From 5f2a8b3b3a2bc388bdb906555048404b743504a4 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 27 Mar 2024 20:57:04 +0800
Subject: [PATCH 001/282] [CoreEngine] 1. For better processing the status in
the unified status center, we split the client runner and server runner into
four base classes: agent, protocol manager, job runner manager, job runner.
2. Add the unified account manager to bind into the MLOps backend. 3.
Abstract the same design paradigm for launching and deployment scheduler with
the following format: agent -> protocol manager -> job runner manager -> job
runner. 4. For better debugging messages, we save the message records to
local files for sender and receiver in the message center. 5. Write the class
diagram and sequence diagram in the lark doc (
https://fedml-inc.larksuite.com/wiki/NpTUwdXnciBlpQkxPkBu79k0sPc, section:
FEDML Launch Python(v2) )
---
python/fedml/__init__.py | 2 +-
.../scheduler/master/base_master_agent.py | 126 +
.../master/base_master_job_runner.py | 622 ++++
.../master/base_master_job_runner_manager.py | 73 +
.../master/base_master_protocol_manager.py | 667 ++++
.../scheduler/master/cloud_server_manager.py | 164 +
.../scheduler/master/deploy_job_launcher.py | 90 +
.../scheduler/master/launch_job_runner.py | 44 +
.../master/launch_job_runner_manager.py | 20 +
.../scheduler/master/master_agent.py | 28 +
.../master/master_protocol_manager.py | 36 +
.../scheduler/master/server_login.py | 406 +--
.../scheduler/master/server_runner.py | 2767 -----------------
.../model_scheduler/device_client_runner.py | 1335 --------
.../model_scheduler/device_model_cache.py | 193 +-
.../model_scheduler/device_model_db.py | 62 +-
.../device_model_deployment.py | 778 ++---
.../model_scheduler/device_model_inference.py | 68 +-
.../device_model_msg_object.py | 64 +-
.../device_replica_controller.py | 437 +++
.../model_scheduler/device_replica_handler.py | 138 +
.../model_scheduler/device_server_runner.py | 2160 -------------
.../model_scheduler/job_runner_msg_sender.py | 204 ++
.../scheduler/model_scheduler/master_agent.py | 27 +
.../model_scheduler/master_job_runner.py | 578 ++++
.../master_job_runner_manager.py | 62 +
.../master_protocol_manager.py | 365 +++
.../model_scheduler/model_device_client.py | 140 +-
.../model_scheduler/model_device_server.py | 142 +-
.../scheduler/model_scheduler/worker_agent.py | 27 +
.../model_scheduler/worker_job_runner.py | 489 +++
.../worker_job_runner_manager.py | 23 +
.../worker_protocol_manager.py | 195 ++
.../scheduler_core/account_manager.py | 460 +++
.../scheduler_core/compute_cache_manager.py | 9 +-
.../scheduler_core/compute_status_cache.py | 76 +
.../scheduler_core/compute_status_db.py | 123 +
.../scheduler_core/endpoint_sync_protocol.py | 25 +-
.../scheduler_core/general_constants.py | 193 ++
.../scheduler_core/master_api_daemon.py | 23 +-
.../scheduler_core/message_center.py | 239 +-
.../scheduler_core/message_common.py | 77 +
.../scheduler/scheduler_core/ota_upgrade.py | 99 +
.../scheduler_base_job_runner.py | 545 ++++
.../scheduler_base_job_runner_manager.py | 66 +
.../scheduler_base_protocol_manager.py | 260 ++
.../scheduler/scheduler_core/status_center.py | 410 +++
.../status_manager_protocols.py | 303 ++
.../scheduler/slave/base_slave_agent.py | 139 +
.../scheduler/slave/base_slave_job_runner.py | 264 ++
.../slave/base_slave_job_runner_manager.py | 12 +
.../slave/base_slave_protocol_manager.py | 571 ++++
.../computing/scheduler/slave/client_login.py | 335 +-
.../scheduler/slave/client_runner.py | 1775 -----------
.../scheduler/slave/launch_job_runner.py | 41 +
.../slave/launch_job_runner_manager.py | 22 +
.../computing/scheduler/slave/slave_agent.py | 26 +
.../scheduler/slave/slave_protocol_manager.py | 104 +
python/fedml/core/mlops/__init__.py | 19 +-
python/fedml/core/mlops/mlops_configs.py | 2 -
python/fedml/core/mlops/mlops_device_perfs.py | 95 +-
python/fedml/core/mlops/mlops_metrics.py | 90 +-
.../customized_workflow.py | 794 ++++-
.../deploy_image_job.yaml | 12 +-
.../deploy_image_job/fedml_model_config.yaml | 19 +-
.../deploy_image_job/mnist_serve_main.py | 37 +
.../deploy_image_job/model/minist_model.py | 11 +
.../model/model_parms_from_mlops | Bin 0 -> 32188 bytes
.../deploy_llm_job.yaml | 29 +
.../deploy_llm_job/.gitignore | 1 +
.../__init__.py | 0
.../app/__init__.py | 0
.../app/pipe/__init__.py | 0
.../app/pipe/constants.py | 0
.../app/pipe/instruct_pipeline.py | 0
.../config/__init__.py | 0
.../deploy_llm_job/fedml_model_config.yaml | 12 +
.../main_entry.py | 0
python/setup.py | 6 +-
79 files changed, 10058 insertions(+), 9798 deletions(-)
create mode 100755 python/fedml/computing/scheduler/master/base_master_agent.py
create mode 100755 python/fedml/computing/scheduler/master/base_master_job_runner.py
create mode 100755 python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
create mode 100755 python/fedml/computing/scheduler/master/base_master_protocol_manager.py
create mode 100755 python/fedml/computing/scheduler/master/cloud_server_manager.py
create mode 100755 python/fedml/computing/scheduler/master/deploy_job_launcher.py
create mode 100755 python/fedml/computing/scheduler/master/launch_job_runner.py
create mode 100755 python/fedml/computing/scheduler/master/launch_job_runner_manager.py
create mode 100755 python/fedml/computing/scheduler/master/master_agent.py
create mode 100755 python/fedml/computing/scheduler/master/master_protocol_manager.py
delete mode 100755 python/fedml/computing/scheduler/master/server_runner.py
delete mode 100755 python/fedml/computing/scheduler/model_scheduler/device_client_runner.py
create mode 100644 python/fedml/computing/scheduler/model_scheduler/device_replica_controller.py
create mode 100644 python/fedml/computing/scheduler/model_scheduler/device_replica_handler.py
delete mode 100755 python/fedml/computing/scheduler/model_scheduler/device_server_runner.py
create mode 100755 python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
create mode 100755 python/fedml/computing/scheduler/model_scheduler/master_agent.py
create mode 100755 python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
create mode 100755 python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
create mode 100755 python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
create mode 100755 python/fedml/computing/scheduler/model_scheduler/worker_agent.py
create mode 100755 python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
create mode 100755 python/fedml/computing/scheduler/model_scheduler/worker_job_runner_manager.py
create mode 100755 python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
create mode 100755 python/fedml/computing/scheduler/scheduler_core/account_manager.py
create mode 100755 python/fedml/computing/scheduler/scheduler_core/compute_status_cache.py
create mode 100755 python/fedml/computing/scheduler/scheduler_core/compute_status_db.py
create mode 100755 python/fedml/computing/scheduler/scheduler_core/general_constants.py
create mode 100755 python/fedml/computing/scheduler/scheduler_core/message_common.py
create mode 100755 python/fedml/computing/scheduler/scheduler_core/ota_upgrade.py
create mode 100755 python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
create mode 100755 python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
create mode 100755 python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
create mode 100755 python/fedml/computing/scheduler/scheduler_core/status_center.py
create mode 100755 python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
create mode 100755 python/fedml/computing/scheduler/slave/base_slave_agent.py
create mode 100755 python/fedml/computing/scheduler/slave/base_slave_job_runner.py
create mode 100755 python/fedml/computing/scheduler/slave/base_slave_job_runner_manager.py
create mode 100755 python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
delete mode 100755 python/fedml/computing/scheduler/slave/client_runner.py
create mode 100755 python/fedml/computing/scheduler/slave/launch_job_runner.py
create mode 100755 python/fedml/computing/scheduler/slave/launch_job_runner_manager.py
create mode 100755 python/fedml/computing/scheduler/slave/slave_agent.py
create mode 100755 python/fedml/computing/scheduler/slave/slave_protocol_manager.py
create mode 100644 python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/mnist_serve_main.py
create mode 100644 python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/model/minist_model.py
create mode 100644 python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/model/model_parms_from_mlops
create mode 100755 python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job.yaml
create mode 100644 python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/.gitignore
rename python/fedml/workflow/driver_example/customized_job_example/{deploy_image_job => deploy_llm_job}/__init__.py (100%)
rename python/fedml/workflow/driver_example/customized_job_example/{deploy_image_job => deploy_llm_job}/app/__init__.py (100%)
rename python/fedml/workflow/driver_example/customized_job_example/{deploy_image_job => deploy_llm_job}/app/pipe/__init__.py (100%)
rename python/fedml/workflow/driver_example/customized_job_example/{deploy_image_job => deploy_llm_job}/app/pipe/constants.py (100%)
rename python/fedml/workflow/driver_example/customized_job_example/{deploy_image_job => deploy_llm_job}/app/pipe/instruct_pipeline.py (100%)
rename python/fedml/workflow/driver_example/customized_job_example/{deploy_image_job => deploy_llm_job}/config/__init__.py (100%)
create mode 100644 python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/fedml_model_config.yaml
rename python/fedml/workflow/driver_example/customized_job_example/{deploy_image_job => deploy_llm_job}/main_entry.py (100%)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index b06c6264a7..f6659cd622 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -34,7 +34,7 @@
_global_training_type = None
_global_comm_backend = None
-__version__ = "0.8.27.dev2"
+__version__ = "0.8.29.dev4"
# This is the deployment environment used for different roles (RD/PM/BD/Public Developers). Potential VALUE: local, dev, test, release
diff --git a/python/fedml/computing/scheduler/master/base_master_agent.py b/python/fedml/computing/scheduler/master/base_master_agent.py
new file mode 100755
index 0000000000..66bc35d96f
--- /dev/null
+++ b/python/fedml/computing/scheduler/master/base_master_agent.py
@@ -0,0 +1,126 @@
+
+from multiprocessing import Process
+from ..comm_utils import sys_utils
+from ..comm_utils.job_cleanup import JobCleanup
+from ....core.mlops import MLOpsRuntimeLog, MLOpsMetrics
+from ..scheduler_core.master_api_daemon import MasterApiDaemon
+from ..scheduler_core.account_manager import FedMLAccountManager
+from ..scheduler_core.general_constants import GeneralConstants
+from abc import ABC, abstractmethod
+
+
+class FedMLBaseMasterAgent(ABC):
+
+ def __init__(self):
+ self.agent_args = None
+ self.master_api_daemon = None
+ self.master_api_process = None
+ self.mlops_metrics = MLOpsMetrics()
+ self.status_reporter = None
+ self.enable_simulation_cloud_agent = True
+ self.use_local_process_as_cloud_server = False
+ self.protocol_mgr = None
+
+ def login(
+ self, user_id, api_key=None, device_id=None,
+ os_name=None, role=None
+ ):
+ # Login account
+ login_result = FedMLAccountManager.get_instance().login(
+ user_id, api_key=api_key, device_id=device_id,
+ os_name=os_name, role=role
+ )
+ if login_result is not None:
+ self.agent_args = login_result
+ else:
+ return None
+
+ # Save the bound info
+ self._save_agent_info(
+ login_result.current_device_id + "." + login_result.os_name, login_result.edge_id)
+
+ # Init the logs for protocol manager
+ self._init_logs(login_result, login_result.edge_id)
+
+ # Create the protocol manager to communicate with the slave agents and MLOps.
+ self._create_protocol_manager(role, login_result)
+
+ # Initialize the protocol manager
+ # noinspection PyBoardException
+ try:
+ self._initialize_protocol_manager()
+ except Exception as e:
+ FedMLAccountManager.write_login_failed_file(is_client=False)
+ self.protocol_mgr.stop()
+ raise e
+
+ # Start the protocol manager to process the messages from MLOps and slave agents.
+ self.protocol_mgr.start()
+
+ @staticmethod
+ def logout():
+ GeneralConstants.cleanup_run_process(None, is_master=True)
+ sys_utils.cleanup_all_fedml_server_api_processes()
+
+ def _create_protocol_manager(self, role, login_result):
+ if self.protocol_mgr is not None:
+ return
+ self.protocol_mgr = self._generate_protocol_manager_instance(
+ login_result, agent_config=login_result.agent_config)
+ self.protocol_mgr.run_as_edge_server_and_agent = True \
+ if role == FedMLAccountManager.ROLE_EDGE_SERVER else False
+ self.protocol_mgr.run_as_cloud_agent = True if role == FedMLAccountManager.ROLE_CLOUD_AGENT else False
+ self.protocol_mgr.run_as_cloud_server = True if role == FedMLAccountManager.ROLE_CLOUD_SERVER else False
+ self.protocol_mgr.args = login_result
+ self.protocol_mgr.edge_id = login_result.edge_id
+ self.protocol_mgr.unique_device_id = login_result.unique_device_id
+ self.protocol_mgr.user_name = login_result.user_name
+ self.protocol_mgr.agent_config = login_result.agent_config
+ self.protocol_mgr.enable_simulation_cloud_agent = self.enable_simulation_cloud_agent
+ self.protocol_mgr.use_local_process_as_cloud_server = self.use_local_process_as_cloud_server
+
+ def _initialize_protocol_manager(self):
+ # Init local database
+ self._init_database()
+
+ # Initialize the master protocol
+ self.protocol_mgr.initialize()
+
+ # Report the IDLE status to MLOps
+ self.mlops_metrics.report_server_training_status(
+ None, GeneralConstants.MSG_MLOPS_SERVER_STATUS_IDLE, edge_id=self.agent_args.edge_id)
+
+ # Cleanup data when startup
+ JobCleanup.get_instance().sync_data_on_startup(self.agent_args.edge_id, is_client=False)
+
+ # Start the API server on master agent
+ self.master_api_daemon = MasterApiDaemon()
+ self.master_api_process = Process(target=self.master_api_daemon.run)
+ self.master_api_process.start()
+
+ def _init_logs(self, agent_args, edge_id):
+ # Init runtime logs
+ in_args = agent_args
+ in_args.log_file_dir = self._get_log_file_dir()
+ in_args.run_id = 0
+ in_args.role = "server"
+ in_args.edge_id = edge_id
+ in_args.using_mlops = True
+ in_args.server_agent_id = edge_id
+ MLOpsRuntimeLog.get_instance(in_args).init_logs()
+
+ @abstractmethod
+ def _get_log_file_dir(self):
+ pass
+
+ @abstractmethod
+ def _save_agent_info(self, unique_device_id, edge_id):
+ pass
+
+ @abstractmethod
+ def _init_database(self):
+ pass
+
+ @abstractmethod
+ def _generate_protocol_manager_instance(self, args, agent_config=None):
+ return None
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner.py b/python/fedml/computing/scheduler/master/base_master_job_runner.py
new file mode 100755
index 0000000000..3dbc1fd891
--- /dev/null
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner.py
@@ -0,0 +1,622 @@
+
+import json
+import logging
+import multiprocessing
+import platform
+import queue
+import os
+import time
+import traceback
+from ..scheduler_entry.constants import Constants
+from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
+from ..master.server_constants import ServerConstants
+from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
+from ..comm_utils import sys_utils
+from .server_data_interface import FedMLServerDataInterface
+from ....core.mlops.mlops_utils import MLOpsUtils
+from ..scheduler_core.log_manager import LogsManager
+from ..scheduler_core.metrics_manager import MetricsManager
+from fedml.utils.debugging import debug
+from ..scheduler_core.status_center import JobStatus
+from ..scheduler_core.compute_cache_manager import ComputeCacheManager
+from multiprocessing import Process, Queue
+from ..scheduler_core.general_constants import GeneralConstants
+from ..scheduler_core.scheduler_base_job_runner import FedMLSchedulerBaseJobRunner, RunnerError, RunnerCompletedError
+from abc import ABC, abstractmethod
+
+
+class FedMLBaseMasterJobRunner(FedMLSchedulerBaseJobRunner, ABC):
+ debug_cloud_server = False
+
+ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id=0,
+ cuda_visible_gpu_ids_str=None,
+ agent_data_dir=None, agent_package_download_dir=None,
+ agent_package_unzip_dir=None, agent_log_file_dir=None):
+ FedMLSchedulerBaseJobRunner.__init__(
+ self, args, edge_id=edge_id, request_json=request_json, agent_config=agent_config, run_id=run_id,
+ cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str, agent_data_dir=agent_data_dir,
+ agent_package_download_dir=agent_package_download_dir,
+ agent_package_unzip_dir=agent_package_unzip_dir,
+ agent_log_file_dir=agent_package_unzip_dir,
+ is_master_runner=True
+ )
+
+ self.run_edge_id_status_queue = Queue()
+ self.run_metrics_queue = Queue()
+ self.run_events_queue = Queue()
+ self.run_artifacts_queue = Queue()
+ self.run_logs_queue = Queue()
+ self.run_edge_device_info_queue = Queue()
+ self.run_edge_device_info_global_queue = Queue()
+ self.run_extend_queue_list = None
+ self.async_check_timeout = 0
+ self.enable_async_cluster = False
+ self.origin_fedml_config_object = None
+ self.server_agent_id = 0
+ if request_json is not None:
+ self.server_agent_id = request_json.get("server_id", 0)
+ self.fedml_data_base_package_dir = os.path.join("/", "fedml", "data")
+ self.fedml_data_local_package_dir = os.path.join("/", "fedml", "fedml-package", "fedml", "data")
+ self.fedml_data_dir = self.fedml_data_base_package_dir
+ self.fedml_config_dir = os.path.join("/", "fedml", "conf")
+
+ @debug
+ def run(
+ self, process_event, completed_event, edge_id_status_queue=None,
+ edge_device_info_queue=None, run_metrics_queue=None, run_event_queue=None,
+ run_artifacts_queue=None, run_logs_queue=None, edge_device_info_global_queue=None,
+ run_extend_queue_list=None, sender_message_center_queue=None, listener_message_queue=None,
+ status_center_queue=None
+ ):
+ print(f"Master job runner process id {os.getpid()}, run id {self.run_id}")
+
+ if platform.system() != "Windows":
+ os.setsid()
+
+ os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
+ os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
+
+ self.run_process_event = process_event
+ self.run_process_completed_event = completed_event
+ try:
+ MLOpsUtils.set_ntp_offset(self.ntp_offset)
+
+ self.rebuild_message_status_center(sender_message_center_queue, listener_message_queue, status_center_queue)
+
+ self.run_impl(
+ edge_id_status_queue, edge_device_info_queue, run_metrics_queue,
+ run_event_queue, run_artifacts_queue, run_logs_queue, edge_device_info_global_queue,
+ run_extend_queue_list=run_extend_queue_list, sender_message_queue=sender_message_center_queue,
+ listener_message_queue=listener_message_queue, status_center_queue=status_center_queue
+ )
+ except RunnerError:
+ logging.info("Runner stopped.")
+ self.status_reporter.report_server_id_status(
+ self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.edge_id)
+ except RunnerCompletedError:
+ logging.info("Runner completed.")
+ except Exception as e:
+ logging.error("Runner exits with exceptions. {}".format(traceback.format_exc()))
+ self.status_reporter.report_server_id_status(
+ self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.edge_id)
+ finally:
+ logging.info("Release resources.")
+ self._process_run_metrics_queue(run_metrics_queue)
+ self._process_run_logs_queue(run_logs_queue)
+ MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, self.edge_id)
+ if self.mlops_metrics is not None:
+ self.mlops_metrics.stop_sys_perf()
+ time.sleep(3)
+ ServerConstants.cleanup_run_process(self.run_id)
+ ServerConstants.cleanup_learning_process(self.run_id)
+ ServerConstants.cleanup_bootstrap_process(self.run_id)
+
+ @debug
+ @abstractmethod
+ def run_impl(
+ self, edge_id_status_queue, edge_device_info_queue, run_metrics_queue,
+ run_event_queue, run_artifacts_queue, run_logs_queue, edge_device_info_global_queue,
+ run_extend_queue_list=None, sender_message_queue=None, listener_message_queue=None,
+ status_center_queue=None
+ ):
+ run_id = self.request_json["runId"]
+ run_config = self.request_json["run_config"]
+ data_config = run_config["data_config"]
+ edge_ids = self.request_json["edgeids"]
+
+ self.check_runner_stop_event()
+
+ self.run_id = run_id
+ self.args.run_id = self.run_id
+ MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+
+ logging.info("Detect all status of Edge ids: " + str(edge_ids))
+
+ status_ok, active_edge_info_dict, inactivate_edges = self.detect_edges_status(
+ edge_device_info_queue, edge_device_info_global_queue=edge_device_info_global_queue,
+ callback_when_edges_ready=self.send_training_request_to_edges)
+ logging.info(f"Status OK: {status_ok}, Active edge info dict: {active_edge_info_dict}, "
+ f"inactivate edges: {inactivate_edges}")
+ if not status_ok:
+ logging.error(f"Status of edge device is not OK. Active edge info dict: {active_edge_info_dict}, "
+ f"Inactivate edges: {inactivate_edges}")
+ return
+
+ if not self.should_continue_run_job(run_id):
+ if FedMLBaseMasterJobRunner.debug_cloud_server:
+ while True:
+ time.sleep(30)
+ # Check if the run status is normal
+ self.aggregate_run_metrics_logs(
+ run_id, edge_ids, edge_id_status_queue, edge_device_info_queue,
+ edge_device_info_global_queue,
+ run_metrics_queue, run_logs_queue)
+ return
+
+ # Start the server job
+ self.start_runner_process(
+ run_id, self.request_json, edge_id=self.edge_id, is_server_job=True,
+ sender_message_queue=sender_message_queue,
+ listener_message_queue=listener_message_queue,
+ status_center_queue=status_center_queue
+ )
+
+ # Check if the run status is normal
+ self.aggregate_run_metrics_logs(
+ run_id, edge_ids, edge_id_status_queue, edge_device_info_queue,
+ edge_device_info_global_queue,
+ run_metrics_queue, run_logs_queue)
+
+ @abstractmethod
+ def _generate_extend_queue_list(self):
+ return list()
+
+ def aggregate_run_metrics_logs(
+ self, run_id, edge_id_list, edge_id_status_queue, edge_device_info_queue,
+ edge_device_info_global_queue, run_metrics_queue, run_logs_queue):
+
+ ComputeCacheManager.get_instance().set_redis_params()
+
+ while True:
+ self.check_runner_stop_event()
+
+ # Process run metrics
+ self._process_run_metrics_queue(run_metrics_queue)
+
+ # Process run logs
+ self._process_run_logs_queue(run_logs_queue)
+
+ # Check the job status
+ job_status = ComputeCacheManager.get_instance().get_status_cache().get_job_status(run_id)
+ if JobStatus.is_job_completed(job_status):
+ break
+
+ def _process_run_metrics_queue(self, run_metrics_queue):
+ # Fetch metrics from the run metrics queue
+ while True:
+ try:
+ metrics_item = run_metrics_queue.get(block=False, timeout=3)
+ MetricsManager.get_instance().save_metrics(metrics_item)
+ metric_json = json.loads(metrics_item)
+ if metric_json.get("is_endpoint", False):
+ metric_json().pop("is_endpoint")
+ self.mlops_metrics.report_endpoint_metric({}, payload=json.dumps(metric_json))
+ else:
+ self.mlops_metrics.report_server_training_metric({}, payload=metrics_item)
+ except queue.Empty as e: # If queue is empty, then break loop
+ break
+
+ def _process_run_logs_queue(self, run_logs_queue):
+ # Fetch logs from the run logs queue
+ while True:
+ try:
+ logs_item = run_logs_queue.get(block=False, timeout=3)
+ LogsManager.save_logs(logs_item)
+ except queue.Empty as e: # If queue is empty, then break loop
+ break
+
+ def run_server_job(
+ self, process_event, completed_event, edge_id_status_queue=None,
+ edge_device_info_queue=None, run_metrics_queue=None,
+ run_event_queue=None, run_artifacts_queue=None, run_logs_queue=None,
+ sender_message_queue=None, listener_message_queue=None,
+ edge_device_info_global_queue=None, status_center_queue=None
+ ):
+ print(f"Server runner process id {os.getpid()}, run id {self.run_id}")
+
+ if platform.system() != "Windows":
+ os.setsid()
+
+ os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
+ os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
+
+ self.run_process_event = process_event
+ self.run_process_completed_event = completed_event
+ try:
+ MLOpsUtils.set_ntp_offset(self.ntp_offset)
+
+ self.rebuild_message_status_center(sender_message_queue, listener_message_queue, status_center_queue)
+
+ self.run_server_job_impl(process_event, completed_event,
+ message_center_queue=sender_message_queue)
+ except RunnerError:
+ logging.info("Runner stopped.")
+ self.status_reporter.report_server_id_status(
+ self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.edge_id)
+ except RunnerCompletedError:
+ logging.info("Runner completed.")
+ except Exception as e:
+ logging.error("Runner exits with exceptions. {}".format(traceback.format_exc()))
+ self.status_reporter.report_server_id_status(
+ self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.edge_id)
+ finally:
+ logging.info("Release resources.")
+ MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, self.edge_id)
+ if self.mlops_metrics is not None:
+ self.mlops_metrics.stop_sys_perf()
+ time.sleep(3)
+ ServerConstants.cleanup_run_process(self.run_id)
+ ServerConstants.cleanup_learning_process(self.run_id)
+ ServerConstants.cleanup_bootstrap_process(self.run_id)
+
+ def run_server_job_impl(self, process_event, completed_event,
+ message_center_queue=None):
+ run_id = self.request_json["runId"]
+ run_config = self.request_json["run_config"]
+ data_config = run_config["data_config"]
+ edge_ids = self.request_json["edgeids"]
+
+ self.check_runner_stop_event()
+
+ self.run_id = run_id
+ self.args.run_id = self.run_id
+ MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+
+ # get training params
+ private_local_data_dir = data_config.get("privateLocalData", "")
+ is_using_local_data = 0
+ # if private_local_data_dir is not None and len(str(private_local_data_dir).strip(' ')) > 0:
+ # is_using_local_data = 1
+
+ # start a run according to the hyper-parameters
+ # fedml_local_data_dir = self.cur_dir + "/fedml_data/run_" + run_id_str + "_edge_" + str(edge_id)
+ fedml_local_data_dir = os.path.join(self.cur_dir, "fedml_data")
+ fedml_local_config_dir = os.path.join(self.cur_dir, "fedml_config")
+ if is_using_local_data:
+ fedml_local_data_dir = private_local_data_dir
+ self.fedml_data_dir = self.fedml_data_local_package_dir
+
+ self.check_runner_stop_event()
+
+ logging.info("download packages and run the bootstrap script...")
+
+ # update local config with real time parameters from server and dynamically replace variables value
+ unzip_package_path, fedml_config_object = self.update_local_fedml_config(run_id, run_config)
+ if unzip_package_path is None or fedml_config_object is None:
+ logging.info("failed to update local fedml config.")
+ self.check_runner_stop_event()
+ self.report_exception_status(run_id)
+ return
+
+ logging.info("cleanup the previous aggregation process and check downloaded packages...")
+
+ entry_file_config = fedml_config_object["entry_config"]
+ dynamic_args_config = fedml_config_object["dynamic_args"]
+ entry_file = str(entry_file_config["entry_file"]).replace('\\', os.sep).replace('/', os.sep)
+ entry_file = os.path.basename(entry_file)
+ conf_file = entry_file_config["conf_file"]
+ conf_file = str(conf_file).replace('\\', os.sep).replace('/', os.sep)
+ ServerConstants.cleanup_learning_process(run_id)
+ self.check_runner_stop_event()
+ if not os.path.exists(unzip_package_path):
+ logging.info("failed to unzip file.")
+ self.check_runner_stop_event()
+ self.report_exception_status(run_id)
+ return
+ os.chdir(os.path.join(unzip_package_path, "fedml"))
+
+ self.check_runner_stop_event()
+
+ logging.info("starting the server user process...")
+
+ entry_file_full_path = os.path.join(unzip_package_path, "fedml", entry_file)
+ conf_file_full_path = os.path.join(unzip_package_path, "fedml", conf_file)
+ logging.info(" ")
+ logging.info(" ")
+ logging.info("====Your Run Logs Begin===")
+
+ process, is_launch_task, error_list = self.execute_job_task(
+ unzip_package_path=unzip_package_path, entry_file_full_path=entry_file_full_path,
+ conf_file_full_path=conf_file_full_path, dynamic_args_config=dynamic_args_config,
+ fedml_config_object=self.fedml_config_object)
+
+ logging.info("====Your Run Logs End===")
+ logging.info(" ")
+ logging.info(" ")
+
+ ret_code, out, err = process.returncode, None, None
+ is_run_ok = sys_utils.is_runner_finished_normally(process.pid)
+ if is_launch_task:
+ is_run_ok = True
+ if error_list is not None and len(error_list) > 0:
+ is_run_ok = False
+ if ret_code is None or ret_code <= 0:
+ self.check_runner_stop_event()
+
+ if is_run_ok:
+ if out is not None:
+ out_str = sys_utils.decode_our_err_result(out)
+ if out_str != "":
+ logging.info("{}".format(out_str))
+
+ self.status_reporter.report_server_id_status(
+ run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.edge_id)
+
+ if is_launch_task:
+ sys_utils.log_return_info(f"job {run_id}", 0)
+ else:
+ sys_utils.log_return_info(entry_file, 0)
+ else:
+ is_run_ok = False
+
+ if not is_run_ok:
+ # If the run status is killed or finished, then return with the normal state.
+ current_job = FedMLServerDataInterface.get_instance().get_job_by_id(run_id)
+ if current_job is not None and (current_job.status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED or
+ current_job.status == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED):
+ return
+
+ self.check_runner_stop_event()
+
+ logging.error("failed to run the aggregation process...")
+
+ if err is not None:
+ err_str = sys_utils.decode_our_err_result(err)
+ if err_str != "":
+ logging.error("{}".format(err_str))
+
+ if is_launch_task:
+ sys_utils.log_return_info(f"job {run_id}", ret_code)
+ else:
+ sys_utils.log_return_info(entry_file, ret_code)
+
+ self.report_exception_status(run_id)
+
+ @abstractmethod
+ def _generate_job_runner_instance(self, args, run_id=None, request_json=None, agent_config=None, edge_id=None):
+ return None
+
+ def start_runner_process(
+ self, run_id, request_json, edge_id=None, is_server_job=False,
+ sender_message_queue=None, listener_message_queue=None,
+ status_center_queue=None,
+ ):
+ server_runner = self._generate_job_runner_instance(
+ self.args, run_id=run_id, request_json=request_json,
+ agent_config=self.agent_config, edge_id=edge_id
+ )
+
+ run_id_str = str(run_id)
+ server_runner.edge_id = self.edge_id
+ server_runner.server_agent_id = self.server_agent_id
+ server_runner.start_request_json = json.dumps(request_json)
+ self.run_process_event = multiprocessing.Event()
+ server_runner.run_process_event = self.run_process_event
+ self.run_process_completed_event = multiprocessing.Event()
+ server_runner.run_process_completed_event = self.run_process_completed_event
+ server_runner.edge_id_status_queue = self.run_edge_id_status_queue
+ server_runner.edge_device_info_queue = self.run_edge_device_info_queue
+ self.run_extend_queue_list = self._generate_extend_queue_list()
+ self.run_process = Process(
+ target=server_runner.run if not is_server_job else server_runner.run_server_job, args=(
+ self.run_process_event, self.run_process_completed_event, self.run_edge_id_status_queue,
+ self.run_edge_device_info_queue, self.run_metrics_queue, self.run_events_queue,
+ self.run_artifacts_queue, self.run_logs_queue, self.run_edge_device_info_global_queue,
+ self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue
+ )
+ )
+ self.run_process.start()
+ ServerConstants.save_run_process(run_id, self.run_process.pid)
+ return self.run_process
+
+ def put_run_edge_device_info_to_queue(self, run_id, device_info):
+ run_id_str = str(run_id)
+ if self.run_edge_device_info_queue is None:
+ self.run_edge_device_info_queue = Queue()
+ self.run_edge_device_info_queue.put(device_info)
+
+ def should_continue_run_job(self, run_id):
+ run_config = self.request_json["run_config"]
+ run_params = run_config.get("parameters", {})
+ job_yaml = run_params.get("job_yaml", {})
+ job_yaml_default_none = run_params.get("job_yaml", None)
+ framework_type = job_yaml.get("framework_type", None)
+ job_type = job_yaml.get("job_type", None)
+ job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
+ if job_yaml_default_none is not None:
+ if job_type == Constants.JOB_TASK_TYPE_FEDERATE:
+ return True
+
+ if framework_type is None or framework_type != Constants.JOB_FRAMEWORK_TYPE_FEDML:
+ self.status_reporter.report_server_id_status(
+ run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_RUNNING, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.edge_id)
+ return False
+
+ return True
+
+ @debug
+ def detect_edges_status(
+ self, edge_device_info_queue, edge_device_info_global_queue=None, callback_when_edges_ready=None,
+ status_timeout=None,
+ need_to_trigger_exception=True, status_check_context=None, given_edge_ids=None,
+ callback_when_detecting=None, args_for_callback_when_detecting=None
+ ):
+ run_id = self.request_json["runId"]
+ run_id_str = str(run_id)
+ edge_id_list = self.request_json["edgeids"]
+ if given_edge_ids is not None:
+ edge_id_list = given_edge_ids
+
+ # Init realtime status of all edges
+ run_edges_realtime_status = dict()
+ run_edges_realtime_status[run_id_str] = dict()
+
+ edge_info_global_dict = dict()
+
+ # Send status message to all edges
+ allowed_cache_edge_status_time = 60
+ for edge_id in edge_id_list:
+ # Check if the edge status was filled allowed_cache_edge_status_time seconds ago,
+ # if so no more checking message would be sent.
+ edge_info = edge_info_global_dict.get(edge_id, None)
+ if edge_info is not None:
+ timestamp = edge_info.get("timestamp", None)
+ time_interval = time.time() - timestamp
+ if time_interval <= allowed_cache_edge_status_time:
+ continue
+
+ self.send_status_check_msg(run_id, edge_id, self.edge_id, context=status_check_context)
+ time.sleep(3)
+
+ total_sleep_seconds = 0
+ status_check_sleep_seconds = 10
+ allowed_status_check_sleep_seconds = 60 * 2 if status_timeout is None else status_timeout
+ allowed_status_check_sleep_seconds_for_async = 30
+ inactivate_edges = list()
+ active_edge_info_dict = dict()
+ while True:
+ if callback_when_detecting is not None:
+ callback_when_detecting(args_for_callback_when_detecting)
+
+ # Fetch edge info from the edge status queue, which will be added to realtime status map
+ while True:
+ self.check_runner_stop_event()
+
+ try:
+ edge_info = edge_device_info_queue.get(block=False, timeout=1)
+ if edge_info is not None:
+ edge_id = edge_info.get("edge_id", None)
+ if edge_id is not None:
+ run_edges_realtime_status[run_id_str][edge_id] = edge_info
+ except queue.Empty as e: # If queue is empty, then break loop
+ break
+
+ self.check_runner_stop_event()
+
+ # Check all edges which don't send response status successfully
+ # and retry to send the status checking message.
+ active_edges_count = 0
+ inactivate_edges.clear()
+ active_edge_info_dict.clear()
+ for edge_id in edge_id_list:
+ edge_info_dict = run_edges_realtime_status.get(run_id_str, {})
+ edge_info = edge_info_dict.get(edge_id, None)
+ edge_info = edge_info_dict.get(str(edge_id), None) if edge_info is None else edge_info
+ if edge_info is not None:
+ active_edges_count += 1
+ active_edge_info_dict[str(edge_id)] = edge_info
+ else:
+ # Check if the edge status was filled allowed_cache_edge_status_time seconds ago,
+ # if so no more checking message would be sent.
+ edge_info = edge_info_global_dict.get(edge_id, None)
+ if edge_info is not None:
+ timestamp = edge_info.get("timestamp", None)
+ time_interval = time.time() - timestamp
+ if time_interval <= allowed_cache_edge_status_time:
+ active_edges_count += 1
+ active_edge_info_dict[str(edge_id)] = edge_info
+ continue
+
+ inactivate_edges.append(edge_id)
+ self.send_status_check_msg(run_id, edge_id, self.edge_id, context=status_check_context)
+
+ # If all edges are ready then send the starting job message to them
+ if active_edges_count == len(edge_id_list):
+ logging.info(f"All edges are ready. Active edge id list is as follows. {active_edge_info_dict}")
+ if callback_when_edges_ready is not None:
+ logging.info("All edges are ready. Start to process the callback function.")
+ callback_when_edges_ready(active_edge_info_dict=active_edge_info_dict)
+ else:
+ logging.info("All edges are ready. No callback function to process.")
+ break
+ else:
+ logging.info(f"All edges are not ready. Active edge id list: {active_edge_info_dict}, "
+ f"Inactive edge id list: {inactivate_edges}")
+
+ # Check if runner needs to stop and sleep specific time
+ self.check_runner_stop_event()
+ time.sleep(status_check_sleep_seconds)
+ total_sleep_seconds += status_check_sleep_seconds
+
+ # Check if the status response message has timed out to receive
+ if total_sleep_seconds >= allowed_status_check_sleep_seconds:
+ # If so, send failed message to MLOps and send exception message to all edges.
+ logging.error(f"There are inactive edge devices. "
+ f"Inactivate edge id list is as follows. {inactivate_edges}")
+ if need_to_trigger_exception:
+ self.status_reporter.report_server_id_status(
+ run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.server_agent_id)
+ self.report_exception_status(run_id)
+ return False, active_edge_info_dict, inactivate_edges
+
+ # If we enable the mode for async cluster, then sleep some time and send messages to all clients.
+ if callback_when_edges_ready is not None and self.should_process_async_cluster is not None:
+ should_async, async_timeout = self.should_process_async_cluster()
+ if should_async and total_sleep_seconds >= allowed_status_check_sleep_seconds_for_async:
+ if async_timeout > allowed_status_check_sleep_seconds_for_async:
+ time.sleep(async_timeout - allowed_status_check_sleep_seconds_for_async)
+ self.send_training_request_to_edges(active_edge_info_dict)
+ return True, active_edge_info_dict, inactivate_edges
+
+ return True, active_edge_info_dict, inactivate_edges
+
+ def send_status_check_msg(self, run_id, edge_id, server_id, context=None):
+ topic_get_model_device_id = "server/client/request_device_info/" + str(edge_id)
+ payload = {"server_id": server_id, "run_id": run_id}
+ if context is not None:
+ payload["context"] = context
+ self.message_center.send_message(topic_get_model_device_id, json.dumps(payload))
+
+ def report_exception_status(self, run_id):
+ self.status_reporter.report_job_status(run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION)
+
+ def callback_run_logs(self, topic, payload):
+ run_id = str(topic).split('/')[-1]
+ run_id_str = str(run_id)
+ if self.run_logs_queue is None:
+ self.run_logs_queue = Queue()
+ self.run_logs_queue.put(payload)
+
+ def callback_run_metrics(self, topic, payload):
+ print(f"callback_run_metrics topic {topic}, payload {payload}")
+ run_id = str(topic).split('/')[-1]
+ run_id_str = str(run_id)
+ if self.run_metrics_queue is None:
+ self.run_metrics_queue = Queue()
+ self.run_metrics_queue.put(payload)
+
+ def send_training_request_to_edges(self, active_edge_info_dict):
+ topic = GeneralConstants.MSG_TOPIC_SEND_TRAINING_REQUEST_TO_EDGES
+ payload = json.dumps(active_edge_info_dict)
+ self.message_center.receive_message(topic, payload)
+
+ def should_process_async_cluster(self):
+ run_config = self.request_json.get("run_config", {})
+ run_params = run_config.get("parameters", {})
+ common_args = run_params.get("common_args", {})
+ self.enable_async_cluster = common_args.get("enable_async_cluster", False)
+ self.async_check_timeout = common_args.get("async_check_timeout", 0)
+ if self.enable_async_cluster:
+ return True, self.async_check_timeout
+
+ return False, self.async_check_timeout
+
+
+
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
new file mode 100755
index 0000000000..694fab5f5f
--- /dev/null
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
@@ -0,0 +1,73 @@
+import base64
+import json
+import logging
+import time
+from abc import ABC
+from multiprocessing import Process
+from .cloud_server_manager import FedMLCloudServerManager
+from ..scheduler_core.scheduler_base_job_runner_manager import FedMLSchedulerBaseJobRunnerManager
+
+
+class FedMLBaseMasterJobRunnerManager(FedMLSchedulerBaseJobRunnerManager, ABC):
+ def __init__(self):
+ FedMLSchedulerBaseJobRunnerManager.__init__(self)
+
+ # Override
+ def start_job_runner(
+ self, run_id, request_json, args=None, edge_id=None, is_server_job=False,
+ sender_message_queue=None, listener_message_queue=None, status_center_queue=None,
+ should_start_cloud_server=False, use_local_process_as_cloud_server=False,
+ cuda_visible_gpu_ids_str=None
+ ):
+ if should_start_cloud_server:
+ self._start_cloud_server(args, run_id, request_json, edge_id=edge_id,
+ use_local_process_as_cloud_server=use_local_process_as_cloud_server)
+ return
+
+ run_id_str = str(run_id)
+ self.job_runners[run_id_str] = self._generate_job_runner_instance(
+ args, run_id=run_id, request_json=request_json,
+ agent_config=args.agent_config, edge_id=edge_id,
+ )
+ self.job_runners[run_id_str].start_runner_process(
+ run_id, request_json, edge_id=edge_id, is_server_job=is_server_job,
+ sender_message_queue=sender_message_queue,
+ listener_message_queue=listener_message_queue,
+ status_center_queue=status_center_queue
+ )
+
+ def _start_cloud_server(
+ self, args, run_id, request_json, edge_id=None,
+ use_local_process_as_cloud_server=False
+ ):
+ run_id_str = str(run_id)
+ cloud_server_mgr = FedMLCloudServerManager(
+ args, run_id=run_id, edge_id=edge_id, request_json=request_json,
+ agent_config=args.agent_config
+ )
+ if not use_local_process_as_cloud_server:
+ self.cloud_run_process_map[run_id_str] = Process(target=cloud_server_mgr.start_cloud_server_process_entry)
+ self.cloud_run_process_map[run_id_str].start()
+ else:
+ message_bytes = json.dumps(request_json).encode("ascii")
+ base64_bytes = base64.b64encode(message_bytes)
+ runner_cmd_encoded = base64_bytes.decode("ascii")
+ cloud_device_id = request_json.get("cloudServerDeviceId", "0")
+
+ logging.info("runner_cmd_encoded: {}".format(runner_cmd_encoded))
+
+ self.cloud_run_process_map[run_id_str] = Process(
+ target=cloud_server_mgr.start_local_cloud_server,
+ args=(args.account_id, args.version, cloud_device_id, runner_cmd_encoded))
+ self.cloud_run_process_map[run_id_str].start()
+ time.sleep(1)
+
+ def callback_run_logs(self, run_id, topic, payload):
+ run_id_str = str(run_id)
+ if self.job_runners.get(run_id_str, None) is not None:
+ self.job_runners[run_id_str].callback_run_logs(topic, payload)
+
+ def callback_run_metrics(self, run_id, topic, payload):
+ run_id_str = str(run_id)
+ if self.job_runners.get(run_id_str, None) is not None:
+ self.job_runners[run_id_str].callback_run_metrics(topic, payload)
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
new file mode 100755
index 0000000000..bf720515d9
--- /dev/null
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -0,0 +1,667 @@
+
+import base64
+import json
+import logging
+import fedml
+from ..scheduler_core.scheduler_matcher import SchedulerMatcher
+from ..comm_utils.constants import SchedulerConstants
+from ..comm_utils.job_utils import JobRunnerUtils
+from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
+from ....core.mlops.mlops_configs import MLOpsConfigs
+from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
+from ..comm_utils import sys_utils
+from ....core.mlops.mlops_utils import MLOpsUtils
+from ..model_scheduler import device_client_constants
+from fedml.utils.debugging import debug
+from ..scheduler_core.compute_cache_manager import ComputeCacheManager
+from ..scheduler_core.ota_upgrade import FedMLOtaUpgrade
+from .deploy_job_launcher import FedMLDeployJobLauncher
+from ..scheduler_core.general_constants import GeneralConstants
+from ..scheduler_core.scheduler_base_protocol_manager import FedMLSchedulerBaseProtocolManager
+from abc import ABC, abstractmethod
+
+
+class FedMLBaseMasterProtocolManager(FedMLSchedulerBaseProtocolManager, ABC):
+ def __init__(self, args, agent_config=None):
+ FedMLSchedulerBaseProtocolManager.__init__(self, args, agent_config=agent_config, is_master=True)
+
+ self.async_check_timeout = 0
+ self.enable_async_cluster = False
+ self.request_json = None
+ self.run_edge_ids = dict()
+ self.version = fedml.get_env_version()
+ self.args = args
+ self.run_id = None
+ self.edge_id = args.edge_id
+ self.server_agent_id = args.edge_id
+ self.current_device_id = args.current_device_id
+ self.unique_device_id = args.unique_device_id
+ self.agent_config = agent_config
+ self.topic_start_train = None
+ self.topic_stop_train = None
+ self.topic_report_status = None
+ self.topic_ota_msg = None
+ self.topic_response_device_info = None
+ self.topic_request_device_info_from_mlops = None
+ self.topic_requesst_job_status = None
+ self.topic_requesst_device_status_in_job = None
+ self.topic_send_training_request_to_edges = None
+ self.run_as_cloud_agent = False
+ self.run_as_cloud_server = False
+ self.run_as_edge_server_and_agent = False
+ self.run_as_cloud_server_and_agent = False
+ self.enable_simulation_cloud_agent = True
+ self.use_local_process_as_cloud_server = False
+ self.ota_upgrade = FedMLOtaUpgrade(edge_id=args.edge_id)
+ self.running_request_json = dict()
+ self.start_request_json = None
+ self.deploy_job_launcher = FedMLDeployJobLauncher()
+
+ @abstractmethod
+ def generate_topics(self):
+ # The MQTT message topic format is as follows: <sender>/<receiver>/<action>
+
+ # The topic for stopping training
+ self.topic_start_train = "mlops/flserver_agent_" + str(self.edge_id) + "/start_train"
+
+ # The topi for stopping training
+ self.topic_stop_train = "mlops/flserver_agent_" + str(self.edge_id) + "/stop_train"
+
+ # The topic for reporting current device status.
+ self.topic_report_status = "mlops/report_device_status"
+
+ # The topic for OTA messages from the MLOps.
+ self.topic_ota_msg = "mlops/flserver_agent_" + str(self.edge_id) + "/ota"
+
+ # The topic for requesting device info from the client.
+ self.topic_response_device_info = "client/server/response_device_info/" + str(self.edge_id)
+
+ # The topic for requesting device info from MLOps.
+ self.topic_request_device_info_from_mlops = f"mlops/master_agent/request_device_info/{self.edge_id}"
+
+ # The topic for getting job status from the status center.
+ self.topic_requesst_job_status = f"anywhere/master_agent/request_job_status/{self.edge_id}"
+
+ # The topic for getting device status of job from the status center.
+ self.topic_requesst_device_status_in_job = f"anywhere/master_agent/request_device_status_in_job/{self.edge_id}"
+
+ # The topic for reporting online status
+ self.topic_active = "flserver_agent/active"
+
+ # The topic for last-will messages.
+ self.topic_last_will = "flserver_agent/last_will_msg"
+
+ # The topic for sending training request to edges (Request from the job runner when all edges are ready)
+ self.topic_send_training_request_to_edges = GeneralConstants.MSG_TOPIC_SEND_TRAINING_REQUEST_TO_EDGES
+
+ # Subscribe topics for starting train, stopping train and fetching client status.
+ self.subscribed_topics.clear()
+ self.add_subscribe_topic(self.topic_start_train)
+ self.add_subscribe_topic(self.topic_stop_train)
+ self.add_subscribe_topic(self.topic_report_status)
+ self.add_subscribe_topic(self.topic_ota_msg)
+ self.add_subscribe_topic(self.topic_response_device_info)
+ self.add_subscribe_topic(self.topic_request_device_info_from_mlops)
+ self.add_subscribe_topic(self.topic_requesst_job_status)
+ self.add_subscribe_topic(self.topic_requesst_device_status_in_job)
+
+ @abstractmethod
+ def add_protocol_handler(self):
+ # Add the message listeners for all topics, the following is an example.
+ # self.add_message_listener(self.topic_start_train, self.callback_start_train)
+ # Add the message listeners for all topics
+ self.add_message_listener(self.topic_start_train, self.callback_start_train)
+ self.add_message_listener(self.topic_stop_train, self.callback_stop_train)
+ self.add_message_listener(self.topic_ota_msg, FedMLBaseMasterProtocolManager.callback_server_ota_msg)
+ self.add_message_listener(self.topic_report_status, self.callback_report_current_status)
+ self.add_message_listener(self.topic_response_device_info, self.callback_response_device_info)
+ self.add_message_listener(self.topic_request_device_info_from_mlops,
+ self.callback_request_device_info_from_mlops)
+ self.add_message_listener(self.topic_requesst_job_status, self.callback_request_job_status)
+ self.add_message_listener(self.topic_requesst_device_status_in_job, self.callback_request_device_status_in_job)
+ self.add_message_listener(self.topic_send_training_request_to_edges,
+ self.callback_send_training_request_to_edges)
+
+ @abstractmethod
+ def _get_job_runner_manager(self):
+ return None
+
+ @abstractmethod
+ def _init_extra_items(self):
+ pass
+
+ def add_subscribe_topic(self, topic):
+ self.subscribed_topics.append(topic)
+
+ def on_agent_communication_connected(self, mqtt_client_object):
+ super().on_agent_communication_connected(mqtt_client_object)
+
+ if self.run_as_cloud_server:
+ # Start the FedML cloud server
+ message_bytes = self.args.runner_cmd.encode("ascii")
+ base64_bytes = base64.b64decode(message_bytes)
+ payload = base64_bytes.decode("ascii")
+ self.receive_message_json(self.topic_start_train, payload)
+
+ def callback_start_train(self, topic=None, payload=None):
+ # Fetch config from MLOps
+ # noinspection PyBroadException
+ try:
+ MLOpsConfigs.fetch_all_configs()
+ except Exception:
+ pass
+
+ # Parse the message when running in the cloud server mode.
+ if self.run_as_cloud_server:
+ message_bytes = payload.encode("ascii")
+ base64_bytes = base64.b64decode(message_bytes)
+ payload = base64_bytes.decode("ascii")
+
+ # Parse the parameters
+ # [NOTES] Example Request JSON:
+ # https://fedml-inc.larksuite.com/wiki/ScnIwUif9iupbjkYS0LuBrd6sod#WjbEdhYrvogmlGxKTOGu98C6sSb
+ request_json = json.loads(payload)
+ is_retain = request_json.get("is_retain", False)
+ if is_retain:
+ return
+ run_id = request_json["runId"]
+ run_id_str = str(run_id)
+
+ # Process the log when running in the edge server mode.
+ if self.run_as_edge_server_and_agent or self.enable_simulation_cloud_agent:
+ # Start log processor for current run
+ self.args.run_id = run_id
+ self.args.edge_id = self.edge_id
+ MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+ MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(
+ run_id, self.edge_id, SchedulerConstants.get_log_source(request_json))
+ # Process the log when running in the cloud agent mode.
+ elif self.run_as_cloud_agent:
+ # Start log processor for current run
+ MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(
+ run_id, request_json.get("server_id", "0"), SchedulerConstants.get_log_source(request_json)
+ )
+ # Process the log when running in the cloud server mode.
+ elif self.run_as_cloud_server:
+ # Parse the parameters.
+ self.server_agent_id = request_json.get("cloud_agent_id", self.edge_id)
+ run_id = request_json["runId"]
+ run_id_str = str(run_id)
+
+ # Start log processor for current run.
+ self.args.run_id = run_id
+ MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(
+ run_id, self.edge_id, SchedulerConstants.get_log_source(request_json))
+
+ # Print the payload
+ logging.info("callback_start_train payload: {}".format(payload))
+ logging.info(
+ f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
+ )
+
+ # Save the parameters
+ self.start_request_json = payload
+ self.run_id = run_id
+ self.request_json = request_json
+ self.running_request_json[run_id_str] = request_json
+ edge_id_list = request_json.get("edgeids", list())
+ self.run_edge_ids[run_id_str] = edge_id_list
+
+ # report server running status to master agent
+ if not self.run_as_cloud_server:
+ self.mlops_metrics.report_server_id_status(
+ run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_STARTING, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.edge_id)
+
+ # Start server with multiprocessing mode
+ if self.run_as_edge_server_and_agent or self.enable_simulation_cloud_agent:
+ self.init_job_task(request_json)
+
+ self.args.run_id = run_id
+
+ self._get_job_runner_manager().start_job_runner(
+ run_id, request_json, args=self.args, edge_id=self.edge_id,
+ sender_message_queue=self.message_center.get_sender_message_queue(),
+ listener_message_queue=self.get_listener_message_queue(),
+ status_center_queue=self.get_status_queue()
+ )
+
+ process = self._get_job_runner_manager().get_runner_process(run_id)
+ if process is not None:
+ GeneralConstants.save_run_process(run_id, process.pid, is_master=True)
+ elif self.run_as_cloud_agent:
+ self.init_job_task(request_json)
+
+ self._get_job_runner_manager().start_job_runner(
+ run_id, request_json, args=self.args, edge_id=self.edge_id,
+ sender_message_queue=self.message_center.get_sender_message_queue(),
+ listener_message_queue=self.get_listener_message_queue(),
+ status_center_queue=self.get_status_queue(), should_start_cloud_server=True,
+ use_local_process_as_cloud_server=self.use_local_process_as_cloud_server
+ )
+
+ process = self._get_job_runner_manager().get_runner_process(run_id, is_cloud_server=True)
+ if process is not None:
+ GeneralConstants.save_run_process(run_id, process.pid, is_master=True)
+ elif self.run_as_cloud_server:
+ self.server_agent_id = request_json.get("cloud_agent_id", self.edge_id)
+ self.start_request_json = json.dumps(request_json)
+ run_id = request_json["runId"]
+ run_id_str = str(run_id)
+
+ self.init_job_task(request_json)
+
+ self.args.run_id = run_id
+
+ self._get_job_runner_manager().start_job_runner(
+ run_id, request_json, args=self.args, edge_id=self.edge_id,
+ sender_message_queue=self.message_center.get_sender_message_queue(),
+ listener_message_queue=self.get_listener_message_queue(),
+ status_center_queue=self.get_status_queue()
+ )
+
+ def callback_stop_train(self, topic, payload, use_payload=None):
+ # Print the payload
+ logging.info(
+ f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
+ )
+
+ # Parse the parameters.
+ request_json = json.loads(payload)
+ run_id = request_json.get("runId", None)
+ run_id = request_json.get("id", None) if run_id is None else run_id
+ run_id_str = str(run_id)
+
+ # Broadcast the job status to all edges
+ self.rebuild_status_center(self.get_status_queue())
+ self.status_reporter.report_job_status(run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_KILLED)
+
+ # Cleanup the cached object
+ if self.running_request_json.get(run_id_str, None) is not None:
+ self.running_request_json.pop(run_id_str)
+
+ # Stop the job runner
+ self._get_job_runner_manager().stop_job_runner(run_id)
+
+ def callback_run_logs(self, topic, payload):
+ run_id = str(topic).split('/')[-1]
+ run_id_str = str(run_id)
+ self._get_job_runner_manager().callback_run_logs(run_id, topic, payload)
+
+ def callback_run_metrics(self, topic, payload):
+ run_id = str(topic).split('/')[-1]
+ run_id_str = str(run_id)
+ self._get_job_runner_manager().callback_run_metrics(run_id, topic, payload)
+
+ def callback_edge_status(self, topic, payload):
+ self.send_status_message(topic, payload)
+
+ def callback_report_current_status(self, topic, payload):
+ logging.info(
+ f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
+ )
+
+ if self.run_as_edge_server_and_agent:
+ self.send_agent_active_msg(self.edge_id)
+ elif self.run_as_cloud_agent:
+ self.send_agent_active_msg(self.edge_id)
+ elif self.run_as_cloud_server:
+ pass
+
+ @staticmethod
+ def callback_server_ota_msg(topic, payload):
+ logging.info(
+ f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
+ )
+
+ request_json = json.loads(payload)
+ cmd = request_json["cmd"]
+
+ if cmd == GeneralConstants.FEDML_OTA_CMD_UPGRADE:
+ # noinspection PyBroadException
+ try:
+ FedMLOtaUpgrade.process_ota_upgrade_msg()
+ # Process(target=FedMLServerRunner.process_ota_upgrade_msg).start()
+ raise Exception("After upgraded, restart runner...")
+ except Exception as e:
+ pass
+ elif cmd == GeneralConstants.FEDML_OTA_CMD_RESTART:
+ raise Exception("Restart runner...")
+
+ def callback_response_device_info(self, topic, payload):
+ # Parse payload
+ payload_json = json.loads(payload)
+ run_id = payload_json.get("run_id", 0)
+ context = payload_json.get("context", None)
+ master_device_id = payload_json.get("master_device_id", 0)
+ slave_device_id = payload_json.get("slave_device_id", 0)
+ slave_device_id_list = payload_json.get("slave_device_id_list", 0)
+ edge_id = payload_json.get("edge_id", 0)
+ device_info = payload_json.get("edge_info", 0)
+ device_info["master_device_id"] = master_device_id
+ device_info["slave_device_id"] = slave_device_id
+ device_info["slave_device_id_list"] = slave_device_id_list
+ run_id_str = str(run_id)
+
+ # Put device info into a multiprocessing queue so master runner checks if all edges are ready
+ if context is None:
+ self._get_job_runner_manager().put_run_edge_device_info_to_queue(run_id, device_info)
+
+ # if self.run_edge_device_info_global_queue is None:
+ # self.run_edge_device_info_global_queue = Array('i', list())
+ #
+ # self.run_edge_device_info_global_queue[len(self.run_edge_device_info_global_queue)] = \
+ # {"timestamp": time.time(), "edge_id": edge_id, "device_info": device_info}
+
+ request_json = self.running_request_json.get(str(run_id), None)
+ if request_json is not None:
+ self.deploy_job_launcher.check_model_device_ready_and_deploy(
+ request_json, run_id, master_device_id, slave_device_id, run_edge_ids=self.run_edge_ids)
+
+ def callback_request_device_info_from_mlops(self, topic, payload):
+ self.response_device_info_to_mlops(topic, payload)
+
+ def callback_request_job_status(self, topic, payload):
+ self.response_job_status(topic, payload)
+
+ def callback_request_device_status_in_job(self, topic, payload):
+ self.response_device_status_in_job(topic, payload)
+
+ def callback_send_training_request_to_edges(self, topic, payload):
+ payload_json = json.loads(payload)
+ self.send_training_request_to_edges(active_edge_info_dict=payload_json)
+
+ def generate_protocol_manager(self):
+ message_status_runner = self._generate_protocol_manager_instance(
+ self.args, agent_config=self.agent_config
+ )
+ message_status_runner.async_check_timeout = self.async_check_timeout
+ message_status_runner.enable_async_cluster = self.enable_async_cluster
+ message_status_runner.request_json = self.request_json
+ message_status_runner.run_edge_ids = self.run_edge_ids
+ message_status_runner.version = self.version
+ message_status_runner.message_center_name = self.message_center_name
+ message_status_runner.run_id = self.run_id
+ message_status_runner.edge_id = self.edge_id
+ message_status_runner.server_agent_id = self.server_agent_id
+ message_status_runner.current_device_id = self.current_device_id
+ message_status_runner.unique_device_id = self.unique_device_id
+ message_status_runner.subscribed_topics = self.subscribed_topics
+ message_status_runner.run_as_cloud_agent = self.run_as_cloud_agent
+ message_status_runner.run_as_cloud_server = self.run_as_cloud_server
+ message_status_runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
+ message_status_runner.run_as_cloud_server_and_agent = self.run_as_cloud_server_and_agent
+ message_status_runner.enable_simulation_cloud_agent = self.enable_simulation_cloud_agent
+ message_status_runner.use_local_process_as_cloud_server = self.use_local_process_as_cloud_server
+ message_status_runner.running_request_json = self.running_request_json
+ message_status_runner.start_request_json = self.start_request_json
+ message_status_runner.user_name = self.user_name
+ message_status_runner.status_queue = self.get_status_queue()
+
+ return message_status_runner
+
+ def response_job_status(self, topic, payload):
+ payload_json = json.loads(payload)
+ if self.mlops_metrics is not None:
+ run_id = payload_json.get("run_id", None)
+ edge_id = payload_json.get("edge_id", None)
+ if run_id is None or edge_id is None:
+ return
+ response_topic = f"master_agent/somewhere/response_job_status/{edge_id}"
+ response_payload = {
+ "run_id": run_id,
+ "master_agent": self.edge_id,
+ "edge_id": edge_id,
+ "job_status": ComputeCacheManager.get_instance().get_status_cache().get_job_status(),
+ "fedml_version": fedml.__version__
+ }
+ self.mlops_metrics.report_json_message(response_topic, json.dumps(response_payload))
+
+ def response_device_status_in_job(self, topic, payload):
+ payload_json = json.loads(payload)
+ if self.mlops_metrics is not None:
+ run_id = payload_json.get("run_id", None)
+ edge_id = payload_json.get("edge_id", None)
+ if run_id is None or edge_id is None:
+ return
+ response_topic = f"master_agent/somewhere/response_device_status_in_job/{edge_id}"
+ response_payload = {
+ "run_id": run_id,
+ "master_agent": self.edge_id,
+ "edge_id": edge_id,
+ "device_status_in_job":
+ ComputeCacheManager.get_instance().get_status_cache().get_device_status_in_job(run_id, edge_id),
+ "fedml_version": fedml.__version__
+ }
+ self.mlops_metrics.report_json_message(response_topic, json.dumps(response_payload))
+
+ def response_device_info_to_mlops(self, topic, payload):
+ response_topic = f"master_agent/mlops/response_device_info"
+ payload_json = json.loads(payload)
+ need_gpu_info = payload_json.get("need_gpu_info", False)
+ if self.mlops_metrics is not None:
+ if not need_gpu_info:
+ response_payload = {
+ "run_id": self.run_id,
+ "master_agent_device_id": self.edge_id,
+ "fedml_version": fedml.__version__
+ }
+ else:
+ total_mem, free_mem, total_disk_size, free_disk_size, cup_utilization, cpu_cores, \
+ gpu_cores_total, gpu_cores_available, sent_bytes, recv_bytes, gpu_available_ids = \
+ sys_utils.get_sys_realtime_stats()
+ gpu_available_ids = JobRunnerUtils.get_instance().get_available_gpu_id_list(self.edge_id)
+ gpu_available_ids = JobRunnerUtils.trim_unavailable_gpu_ids(gpu_available_ids)
+ gpu_cores_available = len(gpu_available_ids)
+ response_payload = {
+ "run_id": self.run_id,
+ "master_agent_device_id": self.edge_id,
+ "memoryTotal": round(total_mem * MLOpsUtils.BYTES_TO_GB, 2),
+ "memoryAvailable": round(free_mem * MLOpsUtils.BYTES_TO_GB, 2),
+ "diskSpaceTotal": round(total_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
+ "diskSpaceAvailable": round(free_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
+ "cpuUtilization": round(cup_utilization, 2),
+ "cpuCores": cpu_cores,
+ "gpuCoresTotal": gpu_cores_total,
+ "gpuCoresAvailable": gpu_cores_available,
+ "networkTraffic": sent_bytes + recv_bytes,
+ "timestamp": int(MLOpsUtils.get_ntp_time()),
+ "fedml_version": fedml.__version__
+ }
+ self.mlops_metrics.report_json_message(response_topic, json.dumps(response_payload))
+
+ def init_job_task(self, request_json):
+ run_id = request_json["runId"]
+ run_config = request_json["run_config"]
+ edge_ids = request_json["edgeids"]
+ run_params = run_config.get("parameters", {})
+ job_yaml = run_params.get("job_yaml", None)
+ server_id = request_json["server_id"]
+ if self.run_as_cloud_agent:
+ server_id = self.edge_id
+
+ self.setup_listeners_for_edge_status(run_id, edge_ids, server_id)
+ self.setup_listener_for_run_metrics(run_id)
+ self.setup_listener_for_run_logs(run_id)
+
+ @debug
+ def send_training_request_to_edges(self, active_edge_info_dict=None):
+ run_id = self.request_json["runId"]
+ edge_id_list = self.request_json["edgeids"]
+ run_config = self.request_json.get("run_config", {})
+ run_params = run_config.get("parameters", {})
+ job_yaml = run_params.get("job_yaml", {})
+ job_yaml_default_none = run_params.get("job_yaml", None)
+ computing = job_yaml.get("computing", {})
+ request_num_gpus = computing.get("minimum_num_gpus", None)
+ job_gpu_id_list = self.request_json.get("job_gpu_id_list", None)
+ assigned_gpu_num_dict = dict()
+ assigned_gpu_ids_dict = dict()
+ master_node_addr = ""
+ master_node_port = 0
+
+ logging.info("Send training request to Edge ids: " + str(edge_id_list))
+
+ should_match_gpu = False
+ if job_yaml_default_none is not None and request_num_gpus is not None and \
+ int(request_num_gpus) > 0 and active_edge_info_dict is not None:
+ should_match_gpu = True
+ SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(active_edge_info_dict, show_gpu_list=True)
+
+ # Match and assign gpus to each device
+ assigned_gpu_num_dict, assigned_gpu_ids_dict = SchedulerMatcher.match_and_assign_gpu_resources_to_devices(
+ request_num_gpus, edge_id_list, active_edge_info_dict, job_gpu_id_list=job_gpu_id_list)
+ if assigned_gpu_num_dict is None or assigned_gpu_ids_dict is None:
+ # If no resources available, send failed message to MLOps and send exception message to all edges.
+ gpu_count, gpu_available_count = SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(
+ active_edge_info_dict, should_print=True)
+ err_info = f"No resources available." \
+ f"Total available GPU count {gpu_available_count} is less than " \
+ f"request GPU count {request_num_gpus}"
+ logging.error(err_info)
+
+ # Bug fix: This mqtt message needs to be sent so platform can clean up the failed run and change the
+ # status from running to failed.
+ self.mlops_metrics.report_server_training_status(
+ run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id
+ )
+
+ self.status_reporter.report_server_id_status(
+ run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.server_agent_id)
+ self.report_exception_status(run_id)
+
+ serving_args = job_yaml.get("serving_args", {})
+ endpoint_id = serving_args.get("endpoint_id", None)
+ if endpoint_id is not None:
+ fedml.mlops.log_endpoint_status(
+ endpoint_id, device_client_constants.ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
+ fedml.mlops.log_run_log_lines(
+ endpoint_id, 0, [err_info],
+ log_source=device_client_constants.ClientConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT
+ )
+ return
+
+ # Generate master node addr and port
+ master_node_addr, master_node_port = SchedulerMatcher.get_master_node_info(edge_id_list,
+ active_edge_info_dict)
+
+ # Generate new edge id list after matched
+ edge_id_list = SchedulerMatcher.generate_new_edge_list_for_gpu_matching(assigned_gpu_num_dict)
+ if len(edge_id_list) <= 0:
+ gpu_count, gpu_available_count = SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(
+ active_edge_info_dict, should_print=True)
+ logging.error(f"Request parameter for GPU num is invalid."
+ f"Total available GPU count {gpu_available_count}."
+ f"Request GPU num {request_num_gpus}")
+ self.status_reporter.report_server_id_status(
+ run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.server_agent_id)
+ self.report_exception_status(run_id)
+ return
+
+ if should_match_gpu:
+ # Report gpu num and related infos to MLOps.
+ serving_args = job_yaml.get("serving_args", {})
+ endpoint_id = serving_args.get("endpoint_id", None)
+ if endpoint_id is not None:
+ endpoint_info = list()
+ for edge_id_item, gpu_num in assigned_gpu_num_dict.items():
+ edge_info = active_edge_info_dict.get(str(edge_id_item), {})
+ endpoint_info.append({
+ "machine_id": edge_id_item, "endpoint_gpu_count": gpu_num,
+ "master_deploy_id": edge_info.get("master_device_id", 0),
+ "slave_deploy_id": edge_info.get("slave_device_id", 0)})
+ topic_name = f"compute/mlops/endpoint"
+ endpoint_info_json = {"endpoint_id": endpoint_id, "endpoint_info": endpoint_info}
+ print(f"endpoint_info_json {endpoint_info_json}")
+ self.message_center.send_message(topic_name, json.dumps(endpoint_info_json))
+
+ client_rank = 1
+ for edge_id in edge_id_list:
+ topic_start_train = "flserver_agent/" + str(edge_id) + "/start_train"
+ logging.info("start_train: send topic " + topic_start_train + " to client...")
+ request_json = self.request_json
+ request_json["client_rank"] = client_rank
+ client_rank += 1
+
+ if active_edge_info_dict is not None:
+ edge_info = active_edge_info_dict.get(str(edge_id), {})
+ model_master_device_id = edge_info.get("master_device_id", None)
+ model_slave_device_id = edge_info.get("slave_device_id", None)
+ model_slave_device_id_list = edge_info.get("slave_device_id_list", None)
+
+ if should_match_gpu:
+ request_json["scheduler_match_info"] = SchedulerMatcher.generate_match_info_for_scheduler(
+ edge_id, edge_id_list, master_node_addr, master_node_port,
+ assigned_gpu_num_dict, assigned_gpu_ids_dict,
+ model_master_device_id=model_master_device_id,
+ model_slave_device_id=model_slave_device_id,
+ model_slave_device_id_list=model_slave_device_id_list
+ )
+
+ self.message_center.send_message(topic_start_train, json.dumps(request_json))
+
+ def setup_listeners_for_edge_status(self, run_id, edge_ids, server_id):
+ edge_status_topic = "fl_client/flclient_agent_" + str(server_id) + "/status"
+ payload = {"run_id": run_id, "init_all_edge_id_list": edge_ids, "init_server_id": server_id}
+ self.callback_edge_status(edge_status_topic, json.dumps(payload))
+
+ for edge_id in edge_ids:
+ edge_status_topic = "fl_client/flclient_agent_" + str(edge_id) + "/status"
+ self.add_message_listener(edge_status_topic, self.callback_edge_status)
+ self.subscribe_msg(edge_status_topic)
+
+ def remove_listeners_for_edge_status(self, edge_ids=None):
+ if edge_ids is None:
+ edge_ids = self.request_json["edgeids"]
+
+ for edge_id in edge_ids:
+ edge_status_topic = "fl_client/flclient_agent_" + str(edge_id) + "/status"
+ self.unsubscribe_msg(edge_status_topic)
+
+ def setup_listener_for_run_metrics(self, run_id):
+ metric_topic = f"fedml_slave/fedml_master/metrics/{run_id}"
+ self.add_message_listener(metric_topic, self.callback_run_metrics)
+ self.subscribe_msg(metric_topic)
+
+ def remove_listener_for_run_metrics(self, run_id):
+ metric_topic = f"fedml_slave/fedml_master/metrics/{run_id}"
+ self.unsubscribe_msg(metric_topic)
+
+ def setup_listener_for_run_logs(self, run_id):
+ logs_topic = f"fedml_slave/fedml_master/logs/{run_id}"
+ self.add_message_listener(logs_topic, self.callback_run_logs)
+ self.subscribe_msg(logs_topic)
+
+ def remove_listener_for_run_logs(self, run_id):
+ logs_topic = f"fedml_slave/fedml_master/logs/{run_id}"
+ self.unsubscribe_msg(logs_topic)
+
+ def send_training_stop_request_to_edges(
+ self, edge_id_list, payload=None, run_id=0):
+ if payload is None:
+ payload_obj = {"runId": run_id, "edgeids": edge_id_list}
+ else:
+ payload_obj = json.loads(payload)
+
+ for edge_id in edge_id_list:
+ topic_stop_train = "flserver_agent/" + str(edge_id) + "/stop_train"
+ logging.info("stop_train: send topic " + topic_stop_train)
+ self.message_center.send_message(topic_stop_train, json.dumps(payload_obj))
+
+ def send_training_stop_request_to_specific_edge(self, edge_id, payload):
+ topic_stop_train = "flserver_agent/" + str(edge_id) + "/stop_train"
+ logging.info("stop_train: send topic " + topic_stop_train)
+ self.message_center.send_message(topic_stop_train, payload)
+
+ def report_exception_status(self, run_id):
+ self.status_reporter.report_job_status(run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION)
+
+ @staticmethod
+ def get_start_train_topic_with_edge_id(edge_id):
+ return "mlops/flserver_agent_" + str(edge_id) + "/start_train"
+
+ @abstractmethod
+ def _generate_protocol_manager_instance(self, args, agent_config=None):
+ return None
diff --git a/python/fedml/computing/scheduler/master/cloud_server_manager.py b/python/fedml/computing/scheduler/master/cloud_server_manager.py
new file mode 100755
index 0000000000..ed39707034
--- /dev/null
+++ b/python/fedml/computing/scheduler/master/cloud_server_manager.py
@@ -0,0 +1,164 @@
+import base64
+import json
+import logging
+import os
+import traceback
+from fedml.computing.scheduler.comm_utils.sys_utils import get_python_program
+
+
+class FedMLCloudServerManager:
+ FEDML_CLOUD_SERVER_PREFIX = "fedml-server-run-"
+ LOCAL_RUNNER_INFO_DIR_NAME = 'runner_infos'
+ STATUS_IDLE = "IDLE"
+
+ def __init__(self, args, run_id=None, edge_id=None, request_json=None, agent_config=None, version=None):
+ self.server_docker_image = None
+ self.args = args
+ self.run_id = run_id
+ self.edge_id = edge_id
+ self.request_json = request_json
+ self.agent_config = agent_config
+ self.version = version
+ image_version = self.version
+ if image_version == "local":
+ image_version = "dev"
+ self.server_docker_base_image = "/fedml-device-image:" + image_version
+ self.cloud_server_name = None
+
+ @staticmethod
+ def start_local_cloud_server(user, version, cloud_device_id, runner_cmd_encoded):
+ print(f"start cloud server, device id {cloud_device_id}, runner cmd {runner_cmd_encoded}")
+ pip_source_dir = os.path.dirname(__file__)
+ login_cmd = os.path.join(pip_source_dir, "server_login.py")
+ run_cmd = f"{get_python_program()} -W ignore {login_cmd} -t login -r cloud_server -u {str(user)} " \
+ f"-v {version} -id {cloud_device_id} -rc {runner_cmd_encoded}"
+ os.system(run_cmd)
+
+ def start_cloud_server_process_entry(self):
+ try:
+ self.start_cloud_server_process()
+ except Exception as e:
+ logging.info(f"Failed to start the cloud server. {traceback.format_exc()}")
+
+ def start_cloud_server_process(self):
+ run_config = self.request_json["run_config"]
+ packages_config = run_config["packages_config"]
+ self.start_cloud_server(packages_config)
+
+ def start_cloud_server(self, packages_config):
+ server_id = self.request_json["server_id"]
+ self.cloud_server_name = f"{FedMLCloudServerManager.FEDML_CLOUD_SERVER_PREFIX}{self.run_id}-{server_id}"
+ self.server_docker_image = (
+ self.agent_config["docker_config"]["registry_server"]
+ + self.agent_config["docker_config"]["registry_dir"]
+ + self.server_docker_base_image
+ )
+
+ logging.info("docker image {}".format(self.server_docker_image))
+ # logging.info("file_sys_driver {}".format(self.agent_config["docker_config"]["file_sys_driver"]))
+
+ registry_secret_cmd = (
+ "kubectl create namespace fedml-devops-aggregator-"
+ + self.version
+ + ";kubectl -n fedml-devops-aggregator-"
+ + self.version
+ + " delete secret secret-"
+ + self.cloud_server_name
+ + " ;kubectl create secret docker-registry secret-"
+ + self.cloud_server_name
+ + " --docker-server="
+ + self.agent_config["docker_config"]["registry_server"]
+ + " --docker-username="
+ + self.agent_config["docker_config"]["user_name"]
+ + " --docker-password=$(aws ecr-public get-login-password --region "
+ + self.agent_config["docker_config"]["public_cloud_region"]
+ + ")"
+ + " --docker-email=fedml@fedml.ai -n fedml-devops-aggregator-"
+ + self.version
+ )
+ logging.info("Create secret cmd: " + registry_secret_cmd)
+ os.system(registry_secret_cmd)
+
+ message_bytes = json.dumps(self.request_json).encode("ascii")
+ base64_bytes = base64.b64encode(message_bytes)
+ runner_cmd_encoded = base64_bytes.decode("ascii")
+ logging.info("runner_cmd_encoded: {}".format(runner_cmd_encoded))
+ # logging.info("runner_cmd_decoded: {}".format(base64.b64decode(runner_cmd_encoded).decode()))
+ cur_dir = os.path.dirname(__file__)
+ run_deployment_cmd = (
+ "export FEDML_AGGREGATOR_NAME="
+ + self.cloud_server_name
+ + ";export FEDML_AGGREGATOR_SVC="
+ + self.cloud_server_name
+ + ";export FEDML_AGGREGATOR_VERSION="
+ + self.version
+ + ';export FEDML_AGGREGATOR_IMAGE_PATH="'
+ + self.server_docker_image
+ + '"'
+ + ";export FEDML_CONF_ID="
+ + self.cloud_server_name
+ + ";export FEDML_DATA_PV_ID="
+ + self.cloud_server_name
+ + ";export FEDML_DATA_PVC_ID="
+ + self.cloud_server_name
+ + ";export FEDML_REGISTRY_SECRET_SUFFIX="
+ + self.cloud_server_name
+ + ";export FEDML_ACCOUNT_ID=0"
+ + ";export FEDML_SERVER_DEVICE_ID="
+ + self.request_json.get("cloudServerDeviceId", "0")
+ + ";export FEDML_VERSION="
+ + self.version
+ + ";export FEDML_PACKAGE_NAME="
+ + packages_config.get("server", "")
+ + ";export FEDML_PACKAGE_URL="
+ + packages_config.get("serverUrl", "")
+ + ";export FEDML_RUNNER_CMD="
+ + runner_cmd_encoded
+ + ";envsubst < "
+ + os.path.join(cur_dir, "templates", "fedml-server-deployment.yaml")
+ + " | kubectl apply -f - "
+ )
+ logging.info("start run with k8s: " + run_deployment_cmd)
+ os.system(run_deployment_cmd)
+
+ def stop_cloud_server(self):
+ self.cloud_server_name = FedMLCloudServerManager.FEDML_CLOUD_SERVER_PREFIX + str(self.run_id) \
+ + "-" + str(self.edge_id)
+ self.server_docker_image = (
+ self.agent_config["docker_config"]["registry_server"]
+ + self.agent_config["docker_config"]["registry_dir"]
+ + self.server_docker_base_image
+ )
+ delete_deployment_cmd = (
+ "export FEDML_AGGREGATOR_NAME="
+ + self.cloud_server_name
+ + ";export FEDML_AGGREGATOR_SVC="
+ + self.cloud_server_name
+ + ";export FEDML_AGGREGATOR_VERSION="
+ + self.version
+ + ';export FEDML_AGGREGATOR_IMAGE_PATH="'
+ + self.server_docker_image
+ + '"'
+ + ";export FEDML_CONF_ID="
+ + self.cloud_server_name
+ + ";export FEDML_DATA_PV_ID="
+ + self.cloud_server_name
+ + ";export FEDML_DATA_PVC_ID="
+ + self.cloud_server_name
+ + ";export FEDML_REGISTRY_SECRET_SUFFIX="
+ + self.cloud_server_name
+ + ";kubectl -n fedml-devops-aggregator-"
+ + self.version
+ + " delete deployment "
+ + self.cloud_server_name
+ + ";kubectl -n fedml-devops-aggregator-"
+ + self.version
+ + " delete svc "
+ + self.cloud_server_name
+ + ";kubectl -n fedml-devops-aggregator-"
+ + self.version
+ + " delete secret secret-"
+ + self.cloud_server_name
+ )
+ logging.info("stop run with k8s: " + delete_deployment_cmd)
+ os.system(delete_deployment_cmd)
diff --git a/python/fedml/computing/scheduler/master/deploy_job_launcher.py b/python/fedml/computing/scheduler/master/deploy_job_launcher.py
new file mode 100755
index 0000000000..e4af2a20be
--- /dev/null
+++ b/python/fedml/computing/scheduler/master/deploy_job_launcher.py
@@ -0,0 +1,90 @@
+import json
+from fedml.computing.scheduler.comm_utils import sys_utils
+from fedml.computing.scheduler.model_scheduler import device_client_constants
+from fedml.computing.scheduler.model_scheduler.device_model_cards import FedMLModelCards
+from fedml.computing.scheduler.scheduler_entry.constants import Constants
+
+
+class FedMLDeployJobLauncher:
+ LOCAL_RUNNER_INFO_DIR_NAME = 'runner_infos'
+ STATUS_IDLE = "IDLE"
+
+ def __init__(self, edge_id=None):
+ self.edge_id = edge_id
+ self.run_model_device_ids = dict()
+
+ @staticmethod
+ def deploy_model(serving_devices, request_json, run_id):
+ run_config = request_json["run_config"]
+ run_params = run_config.get("parameters", {})
+ job_yaml = run_params.get("job_yaml", {})
+ job_type = job_yaml.get("job_type", None)
+ job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
+ if job_type == Constants.JOB_TASK_TYPE_DEPLOY or job_type == Constants.JOB_TASK_TYPE_SERVE:
+ # computing = job_yaml.get("computing", {})
+ # num_gpus = computing.get("minimum_num_gpus", 1)
+ serving_args = run_params.get("serving_args", {})
+ model_id = serving_args.get("model_id", None)
+ model_name = serving_args.get("model_name", None)
+ model_version = serving_args.get("model_version", None)
+ # model_storage_url = serving_args.get("model_storage_url", None)
+ endpoint_name = serving_args.get("endpoint_name", None)
+ endpoint_id = serving_args.get("endpoint_id", None)
+ random = serving_args.get("random", "")
+ random_out = sys_utils.random2(random, "FEDML@9999GREAT")
+ random_list = random_out.split("FEDML@")
+ device_type = device_client_constants.ClientConstants.login_role_list[
+ device_client_constants.ClientConstants.LOGIN_MODE_FEDML_CLOUD_INDEX]
+ FedMLModelCards.get_instance().deploy_model(
+ model_name, device_type, json.dumps(serving_devices),
+ "", random_list[1], None,
+ in_model_id=model_id, in_model_version=model_version,
+ endpoint_name=endpoint_name, endpoint_id=endpoint_id, run_id=run_id)
+
+ def check_model_device_ready_and_deploy(self, request_json, run_id, master_device_id,
+ slave_device_id, run_edge_ids=None):
+ run_config = request_json["run_config"]
+ run_params = run_config.get("parameters", {})
+ job_yaml = run_params.get("job_yaml", {})
+ job_type = job_yaml.get("job_type", None)
+ job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
+ if job_type != Constants.JOB_TASK_TYPE_DEPLOY and job_type != Constants.JOB_TASK_TYPE_SERVE:
+ return
+
+ # Init model device ids for each run
+ run_id_str = str(run_id)
+ if self.run_model_device_ids.get(run_id_str, None) is None:
+ self.run_model_device_ids[run_id_str] = list()
+
+ # Append master device and slave devices to the model devices map
+ self.run_model_device_ids[run_id_str].append({"master_device_id": master_device_id,
+ "slave_device_id": slave_device_id})
+ model_device_ids = self.run_model_device_ids.get(run_id_str, None)
+ if model_device_ids is None:
+ return
+ if run_edge_ids is None:
+ return
+
+ # Check if all model devices are ready
+ if len(model_device_ids) != len(run_edge_ids.get(run_id_str, list())):
+ return
+
+ # Generate model master ids and model slave device ids
+ device_master_ids = list()
+ device_slave_ids = list()
+ for device_ids in model_device_ids:
+ model_master_id = device_ids.get("master_device_id")
+ model_slave_id = device_ids.get("slave_device_id")
+ device_master_ids.append(model_master_id)
+ device_slave_ids.append(model_slave_id)
+
+ if len(device_master_ids) <= 0:
+ return
+
+ # Generate serving devices for deploying
+ serving_devices = list()
+ serving_devices.append(device_master_ids[0])
+ serving_devices.extend(device_slave_ids)
+
+ # Start to deploy the model
+ FedMLDeployJobLauncher.deploy_model(serving_devices, request_json, run_id=run_id)
diff --git a/python/fedml/computing/scheduler/master/launch_job_runner.py b/python/fedml/computing/scheduler/master/launch_job_runner.py
new file mode 100755
index 0000000000..c28458fc0f
--- /dev/null
+++ b/python/fedml/computing/scheduler/master/launch_job_runner.py
@@ -0,0 +1,44 @@
+
+from ..master.server_constants import ServerConstants
+from ..scheduler_core.general_constants import GeneralConstants
+from .base_master_job_runner import FedMLBaseMasterJobRunner
+
+
+class FedMLLaunchMasterJobRunner(FedMLBaseMasterJobRunner):
+
+ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id=0,
+ cuda_visible_gpu_ids_str=None):
+ FedMLBaseMasterJobRunner.__init__(
+ self, args, edge_id=edge_id, request_json=request_json, agent_config=agent_config, run_id=run_id,
+ cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str, agent_data_dir=ServerConstants.get_data_dir(),
+ agent_package_download_dir=ServerConstants.get_package_download_dir(),
+ agent_package_unzip_dir=GeneralConstants.get_package_unzip_dir(ServerConstants.get_package_download_dir()),
+ agent_log_file_dir=ServerConstants.get_log_file_dir()
+ )
+
+ # Override
+ def _generate_job_runner_instance(self, args, run_id=None, request_json=None, agent_config=None, edge_id=None,):
+ return FedMLLaunchMasterJobRunner(
+ args, run_id=run_id, request_json=request_json, agent_config=self.agent_config, edge_id=edge_id
+ )
+
+ # Override
+ def _generate_extend_queue_list(self):
+ return None
+
+ # Override
+ def get_download_package_info(self, packages_config=None):
+ return super().get_download_package_info(packages_config)
+
+ # Override
+ def run_impl(
+ self, edge_id_status_queue, edge_device_info_queue, run_metrics_queue,
+ run_event_queue, run_artifacts_queue, run_logs_queue, edge_device_info_global_queue,
+ run_extend_queue_list=None, sender_message_queue=None, listener_message_queue=None,
+ status_center_queue=None
+ ):
+ super().run_impl(
+ edge_id_status_queue, edge_device_info_queue, run_metrics_queue,
+ run_event_queue, run_artifacts_queue, run_logs_queue, edge_device_info_global_queue,
+ run_extend_queue_list=run_extend_queue_list, sender_message_queue=sender_message_queue,
+ listener_message_queue=listener_message_queue, status_center_queue=status_center_queue)
diff --git a/python/fedml/computing/scheduler/master/launch_job_runner_manager.py b/python/fedml/computing/scheduler/master/launch_job_runner_manager.py
new file mode 100755
index 0000000000..9e94b089a3
--- /dev/null
+++ b/python/fedml/computing/scheduler/master/launch_job_runner_manager.py
@@ -0,0 +1,20 @@
+
+from fedml.core.common.singleton import Singleton
+from .launch_job_runner import FedMLLaunchMasterJobRunner
+from .base_master_job_runner_manager import FedMLBaseMasterJobRunnerManager
+
+
+class FedMLLaunchJobRunnerManager(FedMLBaseMasterJobRunnerManager, Singleton):
+ def __init__(self):
+ FedMLBaseMasterJobRunnerManager.__init__(self)
+
+ @staticmethod
+ def get_instance():
+ return FedMLLaunchJobRunnerManager()
+
+ # Override
+ def _generate_job_runner_instance(
+ self, args, run_id=None, request_json=None, agent_config=None, edge_id=None
+ ):
+ return FedMLLaunchMasterJobRunner(
+ args, run_id=run_id, request_json=request_json, agent_config=agent_config, edge_id=edge_id)
diff --git a/python/fedml/computing/scheduler/master/master_agent.py b/python/fedml/computing/scheduler/master/master_agent.py
new file mode 100755
index 0000000000..9bbf6eb982
--- /dev/null
+++ b/python/fedml/computing/scheduler/master/master_agent.py
@@ -0,0 +1,28 @@
+
+from ..master.server_constants import ServerConstants
+from .server_data_interface import FedMLServerDataInterface
+from .master_protocol_manager import FedMLLaunchMasterProtocolManager
+from .base_master_agent import FedMLBaseMasterAgent
+
+
+class FedMLLaunchMasterAgent(FedMLBaseMasterAgent):
+
+ def __init__(self):
+ FedMLBaseMasterAgent.__init__(self)
+
+ # Override
+ def _get_log_file_dir(self):
+ return ServerConstants.get_log_file_dir()
+
+ # Override
+ def _save_agent_info(self, unique_device_id, edge_id):
+ ServerConstants.save_runner_infos(unique_device_id, edge_id)
+
+ # Override
+ def _init_database(self):
+ FedMLServerDataInterface.get_instance().create_job_table()
+
+ # Override
+ def _generate_protocol_manager_instance(self, args, agent_config=None):
+ return FedMLLaunchMasterProtocolManager(args, agent_config=agent_config)
+
diff --git a/python/fedml/computing/scheduler/master/master_protocol_manager.py b/python/fedml/computing/scheduler/master/master_protocol_manager.py
new file mode 100755
index 0000000000..5eef5914e7
--- /dev/null
+++ b/python/fedml/computing/scheduler/master/master_protocol_manager.py
@@ -0,0 +1,36 @@
+from abc import ABC
+
+from .base_master_protocol_manager import FedMLBaseMasterProtocolManager
+from .launch_job_runner_manager import FedMLLaunchJobRunnerManager
+
+
+class FedMLLaunchMasterProtocolManager(FedMLBaseMasterProtocolManager, ABC):
+ def __init__(self, args, agent_config=None):
+ FedMLBaseMasterProtocolManager.__init__(self, args, agent_config=agent_config)
+
+ # Override
+ def generate_topics(self):
+ super().generate_topics()
+
+ # Override
+ def add_protocol_handler(self):
+ super().add_protocol_handler()
+
+ # Override
+ def _generate_protocol_manager_instance(self, args, agent_config=None):
+ return FedMLLaunchMasterProtocolManager(args, agent_config=agent_config)
+
+ # Override
+ def _get_job_runner_manager(self):
+ return FedMLLaunchJobRunnerManager.get_instance()
+
+ # Override
+ def _init_extra_items(self):
+ # Start the monitor process
+ self.mlops_metrics.stop_device_realtime_perf()
+ self.mlops_metrics.report_device_realtime_perf(
+ self.args, self.args.agent_config["mqtt_config"], is_client=False)
+
+ # Override
+ def print_connected_info(self):
+ super().print_connected_info()
diff --git a/python/fedml/computing/scheduler/master/server_login.py b/python/fedml/computing/scheduler/master/server_login.py
index dee2c83236..3d8d1f6fc9 100755
--- a/python/fedml/computing/scheduler/master/server_login.py
+++ b/python/fedml/computing/scheduler/master/server_login.py
@@ -1,407 +1,11 @@
import argparse
-import logging
import os
-import platform
-import time
-import traceback
-
-import click
import fedml
-from fedml.computing.scheduler.comm_utils import sys_utils
-from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
-from fedml.computing.scheduler.master.server_runner import FedMLServerRunner
-from fedml.computing.scheduler.master.server_constants import ServerConstants
-from fedml.core.mlops.mlops_runtime_log import MLOpsRuntimeLog
-from fedml.core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
-
-
-def __login_as_edge_server_and_agent(args, userid, version, api_key="", use_extra_device_id_suffix=None, role=None):
- setattr(args, "account_id", userid)
- setattr(args, "current_running_dir", ServerConstants.get_fedml_home_dir())
-
- sys_name = platform.system()
- if sys_name == "Darwin":
- sys_name = "MacOS"
- if hasattr(args, "os_name") and args.os_name is not None and args.os_name != "":
- pass
- else:
- setattr(args, "os_name", sys_name)
- setattr(args, "version", version)
- setattr(args, "log_file_dir", ServerConstants.get_log_file_dir())
- is_from_docker = False
- if hasattr(args, "device_id") and args.device_id is not None and args.device_id != "0":
- setattr(args, "current_device_id", args.device_id)
- is_from_docker = True
- else:
- setattr(args, "current_device_id", FedMLServerRunner.get_device_id())
- setattr(args, "config_version", version)
- setattr(args, "cloud_region", "")
-
- # Create server runner for communication with the FedML client.
- runner = FedMLServerRunner(args)
- runner.run_as_edge_server_and_agent = True
-
- # Fetch configs from the MLOps config server.
- service_config = dict()
- config_try_count = 0
- edge_id = 0
- while config_try_count < 5:
- try:
- mqtt_config, s3_config, mlops_config, docker_config = runner.fetch_configs()
- service_config["mqtt_config"] = mqtt_config
- service_config["s3_config"] = s3_config
- service_config["ml_ops_config"] = mlops_config
- service_config["docker_config"] = docker_config
- runner.agent_config = service_config
- log_server_url = mlops_config.get("LOG_SERVER_URL", None)
- if log_server_url is not None:
- setattr(args, "log_server_url", log_server_url)
- setattr(runner.args, "log_server_url", log_server_url)
- break
- except Exception as e:
- click.echo("{}\n{}".format(SchedulerConstants.ERR_MSG_BINDING_EXCEPTION_1, traceback.format_exc()))
- click.echo(SchedulerConstants.ERR_MSG_BINDING_EXIT_RETRYING)
- config_try_count += 1
- time.sleep(3)
- continue
-
- if config_try_count >= 5:
- click.echo("")
- click.echo("[5] Oops, you failed to login the FedML MLOps platform.")
- click.echo("Please check whether your network is normal!")
- return
-
- # Judge whether running from fedml docker hub
- is_from_fedml_docker_hub = False
- dock_loc_file = ServerConstants.get_docker_location_file()
- if os.path.exists(dock_loc_file):
- is_from_fedml_docker_hub = True
-
- # Build unique device id
- if is_from_docker:
- unique_device_id = args.current_device_id + "@" + args.os_name + ".Docker.Edge.Server"
- else:
- unique_device_id = args.current_device_id + "@" + args.os_name + ".Edge.Server"
- setattr(args, "is_from_docker", is_from_docker)
-
- if is_from_fedml_docker_hub:
- unique_device_id = args.current_device_id + "@" + args.os_name + ".DockerHub.Edge.Server"
-
- if use_extra_device_id_suffix is not None:
- unique_device_id = args.current_device_id + "@" + args.os_name + use_extra_device_id_suffix
-
- # Bind account id to FedML® Nexus AI Platform
- register_try_count = 0
- edge_id = -1
- user_name = None
- while register_try_count < 5:
- try:
- edge_id, user_name, extra_url = runner.bind_account_and_device_id(
- service_config["ml_ops_config"]["EDGE_BINDING_URL"], args.account_id, unique_device_id, args.os_name,
- api_key=api_key, role=role
- )
- if edge_id > 0:
- runner.edge_id = edge_id
- break
- except SystemExit as e:
- click.echo("Your account does not exist. Please make sure your account correct.")
- os.system("fedml logout -s")
- return
- except Exception as e:
- click.echo("{}\n{}".format(SchedulerConstants.ERR_MSG_BINDING_EXCEPTION_2, traceback.format_exc()))
- click.echo(SchedulerConstants.ERR_MSG_BINDING_EXIT_RETRYING)
- register_try_count += 1
- time.sleep(3)
- continue
-
- if edge_id <= 0:
- click.echo("")
- click.echo("[6] Oops, you failed to login the FedML MLOps platform.")
- click.echo("Please check whether your network is normal!")
- return
- setattr(args, "server_id", edge_id)
- runner.args = args
- runner.edge_id = edge_id
- init_logs(args, edge_id)
-
- # Log arguments and binding results.
- # logging.info("login: unique_device_id = %s" % str(unique_device_id))
- # logging.info("login: server_id = %s" % str(edge_id))
- runner.unique_device_id = unique_device_id
- runner.user_name = user_name
- ServerConstants.save_runner_infos(args.current_device_id + "." + args.os_name, edge_id)
-
- # Setup MQTT connection for communication with the FedML server.
- try:
- runner.setup_agent_mqtt_connection(service_config)
- except Exception as e:
- login_exit_file = os.path.join(ServerConstants.get_log_file_dir(), "exited.log")
- with open(login_exit_file, "w") as f:
- f.writelines(f"{os.getpid()}.")
- runner.stop_agent()
- raise e
-
- # Start mqtt looper
- runner.start_agent_mqtt_loop()
-
-
-def __login_as_cloud_agent(args, userid, version):
- setattr(args, "account_id", userid)
- setattr(args, "current_running_dir", ServerConstants.get_fedml_home_dir())
-
- sys_name = platform.system()
- if sys_name == "Darwin":
- sys_name = "MacOS"
- setattr(args, "os_name", sys_name)
- setattr(args, "version", version)
- setattr(args, "log_file_dir", ServerConstants.get_log_file_dir())
- if hasattr(args, "device_id") and args.device_id is not None and args.device_id != "0":
- setattr(args, "current_device_id", args.device_id)
- else:
- setattr(args, "current_device_id", FedMLServerRunner.get_device_id())
- setattr(args, "config_version", version)
- setattr(args, "cloud_region", "")
-
- # Create server runner for communication with the FedML client.
- runner = FedMLServerRunner(args)
- runner.run_as_cloud_agent = True
-
- # Fetch configs from the MLOps config server.
- service_config = dict()
- config_try_count = 0
- edge_id = 0
- while config_try_count < 5:
- try:
- mqtt_config, s3_config, mlops_config, docker_config = runner.fetch_configs()
- service_config["mqtt_config"] = mqtt_config
- service_config["s3_config"] = s3_config
- service_config["ml_ops_config"] = mlops_config
- service_config["docker_config"] = docker_config
- runner.agent_config = service_config
- log_server_url = mlops_config.get("LOG_SERVER_URL", None)
- if log_server_url is not None:
- setattr(args, "log_server_url", log_server_url)
- setattr(runner.args, "log_server_url", log_server_url)
- break
- except Exception as e:
- click.echo("{}\n{}".format(SchedulerConstants.ERR_MSG_BINDING_EXCEPTION_1, traceback.format_exc()))
- click.echo(SchedulerConstants.ERR_MSG_BINDING_EXIT_RETRYING)
- config_try_count += 1
- time.sleep(3)
- continue
-
- if config_try_count >= 5:
- click.echo("")
- click.echo("[7] Oops, you failed to login the FedML MLOps platform.")
- click.echo("Please check whether your network is normal!")
- return
-
- # Build unique device id
- if args.current_device_id is not None and len(str(args.current_device_id)) > 0:
- unique_device_id = args.current_device_id + "@" + args.os_name + ".Public.Cloud"
-
- # Bind account id to FedML® Nexus AI Platform
- register_try_count = 0
- if hasattr(args, "server_agent_id") and args.server_agent_id is not None:
- edge_id = args.server_agent_id
- else:
- edge_id = -1
- user_name = None
- while register_try_count < 5:
- try:
- edge_id, user_name, extra_url = runner.bind_account_and_device_id(
- service_config["ml_ops_config"]["EDGE_BINDING_URL"], args.account_id, unique_device_id, args.os_name
- )
- if edge_id > 0:
- runner.edge_id = edge_id
- break
- except SystemExit as e:
- click.echo("Your account does not exist. Please make sure your account correct.")
- os.system("fedml logout -s")
- return
- except Exception as e:
- click.echo("{}\n{}".format(SchedulerConstants.ERR_MSG_BINDING_EXCEPTION_2, traceback.format_exc()))
- click.echo(SchedulerConstants.ERR_MSG_BINDING_EXIT_RETRYING)
- register_try_count += 1
- time.sleep(3)
- continue
-
- if edge_id <= 0:
- click.echo("")
- click.echo("[8] Oops, you failed to login the FedML MLOps platform.")
- click.echo("Please check whether your network is normal!")
- return
- setattr(args, "server_id", edge_id)
- runner.args = args
- runner.edge_id = edge_id
- init_logs(args, edge_id)
- logging.info("args {}".format(args))
-
- # Log arguments and binding results.
- logging.info("login: unique_device_id = %s" % str(unique_device_id))
- logging.info("login: server_id = %s" % str(edge_id))
- runner.unique_device_id = unique_device_id
- runner.user_name = "cloud_agent" if user_name is None else user_name
- ServerConstants.save_runner_infos(args.current_device_id + "." + args.os_name, edge_id)
-
- # Setup MQTT connection for communication with the FedML server.
- try:
- runner.setup_agent_mqtt_connection(service_config)
- except Exception as e:
- login_exit_file = os.path.join(ServerConstants.get_log_file_dir(), "exited.log")
- with open(login_exit_file, "w") as f:
- f.writelines(f"{os.getpid()}.")
- runner.stop_agent()
- raise e
-
- # Start mqtt looper
- runner.start_agent_mqtt_loop()
-
-
-def __login_as_cloud_server(args, userid, version):
- setattr(args, "account_id", userid)
- setattr(args, "current_running_dir", ServerConstants.get_fedml_home_dir())
-
- sys_name = platform.system()
- if sys_name == "Darwin":
- sys_name = "MacOS"
- setattr(args, "os_name", sys_name)
- setattr(args, "version", version)
- setattr(args, "log_file_dir", ServerConstants.get_log_file_dir())
- if hasattr(args, "device_id") and args.device_id is not None and args.device_id != "0":
- setattr(args, "current_device_id", args.device_id)
- else:
- setattr(args, "current_device_id", FedMLServerRunner.get_device_id())
- setattr(args, "config_version", version)
- setattr(args, "cloud_region", "")
-
- # Create server runner for communication with the FedML client.
- runner = FedMLServerRunner(args)
- runner.run_as_cloud_server = True
-
- # Fetch configs from the MLOps config server.
- service_config = dict()
- config_try_count = 0
- edge_id = 0
- while config_try_count < 5:
- try:
- mqtt_config, s3_config, mlops_config, docker_config = runner.fetch_configs()
- service_config["mqtt_config"] = mqtt_config
- service_config["s3_config"] = s3_config
- service_config["ml_ops_config"] = mlops_config
- service_config["docker_config"] = docker_config
- runner.agent_config = service_config
- log_server_url = mlops_config.get("LOG_SERVER_URL", None)
- if log_server_url is not None:
- setattr(args, "log_server_url", log_server_url)
- setattr(runner.args, "log_server_url", log_server_url)
- break
- except Exception as e:
- click.echo("{}\n{}".format(SchedulerConstants.ERR_MSG_BINDING_EXCEPTION_1, traceback.format_exc()))
- click.echo(SchedulerConstants.ERR_MSG_BINDING_EXIT_RETRYING)
- config_try_count += 1
- time.sleep(3)
- continue
-
- if config_try_count >= 5:
- click.echo("")
- click.echo("[9] Oops, you failed to login the FedML MLOps platform.")
- click.echo("Please check whether your network is normal!")
- return
-
- # Build unique device id
- if hasattr(args, "device_id") and args.device_id is not None and args.device_id != "0":
- unique_device_id = args.current_device_id
- else:
- unique_device_id = args.current_device_id + "@" + args.os_name + ".Public.Server"
-
- # Bind account id to FedML® Nexus AI Platform
- register_try_count = 0
- edge_id = -1
- user_name = None
- while register_try_count < 5:
- try:
- edge_id, user_name, extra_url = runner.bind_account_and_device_id(
- service_config["ml_ops_config"]["EDGE_BINDING_URL"], args.account_id, unique_device_id, args.os_name
- )
- if edge_id > 0:
- runner.edge_id = edge_id
- break
- except SystemExit as e:
- click.echo("Your account does not exist. Please make sure your account correct.")
- os.system("fedml logout -s")
- return
- except Exception as e:
- click.echo("{}\n{}".format(SchedulerConstants.ERR_MSG_BINDING_EXCEPTION_2, traceback.format_exc()))
- click.echo(SchedulerConstants.ERR_MSG_BINDING_EXIT_RETRYING)
- register_try_count += 1
- time.sleep(3)
- continue
-
- if edge_id <= 0:
- click.echo("")
- click.echo("[10] Oops, you failed to login the FedML MLOps platform.")
- click.echo("Please check whether your network is normal!")
- return
- setattr(args, "server_id", edge_id)
- runner.args = args
- runner.edge_id = edge_id
- runner.user_name = "cloud_server" if user_name is None else user_name
- init_logs(args, edge_id)
-
- # Log arguments and binding results.
- logging.info("login: unique_device_id = %s" % str(unique_device_id))
- logging.info("login: server_id = %s" % str(edge_id))
- ServerConstants.save_runner_infos(args.current_device_id + "." + args.os_name, edge_id)
-
- # Echo results
- print("\n\nCongratulations, your device is connected to the FedML MLOps platform successfully!")
- print(
- "Your unique device ID is "
- + str(unique_device_id)
- + "\n"
- )
-
- # Setup MQTT connection for communication with the FedML server.
- try:
- runner.setup_agent_mqtt_connection(service_config)
- except Exception as e:
- login_exit_file = os.path.join(ServerConstants.get_log_file_dir(), "exited.log")
- with open(login_exit_file, "w") as f:
- f.writelines(f"{os.getpid()}.")
- runner.stop_agent()
- raise e
-
- # Start mqtt looper
- runner.start_agent_mqtt_loop()
-
-
-def init_logs(args, edge_id):
- # Init runtime logs
- args.log_file_dir = ServerConstants.get_log_file_dir()
- args.run_id = 0
- args.role = "server"
- args.edge_id = edge_id
- setattr(args, "using_mlops", True)
- setattr(args, "server_agent_id", edge_id)
- MLOpsRuntimeLog.get_instance(args).init_logs()
-
-
-def login(args):
- if args.role == ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_LOCAL_INDEX]:
- __login_as_edge_server_and_agent(args, args.user, args.version, api_key=args.api_key)
- elif args.role == ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_CLOUD_AGENT_INDEX]:
- __login_as_cloud_agent(args, args.user, args.version)
- elif args.role == ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_CLOUD_SERVER_INDEX]:
- __login_as_cloud_server(args, args.user, args.version)
- elif args.role == ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_GPU_MASTER_SERVER_INDEX]:
- __login_as_edge_server_and_agent(args, args.user, args.version, api_key=args.api_key,
- use_extra_device_id_suffix=".Edge.GPU.MasterServer", role=args.role)
+from fedml.computing.scheduler.master.master_agent import FedMLLaunchMasterAgent
def logout():
- ServerConstants.cleanup_run_process(None)
- sys_utils.cleanup_all_fedml_server_api_processes()
+ FedMLLaunchMasterAgent.logout()
if __name__ == "__main__":
@@ -432,7 +36,9 @@ def logout():
fedml.set_local_on_premise_platform_port(args.local_on_premise_platform_port)
fedml.set_env_version(args.version)
+ master_agent = FedMLLaunchMasterAgent()
if args.type == 'login':
- login(args)
+ master_agent.login(args.api_key, api_key=args.api_key, device_id=args.device_id,
+ os_name=args.os_name, role=args.role)
else:
- logout()
+ master_agent.logout()
diff --git a/python/fedml/computing/scheduler/master/server_runner.py b/python/fedml/computing/scheduler/master/server_runner.py
deleted file mode 100755
index 0442c99972..0000000000
--- a/python/fedml/computing/scheduler/master/server_runner.py
+++ /dev/null
@@ -1,2767 +0,0 @@
-import base64
-import copy
-import json
-import logging
-import platform
-import queue
-import sys
-
-import multiprocessing
-from multiprocessing import Process, Queue, Value, Array
-import os
-import shutil
-import stat
-import subprocess
-import threading
-
-import time
-import traceback
-import urllib
-import uuid
-import zipfile
-from os import listdir
-from urllib.parse import urljoin, urlparse
-
-import requests
-
-import fedml
-from ..comm_utils.job_cleanup import JobCleanup
-from ..scheduler_core.scheduler_matcher import SchedulerMatcher
-from ..comm_utils.constants import SchedulerConstants
-from ..comm_utils.job_utils import JobRunnerUtils
-from ..comm_utils.run_process_utils import RunProcessUtils
-from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
-
-from ....core.distributed.communication.mqtt.mqtt_manager import MqttManager
-from ..comm_utils.yaml_utils import load_yaml_config
-from ..slave.client_constants import ClientConstants
-from ..master.server_constants import ServerConstants
-
-from ....core.mlops.mlops_metrics import MLOpsMetrics
-
-from ....core.mlops.mlops_configs import MLOpsConfigs
-from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
-from ....core.mlops.mlops_status import MLOpsStatus
-from ..comm_utils.sys_utils import get_sys_runner_info, get_python_program
-from ..comm_utils import sys_utils
-from .server_data_interface import FedMLServerDataInterface
-from ....core.mlops.mlops_utils import MLOpsUtils
-from ..scheduler_entry.constants import Constants
-from ..model_scheduler.model_device_server import FedMLModelDeviceServerRunner
-from ..model_scheduler.device_model_cards import FedMLModelCards
-from ..model_scheduler import device_client_constants
-from ..scheduler_core.log_manager import LogsManager
-from ..scheduler_core.metrics_manager import MetricsManager
-from ..scheduler_core.master_api_daemon import MasterApiDaemon
-from fedml.utils.debugging import debug
-from ..scheduler_core.message_center import FedMLMessageCenter
-
-
-class RunnerError(Exception):
- """ Runner stopped. """
- pass
-
-
-class RunnerCompletedError(Exception):
- """ Runner completed. """
- pass
-
-
-class FedMLServerRunner(FedMLMessageCenter):
- FEDML_CLOUD_SERVER_PREFIX = "fedml-server-run-"
- debug_cloud_server = False
-
- def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id=0):
- super().__init__()
- self.master_api_daemon = None
- self.run_stop_process = None
- self.run_stop_process_map = dict()
- self.run_edge_id_status_queue_map = dict()
- self.run_metrics_queue_map = dict()
- self.run_events_queue_map = dict()
- self.run_artifacts_queue_map = dict()
- self.run_logs_queue_map = dict()
- self.async_check_timeout = 0
- self.enable_async_cluster = False
- self.origin_fedml_config_object = None
- self.package_type = SchedulerConstants.JOB_PACKAGE_TYPE_DEFAULT
- self.local_api_process = None
- self.run_process_event = None
- self.run_process_event_map = dict()
- self.run_process_completed_event = None
- self.run_process_completed_event_map = dict()
- self.run_process_event_map_for_stop = dict()
- self.edge_device_info_queue = None
- self.run_edge_device_info_queue_map = dict()
- self.run_edge_device_info_queue_map_for_stop = dict()
- self.run_edge_device_info_global_queue = None
- self.run_edge_device_info_global_queue_for_stop = None
- self.run_process = None
- self.run_process_map = dict()
- self.start_request_json = None
- self.server_docker_image = None
- self.cloud_server_name = None
- self.run_as_cloud_agent = False
- self.run_as_cloud_server = False
- self.run_as_edge_server_and_agent = False
- self.run_as_cloud_server_and_agent = False
- self.fedml_packages_base_dir = None
- self.fedml_packages_unzip_dir = None
- self.mqtt_mgr = None
- self.running_request_json = dict()
- self.run_id = run_id
- self.unique_device_id = None
- self.edge_id = edge_id
- self.server_agent_id = 0
- if request_json is not None:
- self.server_agent_id = request_json.get("server_id", 0)
- self.process = None
- self.args = args
- self.request_json = copy.deepcopy(request_json)
- self.version = args.version
- self.device_id = args.device_id
- self.cur_dir = os.path.split(os.path.realpath(__file__))[0]
- if args.current_running_dir is not None:
- self.cur_dir = args.current_running_dir
-
- image_version = self.version
- if image_version == "local":
- image_version = "dev"
- self.server_docker_base_image = "/fedml-device-image:" + image_version
-
- self.agent_config = agent_config
- self.fedml_data_base_package_dir = os.path.join("/", "fedml", "data")
- self.fedml_data_local_package_dir = os.path.join("/", "fedml", "fedml-package", "fedml", "data")
- self.fedml_data_dir = self.fedml_data_base_package_dir
- self.fedml_config_dir = os.path.join("/", "fedml", "conf")
-
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES = {
- "${FEDSYS.RUN_ID}": "",
- "${FEDSYS.PRIVATE_LOCAL_DATA}": "",
- "${FEDSYS.CLIENT_ID_LIST}": "",
- "${FEDSYS.SYNTHETIC_DATA_URL}": "",
- "${FEDSYS.IS_USING_LOCAL_DATA}": "",
- "${FEDSYS.CLIENT_NUM}": "",
- "${FEDSYS.CLIENT_INDEX}": "",
- "${FEDSYS.CLIENT_OBJECT_LIST}": "",
- "${FEDSYS.LOG_SERVER_URL}": "",
- }
-
- self.mlops_metrics = None
- self.client_agent_active_list = dict()
- self.server_active_list = dict()
- self.run_status = None
- self.ntp_offset = MLOpsUtils.get_ntp_offset()
- self.runner_list = dict()
- self.enable_simulation_cloud_agent = False
- self.use_local_process_as_cloud_server = False
-
- self.model_device_server = None
- self.run_model_device_ids = dict()
- self.run_edge_ids = dict()
- self.master_api_process = None
-
- self.subscribed_topics = list()
- self.user_name = None
- self.message_center = None
-
- def build_dynamic_constrain_variables(self, run_id, run_config):
- data_config = run_config.get("data_config", {})
- server_edge_id_list = self.request_json["edgeids"]
- is_using_local_data = 0
- private_data_dir = data_config.get("privateLocalData", "")
- synthetic_data_url = data_config.get("syntheticDataUrl", "")
- edges = self.request_json["edges"]
- # if private_data_dir is not None \
- # and len(str(private_data_dir).strip(' ')) > 0:
- # is_using_local_data = 1
- if private_data_dir is None or len(str(private_data_dir).strip(" ")) <= 0:
- params_config = run_config.get("parameters", None)
- private_data_dir = ServerConstants.get_data_dir()
- if synthetic_data_url is None or len(str(synthetic_data_url)) <= 0:
- synthetic_data_url = private_data_dir
-
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.RUN_ID}"] = run_id
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.PRIVATE_LOCAL_DATA}"] = private_data_dir.replace(" ", "")
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_ID_LIST}"] = str(server_edge_id_list).replace(" ", "")
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.SYNTHETIC_DATA_URL}"] = synthetic_data_url.replace(" ", "")
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.IS_USING_LOCAL_DATA}"] = str(is_using_local_data)
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_NUM}"] = len(server_edge_id_list)
- client_objects = str(json.dumps(edges))
- client_objects = client_objects.replace(" ", "").replace("\n", "").replace('"', '\\"')
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_OBJECT_LIST}"] = client_objects
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.LOG_SERVER_URL}"] = self.agent_config["ml_ops_config"][
- "LOG_SERVER_URL"
- ]
-
- def unzip_file(self, zip_file, unzip_file_path) -> str:
- unziped_file_name = ""
- if zipfile.is_zipfile(zip_file):
- with zipfile.ZipFile(zip_file, "r") as zipf:
- zipf.extractall(unzip_file_path)
- unziped_file_name = zipf.namelist()[0]
- else:
- raise Exception("Invalid zip file {}".format(zip_file))
-
- return unziped_file_name
-
- def package_download_progress(self, count, blksize, filesize):
- self.check_runner_stop_event()
-
- downloaded = count * blksize
- downloaded = filesize if downloaded > filesize else downloaded
- progress = (downloaded / filesize * 100) if filesize != 0 else 0
- progress_int = int(progress)
- downloaded_kb = format(downloaded / 1024, '.2f')
-
- # since this hook funtion is stateless, we need a state to avoid printing progress repeatly
- if count == 0:
- self.prev_download_progress = 0
- if progress_int != self.prev_download_progress and progress_int % 5 == 0:
- self.prev_download_progress = progress_int
- logging.info("package downloaded size {} KB, progress {}%".format(downloaded_kb, progress_int))
-
- def retrieve_and_unzip_package(self, package_name, package_url):
- local_package_path = ServerConstants.get_package_download_dir()
- os.makedirs(local_package_path, exist_ok=True)
- filename, filename_without_extension, file_extension = ServerConstants.get_filename_and_extension(package_url)
- local_package_file = os.path.join(local_package_path, f"fedml_run_{self.run_id}_{filename_without_extension}")
- if os.path.exists(local_package_file):
- os.remove(local_package_file)
- package_url_without_query_path = urljoin(package_url, urlparse(package_url).path)
- urllib.request.urlretrieve(package_url_without_query_path, local_package_file,
- reporthook=self.package_download_progress)
- unzip_package_path = os.path.join(ClientConstants.get_package_unzip_dir(),
- f"unzip_fedml_run_{self.run_id}_{filename_without_extension}")
- try:
- shutil.rmtree(unzip_package_path, ignore_errors=True)
- except Exception as e:
- pass
-
- package_dir_name = self.unzip_file(local_package_file, unzip_package_path) # Using unziped folder name
- unzip_package_full_path = os.path.join(unzip_package_path, package_dir_name)
-
- logging.info("local_package_file {}, unzip_package_path {}, unzip file full path {}".format(
- local_package_file, unzip_package_path, unzip_package_full_path))
-
- return unzip_package_full_path
-
- def update_local_fedml_config(self, run_id, run_config):
- packages_config = run_config["packages_config"]
-
- # Copy config file from the client
- server_package_name = packages_config.get("server", None)
- server_package_url = packages_config.get("serverUrl", None)
- unzip_package_path = self.retrieve_and_unzip_package(server_package_name, server_package_url)
- self.fedml_packages_unzip_dir = unzip_package_path
- fedml_local_config_file = os.path.join(unzip_package_path, "conf", "fedml.yaml")
-
- # Load the above config to memory
- config_from_container = load_yaml_config(fedml_local_config_file)
- container_entry_file_config = config_from_container["entry_config"]
- container_dynamic_args_config = config_from_container["dynamic_args"]
- entry_file = container_entry_file_config["entry_file"]
- conf_file = container_entry_file_config["conf_file"]
- self.package_type = container_entry_file_config.get("package_type", SchedulerConstants.JOB_PACKAGE_TYPE_DEFAULT)
- full_conf_path = os.path.join(unzip_package_path, "fedml", "config", os.path.basename(conf_file))
-
- # Dynamically build constrain variable with realtime parameters from server
- self.build_dynamic_constrain_variables(run_id, run_config)
-
- # Update entry arguments value with constrain variable values with realtime parameters from server
- # currently we support the following constrain variables:
- # ${FEDSYS_RUN_ID}: a run id represented one entire Federated Learning flow
- # ${FEDSYS_PRIVATE_LOCAL_DATA}: private local data path in the Federated Learning client
- # ${FEDSYS_CLIENT_ID_LIST}: client list in one entire Federated Learning flow
- # ${FEDSYS_SYNTHETIC_DATA_URL}: synthetic data url from server,
- # if this value is not null, the client will download data from this URL to use it as
- # federated training data set
- # ${FEDSYS_IS_USING_LOCAL_DATA}: whether use private local data as federated training data set
- # container_dynamic_args_config["data_cache_dir"] = "${FEDSYS.PRIVATE_LOCAL_DATA}"
- for constrain_variable_key, constrain_variable_value in self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES.items():
- for argument_key, argument_value in container_dynamic_args_config.items():
- if argument_value is not None and str(argument_value).find(constrain_variable_key) == 0:
- replaced_argument_value = str(argument_value).replace(
- constrain_variable_key, str(constrain_variable_value)
- )
- container_dynamic_args_config[argument_key] = replaced_argument_value
-
- # Merge all container new config sections as new config dictionary
- package_conf_object = dict()
- package_conf_object["entry_config"] = container_entry_file_config
- package_conf_object["dynamic_args"] = container_dynamic_args_config
- package_conf_object["dynamic_args"]["config_version"] = self.args.config_version
- container_dynamic_args_config["mqtt_config_path"] = os.path.join(
- unzip_package_path, "fedml", "config", os.path.basename(container_dynamic_args_config["mqtt_config_path"])
- )
- container_dynamic_args_config["s3_config_path"] = os.path.join(
- unzip_package_path, "fedml", "config", os.path.basename(container_dynamic_args_config["s3_config_path"])
- )
- log_file_dir = ServerConstants.get_log_file_dir()
- os.makedirs(log_file_dir, exist_ok=True)
- package_conf_object["dynamic_args"]["log_file_dir"] = log_file_dir
-
- # Save new config dictionary to local file
- fedml_updated_config_file = os.path.join(unzip_package_path, "conf", "fedml.yaml")
- ServerConstants.generate_yaml_doc(package_conf_object, fedml_updated_config_file)
-
- # Build dynamic arguments and set arguments to fedml config object
- if not self.build_dynamic_args(run_id, run_config, package_conf_object, unzip_package_path):
- return None, None
-
- return unzip_package_path, package_conf_object
-
- def build_dynamic_args(self, run_id, run_config, package_conf_object, base_dir):
- fedml_conf_file = package_conf_object["entry_config"]["conf_file"]
- fedml_conf_file_processed = str(fedml_conf_file).replace('\\', os.sep).replace('/', os.sep)
- fedml_conf_path = os.path.join(base_dir, "fedml", "config",
- os.path.basename(fedml_conf_file_processed))
- fedml_conf_object = load_yaml_config(fedml_conf_path)
- self.origin_fedml_config_object = fedml_conf_object.copy()
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
-
- # Replace local fedml config objects with parameters from MLOps web
- parameters_object = run_config.get("parameters", None)
- if parameters_object is not None:
- for config_k, config_v in fedml_conf_object.items():
- parameter_v = parameters_object.get(config_k, None)
- if parameter_v is not None:
- fedml_conf_object[config_k] = parameter_v
- parameters_object.pop(config_k)
-
- for config_k, config_v in parameters_object.items():
- fedml_conf_object[config_k] = config_v
-
- package_dynamic_args = package_conf_object["dynamic_args"]
- if fedml_conf_object.get("comm_args", None) is not None:
- fedml_conf_object["comm_args"]["mqtt_config_path"] = package_dynamic_args["mqtt_config_path"]
- fedml_conf_object["comm_args"]["s3_config_path"] = package_dynamic_args["s3_config_path"]
- fedml_conf_object["common_args"]["using_mlops"] = True
- if fedml_conf_object.get("train_args", None) is not None:
- fedml_conf_object["train_args"]["run_id"] = package_dynamic_args["run_id"]
- fedml_conf_object["train_args"]["client_id_list"] = package_dynamic_args["client_id_list"]
- fedml_conf_object["train_args"]["client_num_in_total"] = int(package_dynamic_args["client_num_in_total"])
- fedml_conf_object["train_args"]["client_num_per_round"] = int(package_dynamic_args["client_num_in_total"])
- fedml_conf_object["train_args"]["server_id"] = self.edge_id
- fedml_conf_object["train_args"]["server_agent_id"] = self.request_json.get("cloud_agent_id", self.edge_id)
- fedml_conf_object["train_args"]["group_server_id_list"] = self.request_json.get("group_server_id_list",
- list())
- if fedml_conf_object.get("device_args", None) is not None:
- fedml_conf_object["device_args"]["worker_num"] = int(package_dynamic_args["client_num_in_total"])
- # fedml_conf_object["data_args"]["data_cache_dir"] = package_dynamic_args["data_cache_dir"]
- if fedml_conf_object.get("tracking_args", None) is not None:
- fedml_conf_object["tracking_args"]["log_file_dir"] = package_dynamic_args["log_file_dir"]
- fedml_conf_object["tracking_args"]["log_server_url"] = package_dynamic_args["log_server_url"]
-
- bootstrap_script_path = None
- env_args = fedml_conf_object.get("environment_args", None)
- if env_args is not None:
- bootstrap_script_file = env_args.get("bootstrap", None)
- if bootstrap_script_file is not None:
- bootstrap_script_file = str(bootstrap_script_file).replace('\\', os.sep).replace('/', os.sep)
- if platform.system() == 'Windows':
- bootstrap_script_file = bootstrap_script_file.rstrip('.sh') + '.bat'
- if bootstrap_script_file is not None:
- bootstrap_script_dir = os.path.join(base_dir, "fedml", os.path.dirname(bootstrap_script_file))
- bootstrap_script_path = os.path.join(
- bootstrap_script_dir, bootstrap_script_dir, os.path.basename(bootstrap_script_file)
- )
- # try:
- # os.makedirs(package_dynamic_args["data_cache_dir"], exist_ok=True)
- # except Exception as e:
- # pass
- fedml_conf_object["dynamic_args"] = package_dynamic_args
-
- ServerConstants.generate_yaml_doc(fedml_conf_object, fedml_conf_path)
-
- is_bootstrap_run_ok = True
- try:
- if bootstrap_script_path is not None:
- if os.path.exists(bootstrap_script_path):
- bootstrap_stat = os.stat(bootstrap_script_path)
- if platform.system() == 'Windows':
- os.chmod(bootstrap_script_path,
- bootstrap_stat.st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)
- bootstrap_scripts = "{}".format(bootstrap_script_path)
- else:
- os.chmod(bootstrap_script_path,
- bootstrap_stat.st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)
- bootstrap_scripts = "cd {}; ./{}".format(bootstrap_script_dir,
- os.path.basename(bootstrap_script_file))
- bootstrap_scripts = str(bootstrap_scripts).replace('\\', os.sep).replace('/', os.sep)
- logging.info("Bootstrap scripts are being executed...")
- shell_cmd_list = list()
- shell_cmd_list.append(bootstrap_scripts)
- process, error_list = ServerConstants.execute_commands_with_live_logs(
- shell_cmd_list, callback=self.callback_run_bootstrap)
-
- ret_code, out, err = process.returncode, None, None
- if ret_code is None or ret_code <= 0:
- if error_list is not None and len(error_list) > 0:
- is_bootstrap_run_ok = False
- else:
- if out is not None:
- out_str = sys_utils.decode_our_err_result(out)
- if out_str != "":
- logging.info("{}".format(out_str))
-
- sys_utils.log_return_info(bootstrap_script_file, 0)
-
- is_bootstrap_run_ok = True
- else:
- if err is not None:
- err_str = sys_utils.decode_our_err_result(err)
- if err_str != "":
- logging.error("{}".format(err_str))
-
- sys_utils.log_return_info(bootstrap_script_file, ret_code)
-
- is_bootstrap_run_ok = False
- except Exception as e:
- logging.error("Bootstrap scripts error: {}".format(traceback.format_exc()))
-
- is_bootstrap_run_ok = False
-
- return is_bootstrap_run_ok
-
- def callback_run_bootstrap(self, job_pid):
- ServerConstants.save_bootstrap_process(self.run_id, job_pid)
-
- @debug
- def run(
- self, process_event, completed_event, edge_id_status_queue=None,
- edge_device_info_queue=None, run_metrics_queue=None,
- run_event_queue=None, run_artifacts_queue=None, run_logs_queue=None,
- message_center_queue=None, edge_device_info_global_queue=None
- ):
- print(f"Server runner process id {os.getpid()}, run id {self.run_id}")
-
- if platform.system() != "Windows":
- os.setsid()
-
- os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
- os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
-
- self.run_process_event = process_event
- self.run_process_completed_event = completed_event
- try:
- MLOpsUtils.set_ntp_offset(self.ntp_offset)
-
- self.rebuild_message_center(message_center_queue)
-
- self.run_impl(edge_id_status_queue, edge_device_info_queue, run_metrics_queue,
- run_event_queue, run_artifacts_queue, run_logs_queue, edge_device_info_global_queue)
- except RunnerError:
- logging.info("Runner stopped.")
- self.mlops_metrics.report_server_id_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
- except RunnerCompletedError:
- logging.info("Runner completed.")
- except Exception as e:
- logging.error("Runner exits with exceptions. {}".format(traceback.format_exc()))
- self.mlops_metrics.report_server_id_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
- finally:
- logging.info("Release resources.")
- self._process_run_metrics_queue(run_metrics_queue)
- self._process_run_logs_queue(run_logs_queue)
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, self.edge_id)
- if self.mlops_metrics is not None:
- self.mlops_metrics.stop_sys_perf()
- time.sleep(3)
- ServerConstants.cleanup_run_process(self.run_id)
- ServerConstants.cleanup_learning_process(self.run_id)
- ServerConstants.cleanup_bootstrap_process(self.run_id)
-
- def check_runner_stop_event(self):
- if self.run_process_event is not None and self.run_process_event.is_set():
- logging.info("Received stopping event.")
- raise RunnerError("Runner stopped")
-
- if self.run_process_completed_event is not None and self.run_process_completed_event.is_set():
- logging.info("Received completed event.")
- raise RunnerCompletedError("Runner completed")
-
- def deploy_model(self, serving_devices, request_json, run_id):
- run_config = request_json["run_config"]
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
- job_type = job_yaml.get("job_type", None)
- job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
- if job_type == Constants.JOB_TASK_TYPE_DEPLOY or job_type == Constants.JOB_TASK_TYPE_SERVE:
- computing = job_yaml.get("computing", {})
- num_gpus = computing.get("minimum_num_gpus", 1)
- serving_args = run_params.get("serving_args", {})
- model_id = serving_args.get("model_id", None)
- model_name = serving_args.get("model_name", None)
- model_version = serving_args.get("model_version", None)
- model_storage_url = serving_args.get("model_storage_url", None)
- endpoint_name = serving_args.get("endpoint_name", None)
- endpoint_id = serving_args.get("endpoint_id", None)
- random = serving_args.get("random", "")
- random_out = sys_utils.random2(random, "FEDML@9999GREAT")
- random_list = random_out.split("FEDML@")
- device_type = device_client_constants.ClientConstants.login_role_list[
- device_client_constants.ClientConstants.LOGIN_MODE_FEDML_CLOUD_INDEX]
- FedMLModelCards.get_instance().deploy_model(
- model_name, device_type, json.dumps(serving_devices),
- "", random_list[1], None,
- in_model_id=model_id, in_model_version=model_version,
- endpoint_name=endpoint_name, endpoint_id=endpoint_id, run_id=run_id)
-
- @debug
- def run_impl(
- self, edge_id_status_queue, edge_device_info_queue, run_metrics_queue,
- run_event_queue, run_artifacts_queue, run_logs_queue, edge_device_info_global_queue
- ):
- run_id = self.request_json["runId"]
- run_config = self.request_json["run_config"]
- data_config = run_config["data_config"]
- edge_ids = self.request_json["edgeids"]
-
- self.check_runner_stop_event()
-
- self.run_id = run_id
- self.args.run_id = self.run_id
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
-
- # report server running status
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_STARTING, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- logging.info("Detect all status of Edge ids: " + str(edge_ids))
-
- status_ok, active_edge_info_dict, inactivate_edges = self.detect_edges_status(
- edge_device_info_queue, edge_device_info_global_queue=edge_device_info_global_queue,
- callback_when_edges_ready=self.send_training_request_to_edges)
- logging.info(f"Status OK: {status_ok}, Active edge info dict: {active_edge_info_dict}, "
- f"inactivate edges: {inactivate_edges}")
- if not status_ok:
- logging.error(f"Status of edge device is not OK. Active edge info dict: {active_edge_info_dict}, "
- f"Inactivate edges: {inactivate_edges}")
- return
-
- if not self.should_continue_run_job(run_id):
- if FedMLServerRunner.debug_cloud_server:
- while True:
- time.sleep(30)
- # Check if the run status is normal
- self.aggregate_run_status_metrics_logs(
- run_id, edge_ids, edge_id_status_queue, edge_device_info_queue,
- edge_device_info_global_queue,
- run_metrics_queue, run_logs_queue)
- return
-
- # Start the server job
- self._start_runner_process(run_id, self.request_json, is_server_job=True)
-
- # Check if the run status is normal
- self.aggregate_run_status_metrics_logs(
- run_id, edge_ids, edge_id_status_queue, edge_device_info_queue,
- edge_device_info_global_queue,
- run_metrics_queue, run_logs_queue)
-
- def aggregate_run_status_metrics_logs(
- self, run_id, edge_id_list, edge_id_status_queue, edge_device_info_queue,
- edge_device_info_global_queue, run_metrics_queue, run_logs_queue):
- total_sleep_seconds = 0
- sleep_seconds = 3
- allowed_status_check_sleep_seconds = 60 * 25
- server_id = self.edge_id
- normal_response_status_list = [
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE, ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_TRAINING, ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION, ClientConstants.MSG_MLOPS_CLIENT_STATUS_RUNNING
- ]
- edges_id_status_timeout_map = dict()
- number_of_failed_edges = 0
- number_of_finished_edges = 0
- number_of_killed_edges = 0
- running_edges_list = list()
- inactivate_edge_list = list()
- current_edge_id_status_map = dict()
-
- while True:
- self.check_runner_stop_event()
-
- # Process run metrics
- self._process_run_metrics_queue(run_metrics_queue)
-
- # Process run logs
- self._process_run_logs_queue(run_logs_queue)
-
- # Fetch edge id and status from the edge id status queue
- while True:
- try:
- queue_item = edge_id_status_queue.get(block=False, timeout=3)
- if queue_item is not None:
- current_edge_id_status_map.update(queue_item)
- except queue.Empty as e: # If queue is empty, then break loop
- break
-
- # Calc the total completed device number
- server_id = current_edge_id_status_map.get("server", 0)
- running_edges_list.clear()
- number_of_failed_edges = 0
- number_of_finished_edges = 0
- number_of_killed_edges = 0
- for edge_id_item, status_item in current_edge_id_status_map.items():
- if edge_id_item == "server":
- continue
-
- if status_item is None or status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED or \
- status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION:
- number_of_failed_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
- number_of_finished_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED:
- number_of_killed_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE or \
- status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE:
- continue
-
- running_edges_list.append(edge_id_item)
-
- # Process the no response edges and accumulate the counter.
- for edge_id_item in edge_id_list:
- status_dict = edges_id_status_timeout_map.get(str(edge_id_item))
- status_item = current_edge_id_status_map.get(str(edge_id_item))
- if status_item is None:
- continue
- if status_dict is None:
- status_dict = {"status": status_item, "count": 0}
- else:
- if status_item in normal_response_status_list:
- status_dict["count"] = 0
- else:
- status_dict["count"] += 1
- edges_id_status_timeout_map[str(edge_id_item)] = status_dict
-
- # If the completed device number is equal total device number, then break
- if len(running_edges_list) <= 0 and len(current_edge_id_status_map.keys()) == len(edge_id_list) + 1:
- break
-
- # Calc the timeout value to wait to device killed.
- self.check_runner_stop_event()
- time.sleep(sleep_seconds)
- total_sleep_seconds += sleep_seconds
- no_response_edge_ids = list()
- for no_res_edge, no_res_status in edges_id_status_timeout_map.items():
- if no_res_status.get("count") * sleep_seconds > allowed_status_check_sleep_seconds:
- no_response_edge_ids.append(no_res_edge)
-
- # If timeout, then report killed device status
- if len(no_response_edge_ids) > 0:
- for edge_id_item in no_response_edge_ids:
- self.mlops_metrics.report_client_id_status(
- edge_id_item, ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED,
- server_id=self.edge_id, run_id=self.run_id)
-
- # Check if we can get the response device info from edge devices
- # and set the inactive edges to killed status.
- self.check_runner_stop_event()
- given_edge_ids = list(set(edge_id_list) - set(inactivate_edge_list))
- status_ok, active_edge_info_dict, inactivate_edges = self.detect_edges_status(
- edge_device_info_queue, edge_device_info_global_queue=edge_device_info_global_queue,
- need_to_trigger_exception=False, status_timeout=60,
- given_edge_ids=given_edge_ids, callback_when_detecting=self.callback_when_detecting_on_aggregation,
- args_for_callback_when_detecting=(run_metrics_queue, run_logs_queue)
- )
- if not status_ok:
- inactivate_edge_list.extend(inactivate_edges)
- for edge_id_item in inactivate_edges:
- self.mlops_metrics.report_client_id_status(
- edge_id_item, ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE,
- server_id=self.edge_id, run_id=self.run_id)
-
- # Calc the final run status based on the completed device numbers and fault tolerance parameters.
- enable_fault_tolerance, fault_tolerance_rate = self.parse_fault_tolerance_params(run_id)
- running_edges_list = list(set(running_edges_list))
- status_to_report = self.calculate_server_status(
- run_id, len(edge_id_list), number_of_failed_edges, number_of_finished_edges,
- number_of_killed_edges, running_edges_list, enable_fault_tolerance=enable_fault_tolerance,
- fault_tolerance_rate=fault_tolerance_rate)
- if status_to_report is not None:
- logging.info(
- f"Run completed when aggregating status, metrics and logs, will report status {status_to_report}")
- self.mlops_metrics.report_server_id_status(
- self.run_id, status_to_report, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- def callback_when_detecting_on_aggregation(self, detecting_args):
- # Process run metrics
- self._process_run_metrics_queue(detecting_args[0])
-
- # Process run logs
- self._process_run_logs_queue(detecting_args[1])
-
- def _process_run_metrics_queue(self, run_metrics_queue):
- # Fetch metrics from the run metrics queue
- while True:
- try:
- metrics_item = run_metrics_queue.get(block=False, timeout=3)
- MetricsManager.get_instance().save_metrics(metrics_item)
- metric_json = json.loads(metrics_item)
- if metric_json.get("is_endpoint", False):
- metric_json().pop("is_endpoint")
- self.mlops_metrics.report_endpoint_metric({}, payload=json.dumps(metric_json))
- else:
- self.mlops_metrics.report_server_training_metric({}, payload=metrics_item)
- except queue.Empty as e: # If queue is empty, then break loop
- break
-
- def _process_run_logs_queue(self, run_logs_queue):
- # Fetch logs from the run logs queue
- while True:
- try:
- logs_item = run_logs_queue.get(block=False, timeout=3)
- LogsManager.save_logs(logs_item)
- except queue.Empty as e: # If queue is empty, then break loop
- break
-
- def run_server_job_impl(self, process_event, completed_event, edge_id_status_queue=None,
- edge_device_info_queue=None, run_metrics_queue=None,
- run_event_queue=None, run_artifacts_queue=None, run_logs_queue=None,
- message_center_queue=None, edge_device_info_global_queue=None):
- print(f"Server runner process id {os.getpid()}, run id {self.run_id}")
-
- if platform.system() != "Windows":
- os.setsid()
-
- os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
- os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
-
- self.run_process_event = process_event
- self.run_process_completed_event = completed_event
-
- MLOpsUtils.set_ntp_offset(self.ntp_offset)
-
- self.rebuild_message_center(message_center_queue)
-
- run_id = self.request_json["runId"]
- run_config = self.request_json["run_config"]
- data_config = run_config["data_config"]
- edge_ids = self.request_json["edgeids"]
-
- self.check_runner_stop_event()
-
- # get training params
- private_local_data_dir = data_config.get("privateLocalData", "")
- is_using_local_data = 0
- # if private_local_data_dir is not None and len(str(private_local_data_dir).strip(' ')) > 0:
- # is_using_local_data = 1
-
- # start a run according to the hyper-parameters
- # fedml_local_data_dir = self.cur_dir + "/fedml_data/run_" + run_id_str + "_edge_" + str(edge_id)
- fedml_local_data_dir = os.path.join(self.cur_dir, "fedml_data")
- fedml_local_config_dir = os.path.join(self.cur_dir, "fedml_config")
- if is_using_local_data:
- fedml_local_data_dir = private_local_data_dir
- self.fedml_data_dir = self.fedml_data_local_package_dir
-
- self.check_runner_stop_event()
-
- logging.info("download packages and run the bootstrap script...")
-
- # update local config with real time parameters from server and dynamically replace variables value
- unzip_package_path, fedml_config_object = self.update_local_fedml_config(run_id, run_config)
- if unzip_package_path is None or fedml_config_object is None:
- logging.info("failed to update local fedml config.")
- self.check_runner_stop_event()
- self.cleanup_run_when_starting_failed()
- self.send_training_stop_request_to_edges_when_exception(edge_ids, payload=self.start_request_json,
- run_id=run_id)
- return
-
- logging.info("cleanup the previous aggregation process and check downloaded packages...")
-
- entry_file_config = fedml_config_object["entry_config"]
- dynamic_args_config = fedml_config_object["dynamic_args"]
- entry_file = str(entry_file_config["entry_file"]).replace('\\', os.sep).replace('/', os.sep)
- entry_file = os.path.basename(entry_file)
- conf_file = entry_file_config["conf_file"]
- conf_file = str(conf_file).replace('\\', os.sep).replace('/', os.sep)
- ServerConstants.cleanup_learning_process(run_id)
- self.check_runner_stop_event()
- if not os.path.exists(unzip_package_path):
- logging.info("failed to unzip file.")
- self.check_runner_stop_event()
- self.cleanup_run_when_starting_failed()
- self.send_training_stop_request_to_edges_when_exception(edge_ids, payload=self.start_request_json,
- run_id=run_id)
- return
- os.chdir(os.path.join(unzip_package_path, "fedml"))
-
- self.check_runner_stop_event()
-
- logging.info("starting the server user process...")
-
- entry_file_full_path = os.path.join(unzip_package_path, "fedml", entry_file)
- conf_file_full_path = os.path.join(unzip_package_path, "fedml", conf_file)
- logging.info(" ")
- logging.info(" ")
- logging.info("====Your Run Logs Begin===")
- process, is_launch_task, error_list = self.execute_job_task(entry_file_full_path, conf_file_full_path, run_id)
- logging.info("====Your Run Logs End===")
- logging.info(" ")
- logging.info(" ")
-
- ret_code, out, err = process.returncode, None, None
- is_run_ok = sys_utils.is_runner_finished_normally(process.pid)
- if is_launch_task:
- is_run_ok = True
- if error_list is not None and len(error_list) > 0:
- is_run_ok = False
- if ret_code is None or ret_code <= 0:
- self.check_runner_stop_event()
-
- if is_run_ok:
- if out is not None:
- out_str = sys_utils.decode_our_err_result(out)
- if out_str != "":
- logging.info("{}".format(out_str))
-
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- if is_launch_task:
- sys_utils.log_return_info(f"job {run_id}", 0)
- else:
- sys_utils.log_return_info(entry_file, 0)
- else:
- is_run_ok = False
-
- if not is_run_ok:
- # If the run status is killed or finished, then return with the normal state.
- current_job = FedMLServerDataInterface.get_instance().get_job_by_id(run_id)
- if current_job is not None and (current_job.status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED or
- current_job.status == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED):
- return
-
- self.check_runner_stop_event()
-
- logging.error("failed to run the aggregation process...")
-
- if err is not None:
- err_str = sys_utils.decode_our_err_result(err)
- if err_str != "":
- logging.error("{}".format(err_str))
-
- if is_launch_task:
- sys_utils.log_return_info(f"job {run_id}", ret_code)
- else:
- sys_utils.log_return_info(entry_file, ret_code)
-
- self.send_training_stop_request_to_edges_when_exception(edge_ids, run_id=run_id)
-
- def init_job_task(self, request_json):
- run_id = request_json["runId"]
- run_config = request_json["run_config"]
- edge_ids = request_json["edgeids"]
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", None)
- server_id = request_json["server_id"]
- if self.run_as_cloud_agent:
- server_id = self.edge_id
-
- self.setup_listeners_for_edge_status(run_id, edge_ids, server_id)
- self.setup_listener_for_run_metrics(run_id)
- self.setup_listener_for_run_logs(run_id)
-
- def should_continue_run_job(self, run_id):
- run_config = self.request_json["run_config"]
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
- job_yaml_default_none = run_params.get("job_yaml", None)
- framework_type = job_yaml.get("framework_type", None)
- job_type = job_yaml.get("job_type", None)
- job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
- if job_yaml_default_none is not None:
- if job_type == Constants.JOB_TASK_TYPE_FEDERATE:
- return True
-
- if framework_type is None or framework_type != Constants.JOB_FRAMEWORK_TYPE_FEDML:
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_RUNNING, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
- return False
-
- return True
-
- def execute_job_task(self, entry_file_full_path, conf_file_full_path, run_id):
- run_config = self.request_json["run_config"]
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
- job_yaml_default_none = run_params.get("job_yaml", None)
- job_api_key = job_yaml.get("run_api_key", None)
- job_api_key = job_yaml.get("fedml_run_dynamic_params", None) if job_api_key is None else job_api_key
- assigned_gpu_ids = run_params.get("gpu_ids", None)
- framework_type = job_yaml.get("framework_type", None)
- job_type = job_yaml.get("job_type", None)
- job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
- conf_file_object = load_yaml_config(conf_file_full_path)
- entry_args_dict = conf_file_object.get("fedml_entry_args", {})
- entry_args = entry_args_dict.get("arg_items", None)
-
- executable_interpreter = ClientConstants.CLIENT_SHELL_PS \
- if platform.system() == ClientConstants.PLATFORM_WINDOWS else ClientConstants.CLIENT_SHELL_BASH
-
- if job_yaml_default_none is None:
- # Generate the job executing commands for previous federated learning (Compatibility)
- python_program = get_python_program()
- logging.info("Run the server: {} {} --cf {} --rank 0 --role server".format(
- python_program, entry_file_full_path, conf_file_full_path))
- entry_command = f"{python_program} {entry_file_full_path} --cf " \
- f"{conf_file_full_path} --rank 0 --role server"
- shell_cmd_list = [entry_command]
-
- # Run the job executing commands for previous federated learning (Compatibility)
- process, error_list = ClientConstants.execute_commands_with_live_logs(
- shell_cmd_list, callback=self.callback_start_fl_job, should_write_log_file=False)
- is_launch_task = False
- else:
- self.check_runner_stop_event()
-
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_RUNNING, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- # Generate the job executing commands
- job_executing_commands = JobRunnerUtils.generate_job_execute_commands(
- run_id=self.run_id, edge_id=self.edge_id, version=self.version, package_type=self.package_type,
- executable_interpreter=executable_interpreter, entry_file_full_path=entry_file_full_path,
- conf_file_object=conf_file_object, entry_args=entry_args, assigned_gpu_ids=assigned_gpu_ids,
- job_api_key=job_api_key, client_rank=0)
-
- # Run the job executing commands
- logging.info(f"Run the server job with job id {self.run_id}, device id {self.edge_id}.")
- process, error_list = ServerConstants.execute_commands_with_live_logs(
- job_executing_commands, callback=self.start_job_perf, error_processor=self.job_error_processor)
- is_launch_task = True
-
- return process, is_launch_task, error_list
-
- def callback_start_fl_job(self, job_pid):
- ServerConstants.save_learning_process(self.run_id, job_pid)
- self.mlops_metrics.report_sys_perf(
- self.args, self.agent_config["mqtt_config"], job_process_id=job_pid)
-
- def start_job_perf(self, job_pid):
- ServerConstants.save_learning_process(self.run_id, job_pid)
- self.mlops_metrics.report_job_perf(self.args, self.agent_config["mqtt_config"], job_pid)
-
- def job_error_processor(self, error_list):
- self.check_runner_stop_event()
-
- error_str = "\n".join(error_list)
- raise Exception(f"Error occurs when running the job... {error_str}")
-
- def process_job_status(self, run_id, edge_id, status):
- number_of_failed_edges = 0
- number_of_finished_edges = 0
- number_of_killed_edges = 0
- edge_id_status_dict = self.client_agent_active_list.get(f"{run_id}", {})
- server_id = edge_id_status_dict.get("server", 0)
- enable_fault_tolerance, fault_tolerance_rate = self.parse_fault_tolerance_params(run_id)
- running_edges_list = list()
- for edge_id_item, status_item in edge_id_status_dict.items():
- if edge_id_item == "server":
- continue
-
- if status_item is None or status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED or \
- status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION:
- number_of_failed_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
- number_of_finished_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED:
- number_of_killed_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE or \
- status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE:
- continue
-
- running_edges_list.append(edge_id_item)
-
- # Report client status
- edge_status = ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED if status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION else status
- self.mlops_metrics.report_client_training_status(edge_id, edge_status, run_id=run_id)
- self.mlops_metrics.report_client_device_status_to_web_ui(edge_id, edge_status, run_id=run_id)
-
- # Report server status based on the fault tolerance model and parameters
- edge_nums = len(edge_id_status_dict.keys()) - 1
- status_to_report = self.calculate_server_status(
- run_id, edge_nums, number_of_failed_edges, number_of_finished_edges, number_of_killed_edges,
- running_edges_list, enable_fault_tolerance=enable_fault_tolerance,
- fault_tolerance_rate=fault_tolerance_rate)
- if status_to_report is not None:
- logging.info(f"Run completed when processing edge status, will report status {status_to_report}")
- self.report_server_status(run_id, server_id, status_to_report)
-
- def calculate_server_status(
- self, run_id, total_edge_nums, number_of_failed_edges, number_of_finished_edges,
- number_of_killed_edges, running_edges_list, enable_fault_tolerance=False,
- fault_tolerance_rate=0.8
- ):
- # Report server status based on the fault tolerance model and parameters
- actual_failed_rate = number_of_failed_edges / total_edge_nums
- all_edges_run_completed = True if len(running_edges_list) <= 0 else False
- if all_edges_run_completed:
- status_to_report = None
- if enable_fault_tolerance:
- if actual_failed_rate >= fault_tolerance_rate:
- status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED
- self.send_training_stop_request_to_edges_when_exception(
- running_edges_list, run_id=run_id, status=status_to_report)
- return status_to_report
- else:
- if number_of_killed_edges == total_edge_nums:
- status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED
- else:
- status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED
- else:
- if number_of_failed_edges > 0:
- status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED
- elif number_of_finished_edges == total_edge_nums:
- status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED
- elif number_of_killed_edges == total_edge_nums:
- status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED
-
- return status_to_report
-
- def parse_fault_tolerance_params(self, run_id):
- run_json = self.running_request_json.get(str(run_id), None)
- if run_json is None:
- run_json = self.request_json
- run_config = run_json.get("run_config", {})
- run_params = run_config.get("parameters", {})
- common_args = run_params.get("common_args", {})
- enable_fault_tolerance = common_args.get("enable_fault_tolerance", False)
- fault_tolerance_rate = common_args.get("fault_tolerance_rate", 0)
- return enable_fault_tolerance, fault_tolerance_rate
-
- def report_server_status(self, run_id, server_id, status):
- self.mlops_metrics.report_server_id_status(run_id, status, edge_id=self.edge_id,
- server_id=server_id, server_agent_id=self.edge_id)
-
- def stop_run_when_starting_failed(self):
- edge_id_list = self.request_json["edgeids"]
- run_id = self.request_json.get("run_id", 0)
- logging.error("edge ids {}".format(str(edge_id_list)))
-
- payload = self.running_request_json.get(str(run_id))
- if payload is not None:
- self.send_training_stop_request_to_edges(edge_id_list, payload=json.dumps(payload), run_id=run_id)
-
- # logging.info("Stop run successfully when starting failed.")
-
- self.mlops_metrics.report_server_id_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- def cleanup_run_when_finished(self, should_send_server_id_status=True):
- # logging.info("Cleanup run successfully when finished.")
-
- self.mlops_metrics.report_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED, edge_id=self.edge_id
- )
-
- if should_send_server_id_status:
- self.mlops_metrics.report_server_id_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- pass
-
- time.sleep(1)
-
- ServerConstants.cleanup_learning_process(self.run_id)
- ServerConstants.cleanup_bootstrap_process(self.run_id)
-
- try:
- local_package_path = ServerConstants.get_package_download_dir()
- for package_file in listdir(local_package_path):
- if os.path.basename(package_file).startswith("run_" + str(self.run_id)):
- shutil.rmtree(os.path.join(local_package_path, package_file), ignore_errors=True)
- except Exception as e:
- pass
-
- def cleanup_run_when_starting_failed(
- self, status=ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, should_send_server_id_status=True):
- # logging.info("Cleanup run successfully when starting failed.")
-
- self.mlops_metrics.report_server_training_status(
- self.run_id, status, edge_id=self.edge_id)
-
- if should_send_server_id_status:
- self.mlops_metrics.report_server_id_status(
- self.run_id, status, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- pass
-
- time.sleep(1)
-
- ServerConstants.cleanup_learning_process(self.run_id)
- ServerConstants.cleanup_bootstrap_process(self.run_id)
-
- try:
- local_package_path = ServerConstants.get_package_download_dir()
- for package_file in listdir(local_package_path):
- if os.path.basename(package_file).startswith("run_" + str(self.run_id)):
- shutil.rmtree(os.path.join(local_package_path, package_file), ignore_errors=True)
- except Exception as e:
- pass
-
- def should_process_async_cluster(self):
- run_config = self.request_json.get("run_config", {})
- run_params = run_config.get("parameters", {})
- common_args = run_params.get("common_args", {})
- self.enable_async_cluster = common_args.get("enable_async_cluster", False)
- self.async_check_timeout = common_args.get("async_check_timeout", 0)
- if self.enable_async_cluster:
- return True, self.async_check_timeout
-
- return False, self.async_check_timeout
-
- @debug
- def detect_edges_status(
- self, edge_device_info_queue, edge_device_info_global_queue=None, callback_when_edges_ready=None, status_timeout=None,
- need_to_trigger_exception=True, status_check_context=None, given_edge_ids=None,
- callback_when_detecting=None, args_for_callback_when_detecting=None
- ):
- run_id = self.request_json["runId"]
- run_id_str = str(run_id)
- edge_id_list = self.request_json["edgeids"]
- if given_edge_ids is not None:
- edge_id_list = given_edge_ids
-
- # Init realtime status of all edges
- run_edges_realtime_status = dict()
- run_edges_realtime_status[run_id_str] = dict()
-
- edge_info_global_dict = dict()
- if edge_device_info_global_queue is not None:
- for edge_info_global in edge_device_info_global_queue:
- edge_info_id = edge_info_global.get("edge_id")
- edge_info_global_dict[edge_info_id] = edge_info_global
-
- # Send status message to all edges
- allowed_cache_edge_status_time = 60
- for edge_id in edge_id_list:
- # Check if the edge status was filled allowed_cache_edge_status_time seconds ago,
- # if so no more checking message would be sent.
- edge_info = edge_info_global_dict.get(edge_id, None)
- if edge_info is not None:
- timestamp = edge_info.get("timestamp", None)
- time_interval = time.time() - timestamp
- if time_interval <= allowed_cache_edge_status_time:
- continue
-
- self.send_status_check_msg(run_id, edge_id, self.edge_id, context=status_check_context)
- time.sleep(3)
-
- total_sleep_seconds = 0
- status_check_sleep_seconds = 10
- allowed_status_check_sleep_seconds = 60 * 2 if status_timeout is None else status_timeout
- allowed_status_check_sleep_seconds_for_async = 30
- inactivate_edges = list()
- active_edge_info_dict = dict()
- while True:
- if callback_when_detecting is not None:
- callback_when_detecting(args_for_callback_when_detecting)
-
- # Fetch edge info from the edge status queue, which will be added to realtime status map
- while True:
- self.check_runner_stop_event()
-
- try:
- edge_info = edge_device_info_queue.get(block=False, timeout=1)
- if edge_info is not None:
- edge_id = edge_info.get("edge_id", None)
- if edge_id is not None:
- run_edges_realtime_status[run_id_str][edge_id] = edge_info
- except queue.Empty as e: # If queue is empty, then break loop
- break
-
- self.check_runner_stop_event()
-
- # Check all edges which don't send response status successfully
- # and retry to send the status checking message.
- active_edges_count = 0
- inactivate_edges.clear()
- active_edge_info_dict.clear()
- for edge_id in edge_id_list:
- edge_info_dict = run_edges_realtime_status.get(run_id_str, {})
- edge_info = edge_info_dict.get(edge_id, None)
- edge_info = edge_info_dict.get(str(edge_id), None) if edge_info is None else edge_info
- if edge_info is not None:
- active_edges_count += 1
- active_edge_info_dict[str(edge_id)] = edge_info
- else:
- # Check if the edge status was filled allowed_cache_edge_status_time seconds ago,
- # if so no more checking message would be sent.
- edge_info = edge_info_global_dict.get(edge_id, None)
- if edge_info is not None:
- timestamp = edge_info.get("timestamp", None)
- time_interval = time.time() - timestamp
- if time_interval <= allowed_cache_edge_status_time:
- active_edges_count += 1
- active_edge_info_dict[str(edge_id)] = edge_info
- continue
-
- inactivate_edges.append(edge_id)
- self.send_status_check_msg(run_id, edge_id, self.edge_id, context=status_check_context)
-
- # If all edges are ready then send the starting job message to them
- if active_edges_count == len(edge_id_list):
- logging.info(f"All edges are ready. Active edge id list is as follows. {active_edge_info_dict}")
- if callback_when_edges_ready is not None:
- logging.info("All edges are ready. Start to process the callback function.")
- callback_when_edges_ready(active_edge_info_dict=active_edge_info_dict)
- else:
- logging.info("All edges are ready. No callback function to process.")
- break
- else:
- logging.info(f"All edges are not ready. Active edge id list: {active_edge_info_dict}, "
- f"Inactive edge id list: {inactivate_edges}")
-
- # Check if runner needs to stop and sleep specific time
- self.check_runner_stop_event()
- time.sleep(status_check_sleep_seconds)
- total_sleep_seconds += status_check_sleep_seconds
-
- # Check if the status response message has timed out to receive
- if total_sleep_seconds >= allowed_status_check_sleep_seconds:
- # If so, send failed message to MLOps and send exception message to all edges.
- logging.error(f"There are inactive edge devices. "
- f"Inactivate edge id list is as follows. {inactivate_edges}")
- if need_to_trigger_exception:
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.server_agent_id)
- self.send_training_stop_request_to_edges_when_exception(edge_id_list,
- payload=json.dumps(self.request_json),
- run_id=run_id)
- return False, active_edge_info_dict, inactivate_edges
-
- # If we enable the mode for async cluster, then sleep some time and send messages to all clients.
- if callback_when_edges_ready is not None:
- should_async, async_timeout = self.should_process_async_cluster()
- if should_async and total_sleep_seconds >= allowed_status_check_sleep_seconds_for_async:
- if async_timeout > allowed_status_check_sleep_seconds_for_async:
- time.sleep(async_timeout - allowed_status_check_sleep_seconds_for_async)
- self.send_training_request_to_edges()
- return True, active_edge_info_dict, inactivate_edges
-
- return True, active_edge_info_dict, inactivate_edges
-
- def send_status_check_msg(self, run_id, edge_id, server_id, context=None):
- topic_get_model_device_id = "server/client/request_device_info/" + str(edge_id)
- payload = {"server_id": server_id, "run_id": run_id}
- if context is not None:
- payload["context"] = context
- self.message_center.send_message(topic_get_model_device_id, json.dumps(payload))
-
- @debug
- def send_training_request_to_edges(self, active_edge_info_dict=None):
- run_id = self.request_json["runId"]
- edge_id_list = self.request_json["edgeids"]
- run_config = self.request_json.get("run_config", {})
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
- job_yaml_default_none = run_params.get("job_yaml", None)
- computing = job_yaml.get("computing", {})
- request_num_gpus = computing.get("minimum_num_gpus", None)
- job_gpu_id_list = self.request_json.get("job_gpu_id_list", None)
-
- logging.info("Send training request to Edge ids: " + str(edge_id_list))
-
- should_match_gpu = False
- if job_yaml_default_none is not None and request_num_gpus is not None and \
- int(request_num_gpus) > 0 and active_edge_info_dict is not None:
- should_match_gpu = True
- SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(active_edge_info_dict, show_gpu_list=True)
-
- # Match and assign gpus to each device
- assigned_gpu_num_dict, assigned_gpu_ids_dict = SchedulerMatcher.match_and_assign_gpu_resources_to_devices(
- request_num_gpus, edge_id_list, active_edge_info_dict, job_gpu_id_list=job_gpu_id_list)
- if assigned_gpu_num_dict is None or assigned_gpu_ids_dict is None:
- # If no resources available, send failed message to MLOps and send exception message to all edges.
- gpu_count, gpu_available_count = SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(
- active_edge_info_dict, should_print=True)
- err_info = f"No resources available." \
- f"Total available GPU count {gpu_available_count} is less than " \
- f"request GPU count {request_num_gpus}"
- logging.error(err_info)
-
- # Bug fix: This mqtt message needs to be sent so platform can clean up the failed run and change the
- # status from running to failed.
- self.mlops_metrics.report_server_training_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id
- )
-
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.server_agent_id)
- self.send_training_stop_request_to_edges_when_exception(edge_id_list,
- payload=json.dumps(self.request_json),
- run_id=run_id)
-
- serving_args = job_yaml.get("serving_args", {})
- endpoint_id = serving_args.get("endpoint_id", None)
- if endpoint_id is not None:
- fedml.mlops.log_endpoint_status(
- endpoint_id, device_client_constants.ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
- fedml.mlops.log_run_log_lines(
- endpoint_id, 0, [err_info],
- log_source=device_client_constants.ClientConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT
- )
- return
-
- # Generate master node addr and port
- master_node_addr, master_node_port = SchedulerMatcher.get_master_node_info(edge_id_list,
- active_edge_info_dict)
-
- # Generate new edge id list after matched
- edge_id_list = SchedulerMatcher.generate_new_edge_list_for_gpu_matching(assigned_gpu_num_dict)
- if len(edge_id_list) <= 0:
- gpu_count, gpu_available_count = SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(
- active_edge_info_dict, should_print=True)
- logging.error(f"Request parameter for GPU num is invalid."
- f"Total available GPU count {gpu_available_count}."
- f"Request GPU num {request_num_gpus}")
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.server_agent_id)
- self.send_training_stop_request_to_edges_when_exception(edge_id_list,
- payload=json.dumps(self.request_json),
- run_id=run_id)
- return
-
- if should_match_gpu:
- # Report gpu num and related infos to MLOps.
- serving_args = job_yaml.get("serving_args", {})
- endpoint_id = serving_args.get("endpoint_id", None)
- if endpoint_id is not None:
- endpoint_info = list()
- for edge_id_item, gpu_num in assigned_gpu_num_dict.items():
- edge_info = active_edge_info_dict.get(str(edge_id_item), {})
- endpoint_info.append({
- "machine_id": edge_id_item, "endpoint_gpu_count": gpu_num,
- "master_deploy_id": edge_info.get("master_device_id", 0),
- "slave_deploy_id": edge_info.get("slave_device_id", 0)})
- topic_name = f"compute/mlops/endpoint"
- endpoint_info_json = {"endpoint_id": endpoint_id, "endpoint_info": endpoint_info}
- print(f"endpoint_info_json {endpoint_info_json}")
- self.message_center.send_message(topic_name, json.dumps(endpoint_info_json))
-
- client_rank = 1
- for edge_id in edge_id_list:
- topic_start_train = "flserver_agent/" + str(edge_id) + "/start_train"
- logging.info("start_train: send topic " + topic_start_train + " to client...")
- request_json = self.request_json
- request_json["client_rank"] = client_rank
- client_rank += 1
-
- if active_edge_info_dict is not None:
- edge_info = active_edge_info_dict.get(str(edge_id), {})
- model_master_device_id = edge_info.get("master_device_id", None)
- model_slave_device_id = edge_info.get("slave_device_id", None)
- model_slave_device_id_list = edge_info.get("slave_device_id_list", None)
-
- if should_match_gpu:
- request_json["scheduler_match_info"] = SchedulerMatcher.generate_match_info_for_scheduler(
- edge_id, edge_id_list, master_node_addr, master_node_port,
- assigned_gpu_num_dict, assigned_gpu_ids_dict,
- model_master_device_id=model_master_device_id,
- model_slave_device_id=model_slave_device_id,
- model_slave_device_id_list=model_slave_device_id_list
- )
-
- self.message_center.send_message(topic_start_train, json.dumps(request_json))
-
- def setup_listeners_for_edge_status(self, run_id, edge_ids, server_id):
- self.client_agent_active_list[f"{run_id}"] = dict()
- self.client_agent_active_list[f"{run_id}"][f"server"] = server_id
- for edge_id in edge_ids:
- self.client_agent_active_list[f"{run_id}"][f"{edge_id}"] = ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- edge_status_topic = "fl_client/flclient_agent_" + str(edge_id) + "/status"
- self.add_message_listener(edge_status_topic, self.callback_edge_status)
- self.subscribe_msg(edge_status_topic)
-
- def remove_listeners_for_edge_status(self, edge_ids=None):
- if edge_ids is None:
- edge_ids = self.request_json["edgeids"]
-
- for edge_id in edge_ids:
- edge_status_topic = "fl_client/flclient_agent_" + str(edge_id) + "/status"
- self.unsubscribe_msg(edge_status_topic)
-
- def setup_listener_for_run_metrics(self, run_id):
- metric_topic = f"fedml_slave/fedml_master/metrics/{run_id}"
- self.add_message_listener(metric_topic, self.callback_run_metrics)
- self.subscribe_msg(metric_topic)
-
- def remove_listener_for_run_metrics(self, run_id):
- metric_topic = f"fedml_slave/fedml_master/metrics/{run_id}"
- self.unsubscribe_msg(metric_topic)
-
- def setup_listener_for_run_logs(self, run_id):
- logs_topic = f"fedml_slave/fedml_master/logs/{run_id}"
- self.add_message_listener(logs_topic, self.callback_run_logs)
- self.subscribe_msg(logs_topic)
-
- def remove_listener_for_run_logs(self, run_id):
- logs_topic = f"fedml_slave/fedml_master/logs/{run_id}"
- self.unsubscribe_msg(logs_topic)
-
- def callback_run_logs(self, topic, payload):
- run_id = str(topic).split('/')[-1]
- run_id_str = str(run_id)
- if self.run_logs_queue_map.get(run_id_str) is None:
- self.run_logs_queue_map[run_id_str] = Queue()
- self.run_logs_queue_map[run_id_str].put(payload)
-
- def callback_run_metrics(self, topic, payload):
- print(f"callback_run_metrics topic {topic}, payload {payload}")
- run_id = str(topic).split('/')[-1]
- run_id_str = str(run_id)
- if self.run_metrics_queue_map.get(run_id_str) is None:
- self.run_metrics_queue_map[run_id_str] = Queue()
- self.run_metrics_queue_map[run_id_str].put(payload)
-
- def callback_edge_status(self, topic, payload):
- payload_json = json.loads(payload)
- run_id = payload_json.get("run_id", None)
- edge_id = payload_json.get("edge_id", None)
- status = payload_json.get("status", None)
- if run_id is not None and edge_id is not None:
- active_item_dict = self.client_agent_active_list.get(f"{run_id}", None)
- if active_item_dict is None:
- return
- self.client_agent_active_list[f"{run_id}"][f"{edge_id}"] = status
-
- if self.run_edge_id_status_queue_map.get(f"{run_id}") is None:
- self.run_edge_id_status_queue_map[f"{run_id}"] = Queue()
- self.run_edge_id_status_queue_map[f"{run_id}"].put(self.client_agent_active_list[f"{run_id}"])
-
- self.process_job_status(run_id, edge_id, status)
-
- def ota_upgrade(self, payload, request_json):
- run_id = request_json["runId"]
- force_ota = False
- ota_version = None
-
- try:
- run_config = request_json.get("run_config", None)
- parameters = run_config.get("parameters", None)
- common_args = parameters.get("common_args", None)
- force_ota = common_args.get("force_ota", False)
- ota_version = common_args.get("ota_version", None)
- except Exception as e:
- pass
-
- if force_ota and ota_version is not None:
- should_upgrade = True if ota_version != fedml.__version__ else False
- upgrade_version = ota_version
- else:
- try:
- fedml_is_latest_version, local_ver, remote_ver = sys_utils.check_fedml_is_latest_version(self.version)
- except Exception as e:
- return
-
- should_upgrade = False if fedml_is_latest_version else True
- upgrade_version = remote_ver
-
- if should_upgrade:
- job_obj = FedMLServerDataInterface.get_instance().get_job_by_id(run_id)
- if job_obj is None:
- FedMLServerDataInterface.get_instance(). \
- save_started_job(run_id, self.edge_id, time.time(),
- ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING,
- ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING,
- payload)
-
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
- logging.info(f"Upgrade to version {upgrade_version} ...")
-
- sys_utils.do_upgrade(self.version, upgrade_version)
-
- raise Exception("Restarting after upgraded...")
-
- def callback_start_train(self, topic=None, payload=None):
- print("callback_start_train: ")
- try:
- MLOpsConfigs.fetch_all_configs()
- except Exception as e:
- pass
-
- # get training params
- if self.run_as_cloud_server:
- message_bytes = payload.encode("ascii")
- base64_bytes = base64.b64decode(message_bytes)
- payload = base64_bytes.decode("ascii")
-
- # [NOTES] Example Request JSON: https://fedml-inc.larksuite.com/wiki/ScnIwUif9iupbjkYS0LuBrd6sod#WjbEdhYrvogmlGxKTOGu98C6sSb
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
-
- # Process the log
- run_id = request_json["runId"]
- run_id_str = str(run_id)
- if self.run_as_edge_server_and_agent or self.enable_simulation_cloud_agent:
- # Start log processor for current run
- self.args.run_id = run_id
- self.args.edge_id = self.edge_id
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
- MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(
- run_id, self.edge_id, SchedulerConstants.get_log_source(request_json))
- logging.info("start the log processor.")
- elif self.run_as_cloud_agent:
- # Start log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(
- run_id, request_json.get("server_id", "0"), SchedulerConstants.get_log_source(request_json)
- )
- elif self.run_as_cloud_server:
- self.server_agent_id = request_json.get("cloud_agent_id", self.edge_id)
- run_id = request_json["runId"]
- run_id_str = str(run_id)
-
- # Start log processor for current run
- self.args.run_id = run_id
- MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(
- run_id, self.edge_id, SchedulerConstants.get_log_source(request_json))
-
- logging.info("callback_start_train payload: {}".format(payload))
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- if not self.run_as_cloud_agent and not self.run_as_cloud_server:
- self.ota_upgrade(payload, request_json)
-
- self.start_request_json = payload
- self.run_id = run_id
- ServerConstants.save_runner_infos(self.args.device_id + "." + self.args.os_name, self.edge_id, run_id=run_id)
-
- # Start server with multiprocessing mode
- self.request_json = request_json
- self.running_request_json[run_id_str] = request_json
- edge_id_list = request_json.get("edgeids", list())
- self.run_edge_ids[run_id_str] = edge_id_list
-
- logging.info("subscribe the client exception message.")
-
- if self.run_as_edge_server_and_agent or self.enable_simulation_cloud_agent:
- self.init_job_task(request_json)
-
- self.args.run_id = run_id
-
- self._start_runner_process(run_id, request_json)
-
- ServerConstants.save_run_process(run_id, self.run_process_map[run_id_str].pid)
- elif self.run_as_cloud_agent:
- self.init_job_task(request_json)
-
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=request_json, agent_config=self.agent_config
- )
- server_runner.run_as_cloud_agent = self.run_as_cloud_agent
- server_runner.start_request_json = json.dumps(request_json)
- self.run_process_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_event_map[run_id_str].clear()
- server_runner.run_process_event = self.run_process_event_map[run_id_str]
-
- if not self.use_local_process_as_cloud_server:
- self.run_process_map[run_id_str] = Process(target=server_runner.start_cloud_server_process_entry)
- self.run_process_map[run_id_str].start()
- else:
- message_bytes = json.dumps(self.request_json).encode("ascii")
- base64_bytes = base64.b64encode(message_bytes)
- runner_cmd_encoded = base64_bytes.decode("ascii")
- logging.info("runner_cmd_encoded: {}".format(runner_cmd_encoded))
-
- cloud_device_id = request_json.get("cloudServerDeviceId", "0")
-
- self.run_process_map[run_id_str] = Process(
- target=FedMLServerRunner.start_local_cloud_server,
- args=(run_id_str, self.args.user, self.version, cloud_device_id, runner_cmd_encoded))
- self.run_process_map[run_id_str].start()
- time.sleep(1)
-
- ServerConstants.save_run_process(run_id, self.run_process_map[run_id_str].pid)
- elif self.run_as_cloud_server:
- self.server_agent_id = request_json.get("cloud_agent_id", self.edge_id)
- self.start_request_json = json.dumps(request_json)
- run_id = request_json["runId"]
- run_id_str = str(run_id)
-
- self.init_job_task(request_json)
-
- self.args.run_id = run_id
-
- self._start_runner_process(run_id, request_json)
- # ServerConstants.save_run_process(run_id, self.run_process_map[run_id_str].pid)
-
- @staticmethod
- def start_local_cloud_server(run_id, user, version, cloud_device_id, runner_cmd_encoded):
- print(f"start cloud server, device id {cloud_device_id}, runner cmd {runner_cmd_encoded}")
- if not FedMLServerRunner.debug_cloud_server:
- pip_source_dir = os.path.dirname(__file__)
- login_cmd = os.path.join(pip_source_dir, "server_login.py")
- run_cmd = f"{get_python_program()} -W ignore {login_cmd} -t login -r cloud_server -u {str(user)} " \
- f"-v {version} -id {cloud_device_id} -rc {runner_cmd_encoded}"
- os.system(run_cmd)
-
- def _start_runner_process(self, run_id, request_json, is_server_job=False):
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=request_json, agent_config=self.agent_config
- )
- run_id_str = str(run_id)
- server_runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- server_runner.edge_id = self.edge_id
- server_runner.server_agent_id = self.server_agent_id
- server_runner.start_request_json = json.dumps(request_json)
- self.run_process_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_event_map[run_id_str].clear()
- server_runner.run_process_event = self.run_process_event_map[run_id_str]
- self.run_process_completed_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_completed_event_map[run_id_str].clear()
- server_runner.run_process_completed_event = self.run_process_completed_event_map[run_id_str]
- if self.run_edge_id_status_queue_map.get(run_id_str, None) is None:
- self.run_edge_id_status_queue_map[run_id_str] = Queue()
- if self.run_edge_device_info_queue_map.get(run_id_str, None) is None:
- self.run_edge_device_info_queue_map[run_id_str] = Queue()
- if self.run_metrics_queue_map.get(run_id_str, None) is None:
- self.run_metrics_queue_map[run_id_str] = Queue()
- if self.run_events_queue_map.get(run_id_str, None) is None:
- self.run_events_queue_map[run_id_str] = Queue()
- if self.run_artifacts_queue_map.get(run_id_str, None) is None:
- self.run_artifacts_queue_map[run_id_str] = Queue()
- if self.run_logs_queue_map.get(run_id_str, None) is None:
- self.run_logs_queue_map[run_id_str] = Queue()
- # if self.run_edge_device_info_global_queue is None:
- # self.run_edge_device_info_global_queue = Array('i', list())
- server_runner.edge_id_status_queue = self.run_edge_id_status_queue_map[run_id_str]
- server_runner.edge_device_info_queue = self.run_edge_device_info_queue_map[run_id_str]
- self.run_process_map[run_id_str] = Process(
- target=server_runner.run if not is_server_job else server_runner.run_server_job_impl, args=(
- self.run_process_event_map[run_id_str], self.run_process_completed_event_map[run_id_str],
- self.run_edge_id_status_queue_map[run_id_str], self.run_edge_device_info_queue_map[run_id_str],
- self.run_metrics_queue_map[run_id_str], self.run_events_queue_map[run_id_str],
- self.run_artifacts_queue_map[run_id_str], self.run_logs_queue_map[run_id_str],
- self.message_center.get_message_queue(),
- self.run_edge_device_info_global_queue
- )
- )
- self.run_process_map[run_id_str].start()
- ServerConstants.save_run_process(run_id, self.run_process_map[run_id_str].pid)
-
- def start_cloud_server_process_entry(self):
- try:
- self.start_cloud_server_process()
- except Exception as e:
- pass
-
- def start_cloud_server_process(self):
- run_config = self.request_json["run_config"]
- packages_config = run_config["packages_config"]
- self.start_cloud_server(packages_config)
-
- def start_cloud_server(self, packages_config):
- server_id = self.request_json["server_id"]
- self.cloud_server_name = FedMLServerRunner.FEDML_CLOUD_SERVER_PREFIX + str(self.run_id) + "-" + str(server_id)
- self.server_docker_image = (
- self.agent_config["docker_config"]["registry_server"]
- + self.agent_config["docker_config"]["registry_dir"]
- + self.server_docker_base_image
- )
-
- logging.info("docker image {}".format(self.server_docker_image))
- # logging.info("file_sys_driver {}".format(self.agent_config["docker_config"]["file_sys_driver"]))
-
- registry_secret_cmd = (
- "kubectl create namespace fedml-devops-aggregator-"
- + self.version
- + ";kubectl -n fedml-devops-aggregator-"
- + self.version
- + " delete secret secret-"
- + self.cloud_server_name
- + " ;kubectl create secret docker-registry secret-"
- + self.cloud_server_name
- + " --docker-server="
- + self.agent_config["docker_config"]["registry_server"]
- + " --docker-username="
- + self.agent_config["docker_config"]["user_name"]
- + " --docker-password=$(aws ecr-public get-login-password --region "
- + self.agent_config["docker_config"]["public_cloud_region"]
- + ")"
- + " --docker-email=fedml@fedml.ai -n fedml-devops-aggregator-"
- + self.version
- )
- logging.info("Create secret cmd: " + registry_secret_cmd)
- os.system(registry_secret_cmd)
-
- message_bytes = json.dumps(self.request_json).encode("ascii")
- base64_bytes = base64.b64encode(message_bytes)
- runner_cmd_encoded = base64_bytes.decode("ascii")
- logging.info("runner_cmd_encoded: {}".format(runner_cmd_encoded))
- # logging.info("runner_cmd_decoded: {}".format(base64.b64decode(runner_cmd_encoded).decode()))
- cur_dir = os.path.dirname(__file__)
- run_deployment_cmd = (
- "export FEDML_AGGREGATOR_NAME="
- + self.cloud_server_name
- + ";export FEDML_AGGREGATOR_SVC="
- + self.cloud_server_name
- + ";export FEDML_AGGREGATOR_VERSION="
- + self.version
- + ';export FEDML_AGGREGATOR_IMAGE_PATH="'
- + self.server_docker_image
- + '"'
- + ";export FEDML_CONF_ID="
- + self.cloud_server_name
- + ";export FEDML_DATA_PV_ID="
- + self.cloud_server_name
- + ";export FEDML_DATA_PVC_ID="
- + self.cloud_server_name
- + ";export FEDML_REGISTRY_SECRET_SUFFIX="
- + self.cloud_server_name
- + ";export FEDML_ACCOUNT_ID=0"
- + ";export FEDML_SERVER_DEVICE_ID="
- + self.request_json.get("cloudServerDeviceId", "0")
- + ";export FEDML_VERSION="
- + self.version
- + ";export FEDML_PACKAGE_NAME="
- + packages_config.get("server", "")
- + ";export FEDML_PACKAGE_URL="
- + packages_config.get("serverUrl", "")
- + ";export FEDML_RUNNER_CMD="
- + runner_cmd_encoded
- + ";envsubst < "
- + os.path.join(cur_dir, "templates", "fedml-server-deployment.yaml")
- + " | kubectl apply -f - "
- )
- logging.info("FedMLServerRunner.run with k8s: " + run_deployment_cmd)
- os.system(run_deployment_cmd)
-
- def stop_cloud_server(self):
- self.cloud_server_name = FedMLServerRunner.FEDML_CLOUD_SERVER_PREFIX + str(self.run_id) \
- + "-" + str(self.edge_id)
- self.server_docker_image = (
- self.agent_config["docker_config"]["registry_server"]
- + self.agent_config["docker_config"]["registry_dir"]
- + self.server_docker_base_image
- )
- delete_deployment_cmd = (
- "export FEDML_AGGREGATOR_NAME="
- + self.cloud_server_name
- + ";export FEDML_AGGREGATOR_SVC="
- + self.cloud_server_name
- + ";export FEDML_AGGREGATOR_VERSION="
- + self.version
- + ';export FEDML_AGGREGATOR_IMAGE_PATH="'
- + self.server_docker_image
- + '"'
- + ";export FEDML_CONF_ID="
- + self.cloud_server_name
- + ";export FEDML_DATA_PV_ID="
- + self.cloud_server_name
- + ";export FEDML_DATA_PVC_ID="
- + self.cloud_server_name
- + ";export FEDML_REGISTRY_SECRET_SUFFIX="
- + self.cloud_server_name
- + ";kubectl -n fedml-devops-aggregator-"
- + self.version
- + " delete deployment "
- + self.cloud_server_name
- + ";kubectl -n fedml-devops-aggregator-"
- + self.version
- + " delete svc "
- + self.cloud_server_name
- + ";kubectl -n fedml-devops-aggregator-"
- + self.version
- + " delete secret secret-"
- + self.cloud_server_name
- )
- logging.info("FedMLServerRunner.stop_run with k8s: " + delete_deployment_cmd)
- os.system(delete_deployment_cmd)
-
- def setup_message_center(self):
- if self.message_center is not None:
- return
-
- self.message_center = FedMLMessageCenter(agent_config=self.agent_config)
- self.message_center.start_sender()
-
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
- self.mlops_metrics.set_messenger(self.message_center)
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.edge_id = self.edge_id
- self.mlops_metrics.server_agent_id = self.server_agent_id
-
- def rebuild_message_center(self, message_center_queue):
- self.message_center = FedMLMessageCenter(message_queue=message_center_queue)
-
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
- self.mlops_metrics.set_messenger(self.message_center)
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.edge_id = self.edge_id
- self.mlops_metrics.server_agent_id = self.server_agent_id
-
- def release_message_center(self):
- try:
- if self.message_center is not None:
- self.message_center.stop()
- self.message_center = None
-
- except Exception as e:
- logging.error(
- f"Failed to release client mqtt manager with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- def send_training_stop_request_to_edges(
- self, edge_id_list, payload=None, run_id=0):
- if payload is None:
- payload_obj = {"runId": run_id, "edgeids": edge_id_list}
- else:
- payload_obj = json.loads(payload)
-
- for edge_id in edge_id_list:
- topic_stop_train = "flserver_agent/" + str(edge_id) + "/stop_train"
- logging.info("stop_train: send topic " + topic_stop_train)
- self.message_center.send_message(topic_stop_train, json.dumps(payload_obj))
-
- def send_training_stop_request_to_specific_edge(self, edge_id, payload):
- topic_stop_train = "flserver_agent/" + str(edge_id) + "/stop_train"
- logging.info("stop_train: send topic " + topic_stop_train)
- self.message_center.send_message(topic_stop_train, payload)
-
- def send_training_stop_request_to_cloud_server(self, edge_id, payload):
- topic_stop_train = "mlops/flserver_agent_" + str(edge_id) + "/stop_train"
- logging.info("stop_train: send topic " + topic_stop_train)
- self.message_center.send_message(topic_stop_train, payload)
-
- def send_training_stop_request_to_edges_when_exception(
- self, edge_id_list, payload=None, run_id=0, server_id=None, status=None):
- if payload is None:
- payload_obj = {"runId": run_id, "edgeids": edge_id_list}
- if server_id is not None:
- payload_obj["serverId"] = server_id
- else:
- payload_obj = json.loads(payload)
- payload_obj["run_status"] = ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION if status is None else status
- topic_stop_train = "flserver_agent/" + str(self.edge_id) + "/stop_train"
- self.callback_stop_train(topic_stop_train, json.dumps(payload_obj), use_payload=payload_obj)
-
- def callback_stop_train(self, topic, payload, use_payload=None):
- # logging.info("callback_stop_train: topic = %s, payload = %s" % (topic, payload))
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json.get("runId", None)
- if run_id is None:
- run_id = request_json.get("id", None)
-
- edge_id_list = request_json["edgeids"]
- server_id = request_json.get("serverId", None)
- if server_id is None:
- server_id = request_json.get("server_id", None)
-
- if run_id is None or server_id is None:
- logging.info("Json format is not correct!")
- return
-
- # logging.info("Stop run with multiprocessing.")
-
- # Stop server with multiprocessing mode
- run_id_str = str(run_id)
- stop_request_json = self.running_request_json.get(run_id_str, None)
- if stop_request_json is None:
- stop_request_json = request_json
- if use_payload is not None:
- stop_request_json = use_payload
-
- if self.run_process_event_map.get(run_id_str) is not None:
- self.run_process_event_map.get(run_id_str).set()
-
- if self.run_as_edge_server_and_agent or self.enable_simulation_cloud_agent:
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=stop_request_json, agent_config=self.agent_config,
- edge_id=self.edge_id
- )
- server_runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- self.run_process_event_map_for_stop[run_id_str] = multiprocessing.Event()
- if self.run_edge_id_status_queue_map.get(run_id_str, None) is None:
- self.run_edge_id_status_queue_map[run_id_str] = Queue()
- if self.run_edge_device_info_queue_map_for_stop.get(run_id_str, None) is None:
- self.run_edge_device_info_queue_map_for_stop[run_id_str] = Queue()
- # if self.run_edge_device_info_global_queue_for_stop is None:
- # self.run_edge_device_info_global_queue_for_stop = Array('i', list())
-
- self.run_stop_process_map[run_id_str] = Process(
- target=server_runner.run_stop, args=(
- self.run_process_event_map_for_stop[run_id_str],
- self.run_edge_id_status_queue_map[run_id_str],
- self.run_edge_device_info_queue_map_for_stop[run_id_str],
- self.run_edge_device_info_global_queue_for_stop,
- self.message_center.get_message_queue(),
- )
- )
- self.run_stop_process_map[run_id_str].start()
- elif self.run_as_cloud_agent:
- self.send_training_stop_request_to_cloud_server(server_id, payload)
- return
- elif self.run_as_cloud_server:
- # if not self.use_local_process_as_cloud_server:
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=stop_request_json, agent_config=self.agent_config,
- edge_id=server_id
- )
- server_runner.run_as_cloud_agent = self.run_as_cloud_agent
- self.run_process_event_map_for_stop[run_id_str] = multiprocessing.Event()
- if self.run_edge_id_status_queue_map.get(run_id_str, None) is None:
- self.run_edge_id_status_queue_map[run_id_str] = Queue()
- if self.run_edge_device_info_queue_map_for_stop.get(run_id_str, None) is None:
- self.run_edge_device_info_queue_map_for_stop[run_id_str] = Queue()
- # if self.run_edge_device_info_global_queue_for_stop is None:
- # self.run_edge_device_info_global_queue_for_stop = Array('i', list())
-
- self.run_stop_process_map[run_id_str] = Process(
- target=server_runner.run_stop, args=(
- self.run_process_event_map_for_stop[run_id_str],
- self.run_edge_id_status_queue_map[run_id_str],
- self.run_edge_device_info_queue_map_for_stop[run_id_str],
- self.run_edge_device_info_global_queue_for_stop,
- self.message_center.get_message_queue(),
- )
- )
- self.run_stop_process_map[run_id_str].start()
- return
-
- if self.running_request_json.get(run_id_str, None) is not None:
- self.running_request_json.pop(run_id_str)
-
- if self.run_process_map.get(run_id_str, None) is not None:
- self.run_process_map.pop(run_id_str)
-
- def run_stop(self, process_event, edge_id_status_queue, edge_device_info_queue,
- edge_device_info_global_queue, message_center_queue):
- if platform.system() != "Windows":
- os.setsid()
-
- os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
- os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
-
- self.run_process_event = process_event
- try:
- MLOpsUtils.set_ntp_offset(self.ntp_offset)
-
- self.rebuild_message_center(message_center_queue)
-
- self.run_stop_impl(edge_id_status_queue, edge_device_info_queue, edge_device_info_global_queue)
- except Exception as e:
- logging.error("Stop runner exits with exceptions. {}".format(traceback.format_exc()))
- finally:
- logging.info("Release resources.")
-
- def run_stop_impl(self, edge_id_status_queue, edge_device_info_queue, edge_device_info_global_queue):
- run_id_str = str(self.run_id)
- edge_id_list = self.request_json["edgeids"]
-
- # Detect running status of all edges
- status_ok, active_edge_info_dict, inactivate_edges = self.detect_edges_status(
- edge_device_info_queue, edge_device_info_global_queue=edge_device_info_global_queue,
- status_timeout=120, need_to_trigger_exception=False,
- status_check_context=SchedulerConstants.STATUS_CHECK_FRO_RUN_STOP_CONTEXT)
-
- # Send the training stopping request to running edges.
- for edge_id_item, _ in active_edge_info_dict.items():
- self.send_training_stop_request_to_specific_edge(edge_id_item, json.dumps(self.request_json))
- time.sleep(0.2)
- time.sleep(3)
-
- total_sleep_seconds = 0
- allowed_status_check_sleep_seconds = 60
- server_id = self.edge_id
- running_edges_list = list()
- current_edge_id_status_map = dict()
-
- while True:
- # Fetch edge id and status from the edge id status queue
- while True:
- try:
- queue_item = edge_id_status_queue.get(block=False, timeout=3)
- if queue_item is not None:
- current_edge_id_status_map.update(queue_item)
- except queue.Empty as e: # If queue is empty, then break loop
- break
-
- # Calc the total killed device number
- running_edges_list.clear()
- number_of_failed_edges = 0
- number_of_finished_edges = 0
- number_of_killed_edges = 0
- for edge_id_item, status_item in current_edge_id_status_map.items():
- if edge_id_item == "server":
- continue
-
- if status_item is None or status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED or \
- status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION:
- number_of_failed_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
- number_of_finished_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED:
- number_of_killed_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE or \
- status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE:
- continue
-
- running_edges_list.append(edge_id_item)
-
- # If the killed device number is equal total device number, then break
- if len(running_edges_list) <= 0 and len(current_edge_id_status_map.keys()) == len(edge_id_list) + 1:
- break
-
- # Calc the timeout value to wait to device killed.
- time.sleep(3)
- total_sleep_seconds += 3
- if total_sleep_seconds < allowed_status_check_sleep_seconds:
- continue
-
- # If timeout, then report killed device status
- no_response_edges = list(set(edge_id_list) - set(running_edges_list))
- if len(no_response_edges) <= 0:
- break
- for edge_id_item in no_response_edges:
- self.mlops_metrics.report_client_id_status(
- edge_id_item, ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED,
- server_id=self.edge_id, run_id=self.run_id)
-
- if self.run_as_edge_server_and_agent or self.enable_simulation_cloud_agent:
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, self.edge_id)
- elif self.run_as_cloud_agent:
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, server_id)
-
- self.mlops_metrics.report_server_id_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- def set_run_status(self, run_id, status, running_request_json):
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=running_request_json, agent_config=self.agent_config
- )
- server_runner.edge_id = self.edge_id
- server_runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- server_runner.run_status = status
- server_runner.message_center = self.message_center
- server_runner.mlops_metrics = self.mlops_metrics
- server_runner.cleanup_client_with_status()
-
- def callback_runner_id_status(self, topic, payload):
- # logging.info("callback_runner_id_status: topic = %s, payload = %s" % (topic, payload))
- # logging.info(
- # f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- # )
-
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json["run_id"]
- status = request_json["status"]
- edge_id = request_json["edge_id"]
- server_id = request_json.get("server_id", None)
- run_id_str = str(run_id)
-
- if (
- status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED
- or status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED
- or status == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED
- ):
- completed_event = self.run_process_completed_event_map.get(run_id_str, None)
- if completed_event is not None:
- completed_event.set()
-
- FedMLServerDataInterface.get_instance().save_job_status(run_id, self.edge_id, status, status)
-
- # Stop server with multiprocessing mode
- running_request_json = self.running_request_json.get(run_id_str, None)
- if running_request_json is None:
- running_request_json = request_json
- if self.run_as_edge_server_and_agent or self.enable_simulation_cloud_agent:
- self.set_run_status(run_id, status, running_request_json)
-
- run_process = self.run_process_map.get(run_id_str, None)
- if run_process is not None:
- if run_process.pid is not None:
- RunProcessUtils.kill_process(run_process.pid)
-
- self.run_process_map.pop(run_id_str)
-
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- elif self.run_as_cloud_agent:
- pass
- elif self.run_as_cloud_server:
- self.set_run_status(run_id, status, running_request_json)
-
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- if self.use_local_process_as_cloud_server:
- # RunProcessUtils.kill_process(os.getpid())
- cloud_server_process = self.run_process_map.get(run_id_str, None)
- if cloud_server_process is not None:
- RunProcessUtils.kill_process(cloud_server_process.pid)
- else:
- self.stop_cloud_server()
-
- if self.run_process_map.get(run_id_str, None) is not None:
- self.run_process_map.pop(run_id_str)
-
- self.remove_listener_for_run_metrics(self.run_id)
- self.remove_listener_for_run_logs(self.run_id)
- elif (
- status == ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION
- ):
- request_json = self.running_request_json.get(run_id_str, None)
- if request_json is not None:
- edge_id_list = request_json.get("edgeids", list())
- server_id = request_json.get("serverId", None)
- server_id = request_json.get("server_id", None) if server_id is None else server_id
- self.send_training_stop_request_to_edges_when_exception(
- edge_id_list, run_id=run_id, server_id=server_id,
- status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
-
- FedMLServerDataInterface.get_instance().save_job_status(run_id, self.edge_id, status, status)
- else:
- request_json = self.running_request_json.get(run_id_str, None)
- if request_json is None:
- request_json = self.start_request_json
- self.mlops_metrics.report_server_training_status(
- run_id, status, edge_id=self.edge_id, running_json=json.dumps(request_json))
-
- def cleanup_client_with_status(self):
- if self.run_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
- # logging.info("received to finished status.")
- self.cleanup_run_when_finished(should_send_server_id_status=False)
- elif self.run_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
- # logging.info("received to failed status.")
- self.cleanup_run_when_starting_failed(should_send_server_id_status=False)
- elif self.run_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED:
- # logging.info("received to failed status.")
- self.cleanup_run_when_starting_failed(
- status=self.run_status, should_send_server_id_status=False)
-
- def callback_report_current_status(self, topic, payload):
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- request_json = json.loads(payload)
- if self.run_as_edge_server_and_agent:
- self.send_agent_active_msg()
- elif self.run_as_cloud_agent:
- self.send_agent_active_msg()
- elif self.run_as_cloud_server:
- pass
-
- @staticmethod
- def process_ota_upgrade_msg():
- os.system("pip install -U fedml")
-
- def callback_server_ota_msg(self, topic, payload):
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- request_json = json.loads(payload)
- cmd = request_json["cmd"]
-
- if cmd == ServerConstants.FEDML_OTA_CMD_UPGRADE:
- try:
- self.process_ota_upgrade_msg()
- # Process(target=FedMLServerRunner.process_ota_upgrade_msg).start()
- raise Exception("After upgraded, restart runner...")
- except Exception as e:
- pass
- elif cmd == ServerConstants.FEDML_OTA_CMD_RESTART:
- raise Exception("Restart runner...")
-
- def callback_response_device_info(self, topic, payload):
- # Parse payload
- payload_json = json.loads(payload)
- run_id = payload_json.get("run_id", 0)
- context = payload_json.get("context", None)
- master_device_id = payload_json.get("master_device_id", 0)
- slave_device_id = payload_json.get("slave_device_id", 0)
- slave_device_id_list = payload_json.get("slave_device_id_list", 0)
- edge_id = payload_json.get("edge_id", 0)
- device_info = payload_json.get("edge_info", 0)
- device_info["master_device_id"] = master_device_id
- device_info["slave_device_id"] = slave_device_id
- device_info["slave_device_id_list"] = slave_device_id_list
- run_id_str = str(run_id)
-
- # Put device info into a multiprocessing queue so master runner checks if all edges are ready
- if context is None:
- if self.run_edge_device_info_queue_map.get(run_id_str, None) is None:
- self.run_edge_device_info_queue_map[run_id_str] = Queue()
- self.run_edge_device_info_queue_map[run_id_str].put(device_info)
-
- # if self.run_edge_device_info_global_queue is None:
- # self.run_edge_device_info_global_queue = Array('i', list())
- #
- # self.run_edge_device_info_global_queue[len(self.run_edge_device_info_global_queue)] = \
- # {"timestamp": time.time(), "edge_id": edge_id, "device_info": device_info}
-
- self.check_model_device_ready_and_deploy(run_id, master_device_id, slave_device_id,
- slave_device_id_list=slave_device_id_list)
- elif context == SchedulerConstants.STATUS_CHECK_FRO_RUN_STOP_CONTEXT:
- if self.run_edge_device_info_queue_map_for_stop.get(run_id_str, None) is None:
- self.run_edge_device_info_queue_map_for_stop[run_id_str] = Queue()
- self.run_edge_device_info_queue_map_for_stop[run_id_str].put(device_info)
-
- # if self.run_edge_device_info_global_queue_for_stop is None:
- # self.run_edge_device_info_global_queue_for_stop = Array('i', list())
- #
- # self.run_edge_device_info_global_queue_for_stop[len(self.run_edge_device_info_global_queue_for_stop)] = \
- # {"timestamp": time.time(), "edge_id": edge_id, "device_info": device_info}
-
- def check_model_device_ready_and_deploy(self, run_id, master_device_id, slave_device_id, slave_device_id_list=None):
- request_json = self.running_request_json.get(str(run_id), None)
- if request_json is None:
- return
- run_config = request_json["run_config"]
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
- job_type = job_yaml.get("job_type", None)
- job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
- if job_type != Constants.JOB_TASK_TYPE_DEPLOY and job_type != Constants.JOB_TASK_TYPE_SERVE:
- return
-
- # Init model device ids for each run
- run_id_str = str(run_id)
- if self.run_model_device_ids.get(run_id_str, None) is None:
- self.run_model_device_ids[run_id_str] = list()
-
- # Append master device and slave devices to the model devices map
- self.run_model_device_ids[run_id_str].append({"master_device_id": master_device_id,
- "slave_device_id": slave_device_id})
- model_device_ids = self.run_model_device_ids.get(run_id_str, None)
- if model_device_ids is None:
- return
-
- # Check if all model devices are ready
- if len(model_device_ids) != len(self.run_edge_ids.get(run_id_str, list())):
- return
-
- # Generate model master ids and model slave device ids
- device_master_ids = list()
- device_slave_ids = list()
- for device_ids in model_device_ids:
- model_master_id = device_ids.get("master_device_id")
- model_slave_id = device_ids.get("slave_device_id")
- device_master_ids.append(model_master_id)
- device_slave_ids.append(model_slave_id)
-
- if len(device_master_ids) <= 0:
- return
-
- # Generate serving devices for deploying
- serving_devices = list()
- serving_devices.append(device_master_ids[0])
- serving_devices.extend(device_slave_ids)
-
- # Start to deploy the model
- self.deploy_model(serving_devices, request_json, run_id=run_id)
-
- def callback_request_device_info_from_mlops(self, topic, payload):
- self.response_device_info_to_mlops(topic, payload)
-
- def response_device_info_to_mlops(self, topic, payload):
- response_topic = f"master_agent/mlops/response_device_info"
- payload_json = json.loads(payload)
- need_gpu_info = payload_json.get("need_gpu_info", False)
- if self.mlops_metrics is not None:
- if not need_gpu_info:
- response_payload = {
- "run_id": self.run_id,
- "master_agent_device_id": self.edge_id,
- "fedml_version": fedml.__version__
- }
- else:
- total_mem, free_mem, total_disk_size, free_disk_size, cup_utilization, cpu_cores, \
- gpu_cores_total, gpu_cores_available, sent_bytes, recv_bytes, gpu_available_ids = \
- sys_utils.get_sys_realtime_stats()
- gpu_available_ids = JobRunnerUtils.get_instance().get_available_gpu_id_list(self.edge_id)
- gpu_available_ids = JobRunnerUtils.trim_unavailable_gpu_ids(gpu_available_ids)
- gpu_cores_available = len(gpu_available_ids)
- response_payload = {
- "run_id": self.run_id,
- "master_agent_device_id": self.edge_id,
- "memoryTotal": round(total_mem * MLOpsUtils.BYTES_TO_GB, 2),
- "memoryAvailable": round(free_mem * MLOpsUtils.BYTES_TO_GB, 2),
- "diskSpaceTotal": round(total_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
- "diskSpaceAvailable": round(free_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
- "cpuUtilization": round(cup_utilization, 2),
- "cpuCores": cpu_cores,
- "gpuCoresTotal": gpu_cores_total,
- "gpuCoresAvailable": gpu_cores_available,
- "networkTraffic": sent_bytes + recv_bytes,
- "timestamp": int(MLOpsUtils.get_ntp_time()),
- "fedml_version": fedml.__version__
- }
- self.mlops_metrics.report_json_message(response_topic, json.dumps(response_payload))
-
- @staticmethod
- def get_device_id():
- device_file_path = os.path.join(ServerConstants.get_data_dir(), ServerConstants.LOCAL_RUNNER_INFO_DIR_NAME)
- file_for_device_id = os.path.join(device_file_path, "devices.id")
- if not os.path.exists(device_file_path):
- os.makedirs(device_file_path)
- elif os.path.exists(file_for_device_id):
- with open(file_for_device_id, 'r', encoding='utf-8') as f:
- device_id_from_file = f.readline()
- if device_id_from_file is not None and device_id_from_file != "":
- return device_id_from_file
-
- if platform.system() == "Darwin":
- cmd_get_serial_num = "system_profiler SPHardwareDataType | grep Serial | awk '{gsub(/ /,\"\")}{print}' " \
- "|awk -F':' '{print $2}' "
- device_id = os.popen(cmd_get_serial_num).read()
- device_id = device_id.replace('\n', '').replace(' ', '')
- if device_id is None or device_id == "":
- device_id = hex(uuid.getnode())
- else:
- device_id = "0x" + device_id
- else:
- if "nt" in os.name:
-
- def get_uuid():
- guid = ""
- try:
- cmd = "wmic csproduct get uuid"
- guid = str(subprocess.check_output(cmd))
- pos1 = guid.find("\\n") + 2
- guid = guid[pos1:-15]
- except Exception as ex:
- pass
- return str(guid)
-
- device_id = str(get_uuid())
- elif "posix" in os.name:
- device_id = sys_utils.get_device_id_in_docker()
- if device_id is None:
- device_id = hex(uuid.getnode())
- else:
- device_id = sys_utils.run_subprocess_open(
- "hal-get-property --udi /org/freedesktop/Hal/devices/computer --key system.hardware.uuid".split()
- )
- device_id = hex(device_id)
-
- if device_id is not None and device_id != "":
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
- else:
- device_id = hex(uuid.uuid4())
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
-
- return device_id
-
- def bind_account_and_device_id(self, url, account_id, device_id, os_name, api_key="", role=None):
- if role is None:
- role = "edge_server"
- if self.run_as_edge_server_and_agent:
- role = "edge_server"
- elif self.run_as_cloud_agent:
- role = "cloud_agent"
- elif self.run_as_cloud_server:
- role = "cloud_server"
-
- ip = requests.get('https://checkip.amazonaws.com').text.strip()
- fedml_ver, exec_path, os_ver, cpu_info, python_ver, torch_ver, mpi_installed, \
- cpu_usage, available_mem, total_mem, gpu_info, gpu_available_mem, gpu_total_mem, \
- gpu_count, gpu_vendor, cpu_count, gpu_device_name = get_sys_runner_info()
- host_name = sys_utils.get_host_name()
- json_params = {
- "accountid": account_id,
- "deviceid": device_id,
- "type": os_name,
- "state": ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE,
- "status": ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE,
- "processor": cpu_info,
- "core_type": cpu_info,
- "network": "",
- "role": role,
- "os_ver": os_ver,
- "memory": total_mem,
- "ip": ip,
- "api_key": api_key,
- "extra_infos": {"fedml_ver": fedml_ver, "exec_path": exec_path, "os_ver": os_ver,
- "cpu_info": cpu_info, "python_ver": python_ver, "torch_ver": torch_ver,
- "mpi_installed": mpi_installed, "cpu_usage": cpu_usage,
- "available_mem": available_mem, "total_mem": total_mem,
- "cpu_count": cpu_count, "gpu_count": 0, "host_name": host_name}
- }
- if gpu_count > 0:
- if gpu_total_mem is not None:
- json_params["gpu"] = gpu_info if gpu_info is not None else "" + ", Total GPU Memory: " + gpu_total_mem
- else:
- json_params["gpu"] = gpu_info if gpu_info is not None else ""
- json_params["extra_infos"]["gpu_info"] = gpu_info if gpu_info is not None else ""
- if gpu_available_mem is not None:
- json_params["extra_infos"]["gpu_available_mem"] = gpu_available_mem
- if gpu_total_mem is not None:
- json_params["extra_infos"]["gpu_total_mem"] = gpu_total_mem
-
- json_params["extra_infos"]["gpu_count"] = gpu_count
- json_params["extra_infos"]["gpu_vendor"] = gpu_vendor
- json_params["extra_infos"]["gpu_device_name"] = gpu_device_name
-
- gpu_available_id_list = sys_utils.get_available_gpu_id_list(limit=gpu_count)
- gpu_available_count = len(gpu_available_id_list) if gpu_available_id_list is not None else 0
- gpu_list = sys_utils.get_gpu_list()
- json_params["extra_infos"]["gpu_available_count"] = gpu_available_count
- json_params["extra_infos"]["gpu_available_id_list"] = gpu_available_id_list
- json_params["extra_infos"]["gpu_list"] = gpu_list
- else:
- json_params["gpu"] = "None"
- json_params["extra_infos"]["gpu_available_count"] = 0
- json_params["extra_infos"]["gpu_available_id_list"] = []
- json_params["extra_infos"]["gpu_list"] = []
-
- _, cert_path = MLOpsConfigs.get_request_params()
- if cert_path is not None:
- try:
- requests.session().verify = cert_path
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- except requests.exceptions.SSLError as err:
- MLOpsConfigs.install_root_ca_file()
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- else:
- response = requests.post(url, json=json_params, headers={"Connection": "close"})
- edge_id = -1
- user_name = None
- extra_url = None
- if response.status_code != 200:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- pass
- else:
- # print("url = {}, response = {}".format(url, response))
- status_code = response.json().get("code")
- if status_code == "SUCCESS":
- edge_id = response.json().get("data").get("id")
- user_name = response.json().get("data").get("userName", None)
- extra_url = response.json().get("data").get("url", None)
- if edge_id is None or edge_id <= 0:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- else:
- if status_code == SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR:
- raise SystemExit(SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR)
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- return -1, None, None
- return edge_id, user_name, extra_url
-
- def fetch_configs(self):
- return MLOpsConfigs.fetch_all_configs()
-
- def send_agent_active_msg(self):
- active_topic = "flserver_agent/active"
- status = MLOpsStatus.get_instance().get_server_agent_status(self.edge_id)
- if (
- status is not None
- and status != ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE
- and status != ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- ):
- return
-
- if self.run_as_cloud_agent:
- status = ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- else:
- try:
- current_job = FedMLServerDataInterface.get_instance().get_job_by_id(self.run_id)
- except Exception as e:
- current_job = None
- if current_job is None:
- if status is not None and status == ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE:
- status = ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- else:
- return
- else:
- status = ServerConstants.get_device_state_from_run_edge_state(current_job.status)
- active_msg = {"ID": self.edge_id, "status": status}
- MLOpsStatus.get_instance().set_server_agent_status(self.edge_id, status)
- if self.mqtt_mgr is not None:
- self.mqtt_mgr.send_message_json(active_topic, json.dumps(active_msg))
- else:
- self.send_message_json(active_topic, json.dumps(active_msg))
-
- def recover_start_train_msg_after_upgrading(self):
- try:
- current_job = FedMLServerDataInterface.get_instance().get_current_job()
- if current_job is not None and \
- current_job.status == ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING:
- logging.info("start training after upgrading.")
- server_agent_id = self.edge_id
- topic_start_train = "mlops/flserver_agent_" + str(server_agent_id) + "/start_train"
- self.callback_start_train(topic_start_train, current_job.running_json)
- except Exception as e:
- logging.info("recover starting train message after upgrading: {}".format(traceback.format_exc()))
-
- def on_agent_mqtt_connected(self, mqtt_client_object):
- # The MQTT message topic format is as follows: <sender>/<receiver>/<action>
-
- # Setup MQTT message listener for starting training
- server_agent_id = self.edge_id
- topic_start_train = "mlops/flserver_agent_" + str(server_agent_id) + "/start_train"
- self.add_message_listener(topic_start_train, self.callback_start_train)
- self.mqtt_mgr.add_message_listener(topic_start_train, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener for stopping training
- topic_stop_train = "mlops/flserver_agent_" + str(server_agent_id) + "/stop_train"
- self.add_message_listener(topic_stop_train, self.callback_stop_train)
- self.mqtt_mgr.add_message_listener(topic_stop_train, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener for server status switching
- topic_server_status = "fl_server/flserver_agent_" + str(server_agent_id) + "/status"
- self.add_message_listener(topic_server_status, self.callback_runner_id_status)
- self.mqtt_mgr.add_message_listener(topic_server_status, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to report current device status.
- topic_report_status = "mlops/report_device_status"
- self.add_message_listener(topic_report_status, self.callback_report_current_status)
- self.mqtt_mgr.add_message_listener(topic_report_status, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to OTA messages from the MLOps.
- topic_ota_msg = "mlops/flserver_agent_" + str(server_agent_id) + "/ota"
- self.add_message_listener(topic_ota_msg, self.callback_server_ota_msg)
- self.mqtt_mgr.add_message_listener(topic_ota_msg, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to request device info from the client.
- topic_response_device_info = "client/server/response_device_info/" + str(self.edge_id)
- self.add_message_listener(topic_response_device_info, self.callback_response_device_info)
- self.mqtt_mgr.add_message_listener(topic_response_device_info, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to request device info from MLOps.
- topic_request_device_info_from_mlops = f"mlops/master_agent/request_device_info/{self.edge_id}"
- self.add_message_listener(topic_request_device_info_from_mlops, self.callback_request_device_info_from_mlops)
- self.mqtt_mgr.add_message_listener(
- topic_request_device_info_from_mlops, self.listener_message_dispatch_center)
-
- # Subscribe topics for starting train, stopping train and fetching client status.
- mqtt_client_object.subscribe(topic_start_train, qos=2)
- mqtt_client_object.subscribe(topic_stop_train, qos=2)
- mqtt_client_object.subscribe(topic_server_status, qos=2)
- mqtt_client_object.subscribe(topic_report_status, qos=2)
- mqtt_client_object.subscribe(topic_ota_msg, qos=2)
- mqtt_client_object.subscribe(topic_response_device_info, qos=2)
- mqtt_client_object.subscribe(topic_request_device_info_from_mlops, qos=2)
-
- self.subscribed_topics.clear()
- self.subscribed_topics.append(topic_start_train)
- self.subscribed_topics.append(topic_stop_train)
- self.subscribed_topics.append(topic_server_status)
- self.subscribed_topics.append(topic_report_status)
- self.subscribed_topics.append(topic_ota_msg)
- self.subscribed_topics.append(topic_response_device_info)
- self.subscribed_topics.append(topic_request_device_info_from_mlops)
-
- # Broadcast the first active message.
- self.send_agent_active_msg()
-
- if self.run_as_cloud_server:
- # Start the FedML server
- self.callback_start_train(payload=self.args.runner_cmd)
-
- # Echo results
- MLOpsRuntimeLog.get_instance(self.args).enable_show_log_to_stdout()
- print("\nCongratulations, your device is connected to the FedML MLOps platform successfully!")
- print(
- "Your FedML Edge ID is " + str(self.edge_id) + ", unique device ID is "
- + str(self.unique_device_id)
- )
- MLOpsRuntimeLog.get_instance(self.args).enable_show_log_to_stdout(enable=True)
-
- # Start the message center for listener
- self.start_listener(sender_message_queue=self.message_center.get_message_queue(),
- agent_config=self.agent_config)
-
- def on_agent_mqtt_disconnected(self, mqtt_client_object):
- MLOpsStatus.get_instance().set_server_agent_status(
- self.edge_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE
- )
-
- def setup_agent_mqtt_connection(self, service_config):
- # Setup MQTT connection
- self.mqtt_mgr = MqttManager(
- service_config["mqtt_config"]["BROKER_HOST"],
- service_config["mqtt_config"]["BROKER_PORT"],
- service_config["mqtt_config"]["MQTT_USER"],
- service_config["mqtt_config"]["MQTT_PWD"],
- service_config["mqtt_config"]["MQTT_KEEPALIVE"],
- f"FedML_ServerAgent_Daemon_@{self.user_name}@_@{self.args.current_device_id}@_@{str(uuid.uuid4())}@",
- "flserver_agent/last_will_msg",
- json.dumps({"ID": self.edge_id, "status": ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE})
- )
-
- # Init local database
- FedMLServerDataInterface.get_instance().create_job_table()
-
- # Start the message center to process edge related messages.
- self.setup_message_center()
-
- server_api_cmd = "fedml.computing.scheduler.master.server_api:api"
- server_api_pids = RunProcessUtils.get_pid_from_cmd_line(server_api_cmd)
- if server_api_pids is None or len(server_api_pids) <= 0:
- # Start local API services
- cur_dir = os.path.dirname(__file__)
- fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- python_program = get_python_program()
- self.local_api_process = ServerConstants.exec_console_with_script(
- "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
- "--log-level critical".format(
- python_program, server_api_cmd, ServerConstants.LOCAL_SERVER_API_PORT,
- fedml_base_dir
- ),
- should_capture_stdout=False,
- should_capture_stderr=False
- )
- # if self.local_api_process is not None and self.local_api_process.pid is not None:
- # print(f"Server local API process id {self.local_api_process.pid}")
-
- # Setup MQTT connected listener
- self.mqtt_mgr.add_connected_listener(self.on_agent_mqtt_connected)
- self.mqtt_mgr.add_disconnected_listener(self.on_agent_mqtt_disconnected)
- self.mqtt_mgr.connect()
-
- # Report the IDLE status to MLOps
- self.mlops_metrics.report_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE, edge_id=self.edge_id)
- MLOpsStatus.get_instance().set_server_agent_status(
- self.edge_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- )
-
- # MLOpsRuntimeLogDaemon.get_instance(self.args).stop_all_log_processor()
-
- self.mlops_metrics.stop_device_realtime_perf()
- self.mlops_metrics.report_device_realtime_perf(self.args, service_config["mqtt_config"], is_client=False)
-
- if not self.run_as_cloud_server:
- self.recover_start_train_msg_after_upgrading()
-
- JobCleanup.get_instance().sync_data_on_startup(self.edge_id, is_client=False)
-
- self.master_api_daemon = MasterApiDaemon()
- self.master_api_process = Process(target=self.master_api_daemon.run)
- self.master_api_process.start()
-
- # if self.model_device_server is None:
- # self.model_device_server = FedMLModelDeviceServerRunner(self.args, self.args.current_device_id,
- # self.args.os_name, self.args.is_from_docker,
- # self.agent_config)
- # self.model_device_server.start()
-
- def start_agent_mqtt_loop(self):
- # Start MQTT message loop
- try:
- self.mqtt_mgr.loop_forever()
- except Exception as e:
- if str(e) == "Restarting after upgraded...":
- logging.info("Restarting after upgraded...")
- else:
- logging.info("Server tracing: {}".format(traceback.format_exc()))
-
- finally:
- login_exit_file = os.path.join(ServerConstants.get_log_file_dir(), "exited.log")
- with open(login_exit_file, "w") as f:
- f.writelines(f"{os.getpid()}.")
-
- self.stop_agent()
-
- time.sleep(5)
- sys_utils.cleanup_all_fedml_server_login_processes(
- ServerConstants.SERVER_LOGIN_PROGRAM, clean_process_group=False)
- sys.exit(1)
-
- def stop_agent(self):
- if self.run_process_event is not None:
- self.run_process_event.set()
-
- if self.mqtt_mgr is not None:
- try:
- for topic in self.subscribed_topics:
- self.mqtt_mgr.unsubscribe_msg(topic)
- except Exception as e:
- pass
-
- self.mqtt_mgr.loop_stop()
- self.mqtt_mgr.disconnect()
- self.release_message_center()
-
- def get_runner(self):
- runner = FedMLServerRunner(
- self.args, run_id=self.run_id, request_json=self.request_json,
- agent_config=self.agent_config
- )
- runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- runner.edge_id = self.edge_id
- runner.server_agent_id = self.server_agent_id
- runner.start_request_json = self.start_request_json
- runner.unique_device_id = self.unique_device_id
- runner.user_name = self.user_name
- runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- runner.run_as_cloud_agent = self.run_as_cloud_agent
- runner.run_as_cloud_server = self.run_as_cloud_server
- return runner
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_runner.py b/python/fedml/computing/scheduler/model_scheduler/device_client_runner.py
deleted file mode 100755
index e82e8c5542..0000000000
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_runner.py
+++ /dev/null
@@ -1,1335 +0,0 @@
-import json
-import logging
-import multiprocessing
-import sys
-
-from multiprocessing import Process
-import os
-import platform
-import shutil
-import subprocess
-import threading
-
-import time
-import traceback
-import urllib
-import uuid
-import zipfile
-from urllib.parse import urlparse, urljoin
-
-import requests
-import docker
-
-import fedml
-from fedml import mlops
-from fedml.computing.scheduler.model_scheduler.device_model_msg_object import FedMLModelMsgObject
-from fedml.core.distributed.communication.s3.remote_storage import S3Storage
-from .device_model_cache import FedMLModelCache
-from ..comm_utils import sys_utils, security_utils
-
-from ..comm_utils.container_utils import ContainerUtils
-
-from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
-
-from ....core.distributed.communication.mqtt.mqtt_manager import MqttManager
-from ..comm_utils.yaml_utils import load_yaml_config
-from .device_client_constants import ClientConstants
-
-from ....core.mlops.mlops_metrics import MLOpsMetrics
-
-from ....core.mlops.mlops_configs import MLOpsConfigs
-from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
-from ....core.mlops.mlops_status import MLOpsStatus
-from ..comm_utils.sys_utils import get_sys_runner_info, get_python_program
-from .device_model_deployment import start_deployment, run_http_inference_with_curl_request
-from .device_client_data_interface import FedMLClientDataInterface
-from ....core.mlops.mlops_utils import MLOpsUtils
-from ..comm_utils.job_utils import JobRunnerUtils
-from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
-from .device_mqtt_inference_protocol import FedMLMqttInference
-from .device_model_db import FedMLModelDatabase
-from ..comm_utils.constants import SchedulerConstants
-from fedml.computing.scheduler.comm_utils.job_monitor import JobMonitor
-
-
-class RunnerError(Exception):
- """ Runner failed. """
- pass
-
-
-class RunnerCompletedError(Exception):
- """ Runner completed. """
- pass
-
-
-class FedMLClientRunner:
- FEDML_BOOTSTRAP_RUN_OK = "[FedML]Bootstrap Finished"
-
- def __init__(self, args, edge_id=0, request_json=None, agent_config=None, run_id=0):
- self.local_api_process = None
- self.run_process_event = None
- self.run_process_event_map = dict()
- self.run_process_completed_event = None
- self.run_process_completed_event_map = dict()
- self.run_inference_event_map = dict()
- self.run_inference_response_map = dict()
- self.run_process_map = dict()
- self.device_status = None
- self.current_training_status = None
- self.mqtt_mgr = None
- self.client_mqtt_mgr = None
- self.client_mqtt_is_connected = False
- self.client_mqtt_lock = None
- self.edge_id = edge_id
- self.run_id = run_id
- self.unique_device_id = None
- self.args = args
- self.request_json = request_json
- self.version = args.version
- self.device_id = args.device_id
- self.cur_dir = os.path.split(os.path.realpath(__file__))[0]
- if args.current_running_dir is not None:
- self.cur_dir = args.current_running_dir
- self.sudo_cmd = ""
- self.is_mac = False
- if platform.system() == "Darwin":
- self.is_mac = True
-
- self.agent_config = agent_config
- self.fedml_data_base_package_dir = os.path.join("/", "fedml", "data")
- self.fedml_data_local_package_dir = os.path.join("/", "fedml", "fedml-package", "fedml", "data")
- self.fedml_data_dir = self.fedml_data_base_package_dir
- self.fedml_config_dir = os.path.join("/", "fedml", "conf")
-
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES = {}
-
- self.mlops_metrics = None
- self.client_active_list = dict()
- self.infer_host = "127.0.0.1"
- self.redis_addr = "local"
- self.redis_port = "6379"
- self.redis_password = "fedml_default"
- self.model_is_from_open = False
-
- self.model_runner_mapping = dict()
- self.ntp_offset = MLOpsUtils.get_ntp_offset()
- self.running_request_json = dict()
- self.endpoint_inference_runners = dict()
- self.mqtt_inference_obj = None
-
- self.subscribed_topics = list()
- self.user_name = None
-
- def unzip_file(self, zip_file, unzip_file_path) -> str:
- unziped_file_name = ""
- if zipfile.is_zipfile(zip_file):
- with zipfile.ZipFile(zip_file, "r") as zipf:
- zipf.extractall(unzip_file_path)
- unziped_file_name = zipf.namelist()[0]
- else:
- raise Exception("Invalid zip file {}".format(zip_file))
-
- return unziped_file_name
-
- def retrieve_and_unzip_package(self, package_name, package_url):
- local_package_path = ClientConstants.get_model_package_dir()
- os.makedirs(local_package_path, exist_ok=True)
- filename, filename_without_extension, file_extension = ClientConstants.get_filename_and_extension(package_url)
- local_package_file = os.path.join(local_package_path,
- f"fedml_run_{self.run_id}_{self.edge_id}_{filename_without_extension}")
- if os.path.exists(local_package_file):
- os.remove(local_package_file)
- logging.info("Download from package_url {}".format(package_url))
-
- package_url_without_query_path = urljoin(package_url, urlparse(package_url).path)
- urllib.request.urlretrieve(package_url_without_query_path, local_package_file,
- reporthook=self.package_download_progress)
- unzip_package_path = os.path.join(ClientConstants.get_package_unzip_dir(),
- f"unzip_fedml_run_{self.run_id}_{self.edge_id}_{filename_without_extension}")
- try:
- shutil.rmtree(unzip_package_path, ignore_errors=True)
- except Exception as e:
- pass
-
- package_dir_name = self.unzip_file(local_package_file, unzip_package_path) # Using unziped folder name
- unzip_package_full_path = os.path.join(unzip_package_path, package_dir_name)
- model_bin_file = os.path.join(unzip_package_path, "fedml_model.bin")
-
- logging.info("local_package_file {}, unzip_package_path {}, unzip file full path {}".format(
- local_package_file, unzip_package_path, unzip_package_full_path))
-
- return unzip_package_full_path, model_bin_file
-
- def retrieve_binary_model_file(self, package_name, package_url):
- local_package_path = ClientConstants.get_model_package_dir()
- if not os.path.exists(local_package_path):
- os.makedirs(local_package_path, exist_ok=True)
- unzip_package_path = ClientConstants.get_model_dir()
- local_package_file = "{}".format(os.path.join(local_package_path, package_name))
- if os.path.exists(local_package_file):
- os.remove(local_package_file)
- package_url_without_query_path = urljoin(package_url, urlparse(package_url).path)
- urllib.request.urlretrieve(package_url_without_query_path, local_package_file,
- reporthook=self.package_download_progress)
-
- unzip_package_path = os.path.join(unzip_package_path, package_name)
- if not os.path.exists(unzip_package_path):
- os.makedirs(unzip_package_path, exist_ok=True)
- dst_model_file = os.path.join(unzip_package_path, package_name)
- if os.path.exists(local_package_file):
- shutil.copy(local_package_file, dst_model_file)
-
- return unzip_package_path, dst_model_file
-
- def package_download_progress(self, count, blksize, filesize):
- self.check_runner_stop_event()
-
- downloaded = count * blksize
- downloaded = filesize if downloaded > filesize else downloaded
- progress = (downloaded / filesize * 100) if filesize != 0 else 0
- progress_int = int(progress)
- downloaded_kb = format(downloaded / 1024, '.2f')
-
- # since this hook funtion is stateless, we need a state to avoid printing progress repeatly
- if count == 0:
- self.prev_download_progress = 0
- if progress_int != self.prev_download_progress and progress_int % 5 == 0:
- self.prev_download_progress = progress_int
- logging.info("package downloaded size {} KB, progress {}%".format(downloaded_kb, progress_int))
-
- def build_dynamic_constrain_variables(self, run_id, run_config):
- pass
-
- def update_local_fedml_config(self, run_id, model_config, model_config_parameters):
- model_name = model_config["model_name"]
- model_storage_url = model_config["model_storage_url"]
- scale_min = model_config.get("instance_scale_min", 0)
- scale_max = model_config.get("instance_scale_max", 0)
- inference_engine = model_config.get("inference_engine", 0)
- inference_end_point_id = run_id
-
- # Retrieve model package or model binary file.
- if self.model_is_from_open:
- unzip_package_path, model_bin_file = self.retrieve_binary_model_file(model_name, model_storage_url)
- else:
- unzip_package_path, model_bin_file = self.retrieve_and_unzip_package(model_name, model_storage_url)
-
- # Load the config to memory
- package_conf_object = {}
- fedml_local_config_file = os.path.join(unzip_package_path, "fedml_model_config.yaml")
- if os.path.exists(fedml_local_config_file):
- package_conf_object = load_yaml_config(fedml_local_config_file)
- else:
- if model_config_parameters is not None:
- logging.warning(f"The fedml_local_config_file {fedml_local_config_file} does not exist, will \
- create a new one with the model_config_parameters from json.")
- package_conf_object = model_config_parameters
- with open(fedml_local_config_file, 'w') as f:
- json.dump(package_conf_object, f)
- else:
- logging.info(f"The fedml_local_config_file {fedml_local_config_file} does not exist,\
- and the model_config_parameters is None.")
- logging.info("The package_conf_object is {}".format(package_conf_object))
-
- return unzip_package_path, model_bin_file, package_conf_object
-
- def build_dynamic_args(self, run_config, package_conf_object, base_dir):
- pass
-
- def download_model_package(self, package_name, package_url):
- # Copy config file from the client
- unzip_package_path = self.retrieve_and_unzip_package(
- package_name, package_url
- )
-
- return unzip_package_path
-
- def run(self, process_event, completed_event):
- # print(f"Model worker runner process id {os.getpid()}, run id {self.run_id}")
-
- if platform.system() != "Windows":
- os.setsid()
-
- os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
- os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
-
- self.run_process_event = process_event
- self.run_process_completed_event = completed_event
- run_id = self.request_json.get("end_point_id")
-
- try:
- FedMLModelDatabase.get_instance().set_database_base_dir(ClientConstants.get_database_dir())
- FedMLModelDatabase.get_instance().create_table()
-
- MLOpsUtils.set_ntp_offset(self.ntp_offset)
- self.setup_client_mqtt_mgr()
-
- if not self.run_impl():
- logging.info(
- f"[endpoint/device][{run_id}/{self.edge_id}] Release gpu resource when the worker deployment returned false.")
- self.release_gpu_ids(run_id)
- except RunnerError:
- logging.info("Runner stopped.")
- logging.info(
- f"[endpoint/device][{run_id}/{self.edge_id}] Release gpu resource when the worker deployment stopped.")
- self.release_gpu_ids(run_id)
- self.reset_devices_status(self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED)
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- except RunnerCompletedError:
- logging.info(
- f"[endpoint/device][{run_id}/{self.edge_id}] Release gpu resource when the worker deployment completed.")
- self.release_gpu_ids(run_id)
- logging.info("Runner completed.")
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- except Exception as e:
- logging.error("Runner exits with exceptions. {}".format(traceback.format_exc()))
- self.cleanup_run_when_starting_failed()
- self.mlops_metrics.client_send_exit_train_msg(
- run_id, self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
- logging.info(
- f"[endpoint/device][{run_id}/{self.edge_id}] Release gpu resource when the worker deployment occurred exceptions.")
- self.release_gpu_ids(run_id)
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- time.sleep(2)
- sys.exit(1)
- finally:
- logging.info("Release resources.")
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- if self.mlops_metrics is not None:
- self.mlops_metrics.stop_sys_perf()
- time.sleep(3)
- self.release_client_mqtt_mgr()
-
- def release_gpu_ids(self, run_id):
- JobRunnerUtils.get_instance().release_gpu_ids(run_id, self.edge_id)
-
- def check_runner_stop_event(self):
- if self.run_process_event.is_set():
- logging.info("Received stopping event.")
- raise RunnerError("Runner stopped")
-
- if self.run_process_completed_event is not None and self.run_process_completed_event.is_set():
- logging.info("Received completed event.")
- raise RunnerCompletedError("Runner completed")
-
- def inference_run(self):
- # run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
- # model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
- # inference_end_point_id, use_gpu, memory_size, model_version = self.parse_model_run_params(self.request_json)
- #
- # inference_client = FedMLModelServingClient(self.args,
- # end_point_name,
- # model_name,
- # model_version,
- # inference_request=self.request_json)
- # inference_client.run()
- pass
-
- def run_impl(self):
- run_id = self.request_json["end_point_id"]
- end_point_name = self.request_json["end_point_name"]
- token = self.request_json["token"]
- user_id = self.request_json["user_id"]
- user_name = self.request_json["user_name"]
- device_ids = self.request_json["device_ids"]
- device_objs = self.request_json["device_objs"]
- master_ip = self.request_json["master_node_ip"]
-
- model_config = self.request_json["model_config"]
- model_name = model_config["model_name"]
- model_id = model_config["model_id"]
- model_version = model_config["model_version"]
- model_storage_url = model_config["model_storage_url"]
- scale_min = model_config.get("instance_scale_min", 0)
- scale_max = model_config.get("instance_scale_max", 0)
- model_config_parameters = self.request_json["parameters"]
-
- inference_port = model_config_parameters.get("worker_internal_port",
- ClientConstants.MODEL_INFERENCE_DEFAULT_PORT)
- inference_port_external = model_config_parameters.get("worker_external_port", inference_port)
-
- if "using_triton" in model_config_parameters and model_config_parameters["using_triton"]:
- inference_engine = ClientConstants.INFERENCE_ENGINE_TYPE_INT_TRITON
- else:
- inference_engine = ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT
-
- logging.info("[Critical] The inference_engine is: {}".format(inference_engine))
-
- self.model_is_from_open = True if model_config.get("is_from_open", 0) == 1 else False
- if self.model_is_from_open:
- model_net_url = model_config["model_net_url"]
- inference_end_point_id = run_id
- use_gpu = "gpu" # TODO: Get GPU from device infos
- memory_size = "4096m" # TODO: Get Memory size for each instance
-
- self.mlops_metrics.report_sys_perf(self.args, self.agent_config["mqtt_config"], run_id=run_id)
-
- self.check_runner_stop_event()
-
- logging.info("model deployment request: {}".format(self.request_json))
-
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
-
- # Initiate an FedMLInferenceClient object
- # client_runner = FedMLClientRunner(
- # self.args, edge_id=self.edge_id, run_id=self.run_id, request_json=self.request_json,
- # agent_config=self.agent_config
- # )
- # inference_process = Process(target=client_runner.inference_run)
- # inference_process.start()
-
- self.mlops_metrics.report_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_INITIALIZING,
- is_from_model=True, running_json=json.dumps(self.request_json), run_id=run_id)
-
- self.mlops_metrics.report_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_RUNNING,
- is_from_model=True, run_id=run_id)
-
- self.check_runner_stop_event()
-
- # update local config with real time parameters from server and dynamically replace variables value
- logging.info("download and unzip model to local...")
- unzip_package_path, model_bin_file, fedml_config_object = \
- self.update_local_fedml_config(run_id, model_config, model_config_parameters)
- if unzip_package_path is None or fedml_config_object is None:
- logging.info("failed to update local fedml config.")
- self.check_runner_stop_event()
- self.cleanup_run_when_starting_failed()
- self.mlops_metrics.client_send_exit_train_msg(run_id, self.edge_id,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
- return False
-
- logging.info("check downloaded packages...")
- if not os.path.exists(unzip_package_path):
- logging.info("failed to unzip file.")
- self.check_runner_stop_event()
- self.cleanup_run_when_starting_failed()
- self.mlops_metrics.client_send_exit_train_msg(run_id, self.edge_id,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
- return False
-
- # download model net and load into the torch model
- model_from_open = None
- self.model_is_from_open = None
- if self.model_is_from_open:
- logging.info("process the model net from open...")
- self.check_runner_stop_event()
- s3_config = self.agent_config.get("s3_config", None)
- if s3_config is not None and model_net_url is not None and model_net_url != "":
- s3_client = S3Storage(s3_config)
- url_parsed = urlparse(model_net_url)
- path_list = url_parsed.path.split("/")
- if len(path_list) > 0:
- model_key = path_list[-1]
- model_from_open = s3_client.read_model_net(model_key,
- ClientConstants.get_model_cache_dir())
-
- model_input_size, model_input_type = mlops.get_training_model_input_info(model_net_url, s3_config)
- if model_input_size is not None and model_input_type is not None:
- model_config_parameters["input_size"] = model_input_size
- model_config_parameters["input_types"] = model_input_type
- logging.info(
- f"model input size {model_input_size}, input type {model_input_type} from the open platform.")
-
- logging.info("Check if need update / removing existed container...")
- if "diff_devices" in self.request_json and str(self.edge_id) in self.request_json["diff_devices"] and \
- self.request_json["diff_devices"][str(self.edge_id)] == ClientConstants.DEVICE_DIFF_REPLACE_OPERATION:
- self.handle_replaced_device()
-
- logging.info("start the model deployment...")
- self.check_runner_stop_event()
- running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
- "", "", model_version, {}, {}
- try:
- client_ip = self.get_ip_address(self.request_json)
- running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
- start_deployment(
- inference_end_point_id, end_point_name, model_id, model_version,
- unzip_package_path, model_bin_file, model_name, inference_engine,
- ClientConstants.INFERENCE_HTTP_PORT,
- ClientConstants.INFERENCE_GRPC_PORT,
- ClientConstants.INFERENCE_METRIC_PORT,
- use_gpu, memory_size,
- ClientConstants.INFERENCE_CONVERTOR_IMAGE,
- ClientConstants.INFERENCE_SERVER_IMAGE,
- client_ip,
- self.model_is_from_open, model_config_parameters,
- model_from_open,
- token,
- master_ip, self.edge_id, master_device_id=device_ids[0])
- except Exception as e:
- inference_output_url = ""
- logging.error(f"Exception at deployment: {traceback.format_exc()}")
-
- if inference_output_url == "":
- logging.error("failed to deploy the model...")
-
- result_payload = self.send_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
- model_id, model_name, inference_output_url, inference_model_version, inference_port,
- inference_engine, model_metadata, model_config)
-
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.broadcast_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- is_from_model=True, run_id=self.run_id)
-
- self.mlops_metrics.client_send_exit_train_msg(
- run_id, self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
-
- # After sending the deployment status, we should wait for the master to delete the deployment status
- status_payload = self.send_deployment_status(
- end_point_name, self.edge_id, model_id, model_name, model_version, inference_output_url,
- ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED, inference_port=inference_port)
-
- return False
- else:
- logging.info("finished deployment, continue to send results to master...")
- status_payload = self.send_deployment_status( # Send Master the external port
- end_point_name, self.edge_id, model_id, model_name, model_version, inference_output_url,
- ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED, inference_port=inference_port_external)
- result_payload = self.send_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
- model_id, model_name, inference_output_url, model_version, inference_port_external,
- inference_engine, model_metadata, model_config)
-
- if inference_port_external != inference_port: # For Worker, use internal port
- logging.info("inference_port_external {} != inference_port {}".format(
- inference_port_external, inference_port))
- status_payload = self.construct_deployment_status(
- end_point_name, self.edge_id, model_id, model_name, model_version, inference_output_url,
- ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED, inference_port=inference_port)
- result_payload = self.construct_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
- model_id, model_name, inference_output_url, model_version, inference_port,
- inference_engine, model_metadata, model_config)
-
- FedMLModelDatabase.get_instance().set_deployment_result(
- run_id, end_point_name, model_name, model_version, self.edge_id, json.dumps(result_payload))
-
- FedMLModelDatabase.get_instance().set_deployment_status(
- run_id, end_point_name, model_name, model_version, self.edge_id, json.dumps(status_payload))
-
- time.sleep(1)
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.broadcast_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
- is_from_model=True, run_id=self.run_id)
- return True
-
- def handle_replaced_device(self):
- """
- Strategy-1:
- (1) clean local records (2) find and clean current container using diff_version: {device_id: old_version}
- """
- end_point_id = self.request_json["end_point_id"]
- end_point_name = self.request_json["end_point_name"]
- model_config = self.request_json["model_config"]
- model_name = model_config["model_name"]
- model_id = model_config["model_id"]
- new_model_version = model_config["model_version"]
- old_model_version = self.request_json["diff_version"][str(self.edge_id)]
-
- logging.info(f"[endpoint/device][{end_point_id}/{self.edge_id}] "
- f"Start to handle replaced device {self.edge_id} to new version {new_model_version}."
- f"which originally has old version {old_model_version}.")
-
- try:
- JobRunnerUtils.get_instance().release_gpu_ids(end_point_id, self.edge_id)
-
- # Instead of deleting the records, need to change the job status to "UPGRADING"
- FedMLClientDataInterface.get_instance().save_job_status(
- end_point_id, self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING
- )
-
- FedMLModelDatabase.get_instance().delete_deployment_result_with_device_id(
- end_point_id, end_point_name, model_name, self.edge_id)
-
- ClientConstants.remove_deployment(
- end_point_name, model_name, old_model_version,
- end_point_id, model_id, edge_id=self.edge_id)
- except Exception as e:
- # TODO: 1. Check this release action cause the resource seized by other run
- # 2. If this atomic op failed, should rolling back
- logging.info(f"Exception when removing deployment {traceback.format_exc()}")
- pass
-
- def construct_deployment_results(self, end_point_name, device_id, model_status,
- model_id, model_name, model_inference_url,
- model_version, inference_port, inference_engine,
- model_metadata, model_config):
- deployment_results_payload = {"end_point_id": self.run_id, "end_point_name": end_point_name,
- "model_id": model_id, "model_name": model_name,
- "model_url": model_inference_url, "model_version": model_version,
- "port": inference_port,
- "inference_engine": inference_engine,
- "model_metadata": model_metadata,
- "model_config": model_config,
- "model_status": model_status,
- "inference_port": inference_port}
- return deployment_results_payload
-
- def construct_deployment_status(self, end_point_name, device_id,
- model_id, model_name, model_version,
- model_inference_url, model_status,
- inference_port=ClientConstants.MODEL_INFERENCE_DEFAULT_PORT):
- deployment_status_payload = {"end_point_id": self.run_id, "end_point_name": end_point_name,
- "device_id": device_id,
- "model_id": model_id, "model_name": model_name,
- "model_version": model_version,
- "model_url": model_inference_url, "model_status": model_status,
- "inference_port": inference_port}
- return deployment_status_payload
-
- def send_deployment_results(self, end_point_name, device_id, model_status,
- model_id, model_name, model_inference_url,
- model_version, inference_port, inference_engine,
- model_metadata, model_config):
- deployment_results_topic = "model_device/model_device/return_deployment_result/{}".format(device_id)
- deployment_results_payload = self.construct_deployment_results(
- end_point_name, device_id, model_status,
- model_id, model_name, model_inference_url,
- model_version, inference_port, inference_engine,
- model_metadata, model_config)
-
- logging.info("[client] send_deployment_results: topic {}, payload {}.".format(deployment_results_topic,
- deployment_results_payload))
- self.client_mqtt_mgr.send_message_json(deployment_results_topic, json.dumps(deployment_results_payload))
- return deployment_results_payload
-
- def send_deployment_status(self, end_point_name, device_id,
- model_id, model_name, model_version,
- model_inference_url, model_status,
- inference_port=ClientConstants.MODEL_INFERENCE_DEFAULT_PORT):
- deployment_status_topic = "model_device/model_device/return_deployment_status/{}".format(device_id)
- deployment_status_payload = self.construct_deployment_status(
- end_point_name, device_id,
- model_id, model_name, model_version,
- model_inference_url, model_status,
- inference_port=inference_port)
-
- logging.info("[client] send_deployment_status: topic {}, payload {}.".format(deployment_status_topic,
- deployment_status_payload))
- self.client_mqtt_mgr.send_message_json(deployment_status_topic, json.dumps(deployment_status_payload))
- return deployment_status_payload
-
- def reset_devices_status(self, edge_id, status):
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.edge_id = edge_id
- self.mlops_metrics.broadcast_client_training_status(
- edge_id, status, is_from_model=True, run_id=self.run_id)
-
- def cleanup_run_when_starting_failed(self):
- logging.info("Cleanup run successfully when starting failed.")
-
- self.reset_devices_status(self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
-
- time.sleep(2)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- pass
-
- time.sleep(1)
-
- def cleanup_run_when_finished(self):
- logging.info("Cleanup run successfully when finished.")
-
- self.reset_devices_status(self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED)
-
- time.sleep(2)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- pass
-
- time.sleep(1)
-
- def on_client_mqtt_disconnected(self, mqtt_client_object):
- if self.client_mqtt_lock is None:
- self.client_mqtt_lock = threading.Lock()
-
- self.client_mqtt_lock.acquire()
- self.client_mqtt_is_connected = False
- self.client_mqtt_lock.release()
-
- def on_client_mqtt_connected(self, mqtt_client_object):
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
-
- self.mlops_metrics.set_messenger(self.client_mqtt_mgr)
- self.mlops_metrics.run_id = self.run_id
-
- if self.client_mqtt_lock is None:
- self.client_mqtt_lock = threading.Lock()
-
- self.client_mqtt_lock.acquire()
- self.client_mqtt_is_connected = True
- self.client_mqtt_lock.release()
-
- def setup_client_mqtt_mgr(self):
- if self.client_mqtt_mgr is not None:
- return
-
- if self.client_mqtt_lock is None:
- self.client_mqtt_lock = threading.Lock()
-
- self.client_mqtt_mgr = MqttManager(
- self.agent_config["mqtt_config"]["BROKER_HOST"],
- self.agent_config["mqtt_config"]["BROKER_PORT"],
- self.agent_config["mqtt_config"]["MQTT_USER"],
- self.agent_config["mqtt_config"]["MQTT_PWD"],
- self.agent_config["mqtt_config"]["MQTT_KEEPALIVE"],
- "FedML_ModelClientAgent_Metrics_@{}@_{}_{}_{}".format(self.user_name, self.args.current_device_id,
- str(os.getpid()),
- str(uuid.uuid4()))
- )
-
- self.client_mqtt_mgr.add_connected_listener(self.on_client_mqtt_connected)
- self.client_mqtt_mgr.add_disconnected_listener(self.on_client_mqtt_disconnected)
- self.client_mqtt_mgr.connect()
- self.client_mqtt_mgr.loop_start()
-
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
- self.mlops_metrics.set_messenger(self.client_mqtt_mgr)
- self.mlops_metrics.run_id = self.run_id
-
- def release_client_mqtt_mgr(self):
- try:
- if self.client_mqtt_mgr is not None:
- self.client_mqtt_mgr.loop_stop()
- self.client_mqtt_mgr.disconnect()
-
- self.client_mqtt_lock.acquire()
- if self.client_mqtt_mgr is not None:
- self.client_mqtt_is_connected = False
- self.client_mqtt_mgr = None
- self.client_mqtt_lock.release()
- except Exception:
- pass
-
- def ota_upgrade(self, payload, request_json):
- run_id = request_json["end_point_id"]
- force_ota = False
- ota_version = None
-
- try:
- parameters = request_json.get("parameters", None)
- common_args = parameters.get("common_args", None)
- force_ota = common_args.get("force_ota", False)
- ota_version = common_args.get("ota_version", None)
- except Exception as e:
- pass
-
- if force_ota and ota_version is not None:
- should_upgrade = True if ota_version != fedml.__version__ else False
- upgrade_version = ota_version
- else:
- try:
- fedml_is_latest_version, local_ver, remote_ver = sys_utils.check_fedml_is_latest_version(self.version)
- except Exception as e:
- return
-
- should_upgrade = False if fedml_is_latest_version else True
- upgrade_version = remote_ver
-
- if should_upgrade:
- FedMLClientDataInterface.get_instance(). \
- save_started_job(run_id, self.edge_id, time.time(),
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
- payload)
-
- logging.info(f"Upgrade to version {upgrade_version} ...")
-
- sys_utils.do_upgrade(self.version, upgrade_version)
-
- raise Exception("Restarting after upgraded...")
-
- def callback_start_deployment(self, topic, payload):
- """
- topic: model_ops/model_device/start_deployment/model-agent-device-id
- payload: {"model_name": "image-model", "model_storage_url":"s3-url",
- "instance_scale_min":1, "instance_scale_max":3, "inference_engine":"onnx (or tensorrt)"}
- """
- # get deployment params
- request_json = json.loads(payload)
- run_id = request_json["end_point_id"]
- token = request_json["token"]
- user_id = request_json["user_id"]
- user_name = request_json["user_name"]
- device_ids = request_json["device_ids"]
- device_objs = request_json["device_objs"]
-
- model_config = request_json["model_config"]
- model_name = model_config["model_name"]
- model_storage_url = model_config["model_storage_url"]
- scale_min = model_config.get("instance_scale_min", 0)
- scale_max = model_config.get("instance_scale_max", 0)
- inference_engine = model_config.get("inference_engine", 0)
- inference_end_point_id = run_id
-
- try:
- MLOpsConfigs.fetch_all_configs()
- except Exception as e:
- pass
-
- # Start log processor for current run
- run_id = inference_end_point_id
- self.args.run_id = run_id
- self.args.edge_id = self.edge_id
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
- MLOpsRuntimeLogDaemon.get_instance(self.args).set_log_source(
- ClientConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT)
- MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(run_id, self.edge_id)
-
- self.ota_upgrade(payload, request_json)
-
- # Start client with multiprocessing mode
- request_json["run_id"] = run_id
- run_id_str = str(run_id)
- self.request_json = request_json
- self.running_request_json[run_id_str] = request_json
- client_runner = FedMLClientRunner(
- self.args, edge_id=self.edge_id, request_json=request_json, agent_config=self.agent_config, run_id=run_id
- )
- client_runner.infer_host = self.get_ip_address(request_json)
- self.run_process_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_event_map[run_id_str].clear()
- client_runner.run_process_event = self.run_process_event_map[run_id_str]
- self.run_process_completed_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_completed_event_map[run_id_str].clear()
- client_runner.run_process_completed_event = self.run_process_completed_event_map[run_id_str]
- self.model_runner_mapping[run_id_str] = client_runner
- self.run_id = run_id
- self.run_process_map[run_id_str] = Process(target=client_runner.run, args=(
- self.run_process_event_map[run_id_str], self.run_process_completed_event_map[run_id_str]
- ))
- # client_runner.run()
- self.run_process_map[run_id_str].start()
- ClientConstants.save_run_process(run_id, self.run_process_map[run_id_str].pid)
- ClientConstants.save_runner_infos(self.args.device_id + "." + self.args.os_name, self.edge_id, run_id=run_id)
-
- def set_runner_stopped_event(self, run_id):
- run_id_str = str(run_id)
- client_runner = self.model_runner_mapping.get(run_id_str, None)
- if client_runner is not None:
- if client_runner.run_process_event is not None:
- client_runner.run_process_event.set()
- self.model_runner_mapping.pop(run_id_str)
-
- def set_runner_completed_event(self, run_id):
- run_id_str = str(run_id)
- client_runner = self.model_runner_mapping.get(run_id_str, None)
- if client_runner is not None:
- if client_runner.run_process_completed_event is not None:
- client_runner.run_process_completed_event.set()
- self.model_runner_mapping.pop(run_id_str)
-
- def callback_delete_deployment(self, topic, payload):
- logging.info("callback_delete_deployment: topic = %s, payload = %s" % (topic, payload))
-
- # Parse payload as the model message object.
- model_msg_object = FedMLModelMsgObject(topic, payload)
-
- try:
- ClientConstants.remove_deployment(
- model_msg_object.end_point_name, model_msg_object.model_name, model_msg_object.model_version,
- model_msg_object.run_id, model_msg_object.model_id, edge_id=self.edge_id)
- except Exception as e:
- logging.info(f"Exception when removing deployment {traceback.format_exc()}")
- pass
-
- self.set_runner_stopped_event(model_msg_object.run_id)
-
- logging.info(f"[endpoint/device][{model_msg_object.run_id}/{self.edge_id}] "
- f"Release gpu resource when the worker deployment deleted.")
- JobRunnerUtils.get_instance().release_gpu_ids(model_msg_object.run_id, self.edge_id)
-
- if self.running_request_json.get(str(model_msg_object.run_id)) is not None:
- try:
- self.running_request_json.pop(str(model_msg_object.run_id))
- except Exception as e:
- pass
-
- FedMLClientDataInterface.get_instance().delete_job_from_db(model_msg_object.run_id)
- FedMLModelDatabase.get_instance().delete_deployment_result_with_device_id(
- model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_name,
- self.edge_id)
-
- def exit_run_with_exception_entry(self):
- try:
- self.setup_client_mqtt_mgr()
- self.exit_run_with_exception()
- except Exception as e:
- self.release_client_mqtt_mgr()
- sys.exit(1)
- finally:
- self.release_client_mqtt_mgr()
-
- def exit_run_with_exception(self):
- logging.info("Exit run successfully.")
-
- ClientConstants.cleanup_learning_process(self.run_id)
- ClientConstants.cleanup_run_process(self.run_id)
-
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- is_from_model=True, run_id=self.run_id)
-
- time.sleep(1)
-
- def callback_exit_train_with_exception(self, topic, payload):
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json.get("runId", None)
- if run_id is None:
- run_id = request_json.get("run_id", None)
- if run_id is None:
- run_id = request_json.get("id", None)
-
- if run_id is None:
- return
-
- # Stop client with multiprocessing mode
- self.request_json = request_json
- client_runner = FedMLClientRunner(
- self.args, edge_id=self.edge_id, request_json=request_json, agent_config=self.agent_config, run_id=run_id
- )
- try:
- Process(target=client_runner.exit_run_with_exception_entry).start()
- except Exception as e:
- pass
-
- def cleanup_client_with_status(self):
- self.setup_client_mqtt_mgr()
-
- if self.device_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED:
- self.cleanup_run_when_finished()
- elif self.device_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED:
- self.cleanup_run_when_starting_failed()
-
- self.release_client_mqtt_mgr()
-
- def callback_runner_id_status(self, topic, payload):
- # logging.info("callback_runner_id_status: topic = %s, payload = %s" % (topic, payload))
-
- request_json = json.loads(payload)
- run_id = request_json["run_id"]
- edge_id = request_json["edge_id"]
- status = request_json["status"]
-
- self.save_training_status(edge_id, status)
-
- if status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or \
- status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED:
- # Stop client with multiprocessing mode
- self.request_json = request_json
- client_runner = FedMLClientRunner(
- self.args,
- edge_id=self.edge_id,
- request_json=request_json,
- agent_config=self.agent_config,
- run_id=run_id,
- )
- client_runner.device_status = status
- status_process = Process(target=client_runner.cleanup_client_with_status)
- status_process.start()
- status_process.join(15)
-
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, edge_id)
-
- def callback_report_current_status(self, topic, payload):
- self.send_agent_active_msg()
-
- @staticmethod
- def process_ota_upgrade_msg():
- os.system("pip install -U fedml")
-
- def callback_client_ota_msg(self, topic, payload):
- request_json = json.loads(payload)
- cmd = request_json["cmd"]
-
- if cmd == ClientConstants.FEDML_OTA_CMD_UPGRADE:
- FedMLClientRunner.process_ota_upgrade_msg()
- # Process(target=FedMLClientRunner.process_ota_upgrade_msg).start()
- raise Exception("After upgraded, restart runner...")
- elif cmd == ClientConstants.FEDML_OTA_CMD_RESTART:
- raise Exception("Restart runner...")
-
- def save_training_status(self, edge_id, training_status):
- self.current_training_status = training_status
- ClientConstants.save_training_infos(edge_id, training_status)
-
- @staticmethod
- def get_device_id():
- device_file_path = os.path.join(ClientConstants.get_data_dir(),
- ClientConstants.LOCAL_RUNNER_INFO_DIR_NAME)
- file_for_device_id = os.path.join(device_file_path, "devices.id")
- if not os.path.exists(device_file_path):
- os.makedirs(device_file_path)
- elif os.path.exists(file_for_device_id):
- with open(file_for_device_id, 'r', encoding='utf-8') as f:
- device_id_from_file = f.readline()
- if device_id_from_file is not None and device_id_from_file != "":
- return device_id_from_file
-
- if platform.system() == "Darwin":
- cmd_get_serial_num = "system_profiler SPHardwareDataType | grep Serial | awk '{gsub(/ /,\"\")}{print}' " \
- "|awk -F':' '{print $2}' "
- device_id = os.popen(cmd_get_serial_num).read()
- device_id = device_id.replace('\n', '').replace(' ', '')
- if device_id is None or device_id == "":
- device_id = hex(uuid.getnode())
- else:
- device_id = "0x" + device_id
- else:
- if "nt" in os.name:
-
- def get_uuid():
- guid = ""
- try:
- cmd = "wmic csproduct get uuid"
- guid = str(subprocess.check_output(cmd))
- pos1 = guid.find("\\n") + 2
- guid = guid[pos1:-15]
- except Exception as ex:
- pass
- return str(guid)
-
- device_id = str(get_uuid())
- logging.info(device_id)
- elif "posix" in os.name:
- device_id = sys_utils.get_device_id_in_docker()
- if device_id is None:
- device_id = hex(uuid.getnode())
- else:
- device_id = sys_utils.run_subprocess_open(
- "hal-get-property --udi /org/freedesktop/Hal/devices/computer --key system.hardware.uuid".split()
- )
- device_id = hex(device_id)
-
- if device_id is not None and device_id != "":
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
- else:
- device_id = hex(uuid.uuid4())
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
-
- return device_id
-
- def get_ip_address(self, request_json):
- # OPTION 1: Use local ip
- ip = ClientConstants.get_local_ip()
-
- # OPTION 2: Auto detect public ip
- if "parameters" in request_json and \
- ClientConstants.AUTO_DETECT_PUBLIC_IP in request_json["parameters"] and \
- request_json["parameters"][ClientConstants.AUTO_DETECT_PUBLIC_IP]:
- ip = ClientConstants.get_public_ip()
- logging.info("Auto detect public ip for worker: " + ip)
-
- # OPTION 3: Use user indicated ip
- if self.infer_host is not None and self.infer_host != "127.0.0.1" and self.infer_host != "localhost":
- ip = self.infer_host
-
- return ip
-
- def bind_account_and_device_id(self, url, account_id, device_id, os_name, role="md.on_premise_device"):
- ip = requests.get('https://checkip.amazonaws.com').text.strip()
- fedml_ver, exec_path, os_ver, cpu_info, python_ver, torch_ver, mpi_installed, \
- cpu_usage, available_mem, total_mem, gpu_info, gpu_available_mem, gpu_total_mem, \
- gpu_count, gpu_vendor, cpu_count, gpu_device_name = get_sys_runner_info()
- host_name = sys_utils.get_host_name()
- json_params = {
- "accountid": account_id,
- "deviceid": device_id,
- "state": ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE,
- "status": ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE,
- "type": os_name,
- "processor": cpu_info,
- "core_type": cpu_info,
- "network": "",
- "role": role,
- "os_ver": os_ver,
- "memory": total_mem,
- "ip": ip,
- "extra_infos": {"fedml_ver": fedml_ver, "exec_path": exec_path, "os_ver": os_ver,
- "cpu_info": cpu_info, "python_ver": python_ver, "torch_ver": torch_ver,
- "mpi_installed": mpi_installed, "cpu_usage": cpu_usage,
- "available_mem": available_mem, "total_mem": total_mem,
- "cpu_count": cpu_count, "gpu_count": 0, "host_name": host_name}
- }
- if gpu_count > 0:
- if gpu_total_mem is not None:
- json_params["gpu"] = gpu_info if gpu_info is not None else "" + ", Total GPU Memory: " + gpu_total_mem
- else:
- json_params["gpu"] = gpu_info if gpu_info is not None else ""
- json_params["extra_infos"]["gpu_info"] = gpu_info if gpu_info is not None else ""
- if gpu_available_mem is not None:
- json_params["extra_infos"]["gpu_available_mem"] = gpu_available_mem
- if gpu_total_mem is not None:
- json_params["extra_infos"]["gpu_total_mem"] = gpu_total_mem
-
- json_params["extra_infos"]["gpu_count"] = gpu_count
- json_params["extra_infos"]["gpu_vendor"] = gpu_vendor
- json_params["extra_infos"]["gpu_device_name"] = gpu_device_name
-
- gpu_available_id_list = sys_utils.get_available_gpu_id_list(limit=gpu_count)
- gpu_available_count = len(gpu_available_id_list) if gpu_available_id_list is not None else 0
- gpu_list = sys_utils.get_gpu_list()
- json_params["extra_infos"]["gpu_available_count"] = gpu_available_count
- json_params["extra_infos"]["gpu_available_id_list"] = gpu_available_id_list
- json_params["extra_infos"]["gpu_list"] = gpu_list
- else:
- json_params["gpu"] = "None"
- json_params["extra_infos"]["gpu_available_count"] = 0
- json_params["extra_infos"]["gpu_available_id_list"] = []
- json_params["extra_infos"]["gpu_list"] = []
-
- _, cert_path = MLOpsConfigs.get_request_params()
- if cert_path is not None:
- try:
- requests.session().verify = cert_path
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- except requests.exceptions.SSLError as err:
- MLOpsConfigs.install_root_ca_file()
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- else:
- response = requests.post(url, json=json_params, headers={"Connection": "close"})
- edge_id = -1
- user_name = None
- extra_url = None
- if response.status_code != 200:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- pass
- else:
- # print("url = {}, response = {}".format(url, response))
- status_code = response.json().get("code")
- if status_code == "SUCCESS":
- edge_id = response.json().get("data").get("id")
- user_name = response.json().get("data").get("userName", None)
- extra_url = response.json().get("data").get("url", None)
- if edge_id is None or edge_id <= 0:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- else:
- if status_code == SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR:
- raise SystemExit(SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR)
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- return -1, None, None
- return edge_id, user_name, extra_url
-
- def fetch_configs(self):
- return MLOpsConfigs.fetch_all_configs()
-
- def send_agent_active_msg(self):
- active_topic = "flclient_agent/active"
- status = MLOpsStatus.get_instance().get_client_agent_status(self.edge_id)
- if (
- status is not None
- and status != ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE
- and status != ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE
- ):
- return
-
- try:
- current_job = FedMLClientDataInterface.get_instance().get_job_by_id(self.run_id)
- except Exception as e:
- current_job = None
- if current_job is None:
- if status is not None and status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE:
- status = ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE
- else:
- return
- else:
- status = ClientConstants.get_device_state_from_run_edge_state(current_job.status)
- active_msg = {"ID": self.edge_id, "status": status}
- MLOpsStatus.get_instance().set_client_agent_status(self.edge_id, status)
- self.mqtt_mgr.send_message_json(active_topic, json.dumps(active_msg))
-
- def recover_start_deployment_msg_after_upgrading(self):
- try:
- current_job = FedMLClientDataInterface.get_instance().get_current_job()
- if current_job is not None and \
- current_job.status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING:
- logging.info("start deployment after upgrading.")
- topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(self.edge_id))
- self.callback_start_deployment(topic_start_deployment, current_job.running_json)
- except Exception as e:
- logging.info("recover starting deployment message after upgrading: {}".format(traceback.format_exc()))
-
- def on_agent_mqtt_connected(self, mqtt_client_object):
- # The MQTT message topic format is as follows: <sender>/<receiver>/<action>
-
- # Setup MQTT message listener for starting deployment
- topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(self.edge_id))
- self.mqtt_mgr.add_message_listener(topic_start_deployment, self.callback_start_deployment)
-
- # Setup MQTT message listener for delete deployment
- topic_delete_deployment = "model_ops/model_device/delete_deployment/{}".format(str(self.edge_id))
- self.mqtt_mgr.add_message_listener(topic_delete_deployment, self.callback_delete_deployment)
-
- # Setup MQTT message listener for running failed
- topic_exit_train_with_exception = "flserver_agent/" + str(self.edge_id) + "/exit_train_with_exception"
- self.mqtt_mgr.add_message_listener(topic_exit_train_with_exception, self.callback_exit_train_with_exception)
-
- # Setup MQTT message listener for client status switching
- topic_client_status = "fl_client/flclient_agent_" + str(self.edge_id) + "/status"
- self.mqtt_mgr.add_message_listener(topic_client_status, self.callback_runner_id_status)
-
- # Setup MQTT message listener to report current device status.
- topic_report_status = "mlops/report_device_status"
- self.mqtt_mgr.add_message_listener(topic_report_status, self.callback_report_current_status)
-
- # Setup MQTT message listener to OTA messages from the MLOps.
- topic_ota_msg = "mlops/flclient_agent_" + str(self.edge_id) + "/ota"
- self.mqtt_mgr.add_message_listener(topic_ota_msg, self.callback_client_ota_msg)
-
- if self.mqtt_inference_obj is None:
- self.mqtt_inference_obj = FedMLMqttInference(agent_config=self.agent_config, mqtt_mgr=self.mqtt_mgr)
- self.mqtt_inference_obj.setup_listener_for_endpoint_inference_request(self.edge_id)
-
- # Subscribe topics for starting deployment, stopping deployment and fetching client status.
- mqtt_client_object.subscribe(topic_start_deployment, qos=2)
- mqtt_client_object.subscribe(topic_delete_deployment, qos=2)
- mqtt_client_object.subscribe(topic_client_status, qos=2)
- mqtt_client_object.subscribe(topic_report_status, qos=2)
- mqtt_client_object.subscribe(topic_exit_train_with_exception, qos=2)
- mqtt_client_object.subscribe(topic_ota_msg, qos=2)
-
- self.subscribed_topics.clear()
- self.subscribed_topics.append(topic_start_deployment)
- self.subscribed_topics.append(topic_delete_deployment)
- self.subscribed_topics.append(topic_client_status)
- self.subscribed_topics.append(topic_report_status)
- self.subscribed_topics.append(topic_exit_train_with_exception)
- self.subscribed_topics.append(topic_ota_msg)
-
- # Broadcast the first active message.
- self.send_agent_active_msg()
-
- # Echo results
- # print("\n\nCongratulations, your device is connected to the FedML MLOps platform successfully!")
- # print(
- # "Your FedML Edge ID is " + str(self.edge_id) + ", unique device ID is "
- # + str(self.unique_device_id)
- # + "\n"
- # )
-
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
-
- def on_agent_mqtt_disconnected(self, mqtt_client_object):
- MLOpsStatus.get_instance().set_client_agent_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE
- )
-
- try:
- if self.mqtt_inference_obj is not None:
- self.mqtt_inference_obj.remove_listener_for_endpoint_inference_request(self.edge_id)
- except Exception as e:
- pass
-
- def setup_agent_mqtt_connection(self, service_config):
- # Setup MQTT connection
- self.mqtt_mgr = MqttManager(
- service_config["mqtt_config"]["BROKER_HOST"],
- service_config["mqtt_config"]["BROKER_PORT"],
- service_config["mqtt_config"]["MQTT_USER"],
- service_config["mqtt_config"]["MQTT_PWD"],
- service_config["mqtt_config"]["MQTT_KEEPALIVE"],
- "FedML_ModelClientAgent_Daemon_@" + self.user_name + "@_" + self.args.current_device_id + str(uuid.uuid4()),
- "flclient_agent/last_will_msg",
- json.dumps({"ID": self.edge_id, "status": ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE})
- )
- self.agent_config = service_config
-
- # Init local database
- FedMLClientDataInterface.get_instance().create_job_table()
- try:
- FedMLModelDatabase.get_instance().set_database_base_dir(ClientConstants.get_database_dir())
- FedMLModelDatabase.get_instance().create_table()
- except Exception as e:
- pass
-
- client_api_cmd = "fedml.computing.scheduler.model_scheduler.device_client_api:api"
- client_api_pids = RunProcessUtils.get_pid_from_cmd_line(client_api_cmd)
- if client_api_pids is None or len(client_api_pids) <= 0:
- # Start local API services
- cur_dir = os.path.dirname(__file__)
- fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- python_program = get_python_program()
- self.local_api_process = ClientConstants.exec_console_with_script(
- "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
- "--log-level critical".format(
- python_program, client_api_cmd,
- ClientConstants.LOCAL_CLIENT_API_PORT, fedml_base_dir
- ),
- should_capture_stdout=False,
- should_capture_stderr=False
- )
- # if self.local_api_process is not None and self.local_api_process.pid is not None:
- # print(f"Model worker local API process id {self.local_api_process.pid}")
-
- # MLOpsRuntimeLogDaemon.get_instance(self.args).stop_all_log_processor()
-
- # Setup MQTT connected listener
- self.mqtt_mgr.add_connected_listener(self.on_agent_mqtt_connected)
- self.mqtt_mgr.add_disconnected_listener(self.on_agent_mqtt_disconnected)
- self.mqtt_mgr.connect()
-
- self.setup_client_mqtt_mgr()
- self.mlops_metrics.report_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE, is_from_model=True)
- MLOpsStatus.get_instance().set_client_agent_status(self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE)
-
- self.recover_start_deployment_msg_after_upgrading()
-
- def stop_agent(self):
- if self.run_process_event is not None:
- self.run_process_event.set()
-
- if self.mqtt_mgr is not None:
- try:
- for topic in self.subscribed_topics:
- self.mqtt_mgr.unsubscribe_msg(topic)
- except Exception as e:
- pass
-
- self.mqtt_mgr.loop_stop()
- self.mqtt_mgr.disconnect()
-
- self.release_client_mqtt_mgr()
-
- def start_agent_mqtt_loop(self, should_exit_sys=False):
- # Start MQTT message loop
- try:
- self.mqtt_mgr.loop_forever()
- except Exception as e:
- if str(e) == "Restarting after upgraded...":
- logging.info("Restarting after upgraded...")
- else:
- logging.info("Client tracing: {}".format(traceback.format_exc()))
- finally:
- self.stop_agent()
-
- if should_exit_sys:
- time.sleep(5)
- sys.exit(1)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
index 9a997a80e2..f637ccde1d 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
@@ -18,6 +18,11 @@ class FedMLModelCache(Singleton):
FEDML_MODEL_DEVICE_INFO_TAG = "FEDML_MODEL_DEVICE_INFO_TAG-"
FEDML_MODEL_END_POINT_TOKEN_TAG = "FEDML_MODEL_END_POINT_TOKEN_TAG-"
FEDML_MODEL_ROUND_ROBIN_PREVIOUS_DEVICE_TAG = "FEDML_MODEL_ROUND_ROBIN_PREVIOUS_DEVICE_TAG-"
+ FEDML_MODEL_ENDPOINT_REPLICA_NUM_TAG = "FEDML_MODEL_ENDPOINT_REPLICA_NUM_TAG-"
+
+ # On the worker
+ FEDML_MODEL_REPLICA_GPU_IDS_TAG = "FEDML_MODEL_REPLICA_GPU_IDS_TAG-"
+
FEDML_KEY_COUNT_PER_SCAN = 1000
def __init__(self):
@@ -85,16 +90,16 @@ def get_instance(redis_addr="local", redis_port=6379):
return FedMLModelCache()
def set_deployment_result(self, end_point_id, end_point_name,
- model_name, model_version, device_id, deployment_result):
- result_dict = {"cache_device_id": device_id, "result": deployment_result}
+ model_name, model_version, device_id, deployment_result, replica_no):
+ result_dict = {"cache_device_id": device_id, "cache_replica_no": replica_no, "result": deployment_result}
try:
- # Delete old result
+ # Delete old result using (e_id, end_point_name, model_name, device_id, replica_no)
# In this list, find the result's complete record, delete it.
result_list = self.redis_connection.lrange(
self.get_deployment_result_key(end_point_id, end_point_name, model_name), 0, -1)
for result_item in result_list:
- result_device_id, result_payload = self.get_result_item_info(result_item)
- if result_device_id == device_id:
+ res_device_id, res_replica_no, res_payload = self.get_result_item_info(result_item)
+ if res_device_id == device_id and res_replica_no == replica_no:
self.redis_connection.lrem(
self.get_deployment_result_key(end_point_id, end_point_name, model_name), 0, result_item)
@@ -105,18 +110,20 @@ def set_deployment_result(self, end_point_id, end_point_name,
pass
self.model_deployment_db.set_deployment_result(end_point_id, end_point_name,
model_name, model_version,
- device_id, deployment_result)
+ device_id, deployment_result, replica_no)
def set_deployment_status(self, end_point_id, end_point_name,
- model_name, model_version, device_id, deployment_status):
+ model_name, model_version, device_id, deployment_status, replica_no):
status_dict = {"cache_device_id": device_id, "status": deployment_status}
try:
- self.redis_connection.rpush(self.get_deployment_status_key(end_point_id, end_point_name, model_name), json.dumps(status_dict))
+ # rpush could tolerate the same e_id, d_id with different r_no
+ self.redis_connection.rpush(self.get_deployment_status_key(end_point_id, end_point_name, model_name),
+ json.dumps(status_dict))
except Exception as e:
pass
self.model_deployment_db.set_deployment_status(end_point_id, end_point_name,
model_name, model_version,
- device_id, deployment_status)
+ device_id, deployment_status, replica_no)
def delete_deployment_status(self, element: str, end_point_id, end_point_name, model_name):
self.redis_connection.lrem(self.get_deployment_status_key(end_point_id, end_point_name, model_name),
@@ -131,10 +138,32 @@ def delete_deployment_result(self, element: str, end_point_id, end_point_name, m
except Exception as e:
pass
+ def delete_deployment_result_with_device_id_and_replica_no(self, end_point_id, end_point_name, model_name,
+ device_id, replica_no_to_delete):
+ result_item_found = None
+
+ result_list = self.get_deployment_result_list(
+ end_point_id, end_point_name, model_name)
+
+ for result_item in result_list:
+ cache_device_id, cache_replica_no, result_payload = (
+ self.get_result_item_info(result_item))
+
+ if str(cache_device_id) == str(device_id) and cache_replica_no == replica_no_to_delete:
+ result_item_found = result_item
+ break
+
+ # Delete the replica element
+ if result_item_found is not None:
+ self.delete_deployment_result(
+ result_item_found, end_point_id, end_point_name, model_name)
+
def get_deployment_result_list(self, end_point_id, end_point_name, model_name):
try:
- result_list = self.redis_connection.lrange(self.get_deployment_result_key(end_point_id, end_point_name, model_name), 0, -1)
+ result_list = self.redis_connection.lrange(
+ self.get_deployment_result_key(end_point_id, end_point_name, model_name), 0, -1)
except Exception as e:
+ logging.info(e)
result_list = None
if result_list is None or len(result_list) <= 0:
@@ -144,13 +173,14 @@ def get_deployment_result_list(self, end_point_id, end_point_name, model_name):
self.redis_connection.rpush(self.get_deployment_result_key(end_point_id, end_point_name, model_name),
json.dumps(result))
except Exception as e:
+ logging.info(e)
pass
return result_list
def delete_deployment_result(self, element: str, end_point_id, end_point_name, model_name):
self.redis_connection.lrem(self.get_deployment_result_key(end_point_id, end_point_name, model_name),
0, element)
- device_id, _ = self.get_result_item_info(element)
+ device_id, _, _ = self.get_result_item_info(element)
self.model_deployment_db.delete_deployment_result(device_id, end_point_id, end_point_name, model_name)
def get_deployment_result_list_size(self, end_point_id, end_point_name, model_name):
@@ -192,50 +222,43 @@ def get_result_item_info(self, result_item):
result_item_json = json.loads(result_item)
if isinstance(result_item_json, str):
result_item_json = json.loads(result_item_json)
+
device_id = result_item_json["cache_device_id"]
+ replica_no = result_item_json["cache_replica_no"]
+
if isinstance(result_item_json["result"], str):
result_payload = json.loads(result_item_json["result"])
else:
result_payload = result_item_json["result"]
- return device_id, result_payload
+ return device_id, replica_no, result_payload
def get_idle_device(self, end_point_id, end_point_name,
model_name, model_version,
- check_end_point_status=True):
- # Find all deployed devices
- try:
- status_list = self.get_deployment_status_list(end_point_id, end_point_name, model_name) # DEPLOYMENT_STATUS
- except Exception as e:
- logging.error(f"get_deployment_status_list failed {e}")
- return None, None
+ check_end_point_status=True, limit_specific_model_version=False):
+ # Deprecated the model status logic, query directly from the deployment result list
+ idle_device_list = list()
- if len(status_list) == 0:
- return None, None
+ result_list = self.get_deployment_result_list(end_point_id, end_point_name, model_name)
- idle_device_list = list()
- if model_version == "latest":
- model_version = self.get_latest_version(status_list)
- logging.info(f"model_version {model_version}")
+ for result_item in result_list:
+ device_id, _, result_payload = self.get_result_item_info(result_item)
+ found_end_point_id = result_payload["end_point_id"]
+ found_end_point_name = result_payload["end_point_name"]
+ found_model_name = result_payload["model_name"]
+ found_model_version = result_payload["model_version"]
+
+ if (str(found_end_point_id) == str(end_point_id) and found_end_point_name == end_point_name and
+ found_model_name == model_name and
+ (not limit_specific_model_version or found_model_version == model_version)):
+ if "model_status" in result_payload and result_payload["model_status"] == "DEPLOYED":
+ idle_device_list.append({"device_id": device_id, "end_point_id": end_point_id})
+
+ logging.info(f"{len(idle_device_list)} devices has this model on it: {idle_device_list}")
- # iterate all devices, find those with correct version and deployed
- for status_item in status_list:
- try:
- device_id, status_payload = self.get_status_item_info(status_item)
- logging.info(f"status_payload {status_payload}")
- model_status = status_payload["model_status"]
- model_version_cached = status_payload["model_version"]
- end_point_id_cache = status_payload["end_point_id"]
- logging.info(f"model_version {model_version}, model_version_cache {model_version_cached}")
- if (model_version == model_version_cached or model_version == "*") and \
- model_status == ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED:
- idle_device_list.append({"device_id": device_id, "end_point_id": end_point_id_cache})
- except Exception as e:
- logging.info(f"Get idle device list Failed: {e}, status_item {status_item}")
- pass
if len(idle_device_list) <= 0:
return None, None
- logging.info(f"{len(idle_device_list)} devices has this model on it: {idle_device_list}")
- # Randomly shuffle
+
+ # # Randomly shuffle
# shuffle the list of deployed devices and get the first one as the target idle device.
# if len(idle_device_list) <= 0:
# return None, None
@@ -271,18 +294,17 @@ def get_idle_device(self, end_point_id, end_point_name,
# Find deployment result from the target idle device.
try:
- result_list = self.get_deployment_result_list(end_point_id, end_point_name, model_name)
for result_item in result_list:
- device_id, result_payload = self.get_result_item_info(result_item)
+ logging.info("enter the for loop")
+ device_id, _, result_payload = self.get_result_item_info(result_item)
found_end_point_id = result_payload["end_point_id"]
found_end_point_name = result_payload["end_point_name"]
- # Check whether the end point is activated.
- if check_end_point_status:
- end_point_activated = self.get_end_point_activation(found_end_point_id)
- if not end_point_activated:
- continue
+ found_model_status = result_payload["model_status"]
- if found_end_point_id == idle_device_dict["end_point_id"] \
+ if found_model_status != "DEPLOYED":
+ continue
+
+ if str(found_end_point_id) == str(idle_device_dict["end_point_id"]) \
and device_id == idle_device_dict["device_id"]:
if same_model_device_rank > 0:
same_model_device_rank -= 1
@@ -316,10 +338,13 @@ def get_latest_version(self, status_list):
return latest_version
def get_deployment_result_with_device_id(self, end_point_id, end_point_name, model_name, device_id):
+ """"
+ TODO: Return multiple replicas' result for the same device_id
+ """
try:
result_list = self.get_deployment_result_list(end_point_id, end_point_name, model_name)
for result_item in result_list:
- result_device_id, result_payload = self.get_result_item_info(result_item)
+ result_device_id, _, result_payload = self.get_result_item_info(result_item)
found_end_point_id = result_payload["end_point_id"]
end_point_activated = self.get_end_point_activation(found_end_point_id)
@@ -363,9 +388,29 @@ def set_end_point_activation(self, end_point_id, end_point_name, activate_status
pass
self.model_deployment_db.set_end_point_activation(end_point_id, end_point_name, status)
+ def set_replica_gpu_ids(self, end_point_id, end_point_name, model_name, device_id, replica_no, gpu_ids):
+ # Convert the list to string
+ try:
+ self.redis_connection.set(self.get_replica_gpu_ids_key(end_point_id, end_point_name,
+ model_name, device_id, replica_no), str(gpu_ids))
+ except Exception as e:
+ print(e)
+ logging.error(e)
+
+ # TODO: Use Sqlite for the replica backup
+
+ def get_replica_gpu_ids(self, end_point_id, end_point_name, model_name, device_id, replica_no):
+ try:
+ if self.redis_connection.exists(self.get_replica_gpu_ids_key(end_point_id, end_point_name,
+ model_name, device_id, replica_no)):
+ return self.redis_connection.get(self.get_replica_gpu_ids_key(end_point_id, end_point_name,
+ model_name, device_id, replica_no))
+ except Exception as e:
+ pass
+
def delete_end_point(self, end_point_id, end_point_name, model_name, model_version):
try:
- print("Will Delete the realated redis keys permanently")
+ logging.info("Will Delete the related redis keys permanently")
self.redis_connection.expire(self.get_deployment_result_key(end_point_id, end_point_name, model_name), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
self.redis_connection.expire(self.get_deployment_status_key(end_point_id, end_point_name, model_name), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
self.redis_connection.expire(self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
@@ -375,29 +420,16 @@ def delete_end_point(self, end_point_id, end_point_name, model_name, model_versi
self.redis_connection.expire(self.get_end_point_activation_key(end_point_id), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
self.redis_connection.expire(self.get_end_point_status_key(end_point_id), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
except Exception as e:
+ logging.error(f"error when deleting the redis keys: {e}")
pass
def get_end_point_activation(self, end_point_id):
- status_int = -1
- try:
- if self.redis_connection.exists(self.get_end_point_activation_key(end_point_id)):
- status_int = self.redis_connection.get(self.get_end_point_activation_key(end_point_id))
- except Exception as e:
- pass
-
- if status_int == -1:
- status_int = self.model_deployment_db.get_end_point_activation(end_point_id)
- try:
- self.redis_connection.set(self.get_end_point_activation_key(end_point_id), status_int)
- except Exception as e:
- pass
-
- status = True if int(status_int) == 1 else False
- return status
+ # [Deprecated] activation logic is removed
+ return True
def get_end_point_full_key_by_id(self, end_point_id):
- # e.g. FEDML_MODEL_DEPLOYMENT_STATUS--1234-dummy_endpoint_name-dummy_model_name
- target_prefix = f"{FedMLModelCache.FEDML_MODEL_DEPLOYMENT_STATUS_TAG}-{end_point_id}-*"
+ # e.g. FEDML_MODEL_DEPLOYMENT_RESULT--1234-dummy_endpoint_name-dummy_model_name
+ target_prefix = f"{FedMLModelCache.FEDML_MODEL_DEPLOYMENT_RESULT_TAG}-{end_point_id}-*"
status_list = list()
for key in self.redis_connection.scan_iter(target_prefix):
status_list.append(key)
@@ -497,6 +529,19 @@ def get_end_point_token(self, end_point_id, end_point_name, model_name):
return token
+ def get_endpoint_devices_replica_num(self, end_point_id):
+ """
+ Return a endpoint_devices_replica_num dict {id1: 1, id2: 1}, if not exist, return None
+ """
+ try:
+ replica_num = self.redis_connection.get(
+ self.get_endpoint_replica_num_key(end_point_id))
+ except Exception as e:
+ replica_num = None
+ # TODO: Use Sqlite for the replica backup
+
+ return replica_num
+
def get_deployment_result_key(self, end_point_id, end_point_name, model_name):
return "{}-{}-{}-{}".format(FedMLModelCache.FEDML_MODEL_DEPLOYMENT_RESULT_TAG, end_point_id, end_point_name, model_name)
@@ -518,6 +563,14 @@ def get_deployment_token_key(self, end_point_id, end_point_name, model_name):
def get_round_robin_prev_device(self, end_point_id, end_point_name, model_name, version):
return "{}-{}-{}-{}-{}".format(FedMLModelCache.FEDML_MODEL_ROUND_ROBIN_PREVIOUS_DEVICE_TAG, end_point_id, end_point_name, model_name, version)
+ def get_endpoint_replica_num_key(self, end_point_id):
+ return "{}-{}-{}-{}-{}".format(FedMLModelCache.FEDML_MODEL_ENDPOINT_REPLICA_NUM_TAG, end_point_id, "replica_num", "key")
+
+ @staticmethod
+ def get_replica_gpu_ids_key(end_point_id, end_point_name, model_name, device_id, replica_no):
+ return "{}-{}-{}-{}-{}-{}".format(FedMLModelCache.FEDML_MODEL_REPLICA_GPU_IDS_TAG, end_point_id,
+ end_point_name, model_name, device_id, replica_no)
+
def set_monitor_metrics(self, end_point_id, end_point_name,
model_name, model_version,
total_latency, avg_latency,
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_db.py b/python/fedml/computing/scheduler/model_scheduler/device_model_db.py
index e11e098caf..6ee7af1cdd 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_db.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_db.py
@@ -31,20 +31,25 @@ def set_database_base_dir(self, database_base_dir):
self.db_base_dir = database_base_dir
def set_deployment_result(self, end_point_id, end_point_name, model_name, model_version,
- device_id, deployment_result):
+ device_id, deployment_result, replica_no):
self.set_deployment_results_info(end_point_id, end_point_name, model_name, model_version,
- device_id, deployment_result=deployment_result)
+ device_id, deployment_result=deployment_result, replica_no=replica_no)
def set_deployment_status(self, end_point_id, end_point_name, model_name, model_version,
- device_id, deployment_status):
+ device_id, deployment_status, replica_no):
self.set_deployment_results_info(end_point_id, end_point_name, model_name, model_version,
- device_id, deployment_status=deployment_status)
+ device_id, deployment_status=deployment_status, replica_no=replica_no)
def get_deployment_result_list(self, end_point_id, end_point_name, model_name, model_version=None):
+ """
+ query from sqlite db using e_id
+ """
result_list = self.get_deployment_results_info(end_point_id, end_point_name, model_name, model_version)
ret_result_list = list()
for result in result_list:
- result_dict = {"cache_device_id": result.device_id, "result": result.deployment_result}
+ result_dict = {"cache_device_id": result.device_id,
+ "cache_replica_no": result.replica_no,
+ "result": result.deployment_result}
ret_result_list.append(json.dumps(result_dict))
return ret_result_list
@@ -58,18 +63,24 @@ def get_deployment_status_list(self, end_point_id, end_point_name, model_name, m
return ret_status_list
def get_deployment_result_with_device_id(self, end_point_id, end_point_name, model_name, device_id):
+ """
+ Return a list of replica's result given end_point_id, end_point_name, model_name, device_id
+ """
+ replica_result_list = list()
try:
result_list = self.get_deployment_result_list(end_point_id, end_point_name, model_name)
for result_item in result_list:
- result_device_id, result_payload = self.get_result_item_info(result_item)
+ result_device_id, _, result_payload = self.get_result_item_info(result_item)
found_end_point_id = result_payload["end_point_id"]
if str(found_end_point_id) == str(end_point_id) and str(result_device_id) == str(device_id):
- return result_payload
+ replica_result_list.append(result_payload)
except Exception as e:
- logging.info(e)
+ # Do not intervene other endpoints on this device
+ logging.error(f"Error in get_deployment_result_with_device_id: {e}")
+ return None
- return None
+ return replica_result_list
def get_deployment_status_with_device_id(self, end_point_id, end_point_name, model_name, device_id):
try:
@@ -124,6 +135,18 @@ def delete_deployment_result_with_device_id(self, end_point_id, end_point_name,
FedMLDeploymentResultInfoModel.device_id == f'{device_id}')).delete()
self.db_connection.commit()
+ def delete_deployment_result_with_device_id_and_rank(self, end_point_id, end_point_name, model_name,
+ device_id, replica_rank):
+ replica_no = replica_rank + 1
+ self.open_job_db()
+ self.db_connection.query(FedMLDeploymentResultInfoModel).filter(
+ and_(FedMLDeploymentResultInfoModel.end_point_id == f'{end_point_id}',
+ FedMLDeploymentResultInfoModel.end_point_name == f'{end_point_name}',
+ FedMLDeploymentResultInfoModel.model_name == f'{model_name}',
+ FedMLDeploymentResultInfoModel.device_id == f'{device_id}',
+ FedMLDeploymentResultInfoModel.replica_no == f'{replica_no}')).delete()
+ self.db_connection.commit()
+
def delete_deployment_run_info(self, end_point_id):
# db / table -> model-deployment.db / "deployment_run_info"
self.open_job_db()
@@ -136,11 +159,13 @@ def get_result_item_info(self, result_item):
if isinstance(result_item_json, dict):
result_item_json = json.loads(result_item)
device_id = result_item_json["cache_device_id"]
+ replica_no = result_item_json["cache_replica_no"]
+
if isinstance(result_item_json["result"], str):
result_payload = json.loads(result_item_json["result"])
else:
result_payload = result_item_json["result"]
- return device_id, result_payload
+ return device_id, replica_no, result_payload
def get_status_item_info(self, status_item):
status_item_json = json.loads(status_item)
@@ -274,17 +299,19 @@ def get_deployment_results_info(self, end_point_id, end_point_name, model_name,
def set_deployment_results_info(self, end_point_id, end_point_name,
model_name, model_version, device_id,
- deployment_result=None, deployment_status=None):
- '''
- end_point_id + device_id is unique identifier,
+ deployment_result=None, deployment_status=None, replica_no=None):
+ """
+ end_point_id + device_id + replica_no is unique identifier,
we do not allow duplicate records
- '''
+ """
self.open_job_db()
result_info = self.db_connection.query(FedMLDeploymentResultInfoModel). \
filter(and_(FedMLDeploymentResultInfoModel.end_point_id == f'{end_point_id}',
FedMLDeploymentResultInfoModel.end_point_name == f'{end_point_name}',
FedMLDeploymentResultInfoModel.model_name == f'{model_name}',
- FedMLDeploymentResultInfoModel.device_id == f'{device_id}')).first()
+ FedMLDeploymentResultInfoModel.device_id == f'{device_id}',
+ FedMLDeploymentResultInfoModel.replica_no == f'{replica_no}'
+ )).first()
# Insert
if result_info is None:
result_info = FedMLDeploymentResultInfoModel(end_point_id=end_point_id,
@@ -293,7 +320,9 @@ def set_deployment_results_info(self, end_point_id, end_point_name,
model_version=model_version,
device_id=device_id,
deployment_result=deployment_result,
- deployment_status=deployment_status)
+ deployment_status=deployment_status,
+ replica_no=replica_no
+ )
self.db_connection.add(result_info)
self.db_connection.commit()
return
@@ -439,6 +468,7 @@ class FedMLDeploymentResultInfoModel(Base):
device_id = Column(TEXT)
deployment_result = Column(TEXT)
deployment_status = Column(TEXT)
+ replica_no = Column(TEXT)
class FedMLDeploymentRunInfoModel(Base):
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 0a8c3b6ce9..3a6d891e58 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -34,36 +34,15 @@
from .device_http_inference_protocol import FedMLHttpInference
+from fedml.computing.scheduler.model_scheduler.device_model_cache import FedMLModelCache
no_real_gpu_allocation = None
-class CPUUnpickler(pickle.Unpickler):
- def find_class(self, module, name):
- if module == 'torch.storage' and name == '_load_from_bytes':
- return lambda b: torch.load(io.BytesIO(b), map_location='cpu')
- else:
- return super().find_class(module, name)
-
-
def request_gpu_ids_on_deployment(edge_id, end_point_id, num_gpus=None, master_device_id=None):
gpu_ids = None
client_device_id = os.getenv("FEDML_CURRENT_EDGE_ID")
- try:
- ComputeCacheManager.get_instance().set_redis_params()
- with ComputeCacheManager.get_instance().lock(
- ComputeCacheManager.get_instance().get_gpu_cache().get_device_run_lock_key(edge_id, end_point_id)
- ):
- if num_gpus is None:
- num_gpus = ComputeCacheManager.get_instance().get_gpu_cache().get_device_run_num_gpus(edge_id, end_point_id)
- num_gpus = int(num_gpus) if num_gpus is not None and str(num_gpus) != "" else 1
- gpu_ids = ComputeCacheManager.get_instance().get_gpu_cache().get_device_run_gpu_ids(edge_id, end_point_id)
- except Exception as e:
- logging.info(f"Execption when request gpu ids. {traceback.format_exc()}")
- gpu_ids = None
- raise e
-
if gpu_ids is None:
cuda_visable_gpu_ids = JobRunnerUtils.get_instance().occupy_gpu_ids(
end_point_id, num_gpus, client_device_id, inner_id=end_point_id,
@@ -92,234 +71,127 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
inference_use_gpu, inference_memory_size,
inference_convertor_image, inference_server_image,
infer_host, model_is_from_open, model_params,
- model_from_open, token, master_ip, edge_id, master_device_id=None):
+ model_from_open, token, master_ip, edge_id, master_device_id=None, replica_rank=0,
+ gpu_per_replica=1):
logging.info("Model deployment is starting...")
- use_simulation_test_without_triton = False
- model_metadata = {'name': inference_model_name,
- 'versions': ['1'], 'platform': 'onnxruntime_onnx',
- 'inputs': [{'name': 'input2', 'datatype': 'INT32', 'shape': [1, 24]},
- {'name': 'input1', 'datatype': 'FP32', 'shape': [1, 2]}],
- 'outputs': [{'name': 'output', 'datatype': 'FP32', 'shape': [1]}]}
- model_config = {
- "platform": "onnxruntime",
- "max_batch_size": 1,
- "input_size": [[1, 24], [1, 2]],
- "input_types": ["int", "float"],
- "input": [
- {
- "name": "input",
- "data_type": "TYPE_FP32",
- "dims": []
- }
- ],
- "output": [
- {
- "name": "output",
- "data_type": "TYPE_FP32",
- "dims": []
- }
- ]
- }
-
sudo_prefix = "sudo "
sys_name = platform.system()
if sys_name == "Darwin":
sudo_prefix = ""
- num_gpus = 0
+ num_gpus = gpu_per_replica # Real gpu per replica (container)
gpu_ids, gpu_attach_cmd = None, ""
running_model_name = ClientConstants.get_running_model_name(
end_point_name, inference_model_name, model_version, end_point_id, model_id, edge_id=edge_id)
- # Check whether triton server is running.
- triton_server_is_running = False
- if not use_simulation_test_without_triton:
- triton_server_container_name = "{}".format(ClientConstants.FEDML_TRITON_SERVER_CONTAINER_NAME_PREFIX)
- if not ClientConstants.is_running_on_k8s():
- check_triton_server_running_cmds = "{}docker ps |grep {}".format(sudo_prefix, triton_server_container_name)
- running_process = ClientConstants.exec_console_with_script(check_triton_server_running_cmds,
- should_capture_stdout=True,
- should_capture_stderr=True)
- ret_code, out, err = ClientConstants.get_console_pipe_out_err_results(running_process)
- if out is not None:
- out_str = sys_utils.decode_our_err_result(out)
- if str(out_str) != "":
- triton_server_is_running = True
-
- # Convert models from pytorch to onnx format
if model_is_from_open:
- if model_from_open is None:
- return running_model_name, "", model_version, {}, {}
-
- logging.info("model binary file: {}".format(model_bin_file))
- with open(model_bin_file, 'rb') as model_pkl_file:
- if not torch.cuda.is_available():
- try:
- open_model_params = CPUUnpickler(model_pkl_file).load()
- except Exception as ex:
- logging.info("load model exceptions when using CPU_Unpickler: {}".format(traceback.format_exc()))
- return "", "", model_version, model_metadata, model_config
- else:
- open_model_params = pickle.load(model_pkl_file)
- model_from_open.load_state_dict(open_model_params)
- model_from_open.eval()
-
- if inference_engine == ClientConstants.INFERENCE_ENGINE_TYPE_INT_TRITON:
- logging.info("convert the onnx model when the mode is from FedML® Nexus AI Platform..")
- logging.info("Input size {}, input types {}".format(model_params["input_size"],
- model_params["input_types"]))
- input_size = model_params["input_size"]
- input_types = model_params["input_types"]
-
- dummy_input_list = []
- for index, input_i in enumerate(input_size):
- if input_types[index] == "int":
- this_input = torch.randint(0, 1, input_i).clone().detach()
- else:
- this_input = torch.zeros(input_i).clone().detach()
- dummy_input_list.append(this_input)
-
- onnx_model_path = os.path.join(model_storage_local_path,
- ClientConstants.FEDML_CONVERTED_MODEL_DIR_NAME,
- running_model_name, ClientConstants.INFERENCE_MODEL_VERSION)
- if not os.path.exists(onnx_model_path):
- os.makedirs(onnx_model_path, exist_ok=True)
- onnx_model_path = os.path.join(onnx_model_path, "model.onnx")
-
- convert_model_to_onnx(model_from_open, onnx_model_path, dummy_input_list, input_size)
- elif ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT: # we do not convert the model to onnx in llm
- logging.info("LLM model loaded from the open")
+ logging.error("The model is directly export from open, currently do not convert the model to servable format.")
+ return "", "", None, None, None
+
+ # Parse the model config file and get the necessary information for the deployment
+ model_config_path = os.path.join(model_storage_local_path, "fedml_model_config.yaml")
+ with open(model_config_path, 'r') as file:
+ config = yaml.safe_load(file)
+ # Resource related
+ use_gpu = config.get('use_gpu', True)
+ in_gpu_ids = config.get('gpu_ids', gpu_ids)
+ num_gpus_frm_yml = config.get('num_gpus', None)
+ if not use_gpu:
+ num_gpus = 0
else:
- raise Exception("Unsupported inference engine type: {}".format(inference_engine))
- elif model_is_from_open == False or model_is_from_open is None:
- model_location = os.path.join(model_storage_local_path, "fedml_model.bin")
- try:
- model = torch.jit.load(model_location)
- model.eval()
- except Exception as e:
- logging.info(
- "Cannot locate the .bin file, will read it from"
- " the fedml_model_config.yaml with the key [local_model_dir] ")
- model_config_path = os.path.join(model_storage_local_path, "fedml_model_config.yaml")
- with open(model_config_path, 'r') as file:
- config = yaml.safe_load(file)
- # Resource related
- use_gpu = config.get('use_gpu', False)
- in_gpu_ids = config.get('gpu_ids', gpu_ids)
- num_gpus = config.get('num_gpus', None)
- if not use_gpu:
- num_gpus = 0
- else:
- if num_gpus is None:
- num_gpus = len(in_gpu_ids) if in_gpu_ids is not None else 1
- usr_indicated_wait_time = config.get('deploy_timeout', 900)
- usr_indicated_worker_port = config.get('worker_port', "")
- if usr_indicated_worker_port == "":
- usr_indicated_worker_port = os.environ.get("FEDML_WORKER_PORT", "")
- shm_size = config.get('shm_size', None)
- storage_opt = config.get('storage_opt', None)
- tmpfs = config.get('tmpfs', None)
- cpus = config.get('cpus', None)
- if cpus is not None:
- cpus = int(cpus)
- memory = config.get('memory', None)
-
- if usr_indicated_worker_port == "":
- usr_indicated_worker_port = None
- else:
- usr_indicated_worker_port = int(usr_indicated_worker_port)
-
- worker_port_env = os.environ.get("FEDML_WORKER_PORT", "")
- worker_port_from_config = config.get('worker_port', "")
- print(f"usr_indicated_worker_port {usr_indicated_worker_port}, worker port env {worker_port_env}, "
- f"worker port from config {worker_port_from_config}")
-
- usr_indicated_retry_cnt = max(int(usr_indicated_wait_time) // 10, 1)
- inference_image_name = config.get('inference_image_name',
- ClientConstants.INFERENCE_SERVER_CUSTOME_IMAGE)
- image_pull_policy = config.get('image_pull_policy', SchedulerConstants.IMAGE_PULL_POLICY_IF_NOT_PRESENT)
-
- # Source code dir, bootstrap dir, data cache dir
- src_code_dir = os.path.join(model_storage_local_path, config.get('source_code_dir', ""))
-
- # Get the bootstrap and job commands inside the yaml file
- bootstrap_cmds_str_frm_yaml = config.get('bootstrap', "")
- job_cmds_str_frm_yaml = config.get('job', "")
-
- if bootstrap_cmds_str_frm_yaml != "" or job_cmds_str_frm_yaml != "":
- auto_gen_bootstrap_file_name = "fedml-deploy-bootstrap-entry-auto-gen.sh"
- src_bootstrap_file_path = os.path.join(model_storage_local_path, auto_gen_bootstrap_file_name)
- with open(src_bootstrap_file_path, 'w') as f:
- f.write("cd /home/fedml/models_serving/\n")
- f.write(bootstrap_cmds_str_frm_yaml)
- f.write("\n")
- f.write("cd /home/fedml/models_serving/\n")
- f.write(job_cmds_str_frm_yaml)
- else:
- src_bootstrap_file_path = ""
+ if num_gpus_frm_yml is not None:
+ num_gpus = int(num_gpus_frm_yml)
+ usr_indicated_wait_time = config.get('deploy_timeout', 900)
+ usr_indicated_worker_port = config.get('worker_port', "")
+ if usr_indicated_worker_port == "":
+ usr_indicated_worker_port = os.environ.get("FEDML_WORKER_PORT", "")
+ shm_size = config.get('shm_size', None)
+ storage_opt = config.get('storage_opt', None)
+ tmpfs = config.get('tmpfs', None)
+ cpus = config.get('cpus', None)
+ if cpus is not None:
+ cpus = int(cpus)
+ memory = config.get('memory', None)
+
+ if usr_indicated_worker_port == "":
+ usr_indicated_worker_port = None
+ else:
+ usr_indicated_worker_port = int(usr_indicated_worker_port)
+
+ worker_port_env = os.environ.get("FEDML_WORKER_PORT", "")
+ worker_port_from_config = config.get('worker_port', "")
+ print(f"usr_indicated_worker_port {usr_indicated_worker_port}, worker port env {worker_port_env}, "
+ f"worker port from config {worker_port_from_config}")
+
+ usr_indicated_retry_cnt = max(int(usr_indicated_wait_time) // 10, 1)
+ inference_image_name = config.get('inference_image_name',
+ ClientConstants.INFERENCE_SERVER_CUSTOME_IMAGE)
+ image_pull_policy = config.get('image_pull_policy', SchedulerConstants.IMAGE_PULL_POLICY_IF_NOT_PRESENT)
+
+ # Source code dir, bootstrap dir, data cache dir
+ src_code_dir = os.path.join(model_storage_local_path, config.get('source_code_dir', ""))
+
+ # Get the bootstrap and job commands inside the yaml file
+ bootstrap_cmds_str_frm_yaml = config.get('bootstrap', "")
+ job_cmds_str_frm_yaml = config.get('job', "")
+
+ if bootstrap_cmds_str_frm_yaml != "" or job_cmds_str_frm_yaml != "":
+ auto_gen_bootstrap_file_name = "fedml-deploy-bootstrap-entry-auto-gen.sh"
+ src_bootstrap_file_path = os.path.join(model_storage_local_path, auto_gen_bootstrap_file_name)
+ with open(src_bootstrap_file_path, 'w') as f:
+ f.write("cd /home/fedml/models_serving/\n")
+ f.write(bootstrap_cmds_str_frm_yaml)
+ f.write("\n")
+ f.write("cd /home/fedml/models_serving/\n")
+ f.write(job_cmds_str_frm_yaml)
+ else:
+ src_bootstrap_file_path = ""
- data_cache_dir_input = config.get('data_cache_dir', "")
- request_input_example = config.get('request_input_example', None)
- extra_envs = config.get('environment_variables', None)
+ data_cache_dir_input = config.get('data_cache_dir', "")
+ request_input_example = config.get('request_input_example', None)
+ extra_envs = config.get('environment_variables', None)
- # Serving dir inside docker
- dst_model_serving_dir = "/home/fedml/models_serving"
- relative_entry = config.get('entry_point')
- if src_bootstrap_file_path != "":
- dst_bootstrap_dir = os.path.join(dst_model_serving_dir, auto_gen_bootstrap_file_name)
- else:
- dst_bootstrap_dir = ""
+ # Serving dir inside docker
+ dst_model_serving_dir = "/home/fedml/models_serving"
+ relative_entry = config.get('entry_point')
+ if src_bootstrap_file_path != "":
+ dst_bootstrap_dir = os.path.join(dst_model_serving_dir, auto_gen_bootstrap_file_name)
+ else:
+ dst_bootstrap_dir = ""
- # If using customized image, then bootstrap + job will be the entry point
- enable_custom_image = config.get("enable_custom_image", False)
- customized_image_entry_cmd = \
- "/bin/bash /home/fedml/models_serving/fedml-deploy-bootstrap-entry-auto-gen.sh"
+ # If using customized image, then bootstrap + job will be the entry point
+ enable_custom_image = config.get("enable_custom_image", False)
+ customized_image_entry_cmd = \
+ "/bin/bash /home/fedml/models_serving/fedml-deploy-bootstrap-entry-auto-gen.sh"
- docker_registry_user_name = config.get("docker_registry_user_name", "")
- docker_registry_user_password = config.get("docker_registry_user_password", "")
- docker_registry = config.get("docker_registry", "")
+ docker_registry_user_name = config.get("docker_registry_user_name", "")
+ docker_registry_user_password = config.get("docker_registry_user_password", "")
+ docker_registry = config.get("docker_registry", "")
- port_inside_container = int(config.get("port_inside_container", 2345))
- use_triton = config.get("use_triton", False)
- if use_triton:
- inference_type = "triton"
- else:
- inference_type = "default"
-
- if src_code_dir == "":
- raise Exception("Please indicate source_code_dir in the fedml_model_config.yaml")
- if relative_entry == "":
- logging.warning("You missed main_entry in the fedml_model_config.yaml")
-
- if num_gpus > 0:
- gpu_ids, gpu_attach_cmd = request_gpu_ids_on_deployment(
- edge_id, end_point_id, num_gpus=num_gpus, master_device_id=master_device_id)
-
- if inference_engine == ClientConstants.INFERENCE_ENGINE_TYPE_INT_TRITON:
- # configuration passed by user in the Cli
- input_size = model_params["input_size"]
- input_types = model_params["input_types"]
- logging.info("convert the onnx model when the mode is from the general PyTorch...")
- logging.info("Input size {}, input types {}".format(model_params["input_size"],
- model_params["input_types"]))
- dummy_input_list = []
- for index, input_i in enumerate(input_size):
- if input_types[index] == "int":
- this_input = torch.randint(0, 1, input_i).clone().detach()
- else:
- this_input = torch.zeros(input_i).clone().detach()
- dummy_input_list.append(this_input)
+ port_inside_container = int(config.get("port_inside_container", 2345))
+ use_triton = config.get("use_triton", False)
+ if use_triton:
+ inference_type = "triton"
+ else:
+ inference_type = "default"
+
+ # Config check
+ if src_code_dir == "":
+ raise Exception("Please indicate source_code_dir in the fedml_model_config.yaml")
+ if relative_entry == "":
+ logging.warning("You missed main_entry in the fedml_model_config.yaml")
- onnx_model_path = os.path.join(model_storage_local_path,
- ClientConstants.FEDML_CONVERTED_MODEL_DIR_NAME,
- running_model_name, ClientConstants.INFERENCE_MODEL_VERSION)
- logging.info("converted onnx model path: {}".format(onnx_model_path))
- if not os.path.exists(onnx_model_path):
- os.makedirs(onnx_model_path, exist_ok=True)
- onnx_model_path = os.path.join(onnx_model_path, "model.onnx")
+ # Request the GPU ids for the deployment
+ if num_gpus > 0:
+ gpu_ids, gpu_attach_cmd = request_gpu_ids_on_deployment(
+ edge_id, end_point_id, num_gpus=num_gpus, master_device_id=master_device_id)
- convert_model_to_onnx(model, onnx_model_path, dummy_input_list, input_size)
+ # set replica and their gpu ids
+ FedMLModelCache.get_instance().set_redis_params()
+ FedMLModelCache.get_instance().set_replica_gpu_ids(
+ end_point_id, end_point_name, inference_model_name, edge_id, replica_rank+1, gpu_ids)
+ logging.info("GPU ids allocated: {}".format(gpu_ids))
logging.info("move converted model to serving dir for inference...")
model_serving_dir = ClientConstants.get_model_serving_dir()
@@ -339,265 +211,199 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
if not os.path.exists(dst_model_file):
shutil.copyfile(src_model_file, dst_model_file)
- if inference_engine == ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT:
- logging.info(f"master ip: {master_ip}, worker ip: {infer_host}")
- if infer_host == master_ip:
- logging.info("infer_host is the same as master ip, will use 127.0.0.1 to avoid firewall issue")
- infer_host = "127.0.0.1"
+ if inference_engine != ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT:
+ raise Exception(f"inference engine {inference_engine} is not supported")
- try:
- client = docker.from_env()
- if enable_custom_image and docker_registry_user_name != "" and docker_registry_user_password != "" \
- and docker_registry != "":
- client.login(username=docker_registry_user_name, password=docker_registry_user_password,
- registry=docker_registry)
- except Exception:
- logging.error("Failed to connect to the docker daemon, please ensure that you have "
- "installed Docker Desktop or Docker Engine, and the docker is running")
- return "", "", None, None, None
-
- container_prefix = "{}".format(ClientConstants.FEDML_DEFAULT_SERVER_CONTAINER_NAME_PREFIX) + "__" + \
- security_utils.get_content_hash(running_model_name)
-
- same_model_container_rank = ContainerUtils.get_container_rank_same_model(container_prefix)
- if same_model_container_rank == -1:
- logging.error(f"Fail to get existed docker with {end_point_name} {inference_model_name}")
- raise Exception("Failed to get the container rank")
- default_server_container_name = container_prefix + "__" + str(same_model_container_rank)
+ # Get the master device id
+ logging.info(f"master ip: {master_ip}, worker ip: {infer_host}")
+ if infer_host == master_ip:
+ logging.info("infer_host is the same as master ip, will use 127.0.0.1 to avoid firewall issue")
+ infer_host = "127.0.0.1"
- try:
- exist_container_obj = client.containers.get(default_server_container_name)
- except docker.errors.NotFound:
- exist_container_obj = None
- except docker.errors.APIError:
- raise Exception("Failed to get the container object")
-
- if exist_container_obj is not None:
- client.api.remove_container(exist_container_obj.id, v=True, force=True)
- device_requests = []
- if no_real_gpu_allocation is not None:
- use_gpu = not no_real_gpu_allocation
- if use_gpu:
- logging.info("Number of GPUs: {}".format(num_gpus))
- if gpu_ids is not None:
- gpu_id_list = map(lambda x: str(x), gpu_ids)
- device_requests.append(
- docker.types.DeviceRequest(device_ids=list(gpu_id_list), capabilities=[['gpu']]))
+ try:
+ client = docker.from_env()
+ if enable_custom_image and docker_registry_user_name != "" and docker_registry_user_password != "" \
+ and docker_registry != "":
+ client.login(username=docker_registry_user_name, password=docker_registry_user_password,
+ registry=docker_registry)
+ except Exception:
+ logging.error("Failed to connect to the docker daemon, please ensure that you have "
+ "installed Docker Desktop or Docker Engine, and the docker is running")
+ return "", "", None, None, None
+
+ container_prefix = ("{}".format(ClientConstants.FEDML_DEFAULT_SERVER_CONTAINER_NAME_PREFIX) + "__" +
+ security_utils.get_content_hash(running_model_name))
+
+ default_server_container_name = container_prefix + "__" + str(replica_rank)
+
+ try:
+ exist_container_obj = client.containers.get(default_server_container_name)
+ except docker.errors.NotFound:
+ exist_container_obj = None
+ except docker.errors.APIError:
+ raise Exception("Failed to get the container object")
+
+ # Allocate the GPU
+ # TODO: Make sure no competition for each replica in a single deployment
+ if exist_container_obj is not None:
+ client.api.remove_container(exist_container_obj.id, v=True, force=True)
+ device_requests = []
+ if no_real_gpu_allocation is not None:
+ use_gpu = not no_real_gpu_allocation
+ use_gpu = False
+ if use_gpu:
+ logging.info("Number of GPUs: {}".format(num_gpus))
+ if gpu_ids is not None:
+ gpu_id_list = map(lambda x: str(x), gpu_ids)
+ device_requests.append(
+ docker.types.DeviceRequest(device_ids=list(gpu_id_list), capabilities=[['gpu']]))
+ else:
+ device_requests.append(
+ docker.types.DeviceRequest(count=num_gpus, capabilities=[['gpu']]))
+ logging.info(f"device_requests: {device_requests}")
+
+ # Pull the inference image
+ logging.info(f"Start pulling the inference image {inference_image_name}..., may take a few minutes...")
+ ContainerUtils.get_instance().pull_image_with_policy(image_pull_policy, inference_image_name)
+
+ logging.info("Start creating the inference container...")
+ volumns = []
+ binds = {}
+ environment = {}
+
+ # data_cache_dir mounting
+ assert type(data_cache_dir_input) == dict or type(data_cache_dir_input) == str
+ if type(data_cache_dir_input) == str:
+ # In this case, we mount to the same folder, if has ~, we replace it with /home/fedml
+ src_data_cache_dir, dst_data_cache_dir = "", ""
+ if data_cache_dir_input != "":
+ if data_cache_dir_input[0] == "~":
+ src_data_cache_dir = os.path.expanduser(data_cache_dir_input)
+ dst_data_cache_dir = data_cache_dir_input.replace("~", "/home/fedml")
else:
- device_requests.append(
- docker.types.DeviceRequest(count=num_gpus, capabilities=[['gpu']]))
- logging.info(f"device_requests: {device_requests}")
- logging.info(f"Start pulling the inference image {inference_image_name}..., may take a few minutes...")
-
- ContainerUtils.get_instance().pull_image_with_policy(image_pull_policy, inference_image_name)
-
- logging.info("Start creating the inference container...")
- volumns = []
- binds = {}
- environment = {}
-
- assert type(data_cache_dir_input) == dict or type(data_cache_dir_input) == str
- if type(data_cache_dir_input) == str:
- # In this case, we mount to the same folder, if has ~, we replace it with /home/fedml
- src_data_cache_dir, dst_data_cache_dir = "", ""
- if data_cache_dir_input != "":
- if data_cache_dir_input[0] == "~":
- src_data_cache_dir = os.path.expanduser(data_cache_dir_input)
- dst_data_cache_dir = data_cache_dir_input.replace("~", "/home/fedml")
+ # check if the data_cache_dir is a relative path
+ if data_cache_dir_input[0] != "/":
+ raise "data_cache_dir_input has to be an absolute path or start with ~"
else:
- # check if the data_cache_dir is a relative path
- if data_cache_dir_input[0] != "/":
- raise "data_cache_dir_input has to be an absolute path or start with ~"
- else:
- src_data_cache_dir = data_cache_dir_input
- dst_data_cache_dir = data_cache_dir_input
- logging.info(f"src_data_cache_dir: {src_data_cache_dir}, dst_data_cache_dir: {dst_data_cache_dir}")
-
- if type(src_data_cache_dir) == str and src_data_cache_dir != "":
- logging.info("Start copying the data cache to the container...")
- if os.path.exists(src_data_cache_dir):
- volumns.append(src_data_cache_dir)
- binds[src_data_cache_dir] = {
- "bind": dst_data_cache_dir,
- "mode": "rw"
- }
- environment["DATA_CACHE_FOLDER"] = dst_data_cache_dir
- else:
- for k, v in data_cache_dir_input.items():
- if os.path.exists(k):
- volumns.append(v)
- binds[k] = {
- "bind": v,
+ src_data_cache_dir = data_cache_dir_input
+ dst_data_cache_dir = data_cache_dir_input
+ logging.info(f"src_data_cache_dir: {src_data_cache_dir}, dst_data_cache_dir: {dst_data_cache_dir}")
+
+ if type(src_data_cache_dir) == str and src_data_cache_dir != "":
+ logging.info("Start copying the data cache to the container...")
+ if os.path.exists(src_data_cache_dir):
+ volumns.append(src_data_cache_dir)
+ binds[src_data_cache_dir] = {
+ "bind": dst_data_cache_dir,
"mode": "rw"
}
- else:
- logging.warning(f"{k} does not exist, skip mounting it to the container")
- logging.info(f"Data cache mount: {volumns}, {binds}")
-
- # Default
- if not enable_custom_image or (enable_custom_image and relative_entry != ""):
- logging.info("Start copying the source code to the container...")
- volumns.append(src_code_dir)
- binds[src_code_dir] = {
- "bind": dst_model_serving_dir,
- "mode": "rw"
- }
- environment["MAIN_ENTRY"] = relative_entry
-
- if not enable_custom_image:
- # For some image, the default user is root. Unified to fedml.
- environment["HOME"] = "/home/fedml"
- environment["BOOTSTRAP_DIR"] = dst_bootstrap_dir
- environment["FEDML_CURRENT_RUN_ID"] = end_point_id
- environment["FEDML_CURRENT_EDGE_ID"] = edge_id
- environment["FEDML_CURRENT_VERSION"] = fedml.get_env_version()
- environment["FEDML_ENV_VERSION"] = fedml.get_env_version()
- environment["FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_HOST"] = fedml.get_local_on_premise_platform_host()
- environment["FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_PORT"] = fedml.get_local_on_premise_platform_port()
- logging.info(f"volume: {volumns}, binds: {binds}, environment: {environment}")
- logging.info(f"dst_model_serving_dir: {dst_model_serving_dir}")
- logging.info(f"relative_entry: {relative_entry}")
- logging.info(f"src_bootstrap_file_path: {src_bootstrap_file_path}")
- logging.info(f"dst_bootstrap_dir: {dst_bootstrap_dir}")
- logging.info(f"src_code_dir: {src_code_dir}")
- logging.info(f"model_serving_dir: {model_serving_dir}")
-
- if extra_envs is not None:
- for key in extra_envs:
- environment[key] = extra_envs[key]
+ environment["DATA_CACHE_FOLDER"] = dst_data_cache_dir
+ else:
+ for k, v in data_cache_dir_input.items():
+ if os.path.exists(k):
+ volumns.append(v)
+ binds[k] = {
+ "bind": v,
+ "mode": "rw"
+ }
+ else:
+ logging.warning(f"{k} does not exist, skip mounting it to the container")
+ logging.info(f"Data cache mount: {volumns}, {binds}")
+
+ # Default mounting
+ if not enable_custom_image or (enable_custom_image and relative_entry != ""):
+ logging.info("Start copying the source code to the container...")
+ volumns.append(src_code_dir)
+ binds[src_code_dir] = {
+ "bind": dst_model_serving_dir,
+ "mode": "rw"
+ }
+ environment["MAIN_ENTRY"] = relative_entry
+
+ # Environment variables
+ if not enable_custom_image:
+ # For some image, the default user is root. Unified to fedml.
+ environment["HOME"] = "/home/fedml"
+
+ environment["BOOTSTRAP_DIR"] = dst_bootstrap_dir
+ environment["FEDML_CURRENT_RUN_ID"] = end_point_id
+ environment["FEDML_CURRENT_EDGE_ID"] = edge_id
+ environment["FEDML_CURRENT_VERSION"] = fedml.get_env_version()
+ environment["FEDML_ENV_VERSION"] = fedml.get_env_version()
+ environment["FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_HOST"] = fedml.get_local_on_premise_platform_host()
+ environment["FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_PORT"] = fedml.get_local_on_premise_platform_port()
+
+ if extra_envs is not None:
+ for key in extra_envs:
+ environment[key] = extra_envs[key]
+ try:
+ new_container = client.api.create_container(
+ image=inference_image_name,
+ name=default_server_container_name,
+ volumes=volumns,
+ ports=[port_inside_container], # port open inside the container
+ environment=environment,
+ host_config=client.api.create_host_config(
+ binds=binds,
+ port_bindings={
+ port_inside_container: usr_indicated_worker_port # Could be either None or a port number
+ },
+ device_requests=device_requests,
+ shm_size=shm_size,
+ storage_opt=storage_opt,
+ tmpfs=tmpfs,
+ cpu_count=cpus,
+ mem_limit=memory,
+ ),
+ detach=True,
+ command=customized_image_entry_cmd if enable_custom_image else None
+ )
+ client.api.start(container=new_container.get("Id"))
+ except Exception as e:
+ logging.error(f"Failed to create the container with exception {e}, traceback : {traceback.format_exc()}")
+ return "", "", None, None, None
+
+ # Get the port allocation
+ cnt = 0
+ while True:
+ cnt += 1
try:
- new_container = client.api.create_container(
- image=inference_image_name,
- name=default_server_container_name,
- volumes=volumns,
- ports=[port_inside_container], # port open inside the container
- environment=environment,
- host_config=client.api.create_host_config(
- binds=binds,
- port_bindings={
- port_inside_container: usr_indicated_worker_port # Could be either None or a port number
- },
- device_requests=device_requests,
- shm_size=shm_size,
- storage_opt=storage_opt,
- tmpfs=tmpfs,
- cpu_count=cpus,
- mem_limit=memory,
- ),
- detach=True,
- command=customized_image_entry_cmd if enable_custom_image else None
- )
- client.api.start(container=new_container.get("Id"))
- except Exception as e:
- logging.error(f"Failed to create the container with exception {e}, traceback : {traceback.format_exc()}")
+ if usr_indicated_worker_port is not None:
+ inference_http_port = usr_indicated_worker_port
+ break
+ else:
+ # Find the random port
+ port_info = client.api.port(new_container.get("Id"), port_inside_container)
+ inference_http_port = port_info[0]["HostPort"]
+ logging.info("inference_http_port: {}".format(inference_http_port))
+ break
+ except:
+ if cnt >= 5:
+ raise Exception("Failed to get the port allocation")
+ time.sleep(3)
- # Get the port allocation
- cnt = 0
- while True:
- cnt += 1
- try:
- if usr_indicated_worker_port is not None:
- inference_http_port = usr_indicated_worker_port
- break
- else:
- # Find the random port
- port_info = client.api.port(new_container.get("Id"), port_inside_container)
- inference_http_port = port_info[0]["HostPort"]
- logging.info("inference_http_port: {}".format(inference_http_port))
- break
- except:
- if cnt >= 5:
- raise Exception("Failed to get the port allocation")
- time.sleep(3)
-
- # Logging the info from the container
- log_deployment_result(end_point_id, model_id, default_server_container_name,
- ClientConstants.CMD_TYPE_RUN_DEFAULT_SERVER,
- inference_model_name, inference_engine, inference_http_port, inference_type,
- retry_interval=10, deploy_attempt_threshold=usr_indicated_retry_cnt,
- request_input_example=request_input_example, infer_host=infer_host,
- enable_custom_image=enable_custom_image)
-
- # Check if the inference server is ready
- inference_output_url, running_model_version, ret_model_metadata, ret_model_config = \
- get_model_info(inference_model_name, inference_engine, inference_http_port,
- infer_host, False, inference_type, request_input_example=request_input_example,
- enable_custom_image=enable_custom_image)
-
- if inference_output_url == "":
- return running_model_name, "", None, None, None
-
- # testing the inference container
- test_input = ret_model_metadata["inputs"]
-
- # try:
- # inference_response = run_http_inference_with_curl_request(inference_output_url, test_input, [],
- # inference_type="default")
- # logging.info(f"Tested the inference backend with {test_input}, the response is {inference_response}")
- # except Exception as e:
- # logging.info("Tested the inference backend, exceptions occurred: {}".format(traceback.format_exc()))
- # inference_output_url = ""
-
- model_metadata = ret_model_metadata
- logging.info(model_metadata)
- elif inference_engine == ClientConstants.INFERENCE_ENGINE_TYPE_INT_TRITON:
- logging.info("prepare to run triton server...")
- if not use_simulation_test_without_triton:
- if not triton_server_is_running and not ClientConstants.is_running_on_k8s():
- triton_server_cmd = "{}docker stop {}; {}docker rm {}; {}docker run --name {} {} -p{}:8000 " \
- "-p{}:8001 -p{}:8002 " \
- "--shm-size {} " \
- "-v {}:/models {} " \
- "bash -c \"pip install transformers && tritonserver --strict-model-config=false " \
- "--model-control-mode=poll --repository-poll-secs={} " \
- "--model-repository=/models\" ".format(sudo_prefix, triton_server_container_name,
- sudo_prefix, triton_server_container_name,
- sudo_prefix, triton_server_container_name,
- gpu_attach_cmd,
- inference_http_port,
- inference_grpc_port,
- inference_metric_port,
- inference_memory_size,
- model_serving_dir,
- inference_server_image,
- ClientConstants.FEDML_MODEL_SERVING_REPO_SCAN_INTERVAL)
- logging.info("Run triton inference server: {}".format(triton_server_cmd))
- triton_server_process = ClientConstants.exec_console_with_script(triton_server_cmd,
- should_capture_stdout=False,
- should_capture_stderr=False,
- no_sys_out_err=True)
- log_deployment_result(end_point_id, model_id, triton_server_container_name,
- ClientConstants.CMD_TYPE_RUN_TRITON_SERVER, triton_server_process.pid,
- running_model_name, inference_engine, inference_http_port)
-
- inference_output_url, running_model_version, ret_model_metadata, ret_model_config = \
- get_model_info(running_model_name, inference_engine, inference_http_port, infer_host)
- if inference_output_url != "":
- # Send the test request to the inference backend and check if the response is normal
- input_json, output_json = build_inference_req(end_point_name, inference_model_name,
- token, ret_model_metadata)
- try:
- inference_response = run_http_inference_with_curl_request(inference_output_url,
- input_json["inputs"],
- input_json["outputs"])
- logging.info("Tested the inference backend, the response is {}".format(inference_response))
- except Exception as e:
- logging.info("Tested the inference backend, exceptions occurred: {}".format(traceback.format_exc()))
- inference_output_url = ""
-
- if inference_output_url != "":
- logging.info(
- "Deploy model successfully, inference url: {}, model metadata: {}, model config: {}".format(
- inference_output_url, model_metadata, model_config))
- model_metadata = ret_model_metadata
- model_config = ret_model_config
- else:
- inference_output_url = f"http://localhost:{inference_http_port}/v2/models/{running_model_name}/versions/1/infer"
- else:
- raise Exception("inference engine {} is not supported".format(inference_engine))
+ # Logging the info from the container
+ log_deployment_result(end_point_id, model_id, default_server_container_name,
+ ClientConstants.CMD_TYPE_RUN_DEFAULT_SERVER,
+ inference_model_name, inference_engine, inference_http_port, inference_type,
+ retry_interval=10, deploy_attempt_threshold=usr_indicated_retry_cnt,
+ request_input_example=request_input_example, infer_host=infer_host,
+ enable_custom_image=enable_custom_image)
+
+ # Check if the inference server is ready
+ inference_output_url, running_model_version, ret_model_metadata, ret_model_config = \
+ get_model_info(inference_model_name, inference_engine, inference_http_port,
+ infer_host, False, inference_type, request_input_example=request_input_example,
+ enable_custom_image=enable_custom_image)
+
+ if inference_output_url == "":
+ return running_model_name, "", None, None, None
- return running_model_name, inference_output_url, model_version, model_metadata, model_config
+ model_metadata = ret_model_metadata
+ logging.info(model_metadata)
+
+ return running_model_name, inference_output_url, model_version, model_metadata, ret_model_config
def build_inference_req(end_point_name, model_name, token, in_model_metadata):
@@ -719,8 +525,14 @@ def log_deployment_result(end_point_id, model_id, cmd_container_name, cmd_type,
break
if container_obj is not None:
- out_logs = container_obj.logs(stdout=True, stderr=False, stream=False, follow=False, since=last_log_time)
- err_logs = container_obj.logs(stdout=False, stderr=True, stream=False, follow=False, since=last_log_time)
+ try:
+ out_logs = container_obj.logs(stdout=True, stderr=False, stream=False, follow=False,
+ since=last_log_time)
+ err_logs = container_obj.logs(stdout=False, stderr=True, stream=False, follow=False,
+ since=last_log_time)
+ except Exception as e:
+ logging.error(f"Failed to get the logs from the container with exception {e}")
+ pass
last_log_time = datetime.datetime.now()
@@ -741,9 +553,7 @@ def log_deployment_result(end_point_id, model_id, cmd_container_name, cmd_type,
logging.info(f"Logs from docker: {format(out_logs)}")
if container_obj.status == "exited":
- logging.info("Container {} has exited, automatically"
- " remove it ...".format(cmd_container_name))
- client.api.remove_container(container_obj.id, v=True, force=True)
+ logging.info("Container {} has exited".format(cmd_container_name))
break
# should_exit_logs will ping the inference container
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 6e99851d73..faa16e7b4c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -26,34 +26,34 @@
pass
-class Settings(BaseSettings):
- redis_addr: str
- redis_port: str
- redis_password: str
- end_point_name: str
- model_name: str
- model_version: str
- model_infer_url: str
- version: str
- use_mqtt_inference: bool
- use_worker_gateway: bool
- ext_info: str
-
-
-settings = Settings()
-
-# class settings:
-# redis_addr = "127.0.0.1"
-# redis_port = 6379
-# redis_password = "fedml_default"
-# end_point_name = ""
-# model_name = ""
-# model_version = ""
-# model_infer_url = "127.0.0.1"
-# version = "dev"
-# use_mqtt_inference = False
-# use_worker_gateway = False
-# ext_info = "2b34303961245c4f175f2236282d7a272c040b0904747579087f6a760112030109010c215d54505707140005190a051c347f365c4a430c020a7d39120e26032a78730f797f7c031f0901657e75"
+# class Settings(BaseSettings):
+# redis_addr: str
+# redis_port: str
+# redis_password: str
+# end_point_name: str
+# model_name: str
+# model_version: str
+# model_infer_url: str
+# version: str
+# use_mqtt_inference: bool
+# use_worker_gateway: bool
+# ext_info: str
+#
+#
+# settings = Settings()
+
+class settings:
+ redis_addr = "127.0.0.1"
+ redis_port = 6379
+ redis_password = "fedml_default"
+ end_point_name = ""
+ model_name = ""
+ model_version = ""
+ model_infer_url = "127.0.0.1"
+ version = "dev"
+ use_mqtt_inference = False
+ use_worker_gateway = False
+ ext_info = "2b34303961245c4f175f2236282d7a272c040b0904747579087f6a760112030109010c215d54505707140005190a051c347f365c4a430c020a7d39120e26032a78730f797f7c031f0901657e75"
api = FastAPI()
@@ -176,7 +176,8 @@ async def _predict(
idle_device, end_point_id, model_id, model_name, model_version, inference_host, inference_output_url = \
found_idle_inference_device(in_end_point_id, in_end_point_name, in_model_name, in_model_version)
if idle_device is None or idle_device == "":
- return {"error": True, "error_code": status.HTTP_404_NOT_FOUND, "message": "can not found the active endpoint."}
+ return {"error": True, "error_code": status.HTTP_404_NOT_FOUND,
+ "message": "can not found active inference worker for this endpoint."}
# Start timing for model metrics
model_metrics = FedMLModelMetrics(end_point_id, in_end_point_name,
@@ -224,12 +225,17 @@ def retrieve_info_by_endpoint_id(end_point_id, in_end_point_name=None, in_model_
redis_key = FedMLModelCache.get_instance(settings.redis_addr, settings.redis_port). \
get_end_point_full_key_by_id(end_point_id)
if redis_key is not None:
+ end_point_name = ""
+ model_name = ""
if in_end_point_name is not None:
end_point_name = in_end_point_name
model_name = redis_key[len(f"{FedMLModelCache.FEDML_MODEL_DEPLOYMENT_STATUS_TAG}-{end_point_id}-{in_end_point_name}-"):]
else:
# e.g. FEDML_MODEL_DEPLOYMENT_STATUS--1234-dummy_endpoint_name-dummy_model_name
- end_point_id, end_point_name, model_name = redis_key.split("--")[1].split("-")
+ try:
+ end_point_id, end_point_name, model_name = redis_key.split("--")[1].split("-")
+ except Exception as e:
+ logging.warning(f"Failed to parse redis_key: {redis_key}. Could not retrieve only use end_point_id.")
if enable_check:
if end_point_name != in_end_point_name or model_name != in_model_name:
@@ -352,6 +358,6 @@ def logging_inference_request(request, response):
if __name__ == "__main__":
import uvicorn
- port = 2204
+ port = 2203
logging.basicConfig(level=logging.INFO)
uvicorn.run(api, host="0.0.0.0", port=port, log_level="info")
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_msg_object.py b/python/fedml/computing/scheduler/model_scheduler/device_model_msg_object.py
index 062b591853..a6c6244108 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_msg_object.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_msg_object.py
@@ -1,5 +1,5 @@
-
import json
+import logging
class FedMLModelMsgObject(object):
@@ -37,7 +37,10 @@ def __init__(self, topic, payload):
}"""
# get deployment params
- request_json = json.loads(payload)
+ if isinstance(payload, dict):
+ request_json = payload
+ else:
+ request_json = json.loads(payload)
self.msg_topic = topic
self.request_json = request_json
self.run_id = request_json["end_point_id"]
@@ -58,13 +61,54 @@ def __init__(self, topic, payload):
self.inference_engine = self.model_config.get("inference_engine", 0)
self.inference_end_point_id = self.run_id
- self. request_json["run_id"] = self.run_id
+ self.request_json["run_id"] = self.run_id
+
+ self.gpu_topology = self.get_devices_avail_gpus()
+ self.gpu_per_replica = self.get_gpu_per_replica()
+
+ self.max_unavailable_rate = self.model_config.get("max_unavailable_rate", 0.1)
+
+ def get_devices_avail_gpus(self):
+ """
+ {
+ "gpu_topology": {"id1": 1, "id2": 1} # Here the 1 means gpu card, not replica
+ }
+ """
+ # [Test1] using self.request_json["parameters"]["gpu_topology"]
+ # logging.info(f"[Replica Controller] [endpoint {self.run_id} ] devices_avail_gpus:"
+ # f" {self.request_json['parameters']['gpu_topology']}")
+ # res = self.request_json["parameters"]["gpu_topology"]
+
+ # [Test2] Using self.scale_min
+ # res = {}
+ # for id in self.request_json["device_ids"]:
+ # if str(id) == str(self.device_ids[0]):
+ # continue
+ # res[id] = int(self.scale_min)
+ # return res
+
+ # [Prod] Using self.request_json["gpu_topology"]
+ if "gpu_topology" not in self.request_json:
+ logging.warning("gpu_topology not found in request_json, using scale_min instead")
+ res = {}
+ for id in self.request_json["device_ids"]:
+ if str(id) == str(self.device_ids[0]):
+ continue
+ res[id] = int(self.scale_min)
+ return res
+
+ logging.info(f"[Replica Controller] [endpoint {self.run_id}] "
+ f"devices_avail_gpus: {self.request_json['gpu_topology']}")
+
+ return self.request_json["gpu_topology"]
+
+ def get_gpu_per_replica(self):
+ """
+ Read gpu_per_replica from user's config yaml file. Default 1.
+ """
+ if "parameters" in self.request_json and "gpu_per_replica" in self.request_json["parameters"]:
+ return self.request_json["parameters"]["gpu_per_replica"]
+ return 1
def show(self, prefix=""):
- print("{}end point id: {}, model name: {}, model id: {},"
- " model version: {}, model url: {}".format(prefix,
- self.inference_end_point_id,
- self.model_name,
- self.id,
- self.model_version,
- self.model_url))
+ logging.info(f"{prefix} [FedMLModelMsgObject] [run_id {self.run_id}] [end_point_name {self.end_point_name}]")
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_replica_controller.py b/python/fedml/computing/scheduler/model_scheduler/device_replica_controller.py
new file mode 100644
index 0000000000..9c43130687
--- /dev/null
+++ b/python/fedml/computing/scheduler/model_scheduler/device_replica_controller.py
@@ -0,0 +1,437 @@
+import logging
+import copy
+from .device_model_cache import FedMLModelCache
+from .device_model_msg_object import FedMLModelMsgObject
+from .device_client_constants import ClientConstants
+
+
+class FedMLDeviceReplicaController:
+ def __init__(self, master_id, request_json: dict):
+ """
+ For each deployment, we have:
+ master_id: unique id for the master device
+ e_id: unique id (i.e. endpoint_id) for each deployment
+ devices_avail_gpus = {device_id1: gpu_num, device_id2: gpu_num, ...}
+ request_json: json from MLOps for this deployment
+ total_gpu_num: total number of gpus will be used for this deployment
+ gpu_per_replica: number of gpus required per replica
+ min_replica_num: minimum number of replicas required
+ max_replica_num: maximum number of replicas required
+ endpoint_name: endpoint name
+ model_name: model name
+ target_replica_num: target replica number for each device
+ target_replica_version: target replica version
+ curr_replica_num: current replica number for each device
+ intermediate_replica_num: intermediate replica number for each device
+ total_replica_version_diff_num: total replica version difference number
+ max_unavailable_rate: maximum unavailable rate
+ curr_replica_updating_window: current replica updating window
+ curr_replica_version: current replica version for each device
+ intermediate_replica_version: intermediate replica version for each device
+ """
+ self.master_id = master_id
+ self.request_json = request_json
+ self.request_msg_obj = FedMLModelMsgObject("replica_controller", request_json)
+
+ self.e_id = self.request_msg_obj.run_id
+ self.devices_avail_gpus = self.request_msg_obj.gpu_topology
+ self.total_gpu_num = self.calc_total_gpu_num()
+ self.gpu_per_replica = self.request_msg_obj.gpu_per_replica
+ self.min_replica_num = self.request_msg_obj.scale_min
+ self.max_replica_num = self.request_msg_obj.scale_max
+ self.endpoint_name = self.request_msg_obj.end_point_name
+ self.model_name = self.request_msg_obj.model_name
+
+ self.target_replica_num = self.init_id_replica_num()
+
+ self.curr_replica_num = self.get_curr_replica_num_state_frm_db()
+ self.intermediate_replica_num = copy.deepcopy(self.curr_replica_num)
+
+ # Version control
+ self.target_replica_version = self.request_msg_obj.model_version
+ self.max_unavailable_rate = self.request_msg_obj.max_unavailable_rate
+ self.curr_replica_updating_window = {}
+
+ self.curr_replica_version = self.get_curr_replica_version_frm_db()
+ self.intermediate_replica_version = copy.deepcopy(self.curr_replica_version)
+
+ self.total_replica_version_diff_num, self.total_replica_version_diff = self.diff_target_curr_replica_version()
+
+ def calc_total_gpu_num(self):
+ total_gpu_num = 0
+ for device_id, gpu_num in self.devices_avail_gpus.items():
+ total_gpu_num += gpu_num
+ return total_gpu_num
+
+ def init_id_replica_num(self):
+ """
+ Initialize the target replica number for each device.
+ id_replica_num[id] = avail_num // self.gpu_per_replica
+ """
+ id_replica_num = {}
+ for id, avail_num in self.devices_avail_gpus.items():
+ if avail_num % self.gpu_per_replica != 0:
+ raise ValueError("The number of gpus for each device should be divisible by gpu_per_replica")
+ id_replica_num[str(id)] = avail_num // self.gpu_per_replica
+ return id_replica_num
+
+ def diff_target_curr_replica_num(self):
+ logging.info(f"[Replica Controller] [endpoint {self.e_id} ]target_replica_state: {self.target_replica_num}")
+ logging.info(f"[Replica Controller] [endpoint {self.e_id} ]curr_replica_state: {self.curr_replica_num}")
+ diff = self.diff_target_curr_replica_num_impl(self.target_replica_num, self.curr_replica_num)
+ logging.info(
+ f"[Replica Controller] [endpoint {self.e_id} ]diff_target_curr_replica_num: {diff}")
+ return diff
+
+ def diff_target_curr_replica_version(self):
+ logging.info(f"[Replica Controller] [endpoint {self.e_id} ]"
+ f"target_replica_version: {self.target_replica_version}")
+ logging.info(f"[Replica Controller] [endpoint {self.e_id} ]"
+ f"curr_replica_version: {self.curr_replica_version}")
+
+ num_diff, diff = self.diff_target_curr_replica_version_impl(
+ self.target_replica_version, self.curr_replica_version)
+
+ logging.info(
+ f"[Replica Controller] [endpoint {self.e_id} ]diff_target_curr_replica_version: {diff}")
+ return num_diff, diff
+
+ @staticmethod
+ def diff_target_curr_replica_num_impl(target_replica_state, curr_replica_state):
+ """
+ Return the difference between target and current replica number.
+ "op" could only be "add" or "remove".
+ e.g.
+ curr_replica_state = {id1: 1, id2: 1}
+ target_replica_state = {id1: 2, id2: 2}
+
+ return {id1: {"op": "add", "curr_num": 1, "target_num": 2}, id2: {"op": "add", "curr_num": 1, "target_num": 2}}
+ """
+ diff_target_curr_replica_num = {}
+ assert target_replica_state is not None
+
+ if curr_replica_state is None:
+ curr_replica_state = {}
+ for id, target_num in target_replica_state.items():
+ diff_target_curr_replica_num[id] = {"op": "add", "curr_num": 0, "target_num": target_num}
+ return diff_target_curr_replica_num
+
+ for id, target_num in target_replica_state.items():
+ if id not in curr_replica_state:
+ # In one scale-out operation, the device may not be deployed yet.
+ diff_target_curr_replica_num[id] = {"op": "add", "curr_num": 0, "target_num": target_num}
+ elif target_num > curr_replica_state[id]:
+ diff_target_curr_replica_num[id] = {"op": "add", "curr_num": curr_replica_state[id],
+ "target_num": target_num}
+ elif target_num < curr_replica_state[id]:
+ diff_target_curr_replica_num[id] = {"op": "remove", "curr_num": curr_replica_state[id],
+ "target_num": target_num}
+ else:
+ pass
+
+ for id, curr_num in curr_replica_state.items():
+ if id not in target_replica_state:
+ diff_target_curr_replica_num[id] = {"op": "remove", "curr_num": curr_num, "target_num": 0}
+
+ return diff_target_curr_replica_num
+
+ @staticmethod
+ def diff_target_curr_replica_version_impl(target_replica_version: str, curr_replica_version):
+ """
+ Return the number of difference, and difference between target and current replica version.
+ "op" could only be "update".
+ e.g.
+ curr_replica_version = {
+ "id1": {$replica_no: "v1", $replica_no: "v1"},
+ "id2": {$replica_no: "v1", $replica_no: "v1"},
+ }
+ target_replica_version = "v2" # Could be different for each device in the future.
+
+ return {
+ "id1": {
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"},
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"}
+ },
+ "id2": {
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"},
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"}
+ }
+ }
+
+ Return None if curr_replica_version is None.(i.e. this model has not been deployed yet.)
+ """
+ if curr_replica_version is None:
+ return 0, None
+
+ diff_target_curr_replica_version = {}
+ num_diff = 0
+ for device_id, device_replicas_version in curr_replica_version.items():
+ diff_target_curr_replica_version[device_id] = {}
+ for replica_no, curr_version in device_replicas_version.items():
+ if curr_version != target_replica_version:
+ num_diff += 1
+ diff_target_curr_replica_version[device_id][replica_no] = {
+ "op": "update",
+ "new_version": target_replica_version,
+ "old_version": curr_version
+ }
+ if num_diff == 0:
+ return 0, None
+
+ return num_diff, diff_target_curr_replica_version
+
+ def get_curr_replica_num_state_frm_db(self):
+ """
+ Sync the current replica number state from the database.
+ Return the current replica number state.
+ """
+ res_frm_db = FedMLModelCache.get_instance().get_deployment_result_list(
+ self.e_id, self.endpoint_name, self.model_name)
+
+ curr_state = {}
+ if res_frm_db is None or len(res_frm_db) == 0:
+ # First time to get the replica number from the database
+ for id, target_num in self.target_replica_num.items():
+ curr_state[str(id)] = 0
+ else:
+ for result_item in res_frm_db:
+ # Unpack the result_item
+ result_device_id, _, result_payload = FedMLModelCache.get_instance().get_result_item_info(result_item)
+ curr_state[str(result_device_id)] = curr_state.get(str(result_device_id), 0) + 1
+
+ logging.info(f"[Replica Controller] [endpoint {self.e_id} ] curr_replica_state from db: {curr_state}")
+ return curr_state
+
+ def get_curr_replica_version_frm_db(self):
+ """
+ Sync the current replica version from the database.
+ Return the current replica version.
+ {
+ "id1": {$replica_no: "v1", $replica_no: "v2"},
+ "id2": {$replica_no: "v1", $replica_no: "v2"},
+ }
+ Return None if this model has not been deployed yet.
+ """
+ curr_versions = {}
+ res_frm_db = FedMLModelCache.get_instance().get_deployment_result_list(
+ self.e_id, self.endpoint_name, self.model_name)
+ if res_frm_db is None or len(res_frm_db) == 0:
+ return None
+ else:
+ for result_item in res_frm_db:
+ # Unpack the result_item
+ result_device_id, replica_no, result_payload = (FedMLModelCache.get_instance().
+ get_result_item_info(result_item))
+ if str(result_device_id) not in curr_versions:
+ curr_versions[str(result_device_id)] = {}
+ curr_versions[str(result_device_id)][str(replica_no)] = result_payload["model_version"]
+
+ return curr_versions
+
+ def generate_diff_to_request_json(self):
+ """
+ Write the diff (curr <> target) to the self.request_json. e.g.
+ {
+ "replica_num_diff": {
+ id1: {"op": "add", "curr_num": 1, "target_num": 2},
+ id2: {"op": "add", "curr_num": 1, "target_num": 2},
+ id3: {"op": "remove", "curr_num": 1, "target_num": 0}
+ },
+ "replica_version_diff": {
+ {
+ "id1": {
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"},
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"}
+ },
+ "id2": {
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"},
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"}
+ }
+ }
+ "gpus_per_replica": 1,
+ }
+ """
+ replica_num_diff_key = "replica_num_diff"
+ gpu_per_replica_key = "gpus_per_replica"
+
+ replica_num_diff = self.diff_target_curr_replica_num()
+ self.request_json[replica_num_diff_key] = replica_num_diff
+
+ self.request_json[gpu_per_replica_key] = self.gpu_per_replica
+ return self.request_json
+
+ def callback_update_curr_replica_num_state(self, changed_device_id, replica_no, op_type):
+ """
+ Callback function to update the current replica number.
+ curr_state: {id1: 1, id2: 1}
+ target_replica_state = {id1: 2, id2: 2}
+ intermediate_state = {id1: 2, id2: 1}
+ op_type: "add" or "remove"
+ """
+ if (str(changed_device_id) in self.curr_replica_updating_window) and \
+ (str(replica_no) in self.curr_replica_updating_window[str(changed_device_id)]):
+ # Should be viewed as updated, replica number will not be changed.
+ return
+
+ if str(changed_device_id) not in self.intermediate_replica_num:
+ assert op_type == ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED
+
+ # Intermediate state is not initialized yet. Since it may derive from the database.
+ self.intermediate_replica_num[str(changed_device_id)] = 0
+
+ if op_type == ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED:
+ self.intermediate_replica_num[str(changed_device_id)] += 1
+ elif op_type == ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DELETED:
+ self.intermediate_replica_num[str(changed_device_id)] -= 1
+
+ def is_all_replica_num_reconciled(self):
+ """
+ Check if all the replicas are ready. Including the number and version.
+ """
+ for id, replica_no in self.intermediate_replica_num.items():
+ if id not in self.target_replica_num: # Delete all replica in this device
+ if replica_no != 0:
+ return False
+ else:
+ continue
+ if replica_no != self.target_replica_num[id]:
+ return False
+
+ for id, target_replica_num in self.target_replica_num.items():
+ if id not in self.intermediate_replica_num or self.intermediate_replica_num[id] != target_replica_num:
+ return False
+
+ logging.info(f"[Replica Controller] [endpoint {self.e_id} ] Replicas are reconciled as expected.")
+ logging.info(f"[Replica Controller] [endpoint {self.e_id} ] "
+ f"intermediate_replica_num: {self.intermediate_replica_num}")
+ logging.info(f"[Replica Controller] [endpoint {self.e_id} ] "
+ f"target_replica_num: {self.target_replica_num}")
+ return True
+
+ def get_first_chunk_devices_replica_update(self):
+ """
+ Scroll update.
+ Set the schema request json, which, will trans to subprocess (device_server_runner).
+ The subprocess will send the init deployment msg to the worker device(s),
+ then, the callback_deployment_result will handle the rest updating msg.
+
+ e.g.
+ {
+ "replica_version_diff": {
+ "id1": {
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"},
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"}
+ },
+ "id2": {
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"},
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"}
+ }
+ },
+ }
+
+ Return None if there is no replica version difference.
+ """
+ if self.total_replica_version_diff_num == 0:
+ return None
+
+ window_size = max(1, int(self.total_replica_version_diff_num * self.max_unavailable_rate))
+
+ first_chunk_devices_update = {}
+
+ for device_id, device_replicas_version in self.total_replica_version_diff.items():
+ for replica_no, diff in device_replicas_version.items():
+ if len(first_chunk_devices_update) >= window_size:
+ break
+ if device_id not in first_chunk_devices_update:
+ first_chunk_devices_update[device_id] = {}
+ first_chunk_devices_update[device_id][replica_no] = diff
+
+ return first_chunk_devices_update
+
+ def init_update_updating_window(self, first_chunk_devices_update):
+ """
+ Initialize the current replica updating window.
+ """
+ self.curr_replica_updating_window = copy.deepcopy(first_chunk_devices_update)
+
+ def callback_update_updating_window(self, device_id, replica_no):
+ """
+ Update the current replica updating window.
+ """
+ if str(device_id) not in self.curr_replica_updating_window:
+ return
+
+ if str(replica_no) not in self.curr_replica_updating_window[str(device_id)]:
+ return
+
+ # Remove the replica_no from the updating window
+ del self.curr_replica_updating_window[str(device_id)][str(replica_no)]
+
+ if len(self.curr_replica_updating_window[str(device_id)]) == 0:
+ del self.curr_replica_updating_window[str(device_id)]
+
+ # Change this replica's state in the global map
+ self.intermediate_replica_version[str(device_id)][str(replica_no)] = self.target_replica_version
+
+ def get_next_chunk_devices_replica(self):
+ """
+ If no need for updating, return None
+ If the intermediate equal to target, return None
+ If the current updating window is not empty, return None
+ else, determine the next window, and send the request msg to the device -> replica handler.
+ """
+ if self.total_replica_version_diff_num == 0:
+ return None
+
+ if self.is_all_replica_version_reconciled():
+ return None
+
+ if len(self.curr_replica_updating_window) > 0:
+ return None
+
+ # Determine the next window
+ window_size = max(1, int(self.total_replica_version_diff_num * self.max_unavailable_rate))
+
+ next_chunk_devices_replicas_update = {}
+
+ for id, device_replicas_version in self.intermediate_replica_version.items():
+ for replica_no, version in device_replicas_version.items():
+ if version != self.target_replica_version:
+ if id not in next_chunk_devices_replicas_update:
+ next_chunk_devices_replicas_update[id] = {}
+ next_chunk_devices_replicas_update[id][replica_no] = {
+ "op": "update",
+ "new_version": self.target_replica_version,
+ "old_version": version
+ }
+ if len(next_chunk_devices_replicas_update) >= window_size:
+ break
+
+ return next_chunk_devices_replicas_update
+
+ def is_all_replica_version_reconciled(self):
+ """
+ Check if all the replicas are ready. Including the number and version.
+ """
+ if self.total_replica_version_diff_num == 0:
+ return True
+
+ for id, device_replicas_version in self.intermediate_replica_version.items():
+ for replica_no, version in device_replicas_version.items():
+ if version != self.target_replica_version:
+ return False
+ return True
+
+ def init_first_update_device_replica_mapping(self):
+ # Check if there is no replica version difference. return first_chunk_devices_update
+ first_chunk_dict = self.get_first_chunk_devices_replica_update()
+ if first_chunk_dict is None:
+ return self.request_json
+
+ # Update the updating window
+ self.init_update_updating_window(first_chunk_dict)
+
+ # Prepare and return the request json
+ replica_num_diff_key = "replica_version_diff"
+ self.request_json[replica_num_diff_key] = first_chunk_dict
+ return self.request_json
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_replica_handler.py b/python/fedml/computing/scheduler/model_scheduler/device_replica_handler.py
new file mode 100644
index 0000000000..d8865ed854
--- /dev/null
+++ b/python/fedml/computing/scheduler/model_scheduler/device_replica_handler.py
@@ -0,0 +1,138 @@
+import logging
+from ..scheduler_core.compute_cache_manager import ComputeCacheManager
+from ..comm_utils.container_utils import ContainerUtils
+from ..comm_utils import security_utils
+from .device_client_constants import ClientConstants
+from .device_model_msg_object import FedMLModelMsgObject
+
+
+class FedMLDeviceReplicaHandler:
+ def __init__(self, worker_id, request_json: dict):
+ """
+ Handler on the worker to actually exec the reconciliation logic (Including add, remove, update).
+
+ e_id: unique id (i.e. endpoint_id) for each deployment
+ devices_avail_gpus = {device_id1: gpu_num, device_id2: gpu_num, ...}
+ request_json: json from MLOps for this deployment
+ total_gpu_num: total number of gpus will be used for this deployment
+ gpu_per_replica: number of gpus required per replica
+ """
+ self.worker_id = worker_id
+ self.request_json = request_json
+ self.request_msg_obj = FedMLModelMsgObject("replica_handler", request_json)
+ self.e_id = self.request_msg_obj.run_id
+ self.gpu_per_replica = self.request_msg_obj.gpu_per_replica
+
+ self.replica_num_diff = self.get_diff_replica_num_frm_request_json()
+ self.replica_version_diff = self.get_diff_replica_version_frm_request_json()
+
+ self.end_point_name = self.request_msg_obj.end_point_name
+ self.inference_model_name = self.request_msg_obj.model_name
+ self.model_version = self.request_msg_obj.model_version
+ self.model_id = self.request_msg_obj.model_id
+
+ self.device_avail_gpus = self.get_device_avail_gpus_frm_db()
+
+ def get_device_avail_gpus_frm_db(self):
+ """
+ Get the available gpus from db.
+ """
+ available_gpu_ids = ComputeCacheManager.get_instance().get_gpu_cache().get_device_available_gpu_ids(
+ self.worker_id)
+ logging.info(f"[Replica Handler] [endpoint {self.e_id} ] [worker {self.worker_id}] "
+ f"All device_avail_gpus: {available_gpu_ids}")
+ return available_gpu_ids
+
+ def get_diff_replica_num_frm_request_json(self):
+ """
+ Read replica_diff passing by master's request json.
+ Return:
+ {
+ id1_str: {"op": "add", "curr_num": 1, "target_num": 2},
+ id2_str: {"op": "add", "curr_num": 1, "target_num": 2}
+ }
+ """
+ if "replica_num_diff" in self.request_json and str(self.worker_id) in self.request_json["replica_num_diff"]:
+ return self.request_json["replica_num_diff"][str(self.worker_id)]
+ return None
+
+ def get_diff_replica_version_frm_request_json(self):
+ """
+ Read replica_diff passing by master's request json.
+ Return:
+ {
+ "id1": {
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"},
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"}
+ },
+ "id2": {
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"},
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"}
+ }
+ }
+ """
+ if ("replica_version_diff" in self.request_json and
+ str(self.worker_id) in self.request_json["replica_version_diff"]):
+ return self.request_json["replica_version_diff"][str(self.worker_id)]
+
+ return None
+
+ def reconcile_num_replica(self):
+ """
+ To solve the conflict between different reconciliation requests. The request & delete reqs should be
+ executed in order and atomic (i.e. rollback).
+
+ return (op, number of op)
+ """
+ if not self.replica_num_diff:
+ logging.info(f"replica_num_diff is empty, will not reconcile.")
+ return None, None, None
+
+ if self.replica_num_diff["op"] not in ["add", "remove"]:
+ raise ValueError(f"op should be add or remove. Got {self.replica_num_diff['op']}")
+
+ prev_rank = self.replica_num_diff["curr_num"] - 1
+ if self.replica_num_diff["op"] == "add":
+ assert self.replica_num_diff["target_num"] > self.replica_num_diff["curr_num"]
+ op, op_num = (self.replica_num_diff["op"],
+ self.replica_num_diff["target_num"] - self.replica_num_diff["curr_num"])
+ else:
+ assert self.replica_num_diff["target_num"] < self.replica_num_diff["curr_num"]
+ op, op_num = (self.replica_num_diff["op"],
+ self.replica_num_diff["curr_num"] - self.replica_num_diff["target_num"])
+ return prev_rank, op, op_num
+
+ def remove_replica(self, rank):
+ """
+ Remove replica_num replicas from device_id.
+ """
+ running_model_name = ClientConstants.get_running_model_name(
+ self.end_point_name, self.inference_model_name, self.model_version, self.e_id, self.model_id,
+ self.worker_id)
+ container_prefix = ("{}".format(ClientConstants.FEDML_DEFAULT_SERVER_CONTAINER_NAME_PREFIX) + "__" +
+ security_utils.get_content_hash(running_model_name))
+ container_name = container_prefix + "__" + str(rank)
+ logging.info(f"[Replica Handler] [Remove Replica] [Device {self.worker_id}] [Endpoint {self.e_id}]"
+ f" [Replica {rank}] [Container {container_name}]")
+ ContainerUtils.get_instance().remove_container(container_name)
+
+ def reconcile_replica_version(self):
+ """
+ Return a list of replica_rank to be updated.
+ Giving {
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"},
+ $replica_no: {"op": "update", "new_version": "v2", "old_version": "v1"}
+ }
+ for all replicas, update the version. i.e. stop and remove the container, records in db, then start the new
+ container, and report when the new container is ready.
+ """
+ replica_rank_to_update = []
+ ret_op = "update"
+ if not self.replica_version_diff:
+ logging.info(f"replica_version_diff is empty, will not reconcile.")
+ return None, None
+
+ for replica_no, diff in self.replica_version_diff.items():
+ replica_rank_to_update.append(int(replica_no)-1)
+
+ return replica_rank_to_update, ret_op
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_runner.py b/python/fedml/computing/scheduler/model_scheduler/device_server_runner.py
deleted file mode 100755
index 41e2e5cd44..0000000000
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_runner.py
+++ /dev/null
@@ -1,2160 +0,0 @@
-import copy
-import json
-import logging
-import multiprocessing
-import platform
-import sys
-
-from multiprocessing import Process
-import os
-import shutil
-import subprocess
-import threading
-
-import time
-import traceback
-import urllib
-import uuid
-import zipfile
-from os import listdir
-
-import requests
-import torch
-
-import fedml
-from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
-
-from ..comm_utils import sys_utils
-from .device_server_data_interface import FedMLServerDataInterface
-from ..scheduler_core.endpoint_sync_protocol import FedMLEndpointSyncProtocol
-from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
-
-from ....core.distributed.communication.mqtt.mqtt_manager import MqttManager
-from ..comm_utils.yaml_utils import load_yaml_config
-from .device_client_constants import ClientConstants
-from .device_server_constants import ServerConstants
-
-from ....core.mlops.mlops_metrics import MLOpsMetrics
-
-from ....core.mlops.mlops_configs import MLOpsConfigs
-from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
-from ....core.mlops.mlops_status import MLOpsStatus
-from ..comm_utils.sys_utils import get_sys_runner_info, get_python_program
-from .device_model_cache import FedMLModelCache
-from .device_model_msg_object import FedMLModelMsgObject
-#from ....serving.fedml_server import FedMLModelServingServer
-from ....core.mlops.mlops_utils import MLOpsUtils
-from ..comm_utils.constants import SchedulerConstants
-from .device_model_db import FedMLModelDatabase
-
-
-class RunnerError(BaseException):
- """ Runner failed. """
- pass
-
-
-class RunnerCompletedError(Exception):
- """ Runner completed. """
- pass
-
-
-class FedMLServerRunner:
- FEDML_CLOUD_SERVER_PREFIX = "fedml-server-run-"
-
- def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id=0):
- self.inference_gateway_process = None
- self.local_api_process = None
- self.run_process_event = None
- self.run_process_event_map = dict()
- self.run_process_completed_event = None
- self.run_process_completed_event_map = dict()
- self.run_as_cloud_agent = False
- self.run_as_cloud_server = False
- self.run_as_edge_server_and_agent = False
- self.run_as_cloud_server_and_agent = False
- self.fedml_packages_base_dir = None
- self.fedml_packages_unzip_dir = None
- self.mqtt_mgr = None
- self.running_request_json = dict()
- self.run_id = run_id
- self.client_mqtt_mgr = None
- self.client_mqtt_is_connected = False
- self.client_mqtt_lock = None
- self.unique_device_id = None
- self.edge_id = edge_id
- self.server_agent_id = 0
- if request_json is not None:
- self.server_agent_id = request_json.get("server_id", 0)
- self.process = None
- self.args = args
- self.request_json = copy.deepcopy(request_json)
- self.version = args.version
- self.device_id = args.device_id
- self.cur_dir = os.path.split(os.path.realpath(__file__))[0]
- if args.current_running_dir is not None:
- self.cur_dir = args.current_running_dir
-
- self.agent_config = agent_config
- self.fedml_data_base_package_dir = os.path.join("/", "fedml", "data")
- self.fedml_data_local_package_dir = os.path.join("/", "fedml", "fedml-package", "fedml", "data")
- self.fedml_data_dir = self.fedml_data_base_package_dir
- self.fedml_config_dir = os.path.join("/", "fedml", "conf")
-
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES = {}
-
- self.mlops_metrics = None
- self.run_status = None
- self.infer_host = "127.0.0.1"
- self.redis_addr = "local"
- self.redis_port = "6379"
- self.redis_password = "fedml_default"
-
- self.slave_deployment_statuses_mapping = dict()
- self.slave_deployment_results_mapping = dict()
- self.slave_update_result_mapping = dict()
-
- self.model_runner_mapping = dict()
- self.ntp_offset = MLOpsUtils.get_ntp_offset()
-
- self.subscribed_topics = list()
- self.user_name = None
-
- def build_dynamic_constrain_variables(self, run_id, run_config):
- pass
-
- def unzip_file(self, zip_file, unzip_file_path):
- unziped_file_name = ""
- if zipfile.is_zipfile(zip_file):
- with zipfile.ZipFile(zip_file, "r") as zipf:
- zipf.extractall(unzip_file_path)
- unziped_file_name = zipf.namelist()[0]
-
- return unziped_file_name
-
- def package_download_progress(self, count, blksize, filesize):
- self.check_runner_stop_event()
-
- downloaded = count * blksize
- downloaded = filesize if downloaded > filesize else downloaded
- progress = (downloaded / filesize * 100) if filesize != 0 else 0
- progress_int = int(progress)
- downloaded_kb = format(downloaded / 1024, '.2f')
-
- # since this hook funtion is stateless, we need a state to avoid printing progress repeatly
- if count == 0:
- self.prev_download_progress = 0
- if progress_int != self.prev_download_progress and progress_int % 5 == 0:
- self.prev_download_progress = progress_int
- logging.info("package downloaded size {} KB, progress {}%".format(downloaded_kb, progress_int))
-
- def retrieve_and_unzip_package(self, package_name, package_url):
- local_package_path = ServerConstants.get_model_package_dir()
- if not os.path.exists(local_package_path):
- os.makedirs(local_package_path, exist_ok=True)
- local_package_file = "{}.zip".format(os.path.join(local_package_path, package_name))
- if os.path.exists(local_package_file):
- os.remove(local_package_file)
- urllib.request.urlretrieve(package_url, filename=None, reporthook=self.package_download_progress) # do not rename
- unzip_package_path = ServerConstants.get_model_dir()
- self.fedml_packages_base_dir = unzip_package_path
- try:
- shutil.rmtree(
- os.path.join(unzip_package_path, package_name), ignore_errors=True
- )
- except Exception as e:
- pass
- logging.info("local_package_file {}, unzip_package_path {}".format(
- local_package_file, unzip_package_path))
- package_name = self.unzip_file(local_package_file, unzip_package_path)
- unzip_package_path = os.path.join(unzip_package_path, package_name)
- return unzip_package_path
-
- def update_local_fedml_config(self, run_id, run_config):
- model_config = run_config
- model_name = model_config["model_name"]
- model_storage_url = model_config["model_storage_url"]
- scale_min = model_config.get("instance_scale_min", 0)
- scale_max = model_config.get("instance_scale_max", 0)
- inference_engine = model_config.get("inference_engine", 0)
- inference_end_point_id = run_id
-
- # Copy config file from the client
- unzip_package_path = self.retrieve_and_unzip_package(
- model_name, model_storage_url
- )
- fedml_local_config_file = os.path.join(unzip_package_path, "fedml_model_config.yaml")
-
- # Load the above config to memory
- package_conf_object = {}
- if os.path.exists(fedml_local_config_file):
- package_conf_object = load_yaml_config(fedml_local_config_file)
-
- return unzip_package_path, package_conf_object
-
- def get_usr_indicated_token(self, request_json) -> str:
- usr_indicated_token = ""
- if "parameters" in request_json and "authentication_token" in request_json["parameters"]:
- usr_indicated_token = request_json["parameters"]["authentication_token"]
- return usr_indicated_token
-
- def build_dynamic_args(self, run_config, package_conf_object, base_dir):
- pass
-
- def run(self, process_event, completed_event):
- # print(f"Model master runner process id {os.getpid()}, run id {self.run_id}")
-
- if platform.system() != "Windows":
- os.setsid()
-
- os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
- os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
-
- self.run_process_event = process_event
- self.run_process_completed_event = completed_event
- run_id = self.request_json.get("end_point_id")
-
- try:
- MLOpsUtils.set_ntp_offset(self.ntp_offset)
-
- self.setup_client_mqtt_mgr()
-
- self.run_impl()
- except RunnerError:
- logging.info("Runner stopped.")
- self.mlops_metrics.report_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED,
- is_from_model=True, edge_id=self.edge_id)
- except RunnerCompletedError:
- logging.info("Runner completed.")
- except Exception as e:
- logging.error("Runner exits with exceptions.")
- logging.error(traceback.format_exc())
- logging.error(e)
- self.mlops_metrics.report_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED,
- is_from_model=True, edge_id=self.edge_id)
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- if self.mlops_metrics is not None:
- self.mlops_metrics.stop_sys_perf()
- time.sleep(3)
- sys.exit(1)
- finally:
- logging.info("Release resources.")
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- if self.mlops_metrics is not None:
- self.mlops_metrics.stop_sys_perf()
- time.sleep(3)
- if not self.run_as_cloud_server:
- self.release_client_mqtt_mgr()
-
- def parse_model_run_params(self, running_json):
- run_id = running_json["end_point_id"]
- end_point_name = running_json["end_point_name"]
- token = running_json["token"]
- user_id = running_json["user_id"]
- user_name = running_json["user_name"]
- device_ids = running_json["device_ids"]
- device_objs = running_json["device_objs"]
-
- model_config = running_json["model_config"]
- model_name = model_config["model_name"]
- model_id = model_config["model_id"]
- model_storage_url = model_config["model_storage_url"]
- scale_min = model_config.get("instance_scale_min", 0)
- scale_max = model_config.get("instance_scale_max", 0)
- inference_engine = model_config.get("inference_engine", 0)
- model_is_from_open = model_config["is_from_open"]
- inference_end_point_id = run_id
- use_gpu = "gpu" # TODO: Get GPU from device infos
- memory_size = "256m" # TODO: Get Memory size for each instance
- model_version = model_config["model_version"]
- model_config_parameters = running_json.get("parameters", {})
-
- inference_port = model_config_parameters.get("server_internal_port", # Internal port is for the gateway
- ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
- inference_port_external = model_config_parameters.get("server_external_port", inference_port)
-
- return run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
- model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
- inference_end_point_id, use_gpu, memory_size, model_version, inference_port
-
- def inference_run(self):
- # run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
- # model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
- # inference_end_point_id, use_gpu, memory_size, model_version, inference_port = self.parse_model_run_params(self.request_json)
- #
- # inference_server = FedMLModelServingServer(self.args,
- # end_point_name,
- # model_name,
- # model_version,
- # inference_request=self.request_json)
- # inference_server.run()
- pass
-
- def run_impl(self):
- run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
- model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
- inference_end_point_id, use_gpu, memory_size, model_version, inference_port = self.parse_model_run_params(self.request_json)
-
- logging.info("model deployment request: {}".format(self.request_json))
-
- # Initiate an FedMLInferenceServer object which the request will be forwarded to
- # server_runner = FedMLServerRunner(
- # self.args, run_id=self.run_id, request_json=self.request_json, agent_config=self.agent_config
- # )
- # inference_process = Process(target=server_runner.inference_run)
- # inference_process.start()
-
- logging.info("send deployment stages...")
-
- self.mlops_metrics.report_sys_perf(self.args, self.agent_config["mqtt_config"], run_id=run_id)
-
- self.check_runner_stop_event()
-
- # Send stage: MODEL_DEPLOYMENT_STAGE4 = "ForwardRequest2Slave"
- self.send_deployment_stages(self.run_id, model_name, model_id,
- "",
- ServerConstants.MODEL_DEPLOYMENT_STAGE4["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE4["text"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE4["text"])
-
- self.args.run_id = self.run_id
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
-
- # report server running status
- logging.info("report deployment status...")
- self.check_runner_stop_event()
- self.mlops_metrics.report_server_training_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_STARTING,
- is_from_model=True, running_json=json.dumps(self.request_json), edge_id=self.edge_id)
- self.send_deployment_status(self.run_id, end_point_name,
- model_name, "",
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYING)
-
- # start unified inference server
- self.start_device_inference_gateway(
- run_id, end_point_name, model_id, model_name, model_version, inference_port=inference_port)
-
- # start inference monitor server
- self.stop_device_inference_monitor(run_id, end_point_name, model_id, model_name, model_version)
- self.start_device_inference_monitor(run_id, end_point_name, model_id, model_name, model_version)
-
- # Changed the status to "IDLE"
- self.mlops_metrics.broadcast_server_training_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED,
- is_from_model=True, edge_id=self.edge_id)
-
- # forward deployment request to slave devices
- logging.info("send the model inference request to slave devices...")
- self.check_runner_stop_event()
-
- # handle "op:replace"
- first_chunk_devices_update = self.request_json.get("first_chunk_devices_update", list())
- if len(first_chunk_devices_update) > 0:
- self.send_first_scroll_update_msg(first_chunk_devices_update)
-
- # handle "op:add"
- should_added_devices = self.send_deployment_start_request_to_edges()
-
- # handle "op:delete"
- self.send_deployment_delete_request_to_edges(payload=json.dumps(self.request_json), model_msg_object=None)
-
- if len(should_added_devices) == 0 and len(first_chunk_devices_update) == 0:
- '''
- If just including delete op, we do not need to wait for the slave devices to finish the delete.
- '''
- ip = self.get_ip_address(self.request_json)
- master_port = os.getenv("FEDML_MASTER_PORT", None)
- if master_port is not None:
- inference_port = int(master_port)
- model_inference_port = inference_port
- if ip.startswith("http://") or ip.startswith("https://"):
- model_inference_url = "{}/api/v1/predict".format(ip)
- else:
- model_inference_url = "http://{}:{}/api/v1/predict".format(ip, model_inference_port)
-
- self.send_deployment_status(self.run_id, end_point_name,
- model_name,
- model_inference_url,
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED)
-
- while True:
- self.check_runner_stop_event()
- time.sleep(3)
-
- def check_runner_stop_event(self):
- if self.run_process_event is not None and self.run_process_event.is_set():
- logging.info("Received stopping event.")
- raise RunnerError("Runner stopped")
-
- if self.run_process_completed_event is not None and self.run_process_completed_event.is_set():
- logging.info("Received completed event.")
- raise RunnerCompletedError("Runner completed")
-
- def start_device_inference_gateway(
- self, run_id, end_point_name, model_id,
- model_name, model_version, inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT):
- # start unified inference server
- running_model_name = ServerConstants.get_running_model_name(end_point_name,
- model_name, model_version, run_id, model_id)
- python_program = get_python_program()
- master_port = os.getenv("FEDML_MASTER_PORT", None)
- if master_port is not None:
- inference_port = int(master_port)
- if not ServerConstants.is_running_on_k8s():
- logging.info(f"start the model inference gateway, end point {run_id}, "
- f"model name {model_name} at port {inference_port}...")
- self.check_runner_stop_event()
-
- use_mqtt_inference = os.getenv("FEDML_USE_MQTT_INFERENCE", "False")
- use_mqtt_inference = True if use_mqtt_inference.lower() == 'true' else False
- use_worker_gateway = os.getenv("FEDML_USE_WORKER_GATEWAY", "False")
- use_worker_gateway = True if use_worker_gateway.lower() == 'true' else False
- inference_gw_cmd = "fedml.computing.scheduler.model_scheduler.device_model_inference:api"
- inference_gateway_pids = RunProcessUtils.get_pid_from_cmd_line(inference_gw_cmd)
- if inference_gateway_pids is None or len(inference_gateway_pids) <= 0:
- cur_dir = os.path.dirname(__file__)
- fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- connect_str = "@FEDML@"
- ext_info = sys_utils.random1(
- self.agent_config["mqtt_config"]["BROKER_HOST"] + connect_str +
- str(self.agent_config["mqtt_config"]["BROKER_PORT"]) + connect_str +
- self.agent_config["mqtt_config"]["MQTT_USER"] + connect_str +
- self.agent_config["mqtt_config"]["MQTT_PWD"] + connect_str +
- str(self.agent_config["mqtt_config"]["MQTT_KEEPALIVE"]), "FEDML@9999GREAT")
- self.inference_gateway_process = ServerConstants.exec_console_with_script(
- "REDIS_ADDR=\"{}\" REDIS_PORT=\"{}\" REDIS_PASSWORD=\"{}\" "
- "END_POINT_NAME=\"{}\" "
- "MODEL_NAME=\"{}\" MODEL_VERSION=\"{}\" MODEL_INFER_URL=\"{}\" VERSION=\"{}\" "
- "USE_MQTT_INFERENCE={} USE_WORKER_GATEWAY={} EXT_INFO={} "
- "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
- "--log-level critical".format(
- self.redis_addr, self.redis_port, self.redis_password,
- end_point_name,
- model_name, model_version, "", self.args.version,
- use_mqtt_inference, use_worker_gateway, ext_info,
- python_program, inference_gw_cmd, str(inference_port), fedml_base_dir
- ),
- should_capture_stdout=False,
- should_capture_stderr=False
- )
-
- def start_device_inference_monitor(self, run_id, end_point_name,
- model_id, model_name, model_version, check_stopped_event=True):
- # start inference monitor server
- logging.info(f"start the model inference monitor, end point {run_id}, model name {model_name}...")
- if check_stopped_event:
- self.check_runner_stop_event()
- run_id_str = str(run_id)
- pip_source_dir = os.path.dirname(__file__)
- monitor_file = os.path.join(pip_source_dir, "device_model_monitor.py")
- python_program = get_python_program()
- running_model_name = ServerConstants.get_running_model_name(end_point_name,
- model_name, model_version, run_id, model_id)
- self.monitor_process = ServerConstants.exec_console_with_shell_script_list(
- [
- python_program,
- monitor_file,
- "-v",
- self.args.version,
- "-ep",
- run_id_str,
- "-epn",
- str(end_point_name),
- "-mi",
- str(model_id),
- "-mn",
- model_name,
- "-mv",
- model_version,
- "-iu",
- "infer_url",
- "-ra",
- self.redis_addr,
- "-rp",
- self.redis_port,
- "-rpw",
- self.redis_password
- ],
- should_capture_stdout=False,
- should_capture_stderr=False
- )
-
- def stop_device_inference_monitor(self, run_id, end_point_name, model_id, model_name, model_version):
- # stop inference monitor server
- logging.info(f"stop the model inference monitor, end point {run_id}, model name {model_name}...")
- sys_utils.cleanup_model_monitor_processes(run_id, end_point_name,
- model_id, model_name, model_version)
-
- def cleanup_run_when_finished(self):
- logging.info("Cleanup run successfully when finished.")
-
- self.mlops_metrics.broadcast_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED,
- is_from_model=True, edge_id=self.edge_id
- )
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- pass
-
- time.sleep(1)
-
- try:
- local_package_path = ServerConstants.get_package_download_dir()
- for package_file in listdir(local_package_path):
- if os.path.basename(package_file).startswith("run_" + str(self.run_id)):
- shutil.rmtree(os.path.join(local_package_path, package_file), ignore_errors=True)
- except Exception as e:
- pass
-
- def cleanup_run_when_starting_failed(self):
- logging.info("Cleanup run successfully when starting failed.")
-
- self.mlops_metrics.broadcast_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED,
- is_from_model=True, edge_id=self.edge_id)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- pass
-
- time.sleep(1)
-
- try:
- local_package_path = ServerConstants.get_package_download_dir()
- for package_file in listdir(local_package_path):
- if os.path.basename(package_file).startswith("run_" + str(self.run_id)):
- shutil.rmtree(os.path.join(local_package_path, package_file), ignore_errors=True)
- except Exception as e:
- pass
-
- def cleanup_run_when_deploy_failed(self):
- topic = f"model_ops/model_device/delete_deployment/{self.edge_id}"
- self.callback_delete_deployment(topic, payload=json.dumps(self.request_json))
-
- def callback_deployment_result_message(self, topic=None, payload=None):
- # Save deployment result to local cache
- topic_splits = str(topic).split('/')
- device_id = topic_splits[-1]
- payload_json = json.loads(payload)
- end_point_id = payload_json["end_point_id"]
- end_point_name = payload_json["end_point_name"]
- model_id = payload_json["model_id"]
- model_name = payload_json["model_name"]
- model_version = payload_json["model_version"]
- model_status = payload_json["model_status"]
- run_id_str = str(end_point_id)
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_deployment_result(end_point_id, end_point_name,
- model_name, model_version,
- device_id, payload)
- if self.slave_deployment_results_mapping.get(run_id_str, None) is None:
- self.slave_deployment_results_mapping[run_id_str] = dict()
- self.slave_deployment_results_mapping[run_id_str][str(device_id)] = model_status
-
- logging.info("callback_deployment_result_message: topic {}, payload {}, mapping {}.".format(
- topic, payload, self.slave_deployment_results_mapping[run_id_str]))
-
- request_json = self.running_request_json.get(run_id_str, None)
- if request_json is None:
- logging.error(f"The endpoint {end_point_id} is not running.")
- self.send_deployment_status(
- end_point_id, end_point_name, payload_json["model_name"], "",
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
- return
-
- all_device_id_list = request_json["device_ids"]
-
- device_id_list = []
-
- for device in all_device_id_list:
- if str(device) == str(self.edge_id):
- continue
-
- if device in request_json["diff_devices"] and \
- (request_json["diff_devices"][device] == ServerConstants.DEVICE_DIFF_ADD_OPERATION or
- request_json["diff_devices"][device] == ServerConstants.DEVICE_DIFF_REPLACE_OPERATION):
- device_id_list.append(device)
-
- if request_json["diff_devices"].get(int(device_id), None) == ServerConstants.DEVICE_DIFF_REPLACE_OPERATION:
- if model_status == ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED:
- # TODO: Support rollback
- return
- else:
- # Get record from the first message that Java mlops sent
- total_device_objs_list = self.request_json["device_objs"]
- device_obj_to_insert = None
-
- for device_obj in total_device_objs_list:
- if device_obj["id"] == int(device_id):
- device_obj["status"] = ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED
- device_obj_to_insert = device_obj
- break
- if not device_obj_to_insert:
- raise Exception(f"Cannot find device {device_id} in the device list {total_device_objs_list}")
-
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- add_end_point_device_info(request_json["end_point_id"], end_point_name,
- json.dumps(device_obj_to_insert))
-
- self.send_next_scroll_update_msg(int(device_id))
-
- if len(self.slave_deployment_results_mapping[run_id_str].keys()) >= len(device_id_list):
- '''
- When all the devices have finished the add / update operation
- '''
- failed_to_deploy_all_models = False
- for device_item in device_id_list:
- if device_item == self.edge_id: # Skip the master
- continue
- status = self.slave_deployment_results_mapping[run_id_str]. \
- get(str(device_item), ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
- if status == ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED:
- failed_to_deploy_all_models = True
- break
-
- # Failed to deploy models.
- if failed_to_deploy_all_models:
- # Send stage: MODEL_DEPLOYMENT_STAGE5 = "StartInferenceIngress"
- self.send_deployment_stages(end_point_id, model_name, model_id,
- "",
- ServerConstants.MODEL_DEPLOYMENT_STAGE5["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE5["text"],
- "Failed to deploy the model to all devices.")
- FedMLModelDatabase.get_instance().delete_deployment_status(
- run_id_str, end_point_name, model_name, model_version=model_version)
- FedMLModelDatabase.get_instance().delete_deployment_result(
- run_id_str, end_point_name, model_name, model_version=model_version)
-
- # reset slave_deployment_results_mapping, incase user might use this for redeployment
- self.slave_deployment_results_mapping[run_id_str] = dict()
- return
-
- # 1. We should generate one unified inference api
- # Note that here we use the gateway port instead of the inference port that is used by the slave device
- model_config_parameters = request_json["parameters"]
- inference_port = model_config_parameters.get("server_internal_port", ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
- inference_port_external = model_config_parameters.get("server_external_port", inference_port)
- ip = self.get_ip_address(request_json)
-
- if ip.startswith("http://") or ip.startswith("https://"):
- model_inference_url = "{}/inference/{}".format(ip, end_point_id)
- else:
- model_inference_url = "http://{}:{}/inference/{}".format(ip, inference_port_external, end_point_id)
-
- # Send stage: MODEL_DEPLOYMENT_STAGE5 = "StartInferenceIngress"
- self.send_deployment_stages(end_point_id, model_name, model_id,
- model_inference_url,
- ServerConstants.MODEL_DEPLOYMENT_STAGE5["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE5["text"],
- "inference url: {}".format(model_inference_url))
-
- # 2. We should send to MBE(ModelOps Backend)
- model_slave_url = payload_json["model_url"]
- payload_json["model_url"] = model_inference_url
- payload_json["port"] = inference_port_external
- token = FedMLModelCache.get_instance(self.redis_addr, self.redis_port).get_end_point_token(end_point_id, end_point_name, model_name)
-
- model_metadata = payload_json["model_metadata"]
- model_inputs = model_metadata["inputs"]
- ret_inputs = list()
- if "type" in model_metadata and model_metadata["type"] == "default":
- payload_json["input_json"] = {"end_point_name": end_point_name,
- "model_name": model_name,
- "token": str(token),
- "inputs": model_inputs,
- "outputs": []}
- payload_json["output_json"] = model_metadata["outputs"]
- else:
- # triton model, auto generate inputs
- for input_item in model_inputs:
- ret_item = input_item
- shape = ret_item["shape"]
- data_type = ret_item["datatype"]
- if ServerConstants.MODEL_DATA_TYPE_MAPPING[data_type] == ServerConstants.MODEL_DATA_TYPE_INT:
- for i in range(len(shape)):
- if shape[i] == -1: # if input shape is dynamic, we set a default value 1
- shape[i] = 1
- ret_item["data"] = torch.randint(0, 1, shape).tolist()
- else:
- for i in range(len(shape)):
- if shape[i] == -1: # if input shape is dynamic, we set a default value 1
- shape[i] = 1
- ret_item["data"] = torch.zeros(shape).tolist()
- ret_inputs.append(ret_item)
-
- payload_json["input_json"] = {"end_point_name": end_point_name,
- "model_name": model_name,
- "token": str(token),
- "inputs": {"inputs": ret_inputs}, # Nested inputs
- "outputs": model_metadata["outputs"]}
- payload_json["output_json"] = model_metadata["outputs"]
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_deployment_result(end_point_id, end_point_name,
- model_name, model_version,
- self.edge_id, json.dumps(payload_json))
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_activation(end_point_id, end_point_name, True)
- self.send_deployment_results_with_payload(end_point_id, end_point_name, payload_json)
-
- payload_json_saved = payload_json
- payload_json_saved["model_slave_url"] = model_slave_url
- FedMLServerDataInterface.get_instance().save_job_result(end_point_id, self.edge_id,
- json.dumps(payload_json_saved))
-
- self.slave_deployment_results_mapping[run_id_str] = dict()
-
- time.sleep(3)
- self.set_runner_completed_event(end_point_id)
-
- def callback_deployment_status_message(self, topic=None, payload=None):
- # Save deployment status to local cache
- topic_splits = str(topic).split('/')
- device_id = topic_splits[-1]
- payload_json = json.loads(payload)
- end_point_id = payload_json["end_point_id"]
- end_point_name = payload_json["end_point_name"]
- model_name = payload_json["model_name"]
- model_version = payload_json["model_version"]
- inference_port = payload_json.get("inference_external_api_port", ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
- run_id_str = str(end_point_id)
-
- model_status = payload_json["model_status"]
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_deployment_status(end_point_id, end_point_name,
- model_name, model_version,
- device_id, payload)
- if self.slave_deployment_statuses_mapping.get(run_id_str, None) is None:
- self.slave_deployment_statuses_mapping[run_id_str] = dict()
- self.slave_deployment_statuses_mapping[run_id_str][device_id] = model_status
- logging.info("callback_deployment_status_message: topic {}, payload {}, mapping {}.".format(
- topic, payload, self.slave_deployment_statuses_mapping[run_id_str]))
-
- # When all deployments are finished
- request_json = self.running_request_json.get(run_id_str, None)
- if request_json is None:
- logging.error(f"The endpoint {end_point_id} is not running.")
- self.send_deployment_status(
- self.run_id, end_point_name, payload_json["model_name"], "",
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
- return
-
- device_id_list = []
- for device in request_json["device_ids"]:
- if str(device) == str(self.edge_id):
- continue
- if device in request_json["diff_devices"] and \
- (request_json["diff_devices"][device] == ServerConstants.DEVICE_DIFF_ADD_OPERATION or
- request_json["diff_devices"][device] == ServerConstants.DEVICE_DIFF_REPLACE_OPERATION):
- device_id_list.append(device)
-
- if len(self.slave_deployment_statuses_mapping[run_id_str].keys()) >= len(device_id_list):
- failed_to_deploy_all_models = False
- for device_item in device_id_list:
- if device_item == self.edge_id: # Skip the master
- continue
- status = self.slave_deployment_statuses_mapping[run_id_str]. \
- get(str(device_item), ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
- if status == ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED:
- failed_to_deploy_all_models = True
- break
-
- # Failed to deploy the model to all devices
- if failed_to_deploy_all_models:
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_activation(end_point_id, end_point_name, False)
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_status(end_point_id, end_point_name,
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
- self.send_deployment_status(end_point_id, end_point_name,
- payload_json["model_name"], "",
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
-
- time.sleep(2)
- self.cleanup_run_when_deploy_failed()
-
- # reset slave_deployment_statuses_mapping, incase user might use this for redeployment
- self.slave_deployment_statuses_mapping[run_id_str] = dict()
- return
-
- # Send deployment finished message to ModelOps
- ip = self.get_ip_address(request_json)
- master_port = os.getenv("FEDML_MASTER_PORT", None)
- if master_port is not None:
- inference_port = int(master_port)
- model_inference_port = inference_port
- if ip.startswith("http://") or ip.startswith("https://"):
- model_inference_url = "{}/inference/{}".format(ip, end_point_id)
- else:
- model_inference_url = "http://{}:{}/inference/{}".format(ip, model_inference_port, end_point_id)
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_activation(end_point_id, end_point_name, True)
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_status(end_point_id, end_point_name,
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED)
- self.send_deployment_status(end_point_id, end_point_name,
- payload_json["model_name"],
- model_inference_url,
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED)
-
- # Clean the status in case next deployment
- self.slave_deployment_statuses_mapping[run_id_str] = dict()
-
- def send_deployment_start_request_to_edges(self):
- run_id = self.request_json["run_id"]
-
- edge_id_list = []
- for device_id in self.request_json["device_ids"]:
- if device_id in self.request_json["diff_devices"] and \
- (self.request_json["diff_devices"][device_id] == ServerConstants.DEVICE_DIFF_ADD_OPERATION):
- edge_id_list.append(device_id)
-
- logging.info("Edge ids before diff: " + str(self.request_json["device_ids"]))
- logging.info("Edge ids diff: " + str(self.request_json["diff_devices"]))
- logging.info("Edge ids after diff: " + str(edge_id_list))
-
- self.request_json["master_node_ip"] = self.get_ip_address(self.request_json)
- should_added_devices = []
- for edge_id in edge_id_list:
- if edge_id == self.edge_id:
- continue
- should_added_devices.append(edge_id)
- # send start deployment request to each device
- self.send_deployment_start_request_to_edge(edge_id)
- return should_added_devices
-
- def send_deployment_start_request_to_edge(self, edge_id):
- topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(edge_id))
- logging.info("start_deployment: send topic " + topic_start_deployment + " to client...")
- self.client_mqtt_mgr.send_message_json(topic_start_deployment, json.dumps(self.request_json))
-
- def get_ip_address(self, request_json):
- # OPTION 1: Use local ip
- ip = ServerConstants.get_local_ip()
-
- # OPTION 2: Auto detect public ip
- if "parameters" in request_json and \
- ServerConstants.AUTO_DETECT_PUBLIC_IP in request_json["parameters"] and \
- request_json["parameters"][ServerConstants.AUTO_DETECT_PUBLIC_IP]:
- ip = ServerConstants.get_public_ip()
- logging.info("Auto detect public ip for master: " + ip)
-
- # OPTION 3: Use user indicated ip
- if self.infer_host is not None and self.infer_host != "127.0.0.1" and self.infer_host != "localhost":
- ip = self.infer_host
-
- return ip
-
- def send_deployment_delete_request_to_edges(self, payload, model_msg_object):
- if model_msg_object is None: # Called after the diff operation
- if "diff_devices" not in self.request_json or self.request_json["diff_devices"] is None:
- return
- else:
- edge_id_list_to_delete = []
- for device_id in self.request_json["diff_devices"]:
- if self.request_json["diff_devices"][device_id] == ServerConstants.DEVICE_DIFF_DELETE_OPERATION:
- edge_id_list_to_delete.append(device_id)
- if len(edge_id_list_to_delete) == 0:
- return
-
- try:
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port,
- self.redis_password)
-
- # 1. Get & Delete Deployment Status in Redis / SQLite
- devices_status_list = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_deployment_status_list(self.request_json["end_point_id"],
- self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"])
- delete_devices_status_list = []
- for device_status in devices_status_list:
- device_status_dict = json.loads(device_status)
- if int(device_status_dict["cache_device_id"]) in edge_id_list_to_delete:
- delete_devices_status_list.append(device_status)
-
- for delete_item in delete_devices_status_list:
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).delete_deployment_status(
- delete_item, self.request_json["end_point_id"],
- self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"]
- )
-
- # 2. Get & Delete the endpoint device info in Redis / SQLite
- device_objs = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_end_point_device_info(self.request_json["run_id"])
-
- if device_objs is None:
- raise Exception("The device list in local redis is None")
- else:
- total_device_objs_list = json.loads(device_objs)
- for device_obj in total_device_objs_list:
- if device_obj["id"] in edge_id_list_to_delete:
- total_device_objs_list.remove(device_obj)
-
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_end_point_device_info(
- self.request_json["end_point_id"], self.request_json["end_point_name"],
- json.dumps(total_device_objs_list))
-
- # 3. Delete the result in deployment result list in Redis / SQLite
- device_result_list = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_deployment_result_list(self.request_json["end_point_id"],
- self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"])
- delete_device_result_list = []
- for device_result in device_result_list:
- device_result_dict = json.loads(device_result)
- if int(device_result_dict["cache_device_id"]) in edge_id_list_to_delete:
- delete_device_result_list.append(device_result)
-
- for delete_item in delete_device_result_list:
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).delete_deployment_result(
- delete_item, self.request_json["end_point_id"],
- self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"]
- )
-
- except Exception as e:
- run_id = self.request_json["run_id"]
- error_log_path = f"~/.fedml/fedml-model-server/fedml/logs/error_delete_{run_id}.txt"
- if not os.path.exists(os.path.dirname(os.path.expanduser(error_log_path))):
- os.makedirs(os.path.dirname(os.path.expanduser(error_log_path)))
- with open(os.path.expanduser(error_log_path), "w") as f:
- f.write(str(self.request_json))
- f.write(str(e))
- f.write('\n')
- raise e
-
- else: # Delete the whole endpoint
- edge_id_list_to_delete = model_msg_object.device_ids
-
- # For Debug
- if payload is not None:
- debug_log_path = f"~/.fedml/fedml-model-server/fedml/logs/tmp_debug_delete_payload.txt"
- if not os.path.exists(os.path.dirname(os.path.expanduser(debug_log_path))):
- os.makedirs(os.path.dirname(os.path.expanduser(debug_log_path)))
- with open(os.path.expanduser(debug_log_path), "w") as f:
- f.write(str(payload))
-
- logging.info("Device ids to be deleted: " + str(edge_id_list_to_delete))
- for edge_id in edge_id_list_to_delete:
- if edge_id == self.edge_id:
- continue
- # send delete deployment request to each model device
- topic_delete_deployment = "model_ops/model_device/delete_deployment/{}".format(str(edge_id))
- logging.info("delete_deployment: send topic " + topic_delete_deployment + " to client...")
- self.client_mqtt_mgr.send_message_json(topic_delete_deployment, payload)
-
- def ota_upgrade(self, payload, request_json):
- run_id = request_json["end_point_id"]
- force_ota = False
- ota_version = None
-
- try:
- parameters = request_json.get("parameters", None)
- common_args = parameters.get("common_args", None)
- force_ota = common_args.get("force_ota", False)
- ota_version = common_args.get("ota_version", None)
- except Exception as e:
- pass
-
- if force_ota and ota_version is not None:
- should_upgrade = True if ota_version != fedml.__version__ else False
- upgrade_version = ota_version
- else:
- try:
- fedml_is_latest_version, local_ver, remote_ver = sys_utils.check_fedml_is_latest_version(self.version)
- except Exception as e:
- return
-
- should_upgrade = False if fedml_is_latest_version else True
- upgrade_version = remote_ver
-
- if should_upgrade:
- job_obj = FedMLServerDataInterface.get_instance().get_job_by_id(run_id)
- if job_obj is None:
- FedMLServerDataInterface.get_instance(). \
- save_started_job(run_id, self.edge_id, time.time(),
- ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING,
- ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING,
- payload)
-
- logging.info(f"Upgrade to version {upgrade_version} ...")
-
- sys_utils.do_upgrade(self.version, upgrade_version)
-
- raise Exception("Restarting after upgraded...")
-
- def callback_start_deployment(self, topic, payload):
- """
- topic: model_ops/model_device/start_deployment/model-agent-device-id
- payload:
- {
- "timestamp": 1671440005119,
- "end_point_id": 4325,
- "token": "FCpWU",
- "state": "STARTING",
- "user_id": "105",
- "user_name": "alex.liang2",
- "device_ids": [
- 693
- ],
- "device_objs": [
- {
- "device_id": "0xT3630FW2YM@MacOS.Edge.Device",
- "os_type": "MacOS",
- "id": 693,
- "ip": "1.1.1.1",
- "memory": 1024,
- "cpu": "1.7",
- "gpu": "Nvidia",
- "extra_infos": {}
- }
- ],
- "model_config": {
- "model_name": "image-model",
- "model_id": 111,
- "model_version": "v1",
- "is_from_open": 0,
- "model_storage_url": "https://fedml.s3.us-west-1.amazonaws.com/1666239314792client-package.zip",
- "instance_scale_min": 1,
- "instance_scale_max": 3,
- "inference_engine": "onnx"
- },
- "parameters": {
- "hidden_size": 128,
- "hidden_act": "gelu",
- "initializer_range": 0.02,
- "vocab_size": 30522,
- "hidden_dropout_prob": 0.1,
- "num_attention_heads": 2,
- "type_vocab_size": 2,
- "max_position_embeddings": 512,
- "num_hidden_layers": 2,
- "intermediate_size": 512,
- "attention_probs_dropout_prob": 0.1
- }
- }
- """
- try:
- MLOpsConfigs.fetch_all_configs()
- except Exception as e:
- pass
-
- # get deployment params
- request_json = json.loads(payload)
- run_id = request_json["end_point_id"]
- end_point_name = request_json["end_point_name"]
- token = request_json["token"]
- user_id = request_json["user_id"]
- user_name = request_json["user_name"]
- device_ids = request_json["device_ids"]
- device_objs = request_json["device_objs"]
-
- model_config = request_json["model_config"]
- model_name = model_config["model_name"]
- model_id = model_config["model_id"]
- model_storage_url = model_config["model_storage_url"]
- scale_min = model_config.get("instance_scale_min", 0)
- scale_max = model_config.get("instance_scale_max", 0)
- inference_engine = model_config.get("inference_engine", 0)
- inference_end_point_id = run_id
-
- # Start log processor for current run
- self.args.run_id = run_id
- self.args.edge_id = self.edge_id
- MLOpsRuntimeLog.get_instance(self.args).init_logs()
- MLOpsRuntimeLogDaemon.get_instance(self.args).set_log_source(
- ServerConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT)
- MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(run_id, self.edge_id)
-
- logging.info("callback_start_deployment {}".format(payload))
-
- self.ota_upgrade(payload, request_json)
-
- run_id = inference_end_point_id
- self.args.run_id = run_id
- self.run_id = run_id
- request_json["run_id"] = run_id
- self.request_json = request_json
- run_id_str = str(run_id)
- self.running_request_json[run_id_str] = request_json
-
- diff_devices, diff_version = self.get_diff_devices(run_id)
- self.request_json["diff_devices"] = diff_devices
- self.request_json["diff_version"] = diff_version
- self.request_json["master_node_ip"] = self.get_ip_address(self.request_json)
-
- self.init_device_update_map()
-
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
-
- # Target status of the devices
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_device_info(request_json["end_point_id"], end_point_name, json.dumps(device_objs))
-
- usr_indicated_token = self.get_usr_indicated_token(request_json)
- if usr_indicated_token != "":
- logging.info(f"Change Token from{token} to {usr_indicated_token}")
- token = usr_indicated_token
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_token(run_id, end_point_name, model_name, token)
-
- self.subscribe_slave_devices_message(request_json)
-
- # Send stage: MODEL_DEPLOYMENT_STAGE1 = "Received"
- time.sleep(2)
- self.send_deployment_stages(self.run_id, model_name, model_id,
- "",
- ServerConstants.MODEL_DEPLOYMENT_STAGE1["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE1["text"],
- "Received request for end point {}".format(run_id))
- time.sleep(1)
-
- # Send stage: MODEL_DEPLOYMENT_STAGE2 = "Initializing"
- self.send_deployment_stages(self.run_id, model_name, model_id,
- "",
- ServerConstants.MODEL_DEPLOYMENT_STAGE2["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"])
-
- ServerConstants.save_runner_infos(self.args.device_id + "." + self.args.os_name, self.edge_id, run_id=run_id)
- time.sleep(1)
-
- if self.run_as_edge_server_and_agent:
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=request_json, agent_config=self.agent_config
- )
- server_runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- server_runner.edge_id = self.edge_id
- server_runner.infer_host = self.infer_host
- server_runner.redis_addr = self.redis_addr
- server_runner.redis_port = self.redis_port
- server_runner.redis_password = self.redis_password
- self.run_process_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_event_map[run_id_str].clear()
- server_runner.run_process_event = self.run_process_event_map[run_id_str]
- self.run_process_completed_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_completed_event_map[run_id_str].clear()
- server_runner.run_process_completed_event = self.run_process_completed_event_map[run_id_str]
- self.model_runner_mapping[run_id_str] = server_runner
- server_process = Process(target=server_runner.run, args=(
- self.run_process_event_map[run_id_str], self.run_process_completed_event_map[run_id_str]
- ))
- server_process.start()
- ServerConstants.save_run_process(run_id, server_process.pid)
-
- # Send stage: MODEL_DEPLOYMENT_STAGE3 = "StartRunner"
- self.send_deployment_stages(self.run_id, model_name, model_id,
- "",
- ServerConstants.MODEL_DEPLOYMENT_STAGE3["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE3["text"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE3["text"])
-
- def get_diff_devices(self, run_id) -> (dict, dict):
- '''
- {device_id(int): "op: add" | "op: delete" | "op: replace"}
- "op: add" -> need to add
- "op: delete" -> need to delete device
- "op: replace" -> need to restart the container of the device on same port with new (same) model pkg
-
- {device_id(int): "old_version"}
- '''
- try:
- logging.info(f"Get diff devices for run {run_id}")
- request_json = self.running_request_json.get(str(run_id))
-
- diff_devices = {}
- diff_version = {}
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- device_objs = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_end_point_device_info(run_id)
- if device_objs is None:
- for new_device_id in request_json["device_ids"]:
- diff_devices[new_device_id] = ServerConstants.DEVICE_DIFF_ADD_OPERATION
- else:
- device_objs_dict = json.loads(device_objs)
- device_ids_frm_db = [d["id"] for d in device_objs_dict]
-
- for exist_device_id in device_ids_frm_db:
- if exist_device_id not in request_json["device_ids"]:
- diff_devices[exist_device_id] = ServerConstants.DEVICE_DIFF_DELETE_OPERATION
-
- for new_device_id in request_json["device_ids"]:
- if new_device_id not in device_ids_frm_db:
- diff_devices[new_device_id] = ServerConstants.DEVICE_DIFF_ADD_OPERATION
- else:
- if new_device_id == self.edge_id:
- continue
-
- old_version = self.should_update_device(request_json, new_device_id)
- if old_version:
- diff_devices[new_device_id] = ServerConstants.DEVICE_DIFF_REPLACE_OPERATION
- diff_version[new_device_id] = old_version
- else:
- pass
- logging.info(f"Diff devices: {diff_devices}")
- except Exception as e:
- error_log_path = f"~/.fedml/fedml-model-server/fedml/logs/{run_id}_error.txt"
- if not os.path.exists(os.path.dirname(os.path.expanduser(error_log_path))):
- os.makedirs(os.path.dirname(os.path.expanduser(error_log_path)))
- with open(os.path.expanduser(error_log_path), "w") as f:
- f.write(str(e))
- raise e
- return diff_devices, diff_version
-
- def should_update_device(self, payload, new_device_id):
- '''
- Query the device info in local redis, if the device info is different from the payload,
- return the old model version
- '''
- device_result_list = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_deployment_result_list(self.request_json["end_point_id"],
- self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"])
-
- for device_result in device_result_list:
- if device_result is None:
- continue
- device_result_dict = json.loads(device_result)
-
- if int(device_result_dict["cache_device_id"]) == new_device_id:
- result_body = json.loads(device_result_dict["result"])
- if result_body["model_version"] != payload["model_config"]["model_version"]:
- return result_body["model_version"]
- else:
- return None
- return None
-
- def init_device_update_map(self):
- """
- Scroll update.
- Send first scroll update message to the device(s), then the callback_deployment_result will handle the rest
- """
- self.slave_update_result_mapping[self.run_id] = {
- "devices_need_update": [],
- "total_updated_devices": [],
- "curr_update_window": [],
- "max_unavailable_rate": 0.1
- }
-
- for device_id, device_op in self.request_json["diff_devices"].items():
- if device_op == ServerConstants.DEVICE_DIFF_REPLACE_OPERATION:
- self.slave_update_result_mapping[self.run_id]["devices_need_update"].append(device_id)
-
- total_num = len(self.slave_update_result_mapping[self.run_id]["devices_need_update"])
-
- if total_num == 0:
- return
-
- max_unavailable_rate = self.request_json["parameters"].get("max_unavailable_rate", 0.1)
-
- window_size = max(1, int(total_num * max_unavailable_rate))
-
- first_chunk_devices_update = \
- self.slave_update_result_mapping[self.run_id]["devices_need_update"][:window_size].copy()
-
- self.slave_update_result_mapping[self.run_id]["curr_update_window"] = first_chunk_devices_update
-
- self.request_json["first_chunk_devices_update"] = first_chunk_devices_update.copy() # to Notify sub-process
-
- def send_first_scroll_update_msg(self, first_chunk_devices_update):
- """
- Delete the record of the replaced device and send the deployment msg to the devices
- """
- if len(first_chunk_devices_update) == 0:
- return
-
- # Delete the record of the replaced device
- self.delete_device_info_on_master(first_chunk_devices_update)
-
- # Send the deployment msg to the devices, (we reuse the start_deployment msg)
- for edge_id in first_chunk_devices_update:
- if edge_id == self.edge_id:
- continue
- # send start deployment request to each device
- self.send_deployment_start_request_to_edge(edge_id)
- return
-
- def delete_device_info_on_master(self, edge_id_list_to_delete):
- # Remove the record of the replaced device
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- # 1.1 Get & Delete Deployment Status in Redis / SQLite
- devices_status_list = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_deployment_status_list(self.request_json["end_point_id"], self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"])
- delete_devices_status_list = []
- for device_status in devices_status_list:
- device_status_dict = json.loads(device_status)
- if int(device_status_dict["cache_device_id"]) in edge_id_list_to_delete:
- delete_devices_status_list.append(device_status)
-
- for delete_item in delete_devices_status_list:
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).delete_deployment_status(
- delete_item, self.request_json["end_point_id"],
- self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"]
- )
-
- # 1.2 Get & Delete the endpoint device info in Redis / SQLite
- device_objs = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_end_point_device_info(self.request_json["run_id"])
-
- if device_objs is None:
- return
-
- total_device_objs_list = json.loads(device_objs)
- for device_obj in total_device_objs_list:
- if device_obj["id"] in edge_id_list_to_delete:
- total_device_objs_list.remove(device_obj)
-
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_end_point_device_info(
- self.request_json["end_point_id"], self.request_json["end_point_name"],
- json.dumps(total_device_objs_list))
-
- # 1.3 Delete the result in deployment result list in Redis / SQLite
- device_result_list = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_deployment_result_list(self.request_json["end_point_id"], self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"])
- delete_device_result_list = []
- for device_result in device_result_list:
- device_result_dict = json.loads(device_result)
- if int(device_result_dict["cache_device_id"]) in edge_id_list_to_delete:
- delete_device_result_list.append(device_result)
-
- for delete_item in delete_device_result_list:
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).delete_deployment_result(
- delete_item, self.request_json["end_point_id"],
- self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"]
- )
-
- logging.info(f"Deleted the record of the replaced device {edge_id_list_to_delete}")
-
- def send_next_scroll_update_msg(self, device_id: int):
- this_run_meta_data = self.slave_update_result_mapping[self.run_id]
-
- devices_need_update = this_run_meta_data["devices_need_update"]
- devices_updated = this_run_meta_data["total_updated_devices"]
- curr_update_window = this_run_meta_data["curr_update_window"]
- max_unavailable_rate = this_run_meta_data["max_unavailable_rate"]
-
- if (device_id not in devices_need_update) or (device_id in devices_updated):
- # Prevent duplicate message / cross talk
- # TODO: Check the cross talk if multiple run update the same device
- logging.info(f"Device {device_id} is not in the update window nor need to be updated")
- return
-
- devices_updated.append(device_id)
- curr_update_window.remove(device_id)
-
- logging.info(f"Current update window {curr_update_window} after deleting: Device {device_id}")
-
- if len(curr_update_window) == 0:
- remain_devices = list(set(devices_need_update) - set(devices_updated))
- logging.info(f"Devices need to be updated: {remain_devices}")
- if len(remain_devices) == 0: # All devices are updated
- return
- else:
- window_size = max(1, int(len(remain_devices) * max_unavailable_rate))
- edges_in_window = remain_devices[:window_size]
- logging.info(f"Devices in next round window: {edges_in_window}")
- curr_update_window = edges_in_window.copy() # Slide the window
- self.slave_update_result_mapping[self.run_id]["curr_update_window"] = edges_in_window.copy()
-
- self.delete_device_info_on_master(edges_in_window)
-
- # Send the deployment msg to the devices, (we reuse the deployment msg)
- for edge_id in edges_in_window:
- if edge_id == self.edge_id:
- continue
- self.send_deployment_start_request_to_edge(edge_id)
- else:
- pass # Wait for the callback of other devices in this window
-
- def callback_activate_deployment(self, topic, payload):
- logging.info("callback_activate_deployment: topic = %s, payload = %s" % (topic, payload))
-
- # Parse payload as the model message object.
- model_msg_object = FedMLModelMsgObject(topic, payload)
-
- # Get the previous deployment status.
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- endpoint_status = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_end_point_status(model_msg_object.inference_end_point_id)
- if endpoint_status != ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED:
- return
-
- # Set end point as activated status
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_end_point_activation(
- model_msg_object.inference_end_point_id, model_msg_object.end_point_name, True)
-
- def callback_deactivate_deployment(self, topic, payload):
- logging.info("callback_deactivate_deployment: topic = %s, payload = %s" % (topic, payload))
-
- # Parse payload as the model message object.
- model_msg_object = FedMLModelMsgObject(topic, payload)
-
- # Get the endpoint status
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- endpoint_status = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_end_point_status(model_msg_object.inference_end_point_id)
- if endpoint_status != ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED:
- return
-
- # Set end point as deactivated status
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_end_point_activation(
- model_msg_object.inference_end_point_id, model_msg_object.model_name, False)
-
- def set_runner_stopped_event(self, run_id):
- run_id_str = str(run_id)
- server_runner = self.model_runner_mapping.get(run_id_str, None)
- if server_runner is not None:
- if server_runner.run_process_event is not None:
- server_runner.run_process_event.set()
- self.model_runner_mapping.pop(run_id_str)
-
- def set_runner_completed_event(self, run_id):
- run_id_str = str(run_id)
- server_runner = self.model_runner_mapping.get(run_id_str, None)
- if server_runner is not None:
- if server_runner.run_process_completed_event is not None:
- server_runner.run_process_completed_event.set()
- self.model_runner_mapping.pop(run_id_str)
-
- def callback_delete_deployment(self, topic, payload):
- logging.info("callback_delete_deployment: topic = %s, payload = %s" % (topic, payload))
-
- # Parse payload as the model message object.
- model_msg_object = FedMLModelMsgObject(topic, payload)
-
- # Set end point as deactivated status
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_activation(model_msg_object.inference_end_point_id,
- model_msg_object.end_point_name, False)
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- delete_end_point(model_msg_object.inference_end_point_id, model_msg_object.end_point_name, model_msg_object.model_name, model_msg_object.model_version)
-
- self.send_deployment_delete_request_to_edges(payload, model_msg_object)
-
- self.set_runner_stopped_event(model_msg_object.run_id)
-
- self.stop_device_inference_monitor(model_msg_object.run_id, model_msg_object.end_point_name,
- model_msg_object.model_id, model_msg_object.model_name,
- model_msg_object.model_version)
-
- FedMLServerDataInterface.get_instance().delete_job_from_db(model_msg_object.run_id)
- FedMLModelDatabase.get_instance().delete_deployment_status(
- model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_name,
- model_version=model_msg_object.model_version)
- FedMLModelDatabase.get_instance().delete_deployment_result(
- model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_name,
- model_version=model_msg_object.model_version)
- FedMLModelDatabase.get_instance().delete_deployment_run_info(
- end_point_id=model_msg_object.inference_end_point_id)
-
- def send_deployment_results_with_payload(self, end_point_id, end_point_name, payload):
- self.send_deployment_results(end_point_id, end_point_name,
- payload["model_name"], payload["model_url"],
- payload["model_version"], payload["port"],
- payload["inference_engine"],
- payload["model_metadata"],
- payload["model_config"],
- payload["input_json"],
- payload["output_json"])
-
- def send_deployment_results(self, end_point_id, end_point_name,
- model_name, model_inference_url,
- model_version, inference_port, inference_engine,
- model_metadata, model_config, input_json, output_json):
- deployment_results_topic_prefix = "model_ops/model_device/return_deployment_result"
- deployment_results_topic = "{}/{}".format(deployment_results_topic_prefix, end_point_id)
- deployment_results_payload = {"end_point_id": end_point_id, "end_point_name": end_point_name,
- "model_name": model_name, "model_url": model_inference_url,
- "version": model_version, "port": inference_port,
- "inference_engine": inference_engine,
- "model_metadata": model_metadata,
- "model_config": model_config,
- "input_json": input_json,
- "output_json": output_json,
- "timestamp": int(format(time.time_ns() / 1000.0, '.0f'))}
- logging.info(f"[Server] deployment_results_payload to mlops: {deployment_results_payload}")
-
- self.client_mqtt_mgr.send_message_json(deployment_results_topic, json.dumps(deployment_results_payload))
- self.client_mqtt_mgr.send_message_json(deployment_results_topic_prefix, json.dumps(deployment_results_payload))
-
- def send_deployment_status(self, end_point_id, end_point_name, model_name, model_inference_url, model_status):
- deployment_status_topic_prefix = "model_ops/model_device/return_deployment_status"
- deployment_status_topic = "{}/{}".format(deployment_status_topic_prefix, end_point_id)
- deployment_status_payload = {"end_point_id": end_point_id, "end_point_name": end_point_name,
- "model_name": model_name,
- "model_url": model_inference_url,
- "model_status": model_status,
- "timestamp": int(format(time.time_ns() / 1000.0, '.0f'))}
- logging.info(f"[Server] deployment_status_payload to mlops: {deployment_status_payload}")
-
- self.client_mqtt_mgr.send_message_json(deployment_status_topic, json.dumps(deployment_status_payload))
- self.client_mqtt_mgr.send_message_json(deployment_status_topic_prefix, json.dumps(deployment_status_payload))
-
- def send_deployment_stages(self, end_point_id, model_name, model_id, model_inference_url,
- model_stages_index, model_stages_title, model_stage_detail):
- deployment_stages_topic_prefix = "model_ops/model_device/return_deployment_stages"
- deployment_stages_topic = "{}/{}".format(deployment_stages_topic_prefix, end_point_id)
- deployment_stages_payload = {"model_name": model_name,
- "model_id": model_id,
- "model_url": model_inference_url,
- "end_point_id": end_point_id,
- "model_stage_index": model_stages_index,
- "model_stage_title": model_stages_title,
- "model_stage_detail": model_stage_detail,
- "timestamp": int(format(time.time_ns() / 1000.0, '.0f'))}
- logging.info("-------- Stages{}:{} --------".format(model_stages_index, deployment_stages_payload))
-
- self.client_mqtt_mgr.send_message_json(deployment_stages_topic, json.dumps(deployment_stages_payload))
- self.client_mqtt_mgr.send_message_json(deployment_stages_topic_prefix, json.dumps(deployment_stages_payload))
-
- def on_client_mqtt_disconnected(self, mqtt_client_object):
- if self.client_mqtt_lock is None:
- self.client_mqtt_lock = threading.Lock()
-
- self.client_mqtt_lock.acquire()
- self.client_mqtt_is_connected = False
- self.client_mqtt_lock.release()
-
- logging.info("on_client_mqtt_disconnected: {}.".format(self.client_mqtt_is_connected))
-
- def on_client_mqtt_connected(self, mqtt_client_object):
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
-
- self.mlops_metrics.set_messenger(self.client_mqtt_mgr)
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.edge_id = self.edge_id
- self.mlops_metrics.server_agent_id = self.server_agent_id
-
- if self.client_mqtt_lock is None:
- self.client_mqtt_lock = threading.Lock()
-
- self.client_mqtt_lock.acquire()
- self.client_mqtt_is_connected = True
- self.client_mqtt_lock.release()
-
- # logging.info("on_client_mqtt_connected: {}.".format(self.client_mqtt_is_connected))
-
- def setup_client_mqtt_mgr(self):
- if self.client_mqtt_mgr is not None:
- return
-
- if self.client_mqtt_lock is None:
- self.client_mqtt_lock = threading.Lock()
-
- # logging.info(
- # "server agent config: {},{}".format(
- # self.agent_config["mqtt_config"]["BROKER_HOST"], self.agent_config["mqtt_config"]["BROKER_PORT"]
- # )
- # )
-
- self.client_mqtt_mgr = MqttManager(
- self.agent_config["mqtt_config"]["BROKER_HOST"],
- self.agent_config["mqtt_config"]["BROKER_PORT"],
- self.agent_config["mqtt_config"]["MQTT_USER"],
- self.agent_config["mqtt_config"]["MQTT_PWD"],
- self.agent_config["mqtt_config"]["MQTT_KEEPALIVE"],
- "FedML_ModelServerAgent_Metrics_@{}@_{}_{}_{}".format(self.user_name, self.args.current_device_id,
- str(os.getpid()),
- str(uuid.uuid4()))
- )
- self.client_mqtt_mgr.add_connected_listener(self.on_client_mqtt_connected)
- self.client_mqtt_mgr.add_disconnected_listener(self.on_client_mqtt_disconnected)
- self.client_mqtt_mgr.connect()
- self.client_mqtt_mgr.loop_start()
-
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
- self.mlops_metrics.set_messenger(self.client_mqtt_mgr)
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.edge_id = self.edge_id
- self.mlops_metrics.server_agent_id = self.server_agent_id
-
- def release_client_mqtt_mgr(self):
- try:
- if self.client_mqtt_mgr is not None:
- self.client_mqtt_mgr.loop_stop()
- self.client_mqtt_mgr.disconnect()
-
- self.client_mqtt_lock.acquire()
- if self.client_mqtt_mgr is not None:
- self.client_mqtt_is_connected = False
- self.client_mqtt_mgr = None
- self.client_mqtt_lock.release()
- except Exception:
- pass
-
- def send_deployment_stop_request_to_edges(self, edge_id_list, payload):
- for edge_id in edge_id_list:
- topic_stop_deployment = "model_ops/model_device/stop_deployment/{}".format(str(self.edge_id))
- logging.info("stop_deployment: send topic " + topic_stop_deployment)
- self.client_mqtt_mgr.send_message_json(topic_stop_deployment, payload)
-
- def send_exit_train_with_exception_request_to_edges(self, edge_id_list, payload):
- for edge_id in edge_id_list:
- topic_exit_train = "flserver_agent/" + str(edge_id) + "/exit_train_with_exception"
- logging.info("exit_train_with_exception: send topic " + topic_exit_train)
- self.client_mqtt_mgr.send_message_json(topic_exit_train, payload)
-
- def exit_run_with_exception_entry(self):
- try:
- self.setup_client_mqtt_mgr()
- self.exit_run_with_exception()
- except Exception as e:
- self.release_client_mqtt_mgr()
- sys_utils.cleanup_all_fedml_server_login_processes(
- ServerConstants.SERVER_LOGIN_PROGRAM, clean_process_group=False)
- sys.exit(1)
- finally:
- self.release_client_mqtt_mgr()
-
- def exit_run_with_exception(self):
- logging.info("Exit run successfully.")
-
- ServerConstants.cleanup_learning_process(self.run_id)
- ServerConstants.cleanup_run_process(self.run_id)
-
- self.mlops_metrics.report_server_id_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id)
-
- time.sleep(1)
-
- def callback_exit_train_with_exception(self, topic, payload):
- # logging.info("callback_exit_train_with_exception: topic = %s, payload = %s" % (topic, payload))
-
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json.get("runId", None)
- if run_id is None:
- run_id = request_json.get("run_id", None)
- if run_id is None:
- run_id = request_json.get("id", None)
-
- if run_id is None:
- return
-
- edge_ids = request_json.get("edgeids", None)
-
- self.send_exit_train_with_exception_request_to_edges(edge_ids, payload)
-
- # Stop server with multiprocessing mode
- self.request_json = request_json
- server_runner = FedMLServerRunner(
- self.args, edge_id=self.edge_id, request_json=request_json, agent_config=self.agent_config, run_id=run_id
- )
- try:
- Process(target=server_runner.exit_run_with_exception_entry).start()
- except Exception as e:
- pass
-
- def callback_client_exit_train_with_exception(self, topic, payload):
- # logging.info("callback_client_exit_train_with_exception: topic = %s, payload = %s" % (topic, payload))
-
- request_json = json.loads(payload)
- run_id = request_json.get("run_id", None)
- edge_id = request_json.get("edge_id", None)
- if run_id is None:
- logging.info("callback_client_exit_train_with_exception run id is none")
- return
-
- job = FedMLServerDataInterface.get_instance().get_job_by_id(run_id)
- if job is not None and job.running_json is not None and job.running_json != "":
- job_json_obj = json.loads(job.running_json)
- edge_ids = job_json_obj.get("edgeids", None)
-
- self.mlops_metrics.broadcast_server_training_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED,
- is_from_model=True, edge_id=edge_id)
-
- self.send_exit_train_with_exception_request_to_edges(edge_ids, job.running_json)
-
- self.exit_run_with_exception()
-
- def callback_runner_id_status(self, topic, payload):
- logging.info("callback_runner_id_status: topic = %s, payload = %s" % (topic, payload))
-
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json["run_id"]
- status = request_json["status"]
- edge_id = request_json["edge_id"]
- run_id_str = str(run_id)
-
- if (
- status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED
- or status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED
- ):
- # Stop server with multiprocessing mode
- stop_request_json = self.running_request_json.get(run_id_str, None)
- if stop_request_json is None:
- stop_request_json = request_json
- if self.run_as_edge_server_and_agent:
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=stop_request_json, agent_config=self.agent_config
- )
- server_runner.edge_id = self.edge_id
- server_runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- server_runner.run_status = status
- status_process = Process(target=server_runner.cleanup_client_with_status)
- status_process.start()
- status_process.join(10)
-
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
-
- def cleanup_client_with_status(self):
- if self.run_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
- logging.info("received to finished status.")
- self.cleanup_run_when_finished()
- elif self.run_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
- logging.info("received to failed status.")
- self.cleanup_run_when_starting_failed()
-
- def callback_report_current_status(self, topic, payload):
- request_json = json.loads(payload)
- if self.run_as_edge_server_and_agent:
- self.send_agent_active_msg()
- elif self.run_as_cloud_agent:
- self.send_agent_active_msg()
- elif self.run_as_cloud_server:
- pass
-
- @staticmethod
- def process_ota_upgrade_msg():
- os.system("pip install -U fedml")
-
- def callback_server_ota_msg(self, topic, payload):
- request_json = json.loads(payload)
- cmd = request_json["cmd"]
-
- if cmd == ServerConstants.FEDML_OTA_CMD_UPGRADE:
- try:
- self.process_ota_upgrade_msg()
- # Process(target=FedMLServerRunner.process_ota_upgrade_msg).start()
- raise Exception("After upgraded, restart runner...")
- except Exception as e:
- pass
- elif cmd == ServerConstants.FEDML_OTA_CMD_RESTART:
- raise Exception("Restart runner...")
-
- @staticmethod
- def get_device_id():
- device_file_path = os.path.join(ServerConstants.get_data_dir(), ServerConstants.LOCAL_RUNNER_INFO_DIR_NAME)
- file_for_device_id = os.path.join(device_file_path, "devices.id")
- if not os.path.exists(device_file_path):
- os.makedirs(device_file_path)
- elif os.path.exists(file_for_device_id):
- with open(file_for_device_id, 'r', encoding='utf-8') as f:
- device_id_from_file = f.readline()
- if device_id_from_file is not None and device_id_from_file != "":
- return device_id_from_file
-
- if platform.system() == "Darwin":
- cmd_get_serial_num = "system_profiler SPHardwareDataType | grep Serial | awk '{gsub(/ /,\"\")}{print}' " \
- "|awk -F':' '{print $2}' "
- device_id = os.popen(cmd_get_serial_num).read()
- device_id = device_id.replace('\n', '').replace(' ', '')
- if device_id is None or device_id == "":
- device_id = hex(uuid.getnode())
- else:
- device_id = "0x" + device_id
- else:
- if "nt" in os.name:
-
- def get_uuid():
- guid = ""
- try:
- cmd = "wmic csproduct get uuid"
- guid = str(subprocess.check_output(cmd))
- pos1 = guid.find("\\n") + 2
- guid = guid[pos1:-15]
- except Exception as ex:
- pass
- return str(guid)
-
- device_id = str(get_uuid())
- elif "posix" in os.name:
- device_id = sys_utils.get_device_id_in_docker()
- if device_id is None:
- device_id = hex(uuid.getnode())
- else:
- device_id = sys_utils.run_subprocess_open(
- "hal-get-property --udi /org/freedesktop/Hal/devices/computer --key system.hardware.uuid".split()
- )
- device_id = hex(device_id)
-
- if device_id is not None and device_id != "":
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
- else:
- device_id = hex(uuid.uuid4())
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
-
- return device_id
-
- def bind_account_and_device_id(self, url, account_id, device_id, os_name):
- role = ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_ON_PREMISE_MASTER_INDEX]
- if self.run_as_edge_server_and_agent:
- role = ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_ON_PREMISE_MASTER_INDEX]
- elif self.run_as_cloud_agent:
- role = ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_FEDML_CLOUD_MASTER_INDEX]
- elif self.run_as_cloud_server:
- role = ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_INFERENCE_INSTANCE_INDEX]
-
- ip = requests.get('https://checkip.amazonaws.com').text.strip()
- fedml_ver, exec_path, os_ver, cpu_info, python_ver, torch_ver, mpi_installed, \
- cpu_usage, available_mem, total_mem, gpu_info, gpu_available_mem, gpu_total_mem, \
- gpu_count, gpu_vendor, cpu_count, gpu_device_name = get_sys_runner_info()
- host_name = sys_utils.get_host_name()
- json_params = {
- "accountid": account_id,
- "deviceid": device_id,
- "type": os_name,
- "state": ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE,
- "status": ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE,
- "processor": cpu_info,
- "core_type": cpu_info,
- "network": "",
- "role": role,
- "os_ver": os_ver,
- "memory": total_mem,
- "ip": ip,
- "extra_infos": {"fedml_ver": fedml_ver, "exec_path": exec_path, "os_ver": os_ver,
- "cpu_info": cpu_info, "python_ver": python_ver, "torch_ver": torch_ver,
- "mpi_installed": mpi_installed, "cpu_usage": cpu_usage,
- "available_mem": available_mem, "total_mem": total_mem,
- "cpu_count": cpu_count, "gpu_count": 0, "host_name": host_name}
- }
- if gpu_count > 0:
- if gpu_total_mem is not None:
- json_params["gpu"] = gpu_info if gpu_info is not None else "" + ", Total GPU Memory: " + gpu_total_mem
- else:
- json_params["gpu"] = gpu_info if gpu_info is not None else ""
- json_params["extra_infos"]["gpu_info"] = gpu_info if gpu_info is not None else ""
- if gpu_available_mem is not None:
- json_params["extra_infos"]["gpu_available_mem"] = gpu_available_mem
- if gpu_total_mem is not None:
- json_params["extra_infos"]["gpu_total_mem"] = gpu_total_mem
-
- json_params["extra_infos"]["gpu_count"] = gpu_count
- json_params["extra_infos"]["gpu_vendor"] = gpu_vendor
- json_params["extra_infos"]["gpu_device_name"] = gpu_device_name
-
- gpu_available_id_list = sys_utils.get_available_gpu_id_list(limit=gpu_count)
- gpu_available_count = len(gpu_available_id_list) if gpu_available_id_list is not None else 0
- gpu_list = sys_utils.get_gpu_list()
- json_params["extra_infos"]["gpu_available_count"] = gpu_available_count
- json_params["extra_infos"]["gpu_available_id_list"] = gpu_available_id_list
- json_params["extra_infos"]["gpu_list"] = gpu_list
- else:
- json_params["gpu"] = "None"
- json_params["extra_infos"]["gpu_available_count"] = 0
- json_params["extra_infos"]["gpu_available_id_list"] = []
- json_params["extra_infos"]["gpu_list"] = []
-
- _, cert_path = MLOpsConfigs.get_request_params()
- if cert_path is not None:
- try:
- requests.session().verify = cert_path
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- except requests.exceptions.SSLError as err:
- MLOpsConfigs.install_root_ca_file()
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- else:
- response = requests.post(url, json=json_params, headers={"Connection": "close"})
- edge_id = -1
- user_name = None
- extra_url = None
- if response.status_code != 200:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- pass
- else:
- # print("url = {}, response = {}".format(url, response))
- status_code = response.json().get("code")
- if status_code == "SUCCESS":
- edge_id = response.json().get("data").get("id")
- user_name = response.json().get("data").get("userName", None)
- extra_url = response.json().get("data").get("url", None)
- if edge_id is None or edge_id <= 0:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- else:
- if status_code == SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR:
- raise SystemExit(SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR)
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- return -1, None, None
- return edge_id, user_name, extra_url
-
- def fetch_configs(self):
- return MLOpsConfigs.fetch_all_configs()
-
- def send_agent_active_msg(self):
- active_topic = "flserver_agent/active"
- status = MLOpsStatus.get_instance().get_server_agent_status(self.edge_id)
- if (
- status is not None
- and status != ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE
- and status != ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- ):
- return
-
- status = ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- active_msg = {"ID": self.edge_id, "status": status}
- MLOpsStatus.get_instance().set_server_agent_status(self.edge_id, status)
- self.mqtt_mgr.send_message_json(active_topic, json.dumps(active_msg))
-
- def subscribe_slave_devices_message(self, request_json):
- if request_json is None:
- return
- run_id = request_json["run_id"]
- edge_id_list = request_json["device_ids"]
- logging.info("Edge ids: " + str(edge_id_list))
- for edge_id in edge_id_list:
- if str(edge_id) == str(self.edge_id):
- continue
- # subscribe deployment result message for each model device
- deployment_results_topic = "model_device/model_device/return_deployment_result/{}".format(edge_id)
- self.mqtt_mgr.add_message_listener(deployment_results_topic, self.callback_deployment_result_message)
- self.mqtt_mgr.subscribe_msg(deployment_results_topic)
-
- # subscribe deployment status message for each model device
- deployment_status_topic = "model_device/model_device/return_deployment_status/{}".format(edge_id)
- self.mqtt_mgr.add_message_listener(deployment_status_topic, self.callback_deployment_status_message)
- self.mqtt_mgr.subscribe_msg(deployment_status_topic)
-
- logging.info("subscribe device messages {}, {}".format(
- deployment_results_topic, deployment_status_topic))
-
- def on_agent_mqtt_connected(self, mqtt_client_object):
- # The MQTT message topic format is as follows: <sender>/<receiver>/<action>
-
- # Setup MQTT message listener for starting deployment
- server_agent_id = self.edge_id
- topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(self.edge_id))
- self.mqtt_mgr.add_message_listener(topic_start_deployment, self.callback_start_deployment)
-
- # Setup MQTT message listener for activating deployment
- topic_activate_deployment = "model_ops/model_device/activate_deployment/{}".format(str(self.edge_id))
- self.mqtt_mgr.add_message_listener(topic_activate_deployment, self.callback_activate_deployment)
-
- # Setup MQTT message listener for deactivating deployment
- topic_deactivate_deployment = "model_ops/model_device/deactivate_deployment/{}".format(str(self.edge_id))
- self.mqtt_mgr.add_message_listener(topic_deactivate_deployment, self.callback_deactivate_deployment)
-
- # Setup MQTT message listener for delete deployment
- topic_delete_deployment = "model_ops/model_device/delete_deployment/{}".format(str(self.edge_id))
- self.mqtt_mgr.add_message_listener(topic_delete_deployment, self.callback_delete_deployment)
-
- # Setup MQTT message listener for server status switching
- topic_server_status = "fl_server/flserver_agent_" + str(server_agent_id) + "/status"
- self.mqtt_mgr.add_message_listener(topic_server_status, self.callback_runner_id_status)
-
- # Setup MQTT message listener to report current device status.
- topic_report_status = "mlops/report_device_status"
- self.mqtt_mgr.add_message_listener(topic_report_status, self.callback_report_current_status)
-
- # Setup MQTT message listener to OTA messages from the MLOps.
- topic_ota_msg = "mlops/flserver_agent_" + str(server_agent_id) + "/ota"
- self.mqtt_mgr.add_message_listener(topic_ota_msg, self.callback_server_ota_msg)
-
- # Subscribe topics for starting train, stopping train and fetching client status.
- mqtt_client_object.subscribe(topic_start_deployment, qos=2)
- mqtt_client_object.subscribe(topic_activate_deployment, qos=2)
- mqtt_client_object.subscribe(topic_deactivate_deployment, qos=2)
- mqtt_client_object.subscribe(topic_delete_deployment, qos=2)
- mqtt_client_object.subscribe(topic_server_status, qos=2)
- mqtt_client_object.subscribe(topic_report_status, qos=2)
- mqtt_client_object.subscribe(topic_ota_msg, qos=2)
-
- self.subscribed_topics.clear()
- self.subscribed_topics.append(topic_start_deployment)
- self.subscribed_topics.append(topic_activate_deployment)
- self.subscribed_topics.append(topic_deactivate_deployment)
- self.subscribed_topics.append(topic_delete_deployment)
- self.subscribed_topics.append(topic_server_status)
- self.subscribed_topics.append(topic_report_status)
- self.subscribed_topics.append(topic_ota_msg)
-
- self.endpoint_sync_protocol = FedMLEndpointSyncProtocol(agent_config=self.agent_config, mqtt_mgr=self.mqtt_mgr)
- self.endpoint_sync_protocol.setup_listener_for_sync_device_info(self.edge_id)
-
- # Broadcast the first active message.
- self.send_agent_active_msg()
-
- # Echo results
- # print("\n\nCongratulations, your device is connected to the FedML MLOps platform successfully!")
- # print(
- # "Your FedML Edge ID is " + str(self.edge_id) + ", unique device ID is "
- # + str(self.unique_device_id)
- # + "\n"
- # )
-
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
-
- def on_agent_mqtt_disconnected(self, mqtt_client_object):
- MLOpsStatus.get_instance().set_server_agent_status(
- self.edge_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE
- )
-
- def recover_inference_and_monitor(self):
- try:
- history_jobs = FedMLServerDataInterface.get_instance().get_history_jobs()
- for job in history_jobs.job_list:
- if job.running_json is None:
- continue
-
- if job.deployment_result == "":
- continue
-
- run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
- model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
- inference_end_point_id, use_gpu, memory_size, model_version, inference_port = \
- self.parse_model_run_params(json.loads(job.running_json))
-
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- is_activated = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_end_point_activation(run_id)
- if not is_activated:
- continue
-
- self.start_device_inference_gateway(run_id, end_point_name, model_id, model_name, model_version,
- inference_port=inference_port)
-
- self.stop_device_inference_monitor(run_id, end_point_name, model_id, model_name, model_version)
- self.start_device_inference_monitor(run_id, end_point_name, model_id, model_name, model_version)
- except Exception as e:
- logging.info("recover inference and monitor: {}".format(traceback.format_exc()))
-
- def recover_start_deployment_msg_after_upgrading(self):
- try:
- current_job = FedMLServerDataInterface.get_instance().get_current_job()
- if current_job is not None and \
- current_job.status == ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING:
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- is_activated = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_end_point_activation(current_job.job_id)
- if not is_activated:
- return
- logging.info("start deployment after upgrading.")
- topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(self.edge_id))
- self.callback_start_deployment(topic_start_deployment, current_job.running_json)
- except Exception as e:
- logging.info("recover starting deployment message after upgrading: {}".format(traceback.format_exc()))
-
- def setup_agent_mqtt_connection(self, service_config):
- # Setup MQTT connection
- self.mqtt_mgr = MqttManager(
- service_config["mqtt_config"]["BROKER_HOST"],
- service_config["mqtt_config"]["BROKER_PORT"],
- service_config["mqtt_config"]["MQTT_USER"],
- service_config["mqtt_config"]["MQTT_PWD"],
- service_config["mqtt_config"]["MQTT_KEEPALIVE"],
- "FedML_ModelServerAgent_Daemon_@" + self.user_name + "@_" + self.args.current_device_id + str(uuid.uuid4()),
- "flserver_agent/last_will_msg",
- json.dumps({"ID": self.edge_id, "status": ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE})
- )
- self.agent_config = service_config
-
- # Init local database
- FedMLServerDataInterface.get_instance().create_job_table()
- try:
- FedMLModelDatabase.get_instance().set_database_base_dir(ServerConstants.get_database_dir())
- FedMLModelDatabase.get_instance().create_table()
- except Exception as e:
- pass
-
- server_api_cmd = "fedml.computing.scheduler.model_scheduler.device_server_api:api"
- server_api_pids = RunProcessUtils.get_pid_from_cmd_line(server_api_cmd)
- if server_api_pids is None or len(server_api_pids) <= 0:
- # Start local API services
- cur_dir = os.path.dirname(__file__)
- fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- python_program = get_python_program()
- self.local_api_process = ServerConstants.exec_console_with_script(
- "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
- "--log-level critical".format(
- python_program, server_api_cmd, ServerConstants.LOCAL_SERVER_API_PORT,
- fedml_base_dir
- ),
- should_capture_stdout=False,
- should_capture_stderr=False
- )
- # if self.local_api_process is not None and self.local_api_process.pid is not None:
- # print(f"Model master local API process id {self.local_api_process.pid}")
-
- self.recover_inference_and_monitor()
-
- # MLOpsRuntimeLogDaemon.get_instance(self.args).stop_all_log_processor()
-
- # Setup MQTT connected listener
- self.mqtt_mgr.add_connected_listener(self.on_agent_mqtt_connected)
- self.mqtt_mgr.add_disconnected_listener(self.on_agent_mqtt_disconnected)
- self.mqtt_mgr.connect()
-
- self.setup_client_mqtt_mgr()
- self.mlops_metrics.report_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE,
- is_from_model=True, edge_id=self.edge_id)
- MLOpsStatus.get_instance().set_server_agent_status(
- self.edge_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- )
-
- self.recover_start_deployment_msg_after_upgrading()
-
- def stop_agent(self):
- if self.run_process_event is not None:
- self.run_process_event.set()
-
- if self.mqtt_mgr is not None:
- try:
- for topic in self.subscribed_topics:
- self.mqtt_mgr.unsubscribe_msg(topic)
- except Exception as e:
- pass
-
- self.mqtt_mgr.loop_stop()
- self.mqtt_mgr.disconnect()
-
- self.release_client_mqtt_mgr()
-
- def start_agent_mqtt_loop(self, should_exit_sys=True):
- # Start MQTT message loop
- try:
- self.mqtt_mgr.loop_forever()
- except Exception as e:
- if str(e) == "Restarting after upgraded...":
- logging.info("Restarting after upgraded...")
- else:
- print("Server tracing: {}".format(traceback.format_exc()))
- finally:
- self.stop_agent()
-
- if should_exit_sys:
- time.sleep(5)
- sys_utils.cleanup_all_fedml_server_login_processes(
- ServerConstants.SERVER_LOGIN_PROGRAM, clean_process_group=False)
- sys.exit(1)
diff --git a/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py b/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
new file mode 100755
index 0000000000..3fe45401ac
--- /dev/null
+++ b/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
@@ -0,0 +1,204 @@
+
+import json
+import logging
+import os
+import time
+from .device_model_cache import FedMLModelCache
+from .device_server_constants import ServerConstants
+from ..scheduler_core.general_constants import GeneralConstants
+
+
+class FedMLDeployJobRunnerMsgSender(object):
+ def __init__(self):
+ self.infer_host = "127.0.0.1"
+ self.redis_addr = "local"
+ self.redis_port = "6379"
+ self.redis_password = "fedml_default"
+ self.message_center = None
+ self.request_json = None
+ self.edge_id = None
+
+ def send_deployment_results_with_payload(self, end_point_id, end_point_name, payload):
+ self.send_deployment_results(end_point_id, end_point_name,
+ payload["model_name"], payload["model_url"],
+ payload["model_version"], payload["port"],
+ payload["inference_engine"],
+ payload["model_metadata"],
+ payload["model_config"],
+ payload["input_json"],
+ payload["output_json"])
+
+ def send_deployment_results(self, end_point_id, end_point_name,
+ model_name, model_inference_url,
+ model_version, inference_port, inference_engine,
+ model_metadata, model_config, input_json, output_json):
+ deployment_results_topic_prefix = "model_ops/model_device/return_deployment_result"
+ deployment_results_topic = "{}/{}".format(deployment_results_topic_prefix, end_point_id)
+ deployment_results_payload = {"end_point_id": end_point_id, "end_point_name": end_point_name,
+ "model_name": model_name, "model_url": model_inference_url,
+ "version": model_version, "port": inference_port,
+ "inference_engine": inference_engine,
+ "model_metadata": model_metadata,
+ "model_config": model_config,
+ "input_json": input_json,
+ "output_json": output_json,
+ "timestamp": int(format(time.time_ns() / 1000.0, '.0f'))}
+ logging.info(f"[Master] deployment_results_payload is sent to mlops: {deployment_results_payload}")
+
+ self.message_center.send_message_json(deployment_results_topic, json.dumps(deployment_results_payload))
+ self.message_center.send_message_json(deployment_results_topic_prefix, json.dumps(deployment_results_payload))
+
+ @staticmethod
+ def send_deployment_status(
+ end_point_id, end_point_name, model_name, model_inference_url, model_status, message_center=None):
+ if message_center is None:
+ return
+ deployment_status_topic_prefix = "model_ops/model_device/return_deployment_status"
+ deployment_status_topic = "{}/{}".format(deployment_status_topic_prefix, end_point_id)
+ deployment_status_payload = {"end_point_id": end_point_id, "end_point_name": end_point_name,
+ "model_name": model_name,
+ "model_url": model_inference_url,
+ "model_status": model_status,
+ "timestamp": int(format(time.time_ns() / 1000.0, '.0f'))}
+ logging.info(f"[Master] deployment_status_payload is sent to mlops: {deployment_status_payload}")
+
+ message_center.send_message_json(deployment_status_topic, json.dumps(deployment_status_payload))
+ message_center.send_message_json(deployment_status_topic_prefix, json.dumps(deployment_status_payload))
+
+ @staticmethod
+ def send_deployment_stages(end_point_id, model_name, model_id, model_inference_url,
+ model_stages_index, model_stages_title, model_stage_detail,
+ message_center=None):
+ if message_center is None:
+ return
+ deployment_stages_topic_prefix = "model_ops/model_device/return_deployment_stages"
+ deployment_stages_topic = "{}/{}".format(deployment_stages_topic_prefix, end_point_id)
+ deployment_stages_payload = {"model_name": model_name,
+ "model_id": model_id,
+ "model_url": model_inference_url,
+ "end_point_id": end_point_id,
+ "model_stage_index": model_stages_index,
+ "model_stage_title": model_stages_title,
+ "model_stage_detail": model_stage_detail,
+ "timestamp": int(format(time.time_ns() / 1000.0, '.0f'))}
+
+ message_center.send_message_json(deployment_stages_topic, json.dumps(deployment_stages_payload))
+ message_center.send_message_json(deployment_stages_topic_prefix, json.dumps(deployment_stages_payload))
+
+ logging.info(f"-------- Stages has been sent to mlops with stage {model_stages_index} and "
+ f"payload {deployment_stages_payload}")
+
+ def send_deployment_start_request_to_edges(self):
+ # Iterate through replica_num_diff, both add and replace should be sent to the edge devices
+ if "replica_num_diff" not in self.request_json or self.request_json["replica_num_diff"] is None:
+ return []
+
+ edge_id_list = []
+ for device_id in self.request_json["replica_num_diff"].keys():
+ edge_id_list.append(device_id)
+
+ self.request_json["master_node_ip"] = GeneralConstants.get_ip_address(self.request_json)
+ should_added_devices = []
+ for edge_id in edge_id_list:
+ if edge_id == self.edge_id:
+ continue
+ should_added_devices.append(edge_id)
+ # send start deployment request to each device
+ self.send_deployment_start_request_to_edge(edge_id)
+ return should_added_devices
+
+ def send_deployment_start_request_to_edge(self, edge_id):
+ topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(edge_id))
+ logging.info("start_deployment: send topic " + topic_start_deployment + " to client...")
+ self.message_center.send_message_json(topic_start_deployment, json.dumps(self.request_json))
+
+ def send_deployment_delete_request_to_edges(self, payload, model_msg_object):
+ if model_msg_object is None: # Called after the diff operation
+ if "diff_devices" not in self.request_json or self.request_json["diff_devices"] is None:
+ return
+ else:
+ edge_id_list_to_delete = []
+ for device_id in self.request_json["diff_devices"]:
+ if self.request_json["diff_devices"][device_id] == ServerConstants.DEVICE_DIFF_DELETE_OPERATION:
+ edge_id_list_to_delete.append(device_id)
+ if len(edge_id_list_to_delete) == 0:
+ return
+
+ try:
+ FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port,
+ self.redis_password)
+
+ # 1. Get & Delete the endpoint device info in Redis / SQLite
+ device_objs = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ get_end_point_device_info(self.request_json["run_id"])
+
+ if device_objs is None:
+ raise Exception("The device list in local redis is None")
+ else:
+ total_device_objs_list = json.loads(device_objs)
+ for device_obj in total_device_objs_list:
+ if device_obj["id"] in edge_id_list_to_delete:
+ total_device_objs_list.remove(device_obj)
+
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_end_point_device_info(
+ self.request_json["end_point_id"], self.request_json["end_point_name"],
+ json.dumps(total_device_objs_list))
+
+ # 2 Delete the result in deployment result list in Redis / SQLite
+ device_result_list = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ get_deployment_result_list(self.request_json["end_point_id"],
+ self.request_json["end_point_name"],
+ self.request_json["model_config"]["model_name"])
+ delete_device_result_list = []
+ for device_result in device_result_list:
+ device_result_dict = json.loads(device_result)
+ if int(device_result_dict["cache_device_id"]) in edge_id_list_to_delete:
+ delete_device_result_list.append(device_result)
+
+ for delete_item in delete_device_result_list:
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port).delete_deployment_result(
+ delete_item, self.request_json["end_point_id"],
+ self.request_json["end_point_name"],
+ self.request_json["model_config"]["model_name"]
+ )
+
+ except Exception as e:
+ run_id = self.request_json["run_id"]
+ error_log_path = f"~/.fedml/fedml-model-server/fedml/logs/error_delete_{run_id}.txt"
+ if not os.path.exists(os.path.dirname(os.path.expanduser(error_log_path))):
+ os.makedirs(os.path.dirname(os.path.expanduser(error_log_path)))
+ with open(os.path.expanduser(error_log_path), "w") as f:
+ f.write(str(self.request_json))
+ f.write(str(e))
+ f.write('\n')
+ raise e
+
+ else: # Delete the whole endpoint
+ edge_id_list_to_delete = model_msg_object.device_ids
+
+ # For Debug
+ if payload is not None:
+ debug_log_path = f"~/.fedml/fedml-model-server/fedml/logs/tmp_debug_delete_payload.txt"
+ if not os.path.exists(os.path.dirname(os.path.expanduser(debug_log_path))):
+ os.makedirs(os.path.dirname(os.path.expanduser(debug_log_path)))
+ with open(os.path.expanduser(debug_log_path), "w") as f:
+ f.write(str(payload))
+
+ # Remove the model master node id from the list using index 0
+ edge_id_list_to_delete = edge_id_list_to_delete[1:]
+
+ logging.info("Device ids to be deleted: " + str(edge_id_list_to_delete))
+
+ for edge_id in edge_id_list_to_delete:
+ if edge_id == self.edge_id:
+ continue
+ # send delete deployment request to each model device
+ topic_delete_deployment = "model_ops/model_device/delete_deployment/{}".format(str(edge_id))
+ logging.info("delete_deployment: send topic " + topic_delete_deployment + " to client...")
+ self.message_center.send_message_json(topic_delete_deployment, payload)
+
+ def send_deployment_stop_request_to_edges(self, edge_id_list, payload):
+ for edge_id in edge_id_list:
+ topic_stop_deployment = "model_ops/model_device/stop_deployment/{}".format(str(self.edge_id))
+ logging.info("stop_deployment: send topic " + topic_stop_deployment)
+ self.message_center.send_message_json(topic_stop_deployment, payload)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_agent.py b/python/fedml/computing/scheduler/model_scheduler/master_agent.py
new file mode 100755
index 0000000000..2f30ae8b8e
--- /dev/null
+++ b/python/fedml/computing/scheduler/model_scheduler/master_agent.py
@@ -0,0 +1,27 @@
+
+from .device_server_constants import ServerConstants
+from .device_server_data_interface import FedMLServerDataInterface
+from .master_protocol_manager import FedMLDeployMasterProtocolManager
+from ..master.base_master_agent import FedMLBaseMasterAgent
+
+
+class FedMLDeployMasterAgent(FedMLBaseMasterAgent):
+
+ def __init__(self):
+ FedMLBaseMasterAgent.__init__(self)
+
+ # Override
+ def _get_log_file_dir(self):
+ return ServerConstants.get_log_file_dir()
+
+ # Override
+ def _save_agent_info(self, unique_device_id, edge_id):
+ ServerConstants.save_runner_infos(unique_device_id, edge_id)
+
+ # Override
+ def _init_database(self):
+ FedMLServerDataInterface.get_instance().create_job_table()
+
+ # Override
+ def _generate_protocol_manager_instance(self, args, agent_config=None):
+ return FedMLDeployMasterProtocolManager(args, agent_config=agent_config)
\ No newline at end of file
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
new file mode 100755
index 0000000000..f3d68c1f6a
--- /dev/null
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -0,0 +1,578 @@
+import copy
+import json
+import logging
+import os
+import time
+import queue
+import traceback
+from abc import ABC
+from multiprocessing import Queue
+
+import fedml
+from fedml.core.mlops import MLOpsRuntimeLog
+from .device_client_constants import ClientConstants
+from .device_model_cache import FedMLModelCache
+from .device_server_constants import ServerConstants
+from .device_server_data_interface import FedMLServerDataInterface
+from ..comm_utils import sys_utils
+from ..comm_utils.run_process_utils import RunProcessUtils
+from ..comm_utils.sys_utils import get_python_program
+from ..scheduler_core.general_constants import GeneralConstants
+from ..master.base_master_job_runner import FedMLBaseMasterJobRunner
+from .device_replica_controller import FedMLDeviceReplicaController
+from .job_runner_msg_sender import FedMLDeployJobRunnerMsgSender
+
+
+class FedMLDeployMasterJobRunner(FedMLBaseMasterJobRunner, FedMLDeployJobRunnerMsgSender, ABC):
+
+ default_redis_addr = "local"
+ default_redis_port = "6379"
+ default_redis_password = "fedml_default"
+
+ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id=0,
+ cuda_visible_gpu_ids_str=None):
+ FedMLDeployJobRunnerMsgSender.__init__(self)
+ FedMLBaseMasterJobRunner.__init__(
+ self, args, edge_id=edge_id, request_json=request_json, agent_config=agent_config, run_id=run_id,
+ cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str, agent_data_dir=ServerConstants.get_data_dir(),
+ agent_package_download_dir=ServerConstants.get_package_download_dir(),
+ agent_package_unzip_dir=GeneralConstants.get_package_unzip_dir(ServerConstants.get_package_download_dir()),
+ agent_log_file_dir=ServerConstants.get_log_file_dir()
+ )
+
+ self.infer_host = "127.0.0.1"
+ self.redis_addr = "local"
+ self.redis_port = "6379"
+ self.redis_password = "fedml_default"
+ self.inference_gateway_process = None
+ self.monitor_process = None
+ self.replica_controller = None
+ self.deployed_replica_payload = None
+ self.slave_deployment_results_map = dict()
+ self.deployment_result_queue = Queue()
+
+ # Override
+ def _generate_job_runner_instance(self, args, run_id=None, request_json=None, agent_config=None, edge_id=None,):
+ return FedMLDeployMasterJobRunner(
+ args, run_id=run_id, request_json=request_json, agent_config=self.agent_config, edge_id=edge_id
+ )
+
+ # Override
+ def _generate_extend_queue_list(self):
+ return [self.deployment_result_queue]
+
+ # Override
+ def run_impl(
+ self, edge_id_status_queue, edge_device_info_queue, run_metrics_queue,
+ run_event_queue, run_artifacts_queue, run_logs_queue, edge_device_info_global_queue,
+ run_extend_queue_list=None, sender_message_queue=None, listener_message_queue=None,
+ status_center_queue=None
+ ):
+ # Parse the model parameters.
+ run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
+ model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
+ inference_end_point_id, use_gpu, memory_size, model_version, inference_port = \
+ FedMLDeployMasterJobRunner.parse_model_run_params(self.request_json)
+
+ # Print request parameters.
+ logging.info("model deployment request: {}".format(self.request_json))
+ logging.info("send deployment stages...")
+
+ # Generate the replica controller object.
+ self.replica_controller = FedMLDeviceReplicaController(self.edge_id, self.request_json)
+
+ # Start the process to report system performance(cpu,memory,etc.) to MLOps
+ self.mlops_metrics.report_sys_perf(self.args, self.agent_config["mqtt_config"], run_id=run_id)
+
+ # Check if we should stop the runner
+ self.check_runner_stop_event()
+
+ # Send stage: MODEL_DEPLOYMENT_STAGE4 = "ForwardRequest2Slave"
+ self.send_deployment_stages(
+ self.run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE4["index"],
+ ServerConstants.MODEL_DEPLOYMENT_STAGE4["text"], ServerConstants.MODEL_DEPLOYMENT_STAGE4["text"],
+ message_center=self.message_center)
+
+ # Init the runtime logs
+ self.args.run_id = self.run_id
+ MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+
+ # Report server running status
+ logging.info("report deployment status...")
+ self.check_runner_stop_event()
+ self.status_reporter.report_server_id_status(
+ run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_STARTING,
+ is_from_model=True, running_json=json.dumps(self.request_json),
+ server_agent_id=self.edge_id, server_id=self.edge_id, edge_id=self.edge_id)
+ self.send_deployment_status(
+ self.run_id, end_point_name, model_name, "",
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYING,
+ message_center=self.message_center)
+
+ # start unified inference server
+ self.start_device_inference_gateway(
+ run_id, end_point_name, model_id, model_name, model_version,
+ agent_config=self.agent_config, inference_port=inference_port)
+
+ # start inference monitor server
+ self.stop_device_inference_monitor(
+ run_id, end_point_name, model_id, model_name, model_version)
+ self.start_device_inference_monitor(
+ run_id, end_point_name, model_id, model_name, model_version,
+ redis_addr=self.redis_addr, redis_port=self.redis_port, redis_password=self.redis_password
+ )
+
+ # Changed the status to "IDLE"
+ self.status_reporter.report_server_id_status(
+ run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED,
+ is_from_model=True, server_agent_id=self.edge_id, server_id=self.edge_id, edge_id=self.edge_id,)
+
+ # Check if we should stop the runner
+ logging.info("send the model inference request to slave devices...")
+ self.check_runner_stop_event()
+
+ # Forward deployment request to slave devices
+ # Handle "op:add" && "op:remove"
+ devices_sent_add_or_remove_msg = self.send_deployment_start_request_to_edges()
+
+ # Handle "op:update"
+ devices_sent_update_remove_msg = self.send_first_scroll_update_msg()
+
+ if len(devices_sent_add_or_remove_msg) == 0 and len(devices_sent_update_remove_msg) == 0:
+ # No device is added or removed, and no device is updated or removed
+ ip = GeneralConstants.get_ip_address(self.request_json)
+ master_port = os.getenv("FEDML_MASTER_PORT", None)
+ if master_port is not None:
+ inference_port = int(master_port)
+ model_inference_port = inference_port
+ if ip.startswith("http://") or ip.startswith("https://"):
+ model_inference_url = "{}/api/v1/predict".format(ip)
+ else:
+ model_inference_url = "http://{}:{}/api/v1/predict".format(ip, model_inference_port)
+
+ self.send_deployment_status(
+ run_id, end_point_name, model_name, model_inference_url,
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
+ message_center=self.message_center
+ )
+
+ self.trigger_completed_event()
+ return
+
+ self.deployment_result_queue = run_extend_queue_list[0]
+ while True:
+ self.check_runner_stop_event()
+
+ try:
+ deployment_result = self.deployment_result_queue.get(block=False, timeout=0.2)
+ result_topic = deployment_result.get("topic", None)
+ result_payload = deployment_result.get("payload", None)
+ self.process_deployment_result_message(topic=result_topic, payload=result_payload)
+ except queue.Empty as e: # If queue is empty, then continue
+ pass
+
+ time.sleep(0.5)
+
+ def save_deployment_result(self, topic=None, payload=None):
+ self.deployment_result_queue.put({"topic": topic, "payload": payload})
+
+ def process_deployment_result_message(self, topic=None, payload=None):
+ # Parse the parameters
+ topic_splits = str(topic).split('/')
+ device_id = topic_splits[-1]
+ payload_json = json.loads(payload)
+ end_point_id = payload_json["end_point_id"]
+ end_point_name = payload_json["end_point_name"]
+ model_id = payload_json["model_id"]
+ model_name = payload_json["model_name"]
+ model_version = payload_json["model_version"]
+ model_status = payload_json["model_status"]
+ replica_no = payload_json.get("replica_no", None) # Idx start from 1
+ run_id_str = str(end_point_id)
+
+ # Set redis + sqlite deployment result
+ FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+
+ # Save deployment result to local cache
+ if model_status == ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DELETED:
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ delete_deployment_result_with_device_id_and_replica_no(
+ end_point_id, end_point_name, model_name, device_id, replica_no)
+ elif model_status == ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED:
+ # add or update
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ set_deployment_result(end_point_id, end_point_name,
+ model_name, model_version,
+ device_id, payload, replica_no)
+
+ # Note: To display the result in the UI, we need to save successful deployment result to the database
+ self.save_deployed_replica_payload(payload_json)
+ else:
+ if model_status != ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED:
+ logging.error(f"Unsupported model status {model_status}.")
+ self.send_deployment_status(
+ end_point_id, end_point_name, payload_json["model_name"], "",
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
+ message_center=self.message_center
+ )
+
+ # Notify the replica number controller
+ self.callback_update_curr_replica_num_state(device_id, replica_no, model_status)
+
+ # Notify the replica version controller, which might trigger the next rolling update
+ self.send_next_scroll_update_msg(device_id, replica_no)
+
+ # Update the global deployment result mapping
+ self.slave_deployment_results_map[str(device_id)] = model_status
+
+ # Check if the endpoint is running
+ request_json = self.request_json
+ if request_json is None:
+ logging.error(f"The endpoint {end_point_id} is not running.")
+ self.send_deployment_status(
+ end_point_id, end_point_name, payload_json["model_name"], "",
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
+ message_center=self.message_center
+ )
+ return
+
+ # Wait for all replica's result, not device-level
+ if self.is_all_replica_num_reconciled() and self.is_all_replica_version_reconciled():
+ '''
+ When all the devices have finished the add / delete / update operation
+ '''
+ # 1. We should generate one unified inference api
+ # Note that here we use the gateway port instead of the inference port that is used by the slave device
+ model_config_parameters = request_json["parameters"]
+ inference_port = model_config_parameters.get("server_internal_port",
+ ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
+ inference_port_external = model_config_parameters.get("server_external_port", inference_port)
+ ip = GeneralConstants.get_ip_address(request_json)
+
+ if ip.startswith("http://") or ip.startswith("https://"):
+ model_inference_url = "{}/inference/{}".format(ip, end_point_id)
+ else:
+ model_inference_url = "http://{}:{}/inference/{}".format(ip, inference_port_external, end_point_id)
+
+ # Send stage: MODEL_DEPLOYMENT_STAGE5 = "StartInferenceIngress"
+ self.send_deployment_stages(
+ end_point_id, model_name, model_id, model_inference_url,
+ ServerConstants.MODEL_DEPLOYMENT_STAGE5["index"], ServerConstants.MODEL_DEPLOYMENT_STAGE5["text"],
+ "inference url: {}".format(model_inference_url), message_center=self.message_center)
+
+ # Prepare the result to MLOps
+ deployed_replica_payload = self.get_deployed_replica_payload()
+ if deployed_replica_payload is not None:
+ payload_json = deployed_replica_payload
+ model_slave_url = payload_json["model_url"]
+ payload_json["model_url"] = model_inference_url
+ payload_json["port"] = inference_port_external
+ token = FedMLModelCache.get_instance(self.redis_addr, self.redis_port).get_end_point_token(
+ end_point_id, end_point_name, model_name)
+
+ model_metadata = payload_json["model_metadata"]
+ model_inputs = model_metadata["inputs"]
+ ret_inputs = list()
+ if "type" in model_metadata and model_metadata["type"] == "default":
+ payload_json["input_json"] = {
+ "end_point_name": end_point_name, "model_name": model_name, "token": str(token),
+ "inputs": model_inputs, "outputs": []}
+ payload_json["output_json"] = model_metadata["outputs"]
+ else:
+ raise Exception(f"Unsupported model metadata type {model_metadata['type']}")
+
+ self.send_deployment_results_with_payload(
+ end_point_id, end_point_name, payload_json)
+
+ payload_json_saved = payload_json
+ payload_json_saved["model_slave_url"] = model_slave_url
+ FedMLServerDataInterface.get_instance().save_job_result(end_point_id, self.edge_id,
+ json.dumps(payload_json_saved))
+ else:
+ # Arrive here because only contains remove ops, so we do not need to update the model metadata
+ pass
+
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ set_end_point_activation(end_point_id, end_point_name, True)
+
+ self.send_deployment_status(
+ end_point_id, end_point_name, payload_json["model_name"],
+ model_inference_url, ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
+ message_center=self.message_center
+ )
+
+ self.trigger_completed_event()
+
+ @staticmethod
+ def start_device_inference_gateway(
+ run_id, end_point_name, model_id,
+ model_name, model_version, inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
+ agent_config=None, redis_addr=None, redis_port=None, redis_password=None
+ ):
+ # start unified inference server
+ running_model_name = ServerConstants.get_running_model_name(end_point_name,
+ model_name, model_version, run_id, model_id)
+ python_program = get_python_program()
+ master_port = os.getenv("FEDML_MASTER_PORT", None)
+ if master_port is not None:
+ inference_port = int(master_port)
+ if not ServerConstants.is_running_on_k8s():
+ logging.info(f"start the model inference gateway, end point {run_id}, "
+ f"model name {model_name} at port {inference_port}...")
+ use_mqtt_inference = os.getenv("FEDML_USE_MQTT_INFERENCE", "False")
+ use_mqtt_inference = True if use_mqtt_inference.lower() == 'true' else False
+ use_worker_gateway = os.getenv("FEDML_USE_WORKER_GATEWAY", "False")
+ use_worker_gateway = True if use_worker_gateway.lower() == 'true' else False
+ inference_gw_cmd = "fedml.computing.scheduler.model_scheduler.device_model_inference:api"
+ inference_gateway_pids = RunProcessUtils.get_pid_from_cmd_line(inference_gw_cmd)
+ if inference_gateway_pids is None or len(inference_gateway_pids) <= 0:
+ cur_dir = os.path.dirname(__file__)
+ fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
+ connect_str = "@FEDML@"
+ ext_info = sys_utils.random1(
+ agent_config["mqtt_config"]["BROKER_HOST"] + connect_str +
+ str(agent_config["mqtt_config"]["BROKER_PORT"]) + connect_str +
+ agent_config["mqtt_config"]["MQTT_USER"] + connect_str +
+ agent_config["mqtt_config"]["MQTT_PWD"] + connect_str +
+ str(agent_config["mqtt_config"]["MQTT_KEEPALIVE"]), "FEDML@9999GREAT")
+ inference_gateway_process = ServerConstants.exec_console_with_script(
+ "REDIS_ADDR=\"{}\" REDIS_PORT=\"{}\" REDIS_PASSWORD=\"{}\" "
+ "END_POINT_NAME=\"{}\" "
+ "MODEL_NAME=\"{}\" MODEL_VERSION=\"{}\" MODEL_INFER_URL=\"{}\" VERSION=\"{}\" "
+ "USE_MQTT_INFERENCE={} USE_WORKER_GATEWAY={} EXT_INFO={} "
+ "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
+ "--log-level critical".format(
+ redis_addr, redis_port, redis_password, end_point_name,
+ model_name, model_version, "", fedml.get_env_version(), use_mqtt_inference,
+ use_worker_gateway, ext_info, python_program, inference_gw_cmd, str(inference_port),
+ fedml_base_dir),
+ should_capture_stdout=False, should_capture_stderr=False)
+
+ return inference_gateway_process
+
+ return None
+
+ @staticmethod
+ def start_device_inference_monitor(
+ run_id, end_point_name, model_id, model_name, model_version, check_stopped_event=True,
+ redis_addr=None, redis_port=None, redis_password=None
+ ):
+ # start inference monitor server
+ logging.info(f"start the model inference monitor, end point {run_id}, model name {model_name}...")
+ run_id_str = str(run_id)
+ pip_source_dir = os.path.dirname(__file__)
+ monitor_file = os.path.join(pip_source_dir, "device_model_monitor.py")
+ python_program = get_python_program()
+ running_model_name = ServerConstants.get_running_model_name(end_point_name,
+ model_name, model_version, run_id, model_id)
+ monitor_process = ServerConstants.exec_console_with_shell_script_list(
+ [python_program, monitor_file, "-v", fedml.get_env_version(), "-ep", run_id_str,
+ "-epn", str(end_point_name), "-mi", str(model_id), "-mn", model_name,
+ "-mv", model_version, "-iu", "infer_url", "-ra", redis_addr,
+ "-rp", redis_port, "-rpw", redis_password],
+ should_capture_stdout=False, should_capture_stderr=False
+ )
+ return monitor_process
+
+ @staticmethod
+ def stop_device_inference_monitor(run_id, end_point_name, model_id, model_name, model_version):
+ # stop inference monitor server
+ logging.info(f"stop the model inference monitor, end point {run_id}, model name {model_name}...")
+ sys_utils.cleanup_model_monitor_processes(run_id, end_point_name,
+ model_id, model_name, model_version)
+
+ @staticmethod
+ def recover_inference_and_monitor(redis_addr=None, redis_port=None, redis_password=None):
+ # noinspection PyBroadException
+ try:
+ history_jobs = FedMLServerDataInterface.get_instance().get_history_jobs()
+ for job in history_jobs.job_list:
+ if job.running_json is None:
+ continue
+
+ if job.deployment_result == "":
+ continue
+
+ run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
+ model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
+ inference_end_point_id, use_gpu, memory_size, model_version, inference_port = \
+ FedMLDeployMasterJobRunner.parse_model_run_params(json.loads(job.running_json))
+
+ FedMLModelCache.get_instance().set_redis_params(redis_addr, redis_password)
+ is_activated = FedMLModelCache.get_instance(redis_addr, redis_port). \
+ get_end_point_activation(run_id)
+ if not is_activated:
+ continue
+
+ FedMLDeployMasterJobRunner.start_device_inference_gateway(
+ run_id, end_point_name, model_id, model_name, model_version, inference_port=inference_port)
+
+ FedMLDeployMasterJobRunner.stop_device_inference_monitor(
+ run_id, end_point_name, model_id, model_name, model_version)
+ FedMLDeployMasterJobRunner.start_device_inference_monitor(
+ run_id, end_point_name, model_id, model_name, model_version,
+ redis_addr=FedMLDeployMasterJobRunner.default_redis_addr,
+ redis_port=FedMLDeployMasterJobRunner.default_redis_port,
+ redis_password=FedMLDeployMasterJobRunner.default_redis_password
+ )
+ except Exception as e:
+ logging.info("recover inference and monitor: {}".format(traceback.format_exc()))
+
+ def send_first_scroll_update_msg(self):
+ """
+ Replica-level rolling update.
+ Delete the record of the replaced device and send the deployment msg to the devices
+ """
+ if "replica_version_diff" not in self.request_json or self.request_json["replica_version_diff"] is None:
+ return []
+
+ first_chunk_dict = self.request_json["replica_version_diff"]
+
+ # Delete the record of the replaced device
+ self.delete_device_replica_info_on_master(first_chunk_dict)
+
+ # Send the deployment msg to the devices, (we reuse the start_deployment msg)
+ for edge_id in first_chunk_dict.keys():
+ if edge_id == self.edge_id:
+ continue
+ # send start deployment request to each device
+ self.send_deployment_start_request_to_edge(edge_id)
+ return list(first_chunk_dict.keys())
+
+ def send_next_scroll_update_msg(self, device_id, replica_no):
+ if replica_no is None:
+ return
+
+ replica_controller = self.replica_controller
+
+ if replica_controller.total_replica_version_diff_num == 0:
+ return
+
+ replica_controller.callback_update_updating_window(device_id, replica_no)
+
+ # Decide whether to send the next scroll update
+ next_chunk_dict = replica_controller.get_next_chunk_devices_replica()
+
+ replica_controller.curr_replica_updating_window = copy.deepcopy(next_chunk_dict)
+
+ if next_chunk_dict:
+ self.request_json["replica_version_diff"] = next_chunk_dict
+ self.delete_device_replica_info_on_master(next_chunk_dict)
+
+ # Send the deployment msg to the devices, (we reuse the start_deployment msg)
+ for edge_id in next_chunk_dict.keys():
+ if edge_id == self.edge_id:
+ continue
+ # send start deployment request to each device
+ self.send_deployment_start_request_to_edge(edge_id)
+ return
+
+ def delete_device_replica_info_on_master(self, edge_id_replica_no_dict):
+ FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+ # Remove the record of the replaced device
+ # [Deprecated] deployment status & device info
+ # Delete the result in deployment result list in Redis / SQLite
+ device_result_list = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ get_deployment_result_list(self.request_json["end_point_id"], self.request_json["end_point_name"],
+ self.request_json["model_config"]["model_name"])
+ delete_device_result_list = []
+ for device_result in device_result_list:
+ device_result_dict = json.loads(device_result)
+ if (str(device_result_dict["cache_device_id"]) in edge_id_replica_no_dict.keys() and
+ str(device_result_dict["cache_replica_no"]) in
+ edge_id_replica_no_dict[str(device_result_dict["cache_device_id"])]):
+ delete_device_result_list.append(device_result)
+
+ for delete_item in delete_device_result_list:
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port).delete_deployment_result(
+ delete_item, self.request_json["end_point_id"],
+ self.request_json["end_point_name"],
+ self.request_json["model_config"]["model_name"]
+ )
+
+ logging.info(f"Deleted the record of the replaced device {delete_device_result_list}")
+
+ def save_deployed_replica_payload(self, payload_json):
+ self.deployed_replica_payload = copy.deepcopy(payload_json)
+
+ def get_deployed_replica_payload(self):
+ return self.deployed_replica_payload
+
+ def callback_update_curr_replica_num_state(self, changed_device_id, replica_no, op_type):
+ if self.replica_controller is not None:
+ self.replica_controller.callback_update_curr_replica_num_state(changed_device_id, replica_no, op_type)
+
+ def is_all_replica_num_reconciled(self):
+ if self.replica_controller is not None:
+ return self.replica_controller.is_all_replica_num_reconciled()
+
+ return False
+
+ def is_all_replica_version_reconciled(self):
+ if self.replica_controller is not None:
+ return self.replica_controller.is_all_replica_version_reconciled()
+
+ return False
+
+ @staticmethod
+ def generate_request_json_with_replica_diff(run_id, edge_id, request_json):
+ # Replica Controller is per deployment!
+ replica_controller = FedMLDeviceReplicaController(edge_id, request_json)
+ logging.info(f"Start Diff Replica controller for run {run_id} on edge {edge_id}")
+
+ # Prepare num diff
+ run_id_str = str(run_id)
+ new_request_with_num_diff = replica_controller.generate_diff_to_request_json()
+ request_json = new_request_with_num_diff
+
+ # Prepare version diff
+ new_request_with_version_diff = replica_controller.init_first_update_device_replica_mapping()
+ request_json = new_request_with_version_diff
+
+ return request_json
+
+ @staticmethod
+ def parse_model_run_params(running_json):
+ run_id = running_json["end_point_id"]
+ end_point_name = running_json["end_point_name"]
+ token = running_json["token"]
+ user_id = running_json["user_id"]
+ user_name = running_json["user_name"]
+ device_ids = running_json["device_ids"]
+ device_objs = running_json["device_objs"]
+
+ model_config = running_json["model_config"]
+ model_name = model_config["model_name"]
+ model_id = model_config["model_id"]
+ model_storage_url = model_config["model_storage_url"]
+ scale_min = model_config.get("instance_scale_min", 0)
+ scale_max = model_config.get("instance_scale_max", 0)
+ inference_engine = model_config.get("inference_engine", 0)
+ model_is_from_open = model_config["is_from_open"]
+ inference_end_point_id = run_id
+ use_gpu = "gpu" # TODO: Get GPU from device infos
+ memory_size = "256m" # TODO: Get Memory size for each instance
+ model_version = model_config["model_version"]
+ model_config_parameters = running_json.get("parameters", {})
+
+ inference_port = model_config_parameters.get("server_internal_port", # Internal port is for the gateway
+ ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
+ inference_port_external = model_config_parameters.get("server_external_port", inference_port)
+
+ return run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
+ model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
+ inference_end_point_id, use_gpu, memory_size, model_version, inference_port
+
+ # Override
+ def get_download_package_info(self, packages_config=None):
+ model_name = packages_config["model_name"]
+ model_storage_url = packages_config["model_storage_url"]
+ return model_name, model_storage_url
+
+ # Override
+ def build_dynamic_args(self, run_id, run_config, package_conf_object, base_dir):
+ pass
+
+ # Override
+ def build_dynamic_constrain_variables(self, run_id, run_config):
+ pass
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
new file mode 100755
index 0000000000..40896b9ee8
--- /dev/null
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
@@ -0,0 +1,62 @@
+
+import json
+from fedml.core.common.singleton import Singleton
+from ..master.base_master_job_runner_manager import FedMLBaseMasterJobRunnerManager
+from .master_job_runner import FedMLDeployMasterJobRunner
+from ..scheduler_core.general_constants import GeneralConstants
+
+
+class FedMLDeployJobRunnerManager(FedMLBaseMasterJobRunnerManager, Singleton):
+ def __init__(self):
+ FedMLBaseMasterJobRunnerManager.__init__(self)
+
+ @staticmethod
+ def get_instance():
+ return FedMLDeployJobRunnerManager()
+
+ # Override
+ def _generate_job_runner_instance(
+ self, args, run_id=None, request_json=None, agent_config=None, edge_id=None
+ ):
+ job_runner = FedMLDeployMasterJobRunner(
+ args, run_id=run_id, request_json=request_json, agent_config=agent_config, edge_id=edge_id)
+ job_runner.infer_host = GeneralConstants.get_ip_address(request_json)
+ return job_runner
+
+ def save_deployment_result(self, topic, payload):
+ payload_json = json.loads(payload)
+ endpoint_id = payload_json["end_point_id"]
+ run_id_str = str(endpoint_id)
+ if self.job_runners.get(run_id_str, None) is not None:
+ self.job_runners[run_id_str].save_deployment_result(topic=topic, payload=payload)
+
+ def send_deployment_stages(
+ self, end_point_id, model_name, model_id, model_inference_url,
+ model_stages_index, model_stages_title, model_stage_detail, message_center=None
+ ):
+ run_id_str = str(end_point_id)
+ if self.job_runners.get(run_id_str, None) is not None:
+ self.job_runners[run_id_str].send_deployment_stages(
+ end_point_id, model_name, model_id, model_inference_url,
+ model_stages_index, model_stages_title, model_stage_detail,
+ message_center=message_center
+ )
+
+ def send_deployment_delete_request_to_edges(self, end_point_id, payload, model_msg_object):
+ run_id_str = str(end_point_id)
+ if self.job_runners.get(run_id_str, None) is not None:
+ self.job_runners[run_id_str].send_deployment_delete_request_to_edges(payload, model_msg_object)
+
+ def stop_device_inference_monitor(self, run_id, end_point_name, model_id, model_name, model_version):
+ run_id_str = str(run_id)
+ if self.job_runners.get(run_id_str, None) is not None:
+ self.job_runners[run_id_str].stop_device_inference_monitor(
+ run_id, end_point_name, model_id, model_name, model_version)
+
+ @staticmethod
+ def recover_inference_and_monitor():
+ FedMLDeployMasterJobRunner.recover_inference_and_monitor()
+
+ @staticmethod
+ def generate_request_json_with_replica_diff(run_id, edge_id, request_json):
+ return FedMLDeployMasterJobRunner.generate_request_json_with_replica_diff(run_id, edge_id, request_json)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
new file mode 100755
index 0000000000..e8be50f77f
--- /dev/null
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -0,0 +1,365 @@
+
+import json
+import logging
+import os
+from fedml.core.mlops import MLOpsConfigs, MLOpsRuntimeLog, MLOpsRuntimeLogDaemon
+from .device_model_cache import FedMLModelCache
+from .device_model_db import FedMLModelDatabase
+from .device_model_msg_object import FedMLModelMsgObject
+from .device_server_constants import ServerConstants
+from .device_server_data_interface import FedMLServerDataInterface
+from ..master.base_master_protocol_manager import FedMLBaseMasterProtocolManager
+from .master_job_runner_manager import FedMLDeployJobRunnerManager
+from ..scheduler_core.general_constants import GeneralConstants
+from ..scheduler_core.endpoint_sync_protocol import FedMLEndpointSyncProtocol
+
+
+class FedMLDeployMasterProtocolManager(FedMLBaseMasterProtocolManager):
+ def __init__(self, args, agent_config=None):
+ FedMLBaseMasterProtocolManager.__init__(self, args, agent_config=agent_config)
+
+ self.topic_start_deployment = None
+ self.topic_activate_endpoint = None
+ self.topic_deactivate_deployment = None
+ self.topic_delete_deployment = None
+
+ self.infer_host = "127.0.0.1"
+ self.redis_addr = "local"
+ self.redis_port = "6379"
+ self.redis_password = "fedml_default"
+ self.endpoint_sync_protocol = None
+
+ # Override
+ def _generate_protocol_manager_instance(self, args, agent_config=None):
+ return FedMLDeployMasterProtocolManager(args, agent_config=agent_config)
+
+ # Override
+ def generate_topics(self):
+ super().generate_topics()
+
+ # The topic for start deployment
+ self.topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(self.edge_id))
+
+ # The topic for activating endpoint
+ self.topic_activate_endpoint = "model_ops/model_device/activate_deployment/{}".format(str(self.edge_id))
+
+ # The topic for activating endpoint
+ self.topic_deactivate_deployment = "model_ops/model_device/deactivate_deployment/{}".format(str(self.edge_id))
+
+ # The topic for deleting endpoint
+ self.topic_delete_deployment = "model_ops/model_device/delete_deployment/{}".format(str(self.edge_id))
+
+ # Subscribe topics for endpoints
+ self.add_subscribe_topic(self.topic_start_deployment)
+ self.add_subscribe_topic(self.topic_activate_endpoint)
+ self.add_subscribe_topic(self.topic_deactivate_deployment)
+ self.add_subscribe_topic(self.topic_delete_deployment)
+
+ # Override
+ def add_protocol_handler(self):
+ super().add_protocol_handler()
+
+ # Add the message listeners for endpoint related topics
+ self.add_message_listener(self.topic_start_deployment, self.callback_start_deployment)
+ self.add_message_listener(self.topic_activate_endpoint, self.callback_activate_deployment)
+ self.add_message_listener(self.topic_deactivate_deployment, self.callback_deactivate_deployment)
+ self.add_message_listener(self.topic_delete_deployment, self.callback_delete_deployment)
+
+ # Override
+ def _get_job_runner_manager(self):
+ return FedMLDeployJobRunnerManager.get_instance()
+
+ # Override
+ def _init_extra_items(self):
+ # Init local database
+ FedMLServerDataInterface.get_instance().create_job_table()
+ try:
+ FedMLModelDatabase.get_instance().set_database_base_dir(ServerConstants.get_database_dir())
+ FedMLModelDatabase.get_instance().create_table()
+ except Exception as e:
+ pass
+
+ FedMLDeployJobRunnerManager.recover_inference_and_monitor()
+
+ # Override
+ def _process_connection_ready(self):
+ self.endpoint_sync_protocol = FedMLEndpointSyncProtocol(
+ agent_config=self.agent_config, mqtt_mgr=self.message_center)
+ self.endpoint_sync_protocol.setup_listener_for_sync_device_info(self.edge_id)
+
+ MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+
+ # Override
+ def _process_connection_lost(self):
+ pass
+
+ # Override
+ def print_connected_info(self):
+ pass
+
+ def callback_deployment_result_message(self, topic=None, payload=None):
+ logging.info(f"Received deployment result: {self}")
+ FedMLDeployJobRunnerManager.get_instance().save_deployment_result(topic, payload)
+
+ def callback_delete_deployment(self, topic, payload):
+ # Parse payload as the model message object.
+ logging.info("[Master] callback_delete_deployment")
+ model_msg_object = FedMLModelMsgObject(topic, payload)
+
+ # Set end point as deactivated status
+ FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ set_end_point_activation(model_msg_object.inference_end_point_id,
+ model_msg_object.end_point_name, False)
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ delete_end_point(model_msg_object.inference_end_point_id, model_msg_object.end_point_name,
+ model_msg_object.model_name, model_msg_object.model_version)
+
+ FedMLDeployJobRunnerManager.get_instance().send_deployment_delete_request_to_edges(
+ model_msg_object.inference_end_point_id, payload, model_msg_object)
+
+ FedMLDeployJobRunnerManager.get_instance().stop_job_runner(model_msg_object.run_id)
+
+ FedMLDeployJobRunnerManager.get_instance().stop_device_inference_monitor(
+ model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_id,
+ model_msg_object.model_name, model_msg_object.model_version)
+
+ FedMLServerDataInterface.get_instance().delete_job_from_db(model_msg_object.run_id)
+ FedMLModelDatabase.get_instance().delete_deployment_result(
+ model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_name,
+ model_version=model_msg_object.model_version)
+ FedMLModelDatabase.get_instance().delete_deployment_run_info(
+ end_point_id=model_msg_object.inference_end_point_id)
+
+ def callback_start_deployment(self, topic, payload):
+ # noinspection PyBroadException
+ try:
+ MLOpsConfigs.fetch_all_configs()
+ except Exception as e:
+ pass
+
+ # Parse the deployment parameters
+ request_json = json.loads(payload)
+ run_id = request_json["end_point_id"]
+ end_point_name = request_json["end_point_name"]
+ token = request_json["token"]
+ user_id = request_json["user_id"]
+ user_name = request_json["user_name"]
+ device_ids = request_json["device_ids"]
+ device_objs = request_json["device_objs"]
+ model_config = request_json["model_config"]
+ model_name = model_config["model_name"]
+ model_id = model_config["model_id"]
+ model_storage_url = model_config["model_storage_url"]
+ scale_min = model_config.get("instance_scale_min", 0)
+ scale_max = model_config.get("instance_scale_max", 0)
+ inference_engine = model_config.get("inference_engine", 0)
+ inference_end_point_id = run_id
+
+ # Start log processor for current run
+ self.args.run_id = run_id
+ self.args.edge_id = self.edge_id
+ MLOpsRuntimeLog.get_instance(self.args).init_logs()
+ MLOpsRuntimeLogDaemon.get_instance(self.args).set_log_source(
+ ServerConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT)
+ MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(run_id, self.edge_id)
+
+ # Generate the deployment new parameters
+ logging.info("callback_start_deployment {}".format(payload))
+ run_id = inference_end_point_id
+ run_id_str = str(run_id)
+ request_json["run_id"] = run_id
+ self.request_json = request_json
+ self.running_request_json[run_id_str] = request_json
+ diff_devices, diff_version = self.get_diff_devices(run_id)
+ self.request_json["diff_devices"] = diff_devices
+ self.request_json["diff_version"] = diff_version
+ self.request_json["master_node_ip"] = GeneralConstants.get_ip_address(self.request_json)
+
+ # Save the endpoint device info
+ self.init_device_update_map()
+ FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ set_end_point_device_info(request_json["end_point_id"], end_point_name, json.dumps(device_objs))
+
+ # Save the endpoint token
+ usr_indicated_token = FedMLDeployMasterProtocolManager.get_usr_indicated_token(request_json)
+ if usr_indicated_token != "":
+ logging.info(f"Change Token from{token} to {usr_indicated_token}")
+ token = usr_indicated_token
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ set_end_point_token(run_id, end_point_name, model_name, token)
+
+ # Subscribe deployment result messages from slave devices
+ self.subscribe_deployment_messages_from_slave_devices(request_json)
+
+ # Send stage: MODEL_DEPLOYMENT_STAGE1 = "Received"
+ FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
+ self.run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE1["index"],
+ ServerConstants.MODEL_DEPLOYMENT_STAGE1["text"], "Received request for end point {}".format(run_id),
+ message_center=self.message_center)
+
+ # Send stage: MODEL_DEPLOYMENT_STAGE2 = "Initializing"
+ FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
+ self.run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE2["index"],
+ ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"], ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"],
+ message_center=self.message_center)
+
+ # Save the runner info
+ ServerConstants.save_runner_infos(self.args.device_id + "." + self.args.os_name, self.edge_id, run_id=run_id)
+
+ # Start the job runner to deploy models
+ self.running_request_json[run_id_str] = FedMLDeployJobRunnerManager.generate_request_json_with_replica_diff(
+ run_id, self.edge_id, request_json
+ )
+ self._get_job_runner_manager().start_job_runner(
+ run_id, request_json, args=self.args, edge_id=self.edge_id,
+ sender_message_queue=self.message_center.get_sender_message_queue(),
+ listener_message_queue=self.get_listener_message_queue(),
+ status_center_queue=self.get_status_queue()
+ )
+ process = self._get_job_runner_manager().get_runner_process(run_id)
+ if process is not None:
+ ServerConstants.save_run_process(run_id, process.pid)
+
+ # Send stage: MODEL_DEPLOYMENT_STAGE3 = "StartRunner"
+ FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
+ self.run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE3["index"],
+ ServerConstants.MODEL_DEPLOYMENT_STAGE3["text"], ServerConstants.MODEL_DEPLOYMENT_STAGE3["text"],
+ message_center=self.message_center)
+
+ def callback_activate_deployment(self, topic, payload):
+ logging.info("callback_activate_deployment: topic = %s, payload = %s" % (topic, payload))
+
+ # Parse payload as the model message object.
+ model_msg_object = FedMLModelMsgObject(topic, payload)
+
+ # Get the previous deployment status.
+ FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+ endpoint_status = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ get_end_point_status(model_msg_object.inference_end_point_id)
+ if endpoint_status != ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED:
+ return
+
+ # Set end point as activated status
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_end_point_activation(
+ model_msg_object.inference_end_point_id, model_msg_object.end_point_name, True)
+
+ def callback_deactivate_deployment(self, topic, payload):
+ logging.info("callback_deactivate_deployment: topic = %s, payload = %s" % (topic, payload))
+
+ # Parse payload as the model message object.
+ model_msg_object = FedMLModelMsgObject(topic, payload)
+
+ # Get the endpoint status
+ FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+ endpoint_status = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ get_end_point_status(model_msg_object.inference_end_point_id)
+ if endpoint_status != ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED:
+ return
+
+ # Set end point as deactivated status
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_end_point_activation(
+ model_msg_object.inference_end_point_id, model_msg_object.model_name, False)
+
+ def get_diff_devices(self, run_id) -> (dict, dict):
+ """
+ {device_id(int): "op: add" | "op: delete" | "op: replace"}
+ "op: add" -> need to add
+ "op: delete" -> need to delete device
+ "op: replace" -> need to restart the container of the device on same port with new (same) model pkg
+
+ {device_id(int): "old_version"}
+ """
+ try:
+ logging.info(f"Get diff devices for run {run_id}")
+ request_json = self.running_request_json.get(str(run_id))
+
+ diff_devices = {}
+ diff_version = {}
+ FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+ device_objs = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ get_end_point_device_info(run_id)
+ if device_objs is None:
+ for new_device_id in request_json["device_ids"]:
+ diff_devices[new_device_id] = ServerConstants.DEVICE_DIFF_ADD_OPERATION
+ else:
+ device_objs_dict = json.loads(device_objs)
+ device_ids_frm_db = [d["id"] for d in device_objs_dict]
+
+ for exist_device_id in device_ids_frm_db:
+ if exist_device_id not in request_json["device_ids"]:
+ diff_devices[exist_device_id] = ServerConstants.DEVICE_DIFF_DELETE_OPERATION
+
+ for new_device_id in request_json["device_ids"]:
+ if new_device_id not in device_ids_frm_db:
+ diff_devices[new_device_id] = ServerConstants.DEVICE_DIFF_ADD_OPERATION
+ else:
+ if new_device_id == self.edge_id:
+ continue
+
+ old_version = self.should_update_device(request_json, new_device_id)
+ if old_version:
+ diff_devices[new_device_id] = ServerConstants.DEVICE_DIFF_REPLACE_OPERATION
+ diff_version[new_device_id] = old_version
+ else:
+ pass
+ logging.info(f"Diff devices: {diff_devices}")
+ except Exception as e:
+ error_log_path = f"~/.fedml/fedml-model-server/fedml/logs/{run_id}_error.txt"
+ if not os.path.exists(os.path.dirname(os.path.expanduser(error_log_path))):
+ os.makedirs(os.path.dirname(os.path.expanduser(error_log_path)))
+ with open(os.path.expanduser(error_log_path), "w") as f:
+ f.write(str(e))
+ raise e
+ return diff_devices, diff_version
+
+ def should_update_device(self, payload, new_device_id):
+ """
+ Query the device info in local redis, if the device info is different from the payload,
+ return the old model version
+ """
+ device_result_list = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ get_deployment_result_list(self.request_json["end_point_id"],
+ self.request_json["end_point_name"],
+ self.request_json["model_config"]["model_name"])
+
+ for device_result in device_result_list:
+ if device_result is None:
+ continue
+ device_result_dict = json.loads(device_result)
+
+ if int(device_result_dict["cache_device_id"]) == new_device_id:
+ result_body = json.loads(device_result_dict["result"])
+ if result_body["model_version"] != payload["model_config"]["model_version"]:
+ return result_body["model_version"]
+ else:
+ return None
+ return None
+
+ @staticmethod
+ def get_usr_indicated_token(request_json) -> str:
+ usr_indicated_token = ""
+ if "parameters" in request_json and "authentication_token" in request_json["parameters"]:
+ usr_indicated_token = request_json["parameters"]["authentication_token"]
+ return usr_indicated_token
+
+ def init_device_update_map(self):
+ # [Deprecated] Use the replica controller to manage the device update
+ pass
+
+ def subscribe_deployment_messages_from_slave_devices(self, request_json):
+ if request_json is None:
+ return
+ run_id = request_json["run_id"]
+ edge_id_list = request_json["device_ids"]
+ logging.info("Edge ids: " + str(edge_id_list))
+ for edge_id in edge_id_list:
+ if str(edge_id) == str(self.edge_id):
+ continue
+ # subscribe deployment result message for each model device
+ deployment_results_topic = "model_device/model_device/return_deployment_result/{}".format(edge_id)
+ self.add_message_listener(deployment_results_topic, self.callback_deployment_result_message)
+ self.subscribe_msg(deployment_results_topic)
+
+ logging.info("subscribe device messages {}".format(deployment_results_topic))
diff --git a/python/fedml/computing/scheduler/model_scheduler/model_device_client.py b/python/fedml/computing/scheduler/model_scheduler/model_device_client.py
index f397c5421f..05f43afc5f 100755
--- a/python/fedml/computing/scheduler/model_scheduler/model_device_client.py
+++ b/python/fedml/computing/scheduler/model_scheduler/model_device_client.py
@@ -1,16 +1,12 @@
-import json
+
+import copy
import logging
import multiprocessing
-import os
import time
import traceback
from multiprocessing import Process
-
-import click
-from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
-
-from fedml.computing.scheduler.model_scheduler import device_client_runner
-from fedml.computing.scheduler.model_scheduler import device_client_constants
+from ..scheduler_core.account_manager import FedMLAccountManager
+from .worker_agent import FedMLDeployWorkerAgent
class FedMLModelDeviceClientRunner:
@@ -18,8 +14,7 @@ def __init__(self, args, current_device_id, os_name, is_from_docker, service_con
self.agent_process = None
self.agent_runner = None
self.agent_process_event = None
- self.real_client_runner = None
- self.args = args
+ self.args = copy.deepcopy(args)
self.service_config = service_config
self.unique_device_id = None
self.current_device_id = current_device_id
@@ -31,8 +26,6 @@ def __init__(self, args, current_device_id, os_name, is_from_docker, service_con
self.redis_port = "6379"
self.redis_password = "fedml_default"
- self.agent_runner = None
-
def get_edge_id(self):
return self.edge_id
@@ -45,33 +38,34 @@ def start(self):
self.agent_runner.redis_password = self.redis_password
if self.agent_process_event is None:
self.agent_process_event = multiprocessing.Event()
- self.agent_process = Process(target=self.agent_runner.run_entry, args=(self.agent_process_event,))
- self.edge_id = self.bind_device(init_params=False)
+ self.agent_process = Process(target=self.agent_runner.run_entry, args=(self.agent_process_event, self.args,))
+ self.edge_id = self.bind_device()
self.agent_process.start()
- def run_entry(self, process_event):
+ def run_entry(self, process_event, in_args):
# print(f"Model worker process id {os.getpid()}")
self.agent_process_event = process_event
+ worker_agent = FedMLDeployWorkerAgent()
+
while not self.agent_process_event.is_set():
try:
try:
- if self.real_client_runner is not None:
- self.real_client_runner.stop_agent()
+ worker_agent.logout()
except Exception as e:
pass
- self.bind_device()
-
- self.start_agent()
+ worker_agent.login(
+ in_args.account_id, api_key=in_args.api_key, device_id=in_args.device_id,
+ os_name=in_args.os_name, role=FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM
+ )
except Exception as e:
logging.info("Restart model device client: {}".format(traceback.format_exc()))
pass
finally:
try:
- if self.real_client_runner is not None:
- self.real_client_runner.stop_agent()
+ worker_agent.logout()
except Exception as e:
pass
time.sleep(15)
@@ -87,100 +81,18 @@ def check_runner_stop_event(self):
raise Exception("Runner stopped")
def stop(self):
- if self.real_client_runner is not None:
- self.real_client_runner.stop_agent()
+ FedMLDeployWorkerAgent.logout()
if self.agent_process_event is not None:
self.agent_process_event.set()
- def get_binding_unique_device_id(self, current_device_id, os_name, is_from_docker=False):
- role_str = "OnPremise"
-
- # Judge whether running from fedml docker hub
- is_from_fedml_docker_hub = False
- dock_loc_file = device_client_constants.ClientConstants.get_docker_location_file()
- if os.path.exists(dock_loc_file):
- is_from_fedml_docker_hub = True
-
- # Build unique device id
- is_from_k8s = device_client_constants.ClientConstants.is_running_on_k8s()
- if is_from_k8s:
- unique_device_id = current_device_id + "@" + os_name + ".MDA.K8S." + role_str + ".Device"
- elif is_from_docker:
- unique_device_id = current_device_id + "@" + os_name + ".MDA.Docker." + role_str + ".Device"
+ def bind_device(self):
+ # Login account
+ login_result = FedMLAccountManager.get_instance().login(
+ self.args.account_id, api_key=self.args.api_key, device_id=self.args.device_id,
+ os_name=self.args.os_name, role=FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM
+ )
+ if login_result is not None:
+ return login_result.edge_id
else:
- unique_device_id = current_device_id + "@" + os_name + ".MDA." + role_str + ".Device"
- if is_from_fedml_docker_hub:
- unique_device_id = current_device_id + "@" + os_name + ".MDA.DockerHub." + role_str + ".Device"
-
- return unique_device_id
-
- def init_logs_param(self, edge_id):
- # Init runtime logs
- self.args.log_file_dir = device_client_constants.ClientConstants.get_log_file_dir()
- self.args.run_id = 0
- self.args.role = "client"
- client_ids = list()
- client_ids.append(edge_id)
- self.args.client_id_list = json.dumps(client_ids)
- setattr(self.args, "using_mlops", True)
-
- def bind_device(self, init_params=True):
- self.unique_device_id = self.get_binding_unique_device_id(self.current_device_id, self.os_name,
- self.is_from_docker)
-
- # Create client runner for communication with the FedML server.
- if self.real_client_runner is None:
- self.real_client_runner = device_client_runner.FedMLClientRunner(self.args)
-
- # Bind account id to the ModelOps platform.
- register_try_count = 0
- edge_id = -1
- user_name = None
- extra_url = None
- while register_try_count < 5:
- try:
- edge_id, user_name, extra_url = self.real_client_runner.bind_account_and_device_id(
- self.service_config["ml_ops_config"]["EDGE_BINDING_URL"], self.args.account_id,
- self.unique_device_id, self.os_name
- )
- if edge_id > 0:
- self.real_client_runner.edge_id = edge_id
- break
- except Exception as e:
- click.echo("{}\n{}".format(SchedulerConstants.ERR_MSG_BINDING_EXCEPTION_2, traceback.format_exc()))
- click.echo(SchedulerConstants.ERR_MSG_BINDING_EXIT_RETRYING)
- register_try_count += 1
- time.sleep(3)
- continue
-
- if edge_id <= 0:
- click.echo("")
- click.echo("Oops, you failed to login the FedML ModelOps platform.")
- click.echo("Please check whether your network is normal!")
- return
- self.edge_id = edge_id
-
- # Init runtime logs
- if init_params:
- setattr(self.args, "client_id", edge_id)
- self.init_logs_param(edge_id)
- self.real_client_runner.args = self.args
- self.real_client_runner.user_name = user_name
-
- return edge_id
-
- def start_agent(self):
- self.real_client_runner.unique_device_id = self.unique_device_id
- device_client_constants.ClientConstants.save_runner_infos(self.current_device_id + "." + self.os_name,
- self.edge_id, run_id=0)
-
- # Setup MQTT connection for communication with the FedML server.
- self.real_client_runner.infer_host = self.infer_host
- self.real_client_runner.redis_addr = self.redis_addr
- self.real_client_runner.redis_port = self.redis_port
- self.real_client_runner.redis_password = self.redis_password
- self.real_client_runner.setup_agent_mqtt_connection(self.service_config)
-
- # Start mqtt looper
- self.real_client_runner.start_agent_mqtt_loop(should_exit_sys=False)
+ return None
diff --git a/python/fedml/computing/scheduler/model_scheduler/model_device_server.py b/python/fedml/computing/scheduler/model_scheduler/model_device_server.py
index 01228125aa..b2ecd144b1 100755
--- a/python/fedml/computing/scheduler/model_scheduler/model_device_server.py
+++ b/python/fedml/computing/scheduler/model_scheduler/model_device_server.py
@@ -1,16 +1,12 @@
-import json
+
+import copy
import logging
import multiprocessing
-import os
import time
import traceback
from multiprocessing import Process
-
-import click
-from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
-
-from fedml.computing.scheduler.model_scheduler import device_server_runner
-from fedml.computing.scheduler.model_scheduler import device_server_constants
+from ..scheduler_core.account_manager import FedMLAccountManager
+from .master_agent import FedMLDeployMasterAgent
class FedMLModelDeviceServerRunner:
@@ -18,8 +14,7 @@ def __init__(self, args, current_device_id, os_name, is_from_docker, service_con
self.agent_process = None
self.agent_runner = None
self.agent_process_event = None
- self.real_server_runner = None
- self.args = args
+ self.args = copy.deepcopy(args)
self.service_config = service_config
self.unique_device_id = None
self.current_device_id = current_device_id
@@ -30,7 +25,6 @@ def __init__(self, args, current_device_id, os_name, is_from_docker, service_con
self.redis_addr = "local"
self.redis_port = "6379"
self.redis_password = "fedml_default"
- self.agent_runner = None
def get_edge_id(self):
return self.edge_id
@@ -44,33 +38,33 @@ def start(self):
self.agent_runner.redis_password = self.redis_password
if self.agent_process_event is None:
self.agent_process_event = multiprocessing.Event()
- self.agent_process = Process(target=self.agent_runner.run_entry, args=(self.agent_process_event,))
- self.edge_id = self.bind_device(init_params=False)
+ self.agent_process = Process(target=self.agent_runner.run_entry, args=(self.agent_process_event, self.args))
+ self.edge_id = self.bind_device()
self.agent_process.start()
- def run_entry(self, process_event):
+ def run_entry(self, process_event, in_args):
# print(f"Model master process id {os.getpid()}")
self.agent_process_event = process_event
+ master_agent = FedMLDeployMasterAgent()
while not self.agent_process_event.is_set():
try:
try:
- if self.real_server_runner is not None:
- self.real_server_runner.stop_agent()
+ master_agent.logout()
except Exception as e:
pass
- self.bind_device()
-
- self.start_agent()
+ master_agent.login(
+ in_args.account_id, api_key=in_args.api_key, device_id=in_args.device_id,
+ os_name=in_args.os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM
+ )
except Exception as e:
logging.info("Restart model device server: {}".format(traceback.format_exc()))
pass
finally:
try:
- if self.real_server_runner is not None:
- self.real_server_runner.stop_agent()
+ master_agent.logout()
except Exception as e:
pass
time.sleep(15)
@@ -86,104 +80,18 @@ def check_runner_stop_event(self):
raise Exception("Runner stopped")
def stop(self):
- if self.real_server_runner is not None:
- self.real_server_runner.stop_agent()
+ FedMLDeployMasterAgent.logout()
if self.agent_process_event is not None:
self.agent_process_event.set()
- def get_binding_unique_device_id(self, current_device_id, os_name, is_from_docker=False):
- role_str = "OnPremise"
-
- # Judge whether running from fedml docker hub
- is_from_fedml_docker_hub = False
- dock_loc_file = device_server_constants.ServerConstants.get_docker_location_file()
- if os.path.exists(dock_loc_file):
- is_from_fedml_docker_hub = True
-
- # Build unique device id
- is_from_k8s = device_server_constants.ServerConstants.is_running_on_k8s()
- if is_from_k8s:
- unique_device_id = current_device_id + "@" + os_name + ".MDA.K8S." + role_str + ".Master.Device"
- elif is_from_docker:
- unique_device_id = current_device_id + "@" + os_name + ".MDA.Docker." + role_str + ".Master.Device"
+ def bind_device(self):
+ # Login account
+ login_result = FedMLAccountManager.get_instance().login(
+ self.args.account_id, api_key=self.args.api_key, device_id=self.args.device_id,
+ os_name=self.args.os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM
+ )
+ if login_result is not None:
+ return login_result.edge_id
else:
- unique_device_id = current_device_id + "@" + os_name + ".MDA." + role_str + ".Master.Device"
-
- if is_from_fedml_docker_hub:
- unique_device_id = current_device_id + "@" + os_name + ".MDA.DockerHub." + role_str + ".Master.Device"
-
- return unique_device_id
-
- def init_logs_param(self, edge_id):
- self.args.log_file_dir = device_server_constants.ServerConstants.get_log_file_dir()
- self.args.run_id = 0
- self.args.role = "server"
- self.args.edge_id = edge_id
- setattr(self.args, "using_mlops", True)
- setattr(self.args, "server_agent_id", edge_id)
-
- def bind_device(self, init_params=True):
- self.unique_device_id = self.get_binding_unique_device_id(self.current_device_id, self.os_name,
- self.is_from_docker)
-
- # Create client runner for communication with the FedML server.
- if self.real_server_runner is None:
- self.real_server_runner = device_server_runner.FedMLServerRunner(self.args)
-
- # Bind account id to the ModelOps platform.
- register_try_count = 0
- edge_id = -1
- user_name = None
- extra_url = None
- while register_try_count < 5:
- try:
- edge_id, user_name, extra_url = self.real_server_runner.bind_account_and_device_id(
- self.service_config["ml_ops_config"]["EDGE_BINDING_URL"], self.args.account_id,
- self.unique_device_id, self.os_name
- )
- if edge_id > 0:
- self.real_server_runner.edge_id = edge_id
- break
- except Exception as e:
- click.echo("{}\n{}".format(SchedulerConstants.ERR_MSG_BINDING_EXCEPTION_2, traceback.format_exc()))
- click.echo(SchedulerConstants.ERR_MSG_BINDING_EXIT_RETRYING)
- register_try_count += 1
- time.sleep(3)
- continue
-
- if edge_id <= 0:
- click.echo("")
- click.echo("Oops, you failed to login the FedML ModelOps platform.")
- click.echo("Please check whether your network is normal!")
- return
- self.edge_id = edge_id
-
- # Init runtime logs
- if init_params:
- setattr(self.args, "client_id", edge_id)
- self.real_server_runner.infer_host = self.infer_host
- self.real_server_runner.redis_addr = self.redis_addr
- self.real_server_runner.redis_port = self.redis_port
- self.real_server_runner.redis_password = self.redis_password
- self.init_logs_param(edge_id)
- self.real_server_runner.args = self.args
- self.real_server_runner.run_as_edge_server_and_agent = True
- self.real_server_runner.user_name = user_name
-
- return edge_id
-
- def start_agent(self):
- # Log arguments and binding results.
- # logging.info("login: unique_device_id = %s" % str(unique_device_id))
- # logging.info("login: edge_id = %s" % str(edge_id))
- self.real_server_runner.unique_device_id = self.unique_device_id
- device_server_constants.ServerConstants.save_runner_infos(self.current_device_id + "." + self.os_name,
- self.edge_id, run_id=0)
-
- # Setup MQTT connection for communication with the FedML server.
- self.real_server_runner.infer_host = self.infer_host
- self.real_server_runner.setup_agent_mqtt_connection(self.service_config)
-
- # Start mqtt looper
- self.real_server_runner.start_agent_mqtt_loop(should_exit_sys=False)
+ return None
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_agent.py b/python/fedml/computing/scheduler/model_scheduler/worker_agent.py
new file mode 100755
index 0000000000..bdbe5fc143
--- /dev/null
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_agent.py
@@ -0,0 +1,27 @@
+
+from .device_client_constants import ClientConstants
+from .device_client_data_interface import FedMLClientDataInterface
+from .worker_protocol_manager import FedMLDeployWorkerProtocolManager
+from ..slave.base_slave_agent import FedMLBaseSlaveAgent
+
+
+class FedMLDeployWorkerAgent(FedMLBaseSlaveAgent):
+
+ def __init__(self):
+ FedMLBaseSlaveAgent.__init__(self)
+
+ # Override
+ def _get_log_file_dir(self):
+ return ClientConstants.get_log_file_dir()
+
+ # Override
+ def _save_agent_info(self, unique_device_id, edge_id):
+ ClientConstants.save_runner_infos(unique_device_id, edge_id)
+
+ # Override
+ def _init_database(self):
+ FedMLClientDataInterface.get_instance().create_job_table()
+
+ # Override
+ def _generate_protocol_manager_instance(self, args, agent_config=None):
+ return FedMLDeployWorkerProtocolManager(args, agent_config=agent_config)
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
new file mode 100755
index 0000000000..5d6f1a4d8e
--- /dev/null
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -0,0 +1,489 @@
+
+import json
+import logging
+import os
+import shutil
+import time
+import traceback
+import urllib
+from abc import ABC
+from urllib.parse import urljoin, urlparse
+import yaml
+from fedml.computing.scheduler.comm_utils.job_utils import JobRunnerUtils
+from fedml.core.mlops import MLOpsRuntimeLog
+from .device_client_constants import ClientConstants
+from .device_model_cache import FedMLModelCache
+from ..scheduler_core.general_constants import GeneralConstants
+from ..slave.base_slave_job_runner import FedMLBaseSlaveJobRunner
+from .device_model_deployment import start_deployment
+from .device_model_db import FedMLModelDatabase
+from .device_replica_handler import FedMLDeviceReplicaHandler
+
+
+class FedMLDeployWorkerJobRunner(FedMLBaseSlaveJobRunner, ABC):
+
+ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id=0,
+ cuda_visible_gpu_ids_str=None):
+ FedMLBaseSlaveJobRunner.__init__(
+ self, args, edge_id=edge_id, request_json=request_json, agent_config=agent_config, run_id=run_id,
+ cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str, agent_data_dir=ClientConstants.get_data_dir(),
+ agent_package_download_dir=ClientConstants.get_package_download_dir(),
+ agent_package_unzip_dir=GeneralConstants.get_package_unzip_dir(ClientConstants.get_package_download_dir()),
+ agent_log_file_dir=ClientConstants.get_log_file_dir()
+ )
+
+ self.infer_host = "127.0.0.1"
+ self.redis_addr = "local"
+ self.redis_port = "6379"
+ self.redis_password = "fedml_default"
+ self.model_is_from_open = False
+ self.replica_handler = None
+
+ # Override
+ def _generate_job_runner_instance(self, args, run_id=None, request_json=None, agent_config=None, edge_id=None):
+ return FedMLDeployWorkerJobRunner(
+ args, run_id=run_id, request_json=request_json, agent_config=self.agent_config, edge_id=edge_id
+ )
+
+ # Override
+ def _generate_extend_queue_list(self):
+ return None
+
+ def retrieve_binary_model_file(self, package_name, package_url):
+ local_package_path = ClientConstants.get_model_package_dir()
+ if not os.path.exists(local_package_path):
+ os.makedirs(local_package_path, exist_ok=True)
+ unzip_package_path = ClientConstants.get_model_dir()
+ local_package_file = "{}".format(os.path.join(local_package_path, package_name))
+ if os.path.exists(local_package_file):
+ os.remove(local_package_file)
+ package_url_without_query_path = urljoin(package_url, urlparse(package_url).path)
+ urllib.request.urlretrieve(package_url_without_query_path, local_package_file,
+ reporthook=self.package_download_progress)
+
+ unzip_package_path = os.path.join(unzip_package_path, package_name)
+ if not os.path.exists(unzip_package_path):
+ os.makedirs(unzip_package_path, exist_ok=True)
+ dst_model_file = os.path.join(unzip_package_path, package_name)
+ if os.path.exists(local_package_file):
+ shutil.copy(local_package_file, dst_model_file)
+
+ return unzip_package_path, dst_model_file
+
+ @staticmethod
+ def get_model_bin_file(unzip_package_full_path):
+ unzip_package_path = os.path.dirname(unzip_package_full_path)
+ model_bin_file = os.path.join(unzip_package_path, "fedml_model.bin")
+ return model_bin_file
+
+ def update_local_fedml_config(self, run_id, model_config, model_config_parameters=None):
+ model_name = model_config["model_name"]
+ model_storage_url = model_config["model_storage_url"]
+ scale_min = model_config.get("instance_scale_min", 0)
+ scale_max = model_config.get("instance_scale_max", 0)
+ inference_engine = model_config.get("inference_engine", 0)
+ inference_end_point_id = run_id
+
+ # Retrieve model package or model binary file.
+ if self.model_is_from_open:
+ unzip_package_path, model_bin_file = self.retrieve_binary_model_file(model_name, model_storage_url)
+ else:
+ unzip_package_path = self.retrieve_and_unzip_package(model_name, model_storage_url)
+ model_bin_file = FedMLDeployWorkerJobRunner.get_model_bin_file(unzip_package_path)
+
+ # Load the config to memory
+ package_conf_object = {}
+ fedml_local_config_file = os.path.join(unzip_package_path, "fedml_model_config.yaml")
+
+ # Inject the config from UI to pkg yaml
+ package_conf_object = model_config_parameters
+
+ # Save the config to local
+ with open(fedml_local_config_file, "w") as f:
+ yaml.dump(package_conf_object, f)
+
+ logging.info("The package_conf_object is {}".format(package_conf_object))
+
+ return unzip_package_path, model_bin_file, package_conf_object
+
+ def download_model_package(self, package_name, package_url):
+ # Copy config file from the client
+ unzip_package_path = self.retrieve_and_unzip_package(
+ package_name, package_url
+ )
+
+ return unzip_package_path
+
+ # Override
+ def run_impl(self, run_extend_queue_list, sender_message_center,
+ listener_message_queue, status_center_queue):
+ run_id = self.request_json["end_point_id"]
+ end_point_name = self.request_json["end_point_name"]
+ token = self.request_json["token"]
+ user_id = self.request_json["user_id"]
+ user_name = self.request_json["user_name"]
+ device_ids = self.request_json["device_ids"]
+ device_objs = self.request_json["device_objs"]
+ master_ip = self.request_json["master_node_ip"]
+
+ model_config = self.request_json["model_config"]
+ model_name = model_config["model_name"]
+ model_id = model_config["model_id"]
+ model_version = model_config["model_version"]
+ model_storage_url = model_config["model_storage_url"]
+ scale_min = model_config.get("instance_scale_min", 0)
+ scale_max = model_config.get("instance_scale_max", 0)
+ model_config_parameters = self.request_json["parameters"]
+
+ self.replica_handler = FedMLDeviceReplicaHandler(self.edge_id, self.request_json)
+
+ inference_port = model_config_parameters.get("worker_internal_port",
+ ClientConstants.MODEL_INFERENCE_DEFAULT_PORT)
+ inference_port_external = model_config_parameters.get("worker_external_port", inference_port)
+
+ if "using_triton" in model_config_parameters and model_config_parameters["using_triton"]:
+ inference_engine = ClientConstants.INFERENCE_ENGINE_TYPE_INT_TRITON
+ else:
+ inference_engine = ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT
+
+ logging.info("[Critical] The inference_engine is: {}".format(inference_engine))
+
+ self.model_is_from_open = True if model_config.get("is_from_open", 0) == 1 else False
+ if self.model_is_from_open:
+ model_net_url = model_config["model_net_url"]
+ inference_end_point_id = run_id
+ use_gpu = "gpu" # TODO: Get GPU from device infos
+ memory_size = "4096m" # TODO: Get Memory size for each instance
+
+ self.mlops_metrics.report_sys_perf(self.args, self.agent_config["mqtt_config"], run_id=run_id)
+
+ self.check_runner_stop_event()
+
+ logging.info("model deployment request: {}".format(self.request_json))
+
+ MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+
+ self.status_reporter.report_client_id_status(
+ self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_INITIALIZING,
+ is_from_model=True, running_json=json.dumps(self.request_json), run_id=run_id)
+
+ self.status_reporter.report_client_id_status(
+ self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_RUNNING,
+ is_from_model=True, run_id=run_id)
+
+ self.check_runner_stop_event()
+
+ # update local config with real time parameters from server and dynamically replace variables value
+ logging.info("download and unzip model to local...")
+ unzip_package_path, model_bin_file, fedml_config_object = \
+ self.update_local_fedml_config(run_id, model_config, model_config_parameters)
+ if unzip_package_path is None or fedml_config_object is None:
+ logging.info("failed to update local fedml config.")
+ self.check_runner_stop_event()
+ self.status_reporter.report_client_id_status(
+ self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
+ is_from_model=True, run_id=run_id)
+ return False
+
+ logging.info("check downloaded packages...")
+ if not os.path.exists(unzip_package_path):
+ logging.info("failed to unzip file.")
+ self.check_runner_stop_event()
+ self.status_reporter.report_client_id_status(
+ self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
+ is_from_model=True, run_id=run_id)
+ return False
+
+ # download model net and load into the torch model
+ model_from_open = None
+ self.model_is_from_open = None
+
+ logging.info("start the model deployment...")
+ self.check_runner_stop_event()
+ running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
+ "", "", model_version, {}, {}
+
+ # Reconcile the replica number (op: add, remove)
+ prev_rank, op, op_num = self.replica_handler.reconcile_num_replica()
+
+ # Reconcile the replica version (op: update)
+ replica_rank_to_update = []
+ if not op:
+ replica_rank_to_update, op = self.replica_handler.reconcile_replica_version()
+
+ if not op:
+ logging.info("No need to reconcile.")
+ return True
+
+ if op == "add":
+ worker_ip = GeneralConstants.get_ip_address(self.request_json)
+ for rank in range(prev_rank+1, prev_rank+1+op_num):
+ # TODO: Support Rollback if this for loop failed
+ try:
+ running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
+ start_deployment(
+ inference_end_point_id, end_point_name, model_id, model_version,
+ unzip_package_path, model_bin_file, model_name, inference_engine,
+ ClientConstants.INFERENCE_HTTP_PORT,
+ ClientConstants.INFERENCE_GRPC_PORT,
+ ClientConstants.INFERENCE_METRIC_PORT,
+ use_gpu, memory_size,
+ ClientConstants.INFERENCE_CONVERTOR_IMAGE,
+ ClientConstants.INFERENCE_SERVER_IMAGE,
+ worker_ip,
+ self.model_is_from_open, model_config_parameters,
+ model_from_open,
+ token,
+ master_ip, self.edge_id, master_device_id=device_ids[0], replica_rank=rank,
+ gpu_per_replica=int(self.replica_handler.gpu_per_replica)
+ )
+ except Exception as e:
+ inference_output_url = ""
+ logging.error(f"Exception at deployment: {traceback.format_exc()}")
+
+ if inference_output_url == "":
+ logging.error("failed to deploy the model...")
+
+ result_payload = self.send_deployment_results(
+ end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
+ model_id, model_name, inference_output_url, inference_model_version, inference_port,
+ inference_engine, model_metadata, model_config)
+
+ self.status_reporter.report_client_id_status(
+ self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
+ is_from_model=True, run_id=self.run_id)
+ return False
+ else:
+ logging.info("finished deployment, continue to send results to master...")
+ result_payload = self.send_deployment_results(
+ end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
+ model_id, model_name, inference_output_url, model_version, inference_port_external,
+ inference_engine, model_metadata, model_config, replica_no=rank + 1)
+
+ if inference_port_external != inference_port: # Save internal port to local db
+ logging.info("inference_port_external {} != inference_port {}".format(
+ inference_port_external, inference_port))
+ result_payload = self.construct_deployment_results(
+ end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
+ model_id, model_name, inference_output_url, model_version, inference_port,
+ inference_engine, model_metadata, model_config, replica_no=rank + 1)
+
+ FedMLModelDatabase.get_instance().set_deployment_result(
+ run_id, end_point_name, model_name, model_version, self.edge_id,
+ json.dumps(result_payload), replica_no=rank + 1)
+
+ logging.info(f"Deploy replica {rank+1} / {prev_rank+1+op_num} successfully.")
+ time.sleep(5)
+
+ time.sleep(1)
+ self.status_reporter.report_client_id_status(
+ self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
+ is_from_model=True, run_id=self.run_id)
+ return True
+ elif op == "remove":
+ for rank_to_delete in range(prev_rank, prev_rank-op_num, -1):
+ self.replica_handler.remove_replica(rank_to_delete)
+
+ FedMLModelCache.get_instance().set_redis_params()
+ replica_occupied_gpu_ids_str = FedMLModelCache.get_instance().get_replica_gpu_ids(
+ run_id, end_point_name, model_name, self.edge_id, rank_to_delete+1)
+
+ replica_occupied_gpu_ids = json.loads(replica_occupied_gpu_ids_str)
+
+ JobRunnerUtils.get_instance().release_partial_job_gpu(run_id, self.edge_id, replica_occupied_gpu_ids)
+
+ FedMLModelDatabase.get_instance().delete_deployment_result_with_device_id_and_rank(
+ run_id, end_point_name, model_name, self.edge_id, rank_to_delete)
+
+ # Report the deletion msg to master
+ result_payload = self.send_deployment_results(
+ end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DELETED,
+ model_id, model_name, inference_output_url, model_version, inference_port_external,
+ inference_engine, model_metadata, model_config, replica_no=rank_to_delete + 1)
+
+ time.sleep(1)
+ self.status_reporter.report_client_id_status(
+ self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
+ is_from_model=True, run_id=self.run_id)
+
+ # TODO: If delete all replica, then delete the job and related resources
+ if rank_to_delete == 0:
+ pass
+ return True
+ elif op == "update":
+ # Update is combine of delete and add
+ worker_ip = GeneralConstants.get_ip_address(self.request_json)
+ for rank in replica_rank_to_update:
+ # Delete the container
+ self.replica_handler.remove_replica(rank)
+
+ FedMLModelCache.get_instance().set_redis_params()
+ replica_occupied_gpu_ids_str = FedMLModelCache.get_instance().get_replica_gpu_ids(
+ run_id, end_point_name, model_name, self.edge_id, rank + 1)
+
+ replica_occupied_gpu_ids = json.loads(replica_occupied_gpu_ids_str)
+
+ JobRunnerUtils.get_instance().release_partial_job_gpu(run_id, self.edge_id, replica_occupied_gpu_ids)
+
+ # Delete the deployment result from local db
+ FedMLModelDatabase.get_instance().delete_deployment_result_with_device_id_and_rank(
+ run_id, end_point_name, model_name, self.edge_id, rank)
+
+ time.sleep(1)
+
+ # Add the container
+ # TODO: Reduce the duplicated code
+ try:
+ running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
+ start_deployment(
+ inference_end_point_id, end_point_name, model_id, model_version,
+ unzip_package_path, model_bin_file, model_name, inference_engine,
+ ClientConstants.INFERENCE_HTTP_PORT,
+ ClientConstants.INFERENCE_GRPC_PORT,
+ ClientConstants.INFERENCE_METRIC_PORT,
+ use_gpu, memory_size,
+ ClientConstants.INFERENCE_CONVERTOR_IMAGE,
+ ClientConstants.INFERENCE_SERVER_IMAGE,
+ worker_ip,
+ self.model_is_from_open, model_config_parameters,
+ model_from_open,
+ token,
+ master_ip, self.edge_id, master_device_id=device_ids[0], replica_rank=rank,
+ gpu_per_replica=int(self.replica_handler.gpu_per_replica)
+ )
+ except Exception as e:
+ inference_output_url = ""
+ logging.error(f"Exception at deployment: {traceback.format_exc()}")
+
+ if inference_output_url == "":
+ logging.error("failed to deploy the model...")
+
+ result_payload = self.send_deployment_results(
+ end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
+ model_id, model_name, inference_output_url, inference_model_version, inference_port,
+ inference_engine, model_metadata, model_config)
+
+ self.status_reporter.report_client_id_status(
+ self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
+ is_from_model=True, run_id=self.run_id)
+
+ return False
+ else:
+ logging.info("finished deployment, continue to send results to master...")
+ result_payload = self.send_deployment_results(
+ end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
+ model_id, model_name, inference_output_url, model_version, inference_port_external,
+ inference_engine, model_metadata, model_config, replica_no=rank + 1)
+
+ if inference_port_external != inference_port: # Save internal port to local db
+ logging.info("inference_port_external {} != inference_port {}".format(
+ inference_port_external, inference_port))
+ result_payload = self.construct_deployment_results(
+ end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
+ model_id, model_name, inference_output_url, model_version, inference_port,
+ inference_engine, model_metadata, model_config, replica_no=rank + 1)
+
+ FedMLModelDatabase.get_instance().set_deployment_result(
+ run_id, end_point_name, model_name, model_version, self.edge_id,
+ json.dumps(result_payload), replica_no=rank + 1)
+
+ logging.info(f"Update replica with no {rank + 1} successfully. Op num {op_num}")
+ time.sleep(5)
+ time.sleep(1)
+ self.status_reporter.report_client_id_status(
+ self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
+ is_from_model=True, run_id=self.run_id)
+ return True
+
+ else:
+ # The delete op will be handled by callback_delete_deployment
+ logging.error(f"Unsupported op {op} with op num {op_num}")
+ return False
+
+ def construct_deployment_results(self, end_point_name, device_id, model_status,
+ model_id, model_name, model_inference_url,
+ model_version, inference_port, inference_engine,
+ model_metadata, model_config, replica_no=1):
+ deployment_results_payload = {"end_point_id": self.run_id, "end_point_name": end_point_name,
+ "model_id": model_id, "model_name": model_name,
+ "model_url": model_inference_url, "model_version": model_version,
+ "port": inference_port,
+ "inference_engine": inference_engine,
+ "model_metadata": model_metadata,
+ "model_config": model_config,
+ "model_status": model_status,
+ "inference_port": inference_port,
+ "replica_no": replica_no,
+ }
+ return deployment_results_payload
+
+ def construct_deployment_status(self, end_point_name, device_id,
+ model_id, model_name, model_version,
+ model_inference_url, model_status,
+ inference_port=ClientConstants.MODEL_INFERENCE_DEFAULT_PORT,
+ replica_no=1, # start from 1
+ ):
+ deployment_status_payload = {"end_point_id": self.run_id, "end_point_name": end_point_name,
+ "device_id": device_id,
+ "model_id": model_id, "model_name": model_name,
+ "model_version": model_version,
+ "model_url": model_inference_url, "model_status": model_status,
+ "inference_port": inference_port,
+ "replica_no": replica_no,
+ }
+ return deployment_status_payload
+
+ def send_deployment_results(self, end_point_name, device_id, model_status,
+ model_id, model_name, model_inference_url,
+ model_version, inference_port, inference_engine,
+ model_metadata, model_config, replica_no=1):
+ deployment_results_topic = "model_device/model_device/return_deployment_result/{}".format(device_id)
+ deployment_results_payload = self.construct_deployment_results(
+ end_point_name, device_id, model_status,
+ model_id, model_name, model_inference_url,
+ model_version, inference_port, inference_engine,
+ model_metadata, model_config, replica_no=replica_no)
+
+ logging.info("[client] send_deployment_results: topic {}, payload {}.".format(deployment_results_topic,
+ deployment_results_payload))
+ self.message_center.send_message_json(deployment_results_topic, json.dumps(deployment_results_payload))
+ return deployment_results_payload
+
+ def send_deployment_status(self, end_point_name, device_id,
+ model_id, model_name, model_version,
+ model_inference_url, model_status,
+ inference_port=ClientConstants.MODEL_INFERENCE_DEFAULT_PORT,
+ replica_no=1, # start from 1
+ ):
+ deployment_status_topic = "model_device/model_device/return_deployment_status/{}".format(device_id)
+ deployment_status_payload = self.construct_deployment_status(
+ end_point_name, device_id,
+ model_id, model_name, model_version,
+ model_inference_url, model_status,
+ inference_port=inference_port,
+ replica_no=replica_no)
+
+ logging.info("[client] send_deployment_status: topic {}, payload {}.".format(deployment_status_topic,
+ deployment_status_payload))
+ self.message_center.send_message_json(deployment_status_topic, json.dumps(deployment_status_payload))
+ return deployment_status_payload
+
+ def reset_devices_status(self, edge_id, status):
+ self.status_reporter.run_id = self.run_id
+ self.status_reporter.edge_id = edge_id
+ self.status_reporter.report_client_id_status(
+ edge_id, status, is_from_model=True, run_id=self.run_id)
+
+ # Override
+ def get_download_package_info(self, packages_config=None):
+ model_name = packages_config["model_name"]
+ model_storage_url = packages_config["model_storage_url"]
+ return model_name, model_storage_url
+
+ # Override
+ def build_dynamic_args(self, run_id, run_config, package_conf_object, base_dir):
+ pass
+
+ # Override
+ def build_dynamic_constrain_variables(self, run_id, run_config):
+ pass
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner_manager.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner_manager.py
new file mode 100755
index 0000000000..4fe35d5a8a
--- /dev/null
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner_manager.py
@@ -0,0 +1,23 @@
+
+from fedml.core.common.singleton import Singleton
+from .worker_job_runner import FedMLDeployWorkerJobRunner
+from ..scheduler_core.general_constants import GeneralConstants
+from ..slave.base_slave_job_runner_manager import FedMLBaseSlaveJobRunnerManager
+
+
+class FedMLDeployJobRunnerManager(FedMLBaseSlaveJobRunnerManager, Singleton):
+ def __init__(self):
+ FedMLBaseSlaveJobRunnerManager.__init__(self)
+
+ @staticmethod
+ def get_instance():
+ return FedMLDeployJobRunnerManager()
+
+ # Override
+ def _generate_job_runner_instance(
+ self, args, run_id=None, request_json=None, agent_config=None, edge_id=None
+ ):
+ job_runner = FedMLDeployWorkerJobRunner(
+ args, run_id=run_id, request_json=request_json, agent_config=agent_config, edge_id=edge_id)
+ job_runner.infer_host = GeneralConstants.get_ip_address(request_json)
+ return job_runner
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
new file mode 100755
index 0000000000..43bb3c4582
--- /dev/null
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
@@ -0,0 +1,195 @@
+
+import json
+import logging
+import os
+import traceback
+
+from fedml.computing.scheduler.comm_utils.job_utils import JobRunnerUtils
+from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
+from fedml.computing.scheduler.comm_utils.sys_utils import get_python_program
+from fedml.core.mlops import MLOpsConfigs, MLOpsRuntimeLog, MLOpsRuntimeLogDaemon
+from .device_model_db import FedMLModelDatabase
+from .device_model_msg_object import FedMLModelMsgObject
+from .device_client_constants import ClientConstants
+from .device_client_data_interface import FedMLClientDataInterface
+from ..slave.base_slave_protocol_manager import FedMLBaseSlaveProtocolManager
+from .worker_job_runner_manager import FedMLDeployJobRunnerManager
+from .device_mqtt_inference_protocol import FedMLMqttInference
+
+
+class FedMLDeployWorkerProtocolManager(FedMLBaseSlaveProtocolManager):
+ def __init__(self, args, agent_config=None):
+ FedMLBaseSlaveProtocolManager.__init__(self, args, agent_config=agent_config)
+
+ self.topic_start_deployment = None
+ self.topic_delete_deployment = None
+
+ self.infer_host = "127.0.0.1"
+ self.redis_addr = "local"
+ self.redis_port = "6379"
+ self.redis_password = "fedml_default"
+ self.endpoint_sync_protocol = None
+ self.local_api_process = None
+ self.mqtt_inference_obj = None
+
+ # Override
+ def _generate_protocol_manager_instance(self, args, agent_config=None):
+ return FedMLDeployWorkerProtocolManager(args, agent_config=agent_config)
+
+ # Override
+ def generate_topics(self):
+ super().generate_topics()
+
+ # The topic for start deployment
+ self.topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(self.edge_id))
+
+ # The topic for deleting endpoint
+ self.topic_delete_deployment = "model_ops/model_device/delete_deployment/{}".format(str(self.edge_id))
+
+ # Subscribe topics for endpoints
+ self.add_subscribe_topic(self.topic_start_deployment)
+ self.add_subscribe_topic(self.topic_delete_deployment)
+
+ # Override
+ def add_protocol_handler(self):
+ super().add_protocol_handler()
+
+ # Add the message listeners for endpoint related topics
+ self.add_message_listener(self.topic_start_deployment, self.callback_start_deployment)
+ self.add_message_listener(self.topic_delete_deployment, self.callback_delete_deployment)
+
+ # Override
+ def _get_job_runner_manager(self):
+ return FedMLDeployJobRunnerManager.get_instance()
+
+ # Override
+ def _init_extra_items(self):
+ # Init local database
+ FedMLClientDataInterface.get_instance().create_job_table()
+ try:
+ FedMLModelDatabase.get_instance().set_database_base_dir(ClientConstants.get_database_dir())
+ FedMLModelDatabase.get_instance().create_table()
+ except Exception as e:
+ pass
+
+ client_api_cmd = "fedml.computing.scheduler.model_scheduler.device_client_api:api"
+ client_api_pids = RunProcessUtils.get_pid_from_cmd_line(client_api_cmd)
+ if client_api_pids is None or len(client_api_pids) <= 0:
+ # Start local API services
+ cur_dir = os.path.dirname(__file__)
+ fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
+ python_program = get_python_program()
+ self.local_api_process = ClientConstants.exec_console_with_script(
+ "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
+ "--log-level critical".format(
+ python_program, client_api_cmd,
+ ClientConstants.LOCAL_CLIENT_API_PORT, fedml_base_dir
+ ),
+ should_capture_stdout=False,
+ should_capture_stderr=False
+ )
+
+ # Override
+ def _process_connection_ready(self):
+ MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+
+ if self.mqtt_inference_obj is None:
+ self.mqtt_inference_obj = FedMLMqttInference(
+ agent_config=self.agent_config, mqtt_mgr=self.communication_mgr)
+ self.mqtt_inference_obj.setup_listener_for_endpoint_inference_request(self.edge_id)
+
+ # Override
+ def _process_connection_lost(self):
+ try:
+ if self.mqtt_inference_obj is not None:
+ self.mqtt_inference_obj.remove_listener_for_endpoint_inference_request(self.edge_id)
+ except Exception as e:
+ pass
+
+ # Override
+ def print_connected_info(self):
+ pass
+
+ def callback_start_deployment(self, topic, payload):
+ """
+ topic: model_ops/model_device/start_deployment/model-agent-device-id
+ payload: {"model_name": "image-model", "model_storage_url":"s3-url",
+ "instance_scale_min":1, "instance_scale_max":3, "inference_engine":"onnx (or tensorrt)"}
+ """
+ # Parse deployment parameters
+ request_json = json.loads(payload)
+ run_id = request_json["end_point_id"]
+ token = request_json["token"]
+ user_id = request_json["user_id"]
+ user_name = request_json["user_name"]
+ device_ids = request_json["device_ids"]
+ device_objs = request_json["device_objs"]
+ model_config = request_json["model_config"]
+ model_name = model_config["model_name"]
+ model_storage_url = model_config["model_storage_url"]
+ scale_min = model_config.get("instance_scale_min", 0)
+ scale_max = model_config.get("instance_scale_max", 0)
+ inference_engine = model_config.get("inference_engine", 0)
+ inference_end_point_id = run_id
+
+ try:
+ MLOpsConfigs.fetch_all_configs()
+ except Exception as e:
+ pass
+
+ # Start log processor for current run
+ run_id = inference_end_point_id
+ self.args.run_id = run_id
+ self.args.edge_id = self.edge_id
+ MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+ MLOpsRuntimeLogDaemon.get_instance(self.args).set_log_source(
+ ClientConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT)
+ MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(run_id, self.edge_id)
+
+ # Start the job runner
+ request_json["run_id"] = run_id
+ run_id_str = str(run_id)
+ self.request_json = request_json
+ self.running_request_json[run_id_str] = request_json
+ self._get_job_runner_manager().start_job_runner(
+ run_id, request_json, args=self.args, edge_id=self.edge_id,
+ sender_message_queue=self.message_center.get_sender_message_queue(),
+ listener_message_queue=self.get_listener_message_queue(),
+ status_center_queue=self.get_status_queue()
+ )
+ process = self._get_job_runner_manager().get_runner_process(run_id)
+ if process is not None:
+ ClientConstants.save_run_process(run_id, process.pid)
+
+ def callback_delete_deployment(self, topic, payload):
+ logging.info("[Worker] callback_delete_deployment")
+
+ # Parse payload as the model message object.
+ model_msg_object = FedMLModelMsgObject(topic, payload)
+
+ # Delete all replicas on this device
+ try:
+ ClientConstants.remove_deployment(
+ model_msg_object.end_point_name, model_msg_object.model_name, model_msg_object.model_version,
+ model_msg_object.run_id, model_msg_object.model_id, edge_id=self.edge_id)
+ except Exception as e:
+ logging.info(f"Exception when removing deployment {traceback.format_exc()}")
+ pass
+
+ self._get_job_runner_manager().stop_job_runner(model_msg_object.run_id)
+
+ logging.info(f"[endpoint/device][{model_msg_object.run_id}/{self.edge_id}] "
+ f"Release gpu resource when the worker deployment deleted.")
+ JobRunnerUtils.get_instance().release_gpu_ids(model_msg_object.run_id, self.edge_id)
+
+ if self.running_request_json.get(str(model_msg_object.run_id)) is not None:
+ try:
+ self.running_request_json.pop(str(model_msg_object.run_id))
+ except Exception as e:
+ logging.error(f"Error when removing running_request_json: {traceback.format_exc()}")
+ pass
+
+ FedMLClientDataInterface.get_instance().delete_job_from_db(model_msg_object.run_id)
+ FedMLModelDatabase.get_instance().delete_deployment_result_with_device_id(
+ model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_name,
+ self.edge_id)
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
new file mode 100755
index 0000000000..61ffd20988
--- /dev/null
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -0,0 +1,460 @@
+import logging
+import os
+import platform
+import subprocess
+import time
+import traceback
+import uuid
+
+import requests
+
+import fedml
+from fedml.computing.scheduler.comm_utils import sys_utils, security_utils
+from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
+from fedml.computing.scheduler.comm_utils.sys_utils import get_sys_runner_info
+from fedml.computing.scheduler.scheduler_core.general_constants import GeneralConstants
+from fedml.core.common.singleton import Singleton
+from fedml.core.mlops import MLOpsConfigs
+
+
+class FedMLAccountManager(Singleton):
+ LOCAL_RUNNER_INFO_DIR_NAME = 'runner_infos'
+ STATUS_IDLE = "IDLE"
+ ROLE_EDGE_SERVER = "edge_server"
+ ROLE_CLOUD_AGENT = "cloud_agent"
+ ROLE_CLOUD_SERVER = "cloud_server"
+ ROLE_EDGE_DEVICE = "client"
+ ROLE_GPU_PROVIDER = "gpu_supplier"
+ ROLE_DEPLOY_MASTER_ON_PREM = "md.on_premise_device.master"
+ ROLE_DEPLOY_WORKER_ON_PREM = "md.on_premise_device"
+
+ DEVICE_ID_SUFFIX_EDGE_SERVER = ".Edge.Server"
+ DEVICE_ID_SUFFIX_CLOUD_AGENT = ".Public.Cloud"
+ DEVICE_ID_SUFFIX_CLOUD_SERVER = ".Public.Server"
+ DEVICE_ID_SUFFIX_EDGE_DEVICE = ".Edge.Device"
+ DEVICE_ID_SUFFIX_GPU_PROVIDER = ".Edge.GPU.Supplier"
+ DEVICE_ID_SUFFIX_DEPLOY = "MDA"
+ DEVICE_ID_SUFFIX_DEPLOY_MASTER_ON_PREM = ".OnPremise.Master.Device"
+ DEVICE_ID_SUFFIX_DEPLOY_WORKER_ON_PREM = ".OnPremise.Device"
+
+ DEVICE_ID_DOCKER_TAG = ".Docker"
+ DEVICE_ID_DOCKER_HUB_TAG = ".DockerHub"
+
+ def __init__(self):
+ if not hasattr(self, "agent_args"):
+ self.agent_args = None
+
+ @staticmethod
+ def get_instance():
+ return FedMLAccountManager()
+
+ def login(self, user_id, api_key="", device_id=None, os_name=None, role=None):
+ # Build the agent args
+ self.build_agent_args(
+ user_id, api_key=api_key, device_id=device_id, os_name=os_name, role=role
+ )
+
+ # Fetch configs from the MLOps config server.
+ service_config = dict()
+ log_server_url = None
+ config_try_count = 0
+ edge_id = 0
+ while config_try_count < 5:
+ # noinspection PyBroadException
+ try:
+ mqtt_config, s3_config, mlops_config, docker_config = FedMLAccountManager.fetch_configs()
+ service_config["mqtt_config"] = mqtt_config
+ service_config["s3_config"] = s3_config
+ service_config["ml_ops_config"] = mlops_config
+ service_config["docker_config"] = docker_config
+ log_server_url = mlops_config.get("LOG_SERVER_URL", None)
+ break
+ except Exception as e:
+ print("{}\n{}".format(SchedulerConstants.ERR_MSG_BINDING_EXCEPTION_1, traceback.format_exc()))
+ print(SchedulerConstants.ERR_MSG_BINDING_EXIT_RETRYING)
+ config_try_count += 1
+ time.sleep(3)
+ continue
+
+ # Failed to fetch the config after retrying many times.
+ if config_try_count >= 5:
+ print("")
+ print("[5] Oops, you failed to login the FedML MLOps platform.")
+ print("Please check whether your network is normal!")
+ return None
+
+ # Bind account id to FedML® Nexus AI Platform
+ register_try_count = 0
+ edge_id = -1
+ user_name = None
+ extra_url = None
+ general_edge_id = None
+ while register_try_count < 5:
+ # noinspection PyBroadException
+ try:
+ edge_id, user_name, extra_url, general_edge_id = FedMLAccountManager.bind_account_and_device_id(
+ service_config["ml_ops_config"]["EDGE_BINDING_URL"], self.agent_args.account_id,
+ self.agent_args.unique_device_id, self.agent_args.os_name,
+ api_key=api_key, role=role
+ )
+ if edge_id > 0:
+ break
+ except SystemExit as e:
+ print("Your account does not exist. Please make sure your account correct.")
+ os.system("fedml logout -s")
+ return
+ except Exception as e:
+ print("{}\n{}".format(SchedulerConstants.ERR_MSG_BINDING_EXCEPTION_2, traceback.format_exc()))
+ print(SchedulerConstants.ERR_MSG_BINDING_EXIT_RETRYING)
+ register_try_count += 1
+ time.sleep(3)
+ continue
+
+ # Failed to bind your account after retrying many times.
+ if edge_id <= 0:
+ print("")
+ print("[6] Oops, you failed to login the FedML MLOps platform.")
+ print("Please check whether your network is normal!")
+ return None
+
+ # Fill the bound result to agent args.
+ self.fill_argent_args(
+ log_server_url=log_server_url, server_id=edge_id,
+ edge_id=edge_id, general_edge_id=general_edge_id,
+ user_name=user_name, extra_url=extra_url,
+ agent_config=service_config)
+
+ return self.agent_args
+
+ def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None, role=None):
+ # Generate the suffix for device based on the role
+ device_id_suffix = None
+ is_master = False
+ is_deploy = False
+ if role == FedMLAccountManager.ROLE_EDGE_SERVER:
+ device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_EDGE_SERVER
+ is_master = True
+ elif role == FedMLAccountManager.ROLE_CLOUD_AGENT:
+ device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_CLOUD_AGENT
+ is_master = True
+ elif role == FedMLAccountManager.ROLE_CLOUD_SERVER:
+ device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_CLOUD_SERVER
+ is_master = True
+ elif role == FedMLAccountManager.ROLE_EDGE_DEVICE:
+ device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_EDGE_DEVICE
+ elif role == FedMLAccountManager.ROLE_GPU_PROVIDER:
+ device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_GPU_PROVIDER
+ elif role == FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM:
+ device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_DEPLOY_MASTER_ON_PREM
+ is_master = True
+ is_deploy = True
+ elif role == FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM:
+ device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_DEPLOY_WORKER_ON_PREM
+ is_deploy = True
+
+ # Build the agent args
+ version = fedml.get_env_version()
+ if self.agent_args is None:
+ self.agent_args = AgentArgs()
+ self.agent_args.role = role
+ self.agent_args.account_id = user_id
+ self.agent_args.api_key = api_key
+ self.agent_args.current_running_dir = GeneralConstants.get_deploy_fedml_home_dir(is_master=is_master) \
+ if is_deploy else GeneralConstants.get_launch_fedml_home_dir(is_master=is_master)
+ sys_name = platform.system()
+ if sys_name == "Darwin":
+ sys_name = "MacOS"
+ self.agent_args.os_name = sys_name if os_name is None or os_name == "" else os_name
+ self.agent_args.version = version
+ self.agent_args.log_file_dir = GeneralConstants.get_deploy_log_file_dir(is_master=is_master) \
+ if is_deploy else GeneralConstants.get_launch_log_file_dir(is_master=is_master)
+ is_from_docker = False
+ if device_id is not None and device_id != "0":
+ self.agent_args.current_device_id = device_id
+ else:
+ data_dir = GeneralConstants.get_deploy_data_dir(is_master=is_master) \
+ if is_deploy else GeneralConstants.get_launch_data_dir(is_master=is_master)
+ is_gpu_provider = True if role == FedMLAccountManager.ROLE_GPU_PROVIDER else False
+ self.agent_args.current_device_id = FedMLAccountManager.get_device_id(
+ data_dir=data_dir, use_machine_id=is_gpu_provider)
+ self.agent_args.device_id = self.agent_args.current_device_id
+ self.agent_args.config_version = version
+ self.agent_args.cloud_region = ""
+
+ # Check if it is running in the fedml docker hub
+ is_from_fedml_docker_hub = False
+ dock_loc_file = GeneralConstants.get_deploy_docker_location_file(is_master=is_master) \
+ if is_deploy else GeneralConstants.get_deploy_docker_location_file(is_master=is_master)
+ if os.path.exists(dock_loc_file):
+ is_from_fedml_docker_hub = True
+
+ # Build unique device id
+ docker_tag = FedMLAccountManager.DEVICE_ID_DOCKER_TAG if is_from_docker else ""
+ docker_tag = FedMLAccountManager.DEVICE_ID_DOCKER_HUB_TAG if is_from_fedml_docker_hub else docker_tag
+ unique_device_id = f"{self.agent_args.current_device_id}@{self.agent_args.os_name}" \
+ f"{docker_tag}{device_id_suffix}"
+
+ # Set the unique device id
+ self.agent_args.is_from_docker = is_from_docker or is_from_fedml_docker_hub
+ self.agent_args.unique_device_id = unique_device_id
+
+ def fill_argent_args(
+ self, log_server_url=None, server_id=None, edge_id=None,
+ user_name=None, extra_url=None, general_edge_id=None, agent_config=None):
+ self.agent_args.log_server_url = log_server_url
+ self.agent_args.server_id = server_id
+ self.agent_args.edge_id = edge_id
+ self.agent_args.user_name = user_name
+ self.agent_args.extra_url = extra_url
+ self.agent_args.general_edge_id = general_edge_id
+ self.agent_args.agent_config = agent_config
+
+ @staticmethod
+ def write_login_failed_file(is_client=True):
+ login_exit_file = os.path.join(
+ GeneralConstants.get_launch_log_file_dir(is_master=not is_client), "exited.log")
+ with open(login_exit_file, "w") as f:
+ f.writelines(f"{os.getpid()}.")
+
+ @staticmethod
+ def get_device_id(data_dir, use_machine_id=False):
+ device_file_path = os.path.join(data_dir, FedMLAccountManager.LOCAL_RUNNER_INFO_DIR_NAME)
+ file_for_device_id = os.path.join(device_file_path, "devices.id")
+ if not os.path.exists(device_file_path):
+ os.makedirs(device_file_path, exist_ok=True)
+ elif os.path.exists(file_for_device_id):
+ with open(file_for_device_id, 'r', encoding='utf-8') as f:
+ device_id_from_file = f.readline()
+ if device_id_from_file is not None and device_id_from_file != "":
+ return device_id_from_file
+
+ if platform.system() == "Darwin":
+ cmd_get_serial_num = "system_profiler SPHardwareDataType | grep Serial | awk '{gsub(/ /,\"\")}{print}' " \
+ "|awk -F':' '{print $2}' "
+ device_id = os.popen(cmd_get_serial_num).read()
+ device_id = device_id.replace('\n', '').replace(' ', '')
+ if device_id is None or device_id == "":
+ if not use_machine_id:
+ device_id = hex(uuid.getnode())
+ else:
+ device_id = FedMLAccountManager.get_gpu_machine_id()
+ else:
+ device_id = "0x" + device_id
+ else:
+ if "nt" in os.name:
+
+ def get_uuid():
+ guid = ""
+ try:
+ cmd = "wmic csproduct get uuid"
+ guid = str(subprocess.check_output(cmd))
+ pos1 = guid.find("\\n") + 2
+ guid = guid[pos1:-15]
+ except Exception as ex:
+ logging.error(f"Failed to get uuid with Exception {ex}. Traceback: {traceback.format_exc()}")
+ pass
+ return str(guid)
+
+ device_id = str(get_uuid())
+ logging.info(device_id)
+ elif "posix" in os.name:
+ device_id = sys_utils.get_device_id_in_docker()
+ if device_id is None:
+ if not use_machine_id:
+ device_id = hex(uuid.getnode())
+ else:
+ device_id = device_id = FedMLAccountManager.get_gpu_machine_id()
+ else:
+ device_id = sys_utils.run_subprocess_open(
+ "hal-get-property --udi /org/freedesktop/Hal/devices/computer --key system.hardware.uuid".split()
+ )
+ device_id = hex(device_id)
+
+ if device_id is not None and device_id != "":
+ with open(file_for_device_id, 'w', encoding='utf-8') as f:
+ f.write(device_id)
+ else:
+ device_id = hex(uuid.uuid4())
+ with open(file_for_device_id, 'w', encoding='utf-8') as f:
+ f.write(device_id)
+
+ return device_id
+
+ @staticmethod
+ def get_gpu_machine_id():
+ gpu_list = sys_utils.get_gpu_list()
+ gpu_uuids = ""
+ if len(gpu_list) > 0:
+ for gpu in gpu_list:
+ gpu_uuids += gpu.get("uuid", "")
+ else:
+ gpu_uuids = str(uuid.uuid4())
+ device_id_combination = \
+ f"{FedMLAccountManager.get_machine_id()}-{hex(uuid.getnode())}-{gpu_uuids}"
+ device_id = security_utils.get_content_hash(device_id_combination)
+ return device_id
+
+ @staticmethod
+ def get_machine_id():
+ try:
+ import machineid
+ return machineid.id().replace('\n', '').replace('\r\n', '').strip()
+ except Exception as e:
+ logging.error(f"Failed to get machine id with Exception {e}. Traceback: {traceback.format_exc()}")
+ return hex(uuid.getnode())
+
+ @staticmethod
+ def bind_account_and_device_id(
+ url, account_id, device_id, os_name, api_key="",
+ role=ROLE_EDGE_SERVER):
+ ip = requests.get('https://checkip.amazonaws.com').text.strip()
+ fedml_ver, exec_path, os_ver, cpu_info, python_ver, torch_ver, mpi_installed, \
+ cpu_usage, available_mem, total_mem, gpu_info, gpu_available_mem, gpu_total_mem, \
+ gpu_count, gpu_vendor, cpu_count, gpu_device_name = get_sys_runner_info()
+ host_name = sys_utils.get_host_name()
+ json_params = {
+ "accountid": account_id,
+ "deviceid": device_id,
+ "type": os_name,
+ "state": FedMLAccountManager.STATUS_IDLE,
+ "status": FedMLAccountManager.STATUS_IDLE,
+ "processor": cpu_info,
+ "core_type": cpu_info,
+ "network": "",
+ "role": role,
+ "os_ver": os_ver,
+ "memory": total_mem,
+ "ip": ip,
+ "api_key": api_key,
+ "extra_infos": {"fedml_ver": fedml_ver, "exec_path": exec_path, "os_ver": os_ver,
+ "cpu_info": cpu_info, "python_ver": python_ver, "torch_ver": torch_ver,
+ "mpi_installed": mpi_installed, "cpu_usage": cpu_usage,
+ "available_mem": available_mem, "total_mem": total_mem,
+ "cpu_count": cpu_count, "gpu_count": 0, "host_name": host_name}
+ }
+ if gpu_count > 0:
+ if gpu_total_mem is not None:
+ json_params["gpu"] = gpu_info if gpu_info is not None else "" + ", Total GPU Memory: " + gpu_total_mem
+ else:
+ json_params["gpu"] = gpu_info if gpu_info is not None else ""
+ json_params["extra_infos"]["gpu_info"] = gpu_info if gpu_info is not None else ""
+ if gpu_available_mem is not None:
+ json_params["extra_infos"]["gpu_available_mem"] = gpu_available_mem
+ if gpu_total_mem is not None:
+ json_params["extra_infos"]["gpu_total_mem"] = gpu_total_mem
+
+ json_params["extra_infos"]["gpu_count"] = gpu_count
+ json_params["extra_infos"]["gpu_vendor"] = gpu_vendor
+ json_params["extra_infos"]["gpu_device_name"] = gpu_device_name
+
+ gpu_available_id_list = sys_utils.get_available_gpu_id_list(limit=gpu_count)
+ gpu_available_count = len(gpu_available_id_list) if gpu_available_id_list is not None else 0
+ gpu_list = sys_utils.get_gpu_list()
+ json_params["extra_infos"]["gpu_available_count"] = gpu_available_count
+ json_params["extra_infos"]["gpu_available_id_list"] = gpu_available_id_list
+ json_params["extra_infos"]["gpu_list"] = gpu_list
+ else:
+ json_params["gpu"] = "None"
+ json_params["extra_infos"]["gpu_available_count"] = 0
+ json_params["extra_infos"]["gpu_available_id_list"] = []
+ json_params["extra_infos"]["gpu_list"] = []
+
+ _, cert_path = MLOpsConfigs.get_request_params()
+ if cert_path is not None:
+ try:
+ requests.session().verify = cert_path
+ response = requests.post(
+ url, json=json_params, verify=True,
+ headers={"content-type": "application/json", "Connection": "close"}
+ )
+ except requests.exceptions.SSLError as err:
+ logging.error(
+ f"Failed to bind account and device id with error: {err}, traceback: {traceback.format_exc()}")
+ MLOpsConfigs.install_root_ca_file()
+ response = requests.post(
+ url, json=json_params, verify=True,
+ headers={"content-type": "application/json", "Connection": "close"}
+ )
+ else:
+ response = requests.post(url, json=json_params, headers={"Connection": "close"})
+ edge_id, user_name, extra_url, general_edge_id = -1, None, None, None
+ if response.status_code != 200:
+ print(f"Binding to MLOps with response.status_code = {response.status_code}, "
+ f"response.content: {response.content}")
+ pass
+ else:
+ # print("url = {}, response = {}".format(url, response))
+ status_code = response.json().get("code")
+ if status_code == "SUCCESS":
+ edge_id = response.json().get("data").get("id")
+ user_name = response.json().get("data").get("userName", None)
+ extra_url = response.json().get("data").get("url", None)
+ general_edge_id = response.json().get("data").get("general_edge_id", None)
+ if edge_id is None or edge_id <= 0:
+ print(f"Binding to MLOps with response.status_code = {response.status_code}, "
+ f"response.content: {response.content}")
+ else:
+ if status_code == SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR:
+ raise SystemExit(SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR)
+ print(f"Binding to MLOps with response.status_code = {response.status_code}, "
+ f"response.content: {response.content}")
+ return -1, None, None, None
+ return edge_id, user_name, extra_url, general_edge_id
+
+ @staticmethod
+ def fetch_configs():
+ return MLOpsConfigs.fetch_all_configs()
+
+ @staticmethod
+ def _role_is_slave_agent(role):
+ return True if role == FedMLAccountManager.ROLE_EDGE_DEVICE or \
+ role == FedMLAccountManager.ROLE_GPU_PROVIDER else False
+
+
+class AgentArgs:
+ def __init__(self, role=None, account_id=None, api_key=None, server_id=None, current_running_dir=None,
+ os_name=None, version=None, log_file_dir=None, log_server_url=None, device_id=None,
+ current_device_id=None, config_version=None, cloud_region=None, is_from_docker=False,
+ edge_id=None, agent_config=None, user_name=None, extra_url=None, unique_device_id=None):
+ self.role = role
+ self.account_id = account_id
+ self.api_key = api_key
+ self.current_running_dir = current_running_dir
+ self.server_id = server_id
+ self.os_name = os_name
+ self.version = version
+ self.log_file_dir = log_file_dir
+ self.log_server_url = log_server_url
+ self.device_id = device_id
+ self.current_device_id = current_device_id
+ self.config_version = config_version
+ self.cloud_region = cloud_region
+ self.is_from_docker = is_from_docker
+ self.edge_id = edge_id
+ self.client_id = edge_id
+ self.agent_config = agent_config
+ self.user_name = user_name
+ self.extra_url = extra_url
+ self.unique_device_id = unique_device_id
+ self.client_id_list = None
+ self.using_mlops = True
+ self.server_agent_id = None
+ self.general_edge_id = None
+
+ def is_cloud_server(self):
+ return self.role == FedMLAccountManager.ROLE_CLOUD_SERVER
+
+ def is_cloud_agent(self):
+ return self.role == FedMLAccountManager.ROLE_CLOUD_AGENT
+
+ def is_edge_server(self):
+ return self.role == FedMLAccountManager.ROLE_EDGE_SERVER
+
+ def is_edge_device(self):
+ return self.role == FedMLAccountManager.ROLE_EDGE_DEVICE
+
+ def is_gpu_provider(self):
+ return self.role == FedMLAccountManager.ROLE_GPU_PROVIDER
+
+ def is_slave_agent(self):
+ return self.is_edge_device() or self.is_gpu_provider()
diff --git a/python/fedml/computing/scheduler/scheduler_core/compute_cache_manager.py b/python/fedml/computing/scheduler/scheduler_core/compute_cache_manager.py
index f918c785e2..6247cebe4f 100755
--- a/python/fedml/computing/scheduler/scheduler_core/compute_cache_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/compute_cache_manager.py
@@ -1,10 +1,11 @@
-import threading
+import threading
import redis
from .compute_gpu_cache import ComputeGpuCache
from .compute_logs_cache import ComputeLogsCache
from .business_models import LogsUploadModel, MetricsModel
from ..comm_utils.constants import SchedulerConstants
+from .compute_status_cache import ComputeStatusCache
class ComputeCacheManager(object):
@@ -23,6 +24,7 @@ def init(self):
self.redis_connection = None
self.gpu_cache = ComputeGpuCache(self.redis_connection)
self.logs_cache = ComputeLogsCache(self.redis_connection)
+ self.status_cache = ComputeStatusCache(self.redis_connection)
self.local_lock = threading.Lock()
def setup_redis_connection(self, redis_addr, redis_port, redis_password="fedml_default"):
@@ -48,6 +50,7 @@ def setup_redis_connection(self, redis_addr, redis_port, redis_password="fedml_d
self.redis_connection.set("FEDML_TEST_KEYS", "TEST")
self.gpu_cache.redis_connection = self.redis_connection
self.logs_cache.redis_connection = self.redis_connection
+ self.status_cache.redis_connection = self.redis_connection
is_connected = True
except Exception as e:
is_connected = False
@@ -69,6 +72,7 @@ def setup_public_redis_connection(self):
self.redis_connection.set("FEDML_TEST_KEYS", "TEST")
self.gpu_cache.redis_connection = self.redis_connection
self.logs_cache.redis_connection = self.redis_connection
+ self.status_cache.redis_connection = self.redis_connection
is_connected = True
except Exception as e:
pass
@@ -134,6 +138,9 @@ def get_artifact_logs(self):
def get_artifacts(self):
pass
+ def get_status_cache(self):
+ return self.status_cache
+
diff --git a/python/fedml/computing/scheduler/scheduler_core/compute_status_cache.py b/python/fedml/computing/scheduler/scheduler_core/compute_status_cache.py
new file mode 100755
index 0000000000..a1929abbef
--- /dev/null
+++ b/python/fedml/computing/scheduler/scheduler_core/compute_status_cache.py
@@ -0,0 +1,76 @@
+import logging
+import traceback
+from .compute_status_db import ComputeStatusDatabase
+from ..master.server_constants import ServerConstants
+
+
+class ComputeStatusCache(object):
+ FEDML_JOB_STATUS_TAG = "FEDML_JOB_STATUS_TAG-"
+ FEDML_DEVICE_STATUS_IN_JOB_TAG = "FEDML_DEVICE_STATUS_IN_JOB_TAG-"
+
+ def __init__(self, redis_connection):
+ self.redis_connection = redis_connection
+ ComputeStatusDatabase.get_instance().set_database_base_dir(ServerConstants.get_database_dir())
+ ComputeStatusDatabase.get_instance().create_table()
+
+ def save_job_status(self, run_id, status):
+ try:
+ self.redis_connection.set(self._get_job_status_key(run_id), status)
+ except Exception as e:
+ logging.error(f"Error setting job status: {e}, Traceback: {traceback.format_exc()}")
+ pass
+
+ ComputeStatusDatabase.get_instance().set_job_status(run_id, status)
+
+ def get_job_status(self, run_id):
+ status = None
+ try:
+ if self.redis_connection.exists(self._get_job_status_key(run_id)):
+ status = self.redis_connection.get(self._get_job_status_key(run_id))
+ except Exception as e:
+ logging.error(f"Error getting job status: {e}, Traceback: {traceback.format_exc()}")
+ pass
+
+ if status is None:
+ status = ComputeStatusDatabase.get_instance().get_job_status(run_id)
+ try:
+ if status is not None:
+ self.redis_connection.set(self._get_job_status_key(run_id), status)
+ except Exception as e:
+ pass
+
+ return status
+
+ def save_device_status_in_job(self, run_id, device_id, status):
+ try:
+ self.redis_connection.set(self._get_device_status_in_job_key(run_id, device_id), status)
+ except Exception as e:
+ logging.error(f"Error setting device status in job: {e}, Traceback: {traceback.format_exc()}")
+ pass
+
+ ComputeStatusDatabase.get_instance().set_device_status_in_job(run_id, device_id, status)
+
+ def get_device_status_in_job(self, run_id, device_id):
+ status = None
+ try:
+ if self.redis_connection.exists(self._get_device_status_in_job_key(run_id, device_id)):
+ status = self.redis_connection.get(self._get_device_status_in_job_key(run_id, device_id))
+ except Exception as e:
+ logging.error(f"Error getting device status in job: {e}, Traceback: {traceback.format_exc()}")
+ pass
+
+ if status is None:
+ status = ComputeStatusDatabase.get_instance().get_device_status_in_job(run_id, device_id)
+ try:
+ if status is not None:
+ self.redis_connection.set(self._get_device_status_in_job_key(run_id, device_id), status)
+ except Exception as e:
+ pass
+
+ return status
+
+ def _get_job_status_key(self, run_id):
+ return f"{ComputeStatusCache.FEDML_JOB_STATUS_TAG}{run_id}"
+
+ def _get_device_status_in_job_key(self, run_id, device_id):
+ return f"{ComputeStatusCache.FEDML_DEVICE_STATUS_IN_JOB_TAG}{run_id}-{device_id}"
diff --git a/python/fedml/computing/scheduler/scheduler_core/compute_status_db.py b/python/fedml/computing/scheduler/scheduler_core/compute_status_db.py
new file mode 100755
index 0000000000..14219eeb6a
--- /dev/null
+++ b/python/fedml/computing/scheduler/scheduler_core/compute_status_db.py
@@ -0,0 +1,123 @@
+import json
+import os
+import time
+
+from sqlalchemy import Column, String, TEXT, Integer, Float, create_engine, and_
+from sqlalchemy.orm import sessionmaker
+from sqlalchemy.ext.declarative import declarative_base
+from fedml.core.common.singleton import Singleton
+from .base_db import FedMLBaseDb
+from .compute_utils import ComputeUtils
+from ..master.server_constants import ServerConstants
+
+Base = declarative_base()
+
+
+class ComputeStatusDatabase(Singleton, FedMLBaseDb):
+ COMPUTE_STATUS_DB = "compute-status.db"
+
+ def __init__(self):
+ super().__init__()
+
+ @staticmethod
+ def get_instance():
+ return ComputeStatusDatabase()
+
+ def get_job_status(self, run_id):
+ self.open_job_db()
+ job = self.db_connection.query(FedMLJobStatus). \
+ filter(FedMLJobStatus.job_id == f'{run_id}').first()
+ if job is None:
+ return
+
+ return job.job_status
+
+ def get_device_status_in_job(self, device_id, run_id):
+ self.open_job_db()
+ device = self.db_connection.query(FedMLDeviceStatusInJob). \
+ filter(and_(FedMLDeviceStatusInJob.device_id == f'{device_id}',
+ FedMLDeviceStatusInJob.job_id == f'{run_id}')).first()
+
+ return device.device_status
+
+ def set_job_status(self, run_id, job_status):
+ self.open_job_db()
+ job = self.db_connection.query(FedMLJobStatus). \
+ filter(FedMLJobStatus.job_id == f'{run_id}').first()
+ if job is None:
+ job = FedMLJobStatus(job_id=run_id, job_status=job_status)
+ self.db_connection.add(job)
+ self.db_connection.commit()
+ return
+
+ if run_id is not None:
+ job.job_id = run_id
+ if job_status is not None:
+ job.job_status = job_status
+
+ self.db_connection.commit()
+
+ def set_device_status_in_job(self, run_id, device_id, status):
+ self.open_job_db()
+ device = self.db_connection.query(FedMLDeviceStatusInJob). \
+ filter(and_(FedMLDeviceStatusInJob.device_id == f'{device_id}',
+ FedMLDeviceStatusInJob.job_id == f'{run_id}')).first()
+ if device is None:
+ job = FedMLDeviceStatusInJob(job_id=run_id, device_id=device_id, device_status=status)
+ self.db_connection.add(job)
+ self.db_connection.commit()
+ return
+
+ if run_id is not None:
+ device.job_id = run_id
+ if device_id is not None:
+ device.device_id = device_id
+ if status is not None:
+ device.device_status = status
+
+ self.db_connection.commit()
+
+ def set_database_base_dir(self, database_base_dir):
+ self.db_base_dir = database_base_dir
+ self.init_db_path()
+
+ def init_db_path(self):
+ if self.db_base_dir is None:
+ if not os.path.exists(ServerConstants.get_database_dir()):
+ os.makedirs(ServerConstants.get_database_dir(), exist_ok=True)
+ self.db_base_dir = ServerConstants.get_database_dir()
+
+ self.db_path = os.path.join(self.db_base_dir, ComputeStatusDatabase.COMPUTE_STATUS_DB)
+
+ def create_table(self):
+ self.open_job_db()
+ try:
+ Base.metadata.create_all(self.db_engine, checkfirst=True)
+ except Exception as e:
+ pass
+
+ def drop_table(self):
+ self.open_job_db()
+ try:
+ Base.metadata.drop_all(self.db_engine, checkfirst=True)
+ except Exception as e:
+ pass
+
+
+class FedMLJobStatus(Base):
+ __tablename__ = 'job_status'
+
+ id = Column(Integer, primary_key=True)
+ job_id = Column(TEXT)
+ job_status = Column(TEXT)
+ timestamp = Column(Integer)
+
+
+class FedMLDeviceStatusInJob(Base):
+ __tablename__ = 'device_status_in_job'
+
+ id = Column(Integer, primary_key=True)
+ job_id = Column(TEXT)
+ device_id = Column(TEXT)
+ device_status = Column(TEXT)
+ timestamp = Column(Integer)
diff --git a/python/fedml/computing/scheduler/scheduler_core/endpoint_sync_protocol.py b/python/fedml/computing/scheduler/scheduler_core/endpoint_sync_protocol.py
index 545ba75650..91e0815645 100755
--- a/python/fedml/computing/scheduler/scheduler_core/endpoint_sync_protocol.py
+++ b/python/fedml/computing/scheduler/scheduler_core/endpoint_sync_protocol.py
@@ -4,7 +4,8 @@
from ..model_scheduler.device_model_cache import FedMLModelCache
from ..model_scheduler.device_model_db import FedMLModelDatabase
from ..model_scheduler.device_server_data_interface import FedMLServerDataInterface
-from .endpoint_monitor_protocol import EndpointDeviceDeploymentResultModel, EndpointDeviceDeploymentStatusModel, EndpointDeviceDeploymentInfoModel
+from .endpoint_monitor_protocol import EndpointDeviceDeploymentResultModel, \
+ EndpointDeviceDeploymentStatusModel, EndpointDeviceDeploymentInfoModel
from ..model_scheduler.device_server_constants import ServerConstants
from urllib.parse import urlparse
import logging
@@ -82,8 +83,8 @@ def callback_sync_device_result(self, topic, payload):
topic_splits = str(topic).split('/')
device_id = topic_splits[-1]
deployment_result = EndpointDeviceDeploymentResultModel(payload)
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_deployment_result(
+ FedMLModelCache.get_instance().set_redis_params()
+ FedMLModelCache.get_instance().set_deployment_result(
deployment_result.endpoint_id, deployment_result.endpoint_name, deployment_result.model_name,
deployment_result.model_version, device_id, payload)
@@ -97,8 +98,8 @@ def callback_sync_device_status(self, topic, payload):
topic_splits = str(topic).split('/')
device_id = topic_splits[-1]
deployment_status = EndpointDeviceDeploymentStatusModel(payload)
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_deployment_status(
+ FedMLModelCache.get_instance().set_redis_params()
+ FedMLModelCache.get_instance().set_deployment_status(
deployment_status.endpoint_id, deployment_status.endpoint_name, deployment_status.model_name,
deployment_status.model_version, device_id, deployment_status.model_status)
@@ -130,7 +131,7 @@ def callback_sync_device_info(self, topic, payload):
break
if status_item_found is not None:
- #print(f"status_item_found {status_item_found}, status_payload_found {status_payload_found}")
+ # print(f"status_item_found {status_item_found}, status_payload_found {status_payload_found}")
# Delete Status
FedMLModelCache.get_instance().delete_deployment_status(
status_item_found, deployment_info.endpoint_id, deployment_info.endpoint_name,
@@ -143,7 +144,8 @@ def callback_sync_device_info(self, topic, payload):
# Update Status
model_url_parsed = urlparse(status_payload_found.get("model_url", ""))
- status_payload_found["model_url"] = f"http://{model_url_parsed.hostname}:{deployment_info.inference_port}{model_url_parsed.path}"
+ status_payload_found["model_url"] = f"http://{model_url_parsed.hostname}:{deployment_info.inference_port}" \
+ f"{model_url_parsed.path}"
status_payload_found["inference_port"] = deployment_info.inference_port
FedMLModelCache.get_instance().set_deployment_status(
deployment_info.endpoint_id, deployment_info.endpoint_name, deployment_info.model_name,
@@ -163,7 +165,7 @@ def callback_sync_device_info(self, topic, payload):
break
if result_item_found is not None:
- #print(f"result_item_found {result_item_found}, result_payload_found {result_payload_found}")
+ # print(f"result_item_found {result_item_found}, result_payload_found {result_payload_found}")
FedMLModelCache.get_instance().delete_deployment_result(
result_item_found, deployment_info.endpoint_id, deployment_info.endpoint_name,
deployment_info.model_name)
@@ -174,7 +176,8 @@ def callback_sync_device_info(self, topic, payload):
result_payload_found["model_status"] = ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED
model_url_parsed = urlparse(result_payload_found.get("model_url", ""))
- result_payload_found["model_url"] = f"http://{model_url_parsed.hostname}:{deployment_info.inference_port}{model_url_parsed.path}"
+ result_payload_found["model_url"] = f"http://{model_url_parsed.hostname}:{deployment_info.inference_port}" \
+ f"{model_url_parsed.path}"
result_payload_found["inference_port"] = deployment_info.inference_port
FedMLModelCache.get_instance().set_deployment_result(
deployment_info.endpoint_id, deployment_info.endpoint_name, deployment_info.model_name,
@@ -183,12 +186,12 @@ def callback_sync_device_info(self, topic, payload):
def set_local_deployment_status_result(
self, endpoint_id, endpoint_name, model_name, model_version, device_id,
inference_port, status_payload, result_payload):
- '''
+ """
The result and status are saved in the local sqlite table.
They both belong to the table deployment_result_info;
deployment_result column is used to save the result;
deployment_status column is used to save the status.
- '''
+ """
if status_payload is not None:
model_url_parsed = urlparse(status_payload.get("model_url", ""))
status_payload["model_url"] = f"http://{model_url_parsed.hostname}:{inference_port}{model_url_parsed.path}"
diff --git a/python/fedml/computing/scheduler/scheduler_core/general_constants.py b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
new file mode 100755
index 0000000000..e642cacf1b
--- /dev/null
+++ b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
@@ -0,0 +1,193 @@
+import logging
+import os
+
+from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
+from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
+from fedml.computing.scheduler.slave.client_constants import ClientConstants
+from fedml.computing.scheduler.master.server_constants import ServerConstants
+from fedml.computing.scheduler.model_scheduler import device_client_constants
+from fedml.computing.scheduler.model_scheduler import device_server_constants
+
+
+class GeneralConstants:
+ MSG_TOPIC_REQUEST_JOB_STATUS_PREFIX = f"anywhere/master_agent/request_job_status/"
+ MSG_TOPIC_REPORT_DEVICE_STATUS_IN_JOB = f"slave_job/slave_agent/report_device_status_in_job"
+ MSG_TOPIC_SEND_TRAINING_REQUEST_TO_EDGES = "job_runner/master_protocol_manager/send_training_request_to_edges"
+
+ CLIENT_SHELL_BASH = SchedulerConstants.CLIENT_SHELL_BASH
+ CLIENT_SHELL_PS = SchedulerConstants.CLIENT_SHELL_PS
+ PLATFORM_WINDOWS = "Windows"
+
+ MSG_MLOPS_CLIENT_STATUS_OFFLINE = "OFFLINE"
+ MSG_MLOPS_CLIENT_STATUS_PROVISIONING = "PROVISIONING"
+ MSG_MLOPS_CLIENT_STATUS_IDLE = "IDLE"
+ MSG_MLOPS_CLIENT_STATUS_UPGRADING = "UPGRADING"
+ MSG_MLOPS_CLIENT_STATUS_QUEUED = "QUEUED"
+ MSG_MLOPS_CLIENT_STATUS_INITIALIZING = "INITIALIZING"
+ MSG_MLOPS_CLIENT_STATUS_TRAINING = "TRAINING"
+ MSG_MLOPS_CLIENT_STATUS_RUNNING = "RUNNING"
+ MSG_MLOPS_CLIENT_STATUS_STOPPING = "STOPPING"
+ MSG_MLOPS_CLIENT_STATUS_KILLED = "KILLED"
+ MSG_MLOPS_CLIENT_STATUS_FAILED = "FAILED"
+ MSG_MLOPS_CLIENT_STATUS_EXCEPTION = "EXCEPTION"
+ MSG_MLOPS_CLIENT_STATUS_FINISHED = "FINISHED"
+
+ MSG_MLOPS_SERVER_STATUS_OFFLINE = "OFFLINE"
+ MSG_MLOPS_SERVER_STATUS_PROVISIONING = "PROVISIONING"
+ MSG_MLOPS_SERVER_STATUS_IDLE = "IDLE"
+ MSG_MLOPS_SERVER_STATUS_UPGRADING = "UPGRADING"
+ MSG_MLOPS_SERVER_STATUS_STARTING = "STARTING"
+ MSG_MLOPS_SERVER_STATUS_RUNNING = "RUNNING"
+ MSG_MLOPS_SERVER_STATUS_STOPPING = "STOPPING"
+ MSG_MLOPS_SERVER_STATUS_KILLED = "KILLED"
+ MSG_MLOPS_SERVER_STATUS_FAILED = "FAILED"
+ MSG_MLOPS_SERVER_STATUS_FINISHED = "FINISHED"
+ MSG_MLOPS_SERVER_STATUS_EXCEPTION = "EXCEPTION"
+
+ MASTER_LOGIN_PROGRAM = "server_login.py"
+ SLAVE_LOGIN_PROGRAM = "client_login.py"
+
+ CONFIG_KEY_AUTO_DETECT_PUBLIC_IP = "auto_detect_public_ip"
+ FEDML_OTA_CMD_UPGRADE = "upgrade"
+ FEDML_OTA_CMD_RESTART = "restart"
+
+ @staticmethod
+ def get_package_unzip_dir(package_download_dir):
+ package_unzip_dir = package_download_dir
+ if not os.path.exists(package_unzip_dir):
+ os.makedirs(package_unzip_dir, exist_ok=True)
+ return package_unzip_dir
+
+ @staticmethod
+ def get_filename_and_extension(url):
+ return ClientConstants.get_filename_and_extension(url)
+
+ @staticmethod
+ def generate_yaml_doc(run_config_object, yaml_file):
+ ClientConstants.generate_yaml_doc(run_config_object, yaml_file)
+
+ @staticmethod
+ def execute_commands_with_live_logs(cmds, join='&&', should_write_log_file=True,
+ callback=None, error_processor=None):
+ return ClientConstants.execute_commands_with_live_logs(
+ cmds, join=join, should_write_log_file=should_write_log_file,
+ callback=callback, error_processor=error_processor
+ )
+
+ @staticmethod
+ def cleanup_run_process(run_id, is_master=False):
+ if is_master:
+ ServerConstants.cleanup_run_process(run_id)
+ else:
+ ClientConstants.cleanup_run_process(run_id)
+
+ @staticmethod
+ def cleanup_learning_process(run_id, data_dir=None):
+ RunProcessUtils.cleanup_run_process(
+ run_id, data_dir, ClientConstants.LOCAL_RUNNER_INFO_DIR_NAME,
+ info_file_prefix=SchedulerConstants.RUN_PROCESS_TYPE_USER_PROCESS)
+
+ @staticmethod
+ def cleanup_bootstrap_process(run_id, data_dir=None):
+ RunProcessUtils.cleanup_run_process(
+ run_id, data_dir, ClientConstants.LOCAL_RUNNER_INFO_DIR_NAME,
+ info_file_prefix=SchedulerConstants.RUN_PROCESS_TYPE_BOOTSTRAP_PROCESS)
+
+ @staticmethod
+ def save_learning_process(run_id, learning_id, data_dir=None):
+ RunProcessUtils.save_run_process(
+ run_id, learning_id, data_dir, ClientConstants.LOCAL_RUNNER_INFO_DIR_NAME,
+ info_file_prefix=SchedulerConstants.RUN_PROCESS_TYPE_USER_PROCESS)
+
+ @staticmethod
+ def save_bootstrap_process(run_id, process_id, data_dir=None):
+ RunProcessUtils.save_run_process(
+ run_id, process_id, data_dir, ClientConstants.LOCAL_RUNNER_INFO_DIR_NAME,
+ info_file_prefix=SchedulerConstants.RUN_PROCESS_TYPE_BOOTSTRAP_PROCESS)
+
+ @staticmethod
+ def save_run_process(run_id, process_id, is_master=False):
+ RunProcessUtils.save_run_process(
+ run_id, process_id, ServerConstants.get_data_dir() if is_master else ClientConstants.get_data_dir(),
+ ClientConstants.LOCAL_RUNNER_INFO_DIR_NAME)
+
+ @staticmethod
+ def get_learning_process_list(run_id, is_master=False):
+ return RunProcessUtils.get_run_process_list(
+ run_id, ServerConstants.get_data_dir() if is_master else ClientConstants.get_data_dir(),
+ ClientConstants.LOCAL_RUNNER_INFO_DIR_NAME,
+ info_file_prefix=SchedulerConstants.RUN_PROCESS_TYPE_USER_PROCESS)
+
+ @staticmethod
+ def get_launch_fedml_home_dir(is_master=False):
+ return ServerConstants.get_fedml_home_dir() if is_master else ClientConstants.get_fedml_home_dir()
+
+ @staticmethod
+ def get_deploy_fedml_home_dir(is_master=False):
+ return device_server_constants.ServerConstants.get_fedml_home_dir() if is_master \
+ else device_client_constants.ClientConstants.get_fedml_home_dir()
+
+ @staticmethod
+ def get_launch_log_file_dir(is_master=False):
+ return ServerConstants.get_log_file_dir() if is_master else ClientConstants.get_log_file_dir()
+
+ @staticmethod
+ def get_deploy_log_file_dir(is_master=False):
+ return device_server_constants.ServerConstants.get_log_file_dir() if is_master \
+ else device_client_constants.ClientConstants.get_log_file_dir()
+
+ @staticmethod
+ def get_launch_data_dir(is_master=False):
+ return ServerConstants.get_data_dir() if is_master else ClientConstants.get_data_dir()
+
+ @staticmethod
+ def get_deploy_data_dir(is_master=False):
+ return device_server_constants.ServerConstants.get_data_dir() if is_master \
+ else device_client_constants.ClientConstants.get_data_dir()
+
+ @staticmethod
+ def get_deploy_docker_location_file(is_master=False):
+ return device_server_constants.ServerConstants.get_docker_location_file() if is_master \
+ else device_client_constants.ClientConstants.get_docker_location_file()
+
+ @staticmethod
+ def get_launch_docker_location_file(is_master=False):
+ return ServerConstants.get_docker_location_file() if is_master \
+ else ClientConstants.get_docker_location_file()
+
+ @staticmethod
+ def get_local_ip():
+ import socket
+ s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+ conn = s.connect(('8.8.8.8', 53))
+ ip = s.getsockname()[0]
+ s.close()
+ return ip
+
+ @staticmethod
+ def get_public_ip():
+ import requests
+ ip = None
+ try:
+ ip = requests.get('https://checkip.amazonaws.com').text.strip()
+ except Exception as e:
+ logging.info("Failed to get public ip: {}".format(e))
+ return ip
+
+ @staticmethod
+ def get_ip_address(request_json, infer_host=None):
+ # OPTION 1: Use local ip
+ ip = GeneralConstants.get_local_ip()
+
+ # OPTION 2: Auto detect public ip
+ if "parameters" in request_json and \
+ GeneralConstants.CONFIG_KEY_AUTO_DETECT_PUBLIC_IP in request_json["parameters"] and \
+ request_json["parameters"][GeneralConstants.CONFIG_KEY_AUTO_DETECT_PUBLIC_IP]:
+ ip = GeneralConstants.get_public_ip()
+ logging.info("Auto detect public ip for master: " + ip)
+
+ # OPTION 3: Use user indicated ip
+ if infer_host is not None and infer_host != "127.0.0.1" and infer_host != "localhost":
+ ip = infer_host
+
+ return ip
diff --git a/python/fedml/computing/scheduler/scheduler_core/master_api_daemon.py b/python/fedml/computing/scheduler/scheduler_core/master_api_daemon.py
index 5cebf757d6..5876a787ce 100755
--- a/python/fedml/computing/scheduler/scheduler_core/master_api_daemon.py
+++ b/python/fedml/computing/scheduler/scheduler_core/master_api_daemon.py
@@ -1,7 +1,8 @@
from fastapi import FastAPI, Request
-from .log_manager import LogsManager
-from .metrics_manager import MetricsManager
-from ..comm_utils import sys_utils
+from fedml.computing.scheduler.scheduler_core.log_manager import LogsManager
+from fedml.computing.scheduler.scheduler_core.metrics_manager import MetricsManager
+from fedml.computing.scheduler.comm_utils import sys_utils
+from fedml.computing.scheduler.scheduler_core.compute_cache_manager import ComputeCacheManager
import os
@@ -52,6 +53,19 @@ async def update_log(request: Request):
async def ready():
return {"status": "Success"}
+ @api.get("/get_job_status")
+ async def get_job_status(job_id):
+ ComputeCacheManager.get_instance().set_redis_params()
+ job_status = ComputeCacheManager.get_instance().get_status_cache().get_job_status(job_id)
+ return {"job_status": job_status}
+
+ @api.get("/get_device_status_in_job")
+ async def get_device_status_in_job(job_id, device_id):
+ ComputeCacheManager.get_instance().set_redis_params()
+ device_status_in_job = ComputeCacheManager.get_instance().get_status_cache().get_device_status_in_job(
+ job_id, device_id)
+ return {"device_status_in_job": device_status_in_job}
+
import uvicorn
port = 30800
if sys_utils.check_port("localhost", port):
@@ -59,7 +73,6 @@ async def ready():
cur_dir = os.path.dirname(__file__)
fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- uvicorn.run(api, host="0.0.0.0", port=port, reload=True, reload_delay=3, reload_dirs=fedml_base_dir)
-
+ uvicorn.run(api, host="0.0.0.0", port=port)
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_center.py b/python/fedml/computing/scheduler/scheduler_core/message_center.py
index c7af555a99..7ae1e4c0b5 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_center.py
@@ -1,3 +1,4 @@
+import json
import logging
import os
import threading
@@ -7,20 +8,28 @@
import multiprocessing
from multiprocessing import Process, Queue
import queue
+from os.path import expanduser
from fedml.core.distributed.communication.mqtt.mqtt_manager import MqttManager
from ....core.mlops.mlops_metrics import MLOpsMetrics
from operator import methodcaller
+from .message_common import FedMLMessageEntity, FedMLMessageRecord
-class FedMLMessageCenter:
+class FedMLMessageCenter(object):
FUNC_SETUP_MESSAGE_CENTER = "setup_message_center"
FUNC_REBUILD_MESSAGE_CENTER = "rebuild_message_center"
-
- def __init__(self, agent_config=None, message_queue=None, listener_message_queue=None):
+ ENABLE_SAVE_MESSAGE_TO_FILE = True
+ PUBLISH_MESSAGE_RETRY_TIMEOUT = 60 * 1000.0
+ PUBLISH_MESSAGE_RETRY_COUNT = 3
+ MESSAGE_SENT_RECORDS_FILE = "message-sent-records.log"
+ MESSAGE_SENT_SUCCESS_RECORDS_FILE = "message-sent-success-records.log"
+ MESSAGE_RECEIVED_RECORDS_FILE = "message-received-records.log"
+
+ def __init__(self, agent_config=None, sender_message_queue=None, listener_message_queue=None):
self.sender_agent_config = agent_config
self.listener_agent_config = agent_config
- self.message_queue = message_queue
+ self.sender_message_queue = sender_message_queue
self.message_event = None
self.message_center_process = None
self.sender_mqtt_mgr = None
@@ -32,9 +41,15 @@ def __init__(self, agent_config=None, message_queue=None, listener_message_queue
self.listener_payloads = dict()
self.listener_handler_funcs = dict()
self.listener_handler_object = None
- self.listener_message_queue = None
+ self.listener_message_queue = listener_message_queue
self.listener_message_event = None
self.listener_message_center_process = None
+ self.sender_message_list = list()
+ self.receiver_message_list = list()
+ self.published_message_ids = list()
+ self.retry_sending_count_map = dict()
+ self.constants = FedMLMessageCenterConstants()
+ self.message_center_name = None
def __repr__(self):
return "<{klass} @{id:x} {attrs}>".format(
@@ -64,6 +79,10 @@ def on_sender_mqtt_connected(self, mqtt_client_object):
self.sender_mqtt_is_connected = True
self.sender_mqtt_lock.release()
+ def on_sender_mqtt_published(self, mqtt_client_object, obj, mid):
+ self.published_message_ids.append({"message_id": mid, "timestamp": time.time_ns()/100.0/1000.0})
+ self.save_published_message_record(mid)
+
def setup_sender_mqtt_mgr(self):
if self.sender_mqtt_mgr is not None:
return
@@ -82,6 +101,7 @@ def setup_sender_mqtt_mgr(self):
self.sender_mqtt_mgr.add_connected_listener(self.on_sender_mqtt_connected)
self.sender_mqtt_mgr.add_disconnected_listener(self.on_sender_mqtt_disconnected)
+ self.sender_mqtt_mgr.add_published_listener(self.on_sender_mqtt_published)
self.sender_mqtt_mgr.connect()
self.sender_mqtt_mgr.loop_start()
@@ -90,6 +110,7 @@ def setup_sender_mqtt_mgr(self):
self.sender_mlops_metrics.set_messenger(self)
def release_sender_mqtt_mgr(self):
+ # noinspection PyBroadException
try:
if self.sender_mqtt_mgr is not None:
self.sender_mqtt_mgr.loop_stop()
@@ -105,17 +126,19 @@ def release_sender_mqtt_mgr(self):
f"Failed to release sender mqtt manager with Exception {e}. Traceback: {traceback.format_exc()}")
pass
- def get_message_queue(self):
- return self.message_queue
+ def get_sender_message_queue(self):
+ return self.sender_message_queue
- def start_sender(self):
- self.message_queue = Queue()
+ def start_sender(self, message_center_name=None):
+ self.sender_message_queue = Queue()
self.message_event = multiprocessing.Event()
self.message_event.clear()
- message_center = FedMLMessageCenter(agent_config=self.sender_agent_config, message_queue=self.message_queue)
+ message_center = FedMLMessageCenter(agent_config=self.sender_agent_config,
+ sender_message_queue=self.sender_message_queue)
self.message_center_process = Process(
target=message_center.run_sender, args=(
- self.message_event, self.message_queue,
+ self.message_event, self.sender_message_queue,
+ message_center_name
)
)
self.message_center_process.start()
@@ -130,43 +153,96 @@ def stop(self):
def check_message_stop_event(self):
if self.message_event is not None and self.message_event.is_set():
logging.info("Received message center stopping event.")
- raise Exception("Message center stopped (for sender)")
+ raise MessageCenterStoppedException("Message center stopped (for sender)")
def send_message(self, topic, payload, run_id=None):
message_entity = FedMLMessageEntity(topic=topic, payload=payload, run_id=run_id)
- self.message_queue.put(message_entity.get_message_body())
+ self.sender_message_queue.put(message_entity.get_message_body())
def send_message_json(self, topic, payload):
self.send_message(topic, payload)
- def run_sender(self, message_event, message_queue):
+ def retry_sending_undelivered_message(self):
+ for sender_message in self.sender_message_list:
+ # Check if the message is published.
+ message_record = FedMLMessageRecord(json_record=sender_message)
+ is_published = False
+ for published_message in self.published_message_ids:
+ published_message_record = FedMLMessageRecord(json_record=published_message)
+ if published_message_record.message_id == message_record.message_id:
+ is_published = True
+ break
+ if is_published:
+ continue
+
+ # Retry to send the unpublished message based on the timeout value
+ timeout_ms = time.time() * 1000.0 - message_record.timestamp
+ if timeout_ms >= FedMLMessageCenter.PUBLISH_MESSAGE_RETRY_TIMEOUT and \
+ self.retry_sending_count_map.get(message_record.message_id, 0) < \
+ FedMLMessageCenter.PUBLISH_MESSAGE_RETRY_COUNT:
+ # Send the message
+ message_entity = FedMLMessageEntity(message_body=message_record.message_body)
+ message_id = self.sender_mqtt_mgr.send_message_json(message_entity.topic, message_entity.payload)
+ self.retry_sending_count_map[message_record.message_id] += 1
+
+ # Generate the new message record.
+ sent_message_record = FedMLMessageRecord(message_id=message_id,
+ message_body=message_record.message_body)
+
+ # Save the message
+ self.save_message_record(message_entity.run_id, message_entity.device_id, sent_message_record)
+
+ def run_sender(self, message_event, message_queue, message_center_name):
self.message_event = message_event
- self.message_queue = message_queue
+ self.sender_message_queue = message_queue
+ self.message_center_name = message_center_name
self.setup_sender_mqtt_mgr()
- time.sleep(5)
while True:
+ message_entity = None
try:
self.check_message_stop_event()
except MessageCenterStoppedException as e:
break
+ # noinspection PyBroadException
try:
+ # Setup the mqtt connection
self.setup_sender_mqtt_mgr()
+ # Get the message from the queue
try:
- message_body = self.message_queue.get(block=False, timeout=0.1)
+ message_body = message_queue.get(block=False, timeout=0.1)
except queue.Empty as e: # If queue is empty, then break loop
message_body = None
if message_body is None:
time.sleep(0.1)
+ self.retry_sending_undelivered_message()
continue
+ # Generate the message entity object
message_entity = FedMLMessageEntity(message_body=message_body)
- self.sender_mqtt_mgr.send_message_json(message_entity.topic, message_entity.payload)
+
+ # Send the message to mqtt server
+ message_id = self.sender_mqtt_mgr.send_message_json(message_entity.topic, message_entity.payload)
+
+ # Generate the message record.
+ message_record = FedMLMessageRecord(message_id=message_id, message_body=message_body)
+
+ # Cache the message
+ self.cache_message_record(message_record, is_sender=True)
+
+ # Save the message
+ self.save_message_record(message_entity.run_id, message_entity.device_id, message_record)
+
except Exception as e:
- logging.info(
- f"Failed to send the message with topic {message_entity.topic}, payload {message_entity.payload}, {traceback.format_exc()}")
+ if message_entity is not None:
+ logging.info(
+ f"Failed to send the message with topic {message_entity.topic}, "
+ f"payload {message_entity.payload}, {traceback.format_exc()}"
+ )
+ else:
+ logging.info(f"Failed to send the message: {traceback.format_exc()}")
self.release_sender_mqtt_mgr()
@@ -194,7 +270,9 @@ def release_listener_mqtt_mgr(self):
self.listener_mqtt_mgr = None
except Exception as e:
logging.error(
- f"Failed to release listener mqtt manager with Exception {e}. Traceback: {traceback.format_exc()}")
+ f"Failed to release listener mqtt manager with Exception {e}. "
+ f"Traceback: {traceback.format_exc()}"
+ )
pass
def add_message_listener(self, topic, listener_func):
@@ -207,10 +285,13 @@ def remove_message_listener(self, topic):
self.listener_topics.remove(topic)
self.listener_handler_funcs.pop(topic)
- def get_runner(self):
+ def get_message_runner(self):
return None
- def start_listener(self, sender_message_queue=None, agent_config=None):
+ def get_listener_message_queue(self):
+ return self.listener_message_queue
+
+ def start_listener(self, sender_message_queue=None, agent_config=None, message_center_name=None):
if self.listener_message_center_process is not None:
return
@@ -218,12 +299,13 @@ def start_listener(self, sender_message_queue=None, agent_config=None):
self.listener_message_event = multiprocessing.Event()
self.listener_message_event.clear()
self.listener_agent_config = agent_config
- message_runner = self.get_runner()
+ message_runner = self.get_message_runner()
message_runner.listener_agent_config = agent_config
self.listener_message_center_process = Process(
target=message_runner.run_listener_dispatcher, args=(
self.listener_message_event, self.listener_message_queue,
- self.listener_handler_funcs, sender_message_queue
+ self.listener_handler_funcs, sender_message_queue,
+ message_center_name
)
)
self.listener_message_center_process.start()
@@ -231,11 +313,17 @@ def start_listener(self, sender_message_queue=None, agent_config=None):
def check_listener_message_stop_event(self):
if self.listener_message_event is not None and self.listener_message_event.is_set():
logging.info("Received listener message center stopping event.")
- raise Exception("Message center stopped (for listener)")
+ raise MessageCenterStoppedException("Message center stopped (for listener)")
def listener_message_dispatch_center(self, topic, payload):
self.receive_message_json(topic, payload)
+ def listener_message_passthrough_dispatch_center(self, message):
+ payload_obj = json.loads(message.payload)
+ payload_obj["is_retain"] = message.retain
+ payload = json.dumps(payload_obj)
+ self.receive_message_json(message.topic, payload)
+
def receive_message(self, topic, payload, run_id=None):
message_entity = FedMLMessageEntity(topic=topic, payload=payload, run_id=run_id)
self.listener_message_queue.put(message_entity.get_message_body())
@@ -252,10 +340,13 @@ def unsubscribe_msg(self, topic):
self.listener_mqtt_mgr.unsubscribe_msg(topic)
def run_listener_dispatcher(
- self, message_event, message_queue, listener_funcs, sender_message_queue):
+ self, message_event, message_queue, listener_funcs, sender_message_queue,
+ message_center_name
+ ):
self.listener_message_event = message_event
self.listener_message_queue = message_queue
self.listener_handler_funcs = listener_funcs
+ self.message_center_name = message_center_name
self.setup_listener_mqtt_mgr()
@@ -265,51 +356,109 @@ def run_listener_dispatcher(
methodcaller(FedMLMessageCenter.FUNC_REBUILD_MESSAGE_CENTER, sender_message_queue)(self)
while True:
+ message_entity = None
try:
self.check_listener_message_stop_event()
except MessageCenterStoppedException as e:
break
+ # noinspection PyBroadException
try:
+ # Setup the mqtt connection
self.setup_listener_mqtt_mgr()
+ # Get the message from the queue
try:
- message_body = self.listener_message_queue.get(block=False, timeout=0.1)
+ message_body = message_queue.get(block=False, timeout=0.1)
except queue.Empty as e: # If queue is empty, then break loop
message_body = None
if message_body is None:
time.sleep(0.1)
continue
+ # Generate the message entity object
message_entity = FedMLMessageEntity(message_body=message_body)
+ # Generate the message record
+ message_record = FedMLMessageRecord(message_id=str(uuid.uuid4()), message_body=message_body)
+
+ # Cache the message
+ self.cache_message_record(message_record, is_sender=False)
+
+ # Save the message
+ self.save_message_record(message_entity.run_id, message_entity.device_id,
+ message_record, is_sender=False)
+
+ # Dispatch the message to corresponding handler
message_handler_func_name = self.listener_handler_funcs.get(message_entity.topic, None)
if message_handler_func_name is not None:
methodcaller(message_handler_func_name, message_entity.topic, message_entity.payload)(self)
except Exception as e:
- logging.info(
- f"Failed to dispatch messages with topic {message_entity.topic}, payload {message_entity.payload}, {traceback.format_exc()}")
-
+ if message_entity is not None:
+ logging.info(
+ f"Failed to dispatch messages with topic {message_entity.topic}, "
+ f"payload {message_entity.payload}, {traceback.format_exc()}")
+ else:
+ logging.info(f"Failed to dispatch messages: {traceback.format_exc()}")
self.release_listener_mqtt_mgr()
-class FedMLMessageEntity(object):
- def __init__(self, topic=None, payload=None, run_id=None, message_body: dict = None):
- self.topic = topic
- self.payload = payload
- self.run_id = run_id
- if message_body is not None:
- self.from_message_body(message_body=message_body)
+ def cache_message_record(self, message_record, is_sender=True):
+ # Save the message to the cached list.
+ if is_sender:
+ self.sender_message_list.append(message_record.get_json_record())
+ else:
+ self.receiver_message_list.append(message_record.get_json_record())
+
+ def save_message_record(self, run_id, device_id, message_record, is_sender=True):
+ # Check if we enable to log messages to file
+ if not FedMLMessageCenter.ENABLE_SAVE_MESSAGE_TO_FILE:
+ return
+
+ # Log messages to file
+ if is_sender:
+ print(f"save sent message record: {message_record.get_json_record()}")
+ else:
+ print(f"save received message record: {message_record.get_json_record()}")
+ saved_message_file = os.path.join(
+ self.constants.message_log_dir,
+ self.message_center_name,
+ FedMLMessageCenter.MESSAGE_SENT_RECORDS_FILE if is_sender else
+ FedMLMessageCenter.MESSAGE_RECEIVED_RECORDS_FILE
+ )
+ os.makedirs(os.path.dirname(saved_message_file), exist_ok=True)
+ with open(saved_message_file, "a+") as f:
+ f.writelines([json.dumps(message_record.get_json_record()) + "\n"])
+
+ def save_published_message_record(self, message_id):
+ # Check if we enable to log messages to file
+ if not FedMLMessageCenter.ENABLE_SAVE_MESSAGE_TO_FILE:
+ return
- def from_message_body(self, message_body: dict = None):
- self.topic = message_body.get("topic", None)
- self.payload = message_body.get("payload", None)
- self.run_id = message_body.get("run_id", None)
+ # Log published message ids to file
+ message_record = {"message_id": message_id, "timestamp": time.time_ns()/1000.0/1000.0}
+ published_msg_record_file = os.path.join(
+ self.constants.message_log_dir, self.message_center_name,
+ FedMLMessageCenter.MESSAGE_SENT_SUCCESS_RECORDS_FILE)
+ os.makedirs(os.path.dirname(published_msg_record_file), exist_ok=True)
+ print(f"save sent success message record: {message_record}")
+ with open(published_msg_record_file, "a+") as f:
+ f.writelines([json.dumps(message_record) + "\n"])
- def get_message_body(self):
- message_body = {"topic": self.topic, "payload": self.payload, "run_id": self.run_id}
- return message_body
+ @staticmethod
+ def rebuild_message_center_from_queue(sender_message_queue, listener_message_queue=None):
+ message_center = FedMLMessageCenter(sender_message_queue=sender_message_queue,
+ listener_message_queue=listener_message_queue)
+ return message_center
class MessageCenterStoppedException(Exception):
""" Message center stopped. """
pass
+
+
+class FedMLMessageCenterConstants:
+ def __init__(self):
+ self.home_dir = expanduser("~")
+ self.message_center_dir = os.path.join(self.home_dir, ".fedml", "global_services", "message_center")
+ self.message_log_dir = os.path.join(self.message_center_dir, "logs")
+ os.makedirs(self.message_log_dir, exist_ok=True)
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_common.py b/python/fedml/computing/scheduler/scheduler_core/message_common.py
new file mode 100755
index 0000000000..24449af3b5
--- /dev/null
+++ b/python/fedml/computing/scheduler/scheduler_core/message_common.py
@@ -0,0 +1,77 @@
+import json
+import time
+
+
+class FedMLMessageEntity(object):
+ def __init__(self, topic=None, payload=None, run_id=None, device_id=None, message_body: dict = None):
+ self.topic = topic
+ self.payload = payload
+ self.run_id = run_id
+ self.device_id = device_id
+ if message_body is not None:
+ self.from_message_body(message_body=message_body)
+
+ def from_message_body(self, message_body: dict = None):
+ self.topic = message_body.get("topic", None)
+ self.payload = message_body.get("payload", None)
+ if self.payload is not None:
+ payload_json = json.loads(self.payload)
+ self.run_id = payload_json.get("run_id", None)
+ self.run_id = payload_json.get("runId", None) if self.run_id is None else self.run_id
+ self.device_id = payload_json.get("edge_id", None)
+ self.device_id = payload_json.get("ID", None) if self.device_id is None else self.device_id
+
+ def get_message_body(self):
+ message_body = {"topic": self.topic, "payload": self.payload, "run_id": self.run_id}
+ return message_body
+
+
+class FedMLMessageRecord(object):
+ def __init__(self, message_id=None, message_body=None, json_record=None):
+ self.message_id = message_id
+ self.message_body = message_body
+ self.timestamp = time.time_ns() / 1000.0 / 1000.0
+ if json_record is not None:
+ self.from_message_record(json_record=json_record)
+
+ def get_json_record(self):
+ return {"message_id": self.message_id, "message_body": self.message_body, "timestamp": self.timestamp}
+
+ def from_message_record(self, json_record: dict = None):
+ self.message_id = json_record.get("message_id", None)
+ self.message_body = json_record.get("message_body", None)
+ self.timestamp = json_record.get("timestamp", None)
+
+
+class FedMLStatusEntity(object):
+ def __init__(self, topic=None, payload=None, status_msg_body: dict = None):
+ self.topic = topic
+ self.payload = payload
+ self.run_id = None
+ self.edge_id = None
+ self.status = None
+ if status_msg_body is not None:
+ self.from_message_body(status_msg_body=status_msg_body)
+
+ def from_message_body(self, status_msg_body: dict = None):
+ self.topic = status_msg_body.get("topic", None)
+ self.payload = status_msg_body.get("payload", None)
+ if self.payload is not None:
+ payload_json = json.loads(self.payload)
+ self.run_id = payload_json.get("run_id", None)
+ self.run_id = payload_json.get("runId", None) if self.run_id is None else self.run_id
+ self.edge_id = payload_json.get("edge_id", None)
+ self.status = payload_json.get("status", None)
+
+ def get_message_body(self):
+ status_msg_body = {"topic": self.topic, "payload": self.payload, "run_id": self.run_id}
+ return status_msg_body
+
+
+class LogArgs:
+ def __init__(self, role=None, edge_id=None, server_id=None, log_server_url=None, log_file_dir=None):
+ self.role = role
+ self.edge_id = edge_id
+ self.server_id = server_id
+ self.log_server_url = log_server_url
+ self.log_file_dir = log_file_dir
diff --git a/python/fedml/computing/scheduler/scheduler_core/ota_upgrade.py b/python/fedml/computing/scheduler/scheduler_core/ota_upgrade.py
new file mode 100755
index 0000000000..e32f1df806
--- /dev/null
+++ b/python/fedml/computing/scheduler/scheduler_core/ota_upgrade.py
@@ -0,0 +1,99 @@
+import logging
+import os
+import time
+import traceback
+import fedml
+from fedml.computing.scheduler.comm_utils import sys_utils
+from .general_constants import GeneralConstants
+
+
+class FedMLOtaUpgrade:
+ LOCAL_RUNNER_INFO_DIR_NAME = 'runner_infos'
+ STATUS_IDLE = "IDLE"
+
+ def __init__(self, edge_id=None):
+ self.edge_id = edge_id
+ self.version = fedml.get_env_version()
+
+ def ota_upgrade(self, payload, request_json, status_reporter=None,
+ is_master=False, is_deploy=False):
+ run_id = request_json["runId"]
+ force_ota = False
+ ota_version = None
+
+ try:
+ run_config = request_json.get("run_config", None)
+ parameters = run_config.get("parameters", None)
+ common_args = parameters.get("common_args", None)
+ force_ota = common_args.get("force_ota", False) if common_args is not None else False
+ ota_version = common_args.get("ota_version", None) if common_args is not None else None
+ except Exception as e:
+ logging.error(
+ f"Failed to get ota upgrade parameters with Exception {e}. Traceback: {traceback.format_exc()}")
+ pass
+
+ if force_ota and ota_version is not None:
+ should_upgrade = True if ota_version != fedml.__version__ else False
+ upgrade_version = ota_version
+ else:
+ try:
+ fedml_is_latest_version, local_ver, remote_ver = sys_utils.check_fedml_is_latest_version(self.version)
+ except Exception as e:
+ logging.error(f"Failed to check fedml version with Exception {e}. Traceback: {traceback.format_exc()}")
+ return
+
+ should_upgrade = False if fedml_is_latest_version else True
+ upgrade_version = remote_ver
+
+ if should_upgrade:
+ FedMLOtaUpgrade._save_upgrading_job(
+ run_id, self.edge_id, payload, is_master=is_master, is_deploy=is_deploy
+ )
+ if status_reporter is not None:
+ if is_master:
+ status_reporter.report_server_id_status(
+ run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.edge_id)
+ else:
+ status_reporter.report_client_id_status(
+ self.edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING, run_id=run_id)
+
+ logging.info(f"Upgrade to version {upgrade_version} ...")
+
+ sys_utils.do_upgrade(self.version, upgrade_version)
+ raise Exception("Restarting after upgraded...")
+
+ @staticmethod
+ def process_ota_upgrade_msg():
+ os.system("pip install -U fedml")
+
+ @staticmethod
+ def _save_upgrading_job(run_id, edge_id, payload, is_master=False, is_deploy=False):
+ if is_master and is_deploy:
+ from ..model_scheduler.device_server_data_interface import FedMLServerDataInterface
+ FedMLServerDataInterface.get_instance(). \
+ save_started_job(run_id, edge_id, time.time(),
+ GeneralConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
+ GeneralConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
+ payload)
+ elif is_master and not is_deploy:
+ from ..master.server_data_interface import FedMLServerDataInterface
+ FedMLServerDataInterface.get_instance(). \
+ save_started_job(run_id, edge_id, time.time(),
+ GeneralConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
+ GeneralConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
+ payload)
+ elif not is_master and is_deploy:
+ from ..model_scheduler.device_client_data_interface import FedMLClientDataInterface
+ FedMLClientDataInterface.get_instance(). \
+ save_started_job(run_id, edge_id, time.time(),
+ GeneralConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
+ GeneralConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
+ payload)
+ elif not is_master and not is_deploy:
+ from ..slave.client_data_interface import FedMLClientDataInterface
+ FedMLClientDataInterface.get_instance(). \
+ save_started_job(run_id, edge_id, time.time(),
+ GeneralConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
+ GeneralConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
+ payload)
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
new file mode 100755
index 0000000000..e2e090596d
--- /dev/null
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -0,0 +1,545 @@
+import json
+import logging
+import os
+import platform
+import shutil
+import traceback
+import urllib
+import zipfile
+from urllib.parse import urljoin, urlparse
+from ..comm_utils.constants import SchedulerConstants
+from ..comm_utils.job_utils import JobRunnerUtils, DockerArgs
+from ..scheduler_entry.constants import Constants
+from ....core.mlops import MLOpsMetrics
+from ....core.mlops.mlops_device_perfs import MLOpsDevicePerfStats
+from ..comm_utils.yaml_utils import load_yaml_config
+from .general_constants import GeneralConstants
+from ..comm_utils.sys_utils import get_python_program
+from ..comm_utils import sys_utils
+from ....core.mlops.mlops_utils import MLOpsUtils
+from ..scheduler_core.message_center import FedMLMessageCenter
+from ..scheduler_core.status_center import FedMLStatusCenter
+from abc import ABC, abstractmethod
+
+
+class RunnerError(Exception):
+ """ Runner stopped. """
+ pass
+
+
+class RunnerCompletedError(Exception):
+ """ Runner completed. """
+ pass
+
+
+class FedMLSchedulerBaseJobRunner(ABC):
+
+ def __init__(self, args, edge_id=0, request_json=None, agent_config=None, run_id=0,
+ cuda_visible_gpu_ids_str=None, is_master_runner=False,
+ agent_data_dir=None, agent_package_download_dir=None,
+ agent_package_unzip_dir=None, agent_log_file_dir=None):
+ self.args = args
+ self.is_master_runner = is_master_runner
+ self.agent_data_dir = agent_data_dir
+ self.agent_package_download_dir = agent_package_download_dir
+ self.agent_package_unzip_dir = agent_package_unzip_dir
+ self.agent_log_file_dir = agent_log_file_dir
+ self.prev_download_progress = 0
+ self.run_process_event = None
+ self.run_process_completed_event = None
+ self.run_process = None
+ self.running_request_json = dict()
+ self.start_request_json = None
+ self.edge_id = edge_id
+ self.edge_user_name = None
+ self.edge_extra_url = None
+ self.run_id = run_id
+ self.unique_device_id = args.unique_device_id
+ self.request_json = request_json
+ self.version = args.version
+ self.device_id = args.device_id
+ self.cur_dir = os.path.split(os.path.realpath(__file__))[0]
+ self.agent_config = agent_config
+ self.mlops_metrics = None
+ self.status_reporter = None
+ self.ntp_offset = MLOpsUtils.get_ntp_offset()
+ self.server_id = None
+ self.fedml_config_object = None
+ self.package_type = SchedulerConstants.JOB_PACKAGE_TYPE_DEFAULT
+ self.cuda_visible_gpu_ids_str = cuda_visible_gpu_ids_str
+ self.user_name = None
+ self.general_edge_id = None
+ self.message_center = None
+ self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES = {
+ "${FEDSYS.RUN_ID}": "",
+ "${FEDSYS.PRIVATE_LOCAL_DATA}": "",
+ "${FEDSYS.CLIENT_ID_LIST}": "",
+ "${FEDSYS.SYNTHETIC_DATA_URL}": "",
+ "${FEDSYS.IS_USING_LOCAL_DATA}": "",
+ "${FEDSYS.CLIENT_NUM}": "",
+ "${FEDSYS.CLIENT_INDEX}": "",
+ "${FEDSYS.CLIENT_OBJECT_LIST}": "",
+ "${FEDSYS.LOG_SERVER_URL}": "",
+ }
+
+ def __repr__(self):
+ return "<{klass} @{id:x} {attrs}>".format(
+ klass=self.__class__.__name__,
+ id=id(self) & 0xFFFFFF,
+ attrs=" ".join("{}={!r}".format(k, v) for k, v in self.__dict__.items()),
+ )
+
+ def build_dynamic_constrain_variables(self, run_id, run_config):
+ data_config = run_config.get("data_config", {})
+ server_edge_id_list = self.request_json["edgeids"]
+ local_edge_id_list = list()
+ local_edge_id_list.append(int(self.edge_id))
+ is_using_local_data = 0
+ private_data_dir = data_config.get("privateLocalData", "")
+ synthetic_data_url = data_config.get("syntheticDataUrl", "")
+ edges = self.request_json["edges"]
+ # if private_data_dir is not None \
+ # and len(str(private_data_dir).strip(' ')) > 0:
+ # is_using_local_data = 1
+ if private_data_dir is None or len(str(private_data_dir).strip(" ")) <= 0:
+ params_config = run_config.get("parameters", None)
+ private_data_dir = self.agent_data_dir
+ if synthetic_data_url is None or len(str(synthetic_data_url)) <= 0:
+ synthetic_data_url = private_data_dir
+
+ self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.RUN_ID}"] = run_id
+ self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.PRIVATE_LOCAL_DATA}"] = private_data_dir.replace(" ", "")
+ self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_ID_LIST}"] = str(local_edge_id_list).replace(" ", "")
+ self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.SYNTHETIC_DATA_URL}"] = synthetic_data_url.replace(" ", "")
+ self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.IS_USING_LOCAL_DATA}"] = str(is_using_local_data)
+ self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_NUM}"] = len(server_edge_id_list)
+ if not self.is_master_runner:
+ self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_INDEX}"] = 1
+ for cur_index, id_value in enumerate(server_edge_id_list):
+ if str(id_value) == str(self.edge_id):
+ self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_INDEX}"] = cur_index + 1
+ break
+ client_objects = str(json.dumps(edges))
+ client_objects = client_objects.replace(" ", "").replace("\n", "").replace('"', '\\"')
+ self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_OBJECT_LIST}"] = client_objects
+ self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.LOG_SERVER_URL}"] = self.agent_config["ml_ops_config"][
+ "LOG_SERVER_URL"
+ ]
+
+ @staticmethod
+ def unzip_file(zip_file, unzip_file_path) -> str:
+ if zipfile.is_zipfile(zip_file):
+ with zipfile.ZipFile(zip_file, "r") as zipf:
+ zipf.extractall(unzip_file_path)
+ unzipped_file_name = zipf.namelist()[0]
+ else:
+ raise Exception("Invalid zip file {}".format(zip_file))
+
+ return unzipped_file_name
+
+ def package_download_progress(self, count, blksize, filesize):
+ self.check_runner_stop_event()
+
+ downloaded = count * blksize
+ downloaded = filesize if downloaded > filesize else downloaded
+ progress = (downloaded / filesize * 100) if filesize != 0 else 0
+ progress_int = int(progress)
+ downloaded_kb = format(downloaded / 1024, '.2f')
+
+ # since this hook funtion is stateless, we need a state to avoid print progress repeatly
+ if count == 0:
+ self.prev_download_progress = 0
+ if progress_int != self.prev_download_progress and progress_int % 5 == 0:
+ self.prev_download_progress = progress_int
+ logging.info("package downloaded size {} KB, progress {}%".format(downloaded_kb, progress_int))
+
+ def retrieve_and_unzip_package(self, package_name, package_url):
+ local_package_path = self.agent_package_download_dir
+ os.makedirs(local_package_path, exist_ok=True)
+ filename, filename_without_extension, file_extension = GeneralConstants.get_filename_and_extension(package_url)
+ local_package_file = os.path.join(local_package_path, f"fedml_run_{self.run_id}_{filename_without_extension}")
+ if os.path.exists(local_package_file):
+ os.remove(local_package_file)
+ package_url_without_query_path = urljoin(package_url, urlparse(package_url).path)
+ urllib.request.urlretrieve(package_url_without_query_path, local_package_file,
+ reporthook=self.package_download_progress)
+ unzip_package_path = os.path.join(self.agent_package_unzip_dir,
+ f"unzip_fedml_run_{self.run_id}_{filename_without_extension}")
+ try:
+ shutil.rmtree(unzip_package_path, ignore_errors=True)
+ except Exception as e:
+ logging.error(
+ f"Failed to remove directory {unzip_package_path}, Exception: {e}, Traceback: {traceback.format_exc()}")
+ pass
+
+ # Using unzipped folder name
+ package_dir_name = FedMLSchedulerBaseJobRunner.unzip_file(local_package_file, unzip_package_path)
+ unzip_package_full_path = os.path.join(unzip_package_path, package_dir_name)
+
+ logging.info("local_package_file {}, unzip_package_path {}, unzip file full path {}".format(
+ local_package_file, unzip_package_path, unzip_package_full_path))
+
+ return unzip_package_full_path
+
+ @abstractmethod
+ def get_download_package_info(self, packages_config=None):
+ download_package_name = packages_config.get("server", None) if self.is_master_runner \
+ else packages_config["linuxClient"]
+ download_package_url = packages_config.get("serverUrl", None) if self.is_master_runner \
+ else packages_config["linuxClientUrl"]
+ return download_package_name, download_package_url
+
+ def update_local_fedml_config(self, run_id, run_config):
+ # Download the package
+ packages_config = run_config["packages_config"]
+ download_package_name, download_package_url = self.get_download_package_info(packages_config)
+ unzip_package_path = self.retrieve_and_unzip_package(download_package_name, download_package_url)
+ fedml_local_config_file = os.path.join(unzip_package_path, "conf", "fedml.yaml")
+
+ # Load the config file to memory
+ config_from_container = load_yaml_config(fedml_local_config_file)
+ container_entry_file_config = config_from_container["entry_config"]
+ container_dynamic_args_config = config_from_container["dynamic_args"]
+ entry_file = container_entry_file_config["entry_file"]
+ conf_file = container_entry_file_config["conf_file"]
+ self.package_type = container_entry_file_config.get("package_type", SchedulerConstants.JOB_PACKAGE_TYPE_DEFAULT)
+ full_conf_path = os.path.join(unzip_package_path, "fedml", "config", os.path.basename(conf_file))
+
+ # Dynamically build constrain variable with realtime parameters from server
+ self.build_dynamic_constrain_variables(run_id, run_config)
+
+ # Update entry arguments value with constrain variable values with realtime parameters from server
+ # currently we support the following constrain variables:
+ # ${FEDSYS_RUN_ID}: a run id represented one entire Federated Learning flow
+ # ${FEDSYS_PRIVATE_LOCAL_DATA}: private local data path in the Federated Learning client
+ # ${FEDSYS_CLIENT_ID_LIST}: client list in one entire Federated Learning flow
+ # ${FEDSYS_SYNTHETIC_DATA_URL}: synthetic data url from server,
+ # if this value is not null, the client will download data from this URL to use it as
+ # federated training data set
+ # ${FEDSYS_IS_USING_LOCAL_DATA}: whether we use private local data as federated training data set
+ # container_dynamic_args_config["data_cache_dir"] = "${FEDSYS.PRIVATE_LOCAL_DATA}"
+ for constrain_variable_key, constrain_variable_value in self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES.items():
+ for argument_key, argument_value in container_dynamic_args_config.items():
+ if argument_value is not None and str(argument_value).find(constrain_variable_key) == 0:
+ replaced_argument_value = str(argument_value).replace(
+ constrain_variable_key, str(constrain_variable_value)
+ )
+ container_dynamic_args_config[argument_key] = replaced_argument_value
+
+ # Merge all container new config sections as new config dictionary
+ package_conf_object = dict()
+ package_conf_object["entry_config"] = container_entry_file_config
+ package_conf_object["dynamic_args"] = container_dynamic_args_config
+ package_conf_object["dynamic_args"]["config_version"] = self.args.config_version
+ container_dynamic_args_config["mqtt_config_path"] = os.path.join(
+ unzip_package_path, "fedml", "config", os.path.basename(container_dynamic_args_config["mqtt_config_path"])
+ )
+ container_dynamic_args_config["s3_config_path"] = os.path.join(
+ unzip_package_path, "fedml", "config", os.path.basename(container_dynamic_args_config["s3_config_path"])
+ )
+ log_file_dir = self.agent_log_file_dir
+ os.makedirs(log_file_dir, exist_ok=True)
+ package_conf_object["dynamic_args"]["log_file_dir"] = log_file_dir
+
+ # Save new config dictionary to local file
+ fedml_updated_config_file = os.path.join(unzip_package_path, "conf", "fedml.yaml")
+ GeneralConstants.generate_yaml_doc(package_conf_object, fedml_updated_config_file)
+
+ # Build dynamic arguments and set arguments to fedml config object
+ self.build_dynamic_args(run_id, run_config, package_conf_object, unzip_package_path)
+
+ return unzip_package_path, package_conf_object
+
+ def build_dynamic_args(self, run_id, run_config, package_conf_object, base_dir):
+ fedml_conf_file = package_conf_object["entry_config"]["conf_file"]
+ fedml_conf_file_processed = str(fedml_conf_file).replace('\\', os.sep).replace('/', os.sep)
+ fedml_conf_path = os.path.join(base_dir, "fedml", "config",
+ os.path.basename(fedml_conf_file_processed))
+ fedml_conf_object = load_yaml_config(fedml_conf_path)
+ run_params = run_config.get("parameters", {})
+ job_yaml = run_params.get("job_yaml", {})
+
+ # Replace local fedml config objects with parameters from MLOps web
+ parameters_object = run_config.get("parameters", None)
+ if parameters_object is not None:
+ for config_k, config_v in fedml_conf_object.items():
+ parameter_v = parameters_object.get(config_k, None)
+ if parameter_v is not None:
+ fedml_conf_object[config_k] = parameter_v
+ parameters_object.pop(config_k)
+
+ for config_k, config_v in parameters_object.items():
+ fedml_conf_object[config_k] = config_v
+
+ package_dynamic_args = package_conf_object["dynamic_args"]
+ if fedml_conf_object.get("comm_args", None) is not None:
+ fedml_conf_object["comm_args"]["mqtt_config_path"] = package_dynamic_args["mqtt_config_path"]
+ fedml_conf_object["comm_args"]["s3_config_path"] = package_dynamic_args["s3_config_path"]
+ fedml_conf_object["common_args"]["using_mlops"] = True
+ if fedml_conf_object.get("train_args", None) is not None:
+ fedml_conf_object["train_args"]["run_id"] = package_dynamic_args["run_id"]
+ fedml_conf_object["train_args"]["client_id_list"] = package_dynamic_args["client_id_list"]
+ fedml_conf_object["train_args"]["client_num_in_total"] = int(package_dynamic_args["client_num_in_total"])
+ fedml_conf_object["train_args"]["client_num_per_round"] = int(package_dynamic_args["client_num_in_total"])
+ fedml_conf_object["train_args"]["client_id"] = self.edge_id
+ fedml_conf_object["train_args"]["server_id"] = self.request_json.get("server_id", "0")
+ if fedml_conf_object.get("device_args", None) is not None:
+ fedml_conf_object["device_args"]["worker_num"] = int(package_dynamic_args["client_num_in_total"])
+ # fedml_conf_object["data_args"]["data_cache_dir"] = package_dynamic_args["data_cache_dir"]
+ data_args = fedml_conf_object.get("data_args")
+ if data_args is not None:
+ data_cache_dir = fedml_conf_object["data_args"].get("data_cache_dir")
+ if data_cache_dir is not None:
+ data_cache_dir = os.path.join(data_cache_dir, str(self.edge_id))
+ fedml_conf_object["data_args"]["data_cache_dir"] = data_cache_dir
+ if fedml_conf_object.get("tracking_args", None) is not None:
+ fedml_conf_object["tracking_args"]["log_file_dir"] = package_dynamic_args["log_file_dir"]
+ fedml_conf_object["tracking_args"]["log_server_url"] = package_dynamic_args["log_server_url"]
+
+ fedml_conf_object["dynamic_args"] = package_dynamic_args
+ self.fedml_config_object = fedml_conf_object.copy()
+ GeneralConstants.generate_yaml_doc(fedml_conf_object, fedml_conf_path)
+
+ def callback_run_bootstrap(self, job_pid):
+ GeneralConstants.save_bootstrap_process(self.run_id, job_pid, data_dir=self.agent_data_dir)
+
+ def run_bootstrap_script(self, bootstrap_cmd_list, bootstrap_script_file):
+ try:
+ logging.info("Bootstrap commands are being executed...")
+ process, error_list = GeneralConstants.execute_commands_with_live_logs(
+ bootstrap_cmd_list, callback=self.callback_run_bootstrap)
+
+ ret_code, out, err = process.returncode, None, None
+ if ret_code is None or ret_code <= 0:
+ if error_list is not None and len(error_list) > 0:
+ is_bootstrap_run_ok = False
+ else:
+ if out is not None:
+ out_str = sys_utils.decode_our_err_result(out)
+ if out_str != "":
+ logging.info("{}".format(out_str))
+
+ sys_utils.log_return_info(bootstrap_script_file, 0)
+
+ is_bootstrap_run_ok = True
+ else:
+ if err is not None:
+ err_str = sys_utils.decode_our_err_result(err)
+ if err_str != "":
+ logging.error("{}".format(err_str))
+
+ sys_utils.log_return_info(bootstrap_script_file, ret_code)
+
+ is_bootstrap_run_ok = False
+ except Exception as e:
+ logging.error(f"Bootstrap script error: Exception: {e}, Traceback: {traceback.format_exc()}")
+ is_bootstrap_run_ok = False
+ return is_bootstrap_run_ok
+
+ def check_runner_stop_event(self):
+ if self.run_process_event.is_set():
+ logging.info("Received stopping event.")
+ raise RunnerError("Runner stopped")
+
+ if self.run_process_completed_event.is_set():
+ logging.info("Received completed event.")
+ raise RunnerCompletedError("Runner completed")
+
+ def trigger_stop_event(self):
+ if self.run_process_event is not None:
+ self.run_process_event.set()
+
+ def trigger_completed_event(self):
+ if self.run_process_completed_event is not None:
+ self.run_process_completed_event.set()
+
+ def execute_job_task(self, unzip_package_path, entry_file_full_path, conf_file_full_path, dynamic_args_config,
+ fedml_config_object):
+ run_config = self.request_json["run_config"]
+ run_params = run_config.get("parameters", {})
+ client_rank = self.request_json.get("client_rank", 1)
+ job_yaml = run_params.get("job_yaml", {})
+ job_yaml_default_none = run_params.get("job_yaml", None)
+ job_api_key = job_yaml.get("run_api_key", None)
+ job_api_key = job_yaml.get("fedml_run_dynamic_params", None) if job_api_key is None else job_api_key
+ assigned_gpu_ids = run_params.get("gpu_ids", None)
+ job_type = job_yaml.get("job_type", None)
+ containerize = fedml_config_object.get("containerize", None)
+ image_pull_policy = fedml_config_object.get("image_pull_policy", Constants.IMAGE_PULL_POLICY_ALWAYS)
+ job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
+ conf_file_object = load_yaml_config(conf_file_full_path)
+ entry_args_dict = conf_file_object.get("fedml_entry_args", {})
+ entry_args = entry_args_dict.get("arg_items", None)
+ scheduler_match_info = self.request_json.get("scheduler_match_info", {})
+ if job_type == Constants.JOB_TASK_TYPE_TRAIN:
+ containerize = True if containerize is None else containerize
+
+ # Bootstrap Info
+ bootstrap_script_path, bootstrap_script_dir, bootstrap_script_file = [None] * 3
+ env_args = fedml_config_object.get("environment_args", None)
+
+ if env_args is not None:
+ bootstrap_script_file = env_args.get("bootstrap", None)
+ if bootstrap_script_file is not None:
+ bootstrap_script_file = str(bootstrap_script_file).replace('\\', os.sep).replace('/', os.sep)
+ if platform.system() == 'Windows':
+ bootstrap_script_file = bootstrap_script_file.rstrip('.sh') + '.bat'
+ if bootstrap_script_file is not None:
+ bootstrap_script_dir = os.path.join(unzip_package_path, "fedml",
+ os.path.dirname(bootstrap_script_file))
+ bootstrap_script_path = os.path.join(
+ bootstrap_script_dir, bootstrap_script_dir, os.path.basename(bootstrap_script_file)
+ )
+
+ bootstrap_cmd_list = list()
+ if bootstrap_script_path:
+ logging.info("Bootstrap commands are being generated...")
+ bootstrap_cmd_list = JobRunnerUtils.generate_bootstrap_commands(bootstrap_script_path=bootstrap_script_path,
+ bootstrap_script_dir=bootstrap_script_dir,
+ bootstrap_script_file=bootstrap_script_file)
+ logging.info(f"Generated following Bootstrap commands: {bootstrap_cmd_list}")
+
+ if not containerize:
+ if len(bootstrap_cmd_list) and not (job_type == Constants.JOB_TASK_TYPE_DEPLOY or
+ job_type == Constants.JOB_TASK_TYPE_SERVE):
+ bootstrapping_successful = self.run_bootstrap_script(bootstrap_cmd_list=bootstrap_cmd_list,
+ bootstrap_script_file=bootstrap_script_file)
+
+ if not bootstrapping_successful:
+ logging.info("failed to update local fedml config.")
+ self.check_runner_stop_event()
+ # Send failed msg when exceptions.
+ raise Exception(f"Failed to execute following bootstrap commands: {bootstrap_cmd_list}")
+
+ logging.info("cleanup the previous learning process and bootstrap process...")
+ GeneralConstants.cleanup_learning_process(self.request_json["runId"], data_dir=self.agent_data_dir)
+ GeneralConstants.cleanup_bootstrap_process(self.request_json["runId"], data_dir=self.agent_data_dir)
+
+ executable_interpreter = GeneralConstants.CLIENT_SHELL_PS \
+ if platform.system() == GeneralConstants.PLATFORM_WINDOWS else GeneralConstants.CLIENT_SHELL_BASH
+
+ if job_yaml_default_none is None:
+ # Generate the job executing commands for previous federated learning (Compatibility)
+ python_program = get_python_program()
+ logging.info("Run the client: {} {} --cf {} --rank {} --role client".format(
+ python_program, entry_file_full_path, conf_file_full_path, str(dynamic_args_config.get("rank", 1))))
+ rank = str(dynamic_args_config.get("rank", 1))
+ entry_command = f"{python_program} {entry_file_full_path} --cf " \
+ f"{conf_file_full_path} --rank {rank} --role client"
+ shell_cmd_list = [entry_command]
+
+ # Run the job executing commands for previous federated learning (Compatibility)
+ process, error_list = GeneralConstants.execute_commands_with_live_logs(
+ shell_cmd_list, callback=self.callback_start_fl_job, should_write_log_file=False)
+ is_launch_task = False
+ else:
+ self.check_runner_stop_event()
+
+ if self.is_master_runner:
+ self.status_reporter.report_server_id_status(
+ self.run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_RUNNING, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.edge_id)
+ else:
+ self.status_reporter.report_client_id_status(
+ self.edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_RUNNING, run_id=self.run_id)
+
+ # Generate the job executing commands
+ job_executing_commands = JobRunnerUtils.generate_job_execute_commands(
+ self.run_id, self.edge_id, self.version,
+ self.package_type, executable_interpreter, entry_file_full_path,
+ conf_file_object, entry_args, assigned_gpu_ids,
+ job_api_key, client_rank, scheduler_match_info=scheduler_match_info,
+ cuda_visible_gpu_ids_str=self.cuda_visible_gpu_ids_str)
+
+ if containerize is not None and containerize is True:
+ docker_args = fedml_config_object.get("docker", {})
+ docker_args = JobRunnerUtils.create_instance_from_dict(DockerArgs, docker_args)
+ try:
+ job_executing_commands = JobRunnerUtils.generate_launch_docker_command(
+ docker_args=docker_args, run_id=self.run_id, edge_id=self.edge_id,
+ unzip_package_path=unzip_package_path, executable_interpreter=executable_interpreter,
+ entry_file_full_path=entry_file_full_path, bootstrap_cmd_list=bootstrap_cmd_list,
+ cuda_visible_gpu_ids_str=self.cuda_visible_gpu_ids_str, image_pull_policy=image_pull_policy)
+ except Exception as e:
+ logging.error(f"Error occurred while generating containerized launch commands. "
+ f"Exception: {e}, Traceback: {traceback.format_exc()}")
+ return None, None, None
+
+ if not job_executing_commands:
+ raise Exception("Failed to generate docker execution command")
+
+ # Run the job executing commands
+ logging.info(f"Run the client job with job id {self.run_id}, device id {self.edge_id}.")
+ process, error_list = GeneralConstants.execute_commands_with_live_logs(
+ job_executing_commands, callback=self.start_job_perf, error_processor=self.job_error_processor,
+ should_write_log_file=False if job_type == Constants.JOB_TASK_TYPE_FEDERATE else True)
+ is_launch_task = False if job_type == Constants.JOB_TASK_TYPE_FEDERATE else True
+
+ return process, is_launch_task, error_list
+
+ def callback_start_fl_job(self, job_pid):
+ GeneralConstants.save_learning_process(self.run_id, job_pid, data_dir=self.agent_data_dir)
+ self.mlops_metrics.report_sys_perf(
+ self.args, self.agent_config["mqtt_config"], job_process_id=job_pid)
+
+ def start_job_perf(self, job_pid):
+ GeneralConstants.save_learning_process(self.run_id, job_pid, data_dir=self.agent_data_dir)
+ self.mlops_metrics.report_job_perf(self.args, self.agent_config["mqtt_config"], job_pid)
+
+ def job_error_processor(self, error_list):
+ self.check_runner_stop_event()
+
+ error_str = "\n".join(error_list)
+ error_message = f"Error occurred when running the job... {error_str}"
+ logging.error(error_message)
+ raise Exception(error_message)
+
+ def start_runner_process(
+ self, run_id, edge_id, request_json, cuda_visible_gpu_ids_str=None,
+ sender_message_queue=None, status_center_queue=None
+ ):
+ return None
+
+ @staticmethod
+ def cleanup_containers_and_release_gpus(run_id, edge_id):
+ job_type = JobRunnerUtils.get_job_type_from_run_id(run_id)
+
+ if not job_type:
+ logging.info(f"Failed to get job type from run id {run_id}. This is not an error as it would usually "
+ f"happen when the job is not found in the database because job is already finished and "
+ f"cleaned up. Exiting cleanup_containers_and_release_gpus.")
+ return
+
+ # Check if the job type is not "serve" or "deploy"
+ if not (job_type == SchedulerConstants.JOB_TASK_TYPE_SERVE or
+ job_type == SchedulerConstants.JOB_TASK_TYPE_DEPLOY):
+
+ # Terminate the run docker container if exists
+ container_name = JobRunnerUtils.get_run_container_name(run_id)
+ docker_client = JobRunnerUtils.get_docker_client(DockerArgs())
+ logging.info(f"Terminating the run docker container {container_name} if exists...")
+ try:
+ JobRunnerUtils.remove_run_container_if_exists(container_name, docker_client)
+ except Exception as e:
+ logging.error(f"Exception {e} occurred when terminating docker container. "
+ f"Traceback: {traceback.format_exc()}")
+
+ # Release the GPU ids and update the GPU availability in the persistent store
+ JobRunnerUtils.get_instance().release_gpu_ids(run_id, edge_id)
+
+ # Send mqtt message reporting the new gpu availability to the backend
+ MLOpsDevicePerfStats.report_gpu_device_info(edge_id)
+
+ def rebuild_message_status_center(self, sender_message_queue, listener_message_queue, status_queue):
+ self.message_center = FedMLMessageCenter.rebuild_message_center_from_queue(
+ sender_message_queue, listener_message_queue=listener_message_queue)
+ if self.mlops_metrics is None:
+ self.mlops_metrics = MLOpsMetrics()
+ self.mlops_metrics.set_messenger(self.message_center)
+ self.mlops_metrics.run_id = self.run_id
+
+ status_center = FedMLStatusCenter.rebuild_status_center_from_queue(status_queue)
+ if self.status_reporter is None:
+ self.status_reporter = MLOpsMetrics()
+ self.status_reporter.set_messenger(status_center)
+ self.status_reporter.run_id = self.run_id
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
new file mode 100755
index 0000000000..58198b6661
--- /dev/null
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
@@ -0,0 +1,66 @@
+
+from abc import ABC, abstractmethod
+
+
+class FedMLSchedulerBaseJobRunnerManager(ABC):
+
+ def __init__(self):
+ if not hasattr(self, "job_runners"):
+ self.job_runners = dict()
+ if not hasattr(self, "cloud_run_process_map"):
+ self.cloud_run_process_map = dict()
+
+ @abstractmethod
+ def _generate_job_runner_instance(
+ self, args, run_id=None, request_json=None, agent_config=None, edge_id=None
+ ):
+ return None
+
+ def start_job_runner(
+ self, run_id, request_json, args=None, edge_id=None, is_server_job=False,
+ sender_message_queue=None, listener_message_queue=None, status_center_queue=None,
+ should_start_cloud_server=False, use_local_process_as_cloud_server=False,
+ cuda_visible_gpu_ids_str=None
+ ):
+ run_id_str = str(run_id)
+ self.job_runners[run_id_str] = self._generate_job_runner_instance(
+ args, run_id=run_id, request_json=request_json,
+ agent_config=args.agent_config, edge_id=edge_id,
+ )
+ self.job_runners[run_id_str].start_runner_process(
+ run_id, request_json, edge_id=edge_id,
+ sender_message_queue=sender_message_queue,
+ listener_message_queue=listener_message_queue,
+ status_center_queue=status_center_queue
+ )
+
+ def stop_job_runner(self, run_id):
+ run_id_str = str(run_id)
+ if self.job_runners.get(run_id_str, None) is not None:
+ self.job_runners[run_id_str].trigger_stop_event()
+
+ def complete_job_runner(self, run_id):
+ run_id_str = str(run_id)
+ if self.job_runners.get(run_id_str, None) is not None:
+ self.job_runners[run_id_str].trigger_completed_event()
+
+ def put_run_edge_device_info_to_queue(self, run_id, device_info):
+ run_id_str = str(run_id)
+ if self.job_runners.get(run_id_str, None) is not None:
+ self.job_runners[run_id_str].put_run_edge_device_info_to_queue(run_id, device_info)
+
+ def get_runner_process(self, run_id, is_cloud_server=False):
+ run_id_str = str(run_id)
+
+ if self.job_runners.get(run_id_str, None) is None:
+ return None
+
+ return self.job_runners[run_id_str].run_process
+
+ def get_all_runner_pid_map(self):
+ process_id_dict = dict()
+ for run_id, runner in self.job_runners.items():
+ if runner.run_process is not None:
+ process_id_dict[str(run_id)] = runner.run_process.pid
+
+ return process_id_dict
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
new file mode 100755
index 0000000000..4a0c950655
--- /dev/null
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
@@ -0,0 +1,260 @@
+
+import json
+import logging
+import multiprocessing
+import sys
+import time
+import traceback
+import uuid
+import fedml
+from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
+from ....core.distributed.communication.mqtt.mqtt_manager import MqttManager
+from ....core.mlops.mlops_metrics import MLOpsMetrics
+from ..comm_utils import sys_utils
+from ..scheduler_core.message_center import FedMLMessageCenter
+from ..scheduler_core.status_center import FedMLStatusCenter
+from .account_manager import FedMLAccountManager
+from .general_constants import GeneralConstants
+from abc import ABC, abstractmethod
+
+
+class FedMLSchedulerBaseProtocolManager(FedMLMessageCenter, FedMLStatusCenter, ABC):
+
+ def __init__(self, args, agent_config=None, is_master=False):
+ FedMLMessageCenter.__init__(self)
+ FedMLStatusCenter.__init__(self)
+ self.request_json = None
+ self.version = fedml.get_env_version()
+ self.args = args
+ self.is_master_agent = is_master
+ self.message_status_runner = None
+ self.message_center = None
+ self.status_center = None
+ self.message_center_name = "master_agent" if not is_master else "slave_agent"
+ self.run_id = None
+ self.edge_id = args.edge_id
+ self.general_edge_id = None
+ self.server_agent_id = args.edge_id
+ self.current_device_id = args.current_device_id
+ self.unique_device_id = args.unique_device_id
+ self.agent_config = agent_config
+ self.topic_active = None
+ self.topic_last_will = None
+ self.communication_mgr = None
+ self.subscribed_topics = list()
+ self.mlops_metrics = None
+ self.status_reporter = None
+ self.user_name = args.user_name
+
+ if multiprocessing.get_start_method() != "fork":
+ # force all platforms (Windows/Linux/macOS) to use the same way (fork) for multiprocessing
+ multiprocessing.set_start_method("fork", force=True)
+
+ def generate_topics(self):
+ # generate the subscribed topics.
+ self.subscribed_topics.clear()
+ # self.subscribed_topics.append(self.topic_start_train)
+
+ def add_protocol_handler(self):
+ # Add the message listeners for all topics, the following is an example.
+ # self.add_message_listener(self.topic_start_train, self.callback_start_train)
+ pass
+
+ def initialize(self):
+ # Generate the message topics
+ self.generate_topics()
+
+ # Setup MQTT connection
+ self.communication_mgr = MqttManager(
+ self.agent_config["mqtt_config"]["BROKER_HOST"],
+ self.agent_config["mqtt_config"]["BROKER_PORT"],
+ self.agent_config["mqtt_config"]["MQTT_USER"],
+ self.agent_config["mqtt_config"]["MQTT_PWD"],
+ self.agent_config["mqtt_config"]["MQTT_KEEPALIVE"],
+ f"FedML_Agent_Daemon_@{self.user_name}@_@{self.current_device_id}@_@{str(uuid.uuid4())}@",
+ self.topic_last_will,
+ json.dumps({"ID": self.edge_id, "status": GeneralConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE})
+ )
+
+ # Add the message listeners for all topics
+ self.add_protocol_handler()
+
+ # Start the message center to process edge related messages.
+ self.setup_message_center()
+
+ # Start the status center to process edge related status.
+ self.start_status_listener_center()
+
+ # Start the message center for listener
+ self.start_listener(sender_message_queue=self.message_center.get_sender_message_queue(),
+ agent_config=self.agent_config,
+ message_center_name=self.message_center_name)
+
+ # Init extra items, e.g. database, recovery, etc.
+ self._init_extra_items()
+
+ # Setup MQTT connected listener
+ self.communication_mgr.add_connected_listener(self.on_agent_communication_connected)
+ self.communication_mgr.add_disconnected_listener(self.on_agent_communication_disconnected)
+ self.communication_mgr.connect()
+
+ def start(self):
+ # Start MQTT message loop
+ try:
+ self.communication_mgr.loop_forever()
+ except Exception as e:
+ if str(e) == "Restarting after upgraded...":
+ logging.info("Restarting after upgraded...")
+ else:
+ logging.info("Server tracing: {}".format(traceback.format_exc()))
+
+ finally:
+ FedMLAccountManager.write_login_failed_file(is_client=not self.is_master_agent)
+
+ self.stop()
+
+ time.sleep(5)
+ sys_utils.cleanup_all_fedml_server_login_processes(
+ GeneralConstants.MASTER_LOGIN_PROGRAM if self.is_master_agent else GeneralConstants.SLAVE_LOGIN_PROGRAM,
+ clean_process_group=False)
+ sys.exit(1)
+
+ def stop(self):
+ if self.communication_mgr is not None:
+ # noinspection PyBroadException
+ try:
+ for topic in self.subscribed_topics:
+ self.communication_mgr.unsubscribe_msg(topic)
+ except Exception:
+ pass
+
+ self.communication_mgr.loop_stop()
+ self.communication_mgr.disconnect()
+
+ self.release_message_center()
+
+ @abstractmethod
+ def _init_extra_items(self):
+ pass
+
+ def on_agent_communication_connected(self, mqtt_client_object):
+ # Setup MQTT message passthrough listener for all messages
+ self.communication_mgr.add_message_passthrough_listener(self.listener_message_passthrough_dispatch_center)
+
+ # Subscribe topics for starting train, stopping train and fetching client status.
+ for topic in self.subscribed_topics:
+ self.communication_mgr.subscribe_msg(topic)
+
+ # Broadcast the first active message.
+ self.send_agent_active_msg(self.edge_id)
+ if self.general_edge_id is not None:
+ self.send_agent_active_msg(self.general_edge_id)
+
+ # Echo results
+ MLOpsRuntimeLog.get_instance(self.args).enable_show_log_to_stdout()
+ self.print_connected_info()
+ MLOpsRuntimeLog.get_instance(self.args).enable_show_log_to_stdout(enable=True)
+
+ @abstractmethod
+ def print_connected_info(self):
+ print("\nCongratulations, your device is connected to the FedML MLOps platform successfully!")
+ print(
+ "Your FedML Edge ID is " + str(self.edge_id) + ", unique device ID is "
+ + str(self.unique_device_id)
+ )
+
+ def on_agent_communication_disconnected(self, mqtt_client_object):
+ pass
+
+ def setup_message_center(self):
+ if self.message_center is not None:
+ return
+
+ self.message_center = FedMLMessageCenter(agent_config=self.agent_config)
+ self.message_center.start_sender(message_center_name=self.message_center_name)
+
+ if self.mlops_metrics is None:
+ self.mlops_metrics = MLOpsMetrics()
+ self.mlops_metrics.set_messenger(self)
+ self.mlops_metrics.run_id = self.run_id
+ self.mlops_metrics.edge_id = self.edge_id
+ self.mlops_metrics.server_agent_id = self.server_agent_id
+
+ def send_message_json(self, topic, payload):
+ self.message_center.send_message_json(topic, payload)
+
+ def rebuild_message_center(self, message_center_queue):
+ self.message_center = FedMLMessageCenter(sender_message_queue=message_center_queue)
+
+ if self.mlops_metrics is None:
+ self.mlops_metrics = MLOpsMetrics()
+ self.mlops_metrics.set_messenger(self)
+ self.mlops_metrics.run_id = self.run_id
+ self.mlops_metrics.edge_id = self.edge_id
+ self.mlops_metrics.server_agent_id = self.server_agent_id
+
+ def release_message_center(self):
+ try:
+ if self.message_center is not None:
+ self.message_center.stop()
+ self.message_center = None
+
+ except Exception as e:
+ logging.error(
+ f"Failed to release slave communication manager with Exception {e}. "
+ f"Traceback: {traceback.format_exc()}")
+ pass
+
+ def start_status_listener_center(self):
+ self.start_status_center(
+ sender_message_center_queue=self.message_center.get_sender_message_queue(),
+ listener_message_center_queue=self.get_listener_message_queue(),
+ is_slave_agent=not self.is_master_agent
+ )
+
+ if self.status_reporter is None:
+ self.status_reporter = MLOpsMetrics()
+ self.status_reporter.set_messenger(self, send_message_func=self.send_status_message)
+ self.status_reporter.run_id = self.run_id
+ self.status_reporter.edge_id = self.edge_id
+ self.status_reporter.server_agent_id = self.server_agent_id
+
+ def rebuild_status_center(self, status_center_queue):
+ self.status_center = FedMLStatusCenter(message_queue=status_center_queue)
+
+ if self.status_reporter is None:
+ self.status_reporter = MLOpsMetrics()
+ self.status_reporter.set_messenger(self.status_center, send_message_func=self.status_center.send_status_message)
+ self.status_reporter.run_id = self.run_id
+ self.status_reporter.edge_id = self.edge_id
+ self.status_reporter.server_agent_id = self.server_agent_id
+
+ @abstractmethod
+ def generate_protocol_manager(self):
+ # Generate the protocol manager instance and set the attribute values.
+ return None
+
+ def get_message_runner(self):
+ if self.message_status_runner is not None:
+ return self.message_status_runner
+
+ self.message_status_runner = self.generate_protocol_manager()
+ self.message_status_runner.status_queue = self.get_status_queue()
+
+ return self.message_status_runner
+
+ def get_status_runner(self):
+ if self.message_status_runner is None:
+ self.get_message_runner()
+ if self.message_status_runner is not None:
+ self.message_status_runner.sender_message_queue = self.message_center.get_sender_message_queue()
+
+ if self.message_status_runner is not None:
+ self.message_status_runner.sender_message_queue = self.message_center.get_sender_message_queue()
+ return self.message_status_runner
+
+ return None
+
+ def send_agent_active_msg(self, edge_id):
+ active_msg = {"ID": edge_id, "status": GeneralConstants.MSG_MLOPS_SERVER_STATUS_IDLE}
+ self.message_center.send_message_json(self.topic_active, json.dumps(active_msg))
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
new file mode 100755
index 0000000000..569f4d9257
--- /dev/null
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -0,0 +1,410 @@
+import logging
+import time
+
+from ..slave.client_constants import ClientConstants
+from ..master.server_constants import ServerConstants
+from enum import Enum, unique
+import multiprocessing
+from multiprocessing import Process, Queue
+import queue
+from .message_common import FedMLMessageEntity, FedMLStatusEntity
+from .message_center import FedMLMessageCenter
+import traceback
+from .status_manager_protocols import FedMLStatusManager
+from .compute_cache_manager import ComputeCacheManager
+
+
+@unique
+class JobStatus(Enum):
+ STATUS_OFFLINE = "OFFLINE"
+ STATUS_PROVISIONING = "PROVISIONING"
+ STATUS_IDLE = "IDLE"
+ UPGRADING = "UPGRADING"
+ STARTING = "STARTING"
+ STATUS_RUNNING = "RUNNING"
+ STATUS_STOPPING = "STOPPING"
+ STATUS_KILLED = "KILLED"
+ STATUS_FAILED = "FAILED"
+ STATUS_FINISHED = "FINISHED"
+ STATUS_EXCEPTION = "EXCEPTION"
+
+ def __str__(self):
+ return self.value
+
+ @classmethod
+ def get_job_enum_from_str(cls, job_status_str: str):
+ for job_status in cls:
+ if job_status.value == job_status_str:
+ return job_status
+ return cls.STATUS_OFFLINE
+
+ @staticmethod
+ def is_job_completed(job_status_str: str):
+ if job_status_str == JobStatus.STATUS_FINISHED.value or \
+ job_status_str == JobStatus.STATUS_FAILED.value or \
+ job_status_str == JobStatus.STATUS_KILLED.value or \
+ job_status_str == JobStatus.STATUS_EXCEPTION.value:
+ return True
+
+ return False
+
+
+@unique
+class DeviceStatus(Enum):
+ STATUS_OFFLINE = "OFFLINE"
+ STATUS_PROVISIONING = "PROVISIONING"
+ STATUS_IDLE = "IDLE"
+ STATUS_UPGRADING = "UPGRADING"
+ STATUS_QUEUED = "QUEUED"
+ STATUS_INITIALIZING = "INITIALIZING"
+ STATUS_TRAINING = "TRAINING"
+ STATUS_RUNNING = "RUNNING"
+ STATUS_STOPPING = "STOPPING"
+ STATUS_KILLED = "KILLED"
+ STATUS_FAILED = "FAILED"
+ STATUS_EXCEPTION = "EXCEPTION"
+ STATUS_FINISHED = "FINISHED"
+
+ def __str__(self):
+ return self.value
+
+ @classmethod
+ def get_device_enum_from_str(cls, device_status_str: str):
+ for device_status in cls:
+ if device_status.value == device_status_str:
+ return device_status
+ return cls.STATUS_OFFLINE
+
+
+class FedMLStatusCenter(object):
+ TOPIC_MASTER_STATUS_PREFIX = "fl_server/flserver_agent_"
+ TOPIC_SLAVE_STATUS_PREFIX = "fl_client/flclient_agent_"
+ TOPIC_SLAVE_STATUS_TO_MLOPS_PREFIX = "fl_run/fl_client/mlops/status"
+ TOPIC_SLAVE_JOB_LAUNCH_PREFIX = "flserver_agent/"
+ TOPIC_SLAVE_JOB_LAUNCH_SUFFIX = "/start_train"
+ TOPIC_SLAVE_JOB_STOP_PREFIX = "flserver_agent/"
+ TOPIC_SLAVE_JOB_STOP_SUFFIX = "/stop_train"
+
+ def __init__(self, message_queue=None):
+ self.status_queue = message_queue
+ self.job_status_in_slave = dict()
+ self.entire_job_status = None
+ self.job_status_in_master = dict()
+ self.slave_devices_status = dict()
+ self.master_devices_status = dict()
+ self.status_center_process = None
+ self.status_event = None
+ self.status_sender_message_center_queue = None
+ self.status_listener_message_center_queue = None
+ self.status_message_center = None
+ self.status_manager_instance = None
+ self.status_runner = None
+
+ def __repr__(self):
+ return "<{klass} @{id:x} {attrs}>".format(
+ klass=self.__class__.__name__,
+ id=id(self) & 0xFFFFFF,
+ attrs=" ".join("{}={!r}".format(k, v) for k, v in self.__dict__.items()),
+ )
+
+ def add_job_status_in_slave(self, device_id, status):
+ self.job_status_in_slave[device_id] = self._status_transition(status)
+
+ def add_job_status_in_master(self, device_id, status):
+ self.job_status_in_master[device_id] = self._status_transition(status)
+
+ def set_entire_job_status(self, status):
+ self.entire_job_status = status
+
+ def add_slave_device_status(self, device_id, status):
+ self.slave_devices_status[device_id] = self._status_transition(status)
+
+ def add_master_device_status(self, device_id, status):
+ self.master_devices_status[device_id] = self._status_transition(status)
+
+ def get_job_status_in_slave(self, device_id):
+ return self.job_status_in_slave.get(device_id, None)
+
+ def get_job_status_in_master(self, device_id):
+ return self.job_status_in_master.get(device_id, None)
+
+ def get_entire_job_status(self):
+ return self.entire_job_status
+
+ def get_slave_device_status(self, device_id):
+ return self.slave_devices_status.get(device_id, None)
+
+ def get_master_device_status(self, device_id):
+ return self.master_devices_status.get(device_id, None)
+
+ def _status_transition(self, status):
+ transition_status = status
+ if self.entire_job_status is not None:
+ if self.entire_job_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED or \
+ self.entire_job_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
+ if status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED or \
+ status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or \
+ status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED:
+ transition_status = status
+ else:
+ transition_status = ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED
+
+ return transition_status
+
+ def get_status_runner(self):
+ return None
+
+ def start_status_center(self, sender_message_center_queue=None,
+ listener_message_center_queue=None, is_slave_agent=False):
+ self.status_queue = Queue()
+ self.status_event = multiprocessing.Event()
+ self.status_event.clear()
+ self.status_sender_message_center_queue = sender_message_center_queue
+ self.status_listener_message_center_queue = listener_message_center_queue
+ self.status_runner = self.get_status_runner()
+ target_func = self.status_runner.run_status_dispatcher if not is_slave_agent else \
+ self.status_runner.run_status_dispatcher_in_slave
+ self.status_center_process = Process(
+ target=target_func, args=(
+ self.status_event, self.status_queue, self.status_sender_message_center_queue,
+ self.status_listener_message_center_queue
+ )
+ )
+
+ self.status_center_process.start()
+
+ def check_message_stop_event(self):
+ if self.status_event is not None and self.status_event.is_set():
+ logging.info("Received status center stopping event.")
+ raise StatusCenterStoppedException("Status center stopped (for sender)")
+
+ def send_message(self, topic, payload, run_id=None):
+ message_entity = FedMLMessageEntity(topic=topic, payload=payload, run_id=run_id)
+ self.status_queue.put(message_entity.get_message_body())
+
+ def send_message_json(self, topic, payload):
+ self.send_message(topic, payload)
+
+ def send_status_message(self, topic, payload):
+ message_entity = FedMLMessageEntity(topic=topic, payload=payload)
+ self.status_queue.put(message_entity.get_message_body())
+
+ def get_status_queue(self):
+ return self.status_queue
+
+ def status_center_process_master_status(self, topic, payload):
+ pass
+
+ def status_center_process_slave_status(self, topic, payload):
+ pass
+
+ def rebuild_message_center(self, message_center_queue):
+ pass
+
+ def rebuild_status_center(self, status_queue):
+ pass
+
+ @staticmethod
+ def save_job_status(run_id, status):
+ ComputeCacheManager.get_instance().set_redis_params()
+ ComputeCacheManager.get_instance().get_status_cache().save_job_status(run_id, status)
+
+ @staticmethod
+ def save_device_status_in_job(run_id, device_id, status):
+ ComputeCacheManager.get_instance().set_redis_params()
+ ComputeCacheManager.get_instance().get_status_cache().save_device_status_in_job(run_id, device_id, status)
+
+ def run_status_dispatcher(self, status_event, status_queue,
+ sender_message_center_queue,
+ listener_message_center_queue):
+ # Save the parameters
+ self.status_event = status_event
+ self.status_queue = status_queue
+ self.status_sender_message_center_queue = sender_message_center_queue
+ self.status_listener_message_center_queue = listener_message_center_queue
+
+ # Rebuild message center
+ message_center = None
+ if sender_message_center_queue is not None:
+ self.rebuild_message_center(sender_message_center_queue)
+ message_center = FedMLMessageCenter(
+ sender_message_queue=sender_message_center_queue,
+ listener_message_queue=listener_message_center_queue
+ )
+
+ if sender_message_center_queue is not None:
+ self.rebuild_status_center(status_queue)
+
+ # Init status manager instances
+ status_manager_instances = dict()
+
+ while True:
+ message_entity = None
+
+ # Check if we should stop status dispatcher
+ try:
+ self.check_message_stop_event()
+ except StatusCenterStoppedException as e:
+ break
+
+ # Dispatch status messages.
+ # noinspection PyBroadException
+ try:
+ # Get the status message from the queue
+ try:
+ message_body = status_queue.get(block=False, timeout=0.1)
+ except queue.Empty as e: # If queue is empty, then break loop
+ message_body = None
+ if message_body is None:
+ time.sleep(0.1)
+ continue
+
+ # Build message and status entity
+ message_entity = FedMLMessageEntity(message_body=message_body)
+ status_entity = FedMLStatusEntity(status_msg_body=message_body)
+
+ # Generate status manager instance
+ if status_manager_instances.get(status_entity.run_id) is None:
+ status_manager_instances[status_entity.run_id] = FedMLStatusManager(
+ run_id=status_entity.run_id, edge_id=status_entity.edge_id, status_center=self,
+ message_center=message_center)
+ else:
+ status_manager_instances[status_entity.run_id].edge_id = status_entity.edge_id
+
+ # Process the master and slave status.
+ if message_entity.topic.startswith(FedMLStatusCenter.TOPIC_MASTER_STATUS_PREFIX):
+ # Process the job status
+ status_manager_instances[status_entity.run_id].status_center_process_master_status(
+ message_entity.topic, message_entity.payload)
+
+ # Save the job status
+ FedMLStatusCenter.save_job_status(status_entity.run_id, self.get_entire_job_status())
+
+ elif message_entity.topic.startswith(FedMLStatusCenter.TOPIC_SLAVE_STATUS_PREFIX):
+ # Process the slave device status
+ status_manager_instances[status_entity.run_id].status_center_process_slave_status(
+ message_entity.topic, message_entity.payload)
+
+ # Save the device status in job
+ FedMLStatusCenter.save_device_status_in_job(status_entity.run_id, status_entity.edge_id,
+ self.get_job_status_in_slave(status_entity.edge_id))
+
+ except Exception as e:
+ if message_entity is not None:
+ logging.info(
+ f"Failed to process the status with topic {message_entity.topic}, "
+ f"payload {message_entity.payload}, {traceback.format_exc()}")
+ else:
+ logging.info(f"Failed to process the status: {traceback.format_exc()}")
+
+ def run_status_dispatcher_in_slave(self, status_event, status_queue,
+ sender_message_center_queue,
+ listener_message_center_queue):
+ # Save the parameters
+ self.status_event = status_event
+ self.status_queue = status_queue
+ self.status_sender_message_center_queue = sender_message_center_queue
+ self.status_listener_message_center_queue = listener_message_center_queue
+
+ # Rebuild message center
+ message_center = None
+ if sender_message_center_queue is not None:
+ self.rebuild_message_center(sender_message_center_queue)
+ message_center = FedMLMessageCenter(
+ sender_message_queue=sender_message_center_queue,
+ listener_message_queue=listener_message_center_queue
+ )
+
+ if sender_message_center_queue is not None:
+ self.rebuild_status_center(status_queue)
+
+ # Init status manager instances
+ status_manager_instances = dict()
+ job_launch_message_map = dict()
+
+ while True:
+ message_entity = None
+
+ # Check if we should stop status dispatcher
+ try:
+ self.check_message_stop_event()
+ except StatusCenterStoppedException as e:
+ break
+
+ # Dispatch status messages.
+ # noinspection PyBroadException
+ try:
+ # Get the status message from the queue
+ try:
+ message_body = status_queue.get(block=False, timeout=0.1)
+ except queue.Empty as e: # If queue is empty, then break loop
+ message_body = None
+ if message_body is None:
+ time.sleep(0.1)
+ continue
+
+ # Build message and status entity
+ message_entity = FedMLMessageEntity(message_body=message_body)
+ status_entity = FedMLStatusEntity(status_msg_body=message_body)
+
+ # Generate status manager instance
+ if status_manager_instances.get(status_entity.run_id) is None:
+ status_manager_instances[status_entity.run_id] = FedMLStatusManager(
+ run_id=status_entity.run_id, edge_id=status_entity.edge_id, status_center=self,
+ message_center=message_center)
+ else:
+ status_manager_instances[status_entity.run_id].edge_id = status_entity.edge_id
+
+ # Process the slave status
+ if message_entity.topic.startswith(FedMLStatusCenter.TOPIC_SLAVE_STATUS_PREFIX):
+ # Report the slave status to master
+ status_manager_instances[status_entity.run_id]. \
+ status_center_process_slave_status_to_master_in_slave_agent(
+ message_entity.topic, message_entity.payload
+ )
+ elif message_entity.topic.startswith(FedMLStatusCenter.TOPIC_SLAVE_STATUS_TO_MLOPS_PREFIX):
+ # Report slave status to mlops (Active/IDLE message)
+ status_manager_instances[status_entity.run_id]. \
+ status_center_process_slave_status_to_mlops_in_slave_agent(
+ message_entity.topic, message_entity.payload
+ )
+ elif (message_entity.topic.startswith(FedMLStatusCenter.TOPIC_SLAVE_JOB_LAUNCH_PREFIX) and
+ message_entity.topic.endswith(FedMLStatusCenter.TOPIC_SLAVE_JOB_LAUNCH_SUFFIX)):
+ # Async request the job status from master when launching the job
+ job_launch_message_map[status_entity.run_id] = {"topic": message_entity.topic,
+ "payload": message_entity.payload}
+ status_manager_instances[status_entity.run_id]. \
+ status_center_request_job_status_from_master_in_slave_agent(
+ message_entity.topic, message_entity.payload
+ )
+ elif (message_entity.topic.startswith(FedMLStatusCenter.TOPIC_SLAVE_JOB_STOP_PREFIX) and
+ message_entity.topic.endswith(FedMLStatusCenter.TOPIC_SLAVE_JOB_STOP_SUFFIX)):
+ # Cleanup when stopped the job
+ if job_launch_message_map.get(status_entity.run_id, None) is not None:
+ job_launch_message_map.pop(status_entity.run_id)
+
+ except Exception as e:
+ if message_entity is not None:
+ logging.info(
+ f"Failed to process the status with topic {message_entity.topic}, "
+ f"payload {message_entity.payload}, {traceback.format_exc()}")
+ else:
+ logging.info(f"Failed to process the status: {traceback.format_exc()}")
+
+ def register_job_launch_message(self, topic, payload):
+ message_entity = FedMLMessageEntity(topic=topic, payload=payload)
+ self.status_queue.put(message_entity.get_message_body())
+
+ def register_job_stop_message(self, topic, payload):
+ message_entity = FedMLMessageEntity(topic=topic, payload=payload)
+ self.status_queue.put(message_entity.get_message_body())
+
+ @staticmethod
+ def rebuild_status_center_from_queue(status_queue):
+ status_center = FedMLStatusCenter(message_queue=status_queue)
+ return status_center
+
+
+class StatusCenterStoppedException(Exception):
+ """ Status center stopped. """
+ pass
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
new file mode 100755
index 0000000000..06b222cfd1
--- /dev/null
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -0,0 +1,303 @@
+import json
+import logging
+import os
+import shutil
+from os import listdir
+
+from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
+from ....core.mlops.mlops_metrics import MLOpsMetrics
+from ..slave.client_constants import ClientConstants
+from ..master.server_constants import ServerConstants
+from ..master.server_data_interface import FedMLServerDataInterface
+from .message_common import LogArgs
+from .general_constants import GeneralConstants
+
+
+class FedMLStatusManager(object):
+ def __init__(self, run_id=None, edge_id=None, server_id=None,
+ edge_id_list=None, running_scheduler_contract=None,
+ status_center=None, message_center=None):
+ self.run_id = run_id
+ self.edge_id = edge_id
+ self.server_id = server_id
+ self.edge_id_list = edge_id_list
+ self.client_agent_active_list = dict()
+ self.running_scheduler_contract = running_scheduler_contract if running_scheduler_contract is not None else dict()
+ self.message_reporter = MLOpsMetrics()
+ self.message_reporter.set_messenger(message_center)
+ self.status_reporter = MLOpsMetrics()
+ self.status_reporter.set_messenger(status_center, send_message_func=status_center.send_status_message)
+ self.status_center = status_center
+ self.message_center = message_center
+ self.log_args = LogArgs(role="server", edge_id=self.edge_id,
+ server_id=self.server_id, log_file_dir=ServerConstants.get_log_file_dir())
+
+ def __repr__(self):
+ return "<{klass} @{id:x} {attrs}>".format(
+ klass=self.__class__.__name__,
+ id=id(self) & 0xFFFFFF,
+ attrs=" ".join("{}={!r}".format(k, v) for k, v in self.__dict__.items()),
+ )
+
+ def process_job_completed_status(self, master_id, status):
+ # Stop the system performance monitor
+ try:
+ self.message_reporter.stop_sys_perf()
+ except Exception as ex:
+ pass
+
+ # Stop the job process
+ ServerConstants.cleanup_learning_process(self.run_id)
+ ServerConstants.cleanup_bootstrap_process(self.run_id)
+
+ # Remove the package download directory.
+ try:
+ local_package_path = ServerConstants.get_package_download_dir()
+ for package_file in listdir(local_package_path):
+ if os.path.basename(package_file).startswith("run_" + str(self.run_id)):
+ shutil.rmtree(os.path.join(local_package_path, package_file), ignore_errors=True)
+ except Exception as e:
+ pass
+
+ # Stop log processor for current run
+ MLOpsRuntimeLogDaemon.get_instance(self.log_args).stop_log_processor(self.run_id, master_id)
+
+ # RunProcessUtils.kill_process(cloud_server_process.pid)
+ # self.stop_cloud_server()
+ # self.remove_listener_for_run_metrics(self.run_id)
+ # self.remove_listener_for_run_logs(self.run_id)
+
+ def process_job_exception_status(self, master_id, status):
+ # Send the exception status to slave devices.
+ self.report_exception_status(
+ self.edge_id_list, run_id=self.run_id, server_id=master_id,
+ status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
+
+ # Save the job status to local storage
+ FedMLServerDataInterface.get_instance().save_job_status(self.run_id, master_id, status, status)
+
+ def process_job_running_status(self, master_id, status):
+ self.message_reporter.report_server_training_status(
+ self.run_id, status, edge_id=master_id, running_json=self.running_scheduler_contract)
+
+ def status_center_process_master_status(self, topic, payload):
+ request_json = json.loads(payload)
+ is_retain = request_json.get("is_retain", False)
+ if is_retain:
+ return
+ run_id = request_json["run_id"]
+ status = request_json["status"]
+ edge_id = request_json["edge_id"]
+ server_id = request_json.get("server_id", None)
+ run_id_str = str(run_id)
+
+ # Process the job status
+ if status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
+ self.process_job_completed_status(server_id, status)
+ elif status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
+ self.process_job_completed_status(server_id, status)
+ elif status == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED:
+ self.process_job_completed_status(server_id, status)
+ elif status == ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION:
+ self.process_job_exception_status(server_id, status)
+ else:
+ self.process_job_running_status(server_id, status)
+
+ # Process the consensus status
+ self.process_job_status_consensus(run_id, server_id, status)
+
+ def process_job_status_consensus(self, run_id, master_id, status):
+ # Set the master status in the job and entire job status
+ self.status_center.set_entire_job_status(status)
+ self.status_center.add_job_status_in_master(master_id, status)
+ status = self.status_center.get_entire_job_status()
+
+ # Set the device status based on the job status
+ edge_id_status_dict = self.client_agent_active_list.get(f"{run_id}", {})
+ for edge_id_item, edge_status_item in edge_id_status_dict.items():
+ if edge_id_item == "server":
+ continue
+
+ # Calc the device status based on the job status
+ consensus_device_status = FedMLStatusManager.get_device_consensus_status_in_job(
+ status, edge_status_item)
+ if consensus_device_status is not None:
+ self.message_reporter.report_client_training_status(
+ edge_id_item, consensus_device_status, run_id=run_id)
+
+ # Save the job status to local storage
+ FedMLServerDataInterface.get_instance().save_job_status(run_id, master_id, status, status)
+
+ # Report the status to message center
+ self.message_reporter.report_server_training_status(run_id, status, edge_id=master_id)
+
+ # Broadcast the status to slave agents
+ self.message_reporter.report_job_status(run_id, status)
+
+ @staticmethod
+ def get_device_consensus_status_in_job(job_status, device_status):
+ if job_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
+ if device_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED or \
+ device_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or \
+ device_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED:
+ return device_status
+ else:
+ return ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED
+ else:
+ return None
+
+ def get_device_consensus_status_in_current_device(self, edge_id, status):
+ self.status_center.add_job_status_in_slave(edge_id, status)
+ consensus_status = self.status_center.get_job_status_in_slave(edge_id)
+ consensus_status = ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED \
+ if consensus_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION else consensus_status
+ return consensus_status
+
+ def status_center_process_slave_status(self, topic, payload):
+ payload_json = json.loads(payload)
+ run_id = payload_json.get("run_id", None)
+ edge_id = payload_json.get("edge_id", None)
+ status = payload_json.get("status", None)
+ init_edge_id_list = payload_json.get("init_all_edge_id_list", None)
+ init_server_id = payload_json.get("init_server_id", None)
+
+ active_item_dict = self.client_agent_active_list.get(f"{run_id}", None)
+ if active_item_dict is None:
+ self.client_agent_active_list[f"{run_id}"] = dict()
+
+ if init_edge_id_list is not None:
+ self.client_agent_active_list[f"{run_id}"][f"server"] = init_server_id
+ for edge_id_item in init_edge_id_list:
+ self.client_agent_active_list[f"{run_id}"][f"{edge_id_item}"] = \
+ ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE
+
+ if run_id is not None and edge_id is not None:
+ self.client_agent_active_list[f"{run_id}"][f"{edge_id}"] = status
+
+ self.process_device_status(run_id, edge_id, status)
+
+ def process_device_status(self, run_id, edge_id, status):
+ number_of_failed_edges = 0
+ number_of_finished_edges = 0
+ number_of_killed_edges = 0
+ edge_id_status_dict = self.client_agent_active_list.get(f"{run_id}", {})
+ server_id = edge_id_status_dict.get("server", 0)
+ enable_fault_tolerance, fault_tolerance_rate = self.parse_fault_tolerance_params(run_id)
+ running_edges_list = list()
+ for edge_id_item, status_item in edge_id_status_dict.items():
+ if edge_id_item == "server":
+ continue
+
+ if status_item is None or status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED or \
+ status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION:
+ number_of_failed_edges += 1
+ continue
+
+ if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
+ number_of_finished_edges += 1
+ continue
+
+ if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED:
+ number_of_killed_edges += 1
+ continue
+
+ if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE or \
+ status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE:
+ continue
+
+ running_edges_list.append(edge_id_item)
+
+ # Report client status
+ consensus_status = self.get_device_consensus_status_in_current_device(edge_id, status)
+ self.message_reporter.report_client_training_status(edge_id, consensus_status, run_id=run_id)
+
+ # Report server status based on the fault tolerance model and parameters
+ edge_nums = len(edge_id_status_dict.keys()) - 1
+ status_to_report = self.calculate_server_status(
+ run_id, edge_nums, number_of_failed_edges, number_of_finished_edges, number_of_killed_edges,
+ running_edges_list, enable_fault_tolerance=enable_fault_tolerance,
+ fault_tolerance_rate=fault_tolerance_rate)
+ if status_to_report is not None:
+ logging.info(f"Run completed when processing edge status, will report status {status_to_report}")
+ self.report_server_status(run_id, edge_id, server_id, status_to_report)
+
+ def calculate_server_status(
+ self, run_id, total_edge_nums, number_of_failed_edges, number_of_finished_edges,
+ number_of_killed_edges, running_edges_list, enable_fault_tolerance=False,
+ fault_tolerance_rate=0.8
+ ):
+ # Report server status based on the fault tolerance model and parameters
+ actual_failed_rate = number_of_failed_edges / total_edge_nums
+ all_edges_run_completed = True if len(running_edges_list) <= 0 else False
+ if all_edges_run_completed:
+ status_to_report = None
+ if enable_fault_tolerance:
+ if actual_failed_rate >= fault_tolerance_rate:
+ status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED
+ self.report_exception_status(
+ running_edges_list, run_id=run_id, status=status_to_report)
+ return status_to_report
+ else:
+ if number_of_killed_edges == total_edge_nums:
+ status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED
+ else:
+ status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED
+ else:
+ if number_of_failed_edges > 0:
+ status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED
+ elif number_of_finished_edges == total_edge_nums:
+ status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED
+ elif number_of_killed_edges == total_edge_nums:
+ status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED
+
+ return status_to_report
+
+ def parse_fault_tolerance_params(self, run_id):
+ run_json = self.running_scheduler_contract.get(str(run_id), None)
+ if run_json is None:
+ return False, 0
+ run_config = run_json.get("run_config", {})
+ run_params = run_config.get("parameters", {})
+ common_args = run_params.get("common_args", {})
+ enable_fault_tolerance = common_args.get("enable_fault_tolerance", False)
+ fault_tolerance_rate = common_args.get("fault_tolerance_rate", 0)
+ return enable_fault_tolerance, fault_tolerance_rate
+
+ def report_server_status(self, run_id, edge_id, server_id, status):
+ self.status_reporter.report_server_id_status(
+ run_id, status, edge_id=edge_id, server_id=server_id, server_agent_id=edge_id)
+
+ def report_exception_status(
+ self, edge_id_list, run_id=0, server_id=None, status=None, payload=None):
+ if payload is None:
+ payload_obj = {"runId": run_id, "edgeids": edge_id_list}
+ if server_id is not None:
+ payload_obj["serverId"] = server_id
+ else:
+ payload_obj = json.loads(payload)
+ payload_obj["run_status"] = ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION if status is None else status
+ topic_exception = "flserver_agent/" + str(self.edge_id) + "/stop_train"
+ self.message_reporter.send_message(topic_exception, json.dumps(payload_obj))
+
+ def status_center_process_slave_status_to_master_in_slave_agent(self, topic, payload):
+ # Forward the status message to the sender queue of message center.
+ self.message_center.send_message(topic, payload)
+
+ # Post the status message to the listener queue of message center
+ self.message_center.receive_message(GeneralConstants.MSG_TOPIC_REPORT_DEVICE_STATUS_IN_JOB, payload)
+
+ def status_center_process_slave_status_to_mlops_in_slave_agent(self, topic, payload):
+ # Forward the status message to message center.
+ self.message_center.send_message(topic, payload)
+
+ def status_center_request_job_status_from_master_in_slave_agent(self, topic, payload):
+ # Parse the parameters
+ payload_json = json.loads(payload)
+ run_id = payload_json.get("run_id", None)
+ master_id = payload_json.get("master_id", None)
+ edge_id = payload_json.get("edge_id", None)
+
+ # Request the job status from master agent.
+ topic_request_job_status = f"{GeneralConstants.MSG_TOPIC_REQUEST_JOB_STATUS_PREFIX}{master_id}"
+ payload_request_job_status = {"run_id": run_id, "edge_id": edge_id}
+ self.message_center.send_message(topic_request_job_status, payload_request_job_status)
diff --git a/python/fedml/computing/scheduler/slave/base_slave_agent.py b/python/fedml/computing/scheduler/slave/base_slave_agent.py
new file mode 100755
index 0000000000..01c0a39195
--- /dev/null
+++ b/python/fedml/computing/scheduler/slave/base_slave_agent.py
@@ -0,0 +1,139 @@
+
+import json
+import os
+from ..comm_utils import sys_utils
+from ..comm_utils.run_process_utils import RunProcessUtils
+from ..comm_utils.sys_utils import get_python_program
+from ....core.mlops import MLOpsRuntimeLog, MLOpsMetrics
+from .client_data_interface import ClientConstants
+from ..scheduler_core.account_manager import FedMLAccountManager
+from ..scheduler_core.general_constants import GeneralConstants
+from abc import ABC, abstractmethod
+
+
+class FedMLBaseSlaveAgent(ABC):
+ CLIENT_API_CMD = "fedml.computing.scheduler.slave.client_api:api"
+
+ def __init__(self):
+ self.agent_args = None
+ self.local_api_process = None
+ self.process = None
+ self.cur_dir = os.path.split(os.path.realpath(__file__))[0]
+ self.mlops_metrics = MLOpsMetrics()
+ self.protocol_mgr = None
+
+ def login(
+ self, userid, api_key=None, device_id=None,
+ os_name=None, need_to_check_gpu=False, role=None
+ ):
+ # Preprocess the login args
+ if need_to_check_gpu:
+ gpu_count, _ = sys_utils.get_gpu_count_vendor()
+ if gpu_count <= 0:
+ print("We can't find any gpu device on your machine. \n"
+ "With the gpu_supplier(-g) option, you need to check if your machine "
+ "has nvidia GPUs and installs CUDA related drivers.")
+ return
+
+ # Login account
+ login_result = FedMLAccountManager.get_instance().login(
+ userid, api_key=api_key, device_id=device_id,
+ os_name=os_name, role=role
+ )
+ if login_result is not None:
+ self.agent_args = login_result
+ else:
+ return None
+
+ # Save the bound info
+ self._save_agent_info(login_result.current_device_id + "." + login_result.os_name, login_result.edge_id)
+
+ # Init the logs for protocol manager
+ self._init_logs(login_result, login_result.edge_id)
+
+ # Create the protocol manager to communicate with the slave agents and MLOps.
+ self._create_protocol_manager(login_result)
+
+ # Initialize the protocol manager
+ # noinspection PyBoardException
+ try:
+ self._initialize_protocol_manager()
+ except Exception as e:
+ FedMLAccountManager.write_login_failed_file(is_client=True)
+ self.protocol_mgr.stop()
+ raise e
+
+ # Start the protocol manager to process the messages from MLOps and slave agents.
+ self.protocol_mgr.start()
+
+ return login_result
+
+ @staticmethod
+ def logout():
+ GeneralConstants.cleanup_run_process(None)
+ sys_utils.cleanup_all_fedml_client_api_processes()
+
+ def _create_protocol_manager(self, login_result):
+ if self.protocol_mgr is not None:
+ return
+ self.protocol_mgr = self._generate_protocol_manager_instance(
+ login_result, agent_config=login_result.agent_config)
+ self.protocol_mgr.args = login_result
+ self.protocol_mgr.edge_id = login_result.edge_id
+ self.protocol_mgr.unique_device_id = login_result.unique_device_id
+ self.protocol_mgr.user_name = login_result.user_name
+ self.protocol_mgr.agent_config = login_result.agent_config
+
+ def _initialize_protocol_manager(self):
+ # Init local database
+ self._init_database()
+
+ # Initialize the master protocol
+ self.protocol_mgr.initialize()
+
+ # Start the client API process
+ self._start_slave_api()
+
+ def _init_logs(self, login_result, edge_id):
+ # Init runtime logs
+ in_args = login_result
+ in_args.log_file_dir = self._get_log_file_dir()
+ in_args.run_id = 0
+ in_args.role = "client"
+ client_ids = list()
+ client_ids.append(edge_id)
+ in_args.client_id_list = json.dumps(client_ids)
+ in_args.using_mlops = True
+ MLOpsRuntimeLog.get_instance(in_args).init_logs()
+
+ def _start_slave_api(self):
+ # Start the local API services
+ client_api_cmd = FedMLBaseSlaveAgent.CLIENT_API_CMD
+ client_api_pids = RunProcessUtils.get_pid_from_cmd_line(client_api_cmd)
+ if client_api_pids is None or len(client_api_pids) <= 0:
+ python_program = get_python_program()
+ cur_dir = os.path.dirname(__file__)
+ fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
+ self.local_api_process = ClientConstants.exec_console_with_script(
+ "{} -m uvicorn {} --host 0.0.0.0 --port {} "
+ "--reload --reload-delay 3 --reload-dir {} --log-level critical".format(
+ python_program, client_api_cmd, ClientConstants.LOCAL_CLIENT_API_PORT, fedml_base_dir),
+ should_capture_stdout=False,
+ should_capture_stderr=False
+ )
+
+ @abstractmethod
+ def _get_log_file_dir(self):
+ pass
+
+ @abstractmethod
+ def _save_agent_info(self, unique_device_id, edge_id):
+ pass
+
+ @abstractmethod
+ def _init_database(self):
+ pass
+
+ @abstractmethod
+ def _generate_protocol_manager_instance(self, args, agent_config=None):
+ return None
diff --git a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
new file mode 100755
index 0000000000..4448dd49fa
--- /dev/null
+++ b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
@@ -0,0 +1,264 @@
+import logging
+import multiprocessing
+import os
+import platform
+import time
+import traceback
+from abc import ABC, abstractmethod
+
+from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
+from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
+from .client_data_interface import FedMLClientDataInterface
+from ..comm_utils import sys_utils
+from ....core.mlops.mlops_utils import MLOpsUtils
+from multiprocessing import Process
+from ..scheduler_core.scheduler_base_job_runner import FedMLSchedulerBaseJobRunner, RunnerError, RunnerCompletedError
+from ..scheduler_core.general_constants import GeneralConstants
+
+
+class FedMLBaseSlaveJobRunner(FedMLSchedulerBaseJobRunner, ABC):
+
+ def __init__(self, args, edge_id=0, request_json=None, agent_config=None, run_id=0,
+ cuda_visible_gpu_ids_str=None,
+ agent_data_dir=None, agent_package_download_dir=None,
+ agent_package_unzip_dir=None, agent_log_file_dir=None):
+ FedMLSchedulerBaseJobRunner.__init__(
+ self, args, edge_id=edge_id, request_json=request_json, agent_config=agent_config, run_id=run_id,
+ cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str, agent_data_dir=agent_data_dir,
+ agent_package_download_dir=agent_package_download_dir,
+ agent_package_unzip_dir=agent_package_unzip_dir,
+ agent_log_file_dir=agent_log_file_dir
+ )
+
+ self.fedml_data_base_package_dir = os.path.join("/", "fedml", "data")
+ self.fedml_data_local_package_dir = os.path.join("/", "fedml", "fedml-package", "fedml", "data")
+ self.fedml_data_dir = self.fedml_data_base_package_dir
+ self.fedml_config_dir = os.path.join("/", "fedml", "conf")
+ self.run_extend_queue_list = None
+ self.computing_started_time = 0
+
+ def __repr__(self):
+ return "<{klass} @{id:x} {attrs}>".format(
+ klass=self.__class__.__name__,
+ id=id(self) & 0xFFFFFF,
+ attrs=" ".join("{}={!r}".format(k, v) for k, v in self.__dict__.items()),
+ )
+
+ def run(self, process_event, completed_event, run_extend_queue_list,
+ sender_message_center, listener_message_queue, status_center_queue):
+ print(f"Client runner process id {os.getpid()}, run id {self.run_id}")
+
+ if platform.system() != "Windows":
+ os.setsid()
+
+ os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
+ os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
+
+ self.run_process_event = process_event
+ self.run_process_completed_event = completed_event
+ try:
+ MLOpsUtils.set_ntp_offset(self.ntp_offset)
+ self.rebuild_message_status_center(sender_message_center, listener_message_queue, status_center_queue)
+ self.run_impl(run_extend_queue_list, sender_message_center, listener_message_queue, status_center_queue)
+ except RunnerError:
+ logging.info("Runner stopped.")
+ self.reset_devices_status(self.edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_KILLED)
+ except RunnerCompletedError:
+ logging.info("Runner completed.")
+ except Exception as e:
+ logging.error(f"Runner exited with errors. Exception: {e}, Traceback {traceback.format_exc()}")
+ self.status_reporter.report_client_id_status(
+ self.edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
+ server_id=self.server_id, run_id=self.run_id)
+ finally:
+ if self.mlops_metrics is not None:
+ computing_ended_time = MLOpsUtils.get_ntp_time()
+ self.mlops_metrics.report_edge_job_computing_cost(self.run_id, self.edge_id,
+ self.computing_started_time, computing_ended_time,
+ self.args.account_id, self.args.api_key)
+ logging.info("Release resources.")
+ FedMLSchedulerBaseJobRunner.cleanup_containers_and_release_gpus(self.run_id, self.edge_id)
+ MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, self.edge_id)
+ if self.mlops_metrics is not None:
+ self.mlops_metrics.stop_sys_perf()
+ time.sleep(3)
+ GeneralConstants.cleanup_learning_process(self.run_id)
+ GeneralConstants.cleanup_run_process(self.run_id)
+
+ @abstractmethod
+ def run_impl(self, run_extend_queue_list, sender_message_center,
+ listener_message_queue, status_center_queue):
+ run_id = self.request_json["runId"]
+ run_config = self.request_json["run_config"]
+ data_config = run_config.get("data_config", {})
+ packages_config = run_config["packages_config"]
+
+ self.computing_started_time = MLOpsUtils.get_ntp_time()
+ self.mlops_metrics.report_edge_job_computing_cost(run_id, self.edge_id,
+ self.computing_started_time, 0,
+ self.args.account_id, self.args.api_key)
+
+ self.check_runner_stop_event()
+
+ MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+
+ self.status_reporter.report_client_id_status(
+ self.edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_INITIALIZING,
+ running_json=self.start_request_json, run_id=run_id)
+
+ # get training params
+ private_local_data_dir = data_config.get("privateLocalData", "")
+ is_using_local_data = 0
+ # if private_local_data_dir is not None and len(str(private_local_data_dir).strip(' ')) > 0:
+ # is_using_local_data = 1
+
+ # start a run according to the hyper-parameters
+ # fedml_local_data_dir = self.cur_dir + "/fedml_data/run_" + run_id_str + "_edge_" + str(edge_id)
+ fedml_local_data_dir = os.path.join(self.cur_dir, "fedml_data")
+ fedml_local_config_dir = os.path.join(self.cur_dir, "fedml_config")
+ if is_using_local_data:
+ fedml_local_data_dir = private_local_data_dir
+ self.fedml_data_dir = self.fedml_data_local_package_dir
+
+ self.check_runner_stop_event()
+
+ logging.info("Download packages")
+
+ # update local config with real time parameters from server and dynamically replace variables value
+ unzip_package_path, fedml_config_object = self.update_local_fedml_config(run_id, run_config)
+ # if unzip_package_path is None or fedml_config_object is None:
+ # logging.info("failed to update local fedml config.")
+ # self.check_runner_stop_event()
+ # # Send failed msg when exceptions.
+ # self.cleanup_run_when_starting_failed(status=GeneralConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION)
+ # return
+
+ logging.info("Check downloaded packages...")
+
+ entry_file_config = fedml_config_object.get("entry_config", None)
+ dynamic_args_config = fedml_config_object.get("dynamic_args", None)
+ entry_file = str(entry_file_config["entry_file"]).replace('\\', os.sep).replace('/', os.sep)
+ entry_file = os.path.basename(entry_file)
+ conf_file = entry_file_config["conf_file"]
+ conf_file = str(conf_file).replace('\\', os.sep).replace('/', os.sep)
+ #####
+ # GeneralConstants.cleanup_learning_process(run_id)
+ # GeneralConstants.cleanup_bootstrap_process(run_id)
+ #####
+
+ if not os.path.exists(unzip_package_path):
+ logging.info("failed to unzip file.")
+ self.check_runner_stop_event()
+ return
+ os.chdir(os.path.join(unzip_package_path, "fedml"))
+
+ self.check_runner_stop_event()
+
+ logging.info("starting the user process...")
+
+ entry_file_full_path = os.path.join(unzip_package_path, "fedml", entry_file)
+ conf_file_full_path = os.path.join(unzip_package_path, "fedml", conf_file)
+ logging.info("waiting the user process to finish...")
+ logging.info(" ")
+ logging.info(" ")
+ logging.info("====Your Run Logs Begin===")
+
+ process, is_launch_task, error_list = self.execute_job_task(
+ unzip_package_path=unzip_package_path, entry_file_full_path=entry_file_full_path,
+ conf_file_full_path=conf_file_full_path, dynamic_args_config=dynamic_args_config,
+ fedml_config_object=self.fedml_config_object)
+
+ logging.info("====Your Run Logs End===")
+ logging.info(" ")
+ logging.info(" ")
+
+ ret_code, out, err = process.returncode if process else None, None, None
+ is_run_ok = sys_utils.is_runner_finished_normally(process.pid)
+ if is_launch_task:
+ is_run_ok = True
+ if error_list is not None and len(error_list) > 0:
+ is_run_ok = False
+ if ret_code is None or ret_code <= 0:
+ self.check_runner_stop_event()
+
+ if is_run_ok:
+ if out is not None:
+ out_str = sys_utils.decode_our_err_result(out)
+ if out_str != "":
+ logging.info("{}".format(out_str))
+
+ self.status_reporter.report_client_id_status(
+ self.edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
+ server_id=self.server_id, run_id=run_id)
+
+ if is_launch_task:
+ sys_utils.log_return_info(f"job {run_id}", ret_code)
+ else:
+ sys_utils.log_return_info(entry_file, ret_code)
+ else:
+ is_run_ok = False
+
+ if not is_run_ok:
+ # If the run status is killed or finished, then return with the normal state.
+ current_job = FedMLClientDataInterface.get_instance().get_job_by_id(run_id)
+ if current_job is not None and (current_job.status == GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or
+ current_job.status == GeneralConstants.MSG_MLOPS_CLIENT_STATUS_KILLED):
+ return
+
+ self.check_runner_stop_event()
+
+ logging.error("failed to run the learning process...")
+
+ if err is not None:
+ err_str = sys_utils.decode_our_err_result(err)
+ if err_str != "":
+ logging.error("{}".format(err_str))
+
+ if is_launch_task:
+ sys_utils.log_return_info(f"job {run_id}", ret_code)
+ else:
+ sys_utils.log_return_info(entry_file, ret_code)
+
+ # Send failed msg when exceptions.
+ self.status_reporter.report_client_id_status(
+ self.edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
+ server_id=self.server_id, run_id=run_id)
+
+ @abstractmethod
+ def _generate_job_runner_instance(self, args, run_id=None, request_json=None, agent_config=None, edge_id=None):
+ return None
+
+ @abstractmethod
+ def _generate_extend_queue_list(self):
+ return list()
+
+ def reset_devices_status(self, edge_id, status):
+ self.status_reporter.run_id = self.run_id
+ self.status_reporter.edge_id = edge_id
+ self.status_reporter.report_client_id_status(
+ edge_id, status, server_id=self.server_id, run_id=self.run_id)
+
+ def start_runner_process(
+ self, run_id, request_json, edge_id=None,
+ sender_message_queue=None, listener_message_queue=None,
+ status_center_queue=None, cuda_visible_gpu_ids_str=None
+ ):
+ client_runner = self._generate_job_runner_instance(
+ self.args, run_id=run_id, request_json=request_json,
+ agent_config=None, edge_id=edge_id
+ )
+ client_runner.start_request_json = request_json
+ run_id_str = str(run_id)
+ self.run_process_event = multiprocessing.Event()
+ client_runner.run_process_event = self.run_process_event
+ self.run_process_completed_event = multiprocessing.Event()
+ client_runner.run_process_completed_event = self.run_process_completed_event
+ client_runner.server_id = request_json.get("server_id", "0")
+ self.run_extend_queue_list = self._generate_extend_queue_list()
+ logging.info("start the runner process.")
+ self.run_process = Process(target=client_runner.run, args=(
+ self.run_process_event, self.run_process_completed_event, self.run_extend_queue_list,
+ sender_message_queue, listener_message_queue, status_center_queue
+ ))
+ self.run_process.start()
+ return self.run_process
diff --git a/python/fedml/computing/scheduler/slave/base_slave_job_runner_manager.py b/python/fedml/computing/scheduler/slave/base_slave_job_runner_manager.py
new file mode 100755
index 0000000000..c058d5dd0e
--- /dev/null
+++ b/python/fedml/computing/scheduler/slave/base_slave_job_runner_manager.py
@@ -0,0 +1,12 @@
+
+from abc import ABC, abstractmethod
+from ..scheduler_core.scheduler_base_job_runner_manager import FedMLSchedulerBaseJobRunnerManager
+from ..scheduler_core.scheduler_base_job_runner import FedMLSchedulerBaseJobRunner
+
+
+class FedMLBaseSlaveJobRunnerManager(FedMLSchedulerBaseJobRunnerManager, ABC):
+ def __init__(self):
+ FedMLSchedulerBaseJobRunnerManager.__init__(self)
+
+ def cleanup_containers_and_release_gpus(self, run_id, edge_id):
+ FedMLSchedulerBaseJobRunner.cleanup_containers_and_release_gpus(run_id, edge_id)
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
new file mode 100755
index 0000000000..0543459dd0
--- /dev/null
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -0,0 +1,571 @@
+
+import json
+import logging
+import os
+import time
+import traceback
+from abc import ABC, abstractmethod
+
+import fedml
+from ..comm_utils.constants import SchedulerConstants
+from ..comm_utils.job_utils import JobRunnerUtils, DockerArgs
+from ..comm_utils.run_process_utils import RunProcessUtils
+from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
+from ....core.mlops.mlops_configs import MLOpsConfigs
+from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
+from ..comm_utils import sys_utils
+from ....core.mlops.mlops_utils import MLOpsUtils
+from ..scheduler_core.compute_cache_manager import ComputeCacheManager
+from ..scheduler_core.ota_upgrade import FedMLOtaUpgrade
+from .client_data_interface import FedMLClientDataInterface
+from ..scheduler_core.scheduler_base_protocol_manager import FedMLSchedulerBaseProtocolManager
+from ..scheduler_core.general_constants import GeneralConstants
+
+
+class FedMLBaseSlaveProtocolManager(FedMLSchedulerBaseProtocolManager, ABC):
+
+ def __init__(self, args, agent_config=None):
+ FedMLSchedulerBaseProtocolManager.__init__(self, args, agent_config=agent_config)
+
+ self.request_json = None
+ self.disable_client_login = None
+ self.args = args
+ self.message_status_runner = None
+ self.message_center = None
+ self.status_center = None
+ self.message_center_name = "master_agent"
+ self.run_id = None
+ self.edge_id = args.edge_id
+ self.general_edge_id = None
+ self.edge_user_name = args.user_name
+ self.edge_extra_url = args.extra_url
+ self.server_agent_id = args.edge_id
+ self.current_device_id = args.current_device_id
+ self.unique_device_id = args.unique_device_id
+ self.agent_config = agent_config
+ self.topic_start_train = None
+ self.topic_stop_train = None
+ self.topic_report_status = None
+ self.topic_ota_msg = None
+ self.topic_request_device_info = None
+ self.topic_client_logout = None
+ self.topic_response_job_status = None
+ self.topic_report_device_status_in_job = None
+ self.fl_topic_start_train = None
+ self.fl_topic_stop_train = None
+ self.fl_topic_request_device_info = None
+ self.communication_mgr = None
+ self.subscribed_topics = list()
+ self.mlops_metrics = None
+ self.status_reporter = None
+ self.job_runners = dict()
+ self.ota_upgrade = FedMLOtaUpgrade(edge_id=args.edge_id)
+ self.running_request_json = dict()
+ self.start_request_json = None
+ self.user_name = args.user_name
+ self.general_edge_id = args.general_edge_id
+ self.server_id = args.server_id
+ self.model_device_server_id = None
+ self.model_device_client_edge_id_list = None
+ self.model_device_server = None
+ self.model_device_client_list = None
+
+ @abstractmethod
+ def generate_topics(self):
+ # The MQTT message topic format is as follows: <sender>/<receiver>/<action>
+
+ # The topic for stopping training
+ self.topic_start_train = "flserver_agent/" + str(self.edge_id) + "/start_train"
+
+ # The topi for stopping training
+ self.topic_stop_train = "flserver_agent/" + str(self.edge_id) + "/stop_train"
+
+ # The topic for reporting current device status.
+ self.topic_report_status = "mlops/report_device_status"
+
+ # The topic for OTA messages from the MLOps.
+ self.topic_ota_msg = "mlops/flclient_agent_" + str(self.edge_id) + "/ota"
+
+ # The topic for requesting device info from the client.
+ self.topic_request_device_info = "server/client/request_device_info/" + str(self.edge_id)
+
+ # The topic for requesting device info from MLOps.
+ self.topic_client_logout = "mlops/client/logout/" + str(self.edge_id)
+
+ # The topic for getting job status from the status center.
+ self.topic_response_job_status = f"master_agent/somewhere/response_job_status/{self.edge_id}"
+
+ # The topic for getting device status of job from the status center.
+ self.topic_report_device_status_in_job = f"slave_job/slave_agent/report_device_status_in_job"
+
+ # The topic for reporting online status
+ self.topic_active = "flclient_agent/active"
+
+ # The topic for last-will messages.
+ self.topic_last_will = "flclient_agent/last_will_msg"
+
+ if self.general_edge_id is not None:
+ self.fl_topic_start_train = "flserver_agent/" + str(self.general_edge_id) + "/start_train"
+ self.fl_topic_stop_train = "flserver_agent/" + str(self.general_edge_id) + "/stop_train"
+ self.fl_topic_request_device_info = "server/client/request_device_info/" + str(self.general_edge_id)
+
+ # Subscribe topics for starting train, stopping train and fetching client status.
+ self.subscribed_topics.clear()
+ self.add_subscribe_topic(self.topic_start_train)
+ self.add_subscribe_topic(self.topic_stop_train)
+ self.add_subscribe_topic(self.topic_report_status)
+ self.add_subscribe_topic(self.topic_ota_msg)
+ self.add_subscribe_topic(self.topic_request_device_info)
+ self.add_subscribe_topic(self.topic_client_logout)
+ self.add_subscribe_topic(self.topic_response_job_status)
+ self.add_subscribe_topic(self.topic_report_device_status_in_job)
+ if self.general_edge_id is not None:
+ self.add_subscribe_topic(self.fl_topic_start_train)
+ self.add_subscribe_topic(self.fl_topic_stop_train)
+ self.add_subscribe_topic(self.fl_topic_request_device_info)
+
+ @abstractmethod
+ def add_protocol_handler(self):
+ # Add the message listeners for all topics, the following is an example.
+ # self.add_message_listener(self.topic_start_train, self.callback_start_train)
+ # Add the message listeners for all topics
+ self.add_message_listener(self.topic_start_train, self.callback_start_train)
+ self.add_message_listener(self.topic_stop_train, self.callback_stop_train)
+ self.add_message_listener(self.topic_ota_msg, FedMLBaseSlaveProtocolManager.callback_client_ota_msg)
+ self.add_message_listener(self.topic_report_status, self.callback_report_current_status)
+ self.add_message_listener(self.topic_request_device_info, self.callback_report_device_info)
+ self.add_message_listener(self.topic_client_logout, self.callback_client_logout)
+ self.add_message_listener(self.topic_response_job_status, self.callback_response_job_status)
+ self.add_message_listener(self.topic_report_device_status_in_job, self.callback_response_device_status_in_job)
+ self.add_message_listener(self.fl_topic_start_train, self.callback_start_train)
+ self.add_message_listener(self.fl_topic_stop_train, self.callback_stop_train)
+ self.add_message_listener(self.fl_topic_request_device_info, self.callback_report_device_info)
+
+ @abstractmethod
+ def _get_job_runner_manager(self):
+ return None
+
+ @abstractmethod
+ def _init_extra_items(self):
+ os.environ["FEDML_CURRENT_EDGE_ID"] = str(self.edge_id)
+ if not ComputeCacheManager.get_instance().set_redis_params():
+ os.environ["FEDML_DISABLE_REDIS_CONNECTION"] = "1"
+
+ def add_subscribe_topic(self, topic):
+ self.subscribed_topics.append(topic)
+
+ def stop(self):
+ if self.model_device_server is not None:
+ self.model_device_server.stop()
+ self.model_device_server = None
+
+ if self.model_device_client_list is not None:
+ for model_client in self.model_device_client_list:
+ model_client.stop()
+ self.model_device_client_list.clear()
+ self.model_device_client_list = None
+
+ super().stop()
+
+ def on_agent_communication_connected(self, mqtt_client_object):
+ super().on_agent_communication_connected(mqtt_client_object)
+
+ self._process_connection_ready()
+
+ payload = {"model_master_device_id": self.model_device_server_id,
+ "model_slave_device_id_list": self.model_device_client_edge_id_list}
+ self.receive_message(self.topic_request_device_info, json.dumps(payload))
+
+ def on_agent_communication_disconnected(self, mqtt_client_object):
+ super().on_agent_communication_disconnected(mqtt_client_object)
+
+ self._process_connection_lost()
+
+ @abstractmethod
+ def _process_connection_ready(self):
+ pass
+
+ @abstractmethod
+ def _process_connection_lost(self):
+ pass
+
+ def print_connected_info(self):
+ print("\nCongratulations, your device is connected to the FedML MLOps platform successfully!")
+ print(f"Your FedML Edge ID is {str(self.edge_id)}, unique device ID is {str(self.unique_device_id)}, "
+ f"master deploy ID is {str(self.model_device_server_id)}, "
+ f"worker deploy ID is {self.model_device_client_edge_id_list}"
+ )
+ if self.edge_extra_url is not None and self.edge_extra_url != "":
+ print(f"You may visit the following url to fill in more information with your device.\n"
+ f"{self.edge_extra_url}")
+
+ def callback_start_train(self, topic, payload):
+ # Parse the parameters
+ request_json = json.loads(payload)
+ is_retain = request_json.get("is_retain", False)
+ if is_retain:
+ return
+ run_id = request_json["runId"]
+ edge_id = str(topic).split("/")[-2]
+ self.args.run_id = run_id
+ self.args.edge_id = edge_id
+
+ # Start log processor for current run
+ MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+ MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(
+ run_id, edge_id, log_source=SchedulerConstants.get_log_source(request_json))
+ logging.info("start the log processor")
+
+ # Fetch the config
+ try:
+ MLOpsConfigs.fetch_all_configs()
+ except Exception as e:
+ logging.error(f"Failed to fetch all configs with Exception {e}. Traceback: {traceback.format_exc()}")
+ pass
+
+ # Check if the slave agent is disabled.
+ if not FedMLClientDataInterface.get_instance().get_agent_status():
+ request_json = json.loads(payload)
+ run_id = request_json["runId"]
+ logging.error(
+ "FedMLDebug - Receive: topic ({}), payload ({}), but the client agent is disabled. {}".format(
+ topic, payload, traceback.format_exc()
+ )
+ )
+ # Send failed msg when exceptions.
+ self.status_reporter.report_client_id_status(
+ edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION, run_id=run_id,
+ msg=f"the client agent {edge_id} is disabled")
+ MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, edge_id)
+ return
+
+ # Print the payload
+ logging.info(
+ f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
+ )
+
+ # Occupy GPUs
+ server_agent_id = request_json["cloud_agent_id"]
+ scheduler_match_info = request_json.get("scheduler_match_info", {})
+ matched_gpu_num = scheduler_match_info.get("matched_gpu_num", 0)
+ model_master_device_id = scheduler_match_info.get("model_master_device_id", None)
+ model_slave_device_id = scheduler_match_info.get("model_slave_device_id", None)
+ model_slave_device_id_list = scheduler_match_info.get("model_slave_device_id_list", None)
+ run_config = request_json.get("run_config", {})
+ run_params = run_config.get("parameters", {})
+ serving_args = run_params.get("serving_args", {})
+ endpoint_id = serving_args.get("endpoint_id", None)
+ cuda_visible_gpu_ids_str = JobRunnerUtils.get_instance().occupy_gpu_ids(
+ run_id, matched_gpu_num, edge_id, inner_id=endpoint_id,
+ model_master_device_id=model_master_device_id,
+ model_slave_device_id=model_slave_device_id)
+ logging.info(
+ f"Run started, available gpu ids: {JobRunnerUtils.get_instance().get_available_gpu_id_list(edge_id)}")
+
+ # Set the listener for job status from master agent
+ self.setup_listener_job_status(run_id)
+
+ # Start server with multiprocessing mode
+ self.request_json = request_json
+ run_id_str = str(run_id)
+ self.running_request_json[run_id_str] = request_json
+ self._get_job_runner_manager().start_job_runner(
+ run_id, request_json, args=self.args, edge_id=edge_id,
+ sender_message_queue=self.message_center.get_sender_message_queue(),
+ listener_message_queue=self.get_listener_message_queue(),
+ status_center_queue=self.get_status_queue(),
+ cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str,
+ )
+ run_process = self._get_job_runner_manager().get_runner_process(run_id)
+ if run_process is not None:
+ GeneralConstants.save_run_process(run_id, run_process.pid)
+
+ # Register the job launch message into the status center
+ self.register_job_launch_message(topic, payload)
+
+ def callback_stop_train(self, topic, payload):
+ # Parse the parameters.
+ edge_id = str(topic).split("/")[-2]
+ request_json = json.loads(payload)
+ is_retain = request_json.get("is_retain", False)
+ if is_retain:
+ return
+ run_id = request_json.get("runId", None)
+ run_id = request_json.get("id", None) if run_id is None else run_id
+ run_status = request_json.get("run_status", GeneralConstants.MSG_MLOPS_CLIENT_STATUS_KILLED)
+
+ # logging.info("Stop run with multiprocessing...")
+ # Stop client with multiprocessing mode
+ run_id_str = str(run_id)
+ self._get_job_runner_manager().cleanup_containers_and_release_gpus(run_id, edge_id)
+ self.sync_run_stop_status(run_status=run_status)
+
+ # Register the job stopping message into the status center
+ self.register_job_stop_message(topic, payload)
+
+ def callback_report_current_status(self, topic, payload):
+ logging.info(
+ f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
+ )
+
+ self.send_agent_active_msg(self.edge_id)
+ if self.general_edge_id is not None:
+ self.send_agent_active_msg(self.general_edge_id)
+
+ @staticmethod
+ def callback_client_ota_msg(topic, payload):
+ logging.info(
+ f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
+ )
+
+ request_json = json.loads(payload)
+ cmd = request_json["cmd"]
+
+ if cmd == GeneralConstants.FEDML_OTA_CMD_UPGRADE:
+ FedMLOtaUpgrade.process_ota_upgrade_msg()
+ # Process(target=FedMLClientRunner.process_ota_upgrade_msg).start()
+ raise Exception("After upgraded, restart runner...")
+ elif cmd == GeneralConstants.FEDML_OTA_CMD_RESTART:
+ raise Exception("Restart runner...")
+
+ def callback_report_device_info(self, topic, payload):
+ payload_json = json.loads(payload)
+ server_id = payload_json.get("server_id", 0)
+ run_id = payload_json.get("run_id", 0)
+ listen_edge_id = str(topic).split("/")[-1]
+ context = payload_json.get("context", None)
+ need_gpu_info = payload_json.get("need_gpu_info", False)
+ need_running_process_list = payload_json.get("need_running_process_list", False)
+ model_master_device_id = payload_json.get("model_master_device_id", None)
+ model_slave_device_id_list = payload_json.get("model_slave_device_id_list", None)
+ if model_master_device_id is not None:
+ self.model_device_server_id = model_master_device_id
+ if model_slave_device_id_list is not None:
+ self.model_device_client_edge_id_list = model_slave_device_id_list
+ response_topic = f"client/server/response_device_info/{server_id}"
+ if self.mlops_metrics is not None:
+ if not need_gpu_info:
+ device_info_json = {
+ "edge_id": listen_edge_id,
+ "fedml_version": fedml.__version__,
+ "user_id": self.args.user_name
+ }
+ else:
+ total_mem, free_mem, total_disk_size, free_disk_size, cup_utilization, cpu_cores, gpu_cores_total, \
+ gpu_cores_available, sent_bytes, recv_bytes, gpu_available_ids = sys_utils.get_sys_realtime_stats()
+ host_ip = sys_utils.get_host_ip()
+ host_port = sys_utils.get_available_port()
+ gpu_available_ids = JobRunnerUtils.get_available_gpu_id_list(self.edge_id)
+ gpu_available_ids = JobRunnerUtils.trim_unavailable_gpu_ids(gpu_available_ids)
+ gpu_cores_available = len(gpu_available_ids)
+ gpu_list = sys_utils.get_gpu_list()
+ device_info_json = {
+ "edge_id": listen_edge_id,
+ "memoryTotal": round(total_mem * MLOpsUtils.BYTES_TO_GB, 2),
+ "memoryAvailable": round(free_mem * MLOpsUtils.BYTES_TO_GB, 2),
+ "diskSpaceTotal": round(total_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
+ "diskSpaceAvailable": round(free_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
+ "cpuUtilization": round(cup_utilization, 2),
+ "cpuCores": cpu_cores,
+ "gpuCoresTotal": gpu_cores_total,
+ "gpuCoresAvailable": gpu_cores_available,
+ "gpu_available_ids": gpu_available_ids,
+ "gpu_list": gpu_list,
+ "node_ip": host_ip,
+ "node_port": host_port,
+ "networkTraffic": sent_bytes + recv_bytes,
+ "updateTime": int(MLOpsUtils.get_ntp_time()),
+ "fedml_version": fedml.__version__,
+ "user_id": self.args.user_name
+ }
+ if need_running_process_list:
+ device_info_json["run_process_list_map"] = self.get_all_run_process_list_map()
+ salve_device_ids = list()
+ if self.model_device_client_edge_id_list is not None and \
+ isinstance(self.model_device_client_edge_id_list, list):
+ for model_client_edge_id in self.model_device_client_edge_id_list:
+ salve_device_ids.append(model_client_edge_id)
+ response_payload = {"slave_device_id": None if len(salve_device_ids) <= 0 else salve_device_ids[0],
+ "slave_device_id_list": salve_device_ids,
+ "master_device_id": self.model_device_server_id,
+ "run_id": run_id, "edge_id": listen_edge_id,
+ "edge_info": device_info_json}
+ if context is not None:
+ response_payload["context"] = context
+ self.message_center.send_message(response_topic, json.dumps(response_payload), run_id=run_id)
+
+ def callback_client_logout(self, topic, payload):
+ payload_json = json.loads(payload)
+ secret = payload_json.get("auth", None)
+ if secret is None or str(secret) != "246b1be6-0eeb-4b17-b118-7d74de1975d4":
+ return
+ logging.info("Received the logout request.")
+ for runner in self.job_runners:
+ runner.trigger_stop_event()
+ self.disable_client_login = True
+ time.sleep(3)
+ os.system("fedml logout")
+
+ def callback_response_device_status_in_job(self, topic, payload):
+ # Parse the parameters
+ payload_json = json.loads(payload)
+ run_id = payload_json.get("run_id", None)
+ job_status = payload_json.get("status", None)
+ edge_id = payload_json.get("edge_id", None)
+
+ # process the status
+ self.process_status(run_id, job_status, edge_id)
+
+ def callback_response_job_status(self, topic, payload):
+ # Parse the parameters
+ payload_json = json.loads(payload)
+ run_id = payload_json.get("run_id", None)
+ master_agent = payload_json.get("master_agent", None)
+ job_status = payload_json.get("job_status", None)
+ fedml_version = payload_json.get("fedml_version", None)
+ edge_id = payload_json.get("edge_id", None)
+
+ # process the status
+ self.process_status(run_id, job_status, edge_id)
+
+ def callback_broadcasted_job_status(self, topic, payload):
+ # Parse the parameters
+ payload_json = json.loads(payload)
+ run_id = payload_json.get("run_id", None)
+ job_status = payload_json.get("status", None)
+
+ # process the status
+ self.process_status(run_id, job_status, self.edge_id)
+
+ def generate_protocol_manager(self):
+ message_status_runner = self._generate_protocol_manager_instance(
+ self.args, agent_config=self.agent_config
+ )
+ message_status_runner.request_json = self.request_json
+ message_status_runner.disable_client_login = self.disable_client_login
+ message_status_runner.message_center_name = self.message_center_name
+ message_status_runner.run_id = self.run_id
+ message_status_runner.edge_id = self.edge_id
+ message_status_runner.edge_user_name = self.edge_user_name
+ message_status_runner.edge_extra_url = self.edge_extra_url
+ message_status_runner.server_agent_id = self.server_agent_id
+ message_status_runner.current_device_id = self.current_device_id
+ message_status_runner.unique_device_id = self.unique_device_id
+ message_status_runner.subscribed_topics = self.subscribed_topics
+ message_status_runner.running_request_json = self.running_request_json
+ message_status_runner.request_json = self.start_request_json
+ message_status_runner.user_name = self.user_name
+ message_status_runner.general_edge_id = self.general_edge_id
+ message_status_runner.server_id = self.server_id
+ message_status_runner.model_device_server_id = self.model_device_server_id
+ message_status_runner.model_device_client_edge_id_list = self.model_device_client_edge_id_list
+ message_status_runner.status_queue = self.get_status_queue()
+
+ return message_status_runner
+
+ def process_status(self, run_id, status, edge_id):
+ run_id_str = str(run_id)
+
+ # Process the completed status
+ if status == GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or \
+ status == GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FAILED or \
+ status == GeneralConstants.MSG_MLOPS_CLIENT_STATUS_KILLED:
+ if self.job_runners.get(run_id_str, None) is not None:
+ self.job_runners[run_id_str].trigger_completed_event()
+
+ # Stop the sys perf process
+ # noinspection PyBoardException
+ try:
+ self.mlops_metrics.stop_sys_perf()
+ except Exception as ex:
+ logging.error(f"Failed to stop sys perf with Exception {ex}. Traceback: {traceback.format_exc()}")
+ pass
+
+ # Stop the user process
+ try:
+ GeneralConstants.cleanup_learning_process(run_id)
+ GeneralConstants.cleanup_bootstrap_process(run_id)
+ GeneralConstants.cleanup_run_process(run_id)
+ except Exception as e:
+ logging.error(
+ f"Failed to cleanup run when finished with Exception {e}. Traceback: {traceback.format_exc()}")
+ pass
+
+ # Get the running json.
+ running_json = self.running_request_json.get(run_id_str)
+ if running_json is None:
+ try:
+ current_job = FedMLClientDataInterface.get_instance().get_job_by_id(run_id)
+ running_json = json.loads(current_job.running_json)
+ except Exception as e:
+ logging.error(f"Failed to get running json with Exception {e}. Traceback: {traceback.format_exc()}")
+
+ # Cleanup the containers and release the gpu ids.
+ if running_json is not None:
+ job_type = JobRunnerUtils.parse_job_type(running_json)
+ if not SchedulerConstants.is_deploy_job(job_type):
+ logging.info(f"[run/device][{run_id}/{edge_id}] Release gpu resource when run ended.")
+ self._get_job_runner_manager().cleanup_containers_and_release_gpus(run_id, edge_id)
+
+ # Stop the runner process
+ run_process = self._get_job_runner_manager().get_runner_process(run_id)
+ if run_process is not None:
+ if run_process.pid is not None:
+ RunProcessUtils.kill_process(run_process.pid)
+
+ # Terminate the run docker container if exists
+ try:
+ container_name = JobRunnerUtils.get_run_container_name(run_id)
+ docker_client = JobRunnerUtils.get_docker_client(DockerArgs())
+ logging.info(f"Terminating the run docker container {container_name} if exists...")
+ JobRunnerUtils.remove_run_container_if_exists(container_name, docker_client)
+ except Exception as e:
+ logging.error(f"Error occurred when terminating docker container."
+ f"Exception: {e}, Traceback: {traceback.format_exc()}.")
+
+ # Stop log processor for current run
+ MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, edge_id)
+
+ def setup_listener_job_status(self, run_id):
+ # Setup MQTT message listener to receive the job status from master agent;
+ topic_job_status_from_master = f"master_agent/slave_agent/job_status/{run_id}"
+ self.add_message_listener(topic_job_status_from_master, self.callback_broadcasted_job_status)
+ self.subscribe_msg(topic_job_status_from_master)
+
+ def remove_listener_job_status(self, run_id):
+ # Remove MQTT message listener from master agent;
+ topic_job_status_from_master = f"master_agent/slave_agent/job_status/{run_id}"
+ self.remove_message_listener(topic_job_status_from_master)
+ self.unsubscribe_msg(topic_job_status_from_master)
+
+ def sync_run_stop_status(self, run_status=GeneralConstants.MSG_MLOPS_CLIENT_STATUS_KILLED):
+ try:
+ self.status_reporter.report_client_id_status(
+ self.edge_id, run_status, server_id=self.server_id, run_id=self.run_id)
+ except Exception as e:
+ logging.error(f"Failed to sync run stop status with Exception {e}. Traceback: {traceback.format_exc()}")
+ pass
+
+ def get_all_run_process_list_map(self):
+ run_process_dict = dict()
+ all_runner_pid_dict = self._get_job_runner_manager().get_all_runner_pid_map()
+ if all_runner_pid_dict is None:
+ return run_process_dict
+ for run_id_str, process in all_runner_pid_dict.items():
+ cur_run_process_list = GeneralConstants.get_learning_process_list(run_id_str)
+ run_process_dict[run_id_str] = cur_run_process_list
+
+ return run_process_dict
+
+ def stop_job(self, run_id):
+ run_id_str = str(run_id)
+ if self.job_runners.get(run_id_str, None) is not None:
+ self.job_runners[run_id_str].trigger_stop_event()
+
+ @staticmethod
+ def get_start_train_topic_with_edge_id(edge_id):
+ return "flserver_agent/" + str(edge_id) + "/start_train"
+
+ @abstractmethod
+ def _generate_protocol_manager_instance(self, args, agent_config=None):
+ return None
diff --git a/python/fedml/computing/scheduler/slave/client_login.py b/python/fedml/computing/scheduler/slave/client_login.py
index c8123a717c..37a6dc8064 100755
--- a/python/fedml/computing/scheduler/slave/client_login.py
+++ b/python/fedml/computing/scheduler/slave/client_login.py
@@ -1,332 +1,11 @@
-
import argparse
-import json
-import logging
import os
-import platform
-import subprocess
-import time
-import traceback
-
-import click
import fedml
-from fedml.computing.scheduler.comm_utils import sys_utils
-from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
-from fedml.computing.scheduler.slave.client_runner import FedMLClientRunner
-from fedml.computing.scheduler.slave.client_constants import ClientConstants
-from fedml.core.mlops.mlops_runtime_log import MLOpsRuntimeLog
-from fedml.core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
-
-
-def init_logs(args, edge_id):
- # Init runtime logs
- args.log_file_dir = ClientConstants.get_log_file_dir()
- args.run_id = 0
- args.role = "client"
- client_ids = list()
- client_ids.append(edge_id)
- args.client_id_list = json.dumps(client_ids)
- setattr(args, "using_mlops", True)
- MLOpsRuntimeLog.get_instance(args).init_logs()
-
-
-def __login_as_client(args, userid, api_key="", use_extra_device_id_suffix=None, role="client"):
- setattr(args, "account_id", userid)
- setattr(args, "current_running_dir", ClientConstants.get_fedml_home_dir())
-
- sys_name = platform.system()
- if sys_name == "Darwin":
- sys_name = "MacOS"
- if hasattr(args, "os_name") and args.os_name is not None and args.os_name != "":
- pass
- else:
- setattr(args, "os_name", sys_name)
- version = fedml.get_env_version()
- setattr(args, "version", version)
- setattr(args, "log_file_dir", ClientConstants.get_log_file_dir())
- is_from_docker = False
- if hasattr(args, "device_id") and args.device_id is not None and args.device_id != "0":
- setattr(args, "current_device_id", args.device_id)
- is_from_docker = True
- else:
- is_gpu_supplier = (role == ClientConstants.login_role_list[ClientConstants.LOGIN_MODE_GPU_SUPPLIER_INDEX])
- setattr(args, "current_device_id", FedMLClientRunner.get_device_id(use_machine_id=is_gpu_supplier))
- setattr(args, "config_version", version)
- setattr(args, "cloud_region", "")
-
- # Create client runner for communication with the FedML server.
- runner = FedMLClientRunner(args)
-
- # Fetch configs from the MLOps config server.
- service_config = dict()
- config_try_count = 0
- edge_id = 0
- while config_try_count < 5:
- try:
- mqtt_config, s3_config, mlops_config, docker_config = runner.fetch_configs()
- service_config["mqtt_config"] = mqtt_config
- service_config["s3_config"] = s3_config
- service_config["ml_ops_config"] = mlops_config
- service_config["docker_config"] = docker_config
- runner.agent_config = service_config
- # click.echo("service_config = {}".format(service_config))
- log_server_url = mlops_config.get("LOG_SERVER_URL", None)
- if log_server_url is not None:
- setattr(args, "log_server_url", log_server_url)
- setattr(runner.args, "log_server_url", log_server_url)
- break
- except Exception as e:
- click.echo("{}\n{}".format(SchedulerConstants.ERR_MSG_BINDING_EXCEPTION_1, traceback.format_exc()))
- click.echo(SchedulerConstants.ERR_MSG_BINDING_EXIT_RETRYING)
- config_try_count += 1
- time.sleep(3)
- continue
-
- if config_try_count >= 5:
- click.echo("")
- click.echo("[1] Oops, you failed to login the FedML MLOps platform.")
- click.echo("Please check whether your network is normal!")
- return
-
- # Judge whether running from fedml docker hub
- is_from_fedml_docker_hub = False
- dock_loc_file = ClientConstants.get_docker_location_file()
- if os.path.exists(dock_loc_file):
- is_from_fedml_docker_hub = True
-
- # Build unique device id
- if is_from_docker:
- unique_device_id = args.current_device_id + "@" + args.os_name + ".Docker.Edge.Device"
- else:
- unique_device_id = args.current_device_id + "@" + args.os_name + ".Edge.Device"
- if is_from_fedml_docker_hub:
- unique_device_id = args.current_device_id + "@" + args.os_name + ".DockerHub.Edge.Device"
-
- if use_extra_device_id_suffix is not None:
- unique_device_id = args.current_device_id + "@" + args.os_name + use_extra_device_id_suffix
-
- # Bind account id to FedML® Nexus AI Platform
- register_try_count = 0
- edge_id = -1
- user_name = None
- extra_url = None
- general_edge_id = None
- while register_try_count < 5:
- try:
- edge_id, user_name, extra_url, general_edge_id = runner.bind_account_and_device_id(
- service_config["ml_ops_config"]["EDGE_BINDING_URL"], args.account_id, unique_device_id, args.os_name,
- api_key=api_key, role=role
- )
- if edge_id > 0:
- runner.edge_id = edge_id
- runner.edge_user_name = user_name
- runner.edge_extra_url = extra_url
- break
- except SystemExit as e:
- click.echo("Your account does not exist. Please make sure your account correct.")
- os.system("fedml logout -c")
- return
- except Exception as e:
- click.echo("{}\n{}".format(SchedulerConstants.ERR_MSG_BINDING_EXCEPTION_2, traceback.format_exc()))
- click.echo(SchedulerConstants.ERR_MSG_BINDING_EXIT_RETRYING)
- register_try_count += 1
- time.sleep(3)
- continue
-
- if edge_id <= 0:
- click.echo("")
- click.echo("[2] Oops, you failed to login the FedML MLOps platform.")
- click.echo("Please check whether your network is normal!")
- return
-
- # Init runtime logs
- setattr(args, "client_id", edge_id)
- setattr(args, "is_from_docker", is_from_docker)
- runner.args = args
- init_logs(args, edge_id)
- # logging.info("args {}".format(args))
-
- # Log arguments and binding results.
- # logging.info("login: unique_device_id = %s" % str(unique_device_id))
- # logging.info("login: edge_id = %s" % str(edge_id))
- runner.unique_device_id = unique_device_id
- runner.user_name = user_name
- runner.general_edge_id = general_edge_id
- ClientConstants.save_runner_infos(args.current_device_id + "." + args.os_name, edge_id, run_id=0)
-
- # Setup MQTT connection for communication with the FedML server.
- try:
- runner.setup_agent_mqtt_connection(service_config)
- except Exception as e:
- login_exit_file = os.path.join(ClientConstants.get_log_file_dir(), "exited.log")
- with open(login_exit_file, "w") as f:
- f.writelines(f"{os.getpid()}.")
- print("finally")
- runner.stop_agent()
- raise e
-
- # Start mqtt looper
- runner.start_agent_mqtt_loop()
-
-
-def __login_as_simulator(args, userid, mqtt_connection=True):
- setattr(args, "account_id", userid)
- setattr(args, "current_running_dir", ClientConstants.get_fedml_home_dir())
-
- sys_name = platform.system()
- if sys_name == "Darwin":
- sys_name = "MacOS"
- setattr(args, "os_name", sys_name)
- version = fedml.get_env_version()
- setattr(args, "version", version)
- setattr(args, "log_file_dir", ClientConstants.get_log_file_dir())
- setattr(args, "device_id", FedMLClientRunner.get_device_id())
- setattr(args, "current_device_id", FedMLClientRunner.get_device_id())
- setattr(args, "config_version", version)
- setattr(args, "cloud_region", "")
-
-
- # Create client runner for communication with the FedML server.
- runner = FedMLClientRunner(args)
-
- # Fetch configs from the MLOps config server.
- service_config = dict()
- config_try_count = 0
- edge_id = 0
- while config_try_count < 5:
- try:
- mqtt_config, s3_config, mlops_config, docker_config = runner.fetch_configs()
- service_config["mqtt_config"] = mqtt_config
- service_config["s3_config"] = s3_config
- service_config["ml_ops_config"] = mlops_config
- service_config["docker_config"] = docker_config
- runner.agent_config = service_config
- log_server_url = mlops_config.get("LOG_SERVER_URL", None)
- if log_server_url is not None:
- setattr(args, "log_server_url", log_server_url)
- setattr(runner.args, "log_server_url", log_server_url)
- break
- except Exception as e:
- config_try_count += 1
- time.sleep(3)
- continue
-
- if config_try_count >= 5:
- click.echo("")
- click.echo("[3] Oops, you failed to login the FedML MLOps platform.")
- click.echo("Please check whether your network is normal!")
- return False, edge_id, args
-
- # Build unique device id
- if args.device_id is not None and len(str(args.device_id)) > 0:
- unique_device_id = args.device_id + "@" + args.os_name + ".Edge.Simulator"
-
- # Bind account id to FedML® Nexus AI Platform
- register_try_count = 0
- edge_id = -1
- user_name = None
- extra_url = None
- general_edge_id = None
- while register_try_count < 5:
- try:
- edge_id, _, _, _ = runner.bind_account_and_device_id(
- service_config["ml_ops_config"]["EDGE_BINDING_URL"], args.account_id,
- unique_device_id, args.os_name, role="simulator"
- )
- if edge_id > 0:
- runner.edge_id = edge_id
- break
- except SystemExit as e:
- click.echo("Your account does not exist. Please make sure your account correct.")
- os.system("fedml logout -c")
- return
- except Exception as e:
- register_try_count += 1
- time.sleep(3)
- continue
-
- if edge_id <= 0:
- click.echo("")
- click.echo("[4] Oops, you failed to login the FedML MLOps platform.")
- click.echo("Please check whether your network is normal!")
- return False, edge_id, args
-
- # Init runtime logs
- setattr(args, "client_id", edge_id)
- runner.args = args
- #init_logs(args, edge_id)
- logging.info("args {}".format(args))
-
- # Log arguments and binding results.
- logging.info("login: unique_device_id = %s" % str(unique_device_id))
- logging.info("login: edge_id = %s" % str(edge_id))
- runner.unique_device_id = unique_device_id
-
- if mqtt_connection:
- ClientConstants.save_runner_infos(args.device_id + "." + args.os_name, edge_id, run_id=0)
-
- # Setup MQTT connection for communication with the FedML server.
- try:
- runner.setup_agent_mqtt_connection(service_config)
- except Exception as e:
- pass
-
- # Open simulator daemon process to process run status.
- simulator_daemon_cmd = os.path.join(os.path.dirname(__file__), "simulator_daemon.py")
- env_version = fedml.get_env_version()
- simulator_daemon_process = sys_utils.run_subprocess_open(
- [
- sys_utils.get_python_program(),
- simulator_daemon_cmd,
- "-t",
- "login",
- "-u",
- str(args.user),
- "-v",
- env_version,
- "-r",
- args.role,
- "-id",
- args.device_id,
- "-os",
- args.os_name,
- "-rk",
- "1",
- "-lfd",
- args.log_file_dir,
- "-cf",
- env_version,
- "-ci",
- str(edge_id)
- ]
- ).pid
-
- # Start mqtt looper
- runner.start_agent_mqtt_loop()
-
- return True, edge_id, args
-
-
-def login(args):
- if args.role == ClientConstants.login_role_list[ClientConstants.LOGIN_MODE_CLIENT_INDEX]:
- __login_as_client(args, args.user, api_key=args.api_key)
- elif args.role == ClientConstants.login_role_list[ClientConstants.LOGIN_MODE_GPU_SUPPLIER_INDEX]:
- if args.no_gpu_check == 0:
- gpu_count, _ = sys_utils.get_gpu_count_vendor()
- if gpu_count <= 0:
- click.echo("We can't find any gpu device on your machine. \n"
- "With the gpu_supplier(-g) option, you need to check if your machine "
- "has nvidia GPUs and installs CUDA related drivers.")
- return
- __login_as_client(args, args.user, api_key=args.api_key,
- use_extra_device_id_suffix=".Edge.GPU.Supplier", role=args.role)
- elif args.role == ClientConstants.login_role_list[ClientConstants.LOGIN_MODE_EDGE_SIMULATOR_INDEX]:
- __login_as_simulator(args, args.user)
+from fedml.computing.scheduler.slave.slave_agent import FedMLLaunchSlaveAgent
def logout():
- ClientConstants.cleanup_run_process(None)
- sys_utils.cleanup_all_fedml_client_api_processes()
+ FedMLLaunchSlaveAgent.logout()
if __name__ == "__main__":
@@ -351,15 +30,17 @@ def logout():
if args.api_key == "":
args.api_key = args.user
+ fedml.set_env_version("test")
+
if args.local_on_premise_platform_host != "127.0.0.1":
fedml.set_local_on_premise_platform_host(args.local_on_premise_platform_host)
if args.local_on_premise_platform_port != 80:
fedml.set_local_on_premise_platform_port(args.local_on_premise_platform_port)
fedml.set_env_version(args.version)
+ slave_agent = FedMLLaunchSlaveAgent()
if args.type == 'login':
- login(args)
+ slave_agent.login(args.api_key, api_key=args.api_key, device_id=args.device_id,
+ os_name=args.os_name, role=args.role)
else:
- logout()
-
-
+ FedMLLaunchSlaveAgent.logout()
diff --git a/python/fedml/computing/scheduler/slave/client_runner.py b/python/fedml/computing/scheduler/slave/client_runner.py
deleted file mode 100755
index aac57d2174..0000000000
--- a/python/fedml/computing/scheduler/slave/client_runner.py
+++ /dev/null
@@ -1,1775 +0,0 @@
-import json
-import logging
-import multiprocessing
-import sys
-
-from multiprocessing import Process
-import os
-import platform
-import shutil
-import subprocess
-import threading
-
-import time
-import traceback
-import urllib
-import uuid
-import zipfile
-from urllib.parse import urljoin, urlparse
-
-import requests
-
-import fedml
-from ..comm_utils.constants import SchedulerConstants
-from ..comm_utils.job_cleanup import JobCleanup
-from ..comm_utils.job_utils import JobRunnerUtils, DockerArgs
-from ..comm_utils.run_process_utils import RunProcessUtils
-from ..scheduler_entry.constants import Constants
-from ....core.mlops.mlops_device_perfs import MLOpsDevicePerfStats
-from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
-
-from ....core.distributed.communication.mqtt.mqtt_manager import MqttManager
-from ..comm_utils.yaml_utils import load_yaml_config
-from .client_constants import ClientConstants
-
-from ....core.mlops.mlops_metrics import MLOpsMetrics
-
-from ....core.mlops.mlops_configs import MLOpsConfigs
-from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
-from ....core.mlops.mlops_status import MLOpsStatus
-from ..comm_utils.sys_utils import get_sys_runner_info, get_python_program
-from .client_data_interface import FedMLClientDataInterface
-from ..comm_utils import sys_utils
-from ....core.mlops.mlops_utils import MLOpsUtils
-from ..model_scheduler.model_device_client import FedMLModelDeviceClientRunner
-from ..model_scheduler.model_device_server import FedMLModelDeviceServerRunner
-from ..comm_utils import security_utils
-from ..scheduler_core.compute_cache_manager import ComputeCacheManager
-from ..scheduler_core.message_center import FedMLMessageCenter
-
-
-class RunnerError(Exception):
- """ Runner stopped. """
- pass
-
-
-class RunnerCompletedError(Exception):
- """ Runner completed. """
- pass
-
-
-class FedMLClientRunner(FedMLMessageCenter):
-
- def __init__(self, args, edge_id=0, request_json=None, agent_config=None, run_id=0,
- cuda_visible_gpu_ids_str=None):
- super().__init__()
- self.model_device_server_id = None
- self.model_device_client_edge_id_list = None
- self.disable_client_login = False
- self.model_device_server = None
- self.model_device_client_list = None
- self.run_process_event = None
- self.run_process_event_map = dict()
- self.run_process_completed_event = None
- self.run_process_completed_event_map = dict()
- self.run_process = None
- self.run_process_map = dict()
- self.running_request_json = dict()
- self.local_api_process = None
- self.start_request_json = None
- self.device_status = None
- self.current_training_status = None
- self.mqtt_mgr = None
- self.edge_id = edge_id
- self.edge_user_name = None
- self.edge_extra_url = None
- self.run_id = run_id
- self.unique_device_id = None
- self.args = args
- self.request_json = request_json
- self.version = args.version
- self.device_id = args.device_id
- self.cur_dir = os.path.split(os.path.realpath(__file__))[0]
- if args.current_running_dir is not None:
- self.cur_dir = args.current_running_dir
- self.sudo_cmd = ""
- self.is_mac = False
- if platform.system() == "Darwin":
- self.is_mac = True
-
- self.agent_config = agent_config
- self.fedml_data_base_package_dir = os.path.join("/", "fedml", "data")
- self.fedml_data_local_package_dir = os.path.join("/", "fedml", "fedml-package", "fedml", "data")
- self.fedml_data_dir = self.fedml_data_base_package_dir
- self.fedml_config_dir = os.path.join("/", "fedml", "conf")
-
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES = {
- "${FEDSYS.RUN_ID}": "",
- "${FEDSYS.PRIVATE_LOCAL_DATA}": "",
- "${FEDSYS.CLIENT_ID_LIST}": "",
- "${FEDSYS.SYNTHETIC_DATA_URL}": "",
- "${FEDSYS.IS_USING_LOCAL_DATA}": "",
- "${FEDSYS.CLIENT_NUM}": "",
- "${FEDSYS.CLIENT_INDEX}": "",
- "${FEDSYS.CLIENT_OBJECT_LIST}": "",
- "${FEDSYS.LOG_SERVER_URL}": "",
- }
-
- self.mlops_metrics = None
- self.client_active_list = dict()
- self.ntp_offset = MLOpsUtils.get_ntp_offset()
- self.server_id = None
- self.computing_started_time = 0
- self.fedml_config_object = None
- self.package_type = SchedulerConstants.JOB_PACKAGE_TYPE_DEFAULT
- self.cuda_visible_gpu_ids_str = cuda_visible_gpu_ids_str
- # logging.info("Current directory of client agent: " + self.cur_dir)
- self.subscribed_topics = list()
- self.user_name = None
- self.general_edge_id = None
- self.message_center = None
-
- def __repr__(self):
- return "<{klass} @{id:x} {attrs}>".format(
- klass=self.__class__.__name__,
- id=id(self) & 0xFFFFFF,
- attrs=" ".join("{}={!r}".format(k, v) for k, v in self.__dict__.items()),
- )
-
- def copy_runner(self):
- copy_runner = FedMLClientRunner(self.args)
- copy_runner.disable_client_login = self.disable_client_login
- copy_runner.model_device_server = self.model_device_server
- copy_runner.model_device_client_list = self.model_device_client_list
- copy_runner.run_process_event = self.run_process_event
- copy_runner.run_process_event_map = self.run_process_event_map
- copy_runner.run_process_completed_event = self.run_process_completed_event
- copy_runner.run_process_completed_event_map = self.run_process_completed_event_map
- copy_runner.run_process = self.run_process
- copy_runner.run_process_map = self.run_process_map
- copy_runner.running_request_json = self.running_request_json
- copy_runner.local_api_process = self.local_api_process
- copy_runner.start_request_json = self.start_request_json
- copy_runner.device_status = self.device_status
- copy_runner.current_training_status = self.current_training_status
- copy_runner.mqtt_mgr = self.mqtt_mgr
- copy_runner.edge_id = self.edge_id
- copy_runner.edge_user_name = self.edge_user_name
- copy_runner.edge_extra_url = self.edge_extra_url
- copy_runner.run_id = self.run_id
- copy_runner.unique_device_id = self.unique_device_id
- copy_runner.args = self.args
- copy_runner.request_json = self.request_json
- copy_runner.version =self.version
- copy_runner.device_id = self.device_id
- copy_runner.cur_dir = self.cur_dir
- copy_runner.cur_dir = self.cur_dir
- copy_runner.sudo_cmd = self.sudo_cmd
- copy_runner.is_mac = self.is_mac
-
- copy_runner.agent_config = self.agent_config
- copy_runner.fedml_data_base_package_dir = self.fedml_data_base_package_dir
- copy_runner.fedml_data_local_package_dir = self.fedml_data_local_package_dir
- copy_runner.fedml_data_dir = self.fedml_data_dir
- copy_runner.fedml_config_dir = self.fedml_config_dir
-
- copy_runner.FEDML_DYNAMIC_CONSTRAIN_VARIABLES = self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES
-
- copy_runner.mlops_metrics = self.mlops_metrics
- copy_runner.client_active_list = self.client_active_list
- copy_runner.ntp_offset = self.ntp_offset
- copy_runner.server_id = self.server_id
- copy_runner.computing_started_time = self.computing_started_time
- copy_runner.fedml_config_object = self.fedml_config_object
- copy_runner.package_type = self.package_type
- copy_runner.cuda_visible_gpu_ids_str = self.cuda_visible_gpu_ids_str
- copy_runner.subscribed_topics = self.subscribed_topics
- copy_runner.user_name = self.user_name
- copy_runner.general_edge_id = self.general_edge_id
- copy_runner.message_center = self.message_center
-
- return copy_runner
-
- def build_dynamic_constrain_variables(self, run_id, run_config):
- data_config = run_config.get("data_config", {})
- server_edge_id_list = self.request_json["edgeids"]
- local_edge_id_list = list()
- local_edge_id_list.append(int(self.edge_id))
- is_using_local_data = 0
- private_data_dir = data_config.get("privateLocalData", "")
- synthetic_data_url = data_config.get("syntheticDataUrl", "")
- edges = self.request_json["edges"]
- # if private_data_dir is not None \
- # and len(str(private_data_dir).strip(' ')) > 0:
- # is_using_local_data = 1
- if private_data_dir is None or len(str(private_data_dir).strip(" ")) <= 0:
- params_config = run_config.get("parameters", None)
- private_data_dir = ClientConstants.get_data_dir()
- if synthetic_data_url is None or len(str(synthetic_data_url)) <= 0:
- synthetic_data_url = private_data_dir
-
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.RUN_ID}"] = run_id
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.PRIVATE_LOCAL_DATA}"] = private_data_dir.replace(" ", "")
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_ID_LIST}"] = str(local_edge_id_list).replace(" ", "")
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.SYNTHETIC_DATA_URL}"] = synthetic_data_url.replace(" ", "")
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.IS_USING_LOCAL_DATA}"] = str(is_using_local_data)
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_NUM}"] = len(server_edge_id_list)
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_INDEX}"] = 1
- for cur_index, id_value in enumerate(server_edge_id_list):
- if str(id_value) == str(self.edge_id):
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_INDEX}"] = cur_index + 1
- break
- client_objects = str(json.dumps(edges))
- client_objects = client_objects.replace(" ", "").replace("\n", "").replace('"', '\\"')
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_OBJECT_LIST}"] = client_objects
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.LOG_SERVER_URL}"] = self.agent_config["ml_ops_config"][
- "LOG_SERVER_URL"
- ]
-
- def unzip_file(self, zip_file, unzip_file_path) -> str:
- if zipfile.is_zipfile(zip_file):
- with zipfile.ZipFile(zip_file, "r") as zipf:
- zipf.extractall(unzip_file_path)
- unzipped_file_name = zipf.namelist()[0]
- else:
- raise Exception("Invalid zip file {}".format(zip_file))
-
- return unzipped_file_name
-
- def package_download_progress(self, count, blksize, filesize):
- self.check_runner_stop_event()
-
- downloaded = count * blksize
- downloaded = filesize if downloaded > filesize else downloaded
- progress = (downloaded / filesize * 100) if filesize != 0 else 0
- progress_int = int(progress)
- downloaded_kb = format(downloaded / 1024, '.2f')
-
- # since this hook funtion is stateless, we need a state to avoid print progress repeatly
- if count == 0:
- self.prev_download_progress = 0
- if progress_int != self.prev_download_progress and progress_int % 5 == 0:
- self.prev_download_progress = progress_int
- logging.info("package downloaded size {} KB, progress {}%".format(downloaded_kb, progress_int))
-
- def retrieve_and_unzip_package(self, package_name, package_url):
- local_package_path = ClientConstants.get_package_download_dir()
- os.makedirs(local_package_path, exist_ok=True)
- filename, filename_without_extension, file_extension = ClientConstants.get_filename_and_extension(package_url)
- local_package_file = os.path.join(local_package_path, f"fedml_run_{self.run_id}_{filename_without_extension}")
- if os.path.exists(local_package_file):
- os.remove(local_package_file)
- package_url_without_query_path = urljoin(package_url, urlparse(package_url).path)
- urllib.request.urlretrieve(package_url_without_query_path, local_package_file,
- reporthook=self.package_download_progress)
- unzip_package_path = os.path.join(ClientConstants.get_package_unzip_dir(),
- f"unzip_fedml_run_{self.run_id}_{filename_without_extension}")
- try:
- shutil.rmtree(unzip_package_path, ignore_errors=True)
- except Exception as e:
- logging.error(
- f"Failed to remove directory {unzip_package_path}, Exception: {e}, Traceback: {traceback.format_exc()}")
- pass
-
- package_dir_name = self.unzip_file(local_package_file, unzip_package_path) # Using unziped folder name
- unzip_package_full_path = os.path.join(unzip_package_path, package_dir_name)
-
- logging.info("local_package_file {}, unzip_package_path {}, unzip file full path {}".format(
- local_package_file, unzip_package_path, unzip_package_full_path))
-
- return unzip_package_full_path
-
- def update_local_fedml_config(self, run_id, run_config):
- packages_config = run_config["packages_config"]
-
- # Copy config file from the client
- unzip_package_path = self.retrieve_and_unzip_package(
- packages_config["linuxClient"], packages_config["linuxClientUrl"]
- )
- fedml_local_config_file = os.path.join(unzip_package_path, "conf", "fedml.yaml")
-
- # Load the above config to memory
- config_from_container = load_yaml_config(fedml_local_config_file)
- container_entry_file_config = config_from_container["entry_config"]
- container_dynamic_args_config = config_from_container["dynamic_args"]
- entry_file = container_entry_file_config["entry_file"]
- conf_file = container_entry_file_config["conf_file"]
- self.package_type = container_entry_file_config.get("package_type", SchedulerConstants.JOB_PACKAGE_TYPE_DEFAULT)
- full_conf_path = os.path.join(unzip_package_path, "fedml", "config", os.path.basename(conf_file))
-
- # Dynamically build constrain variable with realtime parameters from server
- self.build_dynamic_constrain_variables(run_id, run_config)
-
- # Update entry arguments value with constrain variable values with realtime parameters from server
- # currently we support the following constrain variables:
- # ${FEDSYS_RUN_ID}: a run id represented one entire Federated Learning flow
- # ${FEDSYS_PRIVATE_LOCAL_DATA}: private local data path in the Federated Learning client
- # ${FEDSYS_CLIENT_ID_LIST}: client list in one entire Federated Learning flow
- # ${FEDSYS_SYNTHETIC_DATA_URL}: synthetic data url from server,
- # if this value is not null, the client will download data from this URL to use it as
- # federated training data set
- # ${FEDSYS_IS_USING_LOCAL_DATA}: whether use private local data as federated training data set
- # container_dynamic_args_config["data_cache_dir"] = "${FEDSYS.PRIVATE_LOCAL_DATA}"
- for constrain_variable_key, constrain_variable_value in self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES.items():
- for argument_key, argument_value in container_dynamic_args_config.items():
- if argument_value is not None and str(argument_value).find(constrain_variable_key) == 0:
- replaced_argument_value = str(argument_value).replace(
- constrain_variable_key, str(constrain_variable_value)
- )
- container_dynamic_args_config[argument_key] = replaced_argument_value
-
- # Merge all container new config sections as new config dictionary
- package_conf_object = dict()
- package_conf_object["entry_config"] = container_entry_file_config
- package_conf_object["dynamic_args"] = container_dynamic_args_config
- package_conf_object["dynamic_args"]["config_version"] = self.args.config_version
- container_dynamic_args_config["mqtt_config_path"] = os.path.join(
- unzip_package_path, "fedml", "config", os.path.basename(container_dynamic_args_config["mqtt_config_path"])
- )
- container_dynamic_args_config["s3_config_path"] = os.path.join(
- unzip_package_path, "fedml", "config", os.path.basename(container_dynamic_args_config["s3_config_path"])
- )
- log_file_dir = ClientConstants.get_log_file_dir()
- os.makedirs(log_file_dir, exist_ok=True)
- package_conf_object["dynamic_args"]["log_file_dir"] = log_file_dir
-
- # Save new config dictionary to local file
- fedml_updated_config_file = os.path.join(unzip_package_path, "conf", "fedml.yaml")
- ClientConstants.generate_yaml_doc(package_conf_object, fedml_updated_config_file)
-
- # Build dynamic arguments and set arguments to fedml config object
- self.build_dynamic_args(run_id, run_config, package_conf_object, unzip_package_path)
- return unzip_package_path, package_conf_object
-
- def build_dynamic_args(self, run_id, run_config, package_conf_object, base_dir):
- fedml_conf_file = package_conf_object["entry_config"]["conf_file"]
- fedml_conf_file_processed = str(fedml_conf_file).replace('\\', os.sep).replace('/', os.sep)
- fedml_conf_path = os.path.join(base_dir, "fedml", "config",
- os.path.basename(fedml_conf_file_processed))
- fedml_conf_object = load_yaml_config(fedml_conf_path)
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
-
- # Replace local fedml config objects with parameters from MLOps web
- parameters_object = run_config.get("parameters", None)
- if parameters_object is not None:
- for config_k, config_v in fedml_conf_object.items():
- parameter_v = parameters_object.get(config_k, None)
- if parameter_v is not None:
- fedml_conf_object[config_k] = parameter_v
- parameters_object.pop(config_k)
-
- for config_k, config_v in parameters_object.items():
- fedml_conf_object[config_k] = config_v
-
- package_dynamic_args = package_conf_object["dynamic_args"]
- if fedml_conf_object.get("comm_args", None) is not None:
- fedml_conf_object["comm_args"]["mqtt_config_path"] = package_dynamic_args["mqtt_config_path"]
- fedml_conf_object["comm_args"]["s3_config_path"] = package_dynamic_args["s3_config_path"]
- fedml_conf_object["common_args"]["using_mlops"] = True
- if fedml_conf_object.get("train_args", None) is not None:
- fedml_conf_object["train_args"]["run_id"] = package_dynamic_args["run_id"]
- fedml_conf_object["train_args"]["client_id_list"] = package_dynamic_args["client_id_list"]
- fedml_conf_object["train_args"]["client_num_in_total"] = int(package_dynamic_args["client_num_in_total"])
- fedml_conf_object["train_args"]["client_num_per_round"] = int(package_dynamic_args["client_num_in_total"])
- fedml_conf_object["train_args"]["client_id"] = self.edge_id
- fedml_conf_object["train_args"]["server_id"] = self.request_json.get("server_id", "0")
- if fedml_conf_object.get("device_args", None) is not None:
- fedml_conf_object["device_args"]["worker_num"] = int(package_dynamic_args["client_num_in_total"])
- # fedml_conf_object["data_args"]["data_cache_dir"] = package_dynamic_args["data_cache_dir"]
- data_args = fedml_conf_object.get("data_args")
- if data_args is not None:
- data_cache_dir = fedml_conf_object["data_args"].get("data_cache_dir")
- if data_cache_dir is not None:
- data_cache_dir = os.path.join(data_cache_dir, str(self.edge_id))
- fedml_conf_object["data_args"]["data_cache_dir"] = data_cache_dir
- if fedml_conf_object.get("tracking_args", None) is not None:
- fedml_conf_object["tracking_args"]["log_file_dir"] = package_dynamic_args["log_file_dir"]
- fedml_conf_object["tracking_args"]["log_server_url"] = package_dynamic_args["log_server_url"]
-
- fedml_conf_object["dynamic_args"] = package_dynamic_args
- self.fedml_config_object = fedml_conf_object.copy()
- ClientConstants.generate_yaml_doc(fedml_conf_object, fedml_conf_path)
-
- def run_bootstrap_script(self, bootstrap_cmd_list, bootstrap_script_file):
- try:
- logging.info("Bootstrap commands are being executed...")
- process, error_list = ClientConstants.execute_commands_with_live_logs(bootstrap_cmd_list,
- callback=self.callback_run_bootstrap)
-
- ret_code, out, err = process.returncode, None, None
- if ret_code is None or ret_code <= 0:
- if error_list is not None and len(error_list) > 0:
- is_bootstrap_run_ok = False
- else:
- if out is not None:
- out_str = sys_utils.decode_our_err_result(out)
- if out_str != "":
- logging.info("{}".format(out_str))
-
- sys_utils.log_return_info(bootstrap_script_file, 0)
-
- is_bootstrap_run_ok = True
- else:
- if err is not None:
- err_str = sys_utils.decode_our_err_result(err)
- if err_str != "":
- logging.error("{}".format(err_str))
-
- sys_utils.log_return_info(bootstrap_script_file, ret_code)
-
- is_bootstrap_run_ok = False
- except Exception as e:
- logging.error(f"Bootstrap script error: Exception: {e}, Traceback: {traceback.format_exc()}")
- is_bootstrap_run_ok = False
- return is_bootstrap_run_ok
-
- def callback_run_bootstrap(self, job_pid):
- ClientConstants.save_bootstrap_process(self.run_id, job_pid)
-
- def run(self, process_event, completed_event, message_center_queue):
- print(f"Client runner process id {os.getpid()}, run id {self.run_id}")
-
- if platform.system() != "Windows":
- os.setsid()
-
- os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
- os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
-
- self.run_process_event = process_event
- self.run_process_completed_event = completed_event
- try:
- MLOpsUtils.set_ntp_offset(self.ntp_offset)
- self.rebuild_message_center(message_center_queue)
- self.run_impl()
- except RunnerError:
- logging.info("Runner stopped.")
- self.reset_devices_status(self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED)
- except RunnerCompletedError:
- logging.info("Runner completed.")
- except Exception as e:
- logging.error(f"Runner exited with errors. Exception: {e}, Traceback {traceback.format_exc()}")
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- server_id=self.server_id, run_id=self.run_id)
- finally:
- if self.mlops_metrics is not None:
- computing_ended_time = MLOpsUtils.get_ntp_time()
- self.mlops_metrics.report_edge_job_computing_cost(self.run_id, self.edge_id,
- self.computing_started_time, computing_ended_time,
- self.args.user, self.args.api_key)
- logging.info("Release resources.")
- self.cleanup_containers_and_release_gpus(self.run_id, self.edge_id)
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, self.edge_id)
- if self.mlops_metrics is not None:
- self.mlops_metrics.stop_sys_perf()
- time.sleep(3)
- ClientConstants.cleanup_learning_process(self.run_id)
- ClientConstants.cleanup_run_process(self.run_id)
-
- def check_runner_stop_event(self):
- if self.run_process_event.is_set():
- logging.info("Received stopping event.")
- raise RunnerError("Runner stopped")
-
- if self.run_process_completed_event.is_set():
- logging.info("Received completed event.")
- raise RunnerCompletedError("Runner completed")
-
- def run_impl(self):
- run_id = self.request_json["runId"]
- run_config = self.request_json["run_config"]
- data_config = run_config.get("data_config", {})
- packages_config = run_config["packages_config"]
-
- self.computing_started_time = MLOpsUtils.get_ntp_time()
- self.mlops_metrics.report_edge_job_computing_cost(run_id, self.edge_id,
- self.computing_started_time, 0,
- self.args.user, self.args.api_key)
-
- self.check_runner_stop_event()
-
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
-
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_INITIALIZING,
- running_json=self.start_request_json, run_id=run_id)
-
- # get training params
- private_local_data_dir = data_config.get("privateLocalData", "")
- is_using_local_data = 0
- # if private_local_data_dir is not None and len(str(private_local_data_dir).strip(' ')) > 0:
- # is_using_local_data = 1
-
- # start a run according to the hyper-parameters
- # fedml_local_data_dir = self.cur_dir + "/fedml_data/run_" + run_id_str + "_edge_" + str(edge_id)
- fedml_local_data_dir = os.path.join(self.cur_dir, "fedml_data")
- fedml_local_config_dir = os.path.join(self.cur_dir, "fedml_config")
- if is_using_local_data:
- fedml_local_data_dir = private_local_data_dir
- self.fedml_data_dir = self.fedml_data_local_package_dir
-
- self.check_runner_stop_event()
-
- logging.info("Download packages")
-
- # update local config with real time parameters from server and dynamically replace variables value
- unzip_package_path, fedml_config_object = self.update_local_fedml_config(run_id, run_config)
- # if unzip_package_path is None or fedml_config_object is None:
- # logging.info("failed to update local fedml config.")
- # self.check_runner_stop_event()
- # # Send failed msg when exceptions.
- # self.cleanup_run_when_starting_failed(status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION)
- # return
-
- logging.info("Check downloaded packages...")
-
- entry_file_config = fedml_config_object["entry_config"]
- dynamic_args_config = fedml_config_object["dynamic_args"]
- entry_file = str(entry_file_config["entry_file"]).replace('\\', os.sep).replace('/', os.sep)
- entry_file = os.path.basename(entry_file)
- conf_file = entry_file_config["conf_file"]
- conf_file = str(conf_file).replace('\\', os.sep).replace('/', os.sep)
- #####
- # ClientConstants.cleanup_learning_process(run_id)
- # ClientConstants.cleanup_bootstrap_process(run_id)
- #####
-
- if not os.path.exists(unzip_package_path):
- logging.info("failed to unzip file.")
- self.check_runner_stop_event()
- # Send failed msg when exceptions.
- self.cleanup_run_when_starting_failed(status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION)
- return
- os.chdir(os.path.join(unzip_package_path, "fedml"))
-
- self.check_runner_stop_event()
-
- logging.info("starting the user process...")
-
- entry_file_full_path = os.path.join(unzip_package_path, "fedml", entry_file)
- conf_file_full_path = os.path.join(unzip_package_path, "fedml", conf_file)
- logging.info("waiting the user process to finish...")
- logging.info(" ")
- logging.info(" ")
- logging.info("====Your Run Logs Begin===")
-
- process, is_launch_task, error_list = self.execute_job_task(unzip_package_path=unzip_package_path,
- entry_file_full_path=entry_file_full_path,
- conf_file_full_path=conf_file_full_path,
- dynamic_args_config=dynamic_args_config,
- fedml_config_object=self.fedml_config_object)
-
- logging.info("====Your Run Logs End===")
- logging.info(" ")
- logging.info(" ")
-
- ret_code, out, err = process.returncode if process else None, None, None
- is_run_ok = sys_utils.is_runner_finished_normally(process.pid)
- if is_launch_task:
- is_run_ok = True
- if error_list is not None and len(error_list) > 0:
- is_run_ok = False
- if ret_code is None or ret_code <= 0:
- self.check_runner_stop_event()
-
- if is_run_ok:
- if out is not None:
- out_str = sys_utils.decode_our_err_result(out)
- if out_str != "":
- logging.info("{}".format(out_str))
-
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
- server_id=self.server_id, run_id=run_id)
-
- if is_launch_task:
- sys_utils.log_return_info(f"job {run_id}", ret_code)
- else:
- sys_utils.log_return_info(entry_file, ret_code)
- else:
- is_run_ok = False
-
- if not is_run_ok:
- # If the run status is killed or finished, then return with the normal state.
- current_job = FedMLClientDataInterface.get_instance().get_job_by_id(run_id)
- if current_job is not None and (current_job.status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or
- current_job.status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED):
- return
-
- self.check_runner_stop_event()
-
- logging.error("failed to run the learning process...")
-
- if err is not None:
- err_str = sys_utils.decode_our_err_result(err)
- if err_str != "":
- logging.error("{}".format(err_str))
-
- if is_launch_task:
- sys_utils.log_return_info(f"job {run_id}", ret_code)
- else:
- sys_utils.log_return_info(entry_file, ret_code)
-
- # Send failed msg when exceptions.
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- server_id=self.server_id, run_id=run_id)
-
- def execute_job_task(self, unzip_package_path, entry_file_full_path, conf_file_full_path, dynamic_args_config,
- fedml_config_object):
- run_config = self.request_json["run_config"]
- run_params = run_config.get("parameters", {})
- client_rank = self.request_json.get("client_rank", 1)
- job_yaml = run_params.get("job_yaml", {})
- job_yaml_default_none = run_params.get("job_yaml", None)
- job_api_key = job_yaml.get("run_api_key", None)
- job_api_key = job_yaml.get("fedml_run_dynamic_params", None) if job_api_key is None else job_api_key
- assigned_gpu_ids = run_params.get("gpu_ids", None)
- job_type = job_yaml.get("job_type", None)
- containerize = fedml_config_object.get("containerize", None)
- image_pull_policy = fedml_config_object.get("image_pull_policy", Constants.IMAGE_PULL_POLICY_ALWAYS)
- # TODO: Can we remove task_type?
- job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
- conf_file_object = load_yaml_config(conf_file_full_path)
- entry_args_dict = conf_file_object.get("fedml_entry_args", {})
- entry_args = entry_args_dict.get("arg_items", None)
- scheduler_match_info = self.request_json.get("scheduler_match_info", {})
- if job_type == Constants.JOB_TASK_TYPE_TRAIN:
- containerize = True if containerize is None else containerize
-
- # Bootstrap Info
- bootstrap_script_path, bootstrap_script_dir, bootstrap_script_file = [None] * 3
- env_args = fedml_config_object.get("environment_args", None)
-
- if env_args is not None:
- bootstrap_script_file = env_args.get("bootstrap", None)
- if bootstrap_script_file is not None:
- bootstrap_script_file = str(bootstrap_script_file).replace('\\', os.sep).replace('/', os.sep)
- if platform.system() == 'Windows':
- bootstrap_script_file = bootstrap_script_file.rstrip('.sh') + '.bat'
- if bootstrap_script_file is not None:
- bootstrap_script_dir = os.path.join(unzip_package_path, "fedml",
- os.path.dirname(bootstrap_script_file))
- bootstrap_script_path = os.path.join(
- bootstrap_script_dir, bootstrap_script_dir, os.path.basename(bootstrap_script_file)
- )
-
- bootstrap_cmd_list = list()
- if bootstrap_script_path:
- logging.info("Bootstrap commands are being generated...")
- bootstrap_cmd_list = JobRunnerUtils.generate_bootstrap_commands(bootstrap_script_path=bootstrap_script_path,
- bootstrap_script_dir=bootstrap_script_dir,
- bootstrap_script_file=bootstrap_script_file)
- logging.info(f"Generated following Bootstrap commands: {bootstrap_cmd_list}")
-
- if not containerize:
- if len(bootstrap_cmd_list) and not (job_type == Constants.JOB_TASK_TYPE_DEPLOY or
- job_type == Constants.JOB_TASK_TYPE_SERVE):
- bootstrapping_successful = self.run_bootstrap_script(bootstrap_cmd_list=bootstrap_cmd_list,
- bootstrap_script_file=bootstrap_script_file)
-
- if not bootstrapping_successful:
- logging.info("failed to update local fedml config.")
- self.check_runner_stop_event()
- # Send failed msg when exceptions.
- self.cleanup_run_when_starting_failed(status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION)
- raise Exception(f"Failed to execute following bootstrap commands: {bootstrap_cmd_list}")
-
- logging.info("cleanup the previous learning process and bootstrap process...")
- ClientConstants.cleanup_learning_process(self.request_json["runId"])
- ClientConstants.cleanup_bootstrap_process(self.request_json["runId"])
-
- executable_interpreter = ClientConstants.CLIENT_SHELL_PS \
- if platform.system() == ClientConstants.PLATFORM_WINDOWS else ClientConstants.CLIENT_SHELL_BASH
-
- if job_yaml_default_none is None:
- # Generate the job executing commands for previous federated learning (Compatibility)
- python_program = get_python_program()
- logging.info("Run the client: {} {} --cf {} --rank {} --role client".format(
- python_program, entry_file_full_path, conf_file_full_path, str(dynamic_args_config.get("rank", 1))))
- rank = str(dynamic_args_config.get("rank", 1))
- entry_command = f"{python_program} {entry_file_full_path} --cf " \
- f"{conf_file_full_path} --rank {rank} --role client"
- shell_cmd_list = [entry_command]
-
- # Run the job executing commands for previous federated learning (Compatibility)
- process, error_list = ClientConstants.execute_commands_with_live_logs(
- shell_cmd_list, callback=self.callback_start_fl_job, should_write_log_file=False)
- is_launch_task = False
- else:
- self.check_runner_stop_event()
-
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_RUNNING, run_id=self.run_id)
-
- # Generate the job executing commands
- job_executing_commands = JobRunnerUtils.generate_job_execute_commands(
- self.run_id, self.edge_id, self.version,
- self.package_type, executable_interpreter, entry_file_full_path,
- conf_file_object, entry_args, assigned_gpu_ids,
- job_api_key, client_rank, scheduler_match_info=scheduler_match_info,
- cuda_visible_gpu_ids_str=self.cuda_visible_gpu_ids_str)
-
- if containerize is not None and containerize is True:
- docker_args = fedml_config_object.get("docker", {})
- docker_args = JobRunnerUtils.create_instance_from_dict(DockerArgs, docker_args)
- try:
- job_executing_commands = JobRunnerUtils.generate_launch_docker_command(docker_args=docker_args,
- run_id=self.run_id,
- edge_id=self.edge_id,
- unzip_package_path=unzip_package_path,
- executable_interpreter=executable_interpreter,
- entry_file_full_path=entry_file_full_path,
- bootstrap_cmd_list=bootstrap_cmd_list,
- cuda_visible_gpu_ids_str=self.cuda_visible_gpu_ids_str,
- image_pull_policy=image_pull_policy)
- except Exception as e:
- logging.error(f"Error occurred while generating containerized launch commands. "
- f"Exception: {e}, Traceback: {traceback.format_exc()}")
- return None, None, None
-
- if not job_executing_commands:
- raise Exception("Failed to generate docker execution command")
-
- # Run the job executing commands
- logging.info(f"Run the client job with job id {self.run_id}, device id {self.edge_id}.")
- process, error_list = ClientConstants.execute_commands_with_live_logs(
- job_executing_commands, callback=self.start_job_perf, error_processor=self.job_error_processor,
- should_write_log_file=False if job_type == Constants.JOB_TASK_TYPE_FEDERATE else True)
- is_launch_task = False if job_type == Constants.JOB_TASK_TYPE_FEDERATE else True
-
- return process, is_launch_task, error_list
-
- def callback_start_fl_job(self, job_pid):
- ClientConstants.save_learning_process(self.run_id, job_pid)
- self.mlops_metrics.report_sys_perf(
- self.args, self.agent_config["mqtt_config"], job_process_id=job_pid)
-
- def start_job_perf(self, job_pid):
- ClientConstants.save_learning_process(self.run_id, job_pid)
- self.mlops_metrics.report_job_perf(self.args, self.agent_config["mqtt_config"], job_pid)
-
- def job_error_processor(self, error_list):
- self.check_runner_stop_event()
-
- error_str = "\n".join(error_list)
- error_message = f"Error occurred when running the job... {error_str}"
- logging.error(error_message)
- raise Exception(error_message)
-
- def reset_devices_status(self, edge_id, status, should_send_client_id_status=True):
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.edge_id = edge_id
-
- if should_send_client_id_status:
- if status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED or \
- status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or \
- status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION:
- self.mlops_metrics.report_client_id_status(
- edge_id, status, server_id=self.server_id, run_id=self.run_id)
-
- def sync_run_stop_status(self, run_status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED):
- try:
- if self.run_process_event is not None:
- self.run_process_event.set()
-
- self.mlops_metrics.report_client_id_status(
- self.edge_id, run_status, server_id=self.server_id, run_id=self.run_id)
- except Exception as e:
- logging.error(f"Failed to sync run stop status with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- def cleanup_run_when_starting_failed(
- self, status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED, should_send_client_id_status=True):
- # logging.error("Cleanup run successfully when starting failed.")
-
- self.reset_devices_status(
- self.edge_id, status, should_send_client_id_status=should_send_client_id_status)
-
- time.sleep(2)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- logging.error(f"Failed to stop sys perf with Exception {ex}. Traceback: {traceback.format_exc()}")
- pass
-
- time.sleep(1)
-
- try:
- ClientConstants.cleanup_learning_process(self.run_id)
- ClientConstants.cleanup_bootstrap_process(self.run_id)
- ClientConstants.cleanup_run_process(self.run_id)
- except Exception as e:
- logging.error(
- f"Failed to cleanup run when starting failed with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- def cleanup_run_when_finished(self):
- # logging.info("Cleanup run successfully when finished.")
-
- self.reset_devices_status(self.edge_id,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
- should_send_client_id_status=False)
-
- time.sleep(2)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- logging.error(f"Failed to stop sys perf with Exception {ex}. Traceback: {traceback.format_exc()}")
- pass
-
- time.sleep(1)
-
- try:
- ClientConstants.cleanup_learning_process(self.run_id)
- ClientConstants.cleanup_bootstrap_process(self.run_id)
- ClientConstants.cleanup_run_process(self.run_id)
- except Exception as e:
- logging.error(
- f"Failed to cleanup run when finished with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- def setup_message_center(self):
- if self.message_center is not None:
- return
-
- self.message_center = FedMLMessageCenter(agent_config=self.agent_config)
- self.message_center.start_sender()
-
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
- self.mlops_metrics.set_messenger(self.message_center)
- self.mlops_metrics.run_id = self.run_id
-
- def rebuild_message_center(self, message_center_queue):
- self.message_center = FedMLMessageCenter(message_queue=message_center_queue)
-
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
- self.mlops_metrics.set_messenger(self.message_center)
- self.mlops_metrics.run_id = self.run_id
-
- def release_message_center(self):
- try:
- if self.message_center is not None:
- self.message_center.stop()
- self.message_center = None
-
- except Exception as e:
- logging.error(
- f"Failed to release client mqtt manager with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- def ota_upgrade(self, payload, request_json):
- run_id = request_json["runId"]
- force_ota = False
- ota_version = None
-
- try:
- run_config = request_json.get("run_config", None)
- parameters = run_config.get("parameters", None)
- common_args = parameters.get("common_args", None)
- force_ota = common_args.get("force_ota", False) if common_args is not None else False
- ota_version = common_args.get("ota_version", None) if common_args is not None else None
- except Exception as e:
- logging.error(
- f"Failed to get ota upgrade parameters with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- if force_ota and ota_version is not None:
- should_upgrade = True if ota_version != fedml.__version__ else False
- upgrade_version = ota_version
- else:
- try:
- fedml_is_latest_version, local_ver, remote_ver = sys_utils.check_fedml_is_latest_version(self.version)
- except Exception as e:
- logging.error(f"Failed to check fedml version with Exception {e}. Traceback: {traceback.format_exc()}")
- return
-
- should_upgrade = False if fedml_is_latest_version else True
- upgrade_version = remote_ver
-
- if should_upgrade:
- FedMLClientDataInterface.get_instance(). \
- save_started_job(run_id, self.edge_id, time.time(),
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
- payload)
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING, run_id=run_id)
-
- logging.info(f"Upgrade to version {upgrade_version} ...")
-
- sys_utils.do_upgrade(self.version, upgrade_version)
- raise Exception("Restarting after upgraded...")
-
- def callback_start_train(self, topic, payload):
- # Get training params
-
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json["runId"]
-
- # Start log processor for current run
- train_edge_id = str(topic).split("/")[-2]
- self.args.run_id = run_id
- self.args.edge_id = train_edge_id
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
- MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(
- run_id, train_edge_id, log_source=SchedulerConstants.get_log_source(request_json))
- logging.info("start the log processor")
-
- try:
- MLOpsConfigs.fetch_all_configs()
- except Exception as e:
- logging.error(f"Failed to fetch all configs with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- if not FedMLClientDataInterface.get_instance().get_agent_status():
- request_json = json.loads(payload)
- run_id = request_json["runId"]
- logging.error(
- "FedMLDebug - Receive: topic ({}), payload ({}), but the client agent is disabled. {}".format(
- topic, payload, traceback.format_exc()
- )
- )
- # Send failed msg when exceptions.
- self.mlops_metrics.report_client_id_status(
- train_edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION, run_id=run_id,
- msg=f"the client agent {train_edge_id} is disabled")
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, train_edge_id)
- return
-
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- # Terminate previous process about starting or stopping run command
- logging.info("cleanup and save runner information")
- server_agent_id = request_json["cloud_agent_id"]
- ClientConstants.save_runner_infos(self.args.device_id + "." + self.args.os_name, train_edge_id, run_id=run_id)
-
- # OTA upgrade
- self.ota_upgrade(payload, request_json)
-
- # Occupy GPUs
- scheduler_match_info = request_json.get("scheduler_match_info", {})
- matched_gpu_num = scheduler_match_info.get("matched_gpu_num", 0)
- model_master_device_id = scheduler_match_info.get("model_master_device_id", None)
- model_slave_device_id = scheduler_match_info.get("model_slave_device_id", None)
- model_slave_device_id_list = scheduler_match_info.get("model_slave_device_id_list", None)
- run_config = request_json.get("run_config", {})
- run_params = run_config.get("parameters", {})
- serving_args = run_params.get("serving_args", {})
- endpoint_id = serving_args.get("endpoint_id", None)
- cuda_visible_gpu_ids_str = JobRunnerUtils.get_instance().occupy_gpu_ids(
- run_id, matched_gpu_num, train_edge_id, inner_id=endpoint_id,
- model_master_device_id=model_master_device_id,
- model_slave_device_id=model_slave_device_id)
- logging.info(
- f"Run started, available gpu ids: {JobRunnerUtils.get_instance().get_available_gpu_id_list(train_edge_id)}")
-
- # Start server with multiprocessing mode
- self.request_json = request_json
- run_id_str = str(run_id)
- self.running_request_json[run_id_str] = request_json
- client_runner = FedMLClientRunner(
- self.args, edge_id=train_edge_id, request_json=request_json, agent_config=self.agent_config, run_id=run_id,
- cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str
- )
- client_runner.start_request_json = payload
- self.run_process_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_event_map[run_id_str].clear()
- client_runner.run_process_event = self.run_process_event_map[run_id_str]
- self.run_process_completed_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_completed_event_map[run_id_str].clear()
- client_runner.run_process_completed_event = self.run_process_completed_event_map[run_id_str]
- client_runner.server_id = request_json.get("server_id", "0")
- logging.info("start the runner process.")
- self.run_process_map[run_id_str] = Process(target=client_runner.run, args=(
- self.run_process_event_map[run_id_str], self.run_process_completed_event_map[run_id_str],
- self.message_center.get_message_queue()))
- self.run_process_map[run_id_str].start()
- ClientConstants.save_run_process(run_id, self.run_process_map[run_id_str].pid)
-
- def callback_stop_train(self, topic, payload):
- # logging.info("callback_stop_train: topic = %s, payload = %s" % (topic, payload))
- # logging.info(
- # f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- # )
-
- train_edge_id = str(topic).split("/")[-2]
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json.get("runId", None)
- if run_id is None:
- run_id = request_json.get("id", None)
- run_status = request_json.get("run_status", ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED)
-
- # logging.info("Stop run with multiprocessing...")
-
- # Stop client with multiprocessing mode
- run_id_str = str(run_id)
- client_runner = FedMLClientRunner(
- self.args, edge_id=train_edge_id, request_json=request_json, agent_config=self.agent_config, run_id=run_id
- )
- self.cleanup_containers_and_release_gpus(run_id, train_edge_id)
- client_runner.run_process_event = self.run_process_event_map.get(run_id_str, None)
- client_runner.run_process = self.run_process_map.get(run_id_str, None)
- client_runner.message_center = self.message_center
- client_runner.mlops_metrics = self.mlops_metrics
- client_runner.sync_run_stop_status(run_status=run_status)
-
- def cleanup_containers_and_release_gpus(self, run_id, edge_id):
- job_type = JobRunnerUtils.get_job_type_from_run_id(run_id)
-
- if not job_type:
- logging.info(f"Failed to get job type from run id {run_id}. This is not an error as it would usually "
- f"happen when the job is not found in the database because job is already finished and "
- f"cleaned up. Exiting cleanup_containers_and_release_gpus.")
- return
-
- # Check if the job type is not "serve" or "deploy"
- if not (job_type == SchedulerConstants.JOB_TASK_TYPE_SERVE or
- job_type == SchedulerConstants.JOB_TASK_TYPE_DEPLOY):
-
- # Terminate the run docker container if exists
- container_name = JobRunnerUtils.get_run_container_name(run_id)
- docker_client = JobRunnerUtils.get_docker_client(DockerArgs())
- logging.info(f"Terminating the run docker container {container_name} if exists...")
- try:
- JobRunnerUtils.remove_run_container_if_exists(container_name, docker_client)
- except Exception as e:
- logging.error(f"Exception {e} occurred when terminating docker container. "
- f"Traceback: {traceback.format_exc()}")
-
- # Release the GPU ids and update the GPU availability in the persistent store
- JobRunnerUtils.get_instance().release_gpu_ids(run_id, edge_id)
-
- # Send mqtt message reporting the new gpu availability to the backend
- MLOpsDevicePerfStats.report_gpu_device_info(self.edge_id, mqtt_mgr=self.mqtt_mgr)
-
- def cleanup_client_with_status(self):
- if self.device_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED:
- # logging.info("received to finished status.")
- self.cleanup_run_when_finished()
- elif self.device_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED:
- # logging.error("received to failed status from the server agent")
- self.cleanup_run_when_starting_failed(should_send_client_id_status=False)
- elif self.device_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED:
- # logging.error("received to failed status from the server agent")
- self.cleanup_run_when_starting_failed(status=self.device_status, should_send_client_id_status=False)
-
- def callback_runner_id_status(self, topic, payload):
- # logging.info("callback_runner_id_status: topic = %s, payload = %s" % (topic, payload))
- # logging.info(f"FedMLDebug - Receive: topic ({topic}), payload ({payload})")
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json["run_id"]
- edge_id = str(topic).split("/")[-2].split('_')[-1]
- status = request_json["status"]
- run_id_str = str(run_id)
-
- self.save_training_status(
- edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED
- if status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION else status)
-
- if status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or \
- status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED or \
- status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED:
- completed_event = self.run_process_completed_event_map.get(run_id_str, None)
- if completed_event is not None:
- completed_event.set()
-
- # Stop client with multiprocessing mode
- client_runner = FedMLClientRunner(
- self.args,
- edge_id=edge_id,
- request_json=request_json,
- agent_config=self.agent_config,
- run_id=run_id,
- )
- client_runner.device_status = status
- client_runner.message_center = self.message_center
- client_runner.mlops_metrics = self.mlops_metrics
- client_runner.cleanup_client_with_status()
-
- running_json = self.running_request_json.get(run_id_str)
- if running_json is None:
- try:
- current_job = FedMLClientDataInterface.get_instance().get_job_by_id(run_id)
- running_json = json.loads(current_job.running_json)
- except Exception as e:
- logging.error(f"Failed to get running json with Exception {e}. Traceback: {traceback.format_exc()}")
-
- if running_json is not None:
- job_type = JobRunnerUtils.parse_job_type(running_json)
- if not SchedulerConstants.is_deploy_job(job_type):
- logging.info(f"[run/device][{run_id}/{edge_id}] Release gpu resource when run ended.")
- self.cleanup_containers_and_release_gpus(run_id, edge_id)
-
- run_process = self.run_process_map.get(run_id_str, None)
- if run_process is not None:
- if run_process.pid is not None:
- RunProcessUtils.kill_process(run_process.pid)
-
- # Terminate the run docker container if exists
- try:
- container_name = JobRunnerUtils.get_run_container_name(run_id)
- docker_client = JobRunnerUtils.get_docker_client(DockerArgs())
- logging.info(f"Terminating the run docker container {container_name} if exists...")
- JobRunnerUtils.remove_run_container_if_exists(container_name, docker_client)
- except Exception as e:
- logging.error(f"Error occurred when terminating docker container."
- f"Exception: {e}, Traceback: {traceback.format_exc()}.")
-
- self.run_process_map.pop(run_id_str)
-
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, edge_id)
-
- def callback_report_current_status(self, topic, payload):
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- self.send_agent_active_msg()
- if self.general_edge_id is not None:
- self.send_agent_active_msg(self.general_edge_id)
-
- @staticmethod
- def process_ota_upgrade_msg():
- os.system("pip install -U fedml")
-
- @staticmethod
- def callback_client_ota_msg(topic, payload):
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- request_json = json.loads(payload)
- cmd = request_json["cmd"]
-
- if cmd == ClientConstants.FEDML_OTA_CMD_UPGRADE:
- FedMLClientRunner.process_ota_upgrade_msg()
- # Process(target=FedMLClientRunner.process_ota_upgrade_msg).start()
- raise Exception("After upgraded, restart runner...")
- elif cmd == ClientConstants.FEDML_OTA_CMD_RESTART:
- raise Exception("Restart runner...")
-
- def get_all_run_process_list_map(self):
- run_process_dict = dict()
- for run_id_str, process in self.run_process_map.items():
- cur_run_process_list = ClientConstants.get_learning_process_list(run_id_str)
- run_process_dict[run_id_str] = cur_run_process_list
-
- return run_process_dict
-
- def callback_report_device_info(self, topic, payload):
- payload_json = json.loads(payload)
- server_id = payload_json.get("server_id", 0)
- run_id = payload_json.get("run_id", 0)
- listen_edge_id = str(topic).split("/")[-1]
- context = payload_json.get("context", None)
- need_gpu_info = payload_json.get("need_gpu_info", False)
- need_running_process_list = payload_json.get("need_running_process_list", False)
- response_topic = f"client/server/response_device_info/{server_id}"
- if self.mlops_metrics is not None and self.model_device_client_edge_id_list is not None and \
- self.model_device_server_id is not None:
- if not need_gpu_info:
- device_info_json = {
- "edge_id": listen_edge_id,
- "fedml_version": fedml.__version__,
- "user_id": self.args.user
- }
- else:
- total_mem, free_mem, total_disk_size, free_disk_size, cup_utilization, cpu_cores, gpu_cores_total, \
- gpu_cores_available, sent_bytes, recv_bytes, gpu_available_ids = sys_utils.get_sys_realtime_stats()
- host_ip = sys_utils.get_host_ip()
- host_port = sys_utils.get_available_port()
- gpu_available_ids = JobRunnerUtils.get_available_gpu_id_list(self.edge_id)
- gpu_available_ids = JobRunnerUtils.trim_unavailable_gpu_ids(gpu_available_ids)
- gpu_cores_available = len(gpu_available_ids)
- gpu_list = sys_utils.get_gpu_list()
- device_info_json = {
- "edge_id": listen_edge_id,
- "memoryTotal": round(total_mem * MLOpsUtils.BYTES_TO_GB, 2),
- "memoryAvailable": round(free_mem * MLOpsUtils.BYTES_TO_GB, 2),
- "diskSpaceTotal": round(total_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
- "diskSpaceAvailable": round(free_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
- "cpuUtilization": round(cup_utilization, 2),
- "cpuCores": cpu_cores,
- "gpuCoresTotal": gpu_cores_total,
- "gpuCoresAvailable": gpu_cores_available,
- "gpu_available_ids": gpu_available_ids,
- "gpu_list": gpu_list,
- "node_ip": host_ip,
- "node_port": host_port,
- "networkTraffic": sent_bytes + recv_bytes,
- "updateTime": int(MLOpsUtils.get_ntp_time()),
- "fedml_version": fedml.__version__,
- "user_id": self.args.user
- }
- if need_running_process_list:
- device_info_json["run_process_list_map"] = self.get_all_run_process_list_map()
- salve_device_ids = list()
- for model_client_edge_id in self.model_device_client_edge_id_list:
- salve_device_ids.append(model_client_edge_id)
- response_payload = {"slave_device_id": self.model_device_client_edge_id_list[0],
- "slave_device_id_list": salve_device_ids,
- "master_device_id": self.model_device_server_id,
- "run_id": run_id, "edge_id": listen_edge_id,
- "edge_info": device_info_json}
- if context is not None:
- response_payload["context"] = context
- self.message_center.send_message(response_topic, json.dumps(response_payload), run_id=run_id)
-
- def callback_client_logout(self, topic, payload):
- payload_json = json.loads(payload)
- secret = payload_json.get("auth", None)
- if secret is None or str(secret) != "246b1be6-0eeb-4b17-b118-7d74de1975d4":
- return
- logging.info("Received the logout request.")
- if self.run_process_event is not None:
- self.run_process_event.set()
- if self.run_process_completed_event is not None:
- self.run_process_completed_event.set()
- self.disable_client_login = True
- time.sleep(3)
- os.system("fedml logout")
-
- def save_training_status(self, edge_id, training_status):
- self.current_training_status = training_status
- ClientConstants.save_training_infos(edge_id, training_status)
-
- @staticmethod
- def get_gpu_machine_id():
- gpu_list = sys_utils.get_gpu_list()
- gpu_uuids = ""
- if len(gpu_list) > 0:
- for gpu in gpu_list:
- gpu_uuids += gpu.get("uuid", "")
- else:
- gpu_uuids = str(uuid.uuid4())
- device_id_combination = \
- f"{FedMLClientRunner.get_machine_id()}-{hex(uuid.getnode())}-{gpu_uuids}"
- device_id = security_utils.get_content_hash(device_id_combination)
- return device_id
-
- @staticmethod
- def get_device_id(use_machine_id=False):
- device_file_path = os.path.join(ClientConstants.get_data_dir(),
- ClientConstants.LOCAL_RUNNER_INFO_DIR_NAME)
- file_for_device_id = os.path.join(device_file_path, "devices.id")
- if not os.path.exists(device_file_path):
- os.makedirs(device_file_path, exist_ok=True)
- elif os.path.exists(file_for_device_id):
- with open(file_for_device_id, 'r', encoding='utf-8') as f:
- device_id_from_file = f.readline()
- if device_id_from_file is not None and device_id_from_file != "":
- return device_id_from_file
-
- if platform.system() == "Darwin":
- cmd_get_serial_num = "system_profiler SPHardwareDataType | grep Serial | awk '{gsub(/ /,\"\")}{print}' " \
- "|awk -F':' '{print $2}' "
- device_id = os.popen(cmd_get_serial_num).read()
- device_id = device_id.replace('\n', '').replace(' ', '')
- if device_id is None or device_id == "":
- if not use_machine_id:
- device_id = hex(uuid.getnode())
- else:
- device_id = FedMLClientRunner.get_gpu_machine_id()
- else:
- device_id = "0x" + device_id
- else:
- if "nt" in os.name:
-
- def get_uuid():
- guid = ""
- try:
- cmd = "wmic csproduct get uuid"
- guid = str(subprocess.check_output(cmd))
- pos1 = guid.find("\\n") + 2
- guid = guid[pos1:-15]
- except Exception as ex:
- logging.error(f"Failed to get uuid with Exception {ex}. Traceback: {traceback.format_exc()}")
- pass
- return str(guid)
-
- device_id = str(get_uuid())
- logging.info(device_id)
- elif "posix" in os.name:
- device_id = sys_utils.get_device_id_in_docker()
- if device_id is None:
- if not use_machine_id:
- device_id = hex(uuid.getnode())
- else:
- device_id = device_id = FedMLClientRunner.get_gpu_machine_id()
- else:
- device_id = sys_utils.run_subprocess_open(
- "hal-get-property --udi /org/freedesktop/Hal/devices/computer --key system.hardware.uuid".split()
- )
- device_id = hex(device_id)
-
- if device_id is not None and device_id != "":
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
- else:
- device_id = hex(uuid.uuid4())
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
-
- return device_id
-
- @staticmethod
- def get_machine_id():
- try:
- import machineid
- return machineid.id().replace('\n', '').replace('\r\n', '').strip()
- except Exception as e:
- logging.error(f"Failed to get machine id with Exception {e}. Traceback: {traceback.format_exc()}")
- return hex(uuid.getnode())
-
- @staticmethod
- def bind_account_and_device_id(url, account_id, device_id, os_name, api_key="", role="client"):
- ip = requests.get('https://checkip.amazonaws.com').text.strip()
- fedml_ver, exec_path, os_ver, cpu_info, python_ver, torch_ver, mpi_installed, \
- cpu_usage, available_mem, total_mem, gpu_info, gpu_available_mem, gpu_total_mem, \
- gpu_count, gpu_vendor, cpu_count, gpu_device_name = get_sys_runner_info()
- host_name = sys_utils.get_host_name()
- json_params = {
- "accountid": account_id,
- "deviceid": device_id,
- "type": os_name,
- "state": ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE,
- "status": ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE,
- "processor": cpu_info,
- "core_type": cpu_info,
- "network": "",
- "role": role,
- "os_ver": os_ver,
- "memory": total_mem,
- "ip": ip,
- "api_key": api_key,
- "extra_infos": {"fedml_ver": fedml_ver, "exec_path": exec_path, "os_ver": os_ver,
- "cpu_info": cpu_info, "python_ver": python_ver, "torch_ver": torch_ver,
- "mpi_installed": mpi_installed, "cpu_usage": cpu_usage,
- "available_mem": available_mem, "total_mem": total_mem,
- "cpu_count": cpu_count, "gpu_count": 0, "host_name": host_name}
- }
- if gpu_count > 0:
- if gpu_total_mem is not None:
- json_params["gpu"] = gpu_info if gpu_info is not None else "" + ", Total GPU Memory: " + gpu_total_mem
- else:
- json_params["gpu"] = gpu_info if gpu_info is not None else ""
- json_params["extra_infos"]["gpu_info"] = gpu_info if gpu_info is not None else ""
- if gpu_available_mem is not None:
- json_params["extra_infos"]["gpu_available_mem"] = gpu_available_mem
- if gpu_total_mem is not None:
- json_params["extra_infos"]["gpu_total_mem"] = gpu_total_mem
-
- json_params["extra_infos"]["gpu_count"] = gpu_count
- json_params["extra_infos"]["gpu_vendor"] = gpu_vendor
- json_params["extra_infos"]["gpu_device_name"] = gpu_device_name
-
- gpu_available_id_list = sys_utils.get_available_gpu_id_list(limit=gpu_count)
- gpu_available_count = len(gpu_available_id_list) if gpu_available_id_list is not None else 0
- gpu_list = sys_utils.get_gpu_list()
- json_params["extra_infos"]["gpu_available_count"] = gpu_available_count
- json_params["extra_infos"]["gpu_available_id_list"] = gpu_available_id_list
- json_params["extra_infos"]["gpu_list"] = gpu_list
- else:
- json_params["gpu"] = "None"
- json_params["extra_infos"]["gpu_available_count"] = 0
- json_params["extra_infos"]["gpu_available_id_list"] = []
- json_params["extra_infos"]["gpu_list"] = []
-
- _, cert_path = MLOpsConfigs.get_request_params()
- if cert_path is not None:
- try:
- requests.session().verify = cert_path
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- except requests.exceptions.SSLError as err:
- logging.error(
- f"Failed to bind account and device id with error: {err}, traceback: {traceback.format_exc()}")
- MLOpsConfigs.install_root_ca_file()
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- else:
- response = requests.post(url, json=json_params, headers={"Connection": "close"})
- edge_id, user_name, extra_url, general_edge_id = -1, None, None, None
- if response.status_code != 200:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- pass
- else:
- # print("url = {}, response = {}".format(url, response))
- status_code = response.json().get("code")
- if status_code == "SUCCESS":
- edge_id = response.json().get("data").get("id")
- user_name = response.json().get("data").get("userName", None)
- extra_url = response.json().get("data").get("url", None)
- general_edge_id = response.json().get("data").get("general_edge_id", None)
- if edge_id is None or edge_id <= 0:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- else:
- if status_code == SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR:
- raise SystemExit(SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR)
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- return -1, None, None, None
- return edge_id, user_name, extra_url, general_edge_id
-
- def fetch_configs(self):
- return MLOpsConfigs.fetch_all_configs()
-
- def send_agent_active_msg(self, edge_id):
- active_topic = "flclient_agent/active"
- status = MLOpsStatus.get_instance().get_client_agent_status(edge_id)
- if (
- status is not None
- and status != ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE
- and status != ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE
- ):
- return
-
- try:
- current_job = FedMLClientDataInterface.get_instance().get_job_by_id(self.run_id)
- except Exception as e:
- logging.error(f"Failed to get current job with Exception {e}. Traceback: {traceback.format_exc()}")
- current_job = None
- if current_job is None:
- if status is not None and status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE:
- status = ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE
- else:
- return
- else:
- status = ClientConstants.get_device_state_from_run_edge_state(current_job.status)
- active_msg = {"ID": edge_id, "status": status}
- MLOpsStatus.get_instance().set_client_agent_status(edge_id, status)
- self.mqtt_mgr.send_message_json(active_topic, json.dumps(active_msg))
- logging.info(f"Send agent active msg {active_msg}")
-
- def recover_start_train_msg_after_upgrading(self):
- try:
- current_job = FedMLClientDataInterface.get_instance().get_current_job()
- if current_job is not None and \
- current_job.status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING:
- logging.info("start training after upgrading.")
- topic_start_train = "flserver_agent/" + str(self.edge_id) + "/start_train"
- self.callback_start_train(topic_start_train, current_job.running_json)
- except Exception as e:
- logging.error(f"recover starting train message after upgrading failed with exception {e}, "
- f"Traceback {traceback.format_exc()}")
-
- def on_agent_mqtt_connected(self, mqtt_client_object):
- # The MQTT message topic format is as follows: <sender>/<receiver>/<action>
-
- # Setup MQTT message listener for starting training
- topic_start_train = "flserver_agent/" + str(self.edge_id) + "/start_train"
- self.add_message_listener(topic_start_train, self.callback_start_train)
- self.mqtt_mgr.add_message_listener(topic_start_train, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener for stopping training
- topic_stop_train = "flserver_agent/" + str(self.edge_id) + "/stop_train"
- self.add_message_listener(topic_stop_train, self.callback_stop_train)
- self.mqtt_mgr.add_message_listener(topic_stop_train, self.listener_message_dispatch_center)
-
-
- # Setup MQTT message listener for client status switching
- topic_client_status = "fl_client/flclient_agent_" + str(self.edge_id) + "/status"
- self.add_message_listener(topic_client_status, self.callback_runner_id_status)
- self.mqtt_mgr.add_message_listener(topic_client_status, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to report current device status.
- topic_report_status = "mlops/report_device_status"
- self.add_message_listener(topic_report_status, self.callback_report_current_status)
- self.mqtt_mgr.add_message_listener(topic_report_status, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to OTA messages from the MLOps.
- topic_ota_msg = "mlops/flclient_agent_" + str(self.edge_id) + "/ota"
- self.add_message_listener(topic_ota_msg, self.callback_client_ota_msg)
- self.mqtt_mgr.add_message_listener(topic_ota_msg, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to OTA messages from the MLOps.
- topic_request_device_info = "server/client/request_device_info/" + str(self.edge_id)
- self.add_message_listener(topic_request_device_info, self.callback_report_device_info)
- self.mqtt_mgr.add_message_listener(topic_request_device_info, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to logout from MLOps.
- topic_client_logout = "mlops/client/logout/" + str(self.edge_id)
- self.add_message_listener(topic_client_logout, self.callback_client_logout)
- self.mqtt_mgr.add_message_listener(topic_client_logout, self.listener_message_dispatch_center)
-
- # Subscribe topics for starting train, stopping train and fetching client status.
- mqtt_client_object.subscribe(topic_start_train, qos=2)
- mqtt_client_object.subscribe(topic_stop_train, qos=2)
- mqtt_client_object.subscribe(topic_client_status, qos=2)
- mqtt_client_object.subscribe(topic_report_status, qos=2)
- mqtt_client_object.subscribe(topic_ota_msg, qos=2)
- mqtt_client_object.subscribe(topic_request_device_info, qos=2)
- mqtt_client_object.subscribe(topic_client_logout, qos=2)
-
- self.subscribed_topics.clear()
- self.subscribed_topics.append(topic_start_train)
- self.subscribed_topics.append(topic_stop_train)
- self.subscribed_topics.append(topic_client_status)
- self.subscribed_topics.append(topic_report_status)
- self.subscribed_topics.append(topic_ota_msg)
- self.subscribed_topics.append(topic_request_device_info)
- self.subscribed_topics.append(topic_client_logout)
-
- # Subscribe the messages for federated learning.
- self.subscribe_fl_msgs()
-
- # Broadcast the first active message.
- self.send_agent_active_msg(self.edge_id)
- if self.general_edge_id is not None:
- self.send_agent_active_msg(self.general_edge_id)
-
- # Echo results
- MLOpsRuntimeLog.get_instance(self.args).enable_show_log_to_stdout()
- worker_deploy_id_list = [modeld_device_clint.edge_id for index, modeld_device_clint in
- enumerate(self.model_device_client_list)]
- print("\nCongratulations, your device is connected to the FedML MLOps platform successfully!")
- print(f"Your FedML Edge ID is {str(self.edge_id)}, unique device ID is {str(self.unique_device_id)}, "
- f"master deploy ID is {str(self.model_device_server.edge_id)}, "
- f"worker deploy ID is {worker_deploy_id_list}"
- )
- if self.edge_extra_url is not None and self.edge_extra_url != "":
- print(f"You may visit the following url to fill in more information with your device.\n"
- f"{self.edge_extra_url}")
- MLOpsRuntimeLog.get_instance(self.args).enable_show_log_to_stdout(enable=False)
-
- from fedml.core.mlops import sync_deploy_id
- sync_deploy_id(
- self.edge_id, self.model_device_server.edge_id, worker_deploy_id_list)
-
- # Start the message center for listener
- self.start_listener(sender_message_queue=self.message_center.get_message_queue(),
- agent_config=self.agent_config)
-
- def subscribe_fl_msgs(self):
- if self.general_edge_id is None:
- return
-
- # Setup MQTT message listener for starting training
- topic_start_train = "flserver_agent/" + str(self.general_edge_id) + "/start_train"
- self.add_message_listener(topic_start_train, self.callback_start_train)
- self.mqtt_mgr.add_message_listener(topic_start_train, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener for stopping training
- topic_stop_train = "flserver_agent/" + str(self.general_edge_id) + "/stop_train"
- self.add_message_listener(topic_stop_train, self.callback_stop_train)
- self.mqtt_mgr.add_message_listener(topic_stop_train, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener for client status switching
- topic_client_status = "fl_client/flclient_agent_" + str(self.general_edge_id) + "/status"
- self.add_message_listener(topic_client_status, self.callback_runner_id_status)
- self.mqtt_mgr.add_message_listener(topic_client_status, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to OTA messages from the MLOps.
- topic_request_device_info = "server/client/request_device_info/" + str(self.general_edge_id)
- self.add_message_listener(topic_request_device_info, self.callback_report_device_info)
- self.mqtt_mgr.add_message_listener(topic_request_device_info, self.listener_message_dispatch_center)
-
- # Subscribe topics for starting train, stopping train and fetching client status.
- self.mqtt_mgr.subscribe_msg(topic_start_train)
- self.mqtt_mgr.subscribe_msg(topic_stop_train)
- self.mqtt_mgr.subscribe_msg(topic_client_status)
- self.mqtt_mgr.subscribe_msg(topic_request_device_info)
-
- self.subscribed_topics.append(topic_start_train)
- self.subscribed_topics.append(topic_stop_train)
- self.subscribed_topics.append(topic_client_status)
- self.subscribed_topics.append(topic_request_device_info)
-
- def on_agent_mqtt_disconnected(self, mqtt_client_object):
- MLOpsStatus.get_instance().set_client_agent_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE
- )
- pass
-
- def setup_agent_mqtt_connection(self, service_config):
- # Setup MQTT connection
- self.mqtt_mgr = MqttManager(
- service_config["mqtt_config"]["BROKER_HOST"],
- service_config["mqtt_config"]["BROKER_PORT"],
- service_config["mqtt_config"]["MQTT_USER"],
- service_config["mqtt_config"]["MQTT_PWD"],
- service_config["mqtt_config"]["MQTT_KEEPALIVE"],
- f"FedML_ClientAgent_Daemon_@{self.user_name}@_@{self.args.current_device_id}@_@{str(uuid.uuid4())}@",
- "flclient_agent/last_will_msg",
- json.dumps({"ID": self.edge_id, "status": ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE})
- )
- self.agent_config = service_config
-
- # Init local database
- FedMLClientDataInterface.get_instance().create_job_table()
-
- # Start the message center to process edge related messages.
- self.setup_message_center()
-
- # Start local API services
- client_api_cmd = "fedml.computing.scheduler.slave.client_api:api"
- client_api_pids = RunProcessUtils.get_pid_from_cmd_line(client_api_cmd)
- if client_api_pids is None or len(client_api_pids) <= 0:
- python_program = get_python_program()
- cur_dir = os.path.dirname(__file__)
- fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- self.local_api_process = ClientConstants.exec_console_with_script(
- "{} -m uvicorn {} --host 0.0.0.0 --port {} "
- "--reload --reload-delay 3 --reload-dir {} --log-level critical".format(
- python_program, client_api_cmd, ClientConstants.LOCAL_CLIENT_API_PORT, fedml_base_dir),
- should_capture_stdout=False,
- should_capture_stderr=False
- )
- # if self.local_api_process is not None and self.local_api_process.pid is not None:
- # print(f"Client local API process id {self.local_api_process.pid}")
-
- # Setup MQTT connected listener
- self.mqtt_mgr.add_connected_listener(self.on_agent_mqtt_connected)
- self.mqtt_mgr.add_disconnected_listener(self.on_agent_mqtt_disconnected)
- self.mqtt_mgr.connect()
-
- # Report the IDLE status to MLOps
- self.mlops_metrics.report_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE)
- MLOpsStatus.get_instance().set_client_agent_status(self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE)
-
- # MLOpsRuntimeLogDaemon.get_instance(self.args).stop_all_log_processor()
- self.recover_start_train_msg_after_upgrading()
-
- infer_host = os.getenv("FEDML_INFER_HOST", None)
- infer_redis_addr = os.getenv("FEDML_INFER_REDIS_ADDR", None)
- infer_redis_port = os.getenv("FEDML_INFER_REDIS_PORT", None)
- infer_redis_password = os.getenv("FEDML_INFER_REDIS_PASSWORD", None)
- model_client_num = os.getenv("FEDML_MODEL_WORKER_NUM", None)
- os.environ["FEDML_CURRENT_EDGE_ID"] = str(self.edge_id)
-
- if not ComputeCacheManager.get_instance().set_redis_params():
- os.environ["FEDML_DISABLE_REDIS_CONNECTION"] = "1"
-
- if self.model_device_client_edge_id_list is None:
- self.model_device_client_edge_id_list = list()
- if self.model_device_client_list is None:
- model_client_num = 1 if model_client_num is None else int(model_client_num)
- self.model_device_client_list = list()
- for client_index in range(model_client_num):
- model_device_client = FedMLModelDeviceClientRunner(
- self.args, f"{self.args.current_device_id}_{client_index + 1}", self.args.os_name,
- self.args.is_from_docker, self.agent_config)
- if infer_host is not None:
- model_device_client.infer_host = infer_host
- if infer_redis_addr is not None:
- model_device_client.redis_addr = infer_redis_addr
- if infer_redis_port is not None:
- model_device_client.redis_port = infer_redis_port
- if infer_redis_password is not None:
- model_device_client.redis_password = infer_redis_password
- model_device_client.start()
- self.model_device_client_list.append(model_device_client)
- self.model_device_client_edge_id_list.append(model_device_client.get_edge_id())
-
- if self.model_device_server is None:
- self.model_device_server = FedMLModelDeviceServerRunner(self.args, self.args.current_device_id,
- self.args.os_name, self.args.is_from_docker,
- self.agent_config)
- if infer_host is not None:
- self.model_device_server.infer_host = infer_host
- if infer_redis_addr is not None:
- self.model_device_server.redis_addr = infer_redis_addr
- if infer_redis_port is not None:
- self.model_device_server.redis_port = infer_redis_port
- if infer_redis_password is not None:
- self.model_device_server.redis_password = infer_redis_password
-
- self.model_device_server.start()
- self.model_device_server_id = self.model_device_server.get_edge_id()
-
- JobCleanup.get_instance().sync_data_on_startup(self.edge_id)
-
- os.environ["FEDML_DEPLOY_MASTER_ID"] = str(self.model_device_server.get_edge_id())
- os.environ["FEDML_DEPLOY_WORKER_IDS"] = str([client.get_edge_id() for client in self.model_device_client_list])
- self.mlops_metrics.stop_device_realtime_perf()
- self.mlops_metrics.report_device_realtime_perf(self.args, service_config["mqtt_config"])
-
- def start_agent_mqtt_loop(self):
- # Start MQTT message loop
- try:
- self.mqtt_mgr.loop_forever()
- except Exception as e:
- logging.error(f"Errors in the MQTT loop: Exception {e}, Traceback: {traceback.format_exc()}")
- if str(e) == "Restarting after upgraded...":
- logging.info("Restarting after upgraded...")
- else:
- logging.info("Client tracing: {}".format(traceback.format_exc()))
- finally:
- print("finally")
- login_exit_file = os.path.join(ClientConstants.get_log_file_dir(), "exited.log")
- with open(login_exit_file, "w") as f:
- f.writelines(f"{os.getpid()}.")
-
- self.stop_agent()
-
- time.sleep(5)
- sys_utils.cleanup_all_fedml_client_login_processes(
- ClientConstants.CLIENT_LOGIN_PROGRAM, clean_process_group=False)
- sys.exit(1)
-
- def stop_agent(self):
- if self.run_process_event is not None:
- self.run_process_event.set()
-
- if self.model_device_server is not None:
- self.model_device_server.stop()
- self.model_device_server = None
-
- if self.model_device_client_list is not None:
- for model_client in self.model_device_client_list:
- model_client.stop()
- self.model_device_client_list.clear()
- self.model_device_client_list = None
-
- if self.mqtt_mgr is not None:
- try:
- for topic in self.subscribed_topics:
- self.mqtt_mgr.unsubscribe_msg(topic)
- except Exception as e:
- logging.error(f"Unsubscribe topics error: {e}, Traceback: {traceback.format_exc()}")
- pass
-
- self.mqtt_mgr.loop_stop()
- self.mqtt_mgr.disconnect()
-
- self.release_message_center()
-
- def get_runner(self):
- runner = FedMLClientRunner(
- self.args, edge_id=self.edge_id, request_json=self.request_json,
- agent_config=self.agent_config, run_id=self.run_id,
- cuda_visible_gpu_ids_str=self.cuda_visible_gpu_ids_str
- )
- runner.edge_user_name = self.user_name
- runner.edge_extra_url = self.edge_extra_url
- runner.unique_device_id = self.unique_device_id
- runner.user_name = self.user_name
- runner.general_edge_id = self.general_edge_id
- runner.model_device_client_edge_id_list = self.model_device_client_edge_id_list
- runner.model_device_server_id = self.model_device_server_id
- return runner
diff --git a/python/fedml/computing/scheduler/slave/launch_job_runner.py b/python/fedml/computing/scheduler/slave/launch_job_runner.py
new file mode 100755
index 0000000000..07533af399
--- /dev/null
+++ b/python/fedml/computing/scheduler/slave/launch_job_runner.py
@@ -0,0 +1,41 @@
+from abc import ABC
+
+from .base_slave_job_runner import FedMLBaseSlaveJobRunner
+from .client_constants import ClientConstants
+
+
+class FedMLLaunchSlaveJobRunner(FedMLBaseSlaveJobRunner, ABC):
+
+ def __init__(self, args, edge_id=0, request_json=None, agent_config=None, run_id=0,
+ cuda_visible_gpu_ids_str=None):
+ FedMLBaseSlaveJobRunner.__init__(
+ self, args, edge_id=edge_id, request_json=request_json, agent_config=agent_config, run_id=run_id,
+ cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str, agent_data_dir=ClientConstants.get_data_dir(),
+ agent_package_download_dir=ClientConstants.get_package_download_dir(),
+ agent_package_unzip_dir=ClientConstants.get_package_unzip_dir(),
+ agent_log_file_dir=ClientConstants.get_log_file_dir()
+ )
+
+ # Override
+ def _generate_job_runner_instance(self, args, run_id=None, request_json=None, agent_config=None, edge_id=None):
+ return FedMLLaunchSlaveJobRunner(
+ args, run_id=run_id, request_json=request_json, agent_config=self.agent_config, edge_id=edge_id
+ )
+
+ # Override
+ def _generate_extend_queue_list(self):
+ return None
+
+ # Override
+ def get_download_package_info(self, packages_config=None):
+ return super().get_download_package_info(packages_config)
+
+ # Override
+ def run_impl(
+ self, run_extend_queue_list, sender_message_center,
+ listener_message_queue, status_center_queue
+ ):
+ super().run_impl(
+ run_extend_queue_list, sender_message_center,
+ listener_message_queue, status_center_queue)
+
diff --git a/python/fedml/computing/scheduler/slave/launch_job_runner_manager.py b/python/fedml/computing/scheduler/slave/launch_job_runner_manager.py
new file mode 100755
index 0000000000..3f65438f9e
--- /dev/null
+++ b/python/fedml/computing/scheduler/slave/launch_job_runner_manager.py
@@ -0,0 +1,22 @@
+
+from fedml.core.common.singleton import Singleton
+from .base_slave_job_runner_manager import FedMLBaseSlaveJobRunnerManager
+from .launch_job_runner import FedMLLaunchSlaveJobRunner
+
+
+class FedMLLaunchJobRunnerManager(FedMLBaseSlaveJobRunnerManager, Singleton):
+ def __init__(self):
+ FedMLBaseSlaveJobRunnerManager.__init__(self)
+
+ @staticmethod
+ def get_instance():
+ return FedMLLaunchJobRunnerManager()
+
+ # Override
+ def _generate_job_runner_instance(
+ self, args, run_id=None, request_json=None, agent_config=None, edge_id=None
+ ):
+ return FedMLLaunchSlaveJobRunner(
+ args, run_id=run_id, request_json=request_json, agent_config=agent_config, edge_id=edge_id)
+
+
diff --git a/python/fedml/computing/scheduler/slave/slave_agent.py b/python/fedml/computing/scheduler/slave/slave_agent.py
new file mode 100755
index 0000000000..e9c8b2fc93
--- /dev/null
+++ b/python/fedml/computing/scheduler/slave/slave_agent.py
@@ -0,0 +1,26 @@
+
+from .base_slave_agent import FedMLBaseSlaveAgent
+from .client_constants import ClientConstants
+from .client_data_interface import FedMLClientDataInterface
+from .slave_protocol_manager import FedMLLaunchSlaveProtocolManager
+
+
+class FedMLLaunchSlaveAgent(FedMLBaseSlaveAgent):
+ def __init__(self):
+ FedMLBaseSlaveAgent.__init__(self)
+
+ # Override
+ def _get_log_file_dir(self):
+ return ClientConstants.get_log_file_dir()
+
+ # Override
+ def _save_agent_info(self, unique_device_id, edge_id):
+ ClientConstants.save_runner_infos(unique_device_id, edge_id)
+
+ # Override
+ def _init_database(self):
+ FedMLClientDataInterface.get_instance().create_job_table()
+
+ # Override
+ def _generate_protocol_manager_instance(self, args, agent_config=None):
+ return FedMLLaunchSlaveProtocolManager(args, agent_config=agent_config)
diff --git a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
new file mode 100755
index 0000000000..cd8e40d7e8
--- /dev/null
+++ b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
@@ -0,0 +1,104 @@
+import copy
+import os
+from ..comm_utils.job_cleanup import JobCleanup
+from .base_slave_protocol_manager import FedMLBaseSlaveProtocolManager
+from .launch_job_runner_manager import FedMLLaunchJobRunnerManager
+from ..model_scheduler.model_device_server import FedMLModelDeviceServerRunner
+from ..model_scheduler.model_device_client import FedMLModelDeviceClientRunner
+
+
+class FedMLLaunchSlaveProtocolManager(FedMLBaseSlaveProtocolManager):
+
+ def __init__(self, args, agent_config=None):
+ FedMLBaseSlaveProtocolManager.__init__(self, args, agent_config=agent_config)
+
+ # Override
+ def generate_topics(self):
+ super().generate_topics()
+
+ # Override
+ def add_protocol_handler(self):
+ super().add_protocol_handler()
+
+ # Override
+ def _generate_protocol_manager_instance(self, args, agent_config=None):
+ return FedMLLaunchSlaveProtocolManager(args, agent_config=agent_config)
+
+ # Override
+ def _get_job_runner_manager(self):
+ return FedMLLaunchJobRunnerManager.get_instance()
+
+ # Override
+ def _process_connection_ready(self):
+ from fedml.core.mlops import sync_deploy_id
+ sync_deploy_id(
+ self.edge_id, self.model_device_server.edge_id, self.model_device_client_edge_id_list)
+
+ # Override
+ def _process_connection_lost(self):
+ pass
+
+ # Override
+ def _init_extra_items(self):
+ super()._init_extra_items()
+
+ # Sync the data when startup
+ JobCleanup.get_instance().sync_data_on_startup(self.args.edge_id)
+
+ # Get the environment variables
+ infer_host = os.getenv("FEDML_INFER_HOST", None)
+ infer_redis_addr = os.getenv("FEDML_INFER_REDIS_ADDR", None)
+ infer_redis_port = os.getenv("FEDML_INFER_REDIS_PORT", None)
+ infer_redis_password = os.getenv("FEDML_INFER_REDIS_PASSWORD", None)
+ model_client_num = os.getenv("FEDML_MODEL_WORKER_NUM", None)
+
+ # Start deploy master agent and slave agent
+ in_args = copy.deepcopy(self.args)
+ if self.model_device_client_edge_id_list is None:
+ self.model_device_client_edge_id_list = list()
+ if self.model_device_client_list is None:
+ model_client_num = 1 if model_client_num is None else int(model_client_num)
+ self.model_device_client_list = list()
+ for client_index in range(model_client_num):
+ model_device_client = FedMLModelDeviceClientRunner(
+ in_args, f"{in_args.current_device_id}_{client_index + 1}", in_args.os_name,
+ in_args.is_from_docker, self.agent_config)
+ if infer_host is not None:
+ model_device_client.infer_host = infer_host
+ if infer_redis_addr is not None:
+ model_device_client.redis_addr = infer_redis_addr
+ if infer_redis_port is not None:
+ model_device_client.redis_port = infer_redis_port
+ if infer_redis_password is not None:
+ model_device_client.redis_password = infer_redis_password
+ model_device_client.start()
+ self.model_device_client_list.append(model_device_client)
+ self.model_device_client_edge_id_list.append(model_device_client.get_edge_id())
+
+ self.args = copy.deepcopy(in_args)
+ if self.model_device_server is None:
+ self.model_device_server = FedMLModelDeviceServerRunner(in_args, in_args.current_device_id,
+ in_args.os_name, in_args.is_from_docker,
+ self.agent_config)
+ if infer_host is not None:
+ self.model_device_server.infer_host = infer_host
+ if infer_redis_addr is not None:
+ self.model_device_server.redis_addr = infer_redis_addr
+ if infer_redis_port is not None:
+ self.model_device_server.redis_port = infer_redis_port
+ if infer_redis_password is not None:
+ self.model_device_server.redis_password = infer_redis_password
+
+ self.model_device_server.start()
+ self.model_device_server_id = self.model_device_server.get_edge_id()
+
+ # Save the deployed master and worker id list to the environment variable.
+ os.environ["FEDML_DEPLOY_MASTER_ID"] = str(self.model_device_server_id)
+ os.environ["FEDML_DEPLOY_WORKER_IDS"] = str(self.model_device_client_edge_id_list)
+
+ # Start the monitor process
+ self.args = copy.deepcopy(in_args)
+ self.mlops_metrics.stop_device_realtime_perf()
+ self.mlops_metrics.report_device_realtime_perf(self.args, self.args.agent_config["mqtt_config"])
+ pass
+
diff --git a/python/fedml/core/mlops/__init__.py b/python/fedml/core/mlops/__init__.py
index 9ccd4d2265..a2e9fcc241 100644
--- a/python/fedml/core/mlops/__init__.py
+++ b/python/fedml/core/mlops/__init__.py
@@ -17,8 +17,6 @@
from fedml.core.mlops.mlops_configs import MLOpsConfigs
from ...computing.scheduler.slave.client_constants import ClientConstants
-from ...computing.scheduler.slave.client_runner import FedMLClientRunner
-from ...computing.scheduler.master.server_runner import FedMLServerRunner
from ...constants import FEDML_TRAINING_PLATFORM_SIMULATION, FEDML_TRAINING_PLATFORM_SIMULATION_TYPE
from ...computing.scheduler.master.server_constants import ServerConstants
@@ -35,6 +33,8 @@
from ...computing.scheduler.slave.client_data_interface import FedMLClientDataInterface
from .mlops_utils import MLOpsUtils
from .mlops_constants import MLOpsConstants
+from ...computing.scheduler.master.master_protocol_manager import FedMLLaunchMasterProtocolManager
+from ...computing.scheduler.scheduler_core.account_manager import FedMLAccountManager
FEDML_MLOPS_API_RESPONSE_SUCCESS_CODE = "SUCCESS"
@@ -50,6 +50,8 @@
"log_aggregation_failed_status",
"log_training_failed_status",
"log_endpoint_status",
+ "MLOpsConfigs",
+ "sync_deploy_id"
]
@@ -1244,12 +1246,13 @@ def bind_simulation_device(args, userid):
setattr(args, "version", version)
if args.rank == 0:
setattr(args, "log_file_dir", ServerConstants.get_log_file_dir())
- setattr(args, "device_id", FedMLServerRunner.get_device_id())
- runner = FedMLServerRunner(args)
+ setattr(args, "device_id",
+ FedMLAccountManager.get_device_id(ServerConstants.get_data_dir()))
+ runner = FedMLLaunchMasterProtocolManager(args)
else:
setattr(args, "log_file_dir", ClientConstants.get_log_file_dir())
- setattr(args, "device_id", FedMLClientRunner.get_device_id())
- runner = FedMLClientRunner(args)
+ setattr(args, "device_id", FedMLAccountManager.get_device_id())
+ runner = FedMLSlaveProtocolManager(args)
setattr(args, "config_version", version)
setattr(args, "cloud_region", "")
@@ -1326,10 +1329,10 @@ def fetch_config(args, version="release"):
setattr(args, "version", version)
if args.rank == 0:
setattr(args, "log_file_dir", ServerConstants.get_log_file_dir())
- setattr(args, "device_id", FedMLServerRunner.get_device_id())
+ setattr(args, "device_id", FedMLAccountManager.get_device_id(ServerConstants.get_data_dir()))
else:
setattr(args, "log_file_dir", ClientConstants.get_log_file_dir())
- setattr(args, "device_id", FedMLClientRunner.get_device_id())
+ setattr(args, "device_id", FedMLAccountManager.get_device_id(ClientConstants.get_data_dir()))
setattr(args, "config_version", version)
setattr(args, "cloud_region", "")
diff --git a/python/fedml/core/mlops/mlops_configs.py b/python/fedml/core/mlops/mlops_configs.py
index b83e80a4dd..e0410a880d 100644
--- a/python/fedml/core/mlops/mlops_configs.py
+++ b/python/fedml/core/mlops/mlops_configs.py
@@ -2,8 +2,6 @@
import time
from enum import Enum
-
-
import certifi
import requests
import fedml
diff --git a/python/fedml/core/mlops/mlops_device_perfs.py b/python/fedml/core/mlops/mlops_device_perfs.py
index d488ef27a4..4ed3cd1b6f 100644
--- a/python/fedml/core/mlops/mlops_device_perfs.py
+++ b/python/fedml/core/mlops/mlops_device_perfs.py
@@ -6,7 +6,7 @@
import uuid
from os.path import expanduser
-import multiprocess as multiprocessing
+import multiprocessing
import psutil
from fedml.computing.scheduler.comm_utils import sys_utils
@@ -19,22 +19,14 @@
from .device_info_report_protocol import FedMLDeviceInfoReportProtocol
ROLE_DEVICE_INFO_REPORTER = 1
-ROLE_ENDPOINT_MASTER = 2
-ROLE_ENDPOINT_SLAVE = 3
-ROLE_RUN_MASTER = 4
-ROLE_RUN_SLAVE = 5
-ROLE_ENDPOINT_LOGS = 6
+ROLE_DEVICE_JOB_MONITOR = 2
class MLOpsDevicePerfStats(object):
def __init__(self):
self.device_realtime_stats_process = None
self.device_realtime_stats_event = None
- self.monitor_run_slave_process = None
- self.monitor_run_master_process = None
- self.monitor_endpoint_master_process = None
- self.monitor_endpoint_slave_process = None
- self.monitor_endpoint_logs_process = None
+ self.device_monitor_process = None
self.args = None
self.device_id = None
self.run_id = None
@@ -70,36 +62,15 @@ def setup_realtime_stats_process(self, sys_args):
self.device_realtime_stats_process = multiprocessing.Process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_DEVICE_INFO_REPORTER))
+ args=(self.device_realtime_stats_event, ROLE_DEVICE_INFO_REPORTER, self.is_client))
self.device_realtime_stats_process.start()
- if self.is_client:
- self.monitor_endpoint_slave_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_SLAVE))
- self.monitor_endpoint_slave_process.start()
-
- self.monitor_endpoint_master_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_MASTER))
- self.monitor_endpoint_master_process.start()
-
- self.monitor_run_slave_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_RUN_SLAVE))
- self.monitor_run_slave_process.start()
-
- self.monitor_endpoint_logs_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_LOGS))
- self.monitor_endpoint_logs_process.start()
- else:
- self.monitor_run_master_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_RUN_MASTER))
- self.monitor_run_master_process.start()
-
- def report_device_realtime_stats_entry(self, sys_event, role):
+ self.device_monitor_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_DEVICE_JOB_MONITOR, self.is_client))
+ self.device_monitor_process.start()
+
+ def report_device_realtime_stats_entry(self, sys_event, role, is_client):
# print(f"Report device realtime stats, process id {os.getpid()}")
self.device_realtime_stats_event = sys_event
@@ -117,40 +88,38 @@ def report_device_realtime_stats_entry(self, sys_event, role):
parent_pid = psutil.Process(os.getpid()).ppid()
sys_stats_obj = SysStats(process_id=parent_pid)
- if role == ROLE_RUN_MASTER:
- device_info_reporter = FedMLDeviceInfoReportProtocol(run_id=self.run_id, mqtt_mgr=mqtt_mgr)
+ device_info_reporter = FedMLDeviceInfoReportProtocol(run_id=self.run_id, mqtt_mgr=mqtt_mgr)
JobMonitor.get_instance().mqtt_config = self.args.mqtt_config_path
# Notify MLOps with system information.
- sleep_time_interval = 10
- time_interval_map = {
- ROLE_DEVICE_INFO_REPORTER: 10, ROLE_RUN_SLAVE: 60, ROLE_RUN_MASTER: 70,
- ROLE_ENDPOINT_SLAVE: 80, ROLE_ENDPOINT_MASTER: 90, ROLE_ENDPOINT_LOGS: 30}
+ sleep_time_interval_for_device_info = 60
+ sleep_time_interval_for_client_monitor = 30
+ sleep_time_interval_for_server_monitor = 60
while not self.should_stop_device_realtime_stats():
- try:
- time.sleep(time_interval_map[role])
+ if role == ROLE_DEVICE_INFO_REPORTER:
+ time.sleep(sleep_time_interval_for_device_info)
+ elif role == ROLE_DEVICE_JOB_MONITOR:
+ time.sleep(sleep_time_interval_for_client_monitor if is_client
+ else sleep_time_interval_for_server_monitor)
+ try:
if role == ROLE_DEVICE_INFO_REPORTER:
MLOpsDevicePerfStats.report_gpu_device_info(self.edge_id, mqtt_mgr=mqtt_mgr)
- elif role == ROLE_RUN_SLAVE:
- JobMonitor.get_instance().monitor_slave_run_process_status()
- elif role == ROLE_RUN_MASTER:
- JobMonitor.get_instance().monitor_master_run_process_status(
- self.edge_id, device_info_reporter=device_info_reporter)
- elif role == ROLE_ENDPOINT_SLAVE:
- JobMonitor.get_instance().monitor_slave_endpoint_status()
- elif role == ROLE_ENDPOINT_MASTER:
- JobMonitor.get_instance().monitor_master_endpoint_status()
- elif role == ROLE_ENDPOINT_LOGS:
- JobMonitor.get_instance().monitor_endpoint_logs()
+ elif role == ROLE_DEVICE_JOB_MONITOR:
+ if is_client:
+ JobMonitor.get_instance().monitor_slave_run_process_status()
+ JobMonitor.get_instance().monitor_slave_endpoint_status()
+ JobMonitor.get_instance().monitor_master_endpoint_status()
+ JobMonitor.get_instance().monitor_endpoint_logs()
+ else:
+ JobMonitor.get_instance().monitor_master_run_process_status(
+ self.edge_id, device_info_reporter=device_info_reporter)
except Exception as e:
logging.error(f"exception {e} when reporting device pref: {traceback.format_exc()}.")
pass
- time.sleep(sleep_time_interval)
-
if role == ROLE_DEVICE_INFO_REPORTER:
self.check_fedml_client_parent_process()
@@ -201,6 +170,7 @@ def check_fedml_client_parent_process(self):
if not self.is_client:
return
+ # inspection PyBroadException
try:
home_dir = expanduser("~")
fedml_ppids_dir = os.path.join(home_dir, ".fedml", "fedml-client", "fedml", "data", "ppids")
@@ -222,13 +192,14 @@ def check_fedml_client_parent_process(self):
print(f"Parent client process {file_list} has been killed, so fedml will exit.")
logging.info(f"Parent client process {file_list} has been killed, so fedml will exit.")
os.system("fedml logout")
- except Exception as e:
+ except Exception:
pass
def check_fedml_server_parent_process(self):
if self.is_client:
return
+ # inspection PyBroadException
try:
home_dir = expanduser("~")
fedml_ppids_dir = os.path.join(home_dir, ".fedml", "fedml-server", "fedml", "data", "ppids")
@@ -250,5 +221,5 @@ def check_fedml_server_parent_process(self):
print(f"Parent server process {file_list} has been killed, so fedml will exit.")
logging.info(f"Parent server process {file_list} has been killed, so fedml will exit.")
os.system("fedml logout -s")
- except Exception as e:
+ except Exception:
pass
diff --git a/python/fedml/core/mlops/mlops_metrics.py b/python/fedml/core/mlops/mlops_metrics.py
index 57860ab7cd..ca41df09f2 100644
--- a/python/fedml/core/mlops/mlops_metrics.py
+++ b/python/fedml/core/mlops/mlops_metrics.py
@@ -16,18 +16,12 @@
class MLOpsMetrics(object):
- def __new__(cls, *args, **kw):
- if not hasattr(cls, "_instance"):
- orig = super(MLOpsMetrics, cls)
- cls._instance = orig.__new__(cls, *args, **kw)
- cls._instance.init()
- return cls._instance
-
def __init__(self):
- pass
+ self.init()
def init(self):
self.messenger = None
+ self.send_message_func = None
self.args = None
self.run_id = None
self.edge_id = None
@@ -38,8 +32,9 @@ def init(self):
self.job_perfs = MLOpsJobPerfStats()
self.device_perfs = MLOpsDevicePerfStats()
- def set_messenger(self, msg_messenger, args=None):
+ def set_messenger(self, msg_messenger, args=None, send_message_func=None):
self.messenger = msg_messenger
+ self.send_message_func = send_message_func
if args is not None:
self.args = args
self.run_id = args.run_id
@@ -94,7 +89,7 @@ def report_client_device_status_to_web_ui(self, edge_id, status, run_id=0):
message_json = json.dumps(msg)
logging.info("report_client_device_status. message_json = %s" % message_json)
MLOpsStatus.get_instance().set_client_status(edge_id, status)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def common_report_client_training_status(self, edge_id, status, run_id=0):
# if not self.comm_sanity_check():
@@ -109,7 +104,7 @@ def common_report_client_training_status(self, edge_id, status, run_id=0):
message_json = json.dumps(msg)
logging.info("report_client_training_status. message_json = %s" % message_json)
MLOpsStatus.get_instance().set_client_status(edge_id, status)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def broadcast_client_training_status(self, edge_id, status, is_from_model=False, run_id=0):
# if not self.comm_sanity_check():
@@ -137,14 +132,14 @@ def common_broadcast_client_training_status(self, edge_id, status, run_id=0):
msg = {"edge_id": edge_id, "run_id": run_id, "status": status}
message_json = json.dumps(msg)
logging.info("broadcast_client_training_status. message_json = %s" % message_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def client_send_exit_train_msg(self, run_id, edge_id, status, msg=None):
topic_exit_train_with_exception = "flserver_agent/" + str(run_id) + "/client_exit_train_with_exception"
msg = {"run_id": run_id, "edge_id": edge_id, "status": status, "msg": msg if msg is not None else ""}
message_json = json.dumps(msg)
logging.info("client_send_exit_train_msg.")
- self.messenger.send_message_json(topic_exit_train_with_exception, message_json)
+ self.send_message(topic_exit_train_with_exception, message_json)
def report_client_id_status(self, edge_id, status, running_json=None,
is_from_model=False, server_id="0", run_id=0, msg=""):
@@ -172,7 +167,7 @@ def common_report_client_id_status(self, run_id, edge_id, status, server_id="0",
msg = {"run_id": run_id, "edge_id": edge_id, "status": status, "server_id": server_id, "msg": msg}
message_json = json.dumps(msg)
# logging.info("report_client_id_status. message_json = %s" % message_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def report_server_training_status(self, run_id, status, edge_id=0, role=None, running_json=None, is_from_model=False):
# if not self.comm_sanity_check():
@@ -186,6 +181,13 @@ def report_server_training_status(self, run_id, status, edge_id=0, role=None, ru
from ...computing.scheduler.master.server_data_interface import FedMLServerDataInterface
FedMLServerDataInterface.get_instance().save_job(run_id, self.edge_id, status, running_json)
+ def report_job_status(self, run_id, status):
+ topic_name = "master_agent/slave_agent/job_status"
+ payload = {"run_id": run_id, "status": status}
+
+ message_json = json.dumps(payload)
+ self.send_message(topic_name, message_json)
+
def report_server_device_status_to_web_ui(self, run_id, status, edge_id=0, role=None):
"""
this is used for notifying the server device status to MLOps Frontend
@@ -206,7 +208,7 @@ def report_server_device_status_to_web_ui(self, run_id, status, edge_id=0, role=
# logging.info("report_server_device_status. msg = %s" % msg)
message_json = json.dumps(msg)
MLOpsStatus.get_instance().set_server_status(self.edge_id, status)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def common_report_server_training_status(self, run_id, status, role=None, edge_id=0):
# if not self.comm_sanity_check():
@@ -223,7 +225,7 @@ def common_report_server_training_status(self, run_id, status, role=None, edge_i
# logging.info("report_server_training_status. msg = %s" % msg)
message_json = json.dumps(msg)
MLOpsStatus.get_instance().set_server_status(self.edge_id, status)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def broadcast_server_training_status(self, run_id, status, role=None, is_from_model=False, edge_id=None):
if self.messenger is None:
@@ -239,7 +241,7 @@ def broadcast_server_training_status(self, run_id, status, role=None, is_from_mo
}
logging.info("broadcast_server_training_status. msg = %s" % msg)
message_json = json.dumps(msg)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
if is_from_model:
from ...computing.scheduler.model_scheduler.device_server_data_interface import FedMLServerDataInterface
@@ -248,19 +250,29 @@ def broadcast_server_training_status(self, run_id, status, role=None, is_from_mo
from ...computing.scheduler.master.server_data_interface import FedMLServerDataInterface
FedMLServerDataInterface.get_instance().save_job(run_id, self.edge_id, status)
- def report_server_id_status(self, run_id, status, edge_id=None, server_id=None, server_agent_id=None):
+ def report_server_id_status(self, run_id, status, edge_id=None, server_id=None, server_agent_id=None,
+ is_from_model=False, running_json=None):
# if not self.comm_sanity_check():
# return
topic_name = "fl_server/flserver_agent_" + str(server_agent_id if server_agent_id is not None else
self.server_agent_id) + "/status"
- msg = {"run_id": run_id, "edge_id": edge_id if edge_id is not None else self.edge_id, "status": status}
+ in_edge_id = edge_id if edge_id is not None else self.edge_id
+ msg = {"run_id": run_id, "edge_id": in_edge_id,
+ "status": status, "is_from_model": is_from_model}
if server_id is not None:
msg["server_id"] = server_id
message_json = json.dumps(msg)
logging.info(f"report_server_id_status; topic_name: {topic_name}, msg: {msg}")
# logging.info("report_server_id_status server id {}".format(server_agent_id))
# logging.info("report_server_id_status. message_json = %s" % message_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
+
+ if is_from_model:
+ from ...computing.scheduler.model_scheduler.device_server_data_interface import FedMLServerDataInterface
+ FedMLServerDataInterface.get_instance().save_job(run_id, in_edge_id, status, running_json)
+ else:
+ from ...computing.scheduler.master.server_data_interface import FedMLServerDataInterface
+ FedMLServerDataInterface.get_instance().save_job(run_id, in_edge_id, status, running_json)
def report_client_training_metric(self, metric_json):
# if not self.comm_sanity_check():
@@ -268,7 +280,7 @@ def report_client_training_metric(self, metric_json):
topic_name = "fl_client/mlops/training_metrics"
logging.info("report_client_training_metric. message_json = %s" % metric_json)
message_json = json.dumps(metric_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def report_server_training_metric(self, metric_json, payload=None):
# if not self.comm_sanity_check():
@@ -279,7 +291,7 @@ def report_server_training_metric(self, metric_json, payload=None):
else:
message_json = json.dumps(metric_json)
# logging.info("report_server_training_metric. message_json = %s" % metric_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def report_endpoint_metric(self, metric_json, payload=None):
# if not self.comm_sanity_check():
@@ -290,7 +302,7 @@ def report_endpoint_metric(self, metric_json, payload=None):
else:
message_json = json.dumps(metric_json)
# logging.info("report_endpoint_metric. message_json = %s" % metric_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def report_fedml_train_metric(self, metric_json, run_id=0, is_endpoint=False):
# if not self.comm_sanity_check():
@@ -299,42 +311,42 @@ def report_fedml_train_metric(self, metric_json, run_id=0, is_endpoint=False):
logging.info("report_fedml_train_metric. message_json = %s" % metric_json)
metric_json["is_endpoint"] = is_endpoint
message_json = json.dumps(metric_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def report_fedml_run_logs(self, logs_json, run_id=0):
# if not self.comm_sanity_check():
# return
topic_name = f"fedml_slave/fedml_master/logs/{run_id}"
message_json = json.dumps(logs_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def report_server_training_round_info(self, round_info):
# if not self.comm_sanity_check():
# return
topic_name = "fl_server/mlops/training_roundx"
message_json = json.dumps(round_info)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def report_client_model_info(self, model_info_json):
# if not self.comm_sanity_check():
# return
topic_name = "fl_server/mlops/client_model"
message_json = json.dumps(model_info_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def report_aggregated_model_info(self, model_info_json):
# if not self.comm_sanity_check():
# return
topic_name = "fl_server/mlops/global_aggregated_model"
message_json = json.dumps(model_info_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def report_training_model_net_info(self, model_net_info_json):
# if not self.comm_sanity_check():
# return
topic_name = "fl_server/mlops/training_model_net"
message_json = json.dumps(model_net_info_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def report_llm_record(self, metric_json):
# if not self.comm_sanity_check():
@@ -342,7 +354,7 @@ def report_llm_record(self, metric_json):
topic_name = "model_serving/mlops/llm_input_output_record"
logging.info("report_llm_record. message_json = %s" % metric_json)
message_json = json.dumps(metric_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def report_edge_job_computing_cost(self, job_id, edge_id,
computing_started_time, computing_ended_time,
@@ -359,7 +371,7 @@ def report_edge_job_computing_cost(self, job_id, edge_id,
"computing_ended_time": computing_ended_time,
"duration": duration, "user_id": user_id, "api_key": api_key}
message_json = json.dumps(msg)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
# logging.info("report_job_computing_cost. message_json = %s" % message_json)
def report_logs_updated(self, run_id):
@@ -369,7 +381,7 @@ def report_logs_updated(self, run_id):
msg = {"time": time.time()}
message_json = json.dumps(msg)
logging.info("report_logs_updated. message_json = %s" % message_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def report_artifact_info(self, job_id, edge_id, artifact_name, artifact_type,
artifact_local_path, artifact_url,
@@ -388,7 +400,7 @@ def report_artifact_info(self, job_id, edge_id, artifact_name, artifact_type,
"timestamp": timestamp
}
message_json = json.dumps(artifact_info_json)
- self.messenger.send_message_json(topic_name, message_json)
+ self.send_message(topic_name, message_json)
def report_endpoint_status(self, end_point_id, model_status, timestamp=None,
end_point_name="", model_name="", model_inference_url=""):
@@ -401,8 +413,8 @@ def report_endpoint_status(self, end_point_id, model_status, timestamp=None,
"model_status": model_status,
"timestamp": int(format(time_param, '.0f'))}
- self.messenger.send_message_json(deployment_status_topic, json.dumps(deployment_status_payload))
- self.messenger.send_message_json(deployment_status_topic_prefix, json.dumps(deployment_status_payload))
+ self.send_message(deployment_status_topic, json.dumps(deployment_status_payload))
+ self.send_message(deployment_status_topic_prefix, json.dumps(deployment_status_payload))
def report_run_log(
self, run_id, device_id, log_list, log_source=None, use_mqtt=False
@@ -480,4 +492,10 @@ def stop_device_realtime_perf(self):
self.device_perfs.stop_device_realtime_stats()
def report_json_message(self, topic, payload):
- self.messenger.send_message_json(topic, payload)
\ No newline at end of file
+ self.send_message(topic, payload)
+
+ def send_message(self, topic, payload):
+ if self.send_message_func is not None:
+ self.send_message_func(topic, payload)
+ elif self.messenger is not None:
+ self.messenger.send_message_json(topic, payload)
\ No newline at end of file
diff --git a/python/fedml/workflow/driver_example/customized_job_example/customized_workflow.py b/python/fedml/workflow/driver_example/customized_job_example/customized_workflow.py
index 2a8f2008eb..b948231c96 100644
--- a/python/fedml/workflow/driver_example/customized_job_example/customized_workflow.py
+++ b/python/fedml/workflow/driver_example/customized_job_example/customized_workflow.py
@@ -109,7 +109,7 @@ def create_deploy_workflow(job_api_key=None):
# DeployImageJob.generate_yaml_doc(deploy_image_job_yaml_obj, deploy_image_job_yaml)
# Generate the job object
- endpoint_id = 100 # Here you need to set your own endpoint id
+ endpoint_id = None # Here you need to set your own endpoint id
deploy_image_job = DeployImageJob(
name="deploy_image_job", endpoint_id=endpoint_id,
job_yaml_absolute_path=deploy_image_job_yaml, job_api_key=job_api_key)
@@ -168,7 +168,795 @@ def create_inference_train_workflow(
# workflow.add_job(train_job, dependencies=[inference_jobs[-1]])
# Set the input to the workflow
- input_json = {"text": "What is a good cure for hiccups?"} if input_json is None else input_json
+ # input_json = {"text": "What is a good cure for hiccups?"} if input_json is None else input_json
+ input_json = {
+ "arr": [
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+ -0.0284271584,
+ -0.0506655656,
+ -0.0740332846,
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+ 4.17363552,
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+ 0,
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+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ -0.0132918601,
+ -0.0159980455,
+ -0.0207236291,
+ -0.0266997366,
+ -0.0284703819,
+ -0.0343035092,
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+ -0.0388769026,
+ -0.0316010302,
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+ -0.021736589,
+ -0.0100005,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0
+ ]
+ }
workflow.set_workflow_input(input_json)
# Run workflow
@@ -213,6 +1001,6 @@ def create_inference_train_workflow(
if is_inference and deployed_endpoint_id is not None:
create_inference_train_workflow(
- job_api_key=args.api_key, endpoint_id_list=[deployed_endpoint_id, deployed_endpoint_id],
+ job_api_key=args.api_key, endpoint_id_list=[deployed_endpoint_id],
input_json=args.infer_json)
exit(0)
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job.yaml b/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job.yaml
index 6ec64b0404..52ac79344e 100755
--- a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job.yaml
+++ b/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job.yaml
@@ -6,12 +6,12 @@ workspace: deploy_image_job
# Running entry commands which will be executed as the job entry point.
# Support multiple lines, which can not be empty.
job: |
- echo "current job id: $FEDML_CURRENT_RUN_ID"
- echo "current edge id: $FEDML_CURRENT_EDGE_ID"
- echo "Hello, Here is the FedML Nexus AI platform."
- echo "Current directory is as follows."
- pwd
- sleep 3
+ echo "current job id: $FEDML_CURRENT_RUN_ID"
+ echo "current edge id: $FEDML_CURRENT_EDGE_ID"
+ echo "Hello, Here is the FedML Nexus AI platform."
+ echo "Current directory is as follows."
+ pwd
+ sleep 3
job_type: deploy # options: train, deploy, federate
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/fedml_model_config.yaml b/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/fedml_model_config.yaml
index ab8dbc4747..6992bb37df 100644
--- a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/fedml_model_config.yaml
+++ b/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/fedml_model_config.yaml
@@ -1,12 +1,13 @@
-workspace: "."
-entry_point: "main_entry.py"
+workspace: "./"
+entry_point: "mnist_serve_main.py"
-auto_detect_public_ip: true
-server_external_port: 20215
+data_cache_dir: ""
+bootstrap: ""
+
+server_external_port: 20203
server_internal_port: 2203
-bootstrap: |
- echo "Bootstrap start..."
- pip install -U fedml
- sh ./config/bootstrap.sh
- echo "Bootstrap finished"
+auto_detect_public_ip: true
+
+request_input_example: {"arr":[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,-1.00005000e-02,-1.00005000e-02,-1.00005000e-02,-1.39737990e-02,-1.89315247e-02,-2.31843010e-02,-3.60728861e-02,-3.92619154e-02,-3.80269994e-02,-3.90143887e-02,-3.46046778e-02,-2.57765396e-02,-2.09733754e-02,-2.17809993e-02,-1.44984527e-02,-1.18807892e-02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 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+
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/mnist_serve_main.py b/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/mnist_serve_main.py
new file mode 100644
index 0000000000..6367ea487f
--- /dev/null
+++ b/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/mnist_serve_main.py
@@ -0,0 +1,37 @@
+from fedml.serving import FedMLPredictor
+from fedml.serving import FedMLInferenceRunner
+from model.minist_model import LogisticRegression
+
+# This is the model file that will upload to MLOps
+MODEL_PARMS_DIR = "./model/model_parms_from_mlops"
+# If you do not want to upload the model file to MLOps,
+# (i.e., you want to use the model file in the lcoal DATA_CACHE_DIR)
+# Please use the DATA_CACHE_DIR and specify DATA_CACHE_DIR
+# in the fedml_model_config.yaml
+# DATA_CACHE_DIR = ""
+
+class MnistPredictor(FedMLPredictor):
+ def __init__(self):
+ import pickle
+ import torch
+
+ with open(MODEL_PARMS_DIR, 'rb') as model_file_obj:
+ model_params = pickle.load(model_file_obj)
+
+ output_dim = 10
+
+ self.model = LogisticRegression(28 * 28, output_dim)
+
+ self.model.load_state_dict(model_params)
+
+ self.list_to_tensor_func = torch.tensor
+
+ def predict(self, request):
+ arr = request["arr"]
+ input_tensor = self.list_to_tensor_func(arr)
+ return self.model(input_tensor)
+
+if __name__ == "__main__":
+ predictor = MnistPredictor()
+ fedml_inference_runner = FedMLInferenceRunner(predictor)
+ fedml_inference_runner.run()
\ No newline at end of file
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/model/minist_model.py b/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/model/minist_model.py
new file mode 100644
index 0000000000..25789d4e1c
--- /dev/null
+++ b/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/model/minist_model.py
@@ -0,0 +1,11 @@
+import torch
+class LogisticRegression(torch.nn.Module):
+ def __init__(self, input_dim, output_dim):
+ super(LogisticRegression, self).__init__()
+ self.linear = torch.nn.Linear(input_dim, output_dim)
+
+ def forward(self, x):
+ import torch
+ outputs = torch.sigmoid(self.linear(x))
+ return outputs
+
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/model/model_parms_from_mlops b/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/model/model_parms_from_mlops
new file mode 100644
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zQ0_9pZ1t7Q`i^x>R(C$<>4rhaQg;j%n#YzbkYfwYB^cMIq8Q|H7FQ+gXU>lcVBtC?
zruX9y_~N?}mA2YJm~j?3m~t7@r7@Vb<SwbXz6=V&OEBTqFPv2*%J8mkL)}NpY|_@v
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z%=3GOd!zqgMuj7Pr(OV>bzFmu4x`w~X@wzD;~?s0gX*4&?Ac$h;o`_2`b#pB*3O|Y
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zO2zv$QjqfvV=BO8(gL<_dosz@{Ez=EV0})=_Ios0!U>vA3b0Q-bBT6NA%1%sNgCJB
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z<J@%Ex{+v*J$@BC7iPeUvq#}kQ#{JLYeLoMYj`w-W8%M_0~SYYKv|0Gh=hFO=5h=0
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literal 0
HcmV?d00001
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job.yaml b/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job.yaml
new file mode 100755
index 0000000000..8ac9300165
--- /dev/null
+++ b/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job.yaml
@@ -0,0 +1,29 @@
+# Local directory where your source code resides.
+# It should be the relative path to this job yaml file or the absolute path.
+# If your job doesn't contain any source code, it can be empty.
+workspace: deploy_llm_job
+
+# Running entry commands which will be executed as the job entry point.
+# Support multiple lines, which can not be empty.
+job: |
+ echo "current job id: $FEDML_CURRENT_RUN_ID"
+ echo "current edge id: $FEDML_CURRENT_EDGE_ID"
+ echo "Hello, Here is the FedML Nexus AI platform."
+ echo "Current directory is as follows."
+ pwd
+ sleep 3
+
+job_type: deploy # options: train, deploy, federate
+
+# Bootstrap shell commands which will be executed before running entry commands.
+# Support multiple lines, which can be empty.
+bootstrap: |
+ pip install -r requirements.txt
+ echo "Bootstrap finished."
+
+computing:
+ #resource_type: RTX-3090 # e.g., A100-80G, please check the resource type list by "fedml show-resource-type" or visiting URL: https://open.fedml.ai/accelerator_resource_type
+ resource_type: A100-80GB-SXM
+ minimum_num_gpus: 1 # minimum # of GPUs to provision
+ maximum_cost_per_hour: $10 # max cost per hour of all machines for your job
+ # device_type: GPU # GPU or CPU
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/.gitignore b/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/.gitignore
new file mode 100644
index 0000000000..0d20b6487c
--- /dev/null
+++ b/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/.gitignore
@@ -0,0 +1 @@
+*.pyc
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/__init__.py b/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/__init__.py
similarity index 100%
rename from python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/__init__.py
rename to python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/__init__.py
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/app/__init__.py b/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/app/__init__.py
similarity index 100%
rename from python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/app/__init__.py
rename to python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/app/__init__.py
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/app/pipe/__init__.py b/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/app/pipe/__init__.py
similarity index 100%
rename from python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/app/pipe/__init__.py
rename to python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/app/pipe/__init__.py
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/app/pipe/constants.py b/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/app/pipe/constants.py
similarity index 100%
rename from python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/app/pipe/constants.py
rename to python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/app/pipe/constants.py
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/app/pipe/instruct_pipeline.py b/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/app/pipe/instruct_pipeline.py
similarity index 100%
rename from python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/app/pipe/instruct_pipeline.py
rename to python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/app/pipe/instruct_pipeline.py
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/config/__init__.py b/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/config/__init__.py
similarity index 100%
rename from python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/config/__init__.py
rename to python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/config/__init__.py
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/fedml_model_config.yaml b/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/fedml_model_config.yaml
new file mode 100644
index 0000000000..bff517ef6d
--- /dev/null
+++ b/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/fedml_model_config.yaml
@@ -0,0 +1,12 @@
+workspace: "."
+entry_point: "main_entry.py"
+
+auto_detect_public_ip: true
+server_external_port: 20203
+server_internal_port: 2203
+
+bootstrap: |
+ echo "Bootstrap start..."
+ pip install -U fedml
+ sh ./config/bootstrap.sh
+ echo "Bootstrap finished"
diff --git a/python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/main_entry.py b/python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/main_entry.py
similarity index 100%
rename from python/fedml/workflow/driver_example/customized_job_example/deploy_image_job/main_entry.py
rename to python/fedml/workflow/driver_example/customized_job_example/deploy_llm_job/main_entry.py
diff --git a/python/setup.py b/python/setup.py
index a9d61b352a..c531f722e2 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -41,7 +41,7 @@ def finalize_options(self):
"wandb==0.13.2",
"httpx",
"attrs",
- "fastapi>=0.92.0",
+ "fastapi",
"uvicorn",
"geventhttpclient>=1.4.4,<=2.0.9",
"aiohttp>=3.8.1",
@@ -62,7 +62,7 @@ def finalize_options(self):
"py-machineid",
"cachetools",
"toposort",
- "pydantic>=2.0",
+ "pydantic",
"pydantic-settings",
]
@@ -116,7 +116,7 @@ def finalize_options(self):
setup(
name="fedml",
- version="0.8.27.dev2",
+ version="0.8.29.dev4",
author="FedML Team",
author_email="ch@fedml.ai",
description="A research and production integrated edge-cloud library for "
From 4ea1c7cc76ab0c1ff55ebf38a5d9b7130c9b09eb Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 28 Mar 2024 17:27:14 +0800
Subject: [PATCH 002/282] [CoreEngine] download packages without the ssl
certification.
---
.../scheduler/scheduler_core/scheduler_base_job_runner.py | 5 +++--
1 file changed, 3 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index e2e090596d..46f1e7ff8f 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -20,6 +20,7 @@
from ..scheduler_core.message_center import FedMLMessageCenter
from ..scheduler_core.status_center import FedMLStatusCenter
from abc import ABC, abstractmethod
+import ssl
class RunnerError(Exception):
@@ -160,8 +161,8 @@ def retrieve_and_unzip_package(self, package_name, package_url):
local_package_file = os.path.join(local_package_path, f"fedml_run_{self.run_id}_{filename_without_extension}")
if os.path.exists(local_package_file):
os.remove(local_package_file)
- package_url_without_query_path = urljoin(package_url, urlparse(package_url).path)
- urllib.request.urlretrieve(package_url_without_query_path, local_package_file,
+ ssl._create_default_https_context = ssl._create_unverified_context
+ urllib.request.urlretrieve(package_url, local_package_file,
reporthook=self.package_download_progress)
unzip_package_path = os.path.join(self.agent_package_unzip_dir,
f"unzip_fedml_run_{self.run_id}_{filename_without_extension}")
From 46cbab24068b348c148a7c7e39c7f1c458240b04 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Sat, 6 Apr 2024 21:00:38 +0800
Subject: [PATCH 003/282] [CoreEngine] sync the deployment and launch modules.
---
.../master/base_master_protocol_manager.py | 39 +--
.../model_scheduler/job_runner_msg_sender.py | 87 +----
.../model_scheduler/master_job_runner.py | 299 +++++++++++++-----
.../master_job_runner_manager.py | 8 +-
.../master_protocol_manager.py | 146 +++------
.../model_scheduler/worker_job_runner.py | 287 +++++++++--------
.../slave/base_slave_protocol_manager.py | 13 +-
.../scheduler/slave/slave_protocol_manager.py | 51 +++
8 files changed, 520 insertions(+), 410 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index bf720515d9..25cab5a17c 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -76,8 +76,8 @@ def generate_topics(self):
# The topic for requesting device info from the client.
self.topic_response_device_info = "client/server/response_device_info/" + str(self.edge_id)
- # The topic for requesting device info from MLOps.
- self.topic_request_device_info_from_mlops = f"mlops/master_agent/request_device_info/{self.edge_id}"
+ # The topic for requesting device info from mlops.
+ self.topic_request_device_info_from_mlops = f"deploy/mlops/master_agent/request_device_info/{self.edge_id}"
# The topic for getting job status from the status center.
self.topic_requesst_job_status = f"anywhere/master_agent/request_job_status/{self.edge_id}"
@@ -115,6 +115,7 @@ def add_protocol_handler(self):
self.add_message_listener(self.topic_ota_msg, FedMLBaseMasterProtocolManager.callback_server_ota_msg)
self.add_message_listener(self.topic_report_status, self.callback_report_current_status)
self.add_message_listener(self.topic_response_device_info, self.callback_response_device_info)
+ self.add_message_listener(self.topic_response_device_info, self.callback_response_device_info)
self.add_message_listener(self.topic_request_device_info_from_mlops,
self.callback_request_device_info_from_mlops)
self.add_message_listener(self.topic_requesst_job_status, self.callback_request_job_status)
@@ -436,38 +437,10 @@ def response_device_status_in_job(self, topic, payload):
self.mlops_metrics.report_json_message(response_topic, json.dumps(response_payload))
def response_device_info_to_mlops(self, topic, payload):
- response_topic = f"master_agent/mlops/response_device_info"
- payload_json = json.loads(payload)
- need_gpu_info = payload_json.get("need_gpu_info", False)
+ response_topic = f"deploy/master_agent/mlops/response_device_info"
if self.mlops_metrics is not None:
- if not need_gpu_info:
- response_payload = {
- "run_id": self.run_id,
- "master_agent_device_id": self.edge_id,
- "fedml_version": fedml.__version__
- }
- else:
- total_mem, free_mem, total_disk_size, free_disk_size, cup_utilization, cpu_cores, \
- gpu_cores_total, gpu_cores_available, sent_bytes, recv_bytes, gpu_available_ids = \
- sys_utils.get_sys_realtime_stats()
- gpu_available_ids = JobRunnerUtils.get_instance().get_available_gpu_id_list(self.edge_id)
- gpu_available_ids = JobRunnerUtils.trim_unavailable_gpu_ids(gpu_available_ids)
- gpu_cores_available = len(gpu_available_ids)
- response_payload = {
- "run_id": self.run_id,
- "master_agent_device_id": self.edge_id,
- "memoryTotal": round(total_mem * MLOpsUtils.BYTES_TO_GB, 2),
- "memoryAvailable": round(free_mem * MLOpsUtils.BYTES_TO_GB, 2),
- "diskSpaceTotal": round(total_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
- "diskSpaceAvailable": round(free_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
- "cpuUtilization": round(cup_utilization, 2),
- "cpuCores": cpu_cores,
- "gpuCoresTotal": gpu_cores_total,
- "gpuCoresAvailable": gpu_cores_available,
- "networkTraffic": sent_bytes + recv_bytes,
- "timestamp": int(MLOpsUtils.get_ntp_time()),
- "fedml_version": fedml.__version__
- }
+ response_payload = {"run_id": self.run_id, "master_agent_device_id": self.edge_id,
+ "fedml_version": fedml.__version__}
self.mlops_metrics.report_json_message(response_topic, json.dumps(response_payload))
def init_job_task(self, request_json):
diff --git a/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py b/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
index 3fe45401ac..acce17d20b 100755
--- a/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
+++ b/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
@@ -18,7 +18,7 @@ def __init__(self):
self.request_json = None
self.edge_id = None
- def send_deployment_results_with_payload(self, end_point_id, end_point_name, payload):
+ def send_deployment_results_with_payload(self, end_point_id, end_point_name, payload, replica_id_list=None):
self.send_deployment_results(end_point_id, end_point_name,
payload["model_name"], payload["model_url"],
payload["model_version"], payload["port"],
@@ -26,12 +26,13 @@ def send_deployment_results_with_payload(self, end_point_id, end_point_name, pay
payload["model_metadata"],
payload["model_config"],
payload["input_json"],
- payload["output_json"])
+ payload["output_json"],
+ replica_id_list=replica_id_list)
def send_deployment_results(self, end_point_id, end_point_name,
model_name, model_inference_url,
model_version, inference_port, inference_engine,
- model_metadata, model_config, input_json, output_json):
+ model_metadata, model_config, input_json, output_json, replica_id_list=None):
deployment_results_topic_prefix = "model_ops/model_device/return_deployment_result"
deployment_results_topic = "{}/{}".format(deployment_results_topic_prefix, end_point_id)
deployment_results_payload = {"end_point_id": end_point_id, "end_point_name": end_point_name,
@@ -42,7 +43,8 @@ def send_deployment_results(self, end_point_id, end_point_name,
"model_config": model_config,
"input_json": input_json,
"output_json": output_json,
- "timestamp": int(format(time.time_ns() / 1000.0, '.0f'))}
+ "timestamp": int(format(time.time_ns() / 1000.0, '.0f')),
+ "replica_ids": replica_id_list}
logging.info(f"[Master] deployment_results_payload is sent to mlops: {deployment_results_payload}")
self.message_center.send_message_json(deployment_results_topic, json.dumps(deployment_results_payload))
@@ -104,85 +106,16 @@ def send_deployment_start_request_to_edges(self):
continue
should_added_devices.append(edge_id)
# send start deployment request to each device
- self.send_deployment_start_request_to_edge(edge_id)
+ self.send_deployment_start_request_to_edge(edge_id, self.request_json)
return should_added_devices
- def send_deployment_start_request_to_edge(self, edge_id):
+ def send_deployment_start_request_to_edge(self, edge_id, request_json):
topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(edge_id))
logging.info("start_deployment: send topic " + topic_start_deployment + " to client...")
- self.message_center.send_message_json(topic_start_deployment, json.dumps(self.request_json))
+ self.message_center.send_message_json(topic_start_deployment, json.dumps(request_json))
def send_deployment_delete_request_to_edges(self, payload, model_msg_object):
- if model_msg_object is None: # Called after the diff operation
- if "diff_devices" not in self.request_json or self.request_json["diff_devices"] is None:
- return
- else:
- edge_id_list_to_delete = []
- for device_id in self.request_json["diff_devices"]:
- if self.request_json["diff_devices"][device_id] == ServerConstants.DEVICE_DIFF_DELETE_OPERATION:
- edge_id_list_to_delete.append(device_id)
- if len(edge_id_list_to_delete) == 0:
- return
-
- try:
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port,
- self.redis_password)
-
- # 1. Get & Delete the endpoint device info in Redis / SQLite
- device_objs = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_end_point_device_info(self.request_json["run_id"])
-
- if device_objs is None:
- raise Exception("The device list in local redis is None")
- else:
- total_device_objs_list = json.loads(device_objs)
- for device_obj in total_device_objs_list:
- if device_obj["id"] in edge_id_list_to_delete:
- total_device_objs_list.remove(device_obj)
-
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_end_point_device_info(
- self.request_json["end_point_id"], self.request_json["end_point_name"],
- json.dumps(total_device_objs_list))
-
- # 2 Delete the result in deployment result list in Redis / SQLite
- device_result_list = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_deployment_result_list(self.request_json["end_point_id"],
- self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"])
- delete_device_result_list = []
- for device_result in device_result_list:
- device_result_dict = json.loads(device_result)
- if int(device_result_dict["cache_device_id"]) in edge_id_list_to_delete:
- delete_device_result_list.append(device_result)
-
- for delete_item in delete_device_result_list:
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).delete_deployment_result(
- delete_item, self.request_json["end_point_id"],
- self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"]
- )
-
- except Exception as e:
- run_id = self.request_json["run_id"]
- error_log_path = f"~/.fedml/fedml-model-server/fedml/logs/error_delete_{run_id}.txt"
- if not os.path.exists(os.path.dirname(os.path.expanduser(error_log_path))):
- os.makedirs(os.path.dirname(os.path.expanduser(error_log_path)))
- with open(os.path.expanduser(error_log_path), "w") as f:
- f.write(str(self.request_json))
- f.write(str(e))
- f.write('\n')
- raise e
-
- else: # Delete the whole endpoint
- edge_id_list_to_delete = model_msg_object.device_ids
-
- # For Debug
- if payload is not None:
- debug_log_path = f"~/.fedml/fedml-model-server/fedml/logs/tmp_debug_delete_payload.txt"
- if not os.path.exists(os.path.dirname(os.path.expanduser(debug_log_path))):
- os.makedirs(os.path.dirname(os.path.expanduser(debug_log_path)))
- with open(os.path.expanduser(debug_log_path), "w") as f:
- f.write(str(payload))
+ edge_id_list_to_delete = model_msg_object.device_ids
# Remove the model master node id from the list using index 0
edge_id_list_to_delete = edge_id_list_to_delete[1:]
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index f3d68c1f6a..867f299ccc 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -125,7 +125,7 @@ def run_impl(
# Changed the status to "IDLE"
self.status_reporter.report_server_id_status(
run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED,
- is_from_model=True, server_agent_id=self.edge_id, server_id=self.edge_id, edge_id=self.edge_id,)
+ is_from_model=True, server_agent_id=self.edge_id, server_id=self.edge_id, edge_id=self.edge_id)
# Check if we should stop the runner
logging.info("send the model inference request to slave devices...")
@@ -136,28 +136,32 @@ def run_impl(
devices_sent_add_or_remove_msg = self.send_deployment_start_request_to_edges()
# Handle "op:update"
- devices_sent_update_remove_msg = self.send_first_scroll_update_msg()
-
- if len(devices_sent_add_or_remove_msg) == 0 and len(devices_sent_update_remove_msg) == 0:
- # No device is added or removed, and no device is updated or removed
- ip = GeneralConstants.get_ip_address(self.request_json)
- master_port = os.getenv("FEDML_MASTER_PORT", None)
- if master_port is not None:
- inference_port = int(master_port)
- model_inference_port = inference_port
- if ip.startswith("http://") or ip.startswith("https://"):
- model_inference_url = "{}/api/v1/predict".format(ip)
- else:
- model_inference_url = "http://{}:{}/api/v1/predict".format(ip, model_inference_port)
+ try:
+ devices_sent_update_remove_msg = self.send_first_scroll_update_msg()
+
+ if len(devices_sent_add_or_remove_msg) == 0 and len(devices_sent_update_remove_msg) == 0:
+ # No device is added or removed, and no device is updated or removed
+ logging.info("No device is added, updated or removed. No action needed for reconciliation.")
+ ip = GeneralConstants.get_ip_address(self.request_json)
+ master_port = os.getenv("FEDML_MASTER_PORT", None)
+ if master_port is not None:
+ inference_port = int(master_port)
+ model_inference_port = inference_port
+ if ip.startswith("http://") or ip.startswith("https://"):
+ model_inference_url = "{}/api/v1/predict".format(ip)
+ else:
+ model_inference_url = "http://{}:{}/api/v1/predict".format(ip, model_inference_port)
- self.send_deployment_status(
- run_id, end_point_name, model_name, model_inference_url,
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
- message_center=self.message_center
- )
+ self.send_deployment_status(
+ run_id, end_point_name, model_name, model_inference_url,
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
+ message_center=self.message_center
+ )
- self.trigger_completed_event()
- return
+ self.trigger_completed_event()
+ return
+ except Exception as e:
+ logging.info(f"Exception at run impl {traceback.format_exc()}")
self.deployment_result_queue = run_extend_queue_list[0]
while True:
@@ -187,19 +191,78 @@ def process_deployment_result_message(self, topic=None, payload=None):
model_name = payload_json["model_name"]
model_version = payload_json["model_version"]
model_status = payload_json["model_status"]
- replica_no = payload_json.get("replica_no", None) # Idx start from 1
+ replica_no = payload_json.get("replica_no", None) # "no" Idx start from 1
run_id_str = str(end_point_id)
+ # HotFix(Raphael): logging service cross talk
+ # Change the handler since each handler need to write to different log files
+ try:
+ # Remove the existing file handler
+ root_logger = logging.getLogger()
+ for handler in root_logger.handlers:
+ if isinstance(handler, logging.FileHandler):
+ root_logger.removeHandler(handler)
+
+ # Correct log path: ~/.fedml/fedml-model-server/fedml/logs/fedml-run-$rid-edge-$eid.log
+ log_file = os.path.join(ServerConstants.get_log_file_dir(),
+ f"fedml-run-{run_id_str}-edge-{self.edge_id}.log")
+
+ filehandler = logging.FileHandler(log_file, "a")
+
+ program_prefix = "FedML-Server @device-id-{}".format(self.edge_id)
+ formatter = logging.Formatter(fmt="[" + program_prefix + "] [%(asctime)s] [%(levelname)s] "
+ "[%(filename)s:%(lineno)d:%(funcName)s] %("
+ "message)s",
+ datefmt="%a, %d %b %Y %H:%M:%S")
+
+ filehandler.setFormatter(formatter)
+ root_logger.addHandler(filehandler)
+ except Exception as e:
+ logging.warning(f"Failed to change the logging handler due to {e}.")
+
+ logging.info("========== callback_deployment_result_message ==========\n")
+ # Identify the operation for this run (add, remove, update)
+ if run_id_str not in self.running_request_json:
+ logging.error(f"Run id {run_id_str} is not in the running request json.")
+ return
+
+ # The rolling update and scale out / in operation should not happen at the same time
+ assert not ("replica_num_diff" in self.running_request_json[run_id_str] and
+ len(self.running_request_json[run_id_str]["replica_num_diff"]) > 0 and
+ "replica_version_diff" in self.running_request_json[run_id_str])
+
+ if "replica_version_diff" in self.running_request_json[run_id_str]:
+ run_operation = "UPDATE"
+ elif "replica_num_diff" in self.running_request_json[run_id_str] and \
+ len(self.running_request_json[run_id_str]["replica_num_diff"]) > 0:
+ run_operation = "ADD_OR_REMOVE"
+ else:
+ logging.error(f"Unsupported operation for run id {run_id_str}. and request json "
+ f"{self.running_request_json[run_id_str]}")
+ return
+
+ logging.info(f"End point {end_point_id}; Device {device_id}; replica {replica_no}; "
+ f"run_operation {run_operation} model status {model_status}.")
+
+ # OPTIONAL DEBUG PARAMS
+ # this_run_controller = self.model_runner_mapping[run_id_str].replica_controller
+ # logging.info(f"The current replica controller state is "
+ # f"Total version diff num {this_run_controller.total_replica_version_diff_num}")
+ # logging.info(f"self.request_json now {self.request_json}") # request_json will be deprecated
+ # this_run_request_json = self.running_request_json.get(run_id_str, None)
+ # logging.info(f"self.running_request_json now {this_run_request_json}")
+
# Set redis + sqlite deployment result
FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- # Save deployment result to local cache
+ # Deal with different model status
if model_status == ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DELETED:
+ # remove
FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
delete_deployment_result_with_device_id_and_replica_no(
end_point_id, end_point_name, model_name, device_id, replica_no)
elif model_status == ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED:
- # add or update
+ # add or update or update-failed-rollback
FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
set_deployment_result(end_point_id, end_point_name,
model_name, model_version,
@@ -210,38 +273,66 @@ def process_deployment_result_message(self, topic=None, payload=None):
else:
if model_status != ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED:
logging.error(f"Unsupported model status {model_status}.")
- self.send_deployment_status(
- end_point_id, end_point_name, payload_json["model_name"], "",
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
- message_center=self.message_center
- )
+ # Failure handler
+ if run_operation == "ADD_OR_REMOVE":
+ # TODO(Raphael): Also support rollback for scale out / in operation
+ self.send_deployment_status(
+ end_point_id, end_point_name, payload_json["model_name"], "",
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
+ message_center=self.message_center)
+ return
+ else:
+ # Overwrite the json with the rollback version diff
+ rollback_version_diff = self.replica_controller.rollback_get_replica_version_diff(
+ device_id_trigger=device_id, replica_no_trigger=replica_no)
+
+ # Change the target version to the start version
+ self.replica_controller.rollback_setback_target_replica_version()
+
+ self.running_request_json[run_id_str]["replica_version_diff"] = copy.deepcopy(rollback_version_diff)
+
+ # Send the rollback message to the worker devices
+ self.send_rollback_msg(run_id_str)
+
+ # Set the deployment status to ABORTING
+ self.send_deployment_status(
+ end_point_id, end_point_name, payload_json["model_name"], "",
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTING,
+ message_center=self.message_center)
+
+ # TODO(Raphael): Check if resource left not cleaned up
+ return
+
+ # Move to the next state (rolling update, finish the deployment, etc.)
# Notify the replica number controller
- self.callback_update_curr_replica_num_state(device_id, replica_no, model_status)
+ (self.replica_controller.callback_update_curr_replica_num_state(device_id, replica_no, model_status))
# Notify the replica version controller, which might trigger the next rolling update
- self.send_next_scroll_update_msg(device_id, replica_no)
+ self.send_next_scroll_update_msg(run_id_str, device_id, replica_no)
# Update the global deployment result mapping
self.slave_deployment_results_map[str(device_id)] = model_status
- # Check if the endpoint is running
- request_json = self.request_json
+ logging.info("callback_deployment_result_message: topic {}, payload {}, result mapping {}.".format(
+ topic, payload, self.slave_deployment_results_map))
+
+ request_json = self.running_request_json.get(run_id_str, None)
if request_json is None:
- logging.error(f"The endpoint {end_point_id} is not running.")
+ logging.error(f"The endpoint {end_point_id} is no longer running.")
self.send_deployment_status(
end_point_id, end_point_name, payload_json["model_name"], "",
ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
- message_center=self.message_center
- )
+ message_center=self.message_center)
return
- # Wait for all replica's result, not device-level
- if self.is_all_replica_num_reconciled() and self.is_all_replica_version_reconciled():
+ # Wait for all replica-level's result, not device-level
+ if (self.replica_controller.is_all_replica_num_reconciled() and
+ self.replica_controller.is_all_replica_version_reconciled()):
'''
When all the devices have finished the add / delete / update operation
'''
- # 1. We should generate one unified inference api
+ # Generate one unified inference api
# Note that here we use the gateway port instead of the inference port that is used by the slave device
model_config_parameters = request_json["parameters"]
inference_port = model_config_parameters.get("server_internal_port",
@@ -255,15 +346,16 @@ def process_deployment_result_message(self, topic=None, payload=None):
model_inference_url = "http://{}:{}/inference/{}".format(ip, inference_port_external, end_point_id)
# Send stage: MODEL_DEPLOYMENT_STAGE5 = "StartInferenceIngress"
- self.send_deployment_stages(
- end_point_id, model_name, model_id, model_inference_url,
- ServerConstants.MODEL_DEPLOYMENT_STAGE5["index"], ServerConstants.MODEL_DEPLOYMENT_STAGE5["text"],
- "inference url: {}".format(model_inference_url), message_center=self.message_center)
-
- # Prepare the result to MLOps
- deployed_replica_payload = self.get_deployed_replica_payload()
- if deployed_replica_payload is not None:
- payload_json = deployed_replica_payload
+ self.send_deployment_stages(end_point_id, model_name, model_id,
+ model_inference_url,
+ ServerConstants.MODEL_DEPLOYMENT_STAGE5["index"],
+ ServerConstants.MODEL_DEPLOYMENT_STAGE5["text"],
+ "inference url: {}".format(model_inference_url),
+ message_center=self.message_center)
+
+ # Send the result to MLOps
+ if self.deployed_replica_payload is not None:
+ payload_json = self.deployed_replica_payload
model_slave_url = payload_json["model_url"]
payload_json["model_url"] = model_inference_url
payload_json["port"] = inference_port_external
@@ -274,15 +366,18 @@ def process_deployment_result_message(self, topic=None, payload=None):
model_inputs = model_metadata["inputs"]
ret_inputs = list()
if "type" in model_metadata and model_metadata["type"] == "default":
- payload_json["input_json"] = {
- "end_point_name": end_point_name, "model_name": model_name, "token": str(token),
- "inputs": model_inputs, "outputs": []}
+ payload_json["input_json"] = {"end_point_name": end_point_name,
+ "model_name": model_name,
+ "token": str(token),
+ "inputs": model_inputs,
+ "outputs": []}
payload_json["output_json"] = model_metadata["outputs"]
else:
raise Exception(f"Unsupported model metadata type {model_metadata['type']}")
self.send_deployment_results_with_payload(
- end_point_id, end_point_name, payload_json)
+ end_point_id, end_point_name, payload_json,
+ self.replica_controller.target_replica_ids)
payload_json_saved = payload_json
payload_json_saved["model_slave_url"] = model_slave_url
@@ -295,12 +390,20 @@ def process_deployment_result_message(self, topic=None, payload=None):
FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
set_end_point_activation(end_point_id, end_point_name, True)
- self.send_deployment_status(
- end_point_id, end_point_name, payload_json["model_name"],
- model_inference_url, ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
- message_center=self.message_center
- )
+ if self.replica_controller.under_rollback:
+ self.send_deployment_status(end_point_id, end_point_name,
+ payload_json["model_name"],
+ model_inference_url,
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTED)
+ self.replica_controller.under_rollback = False
+ else:
+ self.send_deployment_status(end_point_id, end_point_name,
+ payload_json["model_name"],
+ model_inference_url,
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
+ message_center=self.message_center)
+ time.sleep(3)
self.trigger_completed_event()
@staticmethod
@@ -429,17 +532,28 @@ def send_first_scroll_update_msg(self):
first_chunk_dict = self.request_json["replica_version_diff"]
# Delete the record of the replaced device
- self.delete_device_replica_info_on_master(first_chunk_dict)
+ try:
+ self.delete_device_replica_info_on_master(
+ self.request_json["end_point_id"], self.request_json["end_point_name"],
+ self.request_json["model_config"]["model_name"], first_chunk_dict)
+ except Exception as e:
+ logging.info(f"Exception at send_first_scroll_update_msg {traceback.format_exc()}")
+
+ logging.info(f"Send the first scroll update msg to the device {first_chunk_dict} ")
# Send the deployment msg to the devices, (we reuse the start_deployment msg)
for edge_id in first_chunk_dict.keys():
if edge_id == self.edge_id:
continue
# send start deployment request to each device
- self.send_deployment_start_request_to_edge(edge_id)
+ self.send_deployment_start_request_to_edge(edge_id, self.request_json)
return list(first_chunk_dict.keys())
- def send_next_scroll_update_msg(self, device_id, replica_no):
+ def send_next_scroll_update_msg(self, run_id_str, device_id, replica_no):
+ """
+ Send the next scroll update msg to the devices if needed.
+ If there is no need for the next scroll update, directly return.
+ """
if replica_no is None:
return
@@ -448,33 +562,70 @@ def send_next_scroll_update_msg(self, device_id, replica_no):
if replica_controller.total_replica_version_diff_num == 0:
return
+ if replica_controller.under_rollback:
+ replica_controller.intermediate_replica_version[device_id][replica_no] = replica_controller.start_version
+ return
+
+ logging.info(f"Curr updating window: {replica_controller.curr_replica_updating_window} "
+ f"Curr version diff num: {replica_controller.total_replica_version_diff_num}")
+
replica_controller.callback_update_updating_window(device_id, replica_no)
# Decide whether to send the next scroll update
next_chunk_dict = replica_controller.get_next_chunk_devices_replica()
- replica_controller.curr_replica_updating_window = copy.deepcopy(next_chunk_dict)
-
if next_chunk_dict:
+ logging.info(f"The next scroll update for end point {run_id_str} is {next_chunk_dict}")
+ # Update curr updating window
+ replica_controller.curr_replica_updating_window = copy.deepcopy(next_chunk_dict)
+
+ # Use global deployment result mapping to decide whether to send the next scroll update
self.request_json["replica_version_diff"] = next_chunk_dict
- self.delete_device_replica_info_on_master(next_chunk_dict)
+
+ # Avoid using the old request_json
+ try:
+ self.delete_device_replica_info_on_master(
+ self.request_json["end_point_id"],
+ self.request_json["end_point_name"],
+ self.request_json["model_config"]["model_name"],
+ next_chunk_dict)
+ except Exception as e:
+ logging.info(f"Exception at send_next_scroll_update_msg {traceback.format_exc()}")
# Send the deployment msg to the devices, (we reuse the start_deployment msg)
for edge_id in next_chunk_dict.keys():
if edge_id == self.edge_id:
continue
# send start deployment request to each device
- self.send_deployment_start_request_to_edge(edge_id)
+ self.send_deployment_start_request_to_edge(edge_id, self.request_json)
return
- def delete_device_replica_info_on_master(self, edge_id_replica_no_dict):
+ def send_rollback_msg(self, run_id_str):
+ # Avoid using the old request_json
+ try:
+ self.delete_device_replica_info_on_master(
+ self.request_json["end_point_id"],
+ self.request_json["end_point_name"],
+ self.request_json["model_config"]["model_name"],
+ self.request_json["replica_version_diff"])
+ except Exception as e:
+ logging.info(f"Exception at send_rollback_msg {traceback.format_exc()}")
+
+ # Send the deployment msg to the devices, (we reuse the start_deployment msg)
+ for edge_id in self.request_json["replica_version_diff"].keys():
+ if edge_id == self.edge_id:
+ continue
+ # send start deployment request to each device
+ self.send_deployment_start_request_to_edge(edge_id, self.request_json)
+
+ def delete_device_replica_info_on_master(self, endpoint_id, endpoint_name, model_name, edge_id_replica_no_dict):
FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
# Remove the record of the replaced device
# [Deprecated] deployment status & device info
# Delete the result in deployment result list in Redis / SQLite
device_result_list = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_deployment_result_list(self.request_json["end_point_id"], self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"])
+ get_deployment_result_list(endpoint_id, endpoint_name, model_name)
+
delete_device_result_list = []
for device_result in device_result_list:
device_result_dict = json.loads(device_result)
@@ -485,9 +636,7 @@ def delete_device_replica_info_on_master(self, edge_id_replica_no_dict):
for delete_item in delete_device_result_list:
FedMLModelCache.get_instance(self.redis_addr, self.redis_port).delete_deployment_result(
- delete_item, self.request_json["end_point_id"],
- self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"]
+ delete_item, endpoint_id, endpoint_name, model_name
)
logging.info(f"Deleted the record of the replaced device {delete_device_result_list}")
@@ -515,7 +664,7 @@ def is_all_replica_version_reconciled(self):
return False
@staticmethod
- def generate_request_json_with_replica_diff(run_id, edge_id, request_json):
+ def generate_request_json_with_replica_num_diff(run_id, edge_id, request_json):
# Replica Controller is per deployment!
replica_controller = FedMLDeviceReplicaController(edge_id, request_json)
logging.info(f"Start Diff Replica controller for run {run_id} on edge {edge_id}")
@@ -525,6 +674,14 @@ def generate_request_json_with_replica_diff(run_id, edge_id, request_json):
new_request_with_num_diff = replica_controller.generate_diff_to_request_json()
request_json = new_request_with_num_diff
+ return request_json
+
+ @staticmethod
+ def generate_request_json_with_replica_version_diff(run_id, edge_id, request_json):
+ # Replica Controller is per deployment!
+ replica_controller = FedMLDeviceReplicaController(edge_id, request_json)
+ logging.info(f"Start Diff Replica controller for run {run_id} on edge {edge_id}")
+
# Prepare version diff
new_request_with_version_diff = replica_controller.init_first_update_device_replica_mapping()
request_json = new_request_with_version_diff
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
index 40896b9ee8..7221a09574 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
@@ -58,5 +58,9 @@ def recover_inference_and_monitor():
FedMLDeployMasterJobRunner.recover_inference_and_monitor()
@staticmethod
- def generate_request_json_with_replica_diff(run_id, edge_id, request_json):
- return FedMLDeployMasterJobRunner.generate_request_json_with_replica_diff(run_id, edge_id, request_json)
+ def generate_request_json_with_replica_num_diff(run_id, edge_id, request_json):
+ return FedMLDeployMasterJobRunner.generate_request_json_with_replica_num_diff(run_id, edge_id, request_json)
+
+ @staticmethod
+ def generate_request_json_with_replica_version_diff(run_id, edge_id, request_json):
+ return FedMLDeployMasterJobRunner.generate_request_json_with_replica_num_diff(run_id, edge_id, request_json)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index e8be50f77f..8566848ec6 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -1,7 +1,6 @@
import json
import logging
-import os
from fedml.core.mlops import MLOpsConfigs, MLOpsRuntimeLog, MLOpsRuntimeLogDaemon
from .device_model_cache import FedMLModelCache
from .device_model_db import FedMLModelDatabase
@@ -102,8 +101,8 @@ def callback_deployment_result_message(self, topic=None, payload=None):
FedMLDeployJobRunnerManager.get_instance().save_deployment_result(topic, payload)
def callback_delete_deployment(self, topic, payload):
- # Parse payload as the model message object.
logging.info("[Master] callback_delete_deployment")
+ # Parse payload as the model message object.
model_msg_object = FedMLModelMsgObject(topic, payload)
# Set end point as deactivated status
@@ -115,8 +114,7 @@ def callback_delete_deployment(self, topic, payload):
delete_end_point(model_msg_object.inference_end_point_id, model_msg_object.end_point_name,
model_msg_object.model_name, model_msg_object.model_version)
- FedMLDeployJobRunnerManager.get_instance().send_deployment_delete_request_to_edges(
- model_msg_object.inference_end_point_id, payload, model_msg_object)
+ FedMLDeployJobRunnerManager.get_instance().send_deployment_delete_request_to_edges(payload, model_msg_object)
FedMLDeployJobRunnerManager.get_instance().stop_job_runner(model_msg_object.run_id)
@@ -138,7 +136,7 @@ def callback_start_deployment(self, topic, payload):
except Exception as e:
pass
- # Parse the deployment parameters
+ # Get deployment params
request_json = json.loads(payload)
run_id = request_json["end_point_id"]
end_point_name = request_json["end_point_name"]
@@ -147,6 +145,7 @@ def callback_start_deployment(self, topic, payload):
user_name = request_json["user_name"]
device_ids = request_json["device_ids"]
device_objs = request_json["device_objs"]
+
model_config = request_json["model_config"]
model_name = model_config["model_name"]
model_id = model_config["model_id"]
@@ -156,62 +155,76 @@ def callback_start_deployment(self, topic, payload):
inference_engine = model_config.get("inference_engine", 0)
inference_end_point_id = run_id
+ logging.info("[Master] received start deployment request for end point {}.".format(run_id))
+
# Start log processor for current run
self.args.run_id = run_id
self.args.edge_id = self.edge_id
- MLOpsRuntimeLog.get_instance(self.args).init_logs()
+ MLOpsRuntimeLog(args=self.args).init_logs()
MLOpsRuntimeLogDaemon.get_instance(self.args).set_log_source(
ServerConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT)
MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(run_id, self.edge_id)
- # Generate the deployment new parameters
- logging.info("callback_start_deployment {}".format(payload))
+ # Add additional parameters to the request_json
run_id = inference_end_point_id
- run_id_str = str(run_id)
+ self.args.run_id = run_id
+ self.run_id = run_id
request_json["run_id"] = run_id
self.request_json = request_json
+ run_id_str = str(run_id)
self.running_request_json[run_id_str] = request_json
- diff_devices, diff_version = self.get_diff_devices(run_id)
- self.request_json["diff_devices"] = diff_devices
- self.request_json["diff_version"] = diff_version
self.request_json["master_node_ip"] = GeneralConstants.get_ip_address(self.request_json)
- # Save the endpoint device info
- self.init_device_update_map()
+ # Target status of the devices
FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
set_end_point_device_info(request_json["end_point_id"], end_point_name, json.dumps(device_objs))
- # Save the endpoint token
- usr_indicated_token = FedMLDeployMasterProtocolManager.get_usr_indicated_token(request_json)
+ # Setup Token
+ usr_indicated_token = self.get_usr_indicated_token(request_json)
if usr_indicated_token != "":
logging.info(f"Change Token from{token} to {usr_indicated_token}")
token = usr_indicated_token
FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
set_end_point_token(run_id, end_point_name, model_name, token)
- # Subscribe deployment result messages from slave devices
self.subscribe_deployment_messages_from_slave_devices(request_json)
- # Send stage: MODEL_DEPLOYMENT_STAGE1 = "Received"
+ # Report stage to mlops: MODEL_DEPLOYMENT_STAGE1 = "Received"
FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
self.run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE1["index"],
ServerConstants.MODEL_DEPLOYMENT_STAGE1["text"], "Received request for end point {}".format(run_id),
message_center=self.message_center)
- # Send stage: MODEL_DEPLOYMENT_STAGE2 = "Initializing"
+ # Report stage to mlops: MODEL_DEPLOYMENT_STAGE2 = "Initializing"
FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
self.run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE2["index"],
ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"], ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"],
message_center=self.message_center)
- # Save the runner info
ServerConstants.save_runner_infos(self.args.device_id + "." + self.args.os_name, self.edge_id, run_id=run_id)
- # Start the job runner to deploy models
- self.running_request_json[run_id_str] = FedMLDeployJobRunnerManager.generate_request_json_with_replica_diff(
+ # Num diff
+ request_json = FedMLDeployJobRunnerManager.generate_request_json_with_replica_num_diff(
+ run_id, self.edge_id, request_json
+ )
+
+ # Listen to extra worker topics, especially when worker's replica remove to zero,
+ # In this case, currently Java will NOT send those worker ids to the master, but still need to listen to it.
+ if "replica_num_diff" in request_json and len(request_json["replica_num_diff"]) > 0:
+ for device_id in request_json["replica_num_diff"].keys():
+ # {"op": "remove", "curr_num": 1, "target_num": 0}
+ if request_json["replica_num_diff"][device_id]["op"] == "remove" and \
+ request_json["replica_num_diff"][device_id]["target_num"] == 0:
+ self.subscribe_spec_device_message(run_id, device_id)
+
+ # Version diff
+ request_json = FedMLDeployJobRunnerManager.generate_request_json_with_replica_version_diff(
run_id, self.edge_id, request_json
)
+ self.running_request_json[run_id_str] = request_json
+
+ # Start the job runner to deploy models
self._get_job_runner_manager().start_job_runner(
run_id, request_json, args=self.args, edge_id=self.edge_id,
sender_message_queue=self.message_center.get_sender_message_queue(),
@@ -262,81 +275,6 @@ def callback_deactivate_deployment(self, topic, payload):
FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_end_point_activation(
model_msg_object.inference_end_point_id, model_msg_object.model_name, False)
- def get_diff_devices(self, run_id) -> (dict, dict):
- """
- {device_id(int): "op: add" | "op: delete" | "op: replace"}
- "op: add" -> need to add
- "op: delete" -> need to delete device
- "op: replace" -> need to restart the container of the device on same port with new (same) model pkg
-
- {device_id(int): "old_version"}
- """
- try:
- logging.info(f"Get diff devices for run {run_id}")
- request_json = self.running_request_json.get(str(run_id))
-
- diff_devices = {}
- diff_version = {}
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- device_objs = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_end_point_device_info(run_id)
- if device_objs is None:
- for new_device_id in request_json["device_ids"]:
- diff_devices[new_device_id] = ServerConstants.DEVICE_DIFF_ADD_OPERATION
- else:
- device_objs_dict = json.loads(device_objs)
- device_ids_frm_db = [d["id"] for d in device_objs_dict]
-
- for exist_device_id in device_ids_frm_db:
- if exist_device_id not in request_json["device_ids"]:
- diff_devices[exist_device_id] = ServerConstants.DEVICE_DIFF_DELETE_OPERATION
-
- for new_device_id in request_json["device_ids"]:
- if new_device_id not in device_ids_frm_db:
- diff_devices[new_device_id] = ServerConstants.DEVICE_DIFF_ADD_OPERATION
- else:
- if new_device_id == self.edge_id:
- continue
-
- old_version = self.should_update_device(request_json, new_device_id)
- if old_version:
- diff_devices[new_device_id] = ServerConstants.DEVICE_DIFF_REPLACE_OPERATION
- diff_version[new_device_id] = old_version
- else:
- pass
- logging.info(f"Diff devices: {diff_devices}")
- except Exception as e:
- error_log_path = f"~/.fedml/fedml-model-server/fedml/logs/{run_id}_error.txt"
- if not os.path.exists(os.path.dirname(os.path.expanduser(error_log_path))):
- os.makedirs(os.path.dirname(os.path.expanduser(error_log_path)))
- with open(os.path.expanduser(error_log_path), "w") as f:
- f.write(str(e))
- raise e
- return diff_devices, diff_version
-
- def should_update_device(self, payload, new_device_id):
- """
- Query the device info in local redis, if the device info is different from the payload,
- return the old model version
- """
- device_result_list = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_deployment_result_list(self.request_json["end_point_id"],
- self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"])
-
- for device_result in device_result_list:
- if device_result is None:
- continue
- device_result_dict = json.loads(device_result)
-
- if int(device_result_dict["cache_device_id"]) == new_device_id:
- result_body = json.loads(device_result_dict["result"])
- if result_body["model_version"] != payload["model_config"]["model_version"]:
- return result_body["model_version"]
- else:
- return None
- return None
-
@staticmethod
def get_usr_indicated_token(request_json) -> str:
usr_indicated_token = ""
@@ -358,8 +296,20 @@ def subscribe_deployment_messages_from_slave_devices(self, request_json):
if str(edge_id) == str(self.edge_id):
continue
# subscribe deployment result message for each model device
- deployment_results_topic = "model_device/model_device/return_deployment_result/{}".format(edge_id)
+ deployment_results_topic = "model_device/model_device/return_deployment_result/{}/{}".format(
+ run_id, edge_id)
self.add_message_listener(deployment_results_topic, self.callback_deployment_result_message)
self.subscribe_msg(deployment_results_topic)
logging.info("subscribe device messages {}".format(deployment_results_topic))
+
+ def subscribe_spec_device_message(self, run_id, device_id):
+ if device_id == self.edge_id:
+ return
+
+ # subscribe deployment result message for each model device
+ deployment_results_topic = "model_device/model_device/return_deployment_result/{}/{}".format(
+ run_id, device_id)
+
+ self.add_message_listener(deployment_results_topic, self.callback_deployment_result_message)
+ self.subscribe_msg(deployment_results_topic)
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index 5d6f1a4d8e..78e2527e0c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -7,7 +7,6 @@
import traceback
import urllib
from abc import ABC
-from urllib.parse import urljoin, urlparse
import yaml
from fedml.computing.scheduler.comm_utils.job_utils import JobRunnerUtils
from fedml.core.mlops import MLOpsRuntimeLog
@@ -27,7 +26,7 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
FedMLBaseSlaveJobRunner.__init__(
self, args, edge_id=edge_id, request_json=request_json, agent_config=agent_config, run_id=run_id,
cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str, agent_data_dir=ClientConstants.get_data_dir(),
- agent_package_download_dir=ClientConstants.get_package_download_dir(),
+ agent_package_download_dir=ClientConstants.get_model_package_dir(),
agent_package_unzip_dir=GeneralConstants.get_package_unzip_dir(ClientConstants.get_package_download_dir()),
agent_log_file_dir=ClientConstants.get_log_file_dir()
)
@@ -57,8 +56,7 @@ def retrieve_binary_model_file(self, package_name, package_url):
local_package_file = "{}".format(os.path.join(local_package_path, package_name))
if os.path.exists(local_package_file):
os.remove(local_package_file)
- package_url_without_query_path = urljoin(package_url, urlparse(package_url).path)
- urllib.request.urlretrieve(package_url_without_query_path, local_package_file,
+ urllib.request.urlretrieve(package_url, local_package_file,
reporthook=self.package_download_progress)
unzip_package_path = os.path.join(unzip_package_path, package_name)
@@ -79,10 +77,6 @@ def get_model_bin_file(unzip_package_full_path):
def update_local_fedml_config(self, run_id, model_config, model_config_parameters=None):
model_name = model_config["model_name"]
model_storage_url = model_config["model_storage_url"]
- scale_min = model_config.get("instance_scale_min", 0)
- scale_max = model_config.get("instance_scale_max", 0)
- inference_engine = model_config.get("inference_engine", 0)
- inference_end_point_id = run_id
# Retrieve model package or model binary file.
if self.model_is_from_open:
@@ -92,7 +86,6 @@ def update_local_fedml_config(self, run_id, model_config, model_config_parameter
model_bin_file = FedMLDeployWorkerJobRunner.get_model_bin_file(unzip_package_path)
# Load the config to memory
- package_conf_object = {}
fedml_local_config_file = os.path.join(unzip_package_path, "fedml_model_config.yaml")
# Inject the config from UI to pkg yaml
@@ -117,92 +110,49 @@ def download_model_package(self, package_name, package_url):
# Override
def run_impl(self, run_extend_queue_list, sender_message_center,
listener_message_queue, status_center_queue):
+ # Get deployment params
run_id = self.request_json["end_point_id"]
end_point_name = self.request_json["end_point_name"]
- token = self.request_json["token"]
- user_id = self.request_json["user_id"]
- user_name = self.request_json["user_name"]
device_ids = self.request_json["device_ids"]
- device_objs = self.request_json["device_objs"]
master_ip = self.request_json["master_node_ip"]
-
model_config = self.request_json["model_config"]
model_name = model_config["model_name"]
model_id = model_config["model_id"]
model_version = model_config["model_version"]
- model_storage_url = model_config["model_storage_url"]
- scale_min = model_config.get("instance_scale_min", 0)
- scale_max = model_config.get("instance_scale_max", 0)
model_config_parameters = self.request_json["parameters"]
-
- self.replica_handler = FedMLDeviceReplicaHandler(self.edge_id, self.request_json)
-
inference_port = model_config_parameters.get("worker_internal_port",
ClientConstants.MODEL_INFERENCE_DEFAULT_PORT)
inference_port_external = model_config_parameters.get("worker_external_port", inference_port)
-
- if "using_triton" in model_config_parameters and model_config_parameters["using_triton"]:
- inference_engine = ClientConstants.INFERENCE_ENGINE_TYPE_INT_TRITON
- else:
- inference_engine = ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT
-
- logging.info("[Critical] The inference_engine is: {}".format(inference_engine))
-
- self.model_is_from_open = True if model_config.get("is_from_open", 0) == 1 else False
- if self.model_is_from_open:
- model_net_url = model_config["model_net_url"]
+ inference_engine = model_config_parameters.get("inference_engine",
+ ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT)
inference_end_point_id = run_id
- use_gpu = "gpu" # TODO: Get GPU from device infos
- memory_size = "4096m" # TODO: Get Memory size for each instance
self.mlops_metrics.report_sys_perf(self.args, self.agent_config["mqtt_config"], run_id=run_id)
+ MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
- self.check_runner_stop_event()
-
- logging.info("model deployment request: {}".format(self.request_json))
+ logging.info(f"[Worker] Received model deployment request from master for endpoint {run_id}.")
+ self.replica_handler = FedMLDeviceReplicaHandler(self.edge_id, self.request_json)
+ if self.replica_handler is not None:
+ logging.info(f"=================Worker replica Handler ======================"
+ f"Reconcile with num diff {self.replica_handler.replica_num_diff} "
+ f"and version diff {self.replica_handler.replica_version_diff}."
+ f"=============================================================")
+ else:
+ logging.error(f"[Worker] Replica handler is None.")
+ return False
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+ self.check_runner_stop_event()
+ # Report the deployment status to mlops
self.status_reporter.report_client_id_status(
self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_INITIALIZING,
is_from_model=True, running_json=json.dumps(self.request_json), run_id=run_id)
-
self.status_reporter.report_client_id_status(
self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_RUNNING,
is_from_model=True, run_id=run_id)
self.check_runner_stop_event()
- # update local config with real time parameters from server and dynamically replace variables value
- logging.info("download and unzip model to local...")
- unzip_package_path, model_bin_file, fedml_config_object = \
- self.update_local_fedml_config(run_id, model_config, model_config_parameters)
- if unzip_package_path is None or fedml_config_object is None:
- logging.info("failed to update local fedml config.")
- self.check_runner_stop_event()
- self.status_reporter.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- is_from_model=True, run_id=run_id)
- return False
-
- logging.info("check downloaded packages...")
- if not os.path.exists(unzip_package_path):
- logging.info("failed to unzip file.")
- self.check_runner_stop_event()
- self.status_reporter.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- is_from_model=True, run_id=run_id)
- return False
-
- # download model net and load into the torch model
- model_from_open = None
- self.model_is_from_open = None
-
- logging.info("start the model deployment...")
- self.check_runner_stop_event()
- running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
- "", "", model_version, {}, {}
-
# Reconcile the replica number (op: add, remove)
prev_rank, op, op_num = self.replica_handler.reconcile_num_replica()
@@ -212,55 +162,134 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
replica_rank_to_update, op = self.replica_handler.reconcile_replica_version()
if not op:
- logging.info("No need to reconcile.")
+ logging.info("[Worker] No need to reconcile.")
return True
+ logging.info(
+ f"================Worker Reconcile Operations ======================\n"
+ f" op: {op}; op num: {op_num}.\n"
+ f"==================================================================\n")
+
+ # If not rollback, download package from MLOps; otherwise, use the backup package
+ if op != "rollback":
+ logging.info("Download and unzip model to local...")
+ unzip_package_path, _, _ = \
+ self.update_local_fedml_config(run_id, model_config, model_config_parameters)
+ if unzip_package_path is None:
+ logging.info("Failed to update local fedml config.")
+ self.check_runner_stop_event()
+ self.status_reporter.report_client_id_status(
+ self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
+ is_from_model=True, run_id=run_id)
+ return False
+
+ if not os.path.exists(unzip_package_path):
+ logging.info("Failed to unzip file.")
+ self.check_runner_stop_event()
+ self.status_reporter.report_client_id_status(
+ self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
+ is_from_model=True, run_id=run_id)
+ return False
+ else:
+ logging.info("Try to use backup package to rollback...")
+ # Find folder under "~/.fedml/fedml-model-client/fedml/model_packages \
+ # /${end_point_id}_${end_point_name}_${model_name}_${model_version}"
+ backup_folder_full_path = None
+ models_root_dir = ClientConstants.get_model_package_dir()
+
+ # Find the version (notified by master) to rollback
+ version_diff_dict = self.request_json["replica_version_diff"][str(self.edge_id)]
+ version_rollback_to = None
+ for replica_no, rollback_ops in version_diff_dict.items():
+ version_rollback_to = rollback_ops["new_version"] # Note that new_version is the version to rollback
+ break
+ if version_rollback_to is None:
+ logging.error(f"No old version found for run_id: {self.run_id} "
+ f"edge_id: {self.edge_id}, rollback failed. No old version found in request_json.")
+ return False
+ model_version = version_rollback_to
+
+ # Format the version to match the folder name
+ model_version_formatted = version_rollback_to.replace(" ", "-")
+ model_version_formatted = model_version_formatted.replace(":", "-")
+
+ last_run_folder_sub_fd = f"{run_id}_{end_point_name}_{model_name}_{model_version_formatted}"
+ for folder in os.listdir(models_root_dir):
+ if last_run_folder_sub_fd in folder:
+ backup_folder_full_path = os.path.join(models_root_dir, folder)
+ break
+ if backup_folder_full_path is None:
+ logging.error(f"No backup folder found for run_id: {self.run_id} edge_id: {self.edge_id} "
+ f"under {models_root_dir} with sub folder {last_run_folder_sub_fd}, rollback failed.")
+ return False
+
+ # Inside backup folder, find unzipped package with prefix unzip_fedml_run
+ unzip_package_path_parent = None
+ for folder in os.listdir(backup_folder_full_path):
+ if folder.startswith("unzip_fedml_run"):
+ unzip_package_path_parent = os.path.join(backup_folder_full_path, folder)
+ break
+
+ # Inside unzip folder, find the unzipped package, should be the only one
+ unzip_package_path = None
+ for folder in os.listdir(unzip_package_path_parent):
+ if os.path.isdir(os.path.join(unzip_package_path_parent, folder)):
+ unzip_package_path = os.path.join(unzip_package_path_parent, folder)
+ break
+
+ if unzip_package_path is None:
+ logging.error(f"No unzipped package found for run_id: {self.run_id} edge_id: {self.edge_id} "
+ f"under {backup_folder_full_path}, rollback failed.")
+ return False
+
+ self.check_runner_stop_event()
+
+ running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
+ "", "", model_version, {}, {}
+
if op == "add":
worker_ip = GeneralConstants.get_ip_address(self.request_json)
- for rank in range(prev_rank+1, prev_rank+1+op_num):
+ for rank in range(prev_rank + 1, prev_rank + 1 + op_num):
# TODO: Support Rollback if this for loop failed
try:
running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
start_deployment(
- inference_end_point_id, end_point_name, model_id, model_version,
- unzip_package_path, model_bin_file, model_name, inference_engine,
- ClientConstants.INFERENCE_HTTP_PORT,
- ClientConstants.INFERENCE_GRPC_PORT,
- ClientConstants.INFERENCE_METRIC_PORT,
- use_gpu, memory_size,
- ClientConstants.INFERENCE_CONVERTOR_IMAGE,
- ClientConstants.INFERENCE_SERVER_IMAGE,
- worker_ip,
- self.model_is_from_open, model_config_parameters,
- model_from_open,
- token,
- master_ip, self.edge_id, master_device_id=device_ids[0], replica_rank=rank,
+ end_point_id=inference_end_point_id, end_point_name=end_point_name, model_id=model_id,
+ model_version=model_version, model_storage_local_path=unzip_package_path,
+ inference_model_name=model_name, inference_engine=inference_engine,
+ infer_host=worker_ip, master_ip=master_ip, edge_id=self.edge_id,
+ master_device_id=device_ids[0], replica_rank=rank,
gpu_per_replica=int(self.replica_handler.gpu_per_replica)
)
except Exception as e:
inference_output_url = ""
- logging.error(f"Exception at deployment: {traceback.format_exc()}")
+ logging.error(f"[Worker] Exception at deployment: {traceback.format_exc()}")
if inference_output_url == "":
- logging.error("failed to deploy the model...")
+ logging.error("[Worker] Failed to deploy the model.")
+ # Send failed result back to master
result_payload = self.send_deployment_results(
end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
model_id, model_name, inference_output_url, inference_model_version, inference_port,
inference_engine, model_metadata, model_config)
+ self.status_reporter.run_id = self.run_id
self.status_reporter.report_client_id_status(
self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
is_from_model=True, run_id=self.run_id)
+
return False
else:
- logging.info("finished deployment, continue to send results to master...")
+ # Send failed successful result back to master
+ logging.info("Finished deployment, continue to send results to master...")
result_payload = self.send_deployment_results(
end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
model_id, model_name, inference_output_url, model_version, inference_port_external,
inference_engine, model_metadata, model_config, replica_no=rank + 1)
- if inference_port_external != inference_port: # Save internal port to local db
+ if inference_port_external != inference_port:
+ # Save internal port to local db
logging.info("inference_port_external {} != inference_port {}".format(
inference_port_external, inference_port))
result_payload = self.construct_deployment_results(
@@ -272,21 +301,22 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
run_id, end_point_name, model_name, model_version, self.edge_id,
json.dumps(result_payload), replica_no=rank + 1)
- logging.info(f"Deploy replica {rank+1} / {prev_rank+1+op_num} successfully.")
+ logging.info(f"Deploy replica {rank + 1} / {prev_rank + 1 + op_num} successfully.")
time.sleep(5)
time.sleep(1)
+ self.status_reporter.run_id = self.run_id
self.status_reporter.report_client_id_status(
self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
is_from_model=True, run_id=self.run_id)
return True
elif op == "remove":
- for rank_to_delete in range(prev_rank, prev_rank-op_num, -1):
+ for rank_to_delete in range(prev_rank, prev_rank - op_num, -1):
self.replica_handler.remove_replica(rank_to_delete)
FedMLModelCache.get_instance().set_redis_params()
replica_occupied_gpu_ids_str = FedMLModelCache.get_instance().get_replica_gpu_ids(
- run_id, end_point_name, model_name, self.edge_id, rank_to_delete+1)
+ run_id, end_point_name, model_name, self.edge_id, rank_to_delete + 1)
replica_occupied_gpu_ids = json.loads(replica_occupied_gpu_ids_str)
@@ -302,6 +332,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
inference_engine, model_metadata, model_config, replica_no=rank_to_delete + 1)
time.sleep(1)
+ self.status_reporter.run_id = self.run_id
self.status_reporter.report_client_id_status(
self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
is_from_model=True, run_id=self.run_id)
@@ -310,11 +341,11 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
if rank_to_delete == 0:
pass
return True
- elif op == "update":
+ elif op == "update" or op == "rollback":
# Update is combine of delete and add
- worker_ip = GeneralConstants.get_ip_address(self.request_json)
+ worker_ip = self.get_ip_address(self.request_json)
for rank in replica_rank_to_update:
- # Delete the container
+ # Delete a replica (container) if exists
self.replica_handler.remove_replica(rank)
FedMLModelCache.get_instance().set_redis_params()
@@ -322,33 +353,36 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
run_id, end_point_name, model_name, self.edge_id, rank + 1)
replica_occupied_gpu_ids = json.loads(replica_occupied_gpu_ids_str)
+ logging.info(f"Release gpu ids {replica_occupied_gpu_ids} for update / rollback.")
- JobRunnerUtils.get_instance().release_partial_job_gpu(run_id, self.edge_id, replica_occupied_gpu_ids)
+ # TODO (Raphael) check if this will allow another job to seize the gpu during high concurrency:
+ try:
+ JobRunnerUtils.get_instance().release_partial_job_gpu(run_id, self.edge_id, replica_occupied_gpu_ids)
+ except Exception as e:
+ if op == "rollback":
+ pass
+ else:
+ logging.error(f"Failed to release gpu ids {replica_occupied_gpu_ids} for update.")
+ return False
# Delete the deployment result from local db
FedMLModelDatabase.get_instance().delete_deployment_result_with_device_id_and_rank(
run_id, end_point_name, model_name, self.edge_id, rank)
+ logging.info(f"Delete replica with no {rank + 1} successfully.")
time.sleep(1)
- # Add the container
+ # Add a replica (container)
# TODO: Reduce the duplicated code
+ logging.info(f"Start to deploy the model with replica no {rank + 1} ...")
try:
running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
start_deployment(
- inference_end_point_id, end_point_name, model_id, model_version,
- unzip_package_path, model_bin_file, model_name, inference_engine,
- ClientConstants.INFERENCE_HTTP_PORT,
- ClientConstants.INFERENCE_GRPC_PORT,
- ClientConstants.INFERENCE_METRIC_PORT,
- use_gpu, memory_size,
- ClientConstants.INFERENCE_CONVERTOR_IMAGE,
- ClientConstants.INFERENCE_SERVER_IMAGE,
- worker_ip,
- self.model_is_from_open, model_config_parameters,
- model_from_open,
- token,
- master_ip, self.edge_id, master_device_id=device_ids[0], replica_rank=rank,
+ end_point_id=inference_end_point_id, end_point_name=end_point_name, model_id=model_id,
+ model_version=model_version, model_storage_local_path=unzip_package_path,
+ inference_model_name=model_name, inference_engine=inference_engine,
+ infer_host=worker_ip, master_ip=master_ip, edge_id=self.edge_id,
+ master_device_id=device_ids[0], replica_rank=rank,
gpu_per_replica=int(self.replica_handler.gpu_per_replica)
)
except Exception as e:
@@ -356,20 +390,30 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
logging.error(f"Exception at deployment: {traceback.format_exc()}")
if inference_output_url == "":
- logging.error("failed to deploy the model...")
+ logging.error("Failed to deploy the model...")
+
+ # If update failed, should release this replica's gpu
+ FedMLModelCache.get_instance().set_redis_params()
+ replica_occupied_gpu_ids_str = FedMLModelCache.get_instance().get_replica_gpu_ids(
+ run_id, end_point_name, model_name, self.edge_id, rank + 1)
+
+ replica_occupied_gpu_ids = json.loads(replica_occupied_gpu_ids_str)
+
+ JobRunnerUtils.get_instance().release_partial_job_gpu(
+ run_id, self.edge_id, replica_occupied_gpu_ids)
result_payload = self.send_deployment_results(
end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
model_id, model_name, inference_output_url, inference_model_version, inference_port,
inference_engine, model_metadata, model_config)
+ self.status_reporter.run_id = self.run_id
self.status_reporter.report_client_id_status(
self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
is_from_model=True, run_id=self.run_id)
-
return False
else:
- logging.info("finished deployment, continue to send results to master...")
+ logging.info("Finished deployment, continue to send results to master...")
result_payload = self.send_deployment_results(
end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
model_id, model_name, inference_output_url, model_version, inference_port_external,
@@ -390,6 +434,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
logging.info(f"Update replica with no {rank + 1} successfully. Op num {op_num}")
time.sleep(5)
time.sleep(1)
+ self.status_reporter.run_id = self.run_id
self.status_reporter.report_client_id_status(
self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
is_from_model=True, run_id=self.run_id)
@@ -437,7 +482,9 @@ def send_deployment_results(self, end_point_name, device_id, model_status,
model_id, model_name, model_inference_url,
model_version, inference_port, inference_engine,
model_metadata, model_config, replica_no=1):
- deployment_results_topic = "model_device/model_device/return_deployment_result/{}".format(device_id)
+ deployment_results_topic = "model_device/model_device/return_deployment_result/{}/{}".format(
+ self.run_id, device_id)
+
deployment_results_payload = self.construct_deployment_results(
end_point_name, device_id, model_status,
model_id, model_name, model_inference_url,
@@ -445,7 +492,7 @@ def send_deployment_results(self, end_point_name, device_id, model_status,
model_metadata, model_config, replica_no=replica_no)
logging.info("[client] send_deployment_results: topic {}, payload {}.".format(deployment_results_topic,
- deployment_results_payload))
+ deployment_results_payload))
self.message_center.send_message_json(deployment_results_topic, json.dumps(deployment_results_payload))
return deployment_results_payload
@@ -455,18 +502,8 @@ def send_deployment_status(self, end_point_name, device_id,
inference_port=ClientConstants.MODEL_INFERENCE_DEFAULT_PORT,
replica_no=1, # start from 1
):
- deployment_status_topic = "model_device/model_device/return_deployment_status/{}".format(device_id)
- deployment_status_payload = self.construct_deployment_status(
- end_point_name, device_id,
- model_id, model_name, model_version,
- model_inference_url, model_status,
- inference_port=inference_port,
- replica_no=replica_no)
-
- logging.info("[client] send_deployment_status: topic {}, payload {}.".format(deployment_status_topic,
- deployment_status_payload))
- self.message_center.send_message_json(deployment_status_topic, json.dumps(deployment_status_payload))
- return deployment_status_payload
+ # Deprecated
+ pass
def reset_devices_status(self, edge_id, status):
self.status_reporter.run_id = self.run_id
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index 0543459dd0..856abdac40 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -255,10 +255,15 @@ def callback_start_train(self, topic, payload):
run_params = run_config.get("parameters", {})
serving_args = run_params.get("serving_args", {})
endpoint_id = serving_args.get("endpoint_id", None)
- cuda_visible_gpu_ids_str = JobRunnerUtils.get_instance().occupy_gpu_ids(
- run_id, matched_gpu_num, edge_id, inner_id=endpoint_id,
- model_master_device_id=model_master_device_id,
- model_slave_device_id=model_slave_device_id)
+ job_yaml = run_params.get("job_yaml", {})
+ job_type = job_yaml.get("job_type", SchedulerConstants.JOB_TASK_TYPE_TRAIN)
+ cuda_visible_gpu_ids_str = None
+ if not (job_type == SchedulerConstants.JOB_TASK_TYPE_SERVE or
+ job_type == SchedulerConstants.JOB_TASK_TYPE_DEPLOY):
+ cuda_visible_gpu_ids_str = JobRunnerUtils.get_instance().occupy_gpu_ids(
+ run_id, matched_gpu_num, edge_id, inner_id=endpoint_id,
+ model_master_device_id=model_master_device_id,
+ model_slave_device_id=model_slave_device_id)
logging.info(
f"Run started, available gpu ids: {JobRunnerUtils.get_instance().get_available_gpu_id_list(edge_id)}")
diff --git a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
index cd8e40d7e8..6e2a03a8b4 100755
--- a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
@@ -1,5 +1,7 @@
import copy
+import json
import os
+import fedml
from ..comm_utils.job_cleanup import JobCleanup
from .base_slave_protocol_manager import FedMLBaseSlaveProtocolManager
from .launch_job_runner_manager import FedMLLaunchJobRunnerManager
@@ -11,15 +13,38 @@ class FedMLLaunchSlaveProtocolManager(FedMLBaseSlaveProtocolManager):
def __init__(self, args, agent_config=None):
FedMLBaseSlaveProtocolManager.__init__(self, args, agent_config=agent_config)
+ self.topic_request_deploy_slave_device_info_from_mlops = None
+ self.topic_request_deploy_master_device_info_from_mlops = None
+ self.topic_request_edge_device_info_from_mlops = None
# Override
def generate_topics(self):
super().generate_topics()
+ # The topic for requesting device info from mlops.
+ self.topic_request_edge_device_info_from_mlops = f"deploy/mlops/slave_agent/request_device_info/{self.edge_id}"
+
+ # The topic for requesting deployment master device info from mlops.
+ self.topic_request_deploy_master_device_info_from_mlops = f"deploy/mlops/master_agent/request_device_info/{self.model_device_server_id}"
+
+ # The topic for requesting deployment slave device info from mlops.
+ self.topic_request_deploy_slave_device_info_from_mlops = f"deploy/mlops/slave_agent/request_device_info/{self.model_device_client_edge_id_list[0]}"
+
+ self.add_subscribe_topic(self.topic_request_edge_device_info_from_mlops)
+ self.add_subscribe_topic(self.topic_request_deploy_master_device_info_from_mlops)
+ self.add_subscribe_topic(self.topic_request_deploy_slave_device_info_from_mlops)
+
# Override
def add_protocol_handler(self):
super().add_protocol_handler()
+ self.add_message_listener(
+ self.topic_request_edge_device_info_from_mlops, self.callback_response_device_info_to_mlops)
+ self.add_message_listener(
+ self.topic_request_deploy_master_device_info_from_mlops, self.callback_response_device_info_to_mlops)
+ self.add_message_listener(
+ self.topic_request_deploy_slave_device_info_from_mlops, self.callback_response_device_info_to_mlops)
+
# Override
def _generate_protocol_manager_instance(self, args, agent_config=None):
return FedMLLaunchSlaveProtocolManager(args, agent_config=agent_config)
@@ -102,3 +127,29 @@ def _init_extra_items(self):
self.mlops_metrics.report_device_realtime_perf(self.args, self.args.agent_config["mqtt_config"])
pass
+ def callback_response_device_info_to_mlops(self, topic, payload):
+ payload_json = json.loads(payload)
+ server_id = payload_json.get("server_id", 0)
+ run_id = payload_json.get("run_id", 0)
+ listen_edge_id = str(topic).split("/")[-1]
+ context = payload_json.get("context", None)
+ response_topic = f"deploy/slave_agent/mlops/response_device_info"
+ if self.mlops_metrics is not None and self.model_device_client_edge_id_list is not None and \
+ self.model_device_server_id is not None:
+ device_info_json = {
+ "edge_id": listen_edge_id,
+ "fedml_version": fedml.__version__,
+ "user_id": self.args.user
+ }
+ salve_device_ids = list()
+ for model_client_edge_id in self.model_device_client_edge_id_list:
+ salve_device_ids.append(model_client_edge_id)
+ response_payload = {"slave_device_id": self.model_device_client_edge_id_list[0],
+ "slave_device_id_list": salve_device_ids,
+ "master_device_id": self.model_device_server_id,
+ "run_id": run_id, "edge_id": listen_edge_id,
+ "edge_info": device_info_json}
+ if context is not None:
+ response_payload["context"] = context
+ self.message_center.send_message(response_topic, json.dumps(response_payload), run_id=run_id)
+
From b3742bb710068ea85f73baf12bd14930633854ba Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Tue, 9 Apr 2024 21:32:13 +0800
Subject: [PATCH 004/282] [CoreEngine] make the latest deployment module work
with the refactored paradigm.
---
.../model_scheduler/job_runner_msg_sender.py | 7 ++--
.../model_scheduler/master_job_runner.py | 26 ++++++--------
.../master_job_runner_manager.py | 5 +--
.../master_protocol_manager.py | 3 +-
.../scheduler_core/compute_status_cache.py | 2 ++
.../scheduler_core/message_center.py | 14 ++++++--
.../scheduler_base_protocol_manager.py | 3 ++
.../status_manager_protocols.py | 2 +-
.../scheduler/slave/slave_protocol_manager.py | 34 +++++++++++--------
9 files changed, 57 insertions(+), 39 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py b/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
index acce17d20b..104dacf716 100755
--- a/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
+++ b/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
@@ -114,7 +114,7 @@ def send_deployment_start_request_to_edge(self, edge_id, request_json):
logging.info("start_deployment: send topic " + topic_start_deployment + " to client...")
self.message_center.send_message_json(topic_start_deployment, json.dumps(request_json))
- def send_deployment_delete_request_to_edges(self, payload, model_msg_object):
+ def send_deployment_delete_request_to_edges(self, payload, model_msg_object, message_center=None):
edge_id_list_to_delete = model_msg_object.device_ids
# Remove the model master node id from the list using index 0
@@ -128,7 +128,10 @@ def send_deployment_delete_request_to_edges(self, payload, model_msg_object):
# send delete deployment request to each model device
topic_delete_deployment = "model_ops/model_device/delete_deployment/{}".format(str(edge_id))
logging.info("delete_deployment: send topic " + topic_delete_deployment + " to client...")
- self.message_center.send_message_json(topic_delete_deployment, payload)
+ if message_center is not None:
+ message_center.send_message_json(topic_delete_deployment, payload)
+ else:
+ self.message_center.send_message_json(topic_delete_deployment, payload)
def send_deployment_stop_request_to_edges(self, edge_id_list, payload):
for edge_id in edge_id_list:
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 867f299ccc..d1cc68dc98 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -221,24 +221,20 @@ def process_deployment_result_message(self, topic=None, payload=None):
logging.warning(f"Failed to change the logging handler due to {e}.")
logging.info("========== callback_deployment_result_message ==========\n")
- # Identify the operation for this run (add, remove, update)
- if run_id_str not in self.running_request_json:
- logging.error(f"Run id {run_id_str} is not in the running request json.")
- return
# The rolling update and scale out / in operation should not happen at the same time
- assert not ("replica_num_diff" in self.running_request_json[run_id_str] and
- len(self.running_request_json[run_id_str]["replica_num_diff"]) > 0 and
- "replica_version_diff" in self.running_request_json[run_id_str])
+ assert not ("replica_num_diff" in self.request_json and
+ len(self.request_json["replica_num_diff"]) > 0 and
+ "replica_version_diff" in self.request_json)
- if "replica_version_diff" in self.running_request_json[run_id_str]:
+ if "replica_version_diff" in self.request_json:
run_operation = "UPDATE"
- elif "replica_num_diff" in self.running_request_json[run_id_str] and \
- len(self.running_request_json[run_id_str]["replica_num_diff"]) > 0:
+ elif "replica_num_diff" in self.request_json and \
+ len(self.request_json["replica_num_diff"]) > 0:
run_operation = "ADD_OR_REMOVE"
else:
logging.error(f"Unsupported operation for run id {run_id_str}. and request json "
- f"{self.running_request_json[run_id_str]}")
+ f"{self.request_json}")
return
logging.info(f"End point {end_point_id}; Device {device_id}; replica {replica_no}; "
@@ -249,8 +245,8 @@ def process_deployment_result_message(self, topic=None, payload=None):
# logging.info(f"The current replica controller state is "
# f"Total version diff num {this_run_controller.total_replica_version_diff_num}")
# logging.info(f"self.request_json now {self.request_json}") # request_json will be deprecated
- # this_run_request_json = self.running_request_json.get(run_id_str, None)
- # logging.info(f"self.running_request_json now {this_run_request_json}")
+ # this_run_request_json = self.request_json
+ # logging.info(f"self.request_json now {this_run_request_json}")
# Set redis + sqlite deployment result
FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
@@ -290,7 +286,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
# Change the target version to the start version
self.replica_controller.rollback_setback_target_replica_version()
- self.running_request_json[run_id_str]["replica_version_diff"] = copy.deepcopy(rollback_version_diff)
+ self.request_json["replica_version_diff"] = copy.deepcopy(rollback_version_diff)
# Send the rollback message to the worker devices
self.send_rollback_msg(run_id_str)
@@ -317,7 +313,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
logging.info("callback_deployment_result_message: topic {}, payload {}, result mapping {}.".format(
topic, payload, self.slave_deployment_results_map))
- request_json = self.running_request_json.get(run_id_str, None)
+ request_json = self.request_json
if request_json is None:
logging.error(f"The endpoint {end_point_id} is no longer running.")
self.send_deployment_status(
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
index 7221a09574..0bfc205b34 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
@@ -42,10 +42,11 @@ def send_deployment_stages(
message_center=message_center
)
- def send_deployment_delete_request_to_edges(self, end_point_id, payload, model_msg_object):
+ def send_deployment_delete_request_to_edges(self, end_point_id, payload, model_msg_object, message_center=None):
run_id_str = str(end_point_id)
if self.job_runners.get(run_id_str, None) is not None:
- self.job_runners[run_id_str].send_deployment_delete_request_to_edges(payload, model_msg_object)
+ self.job_runners[run_id_str].send_deployment_delete_request_to_edges(
+ payload, model_msg_object, message_center=message_center)
def stop_device_inference_monitor(self, run_id, end_point_name, model_id, model_name, model_version):
run_id_str = str(run_id)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index 8566848ec6..962dcbbcb3 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -114,7 +114,8 @@ def callback_delete_deployment(self, topic, payload):
delete_end_point(model_msg_object.inference_end_point_id, model_msg_object.end_point_name,
model_msg_object.model_name, model_msg_object.model_version)
- FedMLDeployJobRunnerManager.get_instance().send_deployment_delete_request_to_edges(payload, model_msg_object)
+ FedMLDeployJobRunnerManager.get_instance().send_deployment_delete_request_to_edges(
+ model_msg_object.run_id, payload, model_msg_object, message_center=self.message_center)
FedMLDeployJobRunnerManager.get_instance().stop_job_runner(model_msg_object.run_id)
diff --git a/python/fedml/computing/scheduler/scheduler_core/compute_status_cache.py b/python/fedml/computing/scheduler/scheduler_core/compute_status_cache.py
index a1929abbef..f224806b8c 100755
--- a/python/fedml/computing/scheduler/scheduler_core/compute_status_cache.py
+++ b/python/fedml/computing/scheduler/scheduler_core/compute_status_cache.py
@@ -42,6 +42,8 @@ def get_job_status(self, run_id):
return status
def save_device_status_in_job(self, run_id, device_id, status):
+ if status is None:
+ return
try:
self.redis_connection.set(self._get_device_status_in_job_key(run_id, device_id), status)
except Exception as e:
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_center.py b/python/fedml/computing/scheduler/scheduler_core/message_center.py
index 7ae1e4c0b5..dcf21d33b7 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_center.py
@@ -200,6 +200,7 @@ def run_sender(self, message_event, message_queue, message_center_name):
while True:
message_entity = None
+ message_body = None
try:
self.check_message_stop_event()
except MessageCenterStoppedException as e:
@@ -242,7 +243,7 @@ def run_sender(self, message_event, message_queue, message_center_name):
f"payload {message_entity.payload}, {traceback.format_exc()}"
)
else:
- logging.info(f"Failed to send the message: {traceback.format_exc()}")
+ logging.info(f"Failed to send the message with body {message_body}, {traceback.format_exc()}")
self.release_sender_mqtt_mgr()
@@ -291,11 +292,18 @@ def get_message_runner(self):
def get_listener_message_queue(self):
return self.listener_message_queue
- def start_listener(self, sender_message_queue=None, agent_config=None, message_center_name=None):
+ def setup_listener_message_queue(self):
+ self.listener_message_queue = Queue()
+
+ def start_listener(self, sender_message_queue=None, listener_message_queue=None, agent_config=None, message_center_name=None):
if self.listener_message_center_process is not None:
return
- self.listener_message_queue = Queue()
+ if listener_message_queue is None:
+ if self.listener_message_queue is None:
+ self.listener_message_queue = Queue()
+ else:
+ self.listener_message_queue = listener_message_queue
self.listener_message_event = multiprocessing.Event()
self.listener_message_event.clear()
self.listener_agent_config = agent_config
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
index 4a0c950655..8c1756880a 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
@@ -82,6 +82,9 @@ def initialize(self):
# Start the message center to process edge related messages.
self.setup_message_center()
+ # Setup the message listener queue
+ self.setup_listener_message_queue()
+
# Start the status center to process edge related status.
self.start_status_listener_center()
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index 06b222cfd1..1a43653bd9 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -300,4 +300,4 @@ def status_center_request_job_status_from_master_in_slave_agent(self, topic, pay
# Request the job status from master agent.
topic_request_job_status = f"{GeneralConstants.MSG_TOPIC_REQUEST_JOB_STATUS_PREFIX}{master_id}"
payload_request_job_status = {"run_id": run_id, "edge_id": edge_id}
- self.message_center.send_message(topic_request_job_status, payload_request_job_status)
+ self.message_center.send_message(topic_request_job_status, json.dumps(payload_request_job_status))
diff --git a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
index 6e2a03a8b4..ef8dac8730 100755
--- a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
@@ -24,27 +24,12 @@ def generate_topics(self):
# The topic for requesting device info from mlops.
self.topic_request_edge_device_info_from_mlops = f"deploy/mlops/slave_agent/request_device_info/{self.edge_id}"
- # The topic for requesting deployment master device info from mlops.
- self.topic_request_deploy_master_device_info_from_mlops = f"deploy/mlops/master_agent/request_device_info/{self.model_device_server_id}"
-
- # The topic for requesting deployment slave device info from mlops.
- self.topic_request_deploy_slave_device_info_from_mlops = f"deploy/mlops/slave_agent/request_device_info/{self.model_device_client_edge_id_list[0]}"
-
self.add_subscribe_topic(self.topic_request_edge_device_info_from_mlops)
- self.add_subscribe_topic(self.topic_request_deploy_master_device_info_from_mlops)
- self.add_subscribe_topic(self.topic_request_deploy_slave_device_info_from_mlops)
# Override
def add_protocol_handler(self):
super().add_protocol_handler()
- self.add_message_listener(
- self.topic_request_edge_device_info_from_mlops, self.callback_response_device_info_to_mlops)
- self.add_message_listener(
- self.topic_request_deploy_master_device_info_from_mlops, self.callback_response_device_info_to_mlops)
- self.add_message_listener(
- self.topic_request_deploy_slave_device_info_from_mlops, self.callback_response_device_info_to_mlops)
-
# Override
def _generate_protocol_manager_instance(self, args, agent_config=None):
return FedMLLaunchSlaveProtocolManager(args, agent_config=agent_config)
@@ -121,6 +106,9 @@ def _init_extra_items(self):
os.environ["FEDML_DEPLOY_MASTER_ID"] = str(self.model_device_server_id)
os.environ["FEDML_DEPLOY_WORKER_IDS"] = str(self.model_device_client_edge_id_list)
+ # Subscribe handshaking messages from MLOps.
+ self.subscribe_handshaking_messages_from_mlops()
+
# Start the monitor process
self.args = copy.deepcopy(in_args)
self.mlops_metrics.stop_device_realtime_perf()
@@ -153,3 +141,19 @@ def callback_response_device_info_to_mlops(self, topic, payload):
response_payload["context"] = context
self.message_center.send_message(response_topic, json.dumps(response_payload), run_id=run_id)
+ def subscribe_handshaking_messages_from_mlops(self):
+ # The topic for requesting deployment master device info from mlops.
+ self.topic_request_deploy_master_device_info_from_mlops = f"deploy/mlops/master_agent/request_device_info/{self.model_device_server_id}"
+
+ # The topic for requesting deployment slave device info from mlops.
+ self.topic_request_deploy_slave_device_info_from_mlops = f"deploy/mlops/slave_agent/request_device_info/{self.model_device_client_edge_id_list[0]}"
+
+ self.add_subscribe_topic(self.topic_request_deploy_master_device_info_from_mlops)
+ self.add_subscribe_topic(self.topic_request_deploy_slave_device_info_from_mlops)
+
+ self.add_message_listener(
+ self.topic_request_edge_device_info_from_mlops, self.callback_response_device_info_to_mlops)
+ self.add_message_listener(
+ self.topic_request_deploy_master_device_info_from_mlops, self.callback_response_device_info_to_mlops)
+ self.add_message_listener(
+ self.topic_request_deploy_slave_device_info_from_mlops, self.callback_response_device_info_to_mlops)
\ No newline at end of file
From d6c5a78e76d18721a634ee66f8ed5a1a2951e62c Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Mon, 15 Apr 2024 22:49:00 +0000
Subject: [PATCH 005/282] Minor Fixes
---
.../computing/scheduler/scheduler_core/account_manager.py | 2 +-
.../scheduler/scheduler_core/status_manager_protocols.py | 8 +++-----
.../scheduler/slave/base_slave_protocol_manager.py | 1 -
python/fedml/computing/scheduler/slave/client_login.py | 2 +-
4 files changed, 5 insertions(+), 8 deletions(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
index 61ffd20988..da04fc3989 100755
--- a/python/fedml/computing/scheduler/scheduler_core/account_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -184,7 +184,7 @@ def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None,
# Check if it is running in the fedml docker hub
is_from_fedml_docker_hub = False
dock_loc_file = GeneralConstants.get_deploy_docker_location_file(is_master=is_master) \
- if is_deploy else GeneralConstants.get_deploy_docker_location_file(is_master=is_master)
+ if is_deploy else GeneralConstants.get_launch_docker_location_file(is_master=is_master)
if os.path.exists(dock_loc_file):
is_from_fedml_docker_hub = True
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index 1a43653bd9..4d2cf3a5ed 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -92,11 +92,9 @@ def status_center_process_master_status(self, topic, payload):
run_id_str = str(run_id)
# Process the job status
- if status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
- self.process_job_completed_status(server_id, status)
- elif status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
- self.process_job_completed_status(server_id, status)
- elif status == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED:
+ if status in (ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED,
+ ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED,
+ ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED):
self.process_job_completed_status(server_id, status)
elif status == ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION:
self.process_job_exception_status(server_id, status)
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index 856abdac40..514aa98cd7 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -33,7 +33,6 @@ def __init__(self, args, agent_config=None):
self.message_status_runner = None
self.message_center = None
self.status_center = None
- self.message_center_name = "master_agent"
self.run_id = None
self.edge_id = args.edge_id
self.general_edge_id = None
diff --git a/python/fedml/computing/scheduler/slave/client_login.py b/python/fedml/computing/scheduler/slave/client_login.py
index 37a6dc8064..a4df2ccb6a 100755
--- a/python/fedml/computing/scheduler/slave/client_login.py
+++ b/python/fedml/computing/scheduler/slave/client_login.py
@@ -30,7 +30,7 @@ def logout():
if args.api_key == "":
args.api_key = args.user
- fedml.set_env_version("test")
+ # fedml.set_env_version("test")
if args.local_on_premise_platform_host != "127.0.0.1":
fedml.set_local_on_premise_platform_host(args.local_on_premise_platform_host)
From 3e6e7d1847009f3e756baab80fb45102623600ee Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Tue, 16 Apr 2024 18:02:50 +0000
Subject: [PATCH 006/282] Fix logging
---
.../scheduler_core/scheduler_base_protocol_manager.py | 2 +-
python/fedml/core/mlops/mlops_utils.py | 10 ++++++----
2 files changed, 7 insertions(+), 5 deletions(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
index 8c1756880a..e3cac7a425 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
@@ -30,7 +30,7 @@ def __init__(self, args, agent_config=None, is_master=False):
self.message_status_runner = None
self.message_center = None
self.status_center = None
- self.message_center_name = "master_agent" if not is_master else "slave_agent"
+ self.message_center_name = "master_agent" if is_master else "slave_agent"
self.run_id = None
self.edge_id = args.edge_id
self.general_edge_id = None
diff --git a/python/fedml/core/mlops/mlops_utils.py b/python/fedml/core/mlops/mlops_utils.py
index 7313141550..1d6db23d02 100644
--- a/python/fedml/core/mlops/mlops_utils.py
+++ b/python/fedml/core/mlops/mlops_utils.py
@@ -128,15 +128,17 @@ def get_program_prefix(args, edge_id):
@staticmethod
def get_edge_id_from_args(args):
if args.role == "server":
- if hasattr(args, "server_id"):
+ # Considering that 0 is a valid value, we need to ensure it is not None rather than solely checking
+ # for truthiness
+ if getattr(args, "server_id", None) is not None:
edge_id = args.server_id
else:
- if hasattr(args, "edge_id"):
+ if getattr(args, "edge_id", None) is not None:
edge_id = args.edge_id
else:
edge_id = 0
else:
- if hasattr(args, "client_id"):
+ if getattr(args, "client_id", None) is not None:
edge_id = args.client_id
elif hasattr(args, "client_id_list"):
if args.client_id_list is None:
@@ -148,7 +150,7 @@ def get_edge_id_from_args(args):
else:
edge_id = 0
else:
- if hasattr(args, "edge_id"):
+ if getattr(args, "client_id", None) is not None:
edge_id = args.edge_id
else:
edge_id = 0
From 8a544172638f2cb3a12dc9865ce0acbef20b8dac Mon Sep 17 00:00:00 2001
From: Alay Dilipbhai Shah <alay11shah@gmail.com>
Date: Tue, 16 Apr 2024 11:04:31 -0700
Subject: [PATCH 007/282] Update client_login.py
---
python/fedml/computing/scheduler/slave/client_login.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/slave/client_login.py b/python/fedml/computing/scheduler/slave/client_login.py
index a4df2ccb6a..37a6dc8064 100755
--- a/python/fedml/computing/scheduler/slave/client_login.py
+++ b/python/fedml/computing/scheduler/slave/client_login.py
@@ -30,7 +30,7 @@ def logout():
if args.api_key == "":
args.api_key = args.user
- # fedml.set_env_version("test")
+ fedml.set_env_version("test")
if args.local_on_premise_platform_host != "127.0.0.1":
fedml.set_local_on_premise_platform_host(args.local_on_premise_platform_host)
From 852a3bf4b6e4d9f65e6a317579c691b7e6a36f21 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Fri, 19 Apr 2024 22:43:52 -0700
Subject: [PATCH 008/282] Update Launch Job Docker Image name
---
python/examples/launch/hello_job_with_container.yaml | 2 +-
python/fedml/computing/scheduler/comm_utils/constants.py | 2 +-
.../driver_example/customized_job_example/train_job.yaml | 2 +-
python/fedml/workflow/driver_example/hello_world_job.yaml | 2 +-
4 files changed, 4 insertions(+), 4 deletions(-)
diff --git a/python/examples/launch/hello_job_with_container.yaml b/python/examples/launch/hello_job_with_container.yaml
index 2c520beb24..26202a3d98 100755
--- a/python/examples/launch/hello_job_with_container.yaml
+++ b/python/examples/launch/hello_job_with_container.yaml
@@ -43,7 +43,7 @@ job_type: train # options: train, deploy, federate
job_subtype: generate_training
docker:
- image: fedml/fedml-default-launch:cu12.1-u22.04
+ image: fedml/fedml-launch-job:cu12.1-u22.04
#registry: docker.io
#username: my_hub_user
#password: my_hub_password
diff --git a/python/fedml/computing/scheduler/comm_utils/constants.py b/python/fedml/computing/scheduler/comm_utils/constants.py
index b1294181bb..f89d5640ce 100644
--- a/python/fedml/computing/scheduler/comm_utils/constants.py
+++ b/python/fedml/computing/scheduler/comm_utils/constants.py
@@ -103,7 +103,7 @@ class SchedulerConstants:
RUN_PROCESS_TYPE_BOOTSTRAP_PROCESS = "bootstrap-process"
FEDML_DEFAULT_LAUNCH_CONTAINER_PREFIX = "fedml_default_launch_container"
- FEDML_DEFAULT_LAUNCH_IMAGE = "fedml/fedml-default-launch:cu12.1-u22.04"
+ FEDML_DEFAULT_LAUNCH_IMAGE = "fedml/fedml-launch-job:cu12.1-u22.04"
FEDML_DEFAULT_LOG_DIR = ".fedml/fedml-client/fedml/logs"
FEDML_DEFAULT_DATA_DIR = ".fedml/fedml-client/fedml/data"
diff --git a/python/fedml/workflow/driver_example/customized_job_example/train_job.yaml b/python/fedml/workflow/driver_example/customized_job_example/train_job.yaml
index e057791431..2ccbc897f0 100755
--- a/python/fedml/workflow/driver_example/customized_job_example/train_job.yaml
+++ b/python/fedml/workflow/driver_example/customized_job_example/train_job.yaml
@@ -7,7 +7,7 @@ workspace: train_job
# It should be the full name of the image with tag.
# If you want to use the default image, it can be empty.
docker:
- image: fedml/fedml-default-launch:cu12.1-u22.04
+ image: fedml/fedml-launch-job:cu12.1-u22.04
# Running entry commands which will be executed as the job entry point.
# Support multiple lines, which can not be empty.
diff --git a/python/fedml/workflow/driver_example/hello_world_job.yaml b/python/fedml/workflow/driver_example/hello_world_job.yaml
index e1dcb02f7e..e63712f99a 100755
--- a/python/fedml/workflow/driver_example/hello_world_job.yaml
+++ b/python/fedml/workflow/driver_example/hello_world_job.yaml
@@ -10,7 +10,7 @@ workspace: hello_world
# It should be the full name of the image with tag.
# If you want to use the default image, it can be empty.
#docker:
-# image: fedml/fedml-default-launch:cu12.1-u22.04
+# image: fedml/fedml-launch-job:cu12.1-u22.04
# Running entry commands which will be executed as the job entry point.
# Support multiple lines, which can not be empty.
From 06ed07fa8c4796a7719a9882cc2086996ea88424 Mon Sep 17 00:00:00 2001
From: bhargav191098 <bhargav3514@gmail.com>
Date: Wed, 1 May 2024 19:14:26 -0700
Subject: [PATCH 009/282] Adding tags to data storage object and the
corresponding pretty table changes
---
python/fedml/api/__init__.py | 5 ++---
python/fedml/api/modules/storage.py | 5 +++--
python/fedml/cli/modules/storage.py | 20 ++++++++++++++------
3 files changed, 19 insertions(+), 11 deletions(-)
diff --git a/python/fedml/api/__init__.py b/python/fedml/api/__init__.py
index 4e004f07d3..3e75b987d6 100755
--- a/python/fedml/api/__init__.py
+++ b/python/fedml/api/__init__.py
@@ -179,13 +179,12 @@ def cluster_killall(api_key=None) -> bool:
return cluster.kill(cluster_names=(), api_key=api_key)
-def upload(data_path, api_key=None, service="R2", name=None, description=None, metadata=None, show_progress=False,
+def upload(data_path, api_key=None, tag_list=[], service="R2", name=None, description=None, metadata=None, show_progress=False,
out_progress_to_err=True, progress_desc=None) -> FedMLResponse:
- return storage.upload(data_path=data_path, api_key=api_key, name=name, description=description,
+ return storage.upload(data_path=data_path, api_key=api_key, name=name, description=description, tag_list =tag_list,
service=service, progress_desc=progress_desc, show_progress=show_progress,
out_progress_to_err=out_progress_to_err, metadata=metadata)
-
def get_storage_user_defined_metadata(data_name, api_key=None) -> FedMLResponse:
return storage.get_user_metadata(data_name=data_name, api_key=api_key)
diff --git a/python/fedml/api/modules/storage.py b/python/fedml/api/modules/storage.py
index 1582788e3a..51f58539bf 100644
--- a/python/fedml/api/modules/storage.py
+++ b/python/fedml/api/modules/storage.py
@@ -18,12 +18,13 @@ def __init__(self, data: dict):
self.createdAt = data.get("createTime", None)
self.updatedAt = data.get("updateTime", None)
self.size = _get_size(data.get("fileSize",None))
+ self.tag_list = data.get("tags", None)
# Todo (alaydshah): Store service name in metadata
# Todo (alaydshah): If data already exists, don't upload again. Instead suggest to use update command
-def upload(data_path, api_key, name, description, service, show_progress, out_progress_to_err, progress_desc,
+def upload(data_path, api_key, name, description, tag_list, service, show_progress, out_progress_to_err, progress_desc,
metadata) -> FedMLResponse:
api_key = authenticate(api_key)
@@ -58,7 +59,7 @@ def upload(data_path, api_key, name, description, service, show_progress, out_pr
"description": description,
"fileSize": file_size,
"fileUrl": file_uploaded_url,
- "tagNameList": [],
+ "tagNameList": tag_list,
}
try:
diff --git a/python/fedml/cli/modules/storage.py b/python/fedml/cli/modules/storage.py
index 93ce273e92..af75cda85f 100644
--- a/python/fedml/cli/modules/storage.py
+++ b/python/fedml/cli/modules/storage.py
@@ -53,6 +53,7 @@ def validate_argument(ctx, param, value):
@click.option("--user_metadata", "-um", type=str, help="User-defined metadata in the form of a dictionary, for instance, "
" {'name':'value'} within double quotes. "" "
"Defaults to None.")
+@click.option("--tags", "-t", type=str, help="Add tags to your data to store. Give tags in comma separated form like 'cv,unet,segmentation' If not provided, the tags will be empty.")
@click.option('--service', "-s", type=click.Choice(['R2']), default="R2", help="Storage service for object storage. "
"Only R2 is supported as of now")
@click.option(
@@ -65,10 +66,11 @@ def validate_argument(ctx, param, value):
default="release",
help=version_help,
)
-def upload(data_path: str, name: str, user_metadata: str, description: str, version: str, api_key: str, service):
+def upload(data_path: str, name: str, user_metadata: str, description: str, version: str, api_key: str, tags:str, service):
metadata = _parse_metadata(user_metadata)
+ tag_list = _parse_tags(tags)
fedml.set_env_version(version)
- response = fedml.api.upload(data_path=data_path, api_key=api_key, name=name, service=service, show_progress=True,
+ response = fedml.api.upload(data_path=data_path, api_key=api_key, name=name, tag_list = tag_list, service=service, show_progress=True,
description=description, metadata=metadata)
if response.code == ResponseCode.SUCCESS:
click.echo(f"Data uploaded successfully. | url: {response.data}")
@@ -96,10 +98,10 @@ def list_data(version, api_key):
if not response.data:
click.echo(f"No stored objects found for account linked with apikey: {api_key}")
return
- object_list_table = PrettyTable(["Data Name", "Data Size", "Description", "Created At", "Updated At"])
+ object_list_table = PrettyTable(["Data Name", "Data Size", "Description", "Data Tags","Created At", "Updated At"])
for stored_object in response.data:
object_list_table.add_row(
- [stored_object.dataName, stored_object.size, stored_object.description, stored_object.createdAt, stored_object.updatedAt])
+ [stored_object.dataName, stored_object.size, stored_object.description, stored_object.tag_list,stored_object.createdAt, stored_object.updatedAt])
click.echo(object_list_table)
else:
click.echo(f"Failed to list stored objects for account linked with apikey {api_key}. "
@@ -157,8 +159,8 @@ def get_metadata(data_name, version, api_key):
return
click.echo(f"Successfully fetched metadata for object {data_name}:")
# Todo (alaydshah): Add file size and tags
- metadata_table = PrettyTable(["Data Name","Data Size","Description", "Created At", "Updated At"])
- metadata_table.add_row([metadata.dataName,metadata.size,metadata.description, metadata.createdAt, metadata.updatedAt])
+ metadata_table = PrettyTable(["Data Name","Data Size","Description","Data Tags","Created At", "Updated At"])
+ metadata_table.add_row([metadata.dataName,metadata.size,metadata.description,metadata.tag_list,metadata.createdAt, metadata.updatedAt])
click.echo(metadata_table)
click.echo("")
else:
@@ -238,3 +240,9 @@ def _parse_metadata(metadata: str):
click.echo(
f"Input metadata cannot be evaluated. Please make sure metadata is in the correct format. Error: {e}.")
exit()
+
+def _parse_tags(tags:str):
+ if not tags:
+ return []
+ tag_list = tags.split(",")
+ return tag_list
\ No newline at end of file
From 6e09a604b7ee5df016dcb4011a613bd4f1f9e103 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Wed, 1 May 2024 20:04:36 -0700
Subject: [PATCH 010/282] Fix Log Rotation Bug
---
python/fedml/core/mlops/__init__.py | 20 ++++++++--------
python/fedml/core/mlops/mlops_runtime_log.py | 24 ++++++++++++++------
2 files changed, 26 insertions(+), 18 deletions(-)
diff --git a/python/fedml/core/mlops/__init__.py b/python/fedml/core/mlops/__init__.py
index 77ad06165e..2b4dc24c95 100644
--- a/python/fedml/core/mlops/__init__.py
+++ b/python/fedml/core/mlops/__init__.py
@@ -9,7 +9,6 @@
import uuid
from multiprocessing import Process
-import click
import requests
import fedml
@@ -95,9 +94,9 @@ def init(args, should_init_logs=True):
if not mlops_parrot_enabled(args):
if not hasattr(args, "config_version"):
args.config_version = "release"
- fetch_config(args, args.config_version)
if should_init_logs:
MLOpsRuntimeLog.get_instance(args).init_logs()
+ fetch_config(args, args.config_version)
return
else:
if hasattr(args, "simulator_daemon"):
@@ -137,7 +136,7 @@ def init(args, should_init_logs=True):
MLOpsStore.mlops_project_id = project_id
MLOpsStore.mlops_run_id = run_id
if result_project is False or result_run is False:
- click.echo("Failed to init project and run.")
+ print("Failed to init project and run.")
return
# Init runtime logs
@@ -973,10 +972,9 @@ def _generate_log_metrics(metrics: dict, step: int = None, customized_step_key:
def log_mlops_running_logs(artifact: fedml.mlops.Artifact, version=None, run_id=None, edge_id=None,
only_push_artifact=False):
- fedml_args = get_fedml_args()
artifact_archive_zip_file, artifact_storage_url = push_artifact_to_s3(
- artifact, version=version if version is not None else fedml_args.config_version, show_progress=False)
+ artifact, version=version if version is not None else fedml.get_env_version(), show_progress=False)
if only_push_artifact:
return artifact_storage_url
@@ -1274,8 +1272,8 @@ def bind_simulation_device(args, userid):
continue
if config_try_count >= 5:
- click.echo("\nNote: Internet is not connected. "
- "Experimental tracking results will not be synchronized to the MLOps (open.fedml.ai).\n")
+ logging.info("\nNote: Internet is not connected. "
+ "Experimental tracking results will not be synchronized to the MLOps (open.fedml.ai).\n")
return False
# Build unique device id
@@ -1301,8 +1299,8 @@ def bind_simulation_device(args, userid):
continue
if edge_id <= 0:
- click.echo("Oops, you failed to login the FedML MLOps platform.")
- click.echo("Please check whether your network is normal!")
+ print("Oops, you failed to login the FedML MLOps platform.")
+ print("Please check whether your network is normal!")
return False
MLOpsStore.mlops_edge_id = edge_id
setattr(MLOpsStore.mlops_args, "client_id", edge_id)
@@ -1353,8 +1351,8 @@ def fetch_config(args, version="release"):
continue
if config_try_count >= 5:
- click.echo("\nNote: Internet is not connected. "
- "Experimental tracking results will not be synchronized to the MLOps (open.fedml.ai).\n")
+ logging.info("\nNote: Internet is not connected. "
+ "Experimental tracking results will not be synchronized to the MLOps (open.fedml.ai).\n")
return False
diff --git a/python/fedml/core/mlops/mlops_runtime_log.py b/python/fedml/core/mlops/mlops_runtime_log.py
index 6992c44555..0bc4dc6b6c 100644
--- a/python/fedml/core/mlops/mlops_runtime_log.py
+++ b/python/fedml/core/mlops/mlops_runtime_log.py
@@ -5,6 +5,7 @@
import sys
import threading
import time
+import shutil
from logging.handlers import TimedRotatingFileHandler
from fedml import mlops
@@ -12,19 +13,19 @@
LOG_LEVEL = logging.INFO
ROTATION_FREQUENCY = 'D'
+# when rollover is done, no more than backupCount files are kept - the oldest ones are deleted.
BACKUP_COUNT = 100
class MLOpsFileHandler(TimedRotatingFileHandler):
def __init__(self, run_id, edge_id, log_config_file, filepath):
- super(MLOpsFileHandler, self).__init__(filename=filepath, when=ROTATION_FREQUENCY, backupCount=BACKUP_COUNT,
- encoding='utf-8')
+ super().__init__(filename=filepath, when=ROTATION_FREQUENCY,
+ backupCount=BACKUP_COUNT,encoding='utf-8')
self.run_id = run_id
self.edge_id = edge_id
self.file_path = filepath
self.rotate_count = 0
- self.backupCount = BACKUP_COUNT
self.rotator: callable = self.update_config_and_rotate
self.log_config_file = log_config_file
self.__initialize_config()
@@ -32,17 +33,26 @@ def __init__(self, run_id, edge_id, log_config_file, filepath):
def update_config_and_rotate(self, source, dest):
# source = current log file name
# dest = log file name (dated)
- if os.path.exists(source):
- os.rename(source, dest)
MLOpsLoggingUtils.acquire_lock()
- config_data = MLOpsLoggingUtils.load_log_config(self.run_id, self.edge_id, self.log_config_file)
+
+ # Check if the source and destination files exist. If it does, return
+ if os.path.exists(source):
+ # Copy the contents of the source file to the destination file
+ shutil.copy(source, dest)
+ # Clear everything in the source file
+ with open(source, 'w') as src_file:
+ src_file.truncate(0)
+ src_file.close()
+
+ config_data = MLOpsLoggingUtils.load_log_config(self.run_id, self.edge_id,
+ self.log_config_file)
# Update file name of current log file
config_data[self.rotate_count].file_path = dest
self.rotate_count += 1
# Store the rotate count, and corresponding log file name in the config file
- rotated_log_file = LogFile(file_path=source, uploaded_file_index=self.backupCount)
+ rotated_log_file = LogFile(file_path=source)
config_data[self.rotate_count] = rotated_log_file
MLOpsLoggingUtils.save_log_config(run_id=self.run_id, device_id=self.edge_id,
log_config_file=self.log_config_file,
From d150998a2a8cf245aef030abbac7896317cac367 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Wed, 1 May 2024 21:50:44 -0700
Subject: [PATCH 011/282] Bug spotted, added FIXME Comment
---
python/fedml/computing/scheduler/comm_utils/job_monitor.py | 2 ++
1 file changed, 2 insertions(+)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index 5874adfef7..fc97a8e077 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -352,6 +352,8 @@ def monitor_slave_run_process_status(self):
continue
# Check if all processes of the specific run are exited
+ # FIXME: Proactively release the gpu ids when the run processes have not even started yet as the docker
+ # image is being pulled
run_process_list = client_constants.ClientConstants.get_learning_process_list(job.job_id)
all_run_processes_exited = True if len(run_process_list) <= 0 else False
if all_run_processes_exited:
From 4ce4c78753ae9ef3aa22b0e0578039d9b28a5140 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Wed, 1 May 2024 18:09:21 +0000
Subject: [PATCH 012/282] Disabling replica release after idle secs.
---
.../model_scheduler/autoscaler/autoscaler.py | 31 ++++++++++---------
.../model_scheduler/autoscaler/policies.py | 2 +-
2 files changed, 17 insertions(+), 16 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
index dd6ca67706..5f0d425505 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
@@ -303,25 +303,26 @@ def scale_operation_endpoint(self,
# If no metric exists then no scaling operation.
return scale_op
- # If we continue here, then it means that there was at least one request.
- # The `most_recent_metric` is of type list, hence we need to access index 0.
- most_recent_metric = metrics[-1]
- latest_request_timestamp_micro_secs = most_recent_metric["timestamp"]
- # The time module does not have a micro-second function built-in, so we need to
- # divide nanoseconds by 1e3 and convert to micro-seconds.
- current_time_micro_seconds = time.time_ns() / 1e3
- # compute elapsed time and convert to seconds
- elapsed_time_secs = \
- (current_time_micro_seconds - latest_request_timestamp_micro_secs) / 1e6
- if elapsed_time_secs > autoscaling_policy.release_replica_after_idle_secs:
+ if autoscaling_policy.release_replica_after_idle_secs:
+ # At this point it means that there was at least one request. The
+ # `most_recent_metric` is of type list, hence we need to access index 0.
+ most_recent_metric = metrics[-1]
+ latest_request_timestamp_micro_secs = most_recent_metric["timestamp"]
+ # The time module does not have a micro-second function built-in,
+ # so we need to divide nanoseconds by 1e3 and convert to micro-seconds.
+ current_time_micro_seconds = time.time_ns() / 1e3
+ # Compute the elapsed time and convert to seconds.
+ elapsed_time_secs = \
+ (current_time_micro_seconds - latest_request_timestamp_micro_secs) / 1e6
# If the elapsed time is greater than the requested idle time,
# in other words there was no incoming request then scale down.
- scale_op = ScaleOp.DOWN_IN_OP
+ if elapsed_time_secs > autoscaling_policy.release_replica_after_idle_secs:
+ scale_op = ScaleOp.DOWN_IN_OP
else:
- # Otherwise, it means there was a request within the elapsed time, then:
+ # Otherwise, it means there was a request within the elapsed time, then,
+ # Check if the current number of running replicas is 0 it means
+ # we need more resources, hence we need to scale up: ScaleOp.UP_OUT_OP.
if autoscaling_policy.current_replicas == 0:
- # Check if the current number of running replicas is 0,
- # then we need more resources, hence ScaleOp.UP_OUT_OP.
scale_op = ScaleOp.UP_OUT_OP
else:
# Else, trigger the autoscaling policy with all existing values.
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/policies.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/policies.py
index 546817ec82..fd49549812 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/policies.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/policies.py
@@ -22,7 +22,7 @@ class AutoscalingPolicy(BaseModel):
min_replicas: NonNegativeInt
max_replicas: NonNegativeInt
previous_triggering_value: float = None
- release_replica_after_idle_secs: NonNegativeInt = 300 # default is after 5 minutes
+ release_replica_after_idle_secs: NonNegativeInt = None
scaledown_delay_secs: NonNegativeInt = 60 # default is 1 minute
scaleup_cost_secs: NonNegativeInt = 300 # default is 5 minutes
From 975e53de8185777f146215f552bbfb7d7f0740b5 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Wed, 1 May 2024 22:34:13 +0000
Subject: [PATCH 013/282] Return scale down operation when no incoming request
with then policy's time frame window.
---
.../model_scheduler/autoscaler/autoscaler.py | 41 ++++++++++++-------
1 file changed, 26 insertions(+), 15 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
index 5f0d425505..03666a5919 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
@@ -55,13 +55,17 @@ def scale_operation_ewm(cls,
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=FutureWarning)
period_data = metrics.last("{}min".format(ewm_policy.ewm_mins))
- # If the data frame window is empty then do nothing more, just return.
- if period_data.empty:
- return ScaleOp.NO_OP
- metric_name = "current_latency" \
- if "ewm_latency" == ewm_policy.metric else "current_qps"
- ewm_period = period_data[metric_name] \
- .ewm(alpha=ewm_policy.ewm_alpha).mean()
+
+ # If the data frame window is empty then it means we
+ # did not have any incoming request, so we need to scale down.
+ if period_data.empty:
+ return ScaleOp.DOWN_IN_OP
+
+ # Otherwise, we proceed as normal.
+ metric_name = "current_latency" \
+ if "ewm_latency" == ewm_policy.metric else "current_qps"
+ ewm_period = period_data[metric_name] \
+ .ewm(alpha=ewm_policy.ewm_alpha).mean()
scale_op = ScaleOp.NO_OP
# If there is no exponential moving average within this
@@ -115,10 +119,14 @@ def scale_operation_query_concurrency(cls,
warnings.simplefilter(action='ignore', category=FutureWarning)
# Here, the number of queries is the number of rows in the short period data frame.
period_data = metrics.last("{}s".format(concurrent_query_policy.window_size_secs))
- # If the data frame window is empty then do nothing more, just return.
- if period_data.empty:
- return ScaleOp.NO_OP
- queries_num = period_data.shape[0]
+
+ # If the data frame window is empty then it means we
+ # did not have any incoming request, so we need to scale down.
+ if period_data.empty:
+ return ScaleOp.DOWN_IN_OP
+
+ # Otherwise, we proceed as normal.
+ queries_num = period_data.shape[0]
try:
# QSR: Queries per Second per Replica: (Number of Queries / Number of Current Replicas) / Window Size
@@ -159,10 +167,13 @@ def scale_operation_meet_traffic_demand(cls,
warnings.simplefilter(action='ignore', category=FutureWarning)
# Here, the number of queries is the number of rows in the short period data frame.
period_data = metrics.last("{}s".format(meet_traffic_demand_policy.window_size_secs))
- # If the data frame window is empty then do nothing more, just return.
- if period_data.empty:
- return ScaleOp.NO_OP
+ # If the data frame window is empty then it means we
+ # did not have any incoming request, so we need to scale down.
+ if period_data.empty:
+ return ScaleOp.DOWN_IN_OP
+
+ # Otherwise, we proceed as normal.
period_requests_num = period_data.shape[0]
all_latencies = metrics["current_latency"]
# Original value is milliseconds, convert to seconds.
@@ -293,7 +304,7 @@ def scale_operation_endpoint(self,
0: do nothing
"""
- # Fetch most recent metric record from the database.
+ # Fetch all metrics record from the database.
metrics = self.fedml_model_cache.get_endpoint_metrics(
endpoint_id=endpoint_id)
From 5fb500f04762f17d797ac6717380ce2a555382d1 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Wed, 1 May 2024 22:58:43 +0000
Subject: [PATCH 014/282] Adding logging to figure out scaling down delay.
---
.../model_scheduler/autoscaler/autoscaler.py | 12 +++++++++++-
1 file changed, 11 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
index 03666a5919..d81e0148eb 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
@@ -51,6 +51,7 @@ def scale_operation_ewm(cls,
ewm_policy: EWMPolicy,
metrics: pd.DataFrame) -> ScaleOp:
+ logging.info("Executing the ExponentialWeightMoving average autoscaling policy.")
# Adding the context below to avoid having a series of warning messages.
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=FutureWarning)
@@ -114,6 +115,7 @@ def scale_operation_query_concurrency(cls,
concurrent_query_policy: ConcurrentQueryPolicy,
metrics: pd.DataFrame) -> ScaleOp:
+ logging.info("Executing the QueryConcurrency autoscaling policy.")
# Adding the context below to avoid having a series of warning messages.
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=FutureWarning)
@@ -162,6 +164,7 @@ def scale_operation_meet_traffic_demand(cls,
meet_traffic_demand_policy: MeetTrafficDemandPolicy,
metrics: pd.DataFrame) -> ScaleOp:
+ logging.info("Executing the MeetTrafficDemand autoscaling policy.")
# Adding the context below to avoid having a series of warning messages.
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=FutureWarning)
@@ -227,6 +230,7 @@ def run_autoscaling_policy(self,
def validate_scaling_bounds(cls,
scale_op: ScaleOp,
autoscaling_policy: AutoscalingPolicy) -> ScaleOp:
+ logging.info("Validating scaling bounds.")
# We cannot be lower than the minimum number of replicas,
# nor exceed the maximum number of requested replicas.
new_running_replicas = autoscaling_policy.current_replicas + scale_op.value
@@ -266,6 +270,7 @@ def enforce_scaling_down_delay_interval(self,
previous_timestamp = \
self.fedml_model_cache.get_endpoint_scaling_down_decision_time(endpoint_id)
diff_secs = (current_timestamp - previous_timestamp) / 1e6
+ logging.info("Difference in seconds between scaling down operations: {}".format(diff_secs))
if diff_secs > autoscaling_policy.scaledown_delay_secs:
# At this point, we will perform the scaling down operation, hence
# we need to delete the previously stored scaling down timestamp (if any).
@@ -279,7 +284,8 @@ def enforce_scaling_down_delay_interval(self,
return scale_op
def clean_up_scaling_down_operation_state(self, endpoint_id) -> bool:
- # We return True if the clean up operation succeeded, else False.
+ # We return True if the cleaning up operation succeeded, else False.
+ logging.info("Not a scaling down operation, cleaning up scale down state from Redis.")
to_clean_up = \
self.fedml_model_cache.exists_endpoint_scaling_down_decision_time(endpoint_id)
if to_clean_up:
@@ -312,6 +318,7 @@ def scale_operation_endpoint(self,
scale_op = ScaleOp.NO_OP
if not metrics:
# If no metric exists then no scaling operation.
+ logging.info("No existing metric, so no scaling operation.")
return scale_op
if autoscaling_policy.release_replica_after_idle_secs:
@@ -328,12 +335,15 @@ def scale_operation_endpoint(self,
# If the elapsed time is greater than the requested idle time,
# in other words there was no incoming request then scale down.
if elapsed_time_secs > autoscaling_policy.release_replica_after_idle_secs:
+ logging.info("Endpoint remained idle for {} seconds, need to scale down.".format(
+ elapsed_time_secs))
scale_op = ScaleOp.DOWN_IN_OP
else:
# Otherwise, it means there was a request within the elapsed time, then,
# Check if the current number of running replicas is 0 it means
# we need more resources, hence we need to scale up: ScaleOp.UP_OUT_OP.
if autoscaling_policy.current_replicas == 0:
+ logging.info("Incoming requests but with 0 replicas, scaling up.")
scale_op = ScaleOp.UP_OUT_OP
else:
# Else, trigger the autoscaling policy with all existing values.
From 14a75a93e86b90feb000b014c9ff8592bd47a606 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Thu, 2 May 2024 01:48:28 +0000
Subject: [PATCH 015/282] Fixed indefinite no scale down. Problem was the data
frame period parsing.
---
.../model_scheduler/autoscaler/autoscaler.py | 44 +++++++++++++++----
1 file changed, 35 insertions(+), 9 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
index d81e0148eb..009345863a 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
@@ -8,7 +8,7 @@
from enum import Enum
from fedml.computing.scheduler.model_scheduler.device_model_cache import FedMLModelCache
from fedml.computing.scheduler.model_scheduler.autoscaler.policies import *
-from utils.singleton import Singleton
+from fedml.computing.scheduler.model_scheduler.autoscaler.utils.singleton import Singleton
class ScaleOp(Enum):
@@ -38,6 +38,26 @@ def get_current_timestamp_micro_seconds(cls):
# in REDIS we record/operate in micro-seconds, hence the division by 1e3!
return int(format(time.time_ns() / 1000.0, '.0f'))
+ @classmethod
+ def filter_by_timestamp(cls,
+ metrics,
+ before_now_minutes=None,
+ before_now_seconds=None) -> pd.DataFrame:
+
+ # We subtract the number of seconds/minutes from the current timestamp, and then we query
+ # the data frame to fetch all the records whose timestamp is within the given range.
+ # By default, we return all records.
+ filtered = metrics
+ if before_now_minutes:
+ less_than_ts = \
+ str(pd.Timestamp.now() - pd.Timedelta(minutes=before_now_minutes))
+ filtered = metrics.query("'{}' <= {}".format(less_than_ts, "timestamp"))
+ if before_now_seconds:
+ less_than_ts = \
+ str(pd.Timestamp.now() - pd.Timedelta(seconds=before_now_seconds))
+ filtered = metrics.query("'{}' <= {}".format(less_than_ts, "timestamp"))
+ return filtered
+
@classmethod
def scale_operation_predictive(cls,
predictive_policy: PredictivePolicy,
@@ -55,7 +75,8 @@ def scale_operation_ewm(cls,
# Adding the context below to avoid having a series of warning messages.
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=FutureWarning)
- period_data = metrics.last("{}min".format(ewm_policy.ewm_mins))
+ period_data = cls.filter_by_timestamp(metrics,
+ before_now_minutes=ewm_policy.ewm_mins)
# If the data frame window is empty then it means we
# did not have any incoming request, so we need to scale down.
@@ -119,8 +140,9 @@ def scale_operation_query_concurrency(cls,
# Adding the context below to avoid having a series of warning messages.
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=FutureWarning)
- # Here, the number of queries is the number of rows in the short period data frame.
- period_data = metrics.last("{}s".format(concurrent_query_policy.window_size_secs))
+ period_data = cls.filter_by_timestamp(
+ metrics,
+ before_now_seconds=concurrent_query_policy.window_size_secs)
# If the data frame window is empty then it means we
# did not have any incoming request, so we need to scale down.
@@ -168,8 +190,9 @@ def scale_operation_meet_traffic_demand(cls,
# Adding the context below to avoid having a series of warning messages.
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=FutureWarning)
- # Here, the number of queries is the number of rows in the short period data frame.
- period_data = metrics.last("{}s".format(meet_traffic_demand_policy.window_size_secs))
+ period_data = cls.filter_by_timestamp(
+ metrics,
+ before_now_seconds=meet_traffic_demand_policy.window_size_secs)
# If the data frame window is empty then it means we
# did not have any incoming request, so we need to scale down.
@@ -257,6 +280,7 @@ def enforce_scaling_down_delay_interval(self,
# If the policy has no scaledown delay then return immediately.
if autoscaling_policy.scaledown_delay_secs == 0:
+ logging.info("No scale down delay, so scale down immediately.")
return ScaleOp.DOWN_IN_OP
# By default, we return a no operation.
@@ -270,11 +294,13 @@ def enforce_scaling_down_delay_interval(self,
previous_timestamp = \
self.fedml_model_cache.get_endpoint_scaling_down_decision_time(endpoint_id)
diff_secs = (current_timestamp - previous_timestamp) / 1e6
- logging.info("Difference in seconds between scaling down operations: {}".format(diff_secs))
if diff_secs > autoscaling_policy.scaledown_delay_secs:
+ logging.info("Scaling down since the time difference: {}secs, "
+ "is above the delay period: {} secs.".format(
+ diff_secs, autoscaling_policy.scaledown_delay_secs))
# At this point, we will perform the scaling down operation, hence
# we need to delete the previously stored scaling down timestamp (if any).
- self.fedml_model_cache.delete_endpoint_scaling_down_decision_time(endpoint_id)
+ self.clean_up_scaling_down_operation_state(endpoint_id)
scale_op = ScaleOp.DOWN_IN_OP
else:
# Record the timestamp of the scaling down operation.
@@ -285,7 +311,7 @@ def enforce_scaling_down_delay_interval(self,
def clean_up_scaling_down_operation_state(self, endpoint_id) -> bool:
# We return True if the cleaning up operation succeeded, else False.
- logging.info("Not a scaling down operation, cleaning up scale down state from Redis.")
+ logging.info("Cleaning up scale down state from Redis.")
to_clean_up = \
self.fedml_model_cache.exists_endpoint_scaling_down_decision_time(endpoint_id)
if to_clean_up:
From 2ff516e59877164f290e93c16cfcd43a48b46ef5 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Thu, 2 May 2024 16:08:46 -0700
Subject: [PATCH 016/282] [Deploy] Fix edge case of readiness check.
---
.../scheduler/comm_utils/job_monitor.py | 32 +++++++++++++++++++
1 file changed, 32 insertions(+)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index 5874adfef7..c24dd2a830 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -574,6 +574,15 @@ def monitor_slave_endpoint_status(self):
is_endpoint_ready = self._check_and_reset_endpoint_status(
job.job_id, job.edge_id, deployment_result, only_check_inference_ready_status=True)
+ # [Hotfix] Under high-concurrency situation, the ready endpoint might not be available
+ # But the container is in health state
+ # In this case, we need to have an exact 503 code, instead of timeout to decide to restart
+ # TODO(Raphael): Split the /ready endpoint and predict endpoint traffic
+ if not self._lenient_check_replica_ready(deployment_result):
+ is_endpoint_ready = False
+ else:
+ is_endpoint_ready = True
+
# Get endpoint container name prefix, prepare for restart
endpoint_container_name_prefix = \
(device_client_constants.ClientConstants.get_endpoint_container_name(
@@ -736,6 +745,28 @@ def monitor_slave_endpoint_status(self):
except Exception as e:
pass
+ def _lenient_check_replica_ready(
+ self, deployment_result
+ ):
+ """
+ Double-check the replica's liveness using /ready api:
+ if 200 -> return True
+ [Critical] if timeout -> Could be under high pressure -> return True
+ if HTTP_202_ACCEPTED -> unhealthy -> return False
+ """
+ result_json = deployment_result
+ inference_url = result_json.get("model_url", None)
+
+ # Make a curl get to inference_url with timeout 5s
+ # TODO(Raphael): Also support PROXY and MQTT to check the readiness
+ response_ok = asyncio.run(FedMLHttpInference.is_inference_ready(inference_url, timeout=5))
+ if response_ok is None:
+ # This means the server return 202
+ return False
+
+ # 200 or Timeout
+ return True
+
def _check_and_reset_endpoint_status(
self, endpoint_id, device_id, deployment_result, only_check_inference_ready_status=False,
should_release_gpu_ids=False
@@ -761,6 +792,7 @@ def _check_and_reset_endpoint_status(
if self.endpoint_unavailable_counter.get(str(endpoint_id)) is None:
self.endpoint_unavailable_counter[str(endpoint_id)] = 0
+
if not response_ok:
self.endpoint_unavailable_counter[str(endpoint_id)] += 1
else:
From 162d896894e829e3adcded97287232307fe75539 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Fri, 3 May 2024 12:13:22 -0700
Subject: [PATCH 017/282] [Deploy] Catch exception when process autoscale.
---
.../scheduler/comm_utils/job_monitor.py | 168 +++++++++---------
1 file changed, 86 insertions(+), 82 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index 75ef647e3f..11123d9c63 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -104,91 +104,95 @@ def autoscaler_reconcile_after_interval(self):
for endpoint_settings in endpoints_settings_list:
endpoint_state = endpoint_settings["state"]
if endpoint_state == "DEPLOYED" and endpoint_settings["enable_auto_scaling"]:
- logging.info(f"After interval, check the autoscaler for async future list."
- f"{self.endpoints_autoscale_predict_future}")
- # TODO(fedml-dimitris): The policy can be set dynamically or be user specific.
- # Set the policy, here we use latency, but other metrics are possible as well, such as qps.
- # For more advanced use cases look for the testing scripts under the autoscaler/test directory.
- autoscaling_policy_config = \
- {
- "current_replicas": int(endpoint_settings["replica_num"]),
- "min_replicas": int(endpoint_settings["scale_min"]),
- "max_replicas": int(endpoint_settings["scale_max"]),
- "queries_per_replica": int(endpoint_settings["target_queries_per_replica"]),
- "window_size_secs": int(endpoint_settings["aggregation_window_size_seconds"]),
- "scaledown_delay_secs": int(endpoint_settings["scale_down_delay_seconds"]),
+ try: # Should not let one endpoint affect the others
+ logging.info(f"After interval, check the autoscaler for async future list."
+ f"{self.endpoints_autoscale_predict_future}")
+ # TODO(fedml-dimitris): The policy can be set dynamically or be user specific.
+ # Set the policy, here we use latency, but other metrics are possible as well, such as qps.
+ # For more advanced use cases look for the testing scripts under the autoscaler/test directory.
+ autoscaling_policy_config = \
+ {
+ "current_replicas": int(endpoint_settings["replica_num"]),
+ "min_replicas": int(endpoint_settings["scale_min"]),
+ "max_replicas": int(endpoint_settings["scale_max"]),
+ "queries_per_replica": int(endpoint_settings["target_queries_per_replica"]),
+ "window_size_secs": int(endpoint_settings["aggregation_window_size_seconds"]),
+ "scaledown_delay_secs": int(endpoint_settings["scale_down_delay_seconds"]),
+ }
+ autoscaling_policy = ConcurrentQueryPolicy(**autoscaling_policy_config)
+
+ e_id, e_name, model_name = endpoint_settings["endpoint_id"], endpoint_settings["endpoint_name"], \
+ endpoint_settings["model_name"]
+
+ logging.info(f"Querying the autoscaler for endpoint {e_id} with user settings {endpoint_settings}.")
+
+ # For every endpoint we just update the policy configuration.
+ autoscaling_policy.min_replicas = endpoint_settings["scale_min"]
+ autoscaling_policy.max_replicas = endpoint_settings["scale_max"]
+ # We retrieve a list of replicas for every endpoint. The number
+ # of running replicas is the length of that list.
+ current_replicas = len(fedml_model_cache.get_endpoint_replicas_results(e_id))
+ autoscaling_policy.current_replicas = current_replicas
+ logging.info(f"Endpoint {e_id} autoscaling policy: {autoscaling_policy}.")
+
+ scale_op = autoscaler.scale_operation_endpoint(
+ autoscaling_policy,
+ str(e_id))
+
+ new_replicas = current_replicas + scale_op.value
+
+ logging.info(f"Scaling operation {scale_op.value} for endpoint {e_id} .")
+ logging.info(f"New Replicas {new_replicas} for endpoint {e_id} .")
+ logging.info(f"Current Replicas {current_replicas} for endpoint {e_id} .")
+ if current_replicas == new_replicas:
+ # Basically the autoscaler decided that no scaling operation should take place.
+ logging.info(f"No scaling operation for endpoint {e_id}.")
+ return
+
+ # Should scale in / out
+ curr_version = fedml.get_env_version()
+
+ if curr_version == "release":
+ mlops_prefix = "https://open.fedml.ai/"
+ elif curr_version == "test":
+ mlops_prefix = "https://open-test.fedml.ai/"
+ else:
+ logging.error(f"Do not support the version {curr_version}.")
+ return
+ autoscale_url_path = "fedmlModelServer/api/v1/endpoint/auto-scale"
+ url = f"{mlops_prefix}{autoscale_url_path}"
+
+ # Get cached token for authorization of autoscale request
+ cached_token = fedml_model_cache.get_end_point_token(e_id, e_name, model_name)
+ if cached_token is None:
+ logging.error(f"Failed to get the cached token for endpoint {e_id}.")
+ return
+
+ req_header = {
+ "Authorization": f"Bearer {cached_token}"
+ }
+ req_body = {
+ "endpointId": int(e_id),
+ "replicasDesired": int(new_replicas)
}
- autoscaling_policy = ConcurrentQueryPolicy(**autoscaling_policy_config)
-
- e_id, e_name, model_name = endpoint_settings["endpoint_id"], endpoint_settings["endpoint_name"], \
- endpoint_settings["model_name"]
-
- logging.info(f"Querying the autoscaler for endpoint {e_id} with user settings {endpoint_settings}.")
-
- # For every endpoint we just update the policy configuration.
- autoscaling_policy.min_replicas = endpoint_settings["scale_min"]
- autoscaling_policy.max_replicas = endpoint_settings["scale_max"]
- # We retrieve a list of replicas for every endpoint. The number
- # of running replicas is the length of that list.
- current_replicas = len(fedml_model_cache.get_endpoint_replicas_results(e_id))
- autoscaling_policy.current_replicas = current_replicas
- logging.info(f"Endpoint {e_id} autoscaling policy: {autoscaling_policy}.")
-
- scale_op = autoscaler.scale_operation_endpoint(
- autoscaling_policy,
- str(e_id))
-
- new_replicas = current_replicas + scale_op.value
-
- logging.info(f"Scaling operation {scale_op.value} for endpoint {e_id} .")
- logging.info(f"New Replicas {new_replicas} for endpoint {e_id} .")
- logging.info(f"Current Replicas {current_replicas} for endpoint {e_id} .")
- if current_replicas == new_replicas:
- # Basically the autoscaler decided that no scaling operation should take place.
- logging.info(f"No scaling operation for endpoint {e_id}.")
- return
-
- # Should scale in / out
- curr_version = fedml.get_env_version()
-
- if curr_version == "release":
- mlops_prefix = "https://open.fedml.ai/"
- elif curr_version == "test":
- mlops_prefix = "https://open-test.fedml.ai/"
- else:
- logging.error(f"Do not support the version {curr_version}.")
- return
- autoscale_url_path = "fedmlModelServer/api/v1/endpoint/auto-scale"
- url = f"{mlops_prefix}{autoscale_url_path}"
-
- # Get cached token for authorization of autoscale request
- cached_token = fedml_model_cache.get_end_point_token(e_id, e_name, model_name)
- if cached_token is None:
- logging.error(f"Failed to get the cached token for endpoint {e_id}.")
- return
-
- req_header = {
- "Authorization": f"Bearer {cached_token}"
- }
- req_body = {
- "endpointId": int(e_id),
- "replicasDesired": int(new_replicas)
- }
- try:
- logging.info(f"Sending the autoscale request to MLOps platform. url {url}, "
- f"body {req_body}., header {req_header}")
- response = requests.post(
- url,
- headers=req_header,
- json=req_body
- )
- if response.status_code != 200:
- logging.error(f"Failed to send the autoscale request to MLOps platform.")
- else:
- logging.info(f"Successfully sent the autoscale request to MLOps platform.")
+ try:
+ logging.info(f"Sending the autoscale request to MLOps platform. url {url}, "
+ f"body {req_body}., header {req_header}")
+ response = requests.post(
+ url,
+ headers=req_header,
+ json=req_body
+ )
+ if response.status_code != 200:
+ logging.error(f"Failed to send the autoscale request to MLOps platform.")
+ else:
+ logging.info(f"Successfully sent the autoscale request to MLOps platform.")
+ except Exception as e:
+ logging.error(f"Failed to send the autoscale request to MLOps platform. {e}")
except Exception as e:
- logging.error(f"Failed to send the autoscale request to MLOps platform. {e}")
+ logging.error(f"Error in autoscaler reconcile after interval. {e}")
+ pass
return
@staticmethod
From 0002dcc2ea9eb12a7a72e92c2f4d887b8cddb06f Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Fri, 3 May 2024 16:53:03 -0700
Subject: [PATCH 018/282] [Deploy] Restrict spacy version.
---
python/setup.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/setup.py b/python/setup.py
index ae1efc0dff..b4b6545808 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -54,7 +54,7 @@ def finalize_options(self):
'redis',
'scikit-learn',
'smart-open==6.3.0',
- 'spacy',
+ 'spacy>=3.2.0,<3.3.0',
'sqlalchemy',
'toposort',
'torch>=1.13.1',
From addd91c8c3412951eed68662c0b1652577770bf2 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Fri, 3 May 2024 17:02:55 -0700
Subject: [PATCH 019/282] [Deploy] Remove pydantic-settings to avoid PyYAML
compatibility issue.
---
python/setup.py | 1 -
1 file changed, 1 deletion(-)
diff --git a/python/setup.py b/python/setup.py
index b4b6545808..c78a597a4b 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -47,7 +47,6 @@ def finalize_options(self):
'prettytable',
'py-machineid',
'pydantic',
- 'pydantic-settings',
'pytest',
'pytest-mock',
'python-rapidjson>=0.9.1',
From 064cfcabb840f9235c283814bf8f347de70cc993 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Fri, 3 May 2024 19:03:06 -0700
Subject: [PATCH 020/282] [Deploy] Put spacy in extra requirement
---
python/setup.py | 6 +++++-
1 file changed, 5 insertions(+), 1 deletion(-)
diff --git a/python/setup.py b/python/setup.py
index c78a597a4b..cce0ddb2ca 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -53,7 +53,6 @@ def finalize_options(self):
'redis',
'scikit-learn',
'smart-open==6.3.0',
- 'spacy>=3.2.0,<3.3.0',
'sqlalchemy',
'toposort',
'torch>=1.13.1',
@@ -112,6 +111,10 @@ def finalize_options(self):
"deepspeed>=0.10.2",
]
+requirements_extra_nlp = [
+ 'spacy>=3.2.0,<3.3.0',
+]
+
# if platform.machine() == "x86_64":
# requirements.append("MNN==1.1.6")
@@ -177,6 +180,7 @@ def finalize_options(self):
"llm": requirements_extra_llm,
"mxnet": requirements_extra_mxnet,
"tensorflow": requirements_extra_tf,
+ "nlp": requirements_extra_nlp,
},
package_data={"": ["py.typed"]},
license="Apache 2.0",
From 282f2e12785de3d04116886cb0343062e7f16ed8 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Mon, 6 May 2024 10:51:34 -0700
Subject: [PATCH 021/282] [Deploy] Handle status when deploy failed.
---
.../model_scheduler/device_server_runner.py | 25 ++++++++++++++++++-
1 file changed, 24 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_runner.py b/python/fedml/computing/scheduler/model_scheduler/device_server_runner.py
index 89e74bbd74..4bcac6d2db 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_server_runner.py
@@ -667,7 +667,30 @@ def callback_deployment_result_message(self, topic=None, payload=None):
self.send_rollback_add_remove_op(run_id_str, rollback_dict)
return
else:
- pass # This is the last worker that failed, so we should continue to "ABORTED" status
+ # This is the last worker that failed, so we should continue to "ABORTED" status
+ model_config_parameters = self.running_request_json[run_id_str]["parameters"]
+ inference_port = model_config_parameters.get("server_internal_port",
+ ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
+ inference_port_external = model_config_parameters.get("server_external_port", inference_port)
+ ip = self.get_ip_address(self.running_request_json[run_id_str])
+ if ip.startswith("http://") or ip.startswith("https://"):
+ model_inference_url = "{}/inference/{}".format(ip, end_point_id)
+ else:
+ model_inference_url = "http://{}:{}/inference/{}".format(ip, inference_port_external,
+ end_point_id)
+
+ self.send_deployment_status(end_point_id, end_point_name,
+ payload_json["model_name"],
+ model_inference_url,
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTED)
+
+ # For auto-scaling, should update the state to "DEPLOYED"
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ update_user_setting_replica_num(end_point_id=end_point_id, state="DEPLOYED")
+
+ self.model_runner_mapping[run_id_str].replica_controller.under_rollback = False
+
+ return
elif run_operation == "UPDATE":
# Overwrite the json with the rollback version diff
rollback_version_diff = \
From e64b6c6b46e0dc81904abcaeed5932b373111ce6 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Mon, 6 May 2024 12:32:23 -0700
Subject: [PATCH 022/282] [Deploy] Saved failed deployment log to a file.
---
.../model_scheduler/device_client_constants.py | 7 +++++++
.../model_scheduler/device_model_deployment.py | 16 ++++++++++++++++
2 files changed, 23 insertions(+)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
index 915690e9a4..d2093569c3 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
@@ -274,6 +274,13 @@ def get_model_serving_dir():
os.makedirs(model_file_dir, exist_ok=True)
return model_file_dir
+ @staticmethod
+ def get_deploy_failed_log_dir():
+ model_file_dir = os.path.join(ClientConstants.get_fedml_home_dir(), "fedml", "logs", "failed_logs")
+ if not os.path.exists(model_file_dir):
+ os.makedirs(model_file_dir, exist_ok=True)
+ return model_file_dir
+
@staticmethod
def get_model_infer_data_dir():
model_infer_data_dir = os.path.join(ClientConstants.get_fedml_home_dir(), "fedml", "models_infer_data")
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 8670633eeb..bd04228355 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -507,6 +507,22 @@ def log_deployment_result(end_point_id, model_id, cmd_container_name, cmd_type,
if container_obj.status == "exited":
logging.info("Container {} has exited, automatically remove it".format(cmd_container_name))
+
+ # Save the failed log into ~/.fedml/fedml-model-client/fedml/logs/failed_logs/
+ # $run_id/$container_name.log
+ try:
+ parent_dir = os.path.join(ClientConstants.get_deploy_failed_log_dir())
+ os.makedirs(parent_dir, exist_ok=True)
+ error_logs_dir = os.path.join(ClientConstants.get_deploy_failed_log_dir(), str(end_point_id))
+ os.makedirs(error_logs_dir, exist_ok=True)
+ error_log_file = os.path.join(error_logs_dir, f"{cmd_container_name}.log")
+ with open(error_log_file, "w") as f:
+ f.write(f"Container {cmd_container_name} has exited\n")
+ f.write(f"Error logs: {err_logs}\n")
+ f.write(f"Output logs: {out_logs}\n")
+ except Exception as e:
+ logging.error(f"Failed to save the error logs with exception {e}")
+
client.api.remove_container(container_obj.id, v=True, force=True)
break
From c6cb5b0d1c9f75de449e8f2fce8f3e3d9e48d60d Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Tue, 7 May 2024 15:39:22 -0400
Subject: [PATCH 023/282] Removing field validator for resolving spacy and
pydantic conflict.
---
.../model_scheduler/autoscaler/policies.py | 9 +++++----
.../autoscaler/test/autoscaler_test.py | 14 +++++++-------
2 files changed, 12 insertions(+), 11 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/policies.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/policies.py
index fd49549812..0ad2cc0d13 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/policies.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/policies.py
@@ -1,4 +1,4 @@
-from pydantic import BaseModel, field_validator, NonNegativeInt, NonNegativeFloat
+from pydantic import BaseModel, NonNegativeInt, NonNegativeFloat, validator
class AutoscalingPolicy(BaseModel):
@@ -70,9 +70,10 @@ class EWMPolicy(AutoscalingPolicy):
ub_threshold: NonNegativeFloat # recommended value: 0.5
lb_threshold: NonNegativeFloat # recommended value: 0.5
- @field_validator("metric")
- def validate_option(cls, v):
- assert v in ["ewm_latency", "ewm_qps"]
+ @validator("metric")
+ def metric_match(cls, v) -> str:
+ if v not in ["ewm_latency", "ewm_qps"]:
+ raise ValueError("Wrong metric name.")
return v
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/autoscaler_test.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/autoscaler_test.py
index 7af1022c7d..eadc2dc9a9 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/autoscaler_test.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/autoscaler_test.py
@@ -111,21 +111,21 @@ def test_validate_scaling_bounds(self):
# Validate scale up.
scale_up = autoscaler.validate_scaling_bounds(ScaleOp.UP_OUT_OP, autoscaling_policy)
- self.assertEquals(scale_up, ScaleOp.UP_OUT_OP)
+ self.assertEqual(scale_up, ScaleOp.UP_OUT_OP)
# Validate scale down.
scale_down = autoscaler.validate_scaling_bounds(ScaleOp.DOWN_IN_OP, autoscaling_policy)
- self.assertEquals(scale_down, ScaleOp.DOWN_IN_OP)
+ self.assertEqual(scale_down, ScaleOp.DOWN_IN_OP)
# Validate max out-of-bounds.
autoscaling_policy.current_replicas = 3
scale_oob_max = autoscaler.validate_scaling_bounds(ScaleOp.UP_OUT_OP, autoscaling_policy)
- self.assertEquals(scale_oob_max, ScaleOp.NO_OP)
+ self.assertEqual(scale_oob_max, ScaleOp.NO_OP)
# Validate min out-of-bounds.
autoscaling_policy.current_replicas = 1
scale_oob_min = autoscaler.validate_scaling_bounds(ScaleOp.DOWN_IN_OP, autoscaling_policy)
- self.assertEquals(scale_oob_min, ScaleOp.NO_OP)
+ self.assertEqual(scale_oob_min, ScaleOp.NO_OP)
def test_enforce_scaling_down_delay_interval(self):
self.populate_redis_with_dummy_metrics()
@@ -140,15 +140,15 @@ def test_enforce_scaling_down_delay_interval(self):
autoscaling_policy.scaledown_delay_secs = 0.0
scale_down = autoscaler.enforce_scaling_down_delay_interval(ENV_ENDPOINT_ID_1, autoscaling_policy)
- self.assertEquals(scale_down, ScaleOp.DOWN_IN_OP)
+ self.assertEqual(scale_down, ScaleOp.DOWN_IN_OP)
autoscaling_policy.scaledown_delay_secs = 1
scale_noop = autoscaler.enforce_scaling_down_delay_interval(ENV_ENDPOINT_ID_1, autoscaling_policy)
- self.assertEquals(scale_noop, ScaleOp.NO_OP)
+ self.assertEqual(scale_noop, ScaleOp.NO_OP)
time.sleep(2)
scale_down = autoscaler.enforce_scaling_down_delay_interval(ENV_ENDPOINT_ID_1, autoscaling_policy)
- self.assertEquals(scale_down, ScaleOp.DOWN_IN_OP)
+ self.assertEqual(scale_down, ScaleOp.DOWN_IN_OP)
self.clear_redis()
From 3cc9bb26ea96cbcaf1833bbf5edd9cf7ff7c6713 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Fri, 10 May 2024 17:02:38 -0700
Subject: [PATCH 024/282] Add Util classes
---
.../scheduler/comm_utils/GPUCardUtil.py | 46 +++++++++++++++++++
.../scheduler/comm_utils/HardwareUtil.py | 34 ++++++++++++++
.../scheduler/comm_utils/NvidiaGPUtil.py | 42 +++++++++++++++++
.../scheduler/comm_utils/QualcommNPUtil.py | 0
.../utils => comm_utils}/singleton.py | 11 ++++-
.../model_scheduler/autoscaler/autoscaler.py | 2 +-
6 files changed, 133 insertions(+), 2 deletions(-)
create mode 100644 python/fedml/computing/scheduler/comm_utils/GPUCardUtil.py
create mode 100644 python/fedml/computing/scheduler/comm_utils/HardwareUtil.py
create mode 100644 python/fedml/computing/scheduler/comm_utils/NvidiaGPUtil.py
create mode 100644 python/fedml/computing/scheduler/comm_utils/QualcommNPUtil.py
rename python/fedml/computing/scheduler/{model_scheduler/autoscaler/utils => comm_utils}/singleton.py (56%)
diff --git a/python/fedml/computing/scheduler/comm_utils/GPUCardUtil.py b/python/fedml/computing/scheduler/comm_utils/GPUCardUtil.py
new file mode 100644
index 0000000000..dd5cb9ff97
--- /dev/null
+++ b/python/fedml/computing/scheduler/comm_utils/GPUCardUtil.py
@@ -0,0 +1,46 @@
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from enum import Enum, auto
+from typing import Optional, List
+
+
+class GPUCardType(Enum):
+ NVIDIA = auto()
+ QUALCOMM = auto()
+ UNKNOWN = auto()
+
+ def __str__(self):
+ return self.name
+
+
+@dataclass
+class GPUCard:
+ id: int
+ uuid: str
+ name: str
+ load: float
+ memoryTotal: float
+ memoryUsed: float
+ memoryFree: float
+ driver: str
+ serial: Optional[str]
+ display_mode: Optional[str]
+ display_active: Optional[str]
+ temperature: Optional[float]
+
+
+class GPUCardUtil(ABC):
+
+ @classmethod
+ def detectGPUCardType(cls) -> GPUCardType:
+ raise NotImplementedError
+
+ @staticmethod
+ @abstractmethod
+ def getAvailableGPUCardIDs() -> List[int]:
+ raise NotImplementedError
+
+ @staticmethod
+ @abstractmethod
+ def getGPUCards() -> List[GPUCard]:
+ raise NotImplementedError
diff --git a/python/fedml/computing/scheduler/comm_utils/HardwareUtil.py b/python/fedml/computing/scheduler/comm_utils/HardwareUtil.py
new file mode 100644
index 0000000000..9d908886b0
--- /dev/null
+++ b/python/fedml/computing/scheduler/comm_utils/HardwareUtil.py
@@ -0,0 +1,34 @@
+import logging
+from typing import Optional, List
+
+from fedml.computing.scheduler.comm_utils.GPUCardUtil import GPUCardUtil, GPUCard
+from fedml.computing.scheduler.comm_utils.singleton import Singleton
+
+
+class HardwareUtil(metaclass=Singleton):
+
+ def __init__(self):
+ self._gpu_util: Optional[GPUCardUtil] = self.__get_util()
+
+ @staticmethod
+ def __get_util() -> Optional[GPUCardUtil]:
+ for cls in GPUCardUtil.__subclasses__():
+ try:
+ if cls.detectGPUCardType() is not None:
+ return cls()
+ except Exception as e:
+ pass
+
+ logging.error("No GPU card detected")
+ return None
+
+ def getGPUs(self) -> List[GPUCard]:
+ if self._gpu_util is None:
+ return []
+ return self._gpu_util.getGPUCards()
+
+ def getAvailableGPUCardIDs(self) -> List[int]:
+ if self._gpu_util is None:
+ return []
+ return self._gpu_util.getAvailableGPUCardIDs()
+
diff --git a/python/fedml/computing/scheduler/comm_utils/NvidiaGPUtil.py b/python/fedml/computing/scheduler/comm_utils/NvidiaGPUtil.py
new file mode 100644
index 0000000000..66317c67c8
--- /dev/null
+++ b/python/fedml/computing/scheduler/comm_utils/NvidiaGPUtil.py
@@ -0,0 +1,42 @@
+import subprocess
+from typing import List
+
+from GPUtil import GPUtil, GPU
+
+from fedml.computing.scheduler.comm_utils.GPUCardUtil import GPUCard, GPUCardUtil, GPUCardType
+
+
+def _convert(gpu: GPU) -> GPUCard:
+ return GPUCard(
+ id=gpu.id,
+ uuid=gpu.uuid,
+ name=gpu.name,
+ load=gpu.load,
+ memoryTotal=gpu.memoryTotal,
+ memoryUsed=gpu.memoryUsed,
+ memoryFree=gpu.memoryFree,
+ driver=gpu.driver,
+ serial=gpu.serial,
+ display_mode=gpu.display_mode,
+ display_active=gpu.display_active,
+ temperature=gpu.temperature
+ )
+
+
+class NvidiaGPUtil(GPUCardUtil):
+
+ @staticmethod
+ def getAvailableGPUCardIDs() -> List[int]:
+ return GPUtil.getAvailable()
+
+ @staticmethod
+ def getGPUCards() -> List[GPUCard]:
+ return [_convert(gpu) for gpu in GPUtil.getGPUs()]
+
+ @classmethod
+ def detectGPUCardType(cls):
+ try:
+ subprocess.check_output(["nvidia-smi"], universal_newlines=True)
+ return GPUCardType.NVIDIA
+ except Exception:
+ return None
diff --git a/python/fedml/computing/scheduler/comm_utils/QualcommNPUtil.py b/python/fedml/computing/scheduler/comm_utils/QualcommNPUtil.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/utils/singleton.py b/python/fedml/computing/scheduler/comm_utils/singleton.py
similarity index 56%
rename from python/fedml/computing/scheduler/model_scheduler/autoscaler/utils/singleton.py
rename to python/fedml/computing/scheduler/comm_utils/singleton.py
index 5c76acea97..dd403965c1 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/utils/singleton.py
+++ b/python/fedml/computing/scheduler/comm_utils/singleton.py
@@ -1,3 +1,6 @@
+import threading
+
+
class Singleton(type):
"""
@@ -8,8 +11,14 @@ class Singleton(type):
"""
_instances = {}
+ # For thread safety
+ _lock = threading.Lock()
def __call__(cls, *args, **kwargs):
if cls not in cls._instances:
- cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
+ with cls._lock:
+ # Another thread might have created the instance before the lock was acquired.
+ # So check again if the instance is already created.
+ if cls not in cls._instances:
+ cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
return cls._instances[cls]
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
index 009345863a..bb2b59e7d9 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
@@ -8,7 +8,7 @@
from enum import Enum
from fedml.computing.scheduler.model_scheduler.device_model_cache import FedMLModelCache
from fedml.computing.scheduler.model_scheduler.autoscaler.policies import *
-from fedml.computing.scheduler.model_scheduler.autoscaler.utils.singleton import Singleton
+from fedml.computing.scheduler.comm_utils.singleton import Singleton
class ScaleOp(Enum):
From e18428603a8e31767cef653e6b37370e68a4cbec Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Fri, 10 May 2024 18:20:39 -0700
Subject: [PATCH 025/282] Rename util files for pattern matching
---
.../scheduler/comm_utils/{GPUCardUtil.py => gpu_utils.py} | 0
.../scheduler/comm_utils/{HardwareUtil.py => hardware_utils.py} | 2 +-
.../scheduler/comm_utils/{NvidiaGPUtil.py => nvidia_utils.py} | 2 +-
.../comm_utils/{QualcommNPUtil.py => qualcomm_utils.py} | 0
4 files changed, 2 insertions(+), 2 deletions(-)
rename python/fedml/computing/scheduler/comm_utils/{GPUCardUtil.py => gpu_utils.py} (100%)
rename python/fedml/computing/scheduler/comm_utils/{HardwareUtil.py => hardware_utils.py} (91%)
rename python/fedml/computing/scheduler/comm_utils/{NvidiaGPUtil.py => nvidia_utils.py} (91%)
rename python/fedml/computing/scheduler/comm_utils/{QualcommNPUtil.py => qualcomm_utils.py} (100%)
diff --git a/python/fedml/computing/scheduler/comm_utils/GPUCardUtil.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils.py
similarity index 100%
rename from python/fedml/computing/scheduler/comm_utils/GPUCardUtil.py
rename to python/fedml/computing/scheduler/comm_utils/gpu_utils.py
diff --git a/python/fedml/computing/scheduler/comm_utils/HardwareUtil.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
similarity index 91%
rename from python/fedml/computing/scheduler/comm_utils/HardwareUtil.py
rename to python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index 9d908886b0..e57b905e4a 100644
--- a/python/fedml/computing/scheduler/comm_utils/HardwareUtil.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -1,7 +1,7 @@
import logging
from typing import Optional, List
-from fedml.computing.scheduler.comm_utils.GPUCardUtil import GPUCardUtil, GPUCard
+from fedml.computing.scheduler.comm_utils.gpu_utils import GPUCardUtil, GPUCard
from fedml.computing.scheduler.comm_utils.singleton import Singleton
diff --git a/python/fedml/computing/scheduler/comm_utils/NvidiaGPUtil.py b/python/fedml/computing/scheduler/comm_utils/nvidia_utils.py
similarity index 91%
rename from python/fedml/computing/scheduler/comm_utils/NvidiaGPUtil.py
rename to python/fedml/computing/scheduler/comm_utils/nvidia_utils.py
index 66317c67c8..0ce49e1ccd 100644
--- a/python/fedml/computing/scheduler/comm_utils/NvidiaGPUtil.py
+++ b/python/fedml/computing/scheduler/comm_utils/nvidia_utils.py
@@ -3,7 +3,7 @@
from GPUtil import GPUtil, GPU
-from fedml.computing.scheduler.comm_utils.GPUCardUtil import GPUCard, GPUCardUtil, GPUCardType
+from fedml.computing.scheduler.comm_utils.gpu_utils import GPUCard, GPUCardUtil, GPUCardType
def _convert(gpu: GPU) -> GPUCard:
diff --git a/python/fedml/computing/scheduler/comm_utils/QualcommNPUtil.py b/python/fedml/computing/scheduler/comm_utils/qualcomm_utils.py
similarity index 100%
rename from python/fedml/computing/scheduler/comm_utils/QualcommNPUtil.py
rename to python/fedml/computing/scheduler/comm_utils/qualcomm_utils.py
From 8c8c1da3a07b7862bc7c7ba1893c77e4080277ae Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Sat, 11 May 2024 03:30:57 +0000
Subject: [PATCH 026/282] MVP
---
.../scheduler/comm_utils/__init__.py | 3 ++
.../__init__.py} | 0
.../comm_utils/{ => gpu_utils}/gpu_utils.py | 5 ++-
.../{ => gpu_utils}/nvidia_utils.py | 23 +++++-----
.../comm_utils/gpu_utils/qualcomm_utils.py | 0
.../scheduler/comm_utils/hardware_utils.py | 42 ++++++++++++-------
6 files changed, 44 insertions(+), 29 deletions(-)
rename python/fedml/computing/scheduler/comm_utils/{qualcomm_utils.py => gpu_utils/__init__.py} (100%)
rename python/fedml/computing/scheduler/comm_utils/{ => gpu_utils}/gpu_utils.py (89%)
rename python/fedml/computing/scheduler/comm_utils/{ => gpu_utils}/nvidia_utils.py (82%)
create mode 100644 python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
diff --git a/python/fedml/computing/scheduler/comm_utils/__init__.py b/python/fedml/computing/scheduler/comm_utils/__init__.py
index e69de29bb2..adf0269b67 100644
--- a/python/fedml/computing/scheduler/comm_utils/__init__.py
+++ b/python/fedml/computing/scheduler/comm_utils/__init__.py
@@ -0,0 +1,3 @@
+import gpu_utils.gpu_utils
+import gpu_utils.qualcomm_utils
+import gpu_utils.nvidia_utils
\ No newline at end of file
diff --git a/python/fedml/computing/scheduler/comm_utils/qualcomm_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/__init__.py
similarity index 100%
rename from python/fedml/computing/scheduler/comm_utils/qualcomm_utils.py
rename to python/fedml/computing/scheduler/comm_utils/gpu_utils/__init__.py
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
similarity index 89%
rename from python/fedml/computing/scheduler/comm_utils/gpu_utils.py
rename to python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
index dd5cb9ff97..e6691b4b5d 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
@@ -1,4 +1,4 @@
-from abc import ABC, abstractmethod
+from abc import ABC, abstractmethod, ABCMeta
from dataclasses import dataclass
from enum import Enum, auto
from typing import Optional, List
@@ -32,7 +32,7 @@ class GPUCard:
class GPUCardUtil(ABC):
@classmethod
- def detectGPUCardType(cls) -> GPUCardType:
+ def detectGPUCardType(cls) -> Optional[GPUCardType]:
raise NotImplementedError
@staticmethod
@@ -44,3 +44,4 @@ def getAvailableGPUCardIDs() -> List[int]:
@abstractmethod
def getGPUCards() -> List[GPUCard]:
raise NotImplementedError
+
diff --git a/python/fedml/computing/scheduler/comm_utils/nvidia_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
similarity index 82%
rename from python/fedml/computing/scheduler/comm_utils/nvidia_utils.py
rename to python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
index 0ce49e1ccd..349230cef5 100644
--- a/python/fedml/computing/scheduler/comm_utils/nvidia_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
@@ -1,9 +1,9 @@
import subprocess
-from typing import List
+from typing import List, Optional
from GPUtil import GPUtil, GPU
-from fedml.computing.scheduler.comm_utils.gpu_utils import GPUCard, GPUCardUtil, GPUCardType
+from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCard, GPUCardUtil, GPUCardType
def _convert(gpu: GPU) -> GPUCard:
@@ -24,19 +24,18 @@ def _convert(gpu: GPU) -> GPUCard:
class NvidiaGPUtil(GPUCardUtil):
-
- @staticmethod
- def getAvailableGPUCardIDs() -> List[int]:
- return GPUtil.getAvailable()
-
- @staticmethod
- def getGPUCards() -> List[GPUCard]:
- return [_convert(gpu) for gpu in GPUtil.getGPUs()]
-
@classmethod
- def detectGPUCardType(cls):
+ def detectGPUCardType(cls) -> Optional[GPUCardType]:
try:
subprocess.check_output(["nvidia-smi"], universal_newlines=True)
return GPUCardType.NVIDIA
except Exception:
return None
+
+ @staticmethod
+ def getGPUCards() -> List[GPUCard]:
+ return [_convert(gpu) for gpu in GPUtil.getGPUs()]
+
+ @staticmethod
+ def getAvailableGPUCardIDs() -> List[int]:
+ return GPUtil.getAvailable()
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index e57b905e4a..d26fb9c5b5 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -1,34 +1,46 @@
import logging
+
from typing import Optional, List
-from fedml.computing.scheduler.comm_utils.gpu_utils import GPUCardUtil, GPUCard
+from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCardUtil, GPUCard
+from fedml.computing.scheduler.comm_utils.gpu_utils.nvidia_utils import NvidiaGPUtil
from fedml.computing.scheduler.comm_utils.singleton import Singleton
class HardwareUtil(metaclass=Singleton):
- def __init__(self):
- self._gpu_util: Optional[GPUCardUtil] = self.__get_util()
+ _gpu_utils = [NvidiaGPUtil]
+ _gpu_util: Optional[GPUCardUtil] = None
@staticmethod
- def __get_util() -> Optional[GPUCardUtil]:
- for cls in GPUCardUtil.__subclasses__():
+ def _get_util() -> Optional[GPUCardUtil]:
+ if HardwareUtil._gpu_util is not None:
+ return HardwareUtil._gpu_util
+
+ for gpu_util in HardwareUtil._gpu_utils:
try:
- if cls.detectGPUCardType() is not None:
- return cls()
+ if gpu_util.detectGPUCardType() is not None:
+ HardwareUtil._gpu_util = gpu_util()
+ return HardwareUtil._gpu_util
except Exception as e:
pass
logging.error("No GPU card detected")
return None
- def getGPUs(self) -> List[GPUCard]:
- if self._gpu_util is None:
- return []
- return self._gpu_util.getGPUCards()
+ @staticmethod
+ def getGPUs() -> List[GPUCard]:
+ gpu_util = HardwareUtil._get_util()
+ return gpu_util.getGPUCards() if gpu_util is not None else []
+
+ @staticmethod
+ def getAvailableGPUCardIDs() -> List[int]:
+ gpu_util = HardwareUtil._get_util()
+ return gpu_util.getAvailainfbleGPUCardIDs() if gpu_util is not None else []
- def getAvailableGPUCardIDs(self) -> List[int]:
- if self._gpu_util is None:
- return []
- return self._gpu_util.getAvailableGPUCardIDs()
+if __name__ == "__main__":
+ gpus = HardwareUtil.getGPUs()
+ get_available_gpu_cards = HardwareUtil.getAvailableGPUCardIDs()
+ print(gpus)
+ print(get_available_gpu_cards)
From a35d433d4d2ec87dff89b3c01ff9057aee70f2d3 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Sat, 11 May 2024 20:28:21 +0800
Subject: [PATCH 027/282] 1. Get the docker container with the timeout value so
that we can avoid the system is blocked. 2. Fix the time error when checking
the run process in the job monitor. 3. Support docker authentication when
launching the job. 4. Send the edge status detecting message once in the
protocol manager, save the status response message to the queue of job
runner, and process the edge status in the job runner. 5. Open a process to
download the package so that we can avoid the request is blocked and check
the timeout. 6. Refactor the status center to make the status sequence work
and stable. 7. Change the retain flag to true when establishing an MQTT
connection so that we can handle messages even if the device is not started.
8. Sync the latest deployment module to this branch.
---
python/fedml/api/api_test.py | 19 +-
.../scheduler/comm_utils/container_utils.py | 4 +-
.../scheduler/comm_utils/job_cleanup.py | 1 +
.../scheduler/comm_utils/job_monitor.py | 15 +-
.../scheduler/comm_utils/job_utils.py | 5 +-
.../master/base_master_job_runner.py | 179 +++++++++++----
.../master/base_master_protocol_manager.py | 156 ++------------
.../scheduler/master/launch_job_runner.py | 2 +-
.../scheduler/master/server_daemon.py | 18 +-
.../model_scheduler/device_client_runner.py | 38 +++-
.../device_model_deployment.py | 4 +-
.../model_scheduler/device_server_runner.py | 203 +++++++++++++-----
.../model_scheduler/job_runner_msg_sender.py | 5 +-
.../model_scheduler/master_job_runner.py | 133 +++++++++---
.../master_protocol_manager.py | 46 ++--
.../model_scheduler/worker_job_runner.py | 26 ++-
.../worker_protocol_manager.py | 24 ++-
.../scheduler_core/general_constants.py | 2 +
.../scheduler_core/message_center.py | 2 +-
.../scheduler_base_job_runner.py | 95 +++++++-
.../scheduler_base_job_runner_manager.py | 7 +-
.../scheduler/scheduler_core/status_center.py | 8 +-
.../status_manager_protocols.py | 12 +-
.../scheduler/slave/base_slave_job_runner.py | 3 +-
.../slave/base_slave_protocol_manager.py | 5 +-
.../scheduler/slave/client_data_interface.py | 14 +-
.../communication/mqtt/mqtt_manager.py | 2 +-
python/fedml/core/mlops/mlops_metrics.py | 49 +++--
28 files changed, 711 insertions(+), 366 deletions(-)
diff --git a/python/fedml/api/api_test.py b/python/fedml/api/api_test.py
index 54da088d0d..fc2fb77b20 100755
--- a/python/fedml/api/api_test.py
+++ b/python/fedml/api/api_test.py
@@ -18,20 +18,23 @@
yaml_file = os.path.join(python_dir, "examples", "launch", "hello_job.yaml")
# Launch job
-for i in range(0, 10):
+launch_result_list = list()
+for i in range(0, 1):
launch_result = fedml.api.launch_job(yaml_file)
+ launch_result_list.append(launch_result)
# launch_result = fedml.api.launch_job_on_cluster(yaml_file, "alex-cluster")
if launch_result.result_code != 0:
print(f"Failed to launch job. Reason: {launch_result.result_message}")
-exit(1)
-
# Get job status
-log_result = fedml.api.run_logs(launch_result.run_id, 1, 100)
-if log_result is None or log_result.run_status is None:
- print(f"Failed to get job status.")
- exit(1)
-print(f"Run status {log_result.run_status}")
+while len(launch_result_list) > 0:
+ for launch_result in launch_result_list:
+ log_result = fedml.api.run_logs(launch_result.run_id, 1, 5)
+ if log_result is None or log_result.run_status is None:
+ print(f"Failed to get job status.")
+ #exit(1)
+ print(f"Run status {log_result.run_status}")
+ time.sleep(0.5)
# Get job logs
time.sleep(30)
diff --git a/python/fedml/computing/scheduler/comm_utils/container_utils.py b/python/fedml/computing/scheduler/comm_utils/container_utils.py
index cfaa5b6457..417aa7ba81 100644
--- a/python/fedml/computing/scheduler/comm_utils/container_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/container_utils.py
@@ -20,7 +20,7 @@ def get_instance():
def get_docker_client(self):
try:
- client = docker.from_env()
+ client = docker.from_env(timeout=5, version="auto")
except Exception:
logging.error("Failed to connect to the docker daemon, please ensure that you have "
"installed Docker Desktop or Docker Engine, and the docker is running")
@@ -175,7 +175,7 @@ def get_container_rank_same_model(prefix: str):
running_model_name = hash("model_endpoint_id_{}_name_{}_model_id_{}_name_{}_ver_{}")
"""
try:
- client = docker.from_env()
+ docker.from_env(timeout=5, version="auto")
except Exception:
logging.error("Failed to connect to the docker daemon, please ensure that you have "
"installed Docker Desktop or Docker Engine, and the docker is running")
diff --git a/python/fedml/computing/scheduler/comm_utils/job_cleanup.py b/python/fedml/computing/scheduler/comm_utils/job_cleanup.py
index ed30c1bf2e..6700b0bc7a 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_cleanup.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_cleanup.py
@@ -44,6 +44,7 @@ def sync_run_process_gpu(self):
ComputeCacheManager.get_instance().get_gpu_cache().get_run_info_sync_lock_key("")
):
count = 0
+ client_data_interface.FedMLClientDataInterface.get_instance().create_job_table()
job_list = client_data_interface.FedMLClientDataInterface.get_instance().get_jobs_from_db()
for job in job_list.job_list:
count += 1
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index 30182a6207..6a9afb9d69 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -81,7 +81,7 @@ def monitor_slave_run_process_status(self):
break
# Calc the timeout
- started_time = int(float(job.started_time))
+ started_time = JobMonitor.get_started_time(job)
timeout = time.time() - started_time
job_type = JobRunnerUtils.parse_job_type(job.running_json)
@@ -157,6 +157,15 @@ def monitor_slave_run_process_status(self):
print(f"Exception when monitoring endpoint process on the slave agent.{traceback.format_exc()}")
pass
+ @staticmethod
+ def get_started_time(job):
+ started_time = int(float(job.started_time))
+ if started_time <= 0:
+ started_time = int(float(job.updated_time))
+ if started_time <= 0:
+ started_time = time.time()
+ return started_time
+
def monitor_master_run_process_status(self, server_id, device_info_reporter=None):
try:
ComputeCacheManager.get_instance().set_redis_params()
@@ -168,7 +177,7 @@ def monitor_master_run_process_status(self, server_id, device_info_reporter=None
break
# Calc the timeout
- started_time = int(float(job.started_time))
+ started_time = JobMonitor.get_started_time(job)
timeout = time.time() - started_time
# Get the timeout threshold
@@ -416,7 +425,7 @@ def monitor_slave_endpoint_status(self):
endpoint_name = endpoint_json.get("end_point_name", None)
device_ids = endpoint_json.get("device_ids", [])
- started_time = int(float(job.started_time))
+ started_time = JobMonitor.get_started_time(job)
timeout = time.time() - started_time
if timeout > SchedulerConstants.ENDPOINT_DEPLOYMENT_DEPLOYING_TIMEOUT:
print(f"[Worker][{job.job_id}:{job.edge_id}] Due to timeout, "
diff --git a/python/fedml/computing/scheduler/comm_utils/job_utils.py b/python/fedml/computing/scheduler/comm_utils/job_utils.py
index 1423c3e6ab..44290de37d 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_utils.py
@@ -568,8 +568,9 @@ def get_run_container_name(run_id: int) -> str:
@staticmethod
def get_docker_client(docker_args: DockerArgs) -> DockerClient:
try:
- client = docker.from_env()
- client.login(username=docker_args.username, password=docker_args.password, registry=docker_args.registry)
+ client = docker.from_env(timeout=5, version="auto")
+ if docker_args.username != "" and docker_args.registry != "":
+ client.login(username=docker_args.username, password=docker_args.password, registry=docker_args.registry)
except Exception as e:
raise Exception(f"Failed to connect to the docker daemon, please ensure that you have "
f"installed Docker Desktop or Docker Engine, and the docker is running. Exception {e}")
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner.py b/python/fedml/computing/scheduler/master/base_master_job_runner.py
index 3dbc1fd891..ce0515160f 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner.py
@@ -23,6 +23,8 @@
from ..scheduler_core.general_constants import GeneralConstants
from ..scheduler_core.scheduler_base_job_runner import FedMLSchedulerBaseJobRunner, RunnerError, RunnerCompletedError
from abc import ABC, abstractmethod
+from ..scheduler_core.scheduler_matcher import SchedulerMatcher
+import fedml
class FedMLBaseMasterJobRunner(FedMLSchedulerBaseJobRunner, ABC):
@@ -424,11 +426,15 @@ def start_runner_process(
ServerConstants.save_run_process(run_id, self.run_process.pid)
return self.run_process
- def put_run_edge_device_info_to_queue(self, run_id, device_info):
- run_id_str = str(run_id)
- if self.run_edge_device_info_queue is None:
- self.run_edge_device_info_queue = Queue()
- self.run_edge_device_info_queue.put(device_info)
+ def put_run_edge_device_info_to_queue(self, run_id, edge_id, device_info):
+ edge_ids = self.request_json.get("edgeids", None)
+ if edge_ids is None:
+ return
+ if int(edge_id) in edge_ids or str(edge_id) in edge_ids:
+ run_id_str = str(run_id)
+ if self.run_edge_device_info_queue is None:
+ self.run_edge_device_info_queue = Queue()
+ self.run_edge_device_info_queue.put(device_info)
def should_continue_run_job(self, run_id):
run_config = self.request_json["run_config"]
@@ -467,23 +473,6 @@ def detect_edges_status(
run_edges_realtime_status = dict()
run_edges_realtime_status[run_id_str] = dict()
- edge_info_global_dict = dict()
-
- # Send status message to all edges
- allowed_cache_edge_status_time = 60
- for edge_id in edge_id_list:
- # Check if the edge status was filled allowed_cache_edge_status_time seconds ago,
- # if so no more checking message would be sent.
- edge_info = edge_info_global_dict.get(edge_id, None)
- if edge_info is not None:
- timestamp = edge_info.get("timestamp", None)
- time_interval = time.time() - timestamp
- if time_interval <= allowed_cache_edge_status_time:
- continue
-
- self.send_status_check_msg(run_id, edge_id, self.edge_id, context=status_check_context)
- time.sleep(3)
-
total_sleep_seconds = 0
status_check_sleep_seconds = 10
allowed_status_check_sleep_seconds = 60 * 2 if status_timeout is None else status_timeout
@@ -522,26 +511,14 @@ def detect_edges_status(
active_edges_count += 1
active_edge_info_dict[str(edge_id)] = edge_info
else:
- # Check if the edge status was filled allowed_cache_edge_status_time seconds ago,
- # if so no more checking message would be sent.
- edge_info = edge_info_global_dict.get(edge_id, None)
- if edge_info is not None:
- timestamp = edge_info.get("timestamp", None)
- time_interval = time.time() - timestamp
- if time_interval <= allowed_cache_edge_status_time:
- active_edges_count += 1
- active_edge_info_dict[str(edge_id)] = edge_info
- continue
-
inactivate_edges.append(edge_id)
- self.send_status_check_msg(run_id, edge_id, self.edge_id, context=status_check_context)
# If all edges are ready then send the starting job message to them
if active_edges_count == len(edge_id_list):
logging.info(f"All edges are ready. Active edge id list is as follows. {active_edge_info_dict}")
if callback_when_edges_ready is not None:
logging.info("All edges are ready. Start to process the callback function.")
- callback_when_edges_ready(active_edge_info_dict=active_edge_info_dict)
+ callback_when_edges_ready(self.request_json, active_edge_info_dict=active_edge_info_dict)
else:
logging.info("All edges are ready. No callback function to process.")
break
@@ -572,18 +549,11 @@ def detect_edges_status(
if should_async and total_sleep_seconds >= allowed_status_check_sleep_seconds_for_async:
if async_timeout > allowed_status_check_sleep_seconds_for_async:
time.sleep(async_timeout - allowed_status_check_sleep_seconds_for_async)
- self.send_training_request_to_edges(active_edge_info_dict)
+ self.send_training_request_to_edges(self.request_json, active_edge_info_dict)
return True, active_edge_info_dict, inactivate_edges
return True, active_edge_info_dict, inactivate_edges
- def send_status_check_msg(self, run_id, edge_id, server_id, context=None):
- topic_get_model_device_id = "server/client/request_device_info/" + str(edge_id)
- payload = {"server_id": server_id, "run_id": run_id}
- if context is not None:
- payload["context"] = context
- self.message_center.send_message(topic_get_model_device_id, json.dumps(payload))
-
def report_exception_status(self, run_id):
self.status_reporter.report_job_status(run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION)
@@ -602,10 +572,125 @@ def callback_run_metrics(self, topic, payload):
self.run_metrics_queue = Queue()
self.run_metrics_queue.put(payload)
- def send_training_request_to_edges(self, active_edge_info_dict):
- topic = GeneralConstants.MSG_TOPIC_SEND_TRAINING_REQUEST_TO_EDGES
- payload = json.dumps(active_edge_info_dict)
- self.message_center.receive_message(topic, payload)
+ # def send_training_request_to_edges(self, active_edge_info_dict):
+ # topic = GeneralConstants.MSG_TOPIC_SEND_TRAINING_REQUEST_TO_EDGES
+ # payload = json.dumps(active_edge_info_dict)
+ # self.message_center.receive_message(topic, payload)
+ def send_training_request_to_edges(self, request_json, active_edge_info_dict=None):
+ run_id = request_json["runId"]
+ edge_id_list = request_json["edgeids"]
+ run_config = request_json.get("run_config", {})
+ run_params = run_config.get("parameters", {})
+ job_yaml = run_params.get("job_yaml", {})
+ job_yaml_default_none = run_params.get("job_yaml", None)
+ computing = job_yaml.get("computing", {})
+ request_num_gpus = computing.get("minimum_num_gpus", None)
+ job_gpu_id_list = request_json.get("job_gpu_id_list", None)
+ assigned_gpu_num_dict = dict()
+ assigned_gpu_ids_dict = dict()
+ master_node_addr = ""
+ master_node_port = 0
+
+ logging.info(f"Send training request to Edge ids: {edge_id_list}, run_id {run_id}")
+
+ should_match_gpu = False
+ if job_yaml_default_none is not None and request_num_gpus is not None and \
+ int(request_num_gpus) > 0 and active_edge_info_dict is not None:
+ should_match_gpu = True
+ SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(active_edge_info_dict, show_gpu_list=True)
+
+ # Match and assign gpus to each device
+ assigned_gpu_num_dict, assigned_gpu_ids_dict = SchedulerMatcher.match_and_assign_gpu_resources_to_devices(
+ request_num_gpus, edge_id_list, active_edge_info_dict, job_gpu_id_list=job_gpu_id_list)
+ if assigned_gpu_num_dict is None or assigned_gpu_ids_dict is None:
+ # If no resources available, send failed message to MLOps and send exception message to all edges.
+ gpu_count, gpu_available_count = SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(
+ active_edge_info_dict, should_print=True)
+ err_info = f"No resources available." \
+ f"Total available GPU count {gpu_available_count} is less than " \
+ f"request GPU count {request_num_gpus}"
+ logging.error(err_info)
+
+ # Bug fix: This mqtt message needs to be sent so platform can clean up the failed run and change the
+ # status from running to failed.
+ self.mlops_metrics.report_server_training_status(
+ run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id
+ )
+
+ self.status_reporter.report_server_id_status(
+ run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.server_agent_id)
+ self.report_exception_status(run_id)
+
+ serving_args = job_yaml.get("serving_args", {})
+ endpoint_id = serving_args.get("endpoint_id", None)
+ if endpoint_id is not None:
+ fedml.mlops.log_endpoint_status(
+ endpoint_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_FAILED)
+ fedml.mlops.log_run_log_lines(
+ endpoint_id, 0, [err_info],
+ log_source=GeneralConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT
+ )
+ return
+
+ # Generate master node addr and port
+ master_node_addr, master_node_port = SchedulerMatcher.get_master_node_info(edge_id_list,
+ active_edge_info_dict)
+
+ # Generate new edge id list after matched
+ edge_id_list = SchedulerMatcher.generate_new_edge_list_for_gpu_matching(assigned_gpu_num_dict)
+ if len(edge_id_list) <= 0:
+ gpu_count, gpu_available_count = SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(
+ active_edge_info_dict, should_print=True)
+ logging.error(f"Request parameter for GPU num is invalid."
+ f"Total available GPU count {gpu_available_count}."
+ f"Request GPU num {request_num_gpus}")
+ self.status_reporter.report_server_id_status(
+ run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.server_agent_id)
+ self.report_exception_status(run_id)
+ return
+
+ if should_match_gpu:
+ # Report gpu num and related infos to MLOps.
+ serving_args = job_yaml.get("serving_args", {})
+ endpoint_id = serving_args.get("endpoint_id", None)
+ if endpoint_id is not None:
+ endpoint_info = list()
+ for edge_id_item, gpu_num in assigned_gpu_num_dict.items():
+ edge_info = active_edge_info_dict.get(str(edge_id_item), {})
+ endpoint_info.append({
+ "machine_id": edge_id_item, "endpoint_gpu_count": gpu_num,
+ "master_deploy_id": edge_info.get("master_device_id", 0),
+ "slave_deploy_id": edge_info.get("slave_device_id", 0)})
+ topic_name = f"compute/mlops/endpoint"
+ endpoint_info_json = {"endpoint_id": endpoint_id, "endpoint_info": endpoint_info}
+ print(f"endpoint_info_json {endpoint_info_json}")
+ self.message_center.send_message(topic_name, json.dumps(endpoint_info_json))
+
+ client_rank = 1
+ for edge_id in edge_id_list:
+ topic_start_train = "flserver_agent/" + str(edge_id) + "/start_train"
+ logging.info("start_train: send topic " + topic_start_train + " to client...")
+ request_json["client_rank"] = client_rank
+ client_rank += 1
+
+ if active_edge_info_dict is not None:
+ edge_info = active_edge_info_dict.get(str(edge_id), {})
+ model_master_device_id = edge_info.get("master_device_id", None)
+ model_slave_device_id = edge_info.get("slave_device_id", None)
+ model_slave_device_id_list = edge_info.get("slave_device_id_list", None)
+
+ if should_match_gpu:
+ request_json["scheduler_match_info"] = SchedulerMatcher.generate_match_info_for_scheduler(
+ edge_id, edge_id_list, master_node_addr, master_node_port,
+ assigned_gpu_num_dict, assigned_gpu_ids_dict,
+ model_master_device_id=model_master_device_id,
+ model_slave_device_id=model_slave_device_id,
+ model_slave_device_id_list=model_slave_device_id_list
+ )
+
+ self.message_center.send_message(topic_start_train, json.dumps(request_json))
def should_process_async_cluster(self):
run_config = self.request_json.get("run_config", {})
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index 25cab5a17c..ef59431ee8 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -3,16 +3,10 @@
import json
import logging
import fedml
-from ..scheduler_core.scheduler_matcher import SchedulerMatcher
from ..comm_utils.constants import SchedulerConstants
-from ..comm_utils.job_utils import JobRunnerUtils
from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
from ....core.mlops.mlops_configs import MLOpsConfigs
from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
-from ..comm_utils import sys_utils
-from ....core.mlops.mlops_utils import MLOpsUtils
-from ..model_scheduler import device_client_constants
-from fedml.utils.debugging import debug
from ..scheduler_core.compute_cache_manager import ComputeCacheManager
from ..scheduler_core.ota_upgrade import FedMLOtaUpgrade
from .deploy_job_launcher import FedMLDeployJobLauncher
@@ -91,9 +85,6 @@ def generate_topics(self):
# The topic for last-will messages.
self.topic_last_will = "flserver_agent/last_will_msg"
- # The topic for sending training request to edges (Request from the job runner when all edges are ready)
- self.topic_send_training_request_to_edges = GeneralConstants.MSG_TOPIC_SEND_TRAINING_REQUEST_TO_EDGES
-
# Subscribe topics for starting train, stopping train and fetching client status.
self.subscribed_topics.clear()
self.add_subscribe_topic(self.topic_start_train)
@@ -115,13 +106,10 @@ def add_protocol_handler(self):
self.add_message_listener(self.topic_ota_msg, FedMLBaseMasterProtocolManager.callback_server_ota_msg)
self.add_message_listener(self.topic_report_status, self.callback_report_current_status)
self.add_message_listener(self.topic_response_device_info, self.callback_response_device_info)
- self.add_message_listener(self.topic_response_device_info, self.callback_response_device_info)
self.add_message_listener(self.topic_request_device_info_from_mlops,
self.callback_request_device_info_from_mlops)
self.add_message_listener(self.topic_requesst_job_status, self.callback_request_job_status)
self.add_message_listener(self.topic_requesst_device_status_in_job, self.callback_request_device_status_in_job)
- self.add_message_listener(self.topic_send_training_request_to_edges,
- self.callback_send_training_request_to_edges)
@abstractmethod
def _get_job_runner_manager(self):
@@ -197,7 +185,7 @@ def callback_start_train(self, topic=None, payload=None):
# Print the payload
logging.info("callback_start_train payload: {}".format(payload))
logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
+ f"FedMLDebug - run id {run_id}, Receive at callback_start_train: topic ({topic}), payload ({payload})"
)
# Save the parameters
@@ -212,7 +200,7 @@ def callback_start_train(self, topic=None, payload=None):
if not self.run_as_cloud_server:
self.mlops_metrics.report_server_id_status(
run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_STARTING, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
+ server_id=self.edge_id, server_agent_id=self.edge_id, running_json=payload)
# Start server with multiprocessing mode
if self.run_as_edge_server_and_agent or self.enable_simulation_cloud_agent:
@@ -230,6 +218,8 @@ def callback_start_train(self, topic=None, payload=None):
process = self._get_job_runner_manager().get_runner_process(run_id)
if process is not None:
GeneralConstants.save_run_process(run_id, process.pid, is_master=True)
+
+ self.send_status_msg_to_edges(edge_id_list, run_id, self.edge_id)
elif self.run_as_cloud_agent:
self.init_job_task(request_json)
@@ -261,6 +251,8 @@ def callback_start_train(self, topic=None, payload=None):
status_center_queue=self.get_status_queue()
)
+ self.send_status_msg_to_edges(edge_id_list, run_id, self.edge_id)
+
def callback_stop_train(self, topic, payload, use_payload=None):
# Print the payload
logging.info(
@@ -346,7 +338,7 @@ def callback_response_device_info(self, topic, payload):
# Put device info into a multiprocessing queue so master runner checks if all edges are ready
if context is None:
- self._get_job_runner_manager().put_run_edge_device_info_to_queue(run_id, device_info)
+ self._get_job_runner_manager().put_run_edge_device_info_to_queue(run_id, edge_id, device_info)
# if self.run_edge_device_info_global_queue is None:
# self.run_edge_device_info_global_queue = Array('i', list())
@@ -368,10 +360,6 @@ def callback_request_job_status(self, topic, payload):
def callback_request_device_status_in_job(self, topic, payload):
self.response_device_status_in_job(topic, payload)
- def callback_send_training_request_to_edges(self, topic, payload):
- payload_json = json.loads(payload)
- self.send_training_request_to_edges(active_edge_info_dict=payload_json)
-
def generate_protocol_manager(self):
message_status_runner = self._generate_protocol_manager_instance(
self.args, agent_config=self.agent_config
@@ -457,124 +445,6 @@ def init_job_task(self, request_json):
self.setup_listener_for_run_metrics(run_id)
self.setup_listener_for_run_logs(run_id)
- @debug
- def send_training_request_to_edges(self, active_edge_info_dict=None):
- run_id = self.request_json["runId"]
- edge_id_list = self.request_json["edgeids"]
- run_config = self.request_json.get("run_config", {})
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
- job_yaml_default_none = run_params.get("job_yaml", None)
- computing = job_yaml.get("computing", {})
- request_num_gpus = computing.get("minimum_num_gpus", None)
- job_gpu_id_list = self.request_json.get("job_gpu_id_list", None)
- assigned_gpu_num_dict = dict()
- assigned_gpu_ids_dict = dict()
- master_node_addr = ""
- master_node_port = 0
-
- logging.info("Send training request to Edge ids: " + str(edge_id_list))
-
- should_match_gpu = False
- if job_yaml_default_none is not None and request_num_gpus is not None and \
- int(request_num_gpus) > 0 and active_edge_info_dict is not None:
- should_match_gpu = True
- SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(active_edge_info_dict, show_gpu_list=True)
-
- # Match and assign gpus to each device
- assigned_gpu_num_dict, assigned_gpu_ids_dict = SchedulerMatcher.match_and_assign_gpu_resources_to_devices(
- request_num_gpus, edge_id_list, active_edge_info_dict, job_gpu_id_list=job_gpu_id_list)
- if assigned_gpu_num_dict is None or assigned_gpu_ids_dict is None:
- # If no resources available, send failed message to MLOps and send exception message to all edges.
- gpu_count, gpu_available_count = SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(
- active_edge_info_dict, should_print=True)
- err_info = f"No resources available." \
- f"Total available GPU count {gpu_available_count} is less than " \
- f"request GPU count {request_num_gpus}"
- logging.error(err_info)
-
- # Bug fix: This mqtt message needs to be sent so platform can clean up the failed run and change the
- # status from running to failed.
- self.mlops_metrics.report_server_training_status(
- run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id
- )
-
- self.status_reporter.report_server_id_status(
- run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.server_agent_id)
- self.report_exception_status(run_id)
-
- serving_args = job_yaml.get("serving_args", {})
- endpoint_id = serving_args.get("endpoint_id", None)
- if endpoint_id is not None:
- fedml.mlops.log_endpoint_status(
- endpoint_id, device_client_constants.ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
- fedml.mlops.log_run_log_lines(
- endpoint_id, 0, [err_info],
- log_source=device_client_constants.ClientConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT
- )
- return
-
- # Generate master node addr and port
- master_node_addr, master_node_port = SchedulerMatcher.get_master_node_info(edge_id_list,
- active_edge_info_dict)
-
- # Generate new edge id list after matched
- edge_id_list = SchedulerMatcher.generate_new_edge_list_for_gpu_matching(assigned_gpu_num_dict)
- if len(edge_id_list) <= 0:
- gpu_count, gpu_available_count = SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(
- active_edge_info_dict, should_print=True)
- logging.error(f"Request parameter for GPU num is invalid."
- f"Total available GPU count {gpu_available_count}."
- f"Request GPU num {request_num_gpus}")
- self.status_reporter.report_server_id_status(
- run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.server_agent_id)
- self.report_exception_status(run_id)
- return
-
- if should_match_gpu:
- # Report gpu num and related infos to MLOps.
- serving_args = job_yaml.get("serving_args", {})
- endpoint_id = serving_args.get("endpoint_id", None)
- if endpoint_id is not None:
- endpoint_info = list()
- for edge_id_item, gpu_num in assigned_gpu_num_dict.items():
- edge_info = active_edge_info_dict.get(str(edge_id_item), {})
- endpoint_info.append({
- "machine_id": edge_id_item, "endpoint_gpu_count": gpu_num,
- "master_deploy_id": edge_info.get("master_device_id", 0),
- "slave_deploy_id": edge_info.get("slave_device_id", 0)})
- topic_name = f"compute/mlops/endpoint"
- endpoint_info_json = {"endpoint_id": endpoint_id, "endpoint_info": endpoint_info}
- print(f"endpoint_info_json {endpoint_info_json}")
- self.message_center.send_message(topic_name, json.dumps(endpoint_info_json))
-
- client_rank = 1
- for edge_id in edge_id_list:
- topic_start_train = "flserver_agent/" + str(edge_id) + "/start_train"
- logging.info("start_train: send topic " + topic_start_train + " to client...")
- request_json = self.request_json
- request_json["client_rank"] = client_rank
- client_rank += 1
-
- if active_edge_info_dict is not None:
- edge_info = active_edge_info_dict.get(str(edge_id), {})
- model_master_device_id = edge_info.get("master_device_id", None)
- model_slave_device_id = edge_info.get("slave_device_id", None)
- model_slave_device_id_list = edge_info.get("slave_device_id_list", None)
-
- if should_match_gpu:
- request_json["scheduler_match_info"] = SchedulerMatcher.generate_match_info_for_scheduler(
- edge_id, edge_id_list, master_node_addr, master_node_port,
- assigned_gpu_num_dict, assigned_gpu_ids_dict,
- model_master_device_id=model_master_device_id,
- model_slave_device_id=model_slave_device_id,
- model_slave_device_id_list=model_slave_device_id_list
- )
-
- self.message_center.send_message(topic_start_train, json.dumps(request_json))
-
def setup_listeners_for_edge_status(self, run_id, edge_ids, server_id):
edge_status_topic = "fl_client/flclient_agent_" + str(server_id) + "/status"
payload = {"run_id": run_id, "init_all_edge_id_list": edge_ids, "init_server_id": server_id}
@@ -628,6 +498,18 @@ def send_training_stop_request_to_specific_edge(self, edge_id, payload):
logging.info("stop_train: send topic " + topic_stop_train)
self.message_center.send_message(topic_stop_train, payload)
+ def send_status_check_msg(self, run_id, edge_id, server_id, context=None):
+ topic_status_check = f"server/client/request_device_info/{edge_id}"
+ payload = {"server_id": server_id, "run_id": run_id}
+ if context is not None:
+ payload["context"] = context
+ self.message_center.send_message(topic_status_check, json.dumps(payload))
+
+ def send_status_msg_to_edges(self, edge_id_list, run_id, server_id, context=None):
+ # Send status message to all edges
+ for edge_id in edge_id_list:
+ self.send_status_check_msg(run_id, edge_id, self.edge_id, context=context)
+
def report_exception_status(self, run_id):
self.status_reporter.report_job_status(run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION)
diff --git a/python/fedml/computing/scheduler/master/launch_job_runner.py b/python/fedml/computing/scheduler/master/launch_job_runner.py
index c28458fc0f..3f26da1ef7 100755
--- a/python/fedml/computing/scheduler/master/launch_job_runner.py
+++ b/python/fedml/computing/scheduler/master/launch_job_runner.py
@@ -19,7 +19,7 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
# Override
def _generate_job_runner_instance(self, args, run_id=None, request_json=None, agent_config=None, edge_id=None,):
return FedMLLaunchMasterJobRunner(
- args, run_id=run_id, request_json=request_json, agent_config=self.agent_config, edge_id=edge_id
+ args, run_id=run_id, request_json=request_json, agent_config=agent_config, edge_id=edge_id
)
# Override
diff --git a/python/fedml/computing/scheduler/master/server_daemon.py b/python/fedml/computing/scheduler/master/server_daemon.py
index bc02621a44..8fe85f3381 100755
--- a/python/fedml/computing/scheduler/master/server_daemon.py
+++ b/python/fedml/computing/scheduler/master/server_daemon.py
@@ -115,18 +115,24 @@
if os.path.exists(login_exit_file):
print(f"[Server] Login process is exited, check the exit file {login_exit_file}")
if retry_count > 3:
- print(f"Retry count is over 3 times, exit the process. Check the log file for more details. "
- f"Login logs: {login_logs}, Exit file: {login_exit_file}")
- exit(1)
+ if args.role == ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_CLOUD_AGENT_INDEX]:
+ retry_count = 0
+ else:
+ print(f"Retry count is over 3 times, exit the process. Check the log file for more details. "
+ f"Login logs: {login_logs}, Exit file: {login_exit_file}")
+ exit(1)
retry_flag = True
if len(login_pids) == 0:
message = f"[Server] Login process is exited, check the log file {login_logs}"
print(message)
if retry_count >= 3:
- print(f"Retry count is over 3 times, exit the process. Check the log file for more details. "
- f"Login logs: {login_logs}, Exit file: {login_exit_file}")
- exit(1)
+ if args.role == ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_CLOUD_AGENT_INDEX]:
+ retry_count = 0
+ else:
+ print(f"Retry count is over 3 times, exit the process. Check the log file for more details. "
+ f"Login logs: {login_logs}, Exit file: {login_exit_file}")
+ exit(1)
retry_flag = True
if retry_flag:
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_runner.py b/python/fedml/computing/scheduler/model_scheduler/device_client_runner.py
index 3c7d0fb05b..8bb03eebbd 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_client_runner.py
@@ -24,6 +24,8 @@
import fedml
from fedml import mlops
from fedml.computing.scheduler.model_scheduler.device_model_msg_object import FedMLModelMsgObject
+from fedml.computing.scheduler.scheduler_core.compute_cache_manager import ComputeCacheManager
+
from fedml.computing.scheduler.scheduler_core.compute_utils import ComputeUtils
from fedml.core.distributed.communication.s3.remote_storage import S3Storage
from .device_model_cache import FedMLModelCache
@@ -356,7 +358,6 @@ def run_impl(self):
ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT)
inference_end_point_id = run_id
- self.mlops_metrics.report_sys_perf(self.args, self.agent_config["mqtt_config"], run_id=run_id)
MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
logging.info(f"[Worker] Received model deployment request from master for endpoint {run_id}.")
@@ -478,7 +479,6 @@ def run_impl(self):
if op == "add":
worker_ip = self.get_ip_address(self.request_json)
for rank in range(prev_rank + 1, prev_rank + 1 + op_num):
- # TODO: Support Rollback if this for loop failed
try:
running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
start_deployment(
@@ -496,6 +496,18 @@ def run_impl(self):
if inference_output_url == "":
logging.error("[Worker] Failed to deploy the model.")
+ # Release the gpu occupancy
+ FedMLModelCache.get_instance().set_redis_params()
+ replica_occupied_gpu_ids_str = FedMLModelCache.get_instance().get_replica_gpu_ids(
+ run_id, end_point_name, model_name, self.edge_id, rank + 1)
+ logging.info(f"Release gpu ids {replica_occupied_gpu_ids_str} for "
+ f"failed deployment of replica no {rank + 1}.")
+
+ if replica_occupied_gpu_ids_str is not None:
+ replica_occupied_gpu_ids = json.loads(replica_occupied_gpu_ids_str)
+ JobRunnerUtils.get_instance().release_partial_job_gpu(run_id,
+ self.edge_id, replica_occupied_gpu_ids)
+
# Send failed result back to master
result_payload = self.send_deployment_results(
end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
@@ -892,7 +904,7 @@ def callback_start_deployment(self, topic, payload):
run_id = inference_end_point_id
self.args.run_id = run_id
self.args.edge_id = self.edge_id
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+ MLOpsRuntimeLog(args=self.args).init_logs()
MLOpsRuntimeLogDaemon.get_instance(self.args).set_log_source(
ClientConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT)
MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(run_id, self.edge_id)
@@ -978,6 +990,26 @@ def callback_delete_deployment(self, topic, payload):
model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_name,
self.edge_id)
+ # Delete FEDML_GLOBAL_ENDPOINT_RUN_ID_MAP_TAG-${run_id} both in redis and local db
+ ComputeCacheManager.get_instance().gpu_cache.delete_endpoint_run_id_map(str(model_msg_object.run_id))
+
+ # Delete FEDML_EDGE_ID_MODEL_DEVICE_ID_MAP_TAG-${run_id} both in redis and local db
+ ComputeCacheManager.get_instance().gpu_cache.delete_edge_model_id_map(str(model_msg_object.run_id))
+
+ # Delete FEDML_GLOBAL_DEVICE_RUN_GPU_IDS_TAG-${run_id}-${device_id} both in redis and local db
+ ComputeCacheManager.get_instance().gpu_cache.delete_device_run_gpu_ids(str(self.edge_id),
+ str(model_msg_object.run_id))
+
+ # Delete FEDML_GLOBAL_DEVICE_RUN_NUM_GPUS_TAG-${run_id}-${device_id} both in redis and local db
+ ComputeCacheManager.get_instance().gpu_cache.delete_device_run_num_gpus(str(self.edge_id),
+ str(model_msg_object.run_id))
+
+ # Delete FEDML_MODEL_REPLICA_GPU_IDS_TAG-${run_id}-${end_point_name}-${model_name}-${device_id}-*
+ FedMLModelCache.get_instance().set_redis_params()
+ FedMLModelCache.get_instance().delete_all_replica_gpu_ids(model_msg_object.run_id,
+ model_msg_object.end_point_name,
+ model_msg_object.model_name, self.edge_id)
+
def exit_run_with_exception_entry(self):
try:
self.setup_client_mqtt_mgr()
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 9b370d9ae4..bf476dd468 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -209,7 +209,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
infer_host = "127.0.0.1"
try:
- client = docker.from_env()
+ client = docker.from_env(timeout=5, version="auto")
if enable_custom_image and docker_registry_user_name != "" and docker_registry_user_password != "" \
and docker_registry != "":
client.login(username=docker_registry_user_name, password=docker_registry_user_password,
@@ -466,7 +466,7 @@ def log_deployment_result(end_point_id, model_id, cmd_container_name, cmd_type,
logging.info(f"Test: {inference_http_port}, Attempt: {deploy_attempt} / {deploy_attempt_threshold}")
try:
- client = docker.from_env()
+ client = docker.from_env(timeout=5, version="auto")
except Exception:
logging.error("Failed to connect to the docker daemon, please ensure that you have "
"installed Docker Desktop or Docker Engine, and the docker is running")
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_runner.py b/python/fedml/computing/scheduler/model_scheduler/device_server_runner.py
index 38acff8d82..4bcac6d2db 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_server_runner.py
@@ -23,6 +23,7 @@
import fedml
from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
+from fedml.core.mlops.mlops_runtime_log import MLOpsFormatter
from ..comm_utils import sys_utils
from .device_server_data_interface import FedMLServerDataInterface
@@ -122,6 +123,8 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
self.replica_controller = None
self.deployed_replica_payload = None
+ self.autoscaler_launcher = None
+
def build_dynamic_constrain_variables(self, run_id, run_config):
pass
@@ -304,6 +307,7 @@ def run_impl(self):
inference_end_point_id, use_gpu, memory_size, model_version, inference_port = self.parse_model_run_params(
self.request_json)
+ # TODO(Raphael): This measurement is for the host machine. Change to container's metrics
self.mlops_metrics.report_sys_perf(self.args, self.agent_config["mqtt_config"], run_id=run_id)
self.check_runner_stop_event()
@@ -347,28 +351,38 @@ def run_impl(self):
devices_sent_add_or_remove_msg = self.send_deployment_start_request_to_edges()
# Handle "op:update"
- devices_sent_update_remove_msg = self.send_first_scroll_update_msg()
-
- if len(devices_sent_add_or_remove_msg) == 0 and len(devices_sent_update_remove_msg) == 0:
- # No device is added, updated or removed
- logging.info("No device is added, updated or removed. No action needed for reconciliation.")
- ip = self.get_ip_address(self.request_json)
- master_port = os.getenv("FEDML_MASTER_PORT", None)
- if master_port is not None:
- inference_port = int(master_port)
- model_inference_port = inference_port
- if ip.startswith("http://") or ip.startswith("https://"):
- model_inference_url = "{}/api/v1/predict".format(ip)
- else:
- model_inference_url = "http://{}:{}/api/v1/predict".format(ip, model_inference_port)
+ try:
+ devices_sent_update_remove_msg = self.send_first_scroll_update_msg()
+
+ if len(devices_sent_add_or_remove_msg) == 0 and len(devices_sent_update_remove_msg) == 0:
+ # No device is added, updated or removed
+ logging.info("No device is added, updated or removed. No action needed for reconciliation.")
+ ip = self.get_ip_address(self.request_json)
+ master_port = os.getenv("FEDML_MASTER_PORT", None)
+ if master_port is not None:
+ inference_port = int(master_port)
+ model_inference_port = inference_port
+ if ip.startswith("http://") or ip.startswith("https://"):
+ model_inference_url = "{}/api/v1/predict".format(ip)
+ else:
+ model_inference_url = "http://{}:{}/api/v1/predict".format(ip, model_inference_port)
- self.set_runner_completed_event(run_id)
+ self.set_runner_completed_event(run_id)
- self.send_deployment_status(run_id, end_point_name,
- model_name,
- model_inference_url,
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED)
- return
+ self.send_deployment_status(run_id, end_point_name,
+ model_name,
+ model_inference_url,
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED)
+
+ # Set setting to "DEPLOYED" for autoscaling service reference
+ FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ update_user_setting_replica_num(end_point_id=run_id, state="DEPLOYED")
+
+ return
+ except Exception as e:
+ logging.error(f"Failed to send first scroll update message due to {e}.")
+ logging.error(f"Exception traceback {traceback.format_exc()}.")
logging.info("Start waiting for result callback from workers ...")
@@ -437,6 +451,7 @@ def start_device_inference_gateway(
def start_device_inference_monitor(self, run_id, end_point_name,
model_id, model_name, model_version, check_stopped_event=True):
# start inference monitor server
+ # Will report the qps related metrics to the MLOps
logging.info(f"start the model inference monitor, end point {run_id}, model name {model_name}...")
if check_stopped_event:
self.check_runner_stop_event()
@@ -563,10 +578,9 @@ def callback_deployment_result_message(self, topic=None, payload=None):
filehandler = logging.FileHandler(log_file, "a")
program_prefix = "FedML-Server @device-id-{}".format(self.edge_id)
- formatter = logging.Formatter(fmt="[" + program_prefix + "] [%(asctime)s] [%(levelname)s] "
- "[%(filename)s:%(lineno)d:%(funcName)s] %("
- "message)s",
- datefmt="%a, %d %b %Y %H:%M:%S")
+ formatter = MLOpsFormatter(fmt="[" + program_prefix + "] [%(asctime)s] [%(levelname)s] "
+ "[%(filename)s:%(lineno)d:%(funcName)s] %("
+ "message)s")
filehandler.setFormatter(formatter)
root_logger.addHandler(filehandler)
@@ -630,21 +644,54 @@ def callback_deployment_result_message(self, topic=None, payload=None):
if model_status != ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED:
logging.error(f"Unsupported model status {model_status}.")
- # Failure handler
- if run_operation == "ADD_OR_REMOVE":
- # TODO(Raphael): Also support rollback for scale out / in operation
+ # Avoid endless loop, if the rollback also failed, we should report the failure to the MLOps
+ if self.model_runner_mapping[run_id_str].replica_controller.under_rollback:
self.send_deployment_status(
end_point_id, end_point_name, payload_json["model_name"], "",
ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
return
- elif run_operation == "UPDATE":
- # Send the rollback message to the worker devices only if it has not been rollback
- if self.model_runner_mapping[run_id_str].replica_controller.under_rollback:
+
+ # Failure handler, send the rollback message to the worker devices only if it has not been rollback
+ if run_operation == "ADD_OR_REMOVE":
+ # During Scale out / in,
+ # the worker that already been scaled out / in should be sent the rollback message
+ rollback_dict = self.model_runner_mapping[run_id_str].replica_controller.rollback_add_or_remove_replica(
+ device_id=device_id, replica_no=replica_no, op_type=run_operation
+ )
+ self.model_runner_mapping[run_id_str].replica_controller.under_rollback = True
+
+ if rollback_dict is not None and len(rollback_dict) > 0:
self.send_deployment_status(
end_point_id, end_point_name, payload_json["model_name"], "",
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTING)
+ self.send_rollback_add_remove_op(run_id_str, rollback_dict)
return
+ else:
+ # This is the last worker that failed, so we should continue to "ABORTED" status
+ model_config_parameters = self.running_request_json[run_id_str]["parameters"]
+ inference_port = model_config_parameters.get("server_internal_port",
+ ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
+ inference_port_external = model_config_parameters.get("server_external_port", inference_port)
+ ip = self.get_ip_address(self.running_request_json[run_id_str])
+ if ip.startswith("http://") or ip.startswith("https://"):
+ model_inference_url = "{}/inference/{}".format(ip, end_point_id)
+ else:
+ model_inference_url = "http://{}:{}/inference/{}".format(ip, inference_port_external,
+ end_point_id)
+
+ self.send_deployment_status(end_point_id, end_point_name,
+ payload_json["model_name"],
+ model_inference_url,
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTED)
+
+ # For auto-scaling, should update the state to "DEPLOYED"
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ update_user_setting_replica_num(end_point_id=end_point_id, state="DEPLOYED")
+
+ self.model_runner_mapping[run_id_str].replica_controller.under_rollback = False
+ return
+ elif run_operation == "UPDATE":
# Overwrite the json with the rollback version diff
rollback_version_diff = \
self.model_runner_mapping[run_id_str].replica_controller.rollback_get_replica_version_diff(
@@ -698,9 +745,9 @@ def callback_deployment_result_message(self, topic=None, payload=None):
# Wait for all replica-level's result, not device-level
if (self.model_runner_mapping[run_id_str].replica_controller.is_all_replica_num_reconciled() and
self.model_runner_mapping[run_id_str].replica_controller.is_all_replica_version_reconciled()):
- '''
+ """
When all the devices have finished the add / delete / update operation
- '''
+ """
# Generate one unified inference api
# Note that here we use the gateway port instead of the inference port that is used by the slave device
model_config_parameters = request_json["parameters"]
@@ -755,16 +802,28 @@ def callback_deployment_result_message(self, topic=None, payload=None):
# Arrive here because only contains remove ops, so we do not need to update the model metadata
pass
+ # For auto-scaling, should update the state to "DEPLOYED"
FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_activation(end_point_id, end_point_name, True)
+ update_user_setting_replica_num(end_point_id=end_point_id, state="DEPLOYED")
if self.model_runner_mapping[run_id_str].replica_controller.under_rollback:
- self.send_deployment_status(end_point_id, end_point_name,
- payload_json["model_name"],
- model_inference_url,
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTED)
+ # If first time failed (Still might need rollback), then send failed message to the MLOps
+ if not (FedMLModelCache.get_instance(self.redis_addr, self.redis_port).
+ get_end_point_activation(end_point_id)):
+ self.send_deployment_status(
+ end_point_id, end_point_name, payload_json["model_name"], "",
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
+ else:
+ self.send_deployment_status(end_point_id, end_point_name,
+ payload_json["model_name"],
+ model_inference_url,
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTED)
self.model_runner_mapping[run_id_str].replica_controller.under_rollback = False
else:
+ # Set the end point activation status to True, for scaling out / in and rolling update
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ set_end_point_activation(end_point_id, end_point_name, True)
+
self.send_deployment_status(end_point_id, end_point_name,
payload_json["model_name"],
model_inference_url,
@@ -781,7 +840,10 @@ def callback_deployment_status_message(self, topic=None, payload=None):
topic, payload))
pass
- def send_deployment_start_request_to_edges(self):
+ def send_deployment_start_request_to_edges(self, in_request_json=None):
+ if in_request_json is not None:
+ self.request_json = in_request_json
+
# Iterate through replica_num_diff, both add and replace should be sent to the edge devices
if "replica_num_diff" not in self.request_json or self.request_json["replica_num_diff"] is None:
return []
@@ -895,15 +957,36 @@ def callback_start_deployment(self, topic, payload):
model_config = request_json["model_config"]
model_name = model_config["model_name"]
+ model_version = model_config["model_version"]
model_id = model_config["model_id"]
model_storage_url = model_config["model_storage_url"]
scale_min = model_config.get("instance_scale_min", 0)
scale_max = model_config.get("instance_scale_max", 0)
inference_engine = model_config.get("inference_engine", 0)
+ enable_auto_scaling = request_json.get("enable_auto_scaling", False)
+ desired_replica_num = request_json.get("desired_replica_num", 1)
+
+ target_queries_per_replica = request_json.get("target_queries_per_replica", 10)
+ aggregation_window_size_seconds = request_json.get("aggregation_window_size_seconds", 60)
+ scale_down_delay_seconds = request_json.get("scale_down_delay_seconds", 120)
+
inference_end_point_id = run_id
logging.info("[Master] received start deployment request for end point {}.".format(run_id))
+ # Set redis config
+ FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+
+ # Save the user setting (about replica number) of this run to Redis, if existed, update it
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_user_setting_replica_num(
+ end_point_id=run_id, end_point_name=end_point_name, model_name=model_name, model_version=model_version,
+ replica_num=desired_replica_num, enable_auto_scaling=enable_auto_scaling,
+ scale_min=scale_min, scale_max=scale_max, state="DEPLOYING",
+ aggregation_window_size_seconds=aggregation_window_size_seconds,
+ target_queries_per_replica=target_queries_per_replica,
+ scale_down_delay_seconds=int(scale_down_delay_seconds)
+ )
+
# Start log processor for current run
self.args.run_id = run_id
self.args.edge_id = self.edge_id
@@ -912,6 +995,7 @@ def callback_start_deployment(self, topic, payload):
ServerConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT)
MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(run_id, self.edge_id)
+ # # Deprecated
# self.ota_upgrade(payload, request_json)
# Add additional parameters to the request_json
@@ -924,8 +1008,7 @@ def callback_start_deployment(self, topic, payload):
self.running_request_json[run_id_str] = request_json
self.request_json["master_node_ip"] = self.get_ip_address(self.request_json)
- # Target status of the devices
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+ # Set the target status of the devices to redis
FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
set_end_point_device_info(request_json["end_point_id"], end_point_name, json.dumps(device_objs))
@@ -944,7 +1027,7 @@ def callback_start_deployment(self, topic, payload):
"",
ServerConstants.MODEL_DEPLOYMENT_STAGE1["index"],
ServerConstants.MODEL_DEPLOYMENT_STAGE1["text"],
- "Received request for end point {}".format(run_id))
+ "Received request for endpoint {}".format(run_id))
# Report stage to mlops: MODEL_DEPLOYMENT_STAGE2 = "Initializing"
self.send_deployment_stages(self.run_id, model_name, model_id,
@@ -1078,7 +1161,7 @@ def delete_device_replica_info_on_master(self, endpoint_id, endpoint_name, model
delete_item, endpoint_id, endpoint_name, model_name
)
- logging.info(f"Deleted the record of the replaced device {delete_device_result_list}")
+ logging.info(f"Deleted the replica record on master: {edge_id_replica_no_dict}")
def send_next_scroll_update_msg(self, run_id_str, device_id, replica_no):
"""
@@ -1128,6 +1211,20 @@ def send_next_scroll_update_msg(self, run_id_str, device_id, replica_no):
self.send_deployment_start_request_to_edge(edge_id, self.running_request_json[run_id_str])
return
+ def send_rollback_add_remove_op(self, run_id, rollback_replica_dict):
+ """
+ This method is used when the original add op failed, we need to rollback by delete the existed replicas
+ Input example:
+ rollback_replica_dict = {'96684': {'curr_num': 2, 'op': 'remove', 'target_num': 1}}
+ """
+ existed_request_json = self.running_request_json[str(run_id)]
+ updated_request_json = copy.deepcopy(existed_request_json)
+
+ # Reverse the replica_num_diff
+ updated_request_json["replica_num_diff"] = rollback_replica_dict
+
+ self.send_deployment_start_request_to_edges(in_request_json=updated_request_json)
+
def callback_activate_deployment(self, topic, payload):
logging.info("callback_activate_deployment: topic = %s, payload = %s" % (topic, payload))
@@ -1183,7 +1280,15 @@ def callback_delete_deployment(self, topic, payload):
# Parse payload as the model message object.
model_msg_object = FedMLModelMsgObject(topic, payload)
- # Set end point as deactivated status
+ # Delete SQLite records
+ FedMLServerDataInterface.get_instance().delete_job_from_db(model_msg_object.run_id)
+ FedMLModelDatabase.get_instance().delete_deployment_result(
+ model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_name,
+ model_version=model_msg_object.model_version)
+ FedMLModelDatabase.get_instance().delete_deployment_run_info(
+ end_point_id=model_msg_object.inference_end_point_id)
+
+ # Delete Redis Records
FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
set_end_point_activation(model_msg_object.inference_end_point_id,
@@ -1192,21 +1297,15 @@ def callback_delete_deployment(self, topic, payload):
delete_end_point(model_msg_object.inference_end_point_id, model_msg_object.end_point_name,
model_msg_object.model_name, model_msg_object.model_version)
+ # Send delete deployment request to the edge devices
self.send_deployment_delete_request_to_edges(payload, model_msg_object)
+ # Stop processes on master
self.set_runner_stopped_event(model_msg_object.run_id)
-
self.stop_device_inference_monitor(model_msg_object.run_id, model_msg_object.end_point_name,
model_msg_object.model_id, model_msg_object.model_name,
model_msg_object.model_version)
- FedMLServerDataInterface.get_instance().delete_job_from_db(model_msg_object.run_id)
- FedMLModelDatabase.get_instance().delete_deployment_result(
- model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_name,
- model_version=model_msg_object.model_version)
- FedMLModelDatabase.get_instance().delete_deployment_run_info(
- end_point_id=model_msg_object.inference_end_point_id)
-
def send_deployment_results_with_payload(self, end_point_id, end_point_name, payload, replica_id_list=None):
self.send_deployment_results(end_point_id, end_point_name,
payload["model_name"], payload["model_url"],
diff --git a/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py b/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
index 104dacf716..482a21b2d4 100755
--- a/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
+++ b/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
@@ -90,7 +90,10 @@ def send_deployment_stages(end_point_id, model_name, model_id, model_inference_u
logging.info(f"-------- Stages has been sent to mlops with stage {model_stages_index} and "
f"payload {deployment_stages_payload}")
- def send_deployment_start_request_to_edges(self):
+ def send_deployment_start_request_to_edges(self, in_request_json=None):
+ if in_request_json is not None:
+ self.request_json = in_request_json
+
# Iterate through replica_num_diff, both add and replace should be sent to the edge devices
if "replica_num_diff" not in self.request_json or self.request_json["replica_num_diff"] is None:
return []
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index d1cc68dc98..13876d0184 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -9,7 +9,8 @@
from multiprocessing import Queue
import fedml
-from fedml.core.mlops import MLOpsRuntimeLog
+from fedml.core.mlops import MLOpsRuntimeLog, MLOpsConfigs
+from fedml.core.mlops.mlops_runtime_log import MLOpsFormatter
from .device_client_constants import ClientConstants
from .device_model_cache import FedMLModelCache
from .device_server_constants import ServerConstants
@@ -82,6 +83,7 @@ def run_impl(
self.replica_controller = FedMLDeviceReplicaController(self.edge_id, self.request_json)
# Start the process to report system performance(cpu,memory,etc.) to MLOps
+ # TODO(Raphael): This measurement is for the host machine. Change to container's metrics
self.mlops_metrics.report_sys_perf(self.args, self.agent_config["mqtt_config"], run_id=run_id)
# Check if we should stop the runner
@@ -140,7 +142,7 @@ def run_impl(
devices_sent_update_remove_msg = self.send_first_scroll_update_msg()
if len(devices_sent_add_or_remove_msg) == 0 and len(devices_sent_update_remove_msg) == 0:
- # No device is added or removed, and no device is updated or removed
+ # No device is added, updated or removed
logging.info("No device is added, updated or removed. No action needed for reconciliation.")
ip = GeneralConstants.get_ip_address(self.request_json)
master_port = os.getenv("FEDML_MASTER_PORT", None)
@@ -158,10 +160,20 @@ def run_impl(
message_center=self.message_center
)
+ # Set setting to "DEPLOYED" for autoscaling service reference
+ FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ update_user_setting_replica_num(end_point_id=run_id, state="DEPLOYED")
+
+ # Complete the job runner
self.trigger_completed_event()
+
return
except Exception as e:
- logging.info(f"Exception at run impl {traceback.format_exc()}")
+ logging.error(f"Failed to send first scroll update message due to {e}.")
+ logging.error(f"Exception traceback {traceback.format_exc()}.")
+
+ logging.info("Start waiting for result callback from workers ...")
self.deployment_result_queue = run_extend_queue_list[0]
while True:
@@ -210,10 +222,9 @@ def process_deployment_result_message(self, topic=None, payload=None):
filehandler = logging.FileHandler(log_file, "a")
program_prefix = "FedML-Server @device-id-{}".format(self.edge_id)
- formatter = logging.Formatter(fmt="[" + program_prefix + "] [%(asctime)s] [%(levelname)s] "
- "[%(filename)s:%(lineno)d:%(funcName)s] %("
- "message)s",
- datefmt="%a, %d %b %Y %H:%M:%S")
+ formatter = MLOpsFormatter(fmt="[" + program_prefix + "] [%(asctime)s] [%(levelname)s] "
+ "[%(filename)s:%(lineno)d:%(funcName)s] %("
+ "message)s")
filehandler.setFormatter(formatter)
root_logger.addHandler(filehandler)
@@ -270,15 +281,55 @@ def process_deployment_result_message(self, topic=None, payload=None):
if model_status != ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED:
logging.error(f"Unsupported model status {model_status}.")
- # Failure handler
- if run_operation == "ADD_OR_REMOVE":
- # TODO(Raphael): Also support rollback for scale out / in operation
+ # Avoid endless loop, if the rollback also failed, we should report the failure to the MLOps
+ if self.replica_controller.under_rollback:
self.send_deployment_status(
end_point_id, end_point_name, payload_json["model_name"], "",
ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
message_center=self.message_center)
return
- else:
+
+ # Failure handler, send the rollback message to the worker devices only if it has not been rollback
+ if run_operation == "ADD_OR_REMOVE":
+ # During Scale out / in,
+ # the worker that already been scaled out / in should be sent the rollback message
+ rollback_dict = self.replica_controller.rollback_add_or_remove_replica(
+ device_id=device_id, replica_no=replica_no, op_type=run_operation
+ )
+ self.replica_controller.under_rollback = True
+
+ if rollback_dict is not None and len(rollback_dict) > 0:
+ self.send_deployment_status(
+ end_point_id, end_point_name, payload_json["model_name"], "",
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTING,
+ message_center=self.message_center)
+ self.send_rollback_add_remove_op(run_id_str, rollback_dict)
+ return
+ else:
+ # This is the last worker that failed, so we should continue to "ABORTED" status
+ model_config_parameters = self.running_request_json[run_id_str]["parameters"]
+ inference_port = model_config_parameters.get("server_internal_port",
+ ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
+ inference_port_external = model_config_parameters.get("server_external_port", inference_port)
+ ip = GeneralConstants.get_ip_address(self.request_json)
+ if ip.startswith("http://") or ip.startswith("https://"):
+ model_inference_url = "{}/inference/{}".format(ip, end_point_id)
+ else:
+ model_inference_url = "http://{}:{}/inference/{}".format(ip, inference_port_external,
+ end_point_id)
+
+ self.send_deployment_status(
+ end_point_id, end_point_name, payload_json["model_name"], model_inference_url,
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTED, message_center=self.message_center)
+
+ # For auto-scaling, should update the state to "DEPLOYED"
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ update_user_setting_replica_num(end_point_id=end_point_id, state="DEPLOYED")
+
+ self.replica_controller.under_rollback = False
+
+ return
+ elif run_operation == "UPDATE":
# Overwrite the json with the rollback version diff
rollback_version_diff = self.replica_controller.rollback_get_replica_version_diff(
device_id_trigger=device_id, replica_no_trigger=replica_no)
@@ -325,9 +376,9 @@ def process_deployment_result_message(self, topic=None, payload=None):
# Wait for all replica-level's result, not device-level
if (self.replica_controller.is_all_replica_num_reconciled() and
self.replica_controller.is_all_replica_version_reconciled()):
- '''
+ """
When all the devices have finished the add / delete / update operation
- '''
+ """
# Generate one unified inference api
# Note that here we use the gateway port instead of the inference port that is used by the slave device
model_config_parameters = request_json["parameters"]
@@ -383,21 +434,31 @@ def process_deployment_result_message(self, topic=None, payload=None):
# Arrive here because only contains remove ops, so we do not need to update the model metadata
pass
+ # For auto-scaling, should update the state to "DEPLOYED"
FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_activation(end_point_id, end_point_name, True)
+ update_user_setting_replica_num(end_point_id=end_point_id, state="DEPLOYED")
if self.replica_controller.under_rollback:
- self.send_deployment_status(end_point_id, end_point_name,
- payload_json["model_name"],
- model_inference_url,
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTED)
+ # If first time failed (Still might need rollback), then send failed message to the MLOps
+ if not (FedMLModelCache.get_instance(self.redis_addr, self.redis_port).
+ get_end_point_activation(end_point_id)):
+ self.send_deployment_status(
+ end_point_id, end_point_name, payload_json["model_name"], "",
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED, message_center=self.message_center)
+ else:
+ self.send_deployment_status(
+ end_point_id, end_point_name, payload_json["model_name"], model_inference_url,
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTED, message_center=self.message_center)
+
self.replica_controller.under_rollback = False
else:
- self.send_deployment_status(end_point_id, end_point_name,
- payload_json["model_name"],
- model_inference_url,
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
- message_center=self.message_center)
+ # Set the end point activation status to True, for scaling out / in and rolling update
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
+ set_end_point_activation(end_point_id, end_point_name, True)
+
+ self.send_deployment_status(
+ end_point_id, end_point_name, payload_json["model_name"], model_inference_url,
+ ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED, message_center=self.message_center)
time.sleep(3)
self.trigger_completed_event()
@@ -457,6 +518,7 @@ def start_device_inference_monitor(
redis_addr=None, redis_port=None, redis_password=None
):
# start inference monitor server
+ # Will report the qps related metrics to the MLOps
logging.info(f"start the model inference monitor, end point {run_id}, model name {model_name}...")
run_id_str = str(run_id)
pip_source_dir = os.path.dirname(__file__)
@@ -503,8 +565,15 @@ def recover_inference_and_monitor(redis_addr=None, redis_port=None, redis_passwo
if not is_activated:
continue
+ agent_config = dict()
+ try:
+ agent_config["mqtt_config"], _, _, _ = MLOpsConfigs.fetch_all_configs()
+ except Exception as e:
+ pass
+
FedMLDeployMasterJobRunner.start_device_inference_gateway(
- run_id, end_point_name, model_id, model_name, model_version, inference_port=inference_port)
+ run_id, end_point_name, model_id, model_name, model_version, inference_port=inference_port,
+ agent_config=agent_config, redis_addr=redis_addr, redis_port=redis_port, redis_password=redis_password)
FedMLDeployMasterJobRunner.stop_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
@@ -614,6 +683,20 @@ def send_rollback_msg(self, run_id_str):
# send start deployment request to each device
self.send_deployment_start_request_to_edge(edge_id, self.request_json)
+ def send_rollback_add_remove_op(self, run_id, rollback_replica_dict):
+ """
+ This method is used when the original add op failed, we need to rollback by delete the existed replicas
+ Input example:
+ rollback_replica_dict = {'96684': {'curr_num': 2, 'op': 'remove', 'target_num': 1}}
+ """
+ existed_request_json = self.request_json
+ updated_request_json = copy.deepcopy(existed_request_json)
+
+ # Reverse the replica_num_diff
+ updated_request_json["replica_num_diff"] = rollback_replica_dict
+
+ self.send_deployment_start_request_to_edges(in_request_json=updated_request_json)
+
def delete_device_replica_info_on_master(self, endpoint_id, endpoint_name, model_name, edge_id_replica_no_dict):
FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
# Remove the record of the replaced device
@@ -635,7 +718,7 @@ def delete_device_replica_info_on_master(self, endpoint_id, endpoint_name, model
delete_item, endpoint_id, endpoint_name, model_name
)
- logging.info(f"Deleted the record of the replaced device {delete_device_result_list}")
+ logging.info(f"Deleted the replica record on master: {edge_id_replica_no_dict}")
def save_deployed_replica_payload(self, payload_json):
self.deployed_replica_payload = copy.deepcopy(payload_json)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index 962dcbbcb3..144d17fd02 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -105,7 +105,15 @@ def callback_delete_deployment(self, topic, payload):
# Parse payload as the model message object.
model_msg_object = FedMLModelMsgObject(topic, payload)
- # Set end point as deactivated status
+ # Delete SQLite records
+ FedMLServerDataInterface.get_instance().delete_job_from_db(model_msg_object.run_id)
+ FedMLModelDatabase.get_instance().delete_deployment_result(
+ model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_name,
+ model_version=model_msg_object.model_version)
+ FedMLModelDatabase.get_instance().delete_deployment_run_info(
+ end_point_id=model_msg_object.inference_end_point_id)
+
+ # Delete Redis Records
FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
set_end_point_activation(model_msg_object.inference_end_point_id,
@@ -114,22 +122,16 @@ def callback_delete_deployment(self, topic, payload):
delete_end_point(model_msg_object.inference_end_point_id, model_msg_object.end_point_name,
model_msg_object.model_name, model_msg_object.model_version)
+ # Send delete deployment request to the edge devices
FedMLDeployJobRunnerManager.get_instance().send_deployment_delete_request_to_edges(
model_msg_object.run_id, payload, model_msg_object, message_center=self.message_center)
+ # Stop processes on master
FedMLDeployJobRunnerManager.get_instance().stop_job_runner(model_msg_object.run_id)
-
FedMLDeployJobRunnerManager.get_instance().stop_device_inference_monitor(
model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_id,
model_msg_object.model_name, model_msg_object.model_version)
- FedMLServerDataInterface.get_instance().delete_job_from_db(model_msg_object.run_id)
- FedMLModelDatabase.get_instance().delete_deployment_result(
- model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_name,
- model_version=model_msg_object.model_version)
- FedMLModelDatabase.get_instance().delete_deployment_run_info(
- end_point_id=model_msg_object.inference_end_point_id)
-
def callback_start_deployment(self, topic, payload):
# noinspection PyBroadException
try:
@@ -149,15 +151,36 @@ def callback_start_deployment(self, topic, payload):
model_config = request_json["model_config"]
model_name = model_config["model_name"]
+ model_version = model_config["model_version"]
model_id = model_config["model_id"]
model_storage_url = model_config["model_storage_url"]
scale_min = model_config.get("instance_scale_min", 0)
scale_max = model_config.get("instance_scale_max", 0)
inference_engine = model_config.get("inference_engine", 0)
+ enable_auto_scaling = request_json.get("enable_auto_scaling", False)
+ desired_replica_num = request_json.get("desired_replica_num", 1)
+
+ target_queries_per_replica = request_json.get("target_queries_per_replica", 10)
+ aggregation_window_size_seconds = request_json.get("aggregation_window_size_seconds", 60)
+ scale_down_delay_seconds = request_json.get("scale_down_delay_seconds", 120)
+
inference_end_point_id = run_id
logging.info("[Master] received start deployment request for end point {}.".format(run_id))
+ # Set redis config
+ FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+
+ # Save the user setting (about replica number) of this run to Redis, if existed, update it
+ FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_user_setting_replica_num(
+ end_point_id=run_id, end_point_name=end_point_name, model_name=model_name, model_version=model_version,
+ replica_num=desired_replica_num, enable_auto_scaling=enable_auto_scaling,
+ scale_min=scale_min, scale_max=scale_max, state="DEPLOYING",
+ aggregation_window_size_seconds=aggregation_window_size_seconds,
+ target_queries_per_replica=target_queries_per_replica,
+ scale_down_delay_seconds=int(scale_down_delay_seconds)
+ )
+
# Start log processor for current run
self.args.run_id = run_id
self.args.edge_id = self.edge_id
@@ -176,8 +199,7 @@ def callback_start_deployment(self, topic, payload):
self.running_request_json[run_id_str] = request_json
self.request_json["master_node_ip"] = GeneralConstants.get_ip_address(self.request_json)
- # Target status of the devices
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+ # Set the target status of the devices to redis
FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
set_end_point_device_info(request_json["end_point_id"], end_point_name, json.dumps(device_objs))
@@ -194,7 +216,7 @@ def callback_start_deployment(self, topic, payload):
# Report stage to mlops: MODEL_DEPLOYMENT_STAGE1 = "Received"
FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
self.run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE1["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE1["text"], "Received request for end point {}".format(run_id),
+ ServerConstants.MODEL_DEPLOYMENT_STAGE1["text"], "Received request for endpoint {}".format(run_id),
message_center=self.message_center)
# Report stage to mlops: MODEL_DEPLOYMENT_STAGE2 = "Initializing"
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index 78e2527e0c..d1cfd3b83c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -77,6 +77,18 @@ def get_model_bin_file(unzip_package_full_path):
def update_local_fedml_config(self, run_id, model_config, model_config_parameters=None):
model_name = model_config["model_name"]
model_storage_url = model_config["model_storage_url"]
+ end_point_name = self.request_json["end_point_name"]
+ model_version = model_config["model_version"]
+
+ # Generate the model package dir for downloading.
+ model_version = model_version.replace(" ", "-") # Avoid using space for folder name
+ model_version = model_version.replace(":", "-") # Since docker mount will conflict with ":"
+ local_package_path = ClientConstants.get_model_package_dir()
+ os.makedirs(local_package_path, exist_ok=True)
+ this_run_model_dir = f"{run_id}_{end_point_name}_{model_name}_{model_version}"
+ this_run_model_full_path = os.path.join(local_package_path, this_run_model_dir)
+ self.agent_package_download_dir = this_run_model_full_path
+ self.agent_package_unzip_dir = this_run_model_full_path
# Retrieve model package or model binary file.
if self.model_is_from_open:
@@ -127,7 +139,6 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT)
inference_end_point_id = run_id
- self.mlops_metrics.report_sys_perf(self.args, self.agent_config["mqtt_config"], run_id=run_id)
MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
logging.info(f"[Worker] Received model deployment request from master for endpoint {run_id}.")
@@ -250,7 +261,6 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
if op == "add":
worker_ip = GeneralConstants.get_ip_address(self.request_json)
for rank in range(prev_rank + 1, prev_rank + 1 + op_num):
- # TODO: Support Rollback if this for loop failed
try:
running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
start_deployment(
@@ -392,15 +402,17 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
if inference_output_url == "":
logging.error("Failed to deploy the model...")
- # If update failed, should release this replica's gpu
+ # Release the gpu occupancy
FedMLModelCache.get_instance().set_redis_params()
replica_occupied_gpu_ids_str = FedMLModelCache.get_instance().get_replica_gpu_ids(
run_id, end_point_name, model_name, self.edge_id, rank + 1)
+ logging.info(f"Release gpu ids {replica_occupied_gpu_ids_str} for "
+ f"failed deployment of replica no {rank + 1}.")
- replica_occupied_gpu_ids = json.loads(replica_occupied_gpu_ids_str)
-
- JobRunnerUtils.get_instance().release_partial_job_gpu(
- run_id, self.edge_id, replica_occupied_gpu_ids)
+ if replica_occupied_gpu_ids_str is not None:
+ replica_occupied_gpu_ids = json.loads(replica_occupied_gpu_ids_str)
+ JobRunnerUtils.get_instance().release_partial_job_gpu(
+ run_id, self.edge_id, replica_occupied_gpu_ids)
result_payload = self.send_deployment_results(
end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
index 43bb3c4582..5f4835d9aa 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
@@ -15,6 +15,8 @@
from ..slave.base_slave_protocol_manager import FedMLBaseSlaveProtocolManager
from .worker_job_runner_manager import FedMLDeployJobRunnerManager
from .device_mqtt_inference_protocol import FedMLMqttInference
+from ..scheduler_core.compute_cache_manager import ComputeCacheManager
+from .device_model_cache import FedMLModelCache
class FedMLDeployWorkerProtocolManager(FedMLBaseSlaveProtocolManager):
@@ -141,7 +143,7 @@ def callback_start_deployment(self, topic, payload):
run_id = inference_end_point_id
self.args.run_id = run_id
self.args.edge_id = self.edge_id
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+ MLOpsRuntimeLog(args=self.args).init_logs()
MLOpsRuntimeLogDaemon.get_instance(self.args).set_log_source(
ClientConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT)
MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(run_id, self.edge_id)
@@ -193,3 +195,23 @@ def callback_delete_deployment(self, topic, payload):
FedMLModelDatabase.get_instance().delete_deployment_result_with_device_id(
model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_name,
self.edge_id)
+
+ # Delete FEDML_GLOBAL_ENDPOINT_RUN_ID_MAP_TAG-${run_id} both in redis and local db
+ ComputeCacheManager.get_instance().gpu_cache.delete_endpoint_run_id_map(str(model_msg_object.run_id))
+
+ # Delete FEDML_EDGE_ID_MODEL_DEVICE_ID_MAP_TAG-${run_id} both in redis and local db
+ ComputeCacheManager.get_instance().gpu_cache.delete_edge_model_id_map(str(model_msg_object.run_id))
+
+ # Delete FEDML_GLOBAL_DEVICE_RUN_GPU_IDS_TAG-${run_id}-${device_id} both in redis and local db
+ ComputeCacheManager.get_instance().gpu_cache.delete_device_run_gpu_ids(str(self.edge_id),
+ str(model_msg_object.run_id))
+
+ # Delete FEDML_GLOBAL_DEVICE_RUN_NUM_GPUS_TAG-${run_id}-${device_id} both in redis and local db
+ ComputeCacheManager.get_instance().gpu_cache.delete_device_run_num_gpus(str(self.edge_id),
+ str(model_msg_object.run_id))
+
+ # Delete FEDML_MODEL_REPLICA_GPU_IDS_TAG-${run_id}-${end_point_name}-${model_name}-${device_id}-*
+ FedMLModelCache.get_instance().set_redis_params()
+ FedMLModelCache.get_instance().delete_all_replica_gpu_ids(model_msg_object.run_id,
+ model_msg_object.end_point_name,
+ model_msg_object.model_name, self.edge_id)
diff --git a/python/fedml/computing/scheduler/scheduler_core/general_constants.py b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
index e642cacf1b..ba8842b30e 100755
--- a/python/fedml/computing/scheduler/scheduler_core/general_constants.py
+++ b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
@@ -51,6 +51,8 @@ class GeneralConstants:
FEDML_OTA_CMD_UPGRADE = "upgrade"
FEDML_OTA_CMD_RESTART = "restart"
+ FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT = "MODEL_END_POINT"
+
@staticmethod
def get_package_unzip_dir(package_download_dir):
package_unzip_dir = package_download_dir
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_center.py b/python/fedml/computing/scheduler/scheduler_core/message_center.py
index dcf21d33b7..869ed6e510 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_center.py
@@ -218,7 +218,7 @@ def run_sender(self, message_event, message_queue, message_center_name):
message_body = None
if message_body is None:
time.sleep(0.1)
- self.retry_sending_undelivered_message()
+ # self.retry_sending_undelivered_message()
continue
# Generate the message entity object
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 46f1e7ff8f..0b4d47d52c 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -2,11 +2,11 @@
import logging
import os
import platform
+import random
import shutil
+import time
import traceback
-import urllib
import zipfile
-from urllib.parse import urljoin, urlparse
from ..comm_utils.constants import SchedulerConstants
from ..comm_utils.job_utils import JobRunnerUtils, DockerArgs
from ..scheduler_entry.constants import Constants
@@ -82,6 +82,8 @@ def __init__(self, args, edge_id=0, request_json=None, agent_config=None, run_id
"${FEDSYS.CLIENT_OBJECT_LIST}": "",
"${FEDSYS.LOG_SERVER_URL}": "",
}
+ self.download_time = time.time()
+ self.download_finished = False
def __repr__(self):
return "<{klass} @{id:x} {attrs}>".format(
@@ -154,18 +156,91 @@ def package_download_progress(self, count, blksize, filesize):
self.prev_download_progress = progress_int
logging.info("package downloaded size {} KB, progress {}%".format(downloaded_kb, progress_int))
+ def download_package_proc(self, package_url, local_package_file):
+ import requests
+ headers = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
+ 'AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36'}
+ user_agent_list = [
+ 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.1 Safari/605.1.15',
+ 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:77.0) Gecko/20100101 Firefox/77.0',
+ 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36',
+ 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:77.0) Gecko/20100101 Firefox/77.0',
+ 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36',
+ ]
+ for _ in user_agent_list:
+ user_agent = random.choice(user_agent_list)
+ headers = {'User-Agent': user_agent}
+
+ # Set the stream to true so that we can reduce the memory footprint when downloading large files.
+ request = requests.get(package_url, headers=headers, timeout=(10, 15), stream=True)
+ with open(local_package_file, 'wb') as f:
+ # 1024 * 1024 is 1MiB
+ download_size = 1024 * 1024
+ total_size = 0
+ for chunk in request.iter_content(download_size):
+ # Write the chunk to the file
+ written_size = f.write(chunk)
+ total_size += written_size
+ logging.info(f"package downloaded size {total_size/1024} KB")
+ self.download_time = time.time()
+ self.download_finished = True
+
def retrieve_and_unzip_package(self, package_name, package_url):
local_package_path = self.agent_package_download_dir
os.makedirs(local_package_path, exist_ok=True)
filename, filename_without_extension, file_extension = GeneralConstants.get_filename_and_extension(package_url)
- local_package_file = os.path.join(local_package_path, f"fedml_run_{self.run_id}_{filename_without_extension}")
+ local_package_file = os.path.join(
+ local_package_path, f"fedml_run_{self.run_id}_{self.edge_id}_{filename_without_extension}")
if os.path.exists(local_package_file):
os.remove(local_package_file)
ssl._create_default_https_context = ssl._create_unverified_context
- urllib.request.urlretrieve(package_url, local_package_file,
- reporthook=self.package_download_progress)
+
+ # Open a process to download the package so that we can avoid the request is blocked and check the timeout.
+ self.download_finished = False
+ self.download_time = time.time()
+ from multiprocessing import Process
+ download_process = Process(target=self.download_package_proc, args=(package_url, local_package_file))
+ download_process.start()
+ allowed_block_download_time = 30
+ while True:
+ block_time = time.time() - self.download_time
+ if block_time > allowed_block_download_time:
+ break
+ if self.download_finished:
+ break
+ time.sleep(3)
+ try:
+ if not self.download_finished:
+ download_process.terminate()
+ download_process.kill()
+ except Exception as e:
+ pass
+
+ # Another method to async download.
+ # import socket
+ # socket.setdefaulttimeout(15)
+ # try:
+ # urllib.request.urlretrieve(package_url, local_package_file,
+ # reporthook=self.package_download_progress)
+ # except socket.timeout:
+ # retry_count = 1
+ # max_retry_num = 5
+ # while retry_count <= max_retry_num:
+ # try:
+ # urllib.request.urlretrieve(package_url, local_package_file,
+ # reporthook=self.package_download_progress)
+ # break
+ # except socket.timeout:
+ # error_info = 'Retry %d time' % retry_count if retry_count == 1 else \
+ # 'Reloading for %d times' % retry_count
+ # logging.info(error_info)
+ # retry_count += 1
+ # if retry_count > max_retry_num:
+ # logging.error("Download failed.")
+ # raise Exception("Download failed")
+
unzip_package_path = os.path.join(self.agent_package_unzip_dir,
- f"unzip_fedml_run_{self.run_id}_{filename_without_extension}")
+ f"unzip_fedml_run_{self.run_id}_{self.edge_id}_{filename_without_extension}")
try:
shutil.rmtree(unzip_package_path, ignore_errors=True)
except Exception as e:
@@ -485,7 +560,7 @@ def callback_start_fl_job(self, job_pid):
def start_job_perf(self, job_pid):
GeneralConstants.save_learning_process(self.run_id, job_pid, data_dir=self.agent_data_dir)
- self.mlops_metrics.report_job_perf(self.args, self.agent_config["mqtt_config"], job_pid)
+ #self.mlops_metrics.report_job_perf(self.args, self.agent_config["mqtt_config"], job_pid)
def job_error_processor(self, error_list):
self.check_runner_stop_event()
@@ -516,10 +591,10 @@ def cleanup_containers_and_release_gpus(run_id, edge_id):
job_type == SchedulerConstants.JOB_TASK_TYPE_DEPLOY):
# Terminate the run docker container if exists
- container_name = JobRunnerUtils.get_run_container_name(run_id)
- docker_client = JobRunnerUtils.get_docker_client(DockerArgs())
- logging.info(f"Terminating the run docker container {container_name} if exists...")
try:
+ container_name = JobRunnerUtils.get_run_container_name(run_id)
+ docker_client = JobRunnerUtils.get_docker_client(DockerArgs())
+ logging.info(f"Terminating the run docker container {container_name} if exists...")
JobRunnerUtils.remove_run_container_if_exists(container_name, docker_client)
except Exception as e:
logging.error(f"Exception {e} occurred when terminating docker container. "
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
index 58198b6661..77768da6c0 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
@@ -44,10 +44,9 @@ def complete_job_runner(self, run_id):
if self.job_runners.get(run_id_str, None) is not None:
self.job_runners[run_id_str].trigger_completed_event()
- def put_run_edge_device_info_to_queue(self, run_id, device_info):
- run_id_str = str(run_id)
- if self.job_runners.get(run_id_str, None) is not None:
- self.job_runners[run_id_str].put_run_edge_device_info_to_queue(run_id, device_info)
+ def put_run_edge_device_info_to_queue(self, run_id, edge_id, device_info):
+ for job_run_id, job_runner in self.job_runners.items():
+ job_runner.put_run_edge_device_info_to_queue(run_id, edge_id, device_info)
def get_runner_process(self, run_id, is_cloud_server=False):
run_id_str = str(run_id)
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index 569f4d9257..76f811993e 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -373,10 +373,10 @@ def run_status_dispatcher_in_slave(self, status_event, status_queue,
# Async request the job status from master when launching the job
job_launch_message_map[status_entity.run_id] = {"topic": message_entity.topic,
"payload": message_entity.payload}
- status_manager_instances[status_entity.run_id]. \
- status_center_request_job_status_from_master_in_slave_agent(
- message_entity.topic, message_entity.payload
- )
+ # status_manager_instances[status_entity.run_id]. \
+ # status_center_request_job_status_from_master_in_slave_agent(
+ # message_entity.topic, message_entity.payload
+ # )
elif (message_entity.topic.startswith(FedMLStatusCenter.TOPIC_SLAVE_JOB_STOP_PREFIX) and
message_entity.topic.endswith(FedMLStatusCenter.TOPIC_SLAVE_JOB_STOP_SUFFIX)):
# Cleanup when stopped the job
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index 4d2cf3a5ed..871b9026bf 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -78,7 +78,7 @@ def process_job_exception_status(self, master_id, status):
def process_job_running_status(self, master_id, status):
self.message_reporter.report_server_training_status(
- self.run_id, status, edge_id=master_id, running_json=self.running_scheduler_contract)
+ self.run_id, status, edge_id=master_id, running_json=self.running_scheduler_contract, update_db=False)
def status_center_process_master_status(self, topic, payload):
request_json = json.loads(payload)
@@ -121,13 +121,13 @@ def process_job_status_consensus(self, run_id, master_id, status):
status, edge_status_item)
if consensus_device_status is not None:
self.message_reporter.report_client_training_status(
- edge_id_item, consensus_device_status, run_id=run_id)
+ edge_id_item, consensus_device_status, run_id=run_id, update_db=False)
# Save the job status to local storage
FedMLServerDataInterface.get_instance().save_job_status(run_id, master_id, status, status)
# Report the status to message center
- self.message_reporter.report_server_training_status(run_id, status, edge_id=master_id)
+ self.message_reporter.report_server_training_status(run_id, status, edge_id=master_id, update_db=False)
# Broadcast the status to slave agents
self.message_reporter.report_job_status(run_id, status)
@@ -207,7 +207,7 @@ def process_device_status(self, run_id, edge_id, status):
# Report client status
consensus_status = self.get_device_consensus_status_in_current_device(edge_id, status)
- self.message_reporter.report_client_training_status(edge_id, consensus_status, run_id=run_id)
+ self.message_reporter.report_client_training_status(edge_id, consensus_status, run_id=run_id, update_db=False)
# Report server status based on the fault tolerance model and parameters
edge_nums = len(edge_id_status_dict.keys()) - 1
@@ -263,7 +263,7 @@ def parse_fault_tolerance_params(self, run_id):
def report_server_status(self, run_id, edge_id, server_id, status):
self.status_reporter.report_server_id_status(
- run_id, status, edge_id=edge_id, server_id=server_id, server_agent_id=edge_id)
+ run_id, status, edge_id=edge_id, server_id=server_id, server_agent_id=edge_id, update_db=False)
def report_exception_status(
self, edge_id_list, run_id=0, server_id=None, status=None, payload=None):
@@ -282,7 +282,7 @@ def status_center_process_slave_status_to_master_in_slave_agent(self, topic, pay
self.message_center.send_message(topic, payload)
# Post the status message to the listener queue of message center
- self.message_center.receive_message(GeneralConstants.MSG_TOPIC_REPORT_DEVICE_STATUS_IN_JOB, payload)
+ #self.message_center.receive_message(GeneralConstants.MSG_TOPIC_REPORT_DEVICE_STATUS_IN_JOB, payload)
def status_center_process_slave_status_to_mlops_in_slave_agent(self, topic, payload):
# Forward the status message to message center.
diff --git a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
index 4448dd49fa..de2956ad94 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
@@ -1,3 +1,4 @@
+import json
import logging
import multiprocessing
import os
@@ -104,7 +105,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
self.status_reporter.report_client_id_status(
self.edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_INITIALIZING,
- running_json=self.start_request_json, run_id=run_id)
+ running_json=json.dumps(self.request_json), run_id=run_id)
# get training params
private_local_data_dir = data_config.get("privateLocalData", "")
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index 514aa98cd7..b3cd154d23 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -240,7 +240,7 @@ def callback_start_train(self, topic, payload):
# Print the payload
logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
+ f"FedMLDebug - run id {run_id}, Receive at callback_start_train: topic ({topic}), payload ({payload})"
)
# Occupy GPUs
@@ -418,6 +418,7 @@ def callback_response_device_status_in_job(self, topic, payload):
edge_id = payload_json.get("edge_id", None)
# process the status
+ logging.info("process status in the device status callback.")
self.process_status(run_id, job_status, edge_id)
def callback_response_job_status(self, topic, payload):
@@ -430,6 +431,7 @@ def callback_response_job_status(self, topic, payload):
edge_id = payload_json.get("edge_id", None)
# process the status
+ logging.info("process status in the job status callback.")
self.process_status(run_id, job_status, edge_id)
def callback_broadcasted_job_status(self, topic, payload):
@@ -439,6 +441,7 @@ def callback_broadcasted_job_status(self, topic, payload):
job_status = payload_json.get("status", None)
# process the status
+ logging.info("process status in the broadcast job status callback.")
self.process_status(run_id, job_status, self.edge_id)
def generate_protocol_manager(self):
diff --git a/python/fedml/computing/scheduler/slave/client_data_interface.py b/python/fedml/computing/scheduler/slave/client_data_interface.py
index 34a7b89bd2..0e9e84381a 100755
--- a/python/fedml/computing/scheduler/slave/client_data_interface.py
+++ b/python/fedml/computing/scheduler/slave/client_data_interface.py
@@ -143,7 +143,7 @@ def create_job_table(self):
updated_time TEXT,
round_index INT,
total_rounds INT,
- running_json TEXT);''')
+ running_json TEXT NULL);''')
self.db_connection.commit()
except Exception as e:
pass
@@ -405,14 +405,14 @@ class FedMLClientJobModel(object):
def __init__(self):
self.job_id = 0
self.edge_id = 0
- self.started_time = ""
- self.ended_time = ""
- self.progress = 0
- self.eta = 0
- self.failed_time = ""
+ self.started_time = "0"
+ self.ended_time = "0"
+ self.progress = 0.0
+ self.eta = 0.0
+ self.failed_time = "0"
self.error_code = -1
self.msg = ""
- self.updated_time = ""
+ self.updated_time = "0"
self.round_index = 0
self.total_rounds = 0
self.status = ""
diff --git a/python/fedml/core/distributed/communication/mqtt/mqtt_manager.py b/python/fedml/core/distributed/communication/mqtt/mqtt_manager.py
index bdafe159c2..937e9f6644 100644
--- a/python/fedml/core/distributed/communication/mqtt/mqtt_manager.py
+++ b/python/fedml/core/distributed/communication/mqtt/mqtt_manager.py
@@ -14,7 +14,7 @@
class MqttManager(object):
def __init__(self, host, port, user, pwd, keepalive_time,
client_id, last_will_topic=None, last_will_msg=None,
- clean_session=True, retain_msg=False):
+ clean_session=True, retain_msg=True):
self._client = None
self.mqtt_connection_id = None
self._host = host
diff --git a/python/fedml/core/mlops/mlops_metrics.py b/python/fedml/core/mlops/mlops_metrics.py
index e0855c74b5..afa96f6870 100644
--- a/python/fedml/core/mlops/mlops_metrics.py
+++ b/python/fedml/core/mlops/mlops_metrics.py
@@ -67,15 +67,17 @@ def comm_sanity_check(self):
else:
return True
- def report_client_training_status(self, edge_id, status, running_json=None, is_from_model=False, run_id=0):
+ def report_client_training_status(self, edge_id, status, running_json=None,
+ is_from_model=False, run_id=0, update_db=True):
self.common_report_client_training_status(edge_id, status, run_id=run_id)
- if is_from_model:
- from ...computing.scheduler.model_scheduler.device_client_data_interface import FedMLClientDataInterface
- FedMLClientDataInterface.get_instance().save_job(run_id, edge_id, status, running_json)
- else:
- from ...computing.scheduler.slave.client_data_interface import FedMLClientDataInterface
- FedMLClientDataInterface.get_instance().save_job(run_id, edge_id, status, running_json)
+ if update_db:
+ if is_from_model:
+ from ...computing.scheduler.model_scheduler.device_client_data_interface import FedMLClientDataInterface
+ FedMLClientDataInterface.get_instance().save_job(run_id, edge_id, status, running_json)
+ else:
+ from ...computing.scheduler.slave.client_data_interface import FedMLClientDataInterface
+ FedMLClientDataInterface.get_instance().save_job(run_id, edge_id, status, running_json)
def report_client_device_status_to_web_ui(self, edge_id, status, run_id=0):
"""
@@ -169,20 +171,22 @@ def common_report_client_id_status(self, run_id, edge_id, status, server_id="0",
# logging.info("report_client_id_status. message_json = %s" % message_json)
self.send_message(topic_name, message_json)
- def report_server_training_status(self, run_id, status, edge_id=0, role=None, running_json=None, is_from_model=False):
+ def report_server_training_status(self, run_id, status, edge_id=0, role=None,
+ running_json=None, is_from_model=False, update_db=True):
# if not self.comm_sanity_check():
# return
self.common_report_server_training_status(run_id, status, role=role, edge_id=edge_id)
- if is_from_model:
- from ...computing.scheduler.model_scheduler.device_server_data_interface import FedMLServerDataInterface
- FedMLServerDataInterface.get_instance().save_job(run_id, self.edge_id, status, running_json)
- else:
- from ...computing.scheduler.master.server_data_interface import FedMLServerDataInterface
- FedMLServerDataInterface.get_instance().save_job(run_id, self.edge_id, status, running_json)
+ if update_db:
+ if is_from_model:
+ from ...computing.scheduler.model_scheduler.device_server_data_interface import FedMLServerDataInterface
+ FedMLServerDataInterface.get_instance().save_job(run_id, self.edge_id, status, running_json)
+ else:
+ from ...computing.scheduler.master.server_data_interface import FedMLServerDataInterface
+ FedMLServerDataInterface.get_instance().save_job(run_id, self.edge_id, status, running_json)
def report_job_status(self, run_id, status):
- topic_name = "master_agent/slave_agent/job_status"
+ topic_name = f"master_agent/slave_agent/job_status/{run_id}"
payload = {"run_id": run_id, "status": status}
message_json = json.dumps(payload)
@@ -251,7 +255,7 @@ def broadcast_server_training_status(self, run_id, status, role=None, is_from_mo
FedMLServerDataInterface.get_instance().save_job(run_id, self.edge_id, status)
def report_server_id_status(self, run_id, status, edge_id=None, server_id=None, server_agent_id=None,
- is_from_model=False, running_json=None):
+ is_from_model=False, running_json=None, update_db=True):
# if not self.comm_sanity_check():
# return
topic_name = "fl_server/flserver_agent_" + str(server_agent_id if server_agent_id is not None else
@@ -267,12 +271,13 @@ def report_server_id_status(self, run_id, status, edge_id=None, server_id=None,
# logging.info("report_server_id_status. message_json = %s" % message_json)
self.send_message(topic_name, message_json)
- if is_from_model:
- from ...computing.scheduler.model_scheduler.device_server_data_interface import FedMLServerDataInterface
- FedMLServerDataInterface.get_instance().save_job(run_id, in_edge_id, status, running_json)
- else:
- from ...computing.scheduler.master.server_data_interface import FedMLServerDataInterface
- FedMLServerDataInterface.get_instance().save_job(run_id, in_edge_id, status, running_json)
+ if update_db:
+ if is_from_model:
+ from ...computing.scheduler.model_scheduler.device_server_data_interface import FedMLServerDataInterface
+ FedMLServerDataInterface.get_instance().save_job(run_id, in_edge_id, status, running_json)
+ else:
+ from ...computing.scheduler.master.server_data_interface import FedMLServerDataInterface
+ FedMLServerDataInterface.get_instance().save_job(run_id, in_edge_id, status, running_json)
def report_client_training_metric(self, metric_json):
# if not self.comm_sanity_check():
From 6b987edd4ed0f421cdd1dbc872b5fa89fe736313 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Sat, 11 May 2024 14:51:15 -0700
Subject: [PATCH 028/282] Add GPU Type Registry
---
.../comm_utils/gpu_utils/gpu_utils.py | 17 +++++++++++++--
.../scheduler/comm_utils/hardware_utils.py | 21 ++++++++-----------
2 files changed, 24 insertions(+), 14 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
index e6691b4b5d..2fc5cf619b 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
@@ -29,9 +29,23 @@ class GPUCard:
temperature: Optional[float]
-class GPUCardUtil(ABC):
+class GPUTypeRegistry(type, ABC):
+ GPU_TYPE_REGISTRY = {}
+
+ def __new__(cls, name, bases, attrs):
+ new_cls = type.__new__(cls, name, bases, attrs)
+ cls.GPU_TYPE_REGISTRY[new_cls.__name__.lower()] = new_cls
+ return new_cls
@classmethod
+ def get_gpu_utils(cls):
+ return cls.GPU_TYPE_REGISTRY.values()
+
+
+class GPUCardUtil(metaclass=GPUTypeRegistry):
+
+ @classmethod
+ @abstractmethod
def detectGPUCardType(cls) -> Optional[GPUCardType]:
raise NotImplementedError
@@ -44,4 +58,3 @@ def getAvailableGPUCardIDs() -> List[int]:
@abstractmethod
def getGPUCards() -> List[GPUCard]:
raise NotImplementedError
-
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index d26fb9c5b5..d12effa826 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -2,22 +2,19 @@
from typing import Optional, List
-from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCardUtil, GPUCard
-from fedml.computing.scheduler.comm_utils.gpu_utils.nvidia_utils import NvidiaGPUtil
+from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCardUtil, GPUCard, GPUTypeRegistry
from fedml.computing.scheduler.comm_utils.singleton import Singleton
class HardwareUtil(metaclass=Singleton):
-
- _gpu_utils = [NvidiaGPUtil]
- _gpu_util: Optional[GPUCardUtil] = None
+ __gpu_util: Optional[GPUCardUtil] = None
@staticmethod
- def _get_util() -> Optional[GPUCardUtil]:
- if HardwareUtil._gpu_util is not None:
- return HardwareUtil._gpu_util
+ def __get_util() -> Optional[GPUCardUtil]:
+ if HardwareUtil.__gpu_util is not None:
+ return HardwareUtil.__gpu_util
- for gpu_util in HardwareUtil._gpu_utils:
+ for gpu_util in GPUTypeRegistry.get_gpu_utils():
try:
if gpu_util.detectGPUCardType() is not None:
HardwareUtil._gpu_util = gpu_util()
@@ -30,13 +27,13 @@ def _get_util() -> Optional[GPUCardUtil]:
@staticmethod
def getGPUs() -> List[GPUCard]:
- gpu_util = HardwareUtil._get_util()
+ gpu_util = HardwareUtil.__get_util()
return gpu_util.getGPUCards() if gpu_util is not None else []
@staticmethod
def getAvailableGPUCardIDs() -> List[int]:
- gpu_util = HardwareUtil._get_util()
- return gpu_util.getAvailainfbleGPUCardIDs() if gpu_util is not None else []
+ gpu_util = HardwareUtil.__get_util()
+ return gpu_util.getAvailableGPUCardIDs() if gpu_util is not None else []
if __name__ == "__main__":
From 436233b752740ddae7304d1581f030baf8536173 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Sat, 11 May 2024 15:11:13 -0700
Subject: [PATCH 029/282] Rolling back GPU Registry change
---
.../computing/scheduler/comm_utils/__init__.py | 3 ---
.../scheduler/comm_utils/gpu_utils/gpu_utils.py | 17 ++---------------
.../scheduler/comm_utils/hardware_utils.py | 8 +++++---
3 files changed, 7 insertions(+), 21 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/__init__.py b/python/fedml/computing/scheduler/comm_utils/__init__.py
index adf0269b67..e69de29bb2 100644
--- a/python/fedml/computing/scheduler/comm_utils/__init__.py
+++ b/python/fedml/computing/scheduler/comm_utils/__init__.py
@@ -1,3 +0,0 @@
-import gpu_utils.gpu_utils
-import gpu_utils.qualcomm_utils
-import gpu_utils.nvidia_utils
\ No newline at end of file
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
index 2fc5cf619b..ced1c53d3e 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
@@ -1,4 +1,4 @@
-from abc import ABC, abstractmethod, ABCMeta
+from abc import ABC, abstractmethod
from dataclasses import dataclass
from enum import Enum, auto
from typing import Optional, List
@@ -29,20 +29,7 @@ class GPUCard:
temperature: Optional[float]
-class GPUTypeRegistry(type, ABC):
- GPU_TYPE_REGISTRY = {}
-
- def __new__(cls, name, bases, attrs):
- new_cls = type.__new__(cls, name, bases, attrs)
- cls.GPU_TYPE_REGISTRY[new_cls.__name__.lower()] = new_cls
- return new_cls
-
- @classmethod
- def get_gpu_utils(cls):
- return cls.GPU_TYPE_REGISTRY.values()
-
-
-class GPUCardUtil(metaclass=GPUTypeRegistry):
+class GPUCardUtil(ABC):
@classmethod
@abstractmethod
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index d12effa826..c468e2181c 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -1,10 +1,12 @@
import logging
-
from typing import Optional, List
-from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCardUtil, GPUCard, GPUTypeRegistry
+from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCardUtil, GPUCard
+from fedml.computing.scheduler.comm_utils.gpu_utils.nvidia_utils import NvidiaGPUtil
from fedml.computing.scheduler.comm_utils.singleton import Singleton
+GPU_CARD_UTILS = [NvidiaGPUtil]
+
class HardwareUtil(metaclass=Singleton):
__gpu_util: Optional[GPUCardUtil] = None
@@ -14,7 +16,7 @@ def __get_util() -> Optional[GPUCardUtil]:
if HardwareUtil.__gpu_util is not None:
return HardwareUtil.__gpu_util
- for gpu_util in GPUTypeRegistry.get_gpu_utils():
+ for gpu_util in GPU_CARD_UTILS:
try:
if gpu_util.detectGPUCardType() is not None:
HardwareUtil._gpu_util = gpu_util()
From 502c031e3838526de681eb2cf062789023659c08 Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Sun, 12 May 2024 02:21:38 +0000
Subject: [PATCH 030/282] Qualcomm Util -> get gpus
---
.../comm_utils/gpu_utils/gpu_utils.py | 26 ++++----
.../comm_utils/gpu_utils/nvidia_utils.py | 45 +++++++-------
.../comm_utils/gpu_utils/qualcomm_utils.py | 60 +++++++++++++++++++
.../scheduler/comm_utils/hardware_utils.py | 29 ++++-----
4 files changed, 112 insertions(+), 48 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
index ced1c53d3e..2731e51a3b 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
@@ -1,5 +1,5 @@
from abc import ABC, abstractmethod
-from dataclasses import dataclass
+from dataclasses import dataclass, field
from enum import Enum, auto
from typing import Optional, List
@@ -16,32 +16,34 @@ def __str__(self):
@dataclass
class GPUCard:
id: int
- uuid: str
name: str
- load: float
+ driver: str
+ serial: str
memoryTotal: float
- memoryUsed: float
memoryFree: float
- driver: str
- serial: Optional[str]
- display_mode: Optional[str]
- display_active: Optional[str]
- temperature: Optional[float]
+ memoryUsed: float
+ memoryUtil: float
+ load: Optional[float] = 0.0
+ uuid: Optional[str] = ""
+ display_mode: Optional[str] = ""
+ display_active: Optional[str] = ""
+ temperature: Optional[float] = 0.0
class GPUCardUtil(ABC):
@classmethod
@abstractmethod
- def detectGPUCardType(cls) -> Optional[GPUCardType]:
+ def detect_gpu_card_type(cls) -> Optional[GPUCardType]:
raise NotImplementedError
@staticmethod
@abstractmethod
- def getAvailableGPUCardIDs() -> List[int]:
+ def get_available_gpu_card_ids(order: str = "memory", limit: int = 1, max_load: float = 0.01,
+ max_memory: float = 0.01) -> List[int]:
raise NotImplementedError
@staticmethod
@abstractmethod
- def getGPUCards() -> List[GPUCard]:
+ def get_gpu_cards() -> List[GPUCard]:
raise NotImplementedError
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
index 349230cef5..f229774ce0 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
@@ -6,26 +6,9 @@
from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCard, GPUCardUtil, GPUCardType
-def _convert(gpu: GPU) -> GPUCard:
- return GPUCard(
- id=gpu.id,
- uuid=gpu.uuid,
- name=gpu.name,
- load=gpu.load,
- memoryTotal=gpu.memoryTotal,
- memoryUsed=gpu.memoryUsed,
- memoryFree=gpu.memoryFree,
- driver=gpu.driver,
- serial=gpu.serial,
- display_mode=gpu.display_mode,
- display_active=gpu.display_active,
- temperature=gpu.temperature
- )
-
-
class NvidiaGPUtil(GPUCardUtil):
@classmethod
- def detectGPUCardType(cls) -> Optional[GPUCardType]:
+ def detect_gpu_card_type(cls) -> Optional[GPUCardType]:
try:
subprocess.check_output(["nvidia-smi"], universal_newlines=True)
return GPUCardType.NVIDIA
@@ -33,9 +16,27 @@ def detectGPUCardType(cls) -> Optional[GPUCardType]:
return None
@staticmethod
- def getGPUCards() -> List[GPUCard]:
- return [_convert(gpu) for gpu in GPUtil.getGPUs()]
+ def get_gpu_cards() -> List[GPUCard]:
+ return [NvidiaGPUtil.__convert(gpu) for gpu in GPUtil.getGPUs()]
+
+ @staticmethod
+ def get_available_gpu_card_ids(order: str = "memory", limit: int = 1, maxLoad: float = 0.01, maxMemory: float = 0.01) -> List[int]:
+ return GPUtil.getAvailable(order='memory', limit=limit, maxLoad=0.01, maxMemory=0.01)
@staticmethod
- def getAvailableGPUCardIDs() -> List[int]:
- return GPUtil.getAvailable()
+ def __convert(gpu: GPU) -> GPUCard:
+ return GPUCard(
+ id=gpu.id,
+ name=gpu.name,
+ driver=gpu.driver,
+ serial=gpu.serial,
+ memoryTotal=gpu.memoryTotal,
+ memoryFree=gpu.memoryFree,
+ memoryUsed=gpu.memoryUsed,
+ memoryUtil=gpu.memoryUtil,
+ load=gpu.load,
+ uuid=gpu.uuid,
+ display_mode=gpu.display_mode,
+ display_active=gpu.display_active,
+ temperature=gpu.temperature
+ )
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
index e69de29bb2..1b56b7d05e 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
@@ -0,0 +1,60 @@
+import logging
+import subprocess
+from typing import List, Optional
+
+from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCard, GPUCardUtil, GPUCardType
+from qaicrt import Util, QIDList, QDevInfo, QStatus
+
+
+class QualcommNPUtil(GPUCardUtil):
+ @classmethod
+ def detect_gpu_card_type(cls) -> Optional[GPUCardType]:
+ try:
+ subprocess.check_output(["/opt/qti-aic/tools/qaic-util"], universal_newlines=True)
+ return GPUCardType.QUALCOMM
+ except Exception:
+ return None
+
+ @staticmethod
+ def get_gpu_cards() -> List[GPUCard]:
+ cards = []
+ util = Util()
+ status, card_list = util.getDeviceIds()
+ if status.value == 0:
+ for card in card_list:
+ status, card_info = util.getDeviceInfo(card)
+ if status.value == 0 and card_info.devStatus.value == 1:
+ cards.append(QualcommNPUtil.__convert(card_info))
+
+ else:
+ logging.error("Qualcomm Card Status not Healthy")
+ return cards
+
+ @staticmethod
+ def get_available_gpu_card_ids(order: str = "memory", limit: int = 1, max_load: float = 0.01,
+ max_memory: float = 0.01) -> List[int]:
+ available_gpu_card_ids = []
+
+ if order != "memory":
+ raise NotImplementedError(f"Qualcomm utils doesn't have support to compute availability based on {order}. "
+ f"Supported criteria: [memory]")
+
+ return available_gpu_card_ids
+
+ @staticmethod
+ def __convert(npu) -> GPUCard:
+ memory_total = npu.devData.resourceInfo.dramTotal / 1024
+ memory_free = npu.devData.resourceInfo.dramFree / 1024
+ memory_used = memory_total - memory_free
+ memory_utilized = float(memory_used) / float(memory_total)
+
+ return GPUCard(
+ id=npu.qid,
+ name=npu.pciInfo.devicename,
+ driver=npu.devData.fwQCImageVersionString,
+ serial=npu.devData.serial,
+ memoryTotal=memory_total,
+ memoryFree=memory_free,
+ memoryUsed=memory_used,
+ memoryUtil=memory_utilized,
+ )
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index c468e2181c..4e6f83e963 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -3,24 +3,25 @@
from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCardUtil, GPUCard
from fedml.computing.scheduler.comm_utils.gpu_utils.nvidia_utils import NvidiaGPUtil
+from fedml.computing.scheduler.comm_utils.gpu_utils.qualcomm_utils import QualcommNPUtil
from fedml.computing.scheduler.comm_utils.singleton import Singleton
-GPU_CARD_UTILS = [NvidiaGPUtil]
+GPU_CARD_UTILS = [NvidiaGPUtil, QualcommNPUtil]
class HardwareUtil(metaclass=Singleton):
__gpu_util: Optional[GPUCardUtil] = None
- @staticmethod
- def __get_util() -> Optional[GPUCardUtil]:
- if HardwareUtil.__gpu_util is not None:
- return HardwareUtil.__gpu_util
+ @classmethod
+ def __get_util(cls) -> Optional[GPUCardUtil]:
+ if cls.__gpu_util is not None:
+ return cls.__gpu_util
for gpu_util in GPU_CARD_UTILS:
try:
- if gpu_util.detectGPUCardType() is not None:
- HardwareUtil._gpu_util = gpu_util()
- return HardwareUtil._gpu_util
+ if gpu_util.detect_gpu_card_type() is not None:
+ cls.__gpu_util = gpu_util()
+ return cls.__gpu_util
except Exception as e:
pass
@@ -28,18 +29,18 @@ def __get_util() -> Optional[GPUCardUtil]:
return None
@staticmethod
- def getGPUs() -> List[GPUCard]:
+ def get_gpus() -> List[GPUCard]:
gpu_util = HardwareUtil.__get_util()
- return gpu_util.getGPUCards() if gpu_util is not None else []
+ return gpu_util.get_gpu_cards() if gpu_util is not None else []
@staticmethod
- def getAvailableGPUCardIDs() -> List[int]:
+ def get_available_gpu_card_ids() -> List[int]:
gpu_util = HardwareUtil.__get_util()
- return gpu_util.getAvailableGPUCardIDs() if gpu_util is not None else []
+ return gpu_util.get_available_gpu_card_ids() if gpu_util is not None else []
if __name__ == "__main__":
- gpus = HardwareUtil.getGPUs()
- get_available_gpu_cards = HardwareUtil.getAvailableGPUCardIDs()
+ gpus = HardwareUtil.get_gpus()
+ get_available_gpu_cards = HardwareUtil.get_available_gpu_card_ids()
print(gpus)
print(get_available_gpu_cards)
From fba65b28d14d3013b6893726ea60bfc8f5200904 Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Sun, 12 May 2024 04:40:22 +0000
Subject: [PATCH 031/282] Qualcomm Util -> get_available_gpu_card_ids
---
.../scheduler/comm_utils/gpu_utils/gpu_utils.py | 3 +--
.../scheduler/comm_utils/gpu_utils/nvidia_utils.py | 2 +-
.../scheduler/comm_utils/gpu_utils/qualcomm_utils.py | 11 +++++++----
.../computing/scheduler/comm_utils/hardware_utils.py | 7 ++++---
4 files changed, 13 insertions(+), 10 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
index 2731e51a3b..e098ce55ac 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
@@ -39,8 +39,7 @@ def detect_gpu_card_type(cls) -> Optional[GPUCardType]:
@staticmethod
@abstractmethod
- def get_available_gpu_card_ids(order: str = "memory", limit: int = 1, max_load: float = 0.01,
- max_memory: float = 0.01) -> List[int]:
+ def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memory: float) -> List[int]:
raise NotImplementedError
@staticmethod
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
index f229774ce0..8da4e89573 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
@@ -20,7 +20,7 @@ def get_gpu_cards() -> List[GPUCard]:
return [NvidiaGPUtil.__convert(gpu) for gpu in GPUtil.getGPUs()]
@staticmethod
- def get_available_gpu_card_ids(order: str = "memory", limit: int = 1, maxLoad: float = 0.01, maxMemory: float = 0.01) -> List[int]:
+ def get_available_gpu_card_ids(order: str, limit: int, maxLoad: float, maxMemory: float) -> List[int]:
return GPUtil.getAvailable(order='memory', limit=limit, maxLoad=0.01, maxMemory=0.01)
@staticmethod
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
index 1b56b7d05e..9ab629a9cc 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
@@ -1,4 +1,5 @@
import logging
+import math
import subprocess
from typing import List, Optional
@@ -31,15 +32,17 @@ def get_gpu_cards() -> List[GPUCard]:
return cards
@staticmethod
- def get_available_gpu_card_ids(order: str = "memory", limit: int = 1, max_load: float = 0.01,
- max_memory: float = 0.01) -> List[int]:
- available_gpu_card_ids = []
+ def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memory: float) -> List[int]:
if order != "memory":
raise NotImplementedError(f"Qualcomm utils doesn't have support to compute availability based on {order}. "
f"Supported criteria: [memory]")
- return available_gpu_card_ids
+ gpu_cards: List[GPUCard] = QualcommNPUtil.get_gpu_cards()
+ gpu_cards = list(filter(lambda card: card.memoryUtil < max_memory, gpu_cards))
+ gpu_cards.sort(key=lambda card: float('inf') if math.isnan(card.memoryUtil) else card.memoryUtil, reverse=False)
+ gpu_cards = gpu_cards[0:min(limit, len(gpu_cards))]
+ return list(map(lambda card: card.id, gpu_cards))
@staticmethod
def __convert(npu) -> GPUCard:
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index 4e6f83e963..0ba8aa664d 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -34,13 +34,14 @@ def get_gpus() -> List[GPUCard]:
return gpu_util.get_gpu_cards() if gpu_util is not None else []
@staticmethod
- def get_available_gpu_card_ids() -> List[int]:
+ def get_available_gpu_card_ids(order: str = "memory", limit: int = 1, max_load: float = 0.01,
+ max_memory: float = 0.01) -> List[int]:
gpu_util = HardwareUtil.__get_util()
- return gpu_util.get_available_gpu_card_ids() if gpu_util is not None else []
+ return gpu_util.get_available_gpu_card_ids(order, limit, max_load, max_memory) if gpu_util is not None else []
if __name__ == "__main__":
gpus = HardwareUtil.get_gpus()
- get_available_gpu_cards = HardwareUtil.get_available_gpu_card_ids()
+ get_available_gpu_cards = HardwareUtil.get_available_gpu_card_ids(limit=len(gpus))
print(gpus)
print(get_available_gpu_cards)
From 63c682ca3e64be70cb9e66e104a4e45dcf17d8b8 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Sat, 11 May 2024 21:53:17 -0700
Subject: [PATCH 032/282] Add sys path in init
---
.../scheduler/comm_utils/gpu_utils/qualcomm_utils.py | 7 ++++++-
1 file changed, 6 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
index 9ab629a9cc..38d95e0836 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
@@ -1,13 +1,16 @@
import logging
import math
import subprocess
+import sys
from typing import List, Optional
from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCard, GPUCardUtil, GPUCardType
-from qaicrt import Util, QIDList, QDevInfo, QStatus
class QualcommNPUtil(GPUCardUtil):
+ def __init__(self):
+ sys.path.append("/opt/qti-aic/dev/lib/x86_64/")
+
@classmethod
def detect_gpu_card_type(cls) -> Optional[GPUCardType]:
try:
@@ -18,6 +21,8 @@ def detect_gpu_card_type(cls) -> Optional[GPUCardType]:
@staticmethod
def get_gpu_cards() -> List[GPUCard]:
+ from qaicrt import Util, QIDList, QDevInfo, QStatus
+
cards = []
util = Util()
status, card_list = util.getDeviceIds()
From d3c081d5ec0cc3285582ce6712b8b3ff7429f42a Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Sat, 11 May 2024 23:09:40 -0700
Subject: [PATCH 033/282] Replace GPUtil with Hardware Util
---
.../scheduler/comm_utils/container_utils.py | 6 +++---
.../comm_utils/gpu_utils/gpu_utils.py | 3 ++-
.../comm_utils/gpu_utils/nvidia_utils.py | 3 ++-
.../comm_utils/gpu_utils/qualcomm_utils.py | 2 ++
.../scheduler/comm_utils/job_utils.py | 16 ---------------
.../scheduler/comm_utils/sys_utils.py | 20 +++++++++++--------
.../computing/scheduler/env/collect_env.py | 6 ++----
7 files changed, 23 insertions(+), 33 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/container_utils.py b/python/fedml/computing/scheduler/comm_utils/container_utils.py
index f337dd9997..4e09315b78 100644
--- a/python/fedml/computing/scheduler/comm_utils/container_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/container_utils.py
@@ -8,10 +8,10 @@
from docker import errors
from fedml.computing.scheduler.comm_utils import sys_utils
+from fedml.computing.scheduler.comm_utils.hardware_utils import HardwareUtil
from fedml.core.common.singleton import Singleton
from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
import time
-from GPUtil import getGPUs
class ContainerUtils(Singleton):
@@ -252,7 +252,7 @@ def get_container_perf(self, c_name) -> ContainerMetrics:
CPU % MEM USAGE / LIMIT MEM % NET I/O BLOCK I/O
0.26% 8.703GiB / 503.5GiB 1.73% 17.4GB / 176MB 545kB / 20.9GB
- GPU: We currently use GPUtil to get the GPU stats on host machine since one GPU is not
+ GPU: We currently use HardwareUtil to get the GPU stats on host machine since one GPU is not
shared by multiple containers
(TODO: get the GPU stats inside the container)
"""
@@ -350,7 +350,7 @@ def gpu_stats(gpu_ids):
utilz, memory, temp = None, None, None
gpu_stats_map = {} # gpu_id: int -> {"gpu_utilization", "gpu_memory_allocated", "gpu_temp"}
try:
- gpus = getGPUs()
+ gpus = HardwareUtil.get_gpus()
for i in gpu_ids:
gpu = gpus[i]
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
index e098ce55ac..3007bc07bc 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
@@ -1,5 +1,5 @@
from abc import ABC, abstractmethod
-from dataclasses import dataclass, field
+from dataclasses import dataclass
from enum import Enum, auto
from typing import Optional, List
@@ -19,6 +19,7 @@ class GPUCard:
name: str
driver: str
serial: str
+ vendor: str
memoryTotal: float
memoryFree: float
memoryUsed: float
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
index 8da4e89573..58c3888e68 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
@@ -30,6 +30,7 @@ def __convert(gpu: GPU) -> GPUCard:
name=gpu.name,
driver=gpu.driver,
serial=gpu.serial,
+ vendor=GPUCardType.NVIDIA.name,
memoryTotal=gpu.memoryTotal,
memoryFree=gpu.memoryFree,
memoryUsed=gpu.memoryUsed,
@@ -38,5 +39,5 @@ def __convert(gpu: GPU) -> GPUCard:
uuid=gpu.uuid,
display_mode=gpu.display_mode,
display_active=gpu.display_active,
- temperature=gpu.temperature
+ temperature=gpu.temperature,
)
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
index 38d95e0836..ca55fdab7c 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
@@ -51,6 +51,7 @@ def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memo
@staticmethod
def __convert(npu) -> GPUCard:
+ # TODO (alaydshah): Add support for load, memoryUtil, temperature
memory_total = npu.devData.resourceInfo.dramTotal / 1024
memory_free = npu.devData.resourceInfo.dramFree / 1024
memory_used = memory_total - memory_free
@@ -61,6 +62,7 @@ def __convert(npu) -> GPUCard:
name=npu.pciInfo.devicename,
driver=npu.devData.fwQCImageVersionString,
serial=npu.devData.serial,
+ vendor=GPUCardType.QUALCOMM.name,
memoryTotal=memory_total,
memoryFree=memory_free,
memoryUsed=memory_used,
diff --git a/python/fedml/computing/scheduler/comm_utils/job_utils.py b/python/fedml/computing/scheduler/comm_utils/job_utils.py
index 384cbacd1d..afa6293396 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_utils.py
@@ -2,7 +2,6 @@
import os
import platform
import traceback
-import GPUtil
import docker
import fedml
from docker import errors, DockerClient
@@ -159,23 +158,8 @@ def occupy_gpu_ids(self, run_id, request_gpu_num, device_id, inner_id=None,
@staticmethod
def search_and_refresh_available_gpu_ids(available_gpu_ids):
trimmed_gpu_ids = JobRunnerUtils.trim_unavailable_gpu_ids(available_gpu_ids)
- # if len(trimmed_gpu_ids) <= 0:
- # available_gpu_ids = JobRunnerUtils.balance_available_gpu_ids(trimmed_gpu_ids)
return trimmed_gpu_ids
- @staticmethod
- def balance_available_gpu_ids(available_gpu_ids):
- gpu_list, realtime_available_gpu_ids = JobRunnerUtils.get_gpu_list_and_realtime_gpu_available_ids()
- available_gpu_ids = realtime_available_gpu_ids
- if len(available_gpu_ids) <= 0:
- for gpu in gpu_list:
- gpu = GPUtil.GPU(gpu)
- if gpu.memoryUtil > 0.8:
- continue
- available_gpu_ids.append(gpu.id)
-
- return available_gpu_ids.copy()
-
@staticmethod
def request_gpu_ids(request_gpu_num, available_gpu_ids):
available_gpu_count = len(available_gpu_ids)
diff --git a/python/fedml/computing/scheduler/comm_utils/sys_utils.py b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
index 64313b0864..f1989fbe5a 100644
--- a/python/fedml/computing/scheduler/comm_utils/sys_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
@@ -10,6 +10,7 @@
import psutil
import yaml
+from fedml.computing.scheduler.comm_utils.hardware_utils import HardwareUtil
from fedml.computing.scheduler.comm_utils.yaml_utils import load_yaml_config
import json
from urllib import request
@@ -18,7 +19,6 @@
from packaging import version
import sys
import subprocess
-import GPUtil
from fedml.computing.scheduler.slave.client_constants import ClientConstants
@@ -95,7 +95,7 @@ def get_sys_runner_info():
pass
try:
- gpus = GPUtil.getGPUs()
+ gpus = HardwareUtil.get_gpus()
memory_total = 0.0
memory_free = 0.0
for gpu in gpus:
@@ -105,9 +105,11 @@ def get_sys_runner_info():
gpu_available_mem = "{:.1f} G".format(memory_free / 1024.0)
gpu_total_mem = "{:.1f}G".format(memory_total / 1024.0)
gpu_count = len(gpus)
- gpu_vendor = "nvidia"
+ if gpu_count:
+ gpu_vendor = gpus[0].vendor
+ gpu_device_name = gpus[0].name
- gpu_device_name = torch.cuda.get_device_name(0)
+ # gpu_device_name = torch.cuda.get_device_name(0)
gpu_info = gpu_device_name
except:
pass
@@ -168,7 +170,7 @@ def get_gpu_list():
return ret_gpu_list[0:simulation_gpu_count]
- gpu_list = GPUtil.getGPUs()
+ gpu_list = HardwareUtil.get_gpus()
ret_gpu_list = list()
for gpu in gpu_list:
ret_gpu_item = {"ID": gpu.id, "uuid": gpu.uuid, "load": gpu.load,
@@ -189,7 +191,8 @@ def get_available_gpu_id_list(limit=1) -> List[int]:
available_gpu_ids.append(count)
return available_gpu_ids[0:simulation_gpu_count]
- gpu_available_list = GPUtil.getAvailable(order='memory', limit=limit, maxLoad=0.01, maxMemory=0.01)
+ gpu_available_list = HardwareUtil.get_available_gpu_card_ids(order='memory', limit=limit, max_load=0.01,
+ max_memory=0.01)
return gpu_available_list
@@ -219,9 +222,10 @@ def get_gpu_count_vendor():
gpu_count = 0
gpu_vendor = ""
try:
- gpus = GPUtil.getGPUs()
+ gpus = HardwareUtil.get_gpus()
gpu_count = len(gpus)
- gpu_vendor = "nvidia"
+ if gpu_count:
+ gpu_vendor = gpus[0].vendor
except:
pass
diff --git a/python/fedml/computing/scheduler/env/collect_env.py b/python/fedml/computing/scheduler/env/collect_env.py
index dcece6a720..63f7e66b85 100644
--- a/python/fedml/computing/scheduler/env/collect_env.py
+++ b/python/fedml/computing/scheduler/env/collect_env.py
@@ -1,9 +1,8 @@
import os
import traceback
-import GPUtil
-
import fedml
+from fedml.computing.scheduler.comm_utils.hardware_utils import HardwareUtil
from fedml.computing.scheduler.slave.client_diagnosis import ClientDiagnosis
@@ -59,8 +58,7 @@ def collect_env():
try:
print("\n======== GPU Configuration ========")
- import GPUtil
- gpus = GPUtil.getGPUs()
+ gpus = HardwareUtil.get_gpus()
memory_total = 0.0
memory_free = 0.0
gpu_name = ""
From 8b2ff80167e085f9763e44083cd7d8de95b17811 Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Sun, 12 May 2024 06:23:05 +0000
Subject: [PATCH 034/282] Make fedml env hardware agnostic
---
.../computing/scheduler/env/collect_env.py | 17 ++++++++---------
1 file changed, 8 insertions(+), 9 deletions(-)
diff --git a/python/fedml/computing/scheduler/env/collect_env.py b/python/fedml/computing/scheduler/env/collect_env.py
index 63f7e66b85..b2f7bd7f5e 100644
--- a/python/fedml/computing/scheduler/env/collect_env.py
+++ b/python/fedml/computing/scheduler/env/collect_env.py
@@ -11,7 +11,7 @@ def collect_env():
print("FedML version: " + str(fedml.__version__))
env_version = fedml.get_env_version()
print("FedML ENV version: " + str(env_version))
-
+
print("Execution path:" + str(os.path.abspath(fedml.__file__)))
print("\n======== Running Environment ========")
@@ -62,26 +62,25 @@ def collect_env():
memory_total = 0.0
memory_free = 0.0
gpu_name = ""
+ vendor = ""
for gpu in gpus:
memory_total += gpu.memoryTotal
memory_free += gpu.memoryFree
gpu_name = gpu.name
+ vendor = gpu.vendor
- print("NVIDIA GPU Info: " + gpu_name)
+ print(f"{vendor} GPU Info: " + gpu_name)
print("Available GPU memory: {:.1f} G / {:.1f}G".format(
memory_free / 1024.0, memory_total / 1024.0))
+ device_count = len(gpus)
+ print("device_count = {}".format(device_count))
+
import torch
torch_is_available = torch.cuda.is_available()
print("torch_is_available = {}".format(torch_is_available))
- device_count = torch.cuda.device_count()
- print("device_count = {}".format(device_count))
-
- device_name = torch.cuda.get_device_name(0)
- print("device_name = {}".format(device_name))
-
except:
print("No GPU devices")
@@ -108,4 +107,4 @@ def collect_env():
print(f"You can not connect to {mqtt_url}.\n")
except Exception as e:
print(f"The connection exception: {traceback.format_exc()}")
- pass
\ No newline at end of file
+ pass
From f9aaee926f31810697d060cc271f5fec5a9d8e5f Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Sun, 12 May 2024 00:09:45 -0700
Subject: [PATCH 035/282] Nit
---
.../fedml/computing/scheduler/comm_utils/hardware_utils.py | 6 +++---
python/fedml/computing/scheduler/comm_utils/sys_utils.py | 4 ++--
2 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index 0ba8aa664d..1aeb5eb0be 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -34,14 +34,14 @@ def get_gpus() -> List[GPUCard]:
return gpu_util.get_gpu_cards() if gpu_util is not None else []
@staticmethod
- def get_available_gpu_card_ids(order: str = "memory", limit: int = 1, max_load: float = 0.01,
- max_memory: float = 0.01) -> List[int]:
+ def get_available_gpu_ids(order: str = "memory", limit: int = 1, max_load: float = 0.01,
+ max_memory: float = 0.01) -> List[int]:
gpu_util = HardwareUtil.__get_util()
return gpu_util.get_available_gpu_card_ids(order, limit, max_load, max_memory) if gpu_util is not None else []
if __name__ == "__main__":
gpus = HardwareUtil.get_gpus()
- get_available_gpu_cards = HardwareUtil.get_available_gpu_card_ids(limit=len(gpus))
+ get_available_gpu_cards = HardwareUtil.get_available_gpu_ids(limit=len(gpus))
print(gpus)
print(get_available_gpu_cards)
diff --git a/python/fedml/computing/scheduler/comm_utils/sys_utils.py b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
index f1989fbe5a..aaa37bc4db 100644
--- a/python/fedml/computing/scheduler/comm_utils/sys_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
@@ -191,8 +191,8 @@ def get_available_gpu_id_list(limit=1) -> List[int]:
available_gpu_ids.append(count)
return available_gpu_ids[0:simulation_gpu_count]
- gpu_available_list = HardwareUtil.get_available_gpu_card_ids(order='memory', limit=limit, max_load=0.01,
- max_memory=0.01)
+ gpu_available_list = HardwareUtil.get_available_gpu_ids(order='memory', limit=limit, max_load=0.01,
+ max_memory=0.01)
return gpu_available_list
From 7e547f4ea78d08202ae23ff19b9ba6a05fffe2f6 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Sun, 12 May 2024 20:29:44 +0800
Subject: [PATCH 036/282] [CoreEngine] make the deployment and federated
learning work.
---
python/fedml/__init__.py | 10 +++++++---
.../scheduler/comm_utils/container_utils.py | 2 +-
.../computing/scheduler/comm_utils/job_monitor.py | 10 +++-------
.../scheduler/master/base_master_job_runner.py | 15 +++++++++------
.../model_scheduler/worker_job_runner.py | 2 +-
.../scheduler_core/scheduler_base_job_runner.py | 15 +++++++++++----
.../scheduler_core/status_manager_protocols.py | 5 ++++-
python/fedml/core/mlops/mlops_device_perfs.py | 13 ++++++++-----
8 files changed, 44 insertions(+), 28 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index 92b72357a0..8044387b65 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -90,9 +90,13 @@ def init(args=None, check_env=True, should_init_logs=True):
# Windows/Linux/MacOS compatability issues on multi-processing
# https://github.com/pytorch/pytorch/issues/3492
"""
- if multiprocessing.get_start_method() != "spawn":
- # force all platforms (Windows/Linux/MacOS) to use the same way (spawn) for multiprocessing
- multiprocessing.set_start_method("spawn", force=True)
+ if multiprocessing.get_start_method() != "fork":
+ # force all platforms (Windows/Linux/macOS) to use the same way (fork) for multiprocessing
+ multiprocessing.set_start_method("fork", force=True)
+
+ # if multiprocessing.get_start_method() != "spawn":
+ # # force all platforms (Windows/Linux/MacOS) to use the same way (spawn) for multiprocessing
+ # multiprocessing.set_start_method("spawn", force=True)
"""
# https://stackoverflow.com/questions/53014306/error-15-initializing-libiomp5-dylib-but-found-libiomp5-dylib-already-initial
diff --git a/python/fedml/computing/scheduler/comm_utils/container_utils.py b/python/fedml/computing/scheduler/comm_utils/container_utils.py
index 9469c8b471..4cbf642a45 100644
--- a/python/fedml/computing/scheduler/comm_utils/container_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/container_utils.py
@@ -178,7 +178,7 @@ def get_container_rank_same_model(prefix: str):
running_model_name = hash("model_endpoint_id_{}_name_{}_model_id_{}_name_{}_ver_{}")
"""
try:
- docker.from_env(timeout=5, version="auto")
+ client = docker.from_env(timeout=5, version="auto")
except Exception:
logging.error("Failed to connect to the docker daemon, please ensure that you have "
"installed Docker Desktop or Docker Engine, and the docker is running")
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index 84723d373a..9bee76e780 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -208,6 +208,8 @@ def monitor_replicas_number():
endpoint_replicas_details = {}
if isinstance(endpoint_detail, str):
endpoint_replicas_details = json.loads(endpoint_detail)
+ if isinstance(endpoint_replicas_details, str):
+ endpoint_replicas_details = json.loads(endpoint_replicas_details)
if "result" in endpoint_replicas_details:
endpoint_replica_details = {}
@@ -220,13 +222,7 @@ def monitor_replicas_number():
for endpoint_id, num_replica in res_to_mlops.items():
curr_version = fedml.get_env_version()
num_replica_url_path = "fedmlModelServer/api/v1/endpoint/replica-info"
- if curr_version == "release":
- mlops_prefix = "https://open.fedml.ai/"
- elif curr_version == "test":
- mlops_prefix = "https://open-test.fedml.ai/"
- else:
- logging.error(f"Do not support the version {curr_version}.")
- return
+ mlops_prefix = fedml._get_backend_service()
url = f"{mlops_prefix}{num_replica_url_path}"
cached_token = FedMLModelCache.get_instance().get_end_point_token_with_eid(endpoint_id)
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner.py b/python/fedml/computing/scheduler/master/base_master_job_runner.py
index ce0515160f..07c297c65d 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner.py
@@ -221,10 +221,10 @@ def _process_run_logs_queue(self, run_logs_queue):
def run_server_job(
self, process_event, completed_event, edge_id_status_queue=None,
- edge_device_info_queue=None, run_metrics_queue=None,
- run_event_queue=None, run_artifacts_queue=None, run_logs_queue=None,
- sender_message_queue=None, listener_message_queue=None,
- edge_device_info_global_queue=None, status_center_queue=None
+ edge_device_info_queue=None, run_metrics_queue=None, run_event_queue=None,
+ run_artifacts_queue=None, run_logs_queue=None, edge_device_info_global_queue=None,
+ run_extend_queue_list=None, sender_message_center_queue=None, listener_message_queue=None,
+ status_center_queue=None
):
print(f"Server runner process id {os.getpid()}, run id {self.run_id}")
@@ -239,10 +239,10 @@ def run_server_job(
try:
MLOpsUtils.set_ntp_offset(self.ntp_offset)
- self.rebuild_message_status_center(sender_message_queue, listener_message_queue, status_center_queue)
+ self.rebuild_message_status_center(sender_message_center_queue, listener_message_queue, status_center_queue)
self.run_server_job_impl(process_event, completed_event,
- message_center_queue=sender_message_queue)
+ message_center_queue=sender_message_center_queue)
except RunnerError:
logging.info("Runner stopped.")
self.status_reporter.report_server_id_status(
@@ -703,5 +703,8 @@ def should_process_async_cluster(self):
return False, self.async_check_timeout
+ def get_client_id_list(self, server_edge_id_list):
+ return server_edge_id_list
+
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index d1cfd3b83c..ac9328592c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -353,7 +353,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
return True
elif op == "update" or op == "rollback":
# Update is combine of delete and add
- worker_ip = self.get_ip_address(self.request_json)
+ worker_ip = GeneralConstants.get_ip_address(self.request_json)
for rank in replica_rank_to_update:
# Delete a replica (container) if exists
self.replica_handler.remove_replica(rank)
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 0b4d47d52c..f40b8ecfb6 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -112,7 +112,8 @@ def build_dynamic_constrain_variables(self, run_id, run_config):
self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.RUN_ID}"] = run_id
self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.PRIVATE_LOCAL_DATA}"] = private_data_dir.replace(" ", "")
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_ID_LIST}"] = str(local_edge_id_list).replace(" ", "")
+ self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_ID_LIST}"] = \
+ str(self.get_client_id_list(server_edge_id_list)).replace(" ", "")
self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.SYNTHETIC_DATA_URL}"] = synthetic_data_url.replace(" ", "")
self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.IS_USING_LOCAL_DATA}"] = str(is_using_local_data)
self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_NUM}"] = len(server_edge_id_list)
@@ -129,6 +130,11 @@ def build_dynamic_constrain_variables(self, run_id, run_config):
"LOG_SERVER_URL"
]
+ def get_client_id_list(self, server_edge_id_list):
+ local_edge_id_list = list()
+ local_edge_id_list.append(int(self.edge_id))
+ return local_edge_id_list
+
@staticmethod
def unzip_file(zip_file, unzip_file_path) -> str:
if zipfile.is_zipfile(zip_file):
@@ -497,11 +503,12 @@ def execute_job_task(self, unzip_package_path, entry_file_full_path, conf_file_f
if job_yaml_default_none is None:
# Generate the job executing commands for previous federated learning (Compatibility)
python_program = get_python_program()
- logging.info("Run the client: {} {} --cf {} --rank {} --role client".format(
- python_program, entry_file_full_path, conf_file_full_path, str(dynamic_args_config.get("rank", 1))))
rank = str(dynamic_args_config.get("rank", 1))
+ role = "server" if rank == "0" else "client"
+ logging.info(f"Run the {role}: {python_program} {entry_file_full_path} --cf {conf_file_full_path} "
+ f"--rank {rank} --role {role}")
entry_command = f"{python_program} {entry_file_full_path} --cf " \
- f"{conf_file_full_path} --rank {rank} --role client"
+ f"{conf_file_full_path} --rank {rank} --role {role}"
shell_cmd_list = [entry_command]
# Run the job executing commands for previous federated learning (Compatibility)
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index 871b9026bf..811ff2a2d5 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -182,10 +182,12 @@ def process_device_status(self, run_id, edge_id, status):
server_id = edge_id_status_dict.get("server", 0)
enable_fault_tolerance, fault_tolerance_rate = self.parse_fault_tolerance_params(run_id)
running_edges_list = list()
+ edge_nums = 0
for edge_id_item, status_item in edge_id_status_dict.items():
if edge_id_item == "server":
continue
+ edge_nums += 1
if status_item is None or status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED or \
status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION:
number_of_failed_edges += 1
@@ -210,7 +212,8 @@ def process_device_status(self, run_id, edge_id, status):
self.message_reporter.report_client_training_status(edge_id, consensus_status, run_id=run_id, update_db=False)
# Report server status based on the fault tolerance model and parameters
- edge_nums = len(edge_id_status_dict.keys()) - 1
+ if edge_nums <= 0:
+ return
status_to_report = self.calculate_server_status(
run_id, edge_nums, number_of_failed_edges, number_of_finished_edges, number_of_killed_edges,
running_edges_list, enable_fault_tolerance=enable_fault_tolerance,
diff --git a/python/fedml/core/mlops/mlops_device_perfs.py b/python/fedml/core/mlops/mlops_device_perfs.py
index b258692645..a0e1e972b6 100644
--- a/python/fedml/core/mlops/mlops_device_perfs.py
+++ b/python/fedml/core/mlops/mlops_device_perfs.py
@@ -166,11 +166,14 @@ def report_device_realtime_stats_entry(self, sys_event, role, is_client=False):
sleep_time_interval_for_server_monitor = 60
while not self.should_stop_device_realtime_stats():
- if role == ROLE_DEVICE_INFO_REPORTER:
- time.sleep(sleep_time_interval_for_device_info)
- elif role == ROLE_DEVICE_JOB_TOTAL_MONITOR:
- time.sleep(sleep_time_interval_for_client_monitor if is_client
- else sleep_time_interval_for_server_monitor)
+ if self.enable_job_total_monitor:
+ if role == ROLE_DEVICE_INFO_REPORTER:
+ time.sleep(sleep_time_interval_for_device_info)
+ elif role == ROLE_DEVICE_JOB_TOTAL_MONITOR:
+ time.sleep(sleep_time_interval_for_client_monitor if is_client
+ else sleep_time_interval_for_server_monitor)
+ else:
+ time.sleep(time_interval_map[role])
try:
if role == ROLE_DEVICE_INFO_REPORTER:
From d1e4fbeaad73a252d4118eceeab9d3414db948db Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Sun, 12 May 2024 20:58:48 +0800
Subject: [PATCH 037/282] [CoreEngine] change the text of package downloading.
---
.../scheduler/scheduler_core/scheduler_base_job_runner.py | 2 +-
python/fedml/core/mlops/mlops_device_perfs.py | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index f40b8ecfb6..054efe437a 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -187,7 +187,7 @@ def download_package_proc(self, package_url, local_package_file):
# Write the chunk to the file
written_size = f.write(chunk)
total_size += written_size
- logging.info(f"package downloaded size {total_size/1024} KB")
+ logging.info("package downloaded size %.2f KB", total_size/1024)
self.download_time = time.time()
self.download_finished = True
diff --git a/python/fedml/core/mlops/mlops_device_perfs.py b/python/fedml/core/mlops/mlops_device_perfs.py
index a0e1e972b6..29183a6e78 100644
--- a/python/fedml/core/mlops/mlops_device_perfs.py
+++ b/python/fedml/core/mlops/mlops_device_perfs.py
@@ -157,7 +157,7 @@ def report_device_realtime_stats_entry(self, sys_event, role, is_client=False):
}
job_monitor_obj = None
- if role == ROLE_AUTO_SCALER:
+ if role == ROLE_AUTO_SCALER or role == ROLE_DEVICE_JOB_TOTAL_MONITOR:
# job_monitor Should be initialized once
job_monitor_obj = JobMonitor.get_instance()
From 4f65467df09602d2ae67b1d89f7d41d5457c8fa4 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Sun, 12 May 2024 22:43:44 +0800
Subject: [PATCH 038/282] [CoreEngine] pass the job type when release gpu ids,
set the message center name for deployments.
---
.../fedml/computing/scheduler/comm_utils/job_utils.py | 3 +++
.../model_scheduler/master_protocol_manager.py | 2 ++
.../model_scheduler/worker_protocol_manager.py | 2 ++
.../scheduler_core/scheduler_base_job_runner.py | 10 +---------
.../computing/scheduler/slave/base_slave_job_runner.py | 4 +++-
.../scheduler/slave/base_slave_job_runner_manager.py | 4 ++--
.../scheduler/slave/base_slave_protocol_manager.py | 5 +++--
7 files changed, 16 insertions(+), 14 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_utils.py b/python/fedml/computing/scheduler/comm_utils/job_utils.py
index ece165e92c..bc5985533b 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_utils.py
@@ -728,6 +728,9 @@ def parse_job_type(running_json):
job_type = job_yaml.get("job_type", None)
job_type = job_yaml.get("task_type",
SchedulerConstants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
+ model_config = running_json_obj.get("model_config", None)
+ if model_config is not None:
+ job_type = SchedulerConstants.JOB_TASK_TYPE_DEPLOY
return job_type
@staticmethod
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index 144d17fd02..a5f2a37dfe 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -17,6 +17,8 @@ class FedMLDeployMasterProtocolManager(FedMLBaseMasterProtocolManager):
def __init__(self, args, agent_config=None):
FedMLBaseMasterProtocolManager.__init__(self, args, agent_config=agent_config)
+ self.message_center_name = "deploy_master_agent"
+
self.topic_start_deployment = None
self.topic_activate_endpoint = None
self.topic_deactivate_deployment = None
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
index 5f4835d9aa..3a0f835b6c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
@@ -23,6 +23,8 @@ class FedMLDeployWorkerProtocolManager(FedMLBaseSlaveProtocolManager):
def __init__(self, args, agent_config=None):
FedMLBaseSlaveProtocolManager.__init__(self, args, agent_config=agent_config)
+ self.message_center_name = "deploy_slave_agent"
+
self.topic_start_deployment = None
self.topic_delete_deployment = None
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 054efe437a..03d3fd5d92 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -584,15 +584,7 @@ def start_runner_process(
return None
@staticmethod
- def cleanup_containers_and_release_gpus(run_id, edge_id):
- job_type = JobRunnerUtils.get_job_type_from_run_id(run_id)
-
- if not job_type:
- logging.info(f"Failed to get job type from run id {run_id}. This is not an error as it would usually "
- f"happen when the job is not found in the database because job is already finished and "
- f"cleaned up. Exiting cleanup_containers_and_release_gpus.")
- return
-
+ def cleanup_containers_and_release_gpus(run_id, edge_id, job_type=SchedulerConstants.JOB_TASK_TYPE_TRAIN):
# Check if the job type is not "serve" or "deploy"
if not (job_type == SchedulerConstants.JOB_TASK_TYPE_SERVE or
job_type == SchedulerConstants.JOB_TASK_TYPE_DEPLOY):
diff --git a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
index de2956ad94..cc7c3c222b 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
@@ -15,6 +15,7 @@
from multiprocessing import Process
from ..scheduler_core.scheduler_base_job_runner import FedMLSchedulerBaseJobRunner, RunnerError, RunnerCompletedError
from ..scheduler_core.general_constants import GeneralConstants
+from ..comm_utils.job_utils import JobRunnerUtils
class FedMLBaseSlaveJobRunner(FedMLSchedulerBaseJobRunner, ABC):
@@ -78,7 +79,8 @@ def run(self, process_event, completed_event, run_extend_queue_list,
self.computing_started_time, computing_ended_time,
self.args.account_id, self.args.api_key)
logging.info("Release resources.")
- FedMLSchedulerBaseJobRunner.cleanup_containers_and_release_gpus(self.run_id, self.edge_id)
+ job_type = JobRunnerUtils.parse_job_type(self.request_json)
+ FedMLSchedulerBaseJobRunner.cleanup_containers_and_release_gpus(self.run_id, self.edge_id, job_type)
MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, self.edge_id)
if self.mlops_metrics is not None:
self.mlops_metrics.stop_sys_perf()
diff --git a/python/fedml/computing/scheduler/slave/base_slave_job_runner_manager.py b/python/fedml/computing/scheduler/slave/base_slave_job_runner_manager.py
index c058d5dd0e..80e486224e 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_job_runner_manager.py
@@ -8,5 +8,5 @@ class FedMLBaseSlaveJobRunnerManager(FedMLSchedulerBaseJobRunnerManager, ABC):
def __init__(self):
FedMLSchedulerBaseJobRunnerManager.__init__(self)
- def cleanup_containers_and_release_gpus(self, run_id, edge_id):
- FedMLSchedulerBaseJobRunner.cleanup_containers_and_release_gpus(run_id, edge_id)
+ def cleanup_containers_and_release_gpus(self, run_id, edge_id, job_type):
+ FedMLSchedulerBaseJobRunner.cleanup_containers_and_release_gpus(run_id, edge_id, job_type)
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index b3cd154d23..fc67ec2ece 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -301,7 +301,8 @@ def callback_stop_train(self, topic, payload):
# logging.info("Stop run with multiprocessing...")
# Stop client with multiprocessing mode
run_id_str = str(run_id)
- self._get_job_runner_manager().cleanup_containers_and_release_gpus(run_id, edge_id)
+ self._get_job_runner_manager().cleanup_containers_and_release_gpus(
+ run_id, edge_id, SchedulerConstants.JOB_TASK_TYPE_TRAIN)
self.sync_run_stop_status(run_status=run_status)
# Register the job stopping message into the status center
@@ -512,7 +513,7 @@ def process_status(self, run_id, status, edge_id):
job_type = JobRunnerUtils.parse_job_type(running_json)
if not SchedulerConstants.is_deploy_job(job_type):
logging.info(f"[run/device][{run_id}/{edge_id}] Release gpu resource when run ended.")
- self._get_job_runner_manager().cleanup_containers_and_release_gpus(run_id, edge_id)
+ self._get_job_runner_manager().cleanup_containers_and_release_gpus(run_id, edge_id, job_type)
# Stop the runner process
run_process = self._get_job_runner_manager().get_runner_process(run_id)
From 906e8b01de21988a358687e31b4854a73885e9b7 Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Sun, 12 May 2024 17:43:57 +0000
Subject: [PATCH 039/282] Minor Bug
---
.../computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
index 58c3888e68..14d8230d06 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
@@ -20,8 +20,8 @@ def get_gpu_cards() -> List[GPUCard]:
return [NvidiaGPUtil.__convert(gpu) for gpu in GPUtil.getGPUs()]
@staticmethod
- def get_available_gpu_card_ids(order: str, limit: int, maxLoad: float, maxMemory: float) -> List[int]:
- return GPUtil.getAvailable(order='memory', limit=limit, maxLoad=0.01, maxMemory=0.01)
+ def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memory: float) -> List[int]:
+ return GPUtil.getAvailable(order=order, limit=limit, maxLoad=max_load, maxMemory=max_memory)
@staticmethod
def __convert(gpu: GPU) -> GPUCard:
From 3a5daf65ac8908cdbc94b8cb9fb0e4bc8b7bb5ca Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Sun, 12 May 2024 19:37:11 +0000
Subject: [PATCH 040/282] Add Hardware specific docker device mapping
---
.../scheduler/comm_utils/gpu_utils/gpu_utils.py | 7 ++++++-
.../scheduler/comm_utils/gpu_utils/nvidia_utils.py | 11 ++++++++++-
.../scheduler/comm_utils/gpu_utils/qualcomm_utils.py | 9 ++++++++-
.../computing/scheduler/comm_utils/hardware_utils.py | 7 ++++++-
4 files changed, 30 insertions(+), 4 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
index 3007bc07bc..c7ce91f694 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
@@ -1,7 +1,7 @@
from abc import ABC, abstractmethod
from dataclasses import dataclass
from enum import Enum, auto
-from typing import Optional, List
+from typing import Optional, List, Dict
class GPUCardType(Enum):
@@ -47,3 +47,8 @@ def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memo
@abstractmethod
def get_gpu_cards() -> List[GPUCard]:
raise NotImplementedError
+
+ @staticmethod
+ @abstractmethod
+ def get_docker_gpu_device_mapping(gpu_ids: List[int]) -> Optional[Dict]:
+ raise NotImplementedError
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
index 14d8230d06..f0da4f8fb4 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
@@ -1,12 +1,15 @@
import subprocess
-from typing import List, Optional
+from typing import List, Optional, Dict
+import docker
+from docker import types
from GPUtil import GPUtil, GPU
from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCard, GPUCardUtil, GPUCardType
class NvidiaGPUtil(GPUCardUtil):
+
@classmethod
def detect_gpu_card_type(cls) -> Optional[GPUCardType]:
try:
@@ -23,6 +26,12 @@ def get_gpu_cards() -> List[GPUCard]:
def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memory: float) -> List[int]:
return GPUtil.getAvailable(order=order, limit=limit, maxLoad=max_load, maxMemory=max_memory)
+ @staticmethod
+ def get_docker_gpu_device_mapping(gpu_ids: List[int]) -> Optional[Dict]:
+ if gpu_ids and len(gpu_ids):
+ return {"device_requests": [docker.types.DeviceRequest(device_ids=gpu_ids, capabilities=[["gpu"]])]}
+ return None
+
@staticmethod
def __convert(gpu: GPU) -> GPUCard:
return GPUCard(
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
index ca55fdab7c..c25c34dcaa 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
@@ -2,12 +2,13 @@
import math
import subprocess
import sys
-from typing import List, Optional
+from typing import List, Optional, Dict
from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCard, GPUCardUtil, GPUCardType
class QualcommNPUtil(GPUCardUtil):
+
def __init__(self):
sys.path.append("/opt/qti-aic/dev/lib/x86_64/")
@@ -49,6 +50,12 @@ def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memo
gpu_cards = gpu_cards[0:min(limit, len(gpu_cards))]
return list(map(lambda card: card.id, gpu_cards))
+ @staticmethod
+ def get_docker_gpu_device_mapping(gpu_ids: List[int]) -> Optional[Dict]:
+ if gpu_ids and len(gpu_ids):
+ return {"devices": [f"/dev/accel/accel{gpu_id}:/dev/accel/accel{gpu_id}" for gpu_id in gpu_ids]}
+ return None
+
@staticmethod
def __convert(npu) -> GPUCard:
# TODO (alaydshah): Add support for load, memoryUtil, temperature
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index 1aeb5eb0be..140f316554 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -1,5 +1,5 @@
import logging
-from typing import Optional, List
+from typing import Optional, List, Dict
from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCardUtil, GPUCard
from fedml.computing.scheduler.comm_utils.gpu_utils.nvidia_utils import NvidiaGPUtil
@@ -39,6 +39,11 @@ def get_available_gpu_ids(order: str = "memory", limit: int = 1, max_load: float
gpu_util = HardwareUtil.__get_util()
return gpu_util.get_available_gpu_card_ids(order, limit, max_load, max_memory) if gpu_util is not None else []
+ @staticmethod
+ def get_docker_gpu_device_mapping(gpu_ids: List[int]) -> Optional[Dict]:
+ gpu_util = HardwareUtil.__get_util()
+ return gpu_util.get_docker_gpu_device_mapping(gpu_ids)
+
if __name__ == "__main__":
gpus = HardwareUtil.get_gpus()
From e57af1eaaa49490a4d28c802a844ca33cf6e42cd Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Sun, 12 May 2024 19:51:36 +0000
Subject: [PATCH 041/282] Bug Fix
---
.../computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
index f0da4f8fb4..79071bf935 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
@@ -29,7 +29,8 @@ def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memo
@staticmethod
def get_docker_gpu_device_mapping(gpu_ids: List[int]) -> Optional[Dict]:
if gpu_ids and len(gpu_ids):
- return {"device_requests": [docker.types.DeviceRequest(device_ids=gpu_ids, capabilities=[["gpu"]])]}
+ gpu_id_list = list(map(lambda x: str(x), gpu_ids))
+ return {"device_requests": [docker.types.DeviceRequest(device_ids=gpu_id_list, capabilities=[["gpu"]])]}
return None
@staticmethod
From e70f56f7b0449e9be8ae59262a1a47bb8eb2ab02 Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Sun, 12 May 2024 22:10:56 +0000
Subject: [PATCH 042/282] Add util function to get gpu_ids from container name
---
.../comm_utils/gpu_utils/gpu_utils.py | 7 +++
.../comm_utils/gpu_utils/nvidia_utils.py | 13 ++++-
.../comm_utils/gpu_utils/qualcomm_utils.py | 55 ++++++++++++++++++-
.../scheduler/comm_utils/hardware_utils.py | 14 ++++-
4 files changed, 86 insertions(+), 3 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
index c7ce91f694..292bcb3624 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
@@ -3,6 +3,8 @@
from enum import Enum, auto
from typing import Optional, List, Dict
+from docker import DockerClient
+
class GPUCardType(Enum):
NVIDIA = auto()
@@ -52,3 +54,8 @@ def get_gpu_cards() -> List[GPUCard]:
@abstractmethod
def get_docker_gpu_device_mapping(gpu_ids: List[int]) -> Optional[Dict]:
raise NotImplementedError
+
+ @staticmethod
+ @abstractmethod
+ def get_docker_gpu_ids_by_container_name(container_name: str, docker_client: DockerClient) -> List[int]:
+ raise NotImplementedError
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
index 79071bf935..0c05b25644 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
@@ -1,8 +1,9 @@
+import logging
import subprocess
from typing import List, Optional, Dict
import docker
-from docker import types
+from docker import types, DockerClient
from GPUtil import GPUtil, GPU
from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCard, GPUCardUtil, GPUCardType
@@ -33,6 +34,16 @@ def get_docker_gpu_device_mapping(gpu_ids: List[int]) -> Optional[Dict]:
return {"device_requests": [docker.types.DeviceRequest(device_ids=gpu_id_list, capabilities=[["gpu"]])]}
return None
+ @staticmethod
+ def get_docker_gpu_ids_by_container_name(container_name: str, docker_client: DockerClient) -> List[int]:
+ try:
+ gpu_ids = docker_client.api.inspect_container(container_name)["HostConfig"]["DeviceRequests"][0]["DeviceIDs"]
+ return list(map(int, gpu_ids))
+ except Exception as e:
+ logging.error(f"Failed to get GPU IDs: {e}")
+ pass
+ return []
+
@staticmethod
def __convert(gpu: GPU) -> GPUCard:
return GPUCard(
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
index c25c34dcaa..5f0eb3b5c6 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
@@ -1,13 +1,17 @@
import logging
import math
+import re
import subprocess
import sys
from typing import List, Optional, Dict
+from docker import DockerClient
+
from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCard, GPUCardUtil, GPUCardType
class QualcommNPUtil(GPUCardUtil):
+ NPU_CARD_PATH = "/dev/accel/accel"
def __init__(self):
sys.path.append("/opt/qti-aic/dev/lib/x86_64/")
@@ -53,9 +57,22 @@ def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memo
@staticmethod
def get_docker_gpu_device_mapping(gpu_ids: List[int]) -> Optional[Dict]:
if gpu_ids and len(gpu_ids):
- return {"devices": [f"/dev/accel/accel{gpu_id}:/dev/accel/accel{gpu_id}" for gpu_id in gpu_ids]}
+ return {
+ "devices": [f"{QualcommNPUtil.NPU_CARD_PATH}{gpu_id}:{QualcommNPUtil.NPU_CARD_PATH}{gpu_id}" for gpu_id
+ in gpu_ids]}
return None
+ @staticmethod
+ def get_docker_gpu_ids_by_container_name(container_name: str, docker_client: DockerClient) -> List[int]:
+ gpu_ids = []
+ try:
+ docker_inspect_info = docker_client.api.inspect_container(container_name)
+ gpu_ids = QualcommNPUtil.__parse_gpu_ids(docker_inspect_info.get("HostConfig", {}))
+ except Exception as e:
+ logging.error(f"Failed to get GPU IDs: {e}")
+ pass
+ return gpu_ids
+
@staticmethod
def __convert(npu) -> GPUCard:
# TODO (alaydshah): Add support for load, memoryUtil, temperature
@@ -75,3 +92,39 @@ def __convert(npu) -> GPUCard:
memoryUsed=memory_used,
memoryUtil=memory_utilized,
)
+
+ @staticmethod
+ def __parse_gpu_ids(host_config: dict) -> List[int]:
+ devices = host_config.get('Devices', [])
+ gpu_ids = []
+ for device in devices:
+ gpu_id = QualcommNPUtil.__extract_integer_from_host_path(device.get('PathOnHost', None))
+
+ # Check explicitly if gpu_id is not None, as gpu_id can be 0, which is a valid value to include.
+ if gpu_id is not None:
+ gpu_ids.append(gpu_id)
+ return gpu_ids
+
+ @staticmethod
+ def __extract_integer_from_host_path(host_path: str) -> Optional[int]:
+ if not host_path:
+ logging.error("Host Path is None; GPU Id extraction Failed")
+ return None
+
+ npu_card_path = QualcommNPUtil.NPU_CARD_PATH
+
+ # Check if host_path starts with npu_card_path
+ if host_path.startswith(npu_card_path):
+
+ # Extract the numeric suffix from the host path
+ suffix = host_path[len(npu_card_path):] # Get the substring after npu_card_path
+ match = re.match(r'^(\d+)', suffix) # Use regex to match the leading integer
+ if match:
+ return int(match.group(1)) # Return the extracted integer
+ else:
+ logging.error(f"Failed to extract GPU id from Host Path {host_path}")
+ else:
+ logging.error(f"Host Path {host_path} doesn't start with NPU Card Path {npu_card_path}")
+
+ # Return None if extraction fails
+ return None
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index 140f316554..a0d27fd7db 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -1,6 +1,8 @@
import logging
from typing import Optional, List, Dict
+from docker import DockerClient
+
from fedml.computing.scheduler.comm_utils.gpu_utils.gpu_utils import GPUCardUtil, GPUCard
from fedml.computing.scheduler.comm_utils.gpu_utils.nvidia_utils import NvidiaGPUtil
from fedml.computing.scheduler.comm_utils.gpu_utils.qualcomm_utils import QualcommNPUtil
@@ -42,7 +44,17 @@ def get_available_gpu_ids(order: str = "memory", limit: int = 1, max_load: float
@staticmethod
def get_docker_gpu_device_mapping(gpu_ids: List[int]) -> Optional[Dict]:
gpu_util = HardwareUtil.__get_util()
- return gpu_util.get_docker_gpu_device_mapping(gpu_ids)
+ if gpu_util is not None:
+ return gpu_util.get_docker_gpu_device_mapping(gpu_ids)
+ return None
+
+ @staticmethod
+ def get_docker_gpu_ids_by_container_name(container_name: str, docker_client: DockerClient) -> List[int]:
+ gpu_ids = []
+ gpu_util = HardwareUtil.__get_util()
+ if gpu_util is not None:
+ gpu_ids = gpu_util.get_docker_gpu_ids_by_container_name(container_name, docker_client)
+ return gpu_ids
if __name__ == "__main__":
From 33da11e4024b617085b497e2fbc7a5a339fe4d45 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Sun, 12 May 2024 15:24:26 -0700
Subject: [PATCH 043/282] Make gpu stats fetching hardware agnostic
---
.../scheduler/comm_utils/container_utils.py | 40 +++++++------------
1 file changed, 14 insertions(+), 26 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/container_utils.py b/python/fedml/computing/scheduler/comm_utils/container_utils.py
index 4e09315b78..3d076c0ffe 100644
--- a/python/fedml/computing/scheduler/comm_utils/container_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/container_utils.py
@@ -320,7 +320,7 @@ def get_container_perf(self, c_name) -> ContainerMetrics:
round(blk_read_bytes / (1024 * 1024), 1), round(blk_write_bytes / (1024 * 1024), 1))
# Calculate the gpu usage
- gpus_stat = self.generate_container_gpu_stats(c_name)
+ gpus_stat = self.generate_container_gpu_stats(container_name=c_name)
# Record timestamp
timestamp = stats["read"]
@@ -328,39 +328,27 @@ def get_container_perf(self, c_name) -> ContainerMetrics:
return ContainerUtils.ContainerMetrics(cpu_percent, mem_gb_used, mem_gb_avail, recv_megabytes, sent_megabytes,
blk_read_bytes, blk_write_bytes, timestamp, gpus_stat)
- def generate_container_gpu_stats(self, c_name):
- gpu_ids = self.get_gpu_ids_by_container_name(c_name)
+ def generate_container_gpu_stats(self, container_name):
+ client = self.get_docker_client()
+ gpu_ids = HardwareUtil.get_docker_gpu_ids_by_container_name(container_name=container_name, docker_client=client)
gpu_stats = self.gpu_stats(gpu_ids)
return gpu_stats
- def get_gpu_ids_by_container_name(self, c_name):
- client = self.get_docker_client()
- gpu_ids = []
- try:
- gpu_ids = client.api.inspect_container(c_name)["HostConfig"]["DeviceRequests"][0]["DeviceIDs"]
- gpu_ids = list(map(int, gpu_ids))
- except Exception as e:
- logging.error(f"Failed to get GPU IDs: {e}")
- pass
-
- return gpu_ids
-
@staticmethod
def gpu_stats(gpu_ids):
utilz, memory, temp = None, None, None
gpu_stats_map = {} # gpu_id: int -> {"gpu_utilization", "gpu_memory_allocated", "gpu_temp"}
+ gpu_ids = set(gpu_ids)
try:
- gpus = HardwareUtil.get_gpus()
-
- for i in gpu_ids:
- gpu = gpus[i]
- gpu_stats_map[i] = {
- "gpu_utilization": gpu.load*100,
- "gpu_memory_allocated": gpu.memoryUtil*100,
- "gpu_temp": gpu.temperature,
- # "gpu_power_usage": pynvml.nvmlDeviceGetPowerUsage(handle) / 1000, # in watts
- # "gpu_time_spent_accessing_memory": utilz.memory # in ms
- }
+ for gpu in HardwareUtil.get_gpus():
+ if gpu.id in gpu_ids:
+ gpu_stats_map[gpu.id] = {
+ "gpu_utilization": gpu.load * 100,
+ "gpu_memory_allocated": gpu.memoryUsed / gpu.memoryTotal * 100,
+ "gpu_temp": gpu.temperature,
+ # "gpu_power_usage": pynvml.nvmlDeviceGetPowerUsage(handle) / 1000, # in watts
+ # "gpu_time_spent_accessing_memory": utilz.memory # in ms
+ }
except Exception as e:
logging.error(f"Failed to get GPU stats: {e}")
From 2d458a8342d7f545fe0991d0d6bb0bf2293a7cc8 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Sun, 12 May 2024 15:27:11 -0700
Subject: [PATCH 044/282] Nits
---
.../fedml/computing/scheduler/comm_utils/container_utils.py | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/container_utils.py b/python/fedml/computing/scheduler/comm_utils/container_utils.py
index 3d076c0ffe..c7645104c0 100644
--- a/python/fedml/computing/scheduler/comm_utils/container_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/container_utils.py
@@ -2,6 +2,8 @@
import os
import traceback
import datetime
+from typing import List
+
from dateutil.parser import isoparse
import docker
@@ -335,7 +337,7 @@ def generate_container_gpu_stats(self, container_name):
return gpu_stats
@staticmethod
- def gpu_stats(gpu_ids):
+ def gpu_stats(gpu_ids: List[int]):
utilz, memory, temp = None, None, None
gpu_stats_map = {} # gpu_id: int -> {"gpu_utilization", "gpu_memory_allocated", "gpu_temp"}
gpu_ids = set(gpu_ids)
From be635dbe2cf2ce29b36508846062fccc112485a9 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Sun, 12 May 2024 16:22:44 -0700
Subject: [PATCH 045/282] Update container creation during deployment
---
.../scheduler/comm_utils/container_utils.py | 5 +-
.../comm_utils/gpu_utils/gpu_utils.py | 2 +-
.../comm_utils/gpu_utils/nvidia_utils.py | 7 ++-
.../comm_utils/gpu_utils/qualcomm_utils.py | 4 +-
.../scheduler/comm_utils/hardware_utils.py | 4 +-
.../device_model_deployment.py | 59 +++++++++----------
6 files changed, 40 insertions(+), 41 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/container_utils.py b/python/fedml/computing/scheduler/comm_utils/container_utils.py
index c7645104c0..2f5fa31fb5 100644
--- a/python/fedml/computing/scheduler/comm_utils/container_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/container_utils.py
@@ -227,9 +227,8 @@ def pull_image_with_policy(self, image_pull_policy, image_name, client=None):
raise Exception(f"Unsupported image pull policy: {image_pull_policy}")
class ContainerMetrics:
- def __init__(self, cpu_percent, mem_used_megabytes, mem_avail_megabytes, network_recv_megabytes, network_sent_megabytes,
- blk_read_megabytes, blk_write_megabytes, timestamp, gpus_stat
- ):
+ def __init__(self, cpu_percent, mem_used_megabytes, mem_avail_megabytes, network_recv_megabytes,
+ network_sent_megabytes, blk_read_megabytes, blk_write_megabytes, timestamp, gpus_stat):
self.cpu_percent = cpu_percent
self.mem_used_megabytes = mem_used_megabytes
self.mem_avail_megabytes = mem_avail_megabytes
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
index 292bcb3624..bc7a3b8216 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
@@ -52,7 +52,7 @@ def get_gpu_cards() -> List[GPUCard]:
@staticmethod
@abstractmethod
- def get_docker_gpu_device_mapping(gpu_ids: List[int]) -> Optional[Dict]:
+ def get_docker_gpu_device_mapping(gpu_ids: Optional[List[int]], num_gpus: int = 0) -> Optional[Dict]:
raise NotImplementedError
@staticmethod
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
index 0c05b25644..34d0c3be1c 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
@@ -28,11 +28,12 @@ def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memo
return GPUtil.getAvailable(order=order, limit=limit, maxLoad=max_load, maxMemory=max_memory)
@staticmethod
- def get_docker_gpu_device_mapping(gpu_ids: List[int]) -> Optional[Dict]:
- if gpu_ids and len(gpu_ids):
+ def get_docker_gpu_device_mapping(gpu_ids: List[int], num_gpus: int = 0) -> Optional[Dict]:
+ if gpu_ids is not None and len(gpu_ids):
gpu_id_list = list(map(lambda x: str(x), gpu_ids))
return {"device_requests": [docker.types.DeviceRequest(device_ids=gpu_id_list, capabilities=[["gpu"]])]}
- return None
+ else:
+ return {"device_requests": [docker.types.DeviceRequest(count=num_gpus, capabilities=[['gpu']])]}
@staticmethod
def get_docker_gpu_ids_by_container_name(container_name: str, docker_client: DockerClient) -> List[int]:
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
index 5f0eb3b5c6..9c7ea21ea9 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
@@ -55,8 +55,8 @@ def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memo
return list(map(lambda card: card.id, gpu_cards))
@staticmethod
- def get_docker_gpu_device_mapping(gpu_ids: List[int]) -> Optional[Dict]:
- if gpu_ids and len(gpu_ids):
+ def get_docker_gpu_device_mapping(gpu_ids: Optional[List[int]], num_gpus: int = 0) -> Optional[Dict]:
+ if gpu_ids is not None and len(gpu_ids):
return {
"devices": [f"{QualcommNPUtil.NPU_CARD_PATH}{gpu_id}:{QualcommNPUtil.NPU_CARD_PATH}{gpu_id}" for gpu_id
in gpu_ids]}
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index a0d27fd7db..0062418631 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -42,10 +42,10 @@ def get_available_gpu_ids(order: str = "memory", limit: int = 1, max_load: float
return gpu_util.get_available_gpu_card_ids(order, limit, max_load, max_memory) if gpu_util is not None else []
@staticmethod
- def get_docker_gpu_device_mapping(gpu_ids: List[int]) -> Optional[Dict]:
+ def get_docker_gpu_device_mapping(gpu_ids: Optional[List[int]], num_gpus: int = 0) -> Optional[Dict]:
gpu_util = HardwareUtil.__get_util()
if gpu_util is not None:
- return gpu_util.get_docker_gpu_device_mapping(gpu_ids)
+ return gpu_util.get_docker_gpu_device_mapping(gpu_ids, num_gpus)
return None
@staticmethod
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index bd04228355..8d3be211a2 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -18,6 +18,7 @@
import fedml
from fedml.computing.scheduler.comm_utils import sys_utils, security_utils
from fedml.computing.scheduler.comm_utils.container_utils import ContainerUtils
+from fedml.computing.scheduler.comm_utils.hardware_utils import HardwareUtil
from fedml.computing.scheduler.comm_utils.job_utils import JobRunnerUtils
for type_name in collections.abc.__all__:
@@ -231,24 +232,6 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
except docker.errors.APIError:
raise Exception("Failed to get the container object")
- # Allocate the GPU
- # TODO: Make sure no competition for each replica in a single deployment
- if exist_container_obj is not None:
- client.api.remove_container(exist_container_obj.id, v=True, force=True)
- device_requests = []
- if no_real_gpu_allocation is not None:
- use_gpu = not no_real_gpu_allocation
- if use_gpu:
- logging.info("Number of GPUs: {}".format(num_gpus))
- if gpu_ids is not None:
- gpu_id_list = map(lambda x: str(x), gpu_ids)
- device_requests.append(
- docker.types.DeviceRequest(device_ids=list(gpu_id_list), capabilities=[['gpu']]))
- else:
- device_requests.append(
- docker.types.DeviceRequest(count=num_gpus, capabilities=[['gpu']]))
- logging.info(f"device_requests: {device_requests}")
-
# Pull the inference image
logging.info(f"Start pulling the inference image {inference_image_name}... with policy {image_pull_policy}")
ContainerUtils.get_instance().pull_image_with_policy(image_pull_policy, inference_image_name)
@@ -306,6 +289,32 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
}
environment["MAIN_ENTRY"] = relative_entry
+ host_config_dict = {
+ "binds": binds,
+ "port_bindings": {
+ port_inside_container: usr_indicated_worker_port
+ },
+ "shm_size": shm_size,
+ "storage_opt": storage_opt,
+ "tmpfs": tmpfs,
+ "cpu_count": cpus,
+ "mem_limit": memory
+ }
+
+ # Allocate the GPU
+ # TODO: Make sure no competition for each replica in a single deployment
+ if exist_container_obj is not None:
+ client.api.remove_container(exist_container_obj.id, v=True, force=True)
+ device_requests = {}
+ if no_real_gpu_allocation is not None:
+ use_gpu = not no_real_gpu_allocation
+ if use_gpu:
+ logging.info("Number of GPUs: {}".format(num_gpus))
+ device_requests = HardwareUtil.get_docker_gpu_device_mapping(gpu_ids, num_gpus)
+ logging.info(f"device_requests: {device_requests}")
+
+ host_config_dict.update(device_requests)
+
# Environment variables
if not enable_custom_image:
# For some image, the default user is root. Unified to fedml.
@@ -325,24 +334,14 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
environment[key] = extra_envs[key]
try:
+ host_config = client.api.create_host_config(**host_config_dict)
new_container = client.api.create_container(
image=inference_image_name,
name=default_server_container_name,
volumes=volumns,
ports=[port_inside_container], # port open inside the container
environment=environment,
- host_config=client.api.create_host_config(
- binds=binds,
- port_bindings={
- port_inside_container: usr_indicated_worker_port # Could be either None or a port number
- },
- device_requests=device_requests,
- shm_size=shm_size,
- storage_opt=storage_opt,
- tmpfs=tmpfs,
- cpu_count=cpus,
- mem_limit=memory,
- ),
+ host_config=host_config,
detach=True,
command=customized_image_entry_cmd if enable_custom_image else None,
entrypoint=customized_image_entry_cmd if enable_custom_image else None
From 9d5b54f1c0ab826e6a900def7bd26743500f4575 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Sun, 12 May 2024 18:02:52 -0700
Subject: [PATCH 046/282] Nit: Update naming
---
.../scheduler/model_scheduler/device_model_deployment.py | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 8d3be211a2..ca83f21c33 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -305,15 +305,15 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
# TODO: Make sure no competition for each replica in a single deployment
if exist_container_obj is not None:
client.api.remove_container(exist_container_obj.id, v=True, force=True)
- device_requests = {}
+ device_mapping = {}
if no_real_gpu_allocation is not None:
use_gpu = not no_real_gpu_allocation
if use_gpu:
logging.info("Number of GPUs: {}".format(num_gpus))
- device_requests = HardwareUtil.get_docker_gpu_device_mapping(gpu_ids, num_gpus)
- logging.info(f"device_requests: {device_requests}")
+ device_mapping = HardwareUtil.get_docker_gpu_device_mapping(gpu_ids, num_gpus)
+ logging.info(f"device_mapping: {device_mapping}")
- host_config_dict.update(device_requests)
+ host_config_dict.update(device_mapping)
# Environment variables
if not enable_custom_image:
From 2fcf57d9ca5b25dba4fe166ec7a95d9ea89c4239 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Sun, 12 May 2024 18:06:51 -0700
Subject: [PATCH 047/282] Add check as device_mapping can be None
---
.../scheduler/model_scheduler/device_model_deployment.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index ca83f21c33..1876373d25 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -313,7 +313,8 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
device_mapping = HardwareUtil.get_docker_gpu_device_mapping(gpu_ids, num_gpus)
logging.info(f"device_mapping: {device_mapping}")
- host_config_dict.update(device_mapping)
+ if device_mapping:
+ host_config_dict.update(device_mapping)
# Environment variables
if not enable_custom_image:
From 3f763957438256e63161bdd5d27ff262a855d340 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Tue, 14 May 2024 01:21:30 +0800
Subject: [PATCH 048/282] [CoreEngine] make the protocol manager and job runner
work on concurrent jobs.
---
.../model_scheduler/master_job_runner.py | 26 +++++++------------
.../master_protocol_manager.py | 8 +++---
.../model_scheduler/worker_job_runner.py | 1 +
.../scheduler_base_protocol_manager.py | 9 +++++++
.../slave/base_slave_protocol_manager.py | 7 +++--
5 files changed, 29 insertions(+), 22 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 13876d0184..e3073700d2 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -74,6 +74,7 @@ def run_impl(
model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
inference_end_point_id, use_gpu, memory_size, model_version, inference_port = \
FedMLDeployMasterJobRunner.parse_model_run_params(self.request_json)
+ self.run_id = run_id
# Print request parameters.
logging.info("model deployment request: {}".format(self.request_json))
@@ -120,9 +121,7 @@ def run_impl(
self.stop_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
self.start_device_inference_monitor(
- run_id, end_point_name, model_id, model_name, model_version,
- redis_addr=self.redis_addr, redis_port=self.redis_port, redis_password=self.redis_password
- )
+ run_id, end_point_name, model_id, model_name, model_version)
# Changed the status to "IDLE"
self.status_reporter.report_server_id_status(
@@ -467,7 +466,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
def start_device_inference_gateway(
run_id, end_point_name, model_id,
model_name, model_version, inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
- agent_config=None, redis_addr=None, redis_port=None, redis_password=None
+ agent_config=None, redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
):
# start unified inference server
running_model_name = ServerConstants.get_running_model_name(end_point_name,
@@ -515,7 +514,7 @@ def start_device_inference_gateway(
@staticmethod
def start_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version, check_stopped_event=True,
- redis_addr=None, redis_port=None, redis_password=None
+ redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
):
# start inference monitor server
# Will report the qps related metrics to the MLOps
@@ -530,7 +529,7 @@ def start_device_inference_monitor(
[python_program, monitor_file, "-v", fedml.get_env_version(), "-ep", run_id_str,
"-epn", str(end_point_name), "-mi", str(model_id), "-mn", model_name,
"-mv", model_version, "-iu", "infer_url", "-ra", redis_addr,
- "-rp", redis_port, "-rpw", redis_password],
+ "-rp", str(redis_port), "-rpw", redis_password],
should_capture_stdout=False, should_capture_stderr=False
)
return monitor_process
@@ -543,7 +542,7 @@ def stop_device_inference_monitor(run_id, end_point_name, model_id, model_name,
model_id, model_name, model_version)
@staticmethod
- def recover_inference_and_monitor(redis_addr=None, redis_port=None, redis_password=None):
+ def recover_inference_and_monitor():
# noinspection PyBroadException
try:
history_jobs = FedMLServerDataInterface.get_instance().get_history_jobs()
@@ -559,9 +558,8 @@ def recover_inference_and_monitor(redis_addr=None, redis_port=None, redis_passwo
inference_end_point_id, use_gpu, memory_size, model_version, inference_port = \
FedMLDeployMasterJobRunner.parse_model_run_params(json.loads(job.running_json))
- FedMLModelCache.get_instance().set_redis_params(redis_addr, redis_password)
- is_activated = FedMLModelCache.get_instance(redis_addr, redis_port). \
- get_end_point_activation(run_id)
+ FedMLModelCache.get_instance().set_redis_params()
+ is_activated = FedMLModelCache.get_instance().get_end_point_activation(run_id)
if not is_activated:
continue
@@ -573,16 +571,12 @@ def recover_inference_and_monitor(redis_addr=None, redis_port=None, redis_passwo
FedMLDeployMasterJobRunner.start_device_inference_gateway(
run_id, end_point_name, model_id, model_name, model_version, inference_port=inference_port,
- agent_config=agent_config, redis_addr=redis_addr, redis_port=redis_port, redis_password=redis_password)
+ agent_config=agent_config)
FedMLDeployMasterJobRunner.stop_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
FedMLDeployMasterJobRunner.start_device_inference_monitor(
- run_id, end_point_name, model_id, model_name, model_version,
- redis_addr=FedMLDeployMasterJobRunner.default_redis_addr,
- redis_port=FedMLDeployMasterJobRunner.default_redis_port,
- redis_password=FedMLDeployMasterJobRunner.default_redis_password
- )
+ run_id, end_point_name, model_id, model_name, model_version)
except Exception as e:
logging.info("recover inference and monitor: {}".format(traceback.format_exc()))
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index a5f2a37dfe..eb23bf1278 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -199,7 +199,7 @@ def callback_start_deployment(self, topic, payload):
self.request_json = request_json
run_id_str = str(run_id)
self.running_request_json[run_id_str] = request_json
- self.request_json["master_node_ip"] = GeneralConstants.get_ip_address(self.request_json)
+ self.request_json["master_node_ip"] = GeneralConstants.get_ip_address(request_json)
# Set the target status of the devices to redis
FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
@@ -217,13 +217,13 @@ def callback_start_deployment(self, topic, payload):
# Report stage to mlops: MODEL_DEPLOYMENT_STAGE1 = "Received"
FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
- self.run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE1["index"],
+ run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE1["index"],
ServerConstants.MODEL_DEPLOYMENT_STAGE1["text"], "Received request for endpoint {}".format(run_id),
message_center=self.message_center)
# Report stage to mlops: MODEL_DEPLOYMENT_STAGE2 = "Initializing"
FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
- self.run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE2["index"],
+ run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE2["index"],
ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"], ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"],
message_center=self.message_center)
@@ -262,7 +262,7 @@ def callback_start_deployment(self, topic, payload):
# Send stage: MODEL_DEPLOYMENT_STAGE3 = "StartRunner"
FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
- self.run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE3["index"],
+ run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE3["index"],
ServerConstants.MODEL_DEPLOYMENT_STAGE3["text"], ServerConstants.MODEL_DEPLOYMENT_STAGE3["text"],
message_center=self.message_center)
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index ac9328592c..f9cfdcd921 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -138,6 +138,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
inference_engine = model_config_parameters.get("inference_engine",
ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT)
inference_end_point_id = run_id
+ self.run_id = run_id
MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
index e3cac7a425..11d6fa44fd 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
@@ -232,6 +232,15 @@ def rebuild_status_center(self, status_center_queue):
self.status_reporter.edge_id = self.edge_id
self.status_reporter.server_agent_id = self.server_agent_id
+ def generate_status_report(self, run_id, edge_id, server_agent_id=None):
+ status_reporter = MLOpsMetrics()
+ status_reporter.set_messenger(self, send_message_func=self.send_status_message)
+ status_reporter.run_id = run_id
+ status_reporter.edge_id = edge_id
+ if server_agent_id is not None:
+ status_reporter.server_agent_id = server_agent_id
+ return status_reporter
+
@abstractmethod
def generate_protocol_manager(self):
# Generate the protocol manager instance and set the attribute values.
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index fc67ec2ece..4ff931e6fd 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -10,6 +10,7 @@
from ..comm_utils.constants import SchedulerConstants
from ..comm_utils.job_utils import JobRunnerUtils, DockerArgs
from ..comm_utils.run_process_utils import RunProcessUtils
+from ....core.mlops import MLOpsMetrics
from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
from ....core.mlops.mlops_configs import MLOpsConfigs
from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
@@ -55,8 +56,6 @@ def __init__(self, args, agent_config=None):
self.fl_topic_request_device_info = None
self.communication_mgr = None
self.subscribed_topics = list()
- self.mlops_metrics = None
- self.status_reporter = None
self.job_runners = dict()
self.ota_upgrade = FedMLOtaUpgrade(edge_id=args.edge_id)
self.running_request_json = dict()
@@ -263,6 +262,10 @@ def callback_start_train(self, topic, payload):
run_id, matched_gpu_num, edge_id, inner_id=endpoint_id,
model_master_device_id=model_master_device_id,
model_slave_device_id=model_slave_device_id)
+ else:
+ self.generate_status_report(run_id, edge_id, server_agent_id=server_agent_id).report_client_id_status(
+ edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED, run_id=run_id)
+ return
logging.info(
f"Run started, available gpu ids: {JobRunnerUtils.get_instance().get_available_gpu_id_list(edge_id)}")
From 3417f300f2d9977042cb5a9d8fb4531eec67ce57 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 15 May 2024 01:17:03 +0800
Subject: [PATCH 049/282] [CoreEngine] make the deployment status more stable.
---
python/fedml/api/api_test.py | 6 +--
.../model_scheduler/job_runner_msg_sender.py | 12 ++----
.../model_scheduler/master_job_runner.py | 3 +-
.../master_protocol_manager.py | 27 ++++++------
.../model_scheduler/worker_job_runner.py | 6 +--
.../worker_protocol_manager.py | 1 +
.../scheduler_core/general_constants.py | 13 ++++++
.../scheduler_base_job_runner.py | 42 +++++++++++++------
.../scheduler_base_protocol_manager.py | 1 +
.../scheduler/scheduler_core/status_center.py | 1 +
.../status_manager_protocols.py | 15 +++++++
.../scheduler/slave/base_slave_job_runner.py | 10 ++---
12 files changed, 91 insertions(+), 46 deletions(-)
diff --git a/python/fedml/api/api_test.py b/python/fedml/api/api_test.py
index fc2fb77b20..5d899bb1fd 100755
--- a/python/fedml/api/api_test.py
+++ b/python/fedml/api/api_test.py
@@ -4,7 +4,7 @@
import fedml
# Login
-fedml.set_env_version("local")
+fedml.set_env_version("test")
fedml.set_local_on_premise_platform_port(18080)
error_code, error_msg = fedml.api.fedml_login(api_key="1316b93c82da40ce90113a2ed12f0b14")
if error_code != 0:
@@ -19,7 +19,7 @@
# Launch job
launch_result_list = list()
-for i in range(0, 1):
+for i in range(0, 10):
launch_result = fedml.api.launch_job(yaml_file)
launch_result_list.append(launch_result)
# launch_result = fedml.api.launch_job_on_cluster(yaml_file, "alex-cluster")
@@ -33,7 +33,7 @@
if log_result is None or log_result.run_status is None:
print(f"Failed to get job status.")
#exit(1)
- print(f"Run status {log_result.run_status}")
+ print(f"Run {launch_result.run_id}, status {log_result.run_status}")
time.sleep(0.5)
# Get job logs
diff --git a/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py b/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
index 482a21b2d4..235c4deb74 100755
--- a/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
+++ b/python/fedml/computing/scheduler/model_scheduler/job_runner_msg_sender.py
@@ -33,8 +33,7 @@ def send_deployment_results(self, end_point_id, end_point_name,
model_name, model_inference_url,
model_version, inference_port, inference_engine,
model_metadata, model_config, input_json, output_json, replica_id_list=None):
- deployment_results_topic_prefix = "model_ops/model_device/return_deployment_result"
- deployment_results_topic = "{}/{}".format(deployment_results_topic_prefix, end_point_id)
+ deployment_results_topic = "model_ops/model_device/return_deployment_result"
deployment_results_payload = {"end_point_id": end_point_id, "end_point_name": end_point_name,
"model_name": model_name, "model_url": model_inference_url,
"version": model_version, "port": inference_port,
@@ -48,15 +47,13 @@ def send_deployment_results(self, end_point_id, end_point_name,
logging.info(f"[Master] deployment_results_payload is sent to mlops: {deployment_results_payload}")
self.message_center.send_message_json(deployment_results_topic, json.dumps(deployment_results_payload))
- self.message_center.send_message_json(deployment_results_topic_prefix, json.dumps(deployment_results_payload))
@staticmethod
def send_deployment_status(
end_point_id, end_point_name, model_name, model_inference_url, model_status, message_center=None):
if message_center is None:
return
- deployment_status_topic_prefix = "model_ops/model_device/return_deployment_status"
- deployment_status_topic = "{}/{}".format(deployment_status_topic_prefix, end_point_id)
+ deployment_status_topic = "model_ops/model_device/return_deployment_status"
deployment_status_payload = {"end_point_id": end_point_id, "end_point_name": end_point_name,
"model_name": model_name,
"model_url": model_inference_url,
@@ -65,7 +62,6 @@ def send_deployment_status(
logging.info(f"[Master] deployment_status_payload is sent to mlops: {deployment_status_payload}")
message_center.send_message_json(deployment_status_topic, json.dumps(deployment_status_payload))
- message_center.send_message_json(deployment_status_topic_prefix, json.dumps(deployment_status_payload))
@staticmethod
def send_deployment_stages(end_point_id, model_name, model_id, model_inference_url,
@@ -73,8 +69,7 @@ def send_deployment_stages(end_point_id, model_name, model_id, model_inference_u
message_center=None):
if message_center is None:
return
- deployment_stages_topic_prefix = "model_ops/model_device/return_deployment_stages"
- deployment_stages_topic = "{}/{}".format(deployment_stages_topic_prefix, end_point_id)
+ deployment_stages_topic = "model_ops/model_device/return_deployment_stages"
deployment_stages_payload = {"model_name": model_name,
"model_id": model_id,
"model_url": model_inference_url,
@@ -85,7 +80,6 @@ def send_deployment_stages(end_point_id, model_name, model_id, model_inference_u
"timestamp": int(format(time.time_ns() / 1000.0, '.0f'))}
message_center.send_message_json(deployment_stages_topic, json.dumps(deployment_stages_payload))
- message_center.send_message_json(deployment_stages_topic_prefix, json.dumps(deployment_stages_payload))
logging.info(f"-------- Stages has been sent to mlops with stage {model_stages_index} and "
f"payload {deployment_stages_payload}")
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index e3073700d2..8ce9d0e102 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -41,6 +41,7 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
agent_log_file_dir=ServerConstants.get_log_file_dir()
)
+ self.is_deployment_runner = True
self.infer_host = "127.0.0.1"
self.redis_addr = "local"
self.redis_port = "6379"
@@ -306,7 +307,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
return
else:
# This is the last worker that failed, so we should continue to "ABORTED" status
- model_config_parameters = self.running_request_json[run_id_str]["parameters"]
+ model_config_parameters = self.request_json["parameters"]
inference_port = model_config_parameters.get("server_internal_port",
ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
inference_port_external = model_config_parameters.get("server_external_port", inference_port)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index eb23bf1278..b4c5b41d74 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -18,6 +18,7 @@ def __init__(self, args, agent_config=None):
FedMLBaseMasterProtocolManager.__init__(self, args, agent_config=agent_config)
self.message_center_name = "deploy_master_agent"
+ self.is_deployment_status_center = True
self.topic_start_deployment = None
self.topic_activate_endpoint = None
@@ -215,18 +216,6 @@ def callback_start_deployment(self, topic, payload):
self.subscribe_deployment_messages_from_slave_devices(request_json)
- # Report stage to mlops: MODEL_DEPLOYMENT_STAGE1 = "Received"
- FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
- run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE1["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE1["text"], "Received request for endpoint {}".format(run_id),
- message_center=self.message_center)
-
- # Report stage to mlops: MODEL_DEPLOYMENT_STAGE2 = "Initializing"
- FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
- run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE2["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"], ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"],
- message_center=self.message_center)
-
ServerConstants.save_runner_infos(self.args.device_id + "." + self.args.os_name, self.edge_id, run_id=run_id)
# Num diff
@@ -260,6 +249,18 @@ def callback_start_deployment(self, topic, payload):
if process is not None:
ServerConstants.save_run_process(run_id, process.pid)
+ # Report stage to mlops: MODEL_DEPLOYMENT_STAGE1 = "Received"
+ FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
+ run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE1["index"],
+ ServerConstants.MODEL_DEPLOYMENT_STAGE1["text"], "Received request for endpoint {}".format(run_id),
+ message_center=self.message_center)
+
+ # Report stage to mlops: MODEL_DEPLOYMENT_STAGE2 = "Initializing"
+ FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
+ run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE2["index"],
+ ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"], ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"],
+ message_center=self.message_center)
+
# Send stage: MODEL_DEPLOYMENT_STAGE3 = "StartRunner"
FedMLDeployJobRunnerManager.get_instance().send_deployment_stages(
run_id, model_name, model_id, "", ServerConstants.MODEL_DEPLOYMENT_STAGE3["index"],
@@ -328,6 +329,8 @@ def subscribe_deployment_messages_from_slave_devices(self, request_json):
logging.info("subscribe device messages {}".format(deployment_results_topic))
+ self.setup_listeners_for_edge_status(run_id, edge_id_list, self.edge_id)
+
def subscribe_spec_device_message(self, run_id, device_id):
if device_id == self.edge_id:
return
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index f9cfdcd921..332dab2547 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -31,6 +31,7 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
agent_log_file_dir=ClientConstants.get_log_file_dir()
)
+ self.is_deployment_runner = True
self.infer_host = "127.0.0.1"
self.redis_addr = "local"
self.redis_port = "6379"
@@ -286,11 +287,8 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
inference_engine, model_metadata, model_config)
self.status_reporter.run_id = self.run_id
- self.status_reporter.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- is_from_model=True, run_id=self.run_id)
- return False
+ raise Exception("[Worker] Failed to deploy the model.")
else:
# Send failed successful result back to master
logging.info("Finished deployment, continue to send results to master...")
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
index 3a0f835b6c..f9bc70452d 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
@@ -24,6 +24,7 @@ def __init__(self, args, agent_config=None):
FedMLBaseSlaveProtocolManager.__init__(self, args, agent_config=agent_config)
self.message_center_name = "deploy_slave_agent"
+ self.is_deployment_status_center = True
self.topic_start_deployment = None
self.topic_delete_deployment = None
diff --git a/python/fedml/computing/scheduler/scheduler_core/general_constants.py b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
index ba8842b30e..0cc6044d4b 100755
--- a/python/fedml/computing/scheduler/scheduler_core/general_constants.py
+++ b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
@@ -44,6 +44,19 @@ class GeneralConstants:
MSG_MLOPS_SERVER_STATUS_FINISHED = "FINISHED"
MSG_MLOPS_SERVER_STATUS_EXCEPTION = "EXCEPTION"
+ MSG_MODELOPS_DEPLOYMENT_STATUS_INITIALIZING = "INITIALIZING"
+ MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYING = "DEPLOYING"
+ MSG_MODELOPS_DEPLOYMENT_STATUS_INFERRING = "INFERRING"
+ MSG_MODELOPS_DEPLOYMENT_STATUS_OVERLOAD = "OVERLOAD"
+ MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED = "FAILED"
+ MSG_MODELOPS_DEPLOYMENT_STATUS_RESCALING = "RESCALING"
+ MSG_MODELOPS_DEPLOYMENT_STATUS_UPDATING = "UPDATING"
+ MSG_MODELOPS_DEPLOYMENT_STATUS_UPDATING_FAILED = "UPDATING_FAILED"
+ MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTING = "ABORTING"
+ MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTED = "ABORTED"
+ MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED = "DEPLOYED"
+ MSG_MODELOPS_DEPLOYMENT_STATUS_KILLED = "KILLED"
+
MASTER_LOGIN_PROGRAM = "server_login.py"
SLAVE_LOGIN_PROGRAM = "client_login.py"
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 03d3fd5d92..69b69f4d4c 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -1,5 +1,6 @@
import json
import logging
+import multiprocessing
import os
import platform
import random
@@ -7,6 +8,7 @@
import time
import traceback
import zipfile
+import queue
from ..comm_utils.constants import SchedulerConstants
from ..comm_utils.job_utils import JobRunnerUtils, DockerArgs
from ..scheduler_entry.constants import Constants
@@ -82,8 +84,7 @@ def __init__(self, args, edge_id=0, request_json=None, agent_config=None, run_id
"${FEDSYS.CLIENT_OBJECT_LIST}": "",
"${FEDSYS.LOG_SERVER_URL}": "",
}
- self.download_time = time.time()
- self.download_finished = False
+ self.is_deployment_runner = False
def __repr__(self):
return "<{klass} @{id:x} {attrs}>".format(
@@ -162,7 +163,7 @@ def package_download_progress(self, count, blksize, filesize):
self.prev_download_progress = progress_int
logging.info("package downloaded size {} KB, progress {}%".format(downloaded_kb, progress_int))
- def download_package_proc(self, package_url, local_package_file):
+ def download_package_proc(self, package_url, local_package_file, completed_event, info_queue):
import requests
headers = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
'AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36'}
@@ -188,8 +189,8 @@ def download_package_proc(self, package_url, local_package_file):
written_size = f.write(chunk)
total_size += written_size
logging.info("package downloaded size %.2f KB", total_size/1024)
- self.download_time = time.time()
- self.download_finished = True
+ info_queue.put(time.time())
+ completed_event.set()
def retrieve_and_unzip_package(self, package_name, package_url):
local_package_path = self.agent_package_download_dir
@@ -202,26 +203,43 @@ def retrieve_and_unzip_package(self, package_name, package_url):
ssl._create_default_https_context = ssl._create_unverified_context
# Open a process to download the package so that we can avoid the request is blocked and check the timeout.
- self.download_finished = False
- self.download_time = time.time()
from multiprocessing import Process
- download_process = Process(target=self.download_package_proc, args=(package_url, local_package_file))
+ completed_event = multiprocessing.Event()
+ info_queue = multiprocessing.Queue()
+ download_process = Process(target=self.download_package_proc,
+ args=(package_url, local_package_file, completed_event, info_queue))
download_process.start()
- allowed_block_download_time = 30
+ allowed_block_download_time = 60
+ download_finished = False
+ download_time = time.time()
while True:
- block_time = time.time() - self.download_time
+ try:
+ queue_time = info_queue.get(block=False, timeout=3)
+ download_time = queue_time
+ except queue.Empty as e:
+ pass
+
+ block_time = time.time() - download_time
if block_time > allowed_block_download_time:
break
- if self.download_finished:
+
+ if completed_event.is_set():
+ download_finished = True
break
time.sleep(3)
try:
- if not self.download_finished:
+ if not download_finished:
download_process.terminate()
download_process.kill()
except Exception as e:
pass
+ if not download_finished:
+ raise Exception("Download timeout, please check if your network is stable.")
+
+ if not os.path.exists(local_package_file):
+ raise Exception(f"Failed to download, the zip file is not exist at {local_package_file}.")
+
# Another method to async download.
# import socket
# socket.setdefaulttimeout(15)
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
index 11d6fa44fd..9bb8b7a7ec 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
@@ -224,6 +224,7 @@ def start_status_listener_center(self):
def rebuild_status_center(self, status_center_queue):
self.status_center = FedMLStatusCenter(message_queue=status_center_queue)
+ self.status_center.is_deployment_status_center = self.is_deployment_status_center
if self.status_reporter is None:
self.status_reporter = MLOpsMetrics()
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index 76f811993e..4a55dbb679 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -99,6 +99,7 @@ def __init__(self, message_queue=None):
self.status_message_center = None
self.status_manager_instance = None
self.status_runner = None
+ self.is_deployment_status_center = False
def __repr__(self):
return "<{klass} @{id:x} {attrs}>".format(
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index 811ff2a2d5..01caf7db67 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -2,6 +2,7 @@
import logging
import os
import shutil
+import time
from os import listdir
from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
@@ -67,6 +68,9 @@ def process_job_completed_status(self, master_id, status):
# self.remove_listener_for_run_metrics(self.run_id)
# self.remove_listener_for_run_logs(self.run_id)
+ if self.status_center.is_deployment_status_center and status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
+ self.report_deployment_status(self.run_id, GeneralConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
+
def process_job_exception_status(self, master_id, status):
# Send the exception status to slave devices.
self.report_exception_status(
@@ -302,3 +306,14 @@ def status_center_request_job_status_from_master_in_slave_agent(self, topic, pay
topic_request_job_status = f"{GeneralConstants.MSG_TOPIC_REQUEST_JOB_STATUS_PREFIX}{master_id}"
payload_request_job_status = {"run_id": run_id, "edge_id": edge_id}
self.message_center.send_message(topic_request_job_status, json.dumps(payload_request_job_status))
+
+ def report_deployment_status(self, run_id, status):
+ deployment_status_topic = "model_ops/model_device/return_deployment_status"
+ deployment_status_payload = {"end_point_id": run_id, "end_point_name": "",
+ "model_name": "",
+ "model_url": "",
+ "model_status": status,
+ "timestamp": int(format(time.time_ns() / 1000.0, '.0f'))}
+ logging.info(f"[StatusCenter] deployment_status_payload is sent to mlops: {deployment_status_payload}")
+
+ self.message_center.send_message_json(deployment_status_topic, json.dumps(deployment_status_payload))
diff --git a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
index cc7c3c222b..5e530dbba7 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
@@ -71,7 +71,7 @@ def run(self, process_event, completed_event, run_extend_queue_list,
logging.error(f"Runner exited with errors. Exception: {e}, Traceback {traceback.format_exc()}")
self.status_reporter.report_client_id_status(
self.edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- server_id=self.server_id, run_id=self.run_id)
+ is_from_model=self.is_deployment_runner, server_id=self.server_id, run_id=self.run_id)
finally:
if self.mlops_metrics is not None:
computing_ended_time = MLOpsUtils.get_ntp_time()
@@ -107,7 +107,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
self.status_reporter.report_client_id_status(
self.edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_INITIALIZING,
- running_json=json.dumps(self.request_json), run_id=run_id)
+ is_from_model=self.is_deployment_runner, running_json=json.dumps(self.request_json), run_id=run_id)
# get training params
private_local_data_dir = data_config.get("privateLocalData", "")
@@ -192,7 +192,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
self.status_reporter.report_client_id_status(
self.edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
- server_id=self.server_id, run_id=run_id)
+ is_from_model=self.is_deployment_runner, server_id=self.server_id, run_id=run_id)
if is_launch_task:
sys_utils.log_return_info(f"job {run_id}", ret_code)
@@ -225,7 +225,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
# Send failed msg when exceptions.
self.status_reporter.report_client_id_status(
self.edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- server_id=self.server_id, run_id=run_id)
+ is_from_model=self.is_deployment_runner, server_id=self.server_id, run_id=run_id)
@abstractmethod
def _generate_job_runner_instance(self, args, run_id=None, request_json=None, agent_config=None, edge_id=None):
@@ -239,7 +239,7 @@ def reset_devices_status(self, edge_id, status):
self.status_reporter.run_id = self.run_id
self.status_reporter.edge_id = edge_id
self.status_reporter.report_client_id_status(
- edge_id, status, server_id=self.server_id, run_id=self.run_id)
+ edge_id, status, is_from_model=self.is_deployment_runner, server_id=self.server_id, run_id=self.run_id)
def start_runner_process(
self, run_id, request_json, edge_id=None,
From 060eef68749c68cf1d5ad187c8c8f82f2dd78e1c Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 15 May 2024 01:27:53 +0800
Subject: [PATCH 050/282] [CoreEngine] update the keys.
---
python/fedml/api/api_test.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/api/api_test.py b/python/fedml/api/api_test.py
index 5d899bb1fd..5a01a76448 100755
--- a/python/fedml/api/api_test.py
+++ b/python/fedml/api/api_test.py
@@ -6,7 +6,7 @@
# Login
fedml.set_env_version("test")
fedml.set_local_on_premise_platform_port(18080)
-error_code, error_msg = fedml.api.fedml_login(api_key="1316b93c82da40ce90113a2ed12f0b14")
+error_code, error_msg = fedml.api.fedml_login(api_key="")
if error_code != 0:
print("API Key is invalid!")
exit(1)
From 66af3db9bbe06fd13f91ead915dd4e860dd83bac Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 15 May 2024 01:57:35 +0800
Subject: [PATCH 051/282] [CoreEngine] make the client and server runner
deprecated.
---
.../master/{server_runner.py => server_runner_deprecated.py} | 0
...device_client_runner.py => device_client_runner_deprecated.py} | 0
...device_server_runner.py => device_server_runner_deprecated.py} | 0
.../slave/{client_runner.py => client_runner_deprecated.py} | 0
4 files changed, 0 insertions(+), 0 deletions(-)
rename python/fedml/computing/scheduler/master/{server_runner.py => server_runner_deprecated.py} (100%)
rename python/fedml/computing/scheduler/model_scheduler/{device_client_runner.py => device_client_runner_deprecated.py} (100%)
rename python/fedml/computing/scheduler/model_scheduler/{device_server_runner.py => device_server_runner_deprecated.py} (100%)
rename python/fedml/computing/scheduler/slave/{client_runner.py => client_runner_deprecated.py} (100%)
diff --git a/python/fedml/computing/scheduler/master/server_runner.py b/python/fedml/computing/scheduler/master/server_runner_deprecated.py
similarity index 100%
rename from python/fedml/computing/scheduler/master/server_runner.py
rename to python/fedml/computing/scheduler/master/server_runner_deprecated.py
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_runner.py b/python/fedml/computing/scheduler/model_scheduler/device_client_runner_deprecated.py
similarity index 100%
rename from python/fedml/computing/scheduler/model_scheduler/device_client_runner.py
rename to python/fedml/computing/scheduler/model_scheduler/device_client_runner_deprecated.py
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_runner.py b/python/fedml/computing/scheduler/model_scheduler/device_server_runner_deprecated.py
similarity index 100%
rename from python/fedml/computing/scheduler/model_scheduler/device_server_runner.py
rename to python/fedml/computing/scheduler/model_scheduler/device_server_runner_deprecated.py
diff --git a/python/fedml/computing/scheduler/slave/client_runner.py b/python/fedml/computing/scheduler/slave/client_runner_deprecated.py
similarity index 100%
rename from python/fedml/computing/scheduler/slave/client_runner.py
rename to python/fedml/computing/scheduler/slave/client_runner_deprecated.py
From 54f8b00201dad42b04f232b3d9cb310c214a1ed2 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 15 May 2024 18:12:16 +0800
Subject: [PATCH 052/282] [CoreEngine] make the cloud agent work.
---
.../scheduler/master/base_master_agent.py | 6 ++---
.../master/base_master_job_runner.py | 4 ++++
.../master/base_master_job_runner_manager.py | 13 ++++++++++
.../master/base_master_protocol_manager.py | 24 ++++++++++++-------
.../scheduler/master/cloud_server_manager.py | 6 ++++-
.../scheduler/master/server_login.py | 2 +-
.../scheduler_core/account_manager.py | 8 ++++---
7 files changed, 47 insertions(+), 16 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_agent.py b/python/fedml/computing/scheduler/master/base_master_agent.py
index 66bc35d96f..3aff523c24 100755
--- a/python/fedml/computing/scheduler/master/base_master_agent.py
+++ b/python/fedml/computing/scheduler/master/base_master_agent.py
@@ -17,18 +17,18 @@ def __init__(self):
self.master_api_process = None
self.mlops_metrics = MLOpsMetrics()
self.status_reporter = None
- self.enable_simulation_cloud_agent = True
+ self.enable_simulation_cloud_agent = False
self.use_local_process_as_cloud_server = False
self.protocol_mgr = None
def login(
self, user_id, api_key=None, device_id=None,
- os_name=None, role=None
+ os_name=None, role=None, runner_cmd=None
):
# Login account
login_result = FedMLAccountManager.get_instance().login(
user_id, api_key=api_key, device_id=device_id,
- os_name=os_name, role=role
+ os_name=os_name, role=role, runner_cmd=runner_cmd
)
if login_result is not None:
self.agent_args = login_result
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner.py b/python/fedml/computing/scheduler/master/base_master_job_runner.py
index 07c297c65d..1827de481d 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner.py
@@ -136,6 +136,10 @@ def run_impl(
logging.info("Detect all status of Edge ids: " + str(edge_ids))
+ self.status_reporter.report_server_id_status(
+ self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_STARTING, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.edge_id)
+
status_ok, active_edge_info_dict, inactivate_edges = self.detect_edges_status(
edge_device_info_queue, edge_device_info_global_queue=edge_device_info_global_queue,
callback_when_edges_ready=self.send_training_request_to_edges)
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
index 694fab5f5f..b1066910c1 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
@@ -36,6 +36,19 @@ def start_job_runner(
status_center_queue=status_center_queue
)
+ def stop_job_runner(
+ self, run_id, args=None, edge_id=None, request_json=None,
+ run_as_cloud_agent=False
+ ):
+ super().stop_job_runner(run_id)
+
+ if run_as_cloud_agent:
+ cloud_server_mgr = FedMLCloudServerManager(
+ args, run_id=run_id, edge_id=edge_id, request_json=request_json,
+ agent_config=args.agent_config
+ )
+ cloud_server_mgr.stop_cloud_server()
+
def _start_cloud_server(
self, args, run_id, request_json, edge_id=None,
use_local_process_as_cloud_server=False
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index ef59431ee8..b8479ebc03 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -44,7 +44,7 @@ def __init__(self, args, agent_config=None):
self.run_as_cloud_server = False
self.run_as_edge_server_and_agent = False
self.run_as_cloud_server_and_agent = False
- self.enable_simulation_cloud_agent = True
+ self.enable_simulation_cloud_agent = False
self.use_local_process_as_cloud_server = False
self.ota_upgrade = FedMLOtaUpgrade(edge_id=args.edge_id)
self.running_request_json = dict()
@@ -140,12 +140,6 @@ def callback_start_train(self, topic=None, payload=None):
except Exception:
pass
- # Parse the message when running in the cloud server mode.
- if self.run_as_cloud_server:
- message_bytes = payload.encode("ascii")
- base64_bytes = base64.b64decode(message_bytes)
- payload = base64_bytes.decode("ascii")
-
# Parse the parameters
# [NOTES] Example Request JSON:
# https://fedml-inc.larksuite.com/wiki/ScnIwUif9iupbjkYS0LuBrd6sod#WjbEdhYrvogmlGxKTOGu98C6sSb
@@ -264,6 +258,9 @@ def callback_stop_train(self, topic, payload, use_payload=None):
run_id = request_json.get("runId", None)
run_id = request_json.get("id", None) if run_id is None else run_id
run_id_str = str(run_id)
+ server_id = request_json.get("serverId", None)
+ if server_id is None:
+ server_id = request_json.get("server_id", None)
# Broadcast the job status to all edges
self.rebuild_status_center(self.get_status_queue())
@@ -273,8 +270,14 @@ def callback_stop_train(self, topic, payload, use_payload=None):
if self.running_request_json.get(run_id_str, None) is not None:
self.running_request_json.pop(run_id_str)
+ # Send the stopping request to edges
+ if self.run_as_cloud_agent:
+ self.send_training_stop_request_to_cloud_server(server_id, payload)
+
# Stop the job runner
- self._get_job_runner_manager().stop_job_runner(run_id)
+ self._get_job_runner_manager().stop_job_runner(
+ run_id, args=self.args, edge_id=self.edge_id, request_json=request_json,
+ run_as_cloud_agent=self.run_as_cloud_agent)
def callback_run_logs(self, topic, payload):
run_id = str(topic).split('/')[-1]
@@ -498,6 +501,11 @@ def send_training_stop_request_to_specific_edge(self, edge_id, payload):
logging.info("stop_train: send topic " + topic_stop_train)
self.message_center.send_message(topic_stop_train, payload)
+ def send_training_stop_request_to_cloud_server(self, edge_id, payload):
+ topic_stop_train = "mlops/flserver_agent_" + str(edge_id) + "/stop_train"
+ logging.info("stop_train: send topic " + topic_stop_train)
+ self.message_center.send_message(topic_stop_train, payload)
+
def send_status_check_msg(self, run_id, edge_id, server_id, context=None):
topic_status_check = f"server/client/request_device_info/{edge_id}"
payload = {"server_id": server_id, "run_id": run_id}
diff --git a/python/fedml/computing/scheduler/master/cloud_server_manager.py b/python/fedml/computing/scheduler/master/cloud_server_manager.py
index ed39707034..342d785afe 100755
--- a/python/fedml/computing/scheduler/master/cloud_server_manager.py
+++ b/python/fedml/computing/scheduler/master/cloud_server_manager.py
@@ -3,6 +3,8 @@
import logging
import os
import traceback
+
+import fedml
from fedml.computing.scheduler.comm_utils.sys_utils import get_python_program
@@ -18,10 +20,12 @@ def __init__(self, args, run_id=None, edge_id=None, request_json=None, agent_con
self.edge_id = edge_id
self.request_json = request_json
self.agent_config = agent_config
+ if version is None:
+ version = fedml.get_env_version()
self.version = version
image_version = self.version
if image_version == "local":
- image_version = "dev"
+ image_version = "test"
self.server_docker_base_image = "/fedml-device-image:" + image_version
self.cloud_server_name = None
diff --git a/python/fedml/computing/scheduler/master/server_login.py b/python/fedml/computing/scheduler/master/server_login.py
index 3d8d1f6fc9..8dd0696bc8 100755
--- a/python/fedml/computing/scheduler/master/server_login.py
+++ b/python/fedml/computing/scheduler/master/server_login.py
@@ -39,6 +39,6 @@ def logout():
master_agent = FedMLLaunchMasterAgent()
if args.type == 'login':
master_agent.login(args.api_key, api_key=args.api_key, device_id=args.device_id,
- os_name=args.os_name, role=args.role)
+ os_name=args.os_name, role=args.role, runner_cmd=args.runner_cmd)
else:
master_agent.logout()
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
index da04fc3989..4b4cc9fd31 100755
--- a/python/fedml/computing/scheduler/scheduler_core/account_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -48,10 +48,10 @@ def __init__(self):
def get_instance():
return FedMLAccountManager()
- def login(self, user_id, api_key="", device_id=None, os_name=None, role=None):
+ def login(self, user_id, api_key="", device_id=None, os_name=None, role=None, runner_cmd=None):
# Build the agent args
self.build_agent_args(
- user_id, api_key=api_key, device_id=device_id, os_name=os_name, role=role
+ user_id, api_key=api_key, device_id=device_id, os_name=os_name, role=role, runner_cmd=runner_cmd
)
# Fetch configs from the MLOps config server.
@@ -126,7 +126,7 @@ def login(self, user_id, api_key="", device_id=None, os_name=None, role=None):
return self.agent_args
- def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None, role=None):
+ def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None, role=None, runner_cmd=None):
# Generate the suffix for device based on the role
device_id_suffix = None
is_master = False
@@ -197,6 +197,7 @@ def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None,
# Set the unique device id
self.agent_args.is_from_docker = is_from_docker or is_from_fedml_docker_hub
self.agent_args.unique_device_id = unique_device_id
+ self.agent_args.runner_cmd = runner_cmd
def fill_argent_args(
self, log_server_url=None, server_id=None, edge_id=None,
@@ -440,6 +441,7 @@ def __init__(self, role=None, account_id=None, api_key=None, server_id=None, cur
self.using_mlops = True
self.server_agent_id = None
self.general_edge_id = None
+ self.runner_cmd = None
def is_cloud_server(self):
return self.role == FedMLAccountManager.ROLE_CLOUD_SERVER
From 75cd1781468de07756af9d194d4b77b8eba2432b Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 15 May 2024 18:19:29 +0800
Subject: [PATCH 053/282] [CoreEngine] update the version.
---
python/fedml/__init__.py | 2 +-
python/setup.py | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index 8044387b65..6b3ac3f61b 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -34,7 +34,7 @@
_global_training_type = None
_global_comm_backend = None
-__version__ = "0.8.30"
+__version__ = "0.8.31"
# This is the deployment environment used for different roles (RD/PM/BD/Public Developers). Potential VALUE: local, dev, test, release
diff --git a/python/setup.py b/python/setup.py
index cce0ddb2ca..fa425c98f7 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -120,7 +120,7 @@ def finalize_options(self):
setup(
name="fedml",
- version="0.8.30",
+ version="0.8.31",
author="FedML Team",
author_email="ch@fedml.ai",
description="A research and production integrated edge-cloud library for "
From 90dc19bb957fb0b175d5e1f75e1bc3e91c18b700 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 15 May 2024 19:32:36 +0800
Subject: [PATCH 054/282] [CoreEngine] stop the run in the cloud agent.
---
.../master/base_master_job_runner_manager.py | 10 ++--
.../master/base_master_protocol_manager.py | 25 +++++++--
.../scheduler/master/cloud_server_manager.py | 53 +++++++++++--------
.../scheduler_core/general_constants.py | 5 ++
.../status_manager_protocols.py | 3 ++
5 files changed, 64 insertions(+), 32 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
index b1066910c1..f4735227bc 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
@@ -37,17 +37,15 @@ def start_job_runner(
)
def stop_job_runner(
- self, run_id, args=None, edge_id=None, request_json=None,
+ self, run_id, args=None, server_id=None, request_json=None,
run_as_cloud_agent=False
):
super().stop_job_runner(run_id)
if run_as_cloud_agent:
- cloud_server_mgr = FedMLCloudServerManager(
- args, run_id=run_id, edge_id=edge_id, request_json=request_json,
- agent_config=args.agent_config
- )
- cloud_server_mgr.stop_cloud_server()
+ stopping_process = Process(
+ target=FedMLCloudServerManager.stop_cloud_server, args=(run_id, server_id, args.agent_config))
+ stopping_process.start()
def _start_cloud_server(
self, args, run_id, request_json, edge_id=None,
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index b8479ebc03..0873548a42 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -33,6 +33,7 @@ def __init__(self, args, agent_config=None):
self.agent_config = agent_config
self.topic_start_train = None
self.topic_stop_train = None
+ self.topic_exit_train = None
self.topic_report_status = None
self.topic_ota_msg = None
self.topic_response_device_info = None
@@ -61,6 +62,9 @@ def generate_topics(self):
# The topi for stopping training
self.topic_stop_train = "mlops/flserver_agent_" + str(self.edge_id) + "/stop_train"
+ # The topi for exiting training
+ self.topic_exit_train = GeneralConstants.get_topic_exit_train(self.edge_id)
+
# The topic for reporting current device status.
self.topic_report_status = "mlops/report_device_status"
@@ -89,6 +93,7 @@ def generate_topics(self):
self.subscribed_topics.clear()
self.add_subscribe_topic(self.topic_start_train)
self.add_subscribe_topic(self.topic_stop_train)
+ self.add_subscribe_topic(self.topic_exit_train)
self.add_subscribe_topic(self.topic_report_status)
self.add_subscribe_topic(self.topic_ota_msg)
self.add_subscribe_topic(self.topic_response_device_info)
@@ -103,6 +108,7 @@ def add_protocol_handler(self):
# Add the message listeners for all topics
self.add_message_listener(self.topic_start_train, self.callback_start_train)
self.add_message_listener(self.topic_stop_train, self.callback_stop_train)
+ self.add_message_listener(self.topic_exit_train, self.callback_exit_train)
self.add_message_listener(self.topic_ota_msg, FedMLBaseMasterProtocolManager.callback_server_ota_msg)
self.add_message_listener(self.topic_report_status, self.callback_report_current_status)
self.add_message_listener(self.topic_response_device_info, self.callback_response_device_info)
@@ -270,13 +276,24 @@ def callback_stop_train(self, topic, payload, use_payload=None):
if self.running_request_json.get(run_id_str, None) is not None:
self.running_request_json.pop(run_id_str)
- # Send the stopping request to edges
- if self.run_as_cloud_agent:
- self.send_training_stop_request_to_cloud_server(server_id, payload)
+ # Stop the job runner
+ self._get_job_runner_manager().stop_job_runner(
+ run_id, args=self.args, server_id=server_id, request_json=request_json,
+ run_as_cloud_agent=self.run_as_cloud_agent)
+
+ def callback_exit_train(self, topic, payload):
+ # Parse the parameters.
+ request_json = json.loads(payload)
+ run_id = request_json.get("runId", None)
+ run_id = request_json.get("id", None) if run_id is None else run_id
+ run_id_str = str(run_id)
+ server_id = request_json.get("serverId", None)
+ if server_id is None:
+ server_id = request_json.get("server_id", None)
# Stop the job runner
self._get_job_runner_manager().stop_job_runner(
- run_id, args=self.args, edge_id=self.edge_id, request_json=request_json,
+ run_id, args=self.args, server_id=server_id, request_json=request_json,
run_as_cloud_agent=self.run_as_cloud_agent)
def callback_run_logs(self, topic, payload):
diff --git a/python/fedml/computing/scheduler/master/cloud_server_manager.py b/python/fedml/computing/scheduler/master/cloud_server_manager.py
index 342d785afe..040a0f38a3 100755
--- a/python/fedml/computing/scheduler/master/cloud_server_manager.py
+++ b/python/fedml/computing/scheduler/master/cloud_server_manager.py
@@ -12,6 +12,7 @@ class FedMLCloudServerManager:
FEDML_CLOUD_SERVER_PREFIX = "fedml-server-run-"
LOCAL_RUNNER_INFO_DIR_NAME = 'runner_infos'
STATUS_IDLE = "IDLE"
+ FEDML_SERVER_BASE_IMAGE = "/fedml-device-image:"
def __init__(self, args, run_id=None, edge_id=None, request_json=None, agent_config=None, version=None):
self.server_docker_image = None
@@ -26,7 +27,7 @@ def __init__(self, args, run_id=None, edge_id=None, request_json=None, agent_con
image_version = self.version
if image_version == "local":
image_version = "test"
- self.server_docker_base_image = "/fedml-device-image:" + image_version
+ self.server_docker_base_image = FedMLCloudServerManager._get_server_base_image(image_version)
self.cloud_server_name = None
@staticmethod
@@ -125,44 +126,52 @@ def start_cloud_server(self, packages_config):
logging.info("start run with k8s: " + run_deployment_cmd)
os.system(run_deployment_cmd)
- def stop_cloud_server(self):
- self.cloud_server_name = FedMLCloudServerManager.FEDML_CLOUD_SERVER_PREFIX + str(self.run_id) \
- + "-" + str(self.edge_id)
- self.server_docker_image = (
- self.agent_config["docker_config"]["registry_server"]
- + self.agent_config["docker_config"]["registry_dir"]
- + self.server_docker_base_image
+ @staticmethod
+ def stop_cloud_server(run_id, server_id, agent_config):
+ cloud_server_name = FedMLCloudServerManager._get_cloud_server_name(run_id, server_id)
+ server_docker_image = (
+ agent_config["docker_config"]["registry_server"]
+ + agent_config["docker_config"]["registry_dir"]
+ + FedMLCloudServerManager._get_server_base_image(fedml.get_env_version())
)
delete_deployment_cmd = (
"export FEDML_AGGREGATOR_NAME="
- + self.cloud_server_name
+ + cloud_server_name
+ ";export FEDML_AGGREGATOR_SVC="
- + self.cloud_server_name
+ + cloud_server_name
+ ";export FEDML_AGGREGATOR_VERSION="
- + self.version
+ + fedml.get_env_version()
+ ';export FEDML_AGGREGATOR_IMAGE_PATH="'
- + self.server_docker_image
+ + server_docker_image
+ '"'
+ ";export FEDML_CONF_ID="
- + self.cloud_server_name
+ + cloud_server_name
+ ";export FEDML_DATA_PV_ID="
- + self.cloud_server_name
+ + cloud_server_name
+ ";export FEDML_DATA_PVC_ID="
- + self.cloud_server_name
+ + cloud_server_name
+ ";export FEDML_REGISTRY_SECRET_SUFFIX="
- + self.cloud_server_name
+ + cloud_server_name
+ ";kubectl -n fedml-devops-aggregator-"
- + self.version
+ + fedml.get_env_version()
+ " delete deployment "
- + self.cloud_server_name
+ + cloud_server_name
+ ";kubectl -n fedml-devops-aggregator-"
- + self.version
+ + fedml.get_env_version()
+ " delete svc "
- + self.cloud_server_name
+ + cloud_server_name
+ ";kubectl -n fedml-devops-aggregator-"
- + self.version
+ + fedml.get_env_version()
+ " delete secret secret-"
- + self.cloud_server_name
+ + cloud_server_name
)
logging.info("stop run with k8s: " + delete_deployment_cmd)
os.system(delete_deployment_cmd)
+
+ @staticmethod
+ def _get_server_base_image(version):
+ return f"{FedMLCloudServerManager.FEDML_SERVER_BASE_IMAGE}{version}"
+
+ @staticmethod
+ def _get_cloud_server_name(run_id, server_id):
+ return f"{FedMLCloudServerManager.FEDML_CLOUD_SERVER_PREFIX}{run_id}-{server_id}"
diff --git a/python/fedml/computing/scheduler/scheduler_core/general_constants.py b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
index 0cc6044d4b..347f157333 100755
--- a/python/fedml/computing/scheduler/scheduler_core/general_constants.py
+++ b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
@@ -206,3 +206,8 @@ def get_ip_address(request_json, infer_host=None):
ip = infer_host
return ip
+
+ @staticmethod
+ def get_topic_exit_train(server_id):
+ topic_exit_train = f"status_center/flserver_agent_{server_id}/exit_train"
+ return topic_exit_train
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index 01caf7db67..921632472b 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -67,6 +67,9 @@ def process_job_completed_status(self, master_id, status):
# self.stop_cloud_server()
# self.remove_listener_for_run_metrics(self.run_id)
# self.remove_listener_for_run_logs(self.run_id)
+ payload_exit_train = {"runId": self.run_id, "serverId": master_id}
+ self.message_center.receive_message(
+ GeneralConstants.get_topic_exit_train(master_id), json.dumps(payload_exit_train))
if self.status_center.is_deployment_status_center and status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
self.report_deployment_status(self.run_id, GeneralConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
From 7064783779837b7bd73d68fcdc4d7ac269b4586f Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 15 May 2024 20:17:11 +0800
Subject: [PATCH 055/282] [CoreEngine] stop the run in the cloud agent.
---
.../scheduler/master/base_master_job_runner_manager.py | 4 ++--
.../scheduler/master/base_master_protocol_manager.py | 2 +-
2 files changed, 3 insertions(+), 3 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
index f4735227bc..2b6c4d3b5a 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
@@ -38,11 +38,11 @@ def start_job_runner(
def stop_job_runner(
self, run_id, args=None, server_id=None, request_json=None,
- run_as_cloud_agent=False
+ run_as_cloud_agent=False, run_as_cloud_server=False
):
super().stop_job_runner(run_id)
- if run_as_cloud_agent:
+ if run_as_cloud_agent or run_as_cloud_server:
stopping_process = Process(
target=FedMLCloudServerManager.stop_cloud_server, args=(run_id, server_id, args.agent_config))
stopping_process.start()
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index 0873548a42..88b25f33ab 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -294,7 +294,7 @@ def callback_exit_train(self, topic, payload):
# Stop the job runner
self._get_job_runner_manager().stop_job_runner(
run_id, args=self.args, server_id=server_id, request_json=request_json,
- run_as_cloud_agent=self.run_as_cloud_agent)
+ run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server)
def callback_run_logs(self, topic, payload):
run_id = str(topic).split('/')[-1]
From 162f7598cdd84a19d3c6d230f27985b229aaaa7b Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 15 May 2024 20:57:47 +0800
Subject: [PATCH 056/282] [CoreEngine] make the cloud server id work.
---
.../computing/scheduler/scheduler_core/account_manager.py | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
index 4b4cc9fd31..3491e102f6 100755
--- a/python/fedml/computing/scheduler/scheduler_core/account_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -138,7 +138,7 @@ def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None,
device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_CLOUD_AGENT
is_master = True
elif role == FedMLAccountManager.ROLE_CLOUD_SERVER:
- device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_CLOUD_SERVER
+ device_id_suffix = ""
is_master = True
elif role == FedMLAccountManager.ROLE_EDGE_DEVICE:
device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_EDGE_DEVICE
@@ -193,6 +193,8 @@ def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None,
docker_tag = FedMLAccountManager.DEVICE_ID_DOCKER_HUB_TAG if is_from_fedml_docker_hub else docker_tag
unique_device_id = f"{self.agent_args.current_device_id}@{self.agent_args.os_name}" \
f"{docker_tag}{device_id_suffix}"
+ if role == FedMLAccountManager.ROLE_CLOUD_SERVER:
+ unique_device_id = self.agent_args.current_device_id
# Set the unique device id
self.agent_args.is_from_docker = is_from_docker or is_from_fedml_docker_hub
From 496732af205ca526da3f6c5fbf0c3d9ca7e77fa3 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 02:47:13 +0800
Subject: [PATCH 057/282] [CoreEngine] make the deployment work.
---
.../master/base_master_job_runner_manager.py | 11 +++++++++++
.../scheduler/master/base_master_protocol_manager.py | 2 +-
.../scheduler/model_scheduler/master_job_runner.py | 3 +--
.../model_scheduler/master_protocol_manager.py | 5 ++++-
4 files changed, 17 insertions(+), 4 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
index 2b6c4d3b5a..6831c9d034 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
@@ -47,6 +47,17 @@ def stop_job_runner(
target=FedMLCloudServerManager.stop_cloud_server, args=(run_id, server_id, args.agent_config))
stopping_process.start()
+ def complete_job_runner(
+ self, run_id, args=None, server_id=None, request_json=None,
+ run_as_cloud_agent=False, run_as_cloud_server=False
+ ):
+ super().complete_job_runner(run_id)
+
+ if run_as_cloud_agent or run_as_cloud_server:
+ stopping_process = Process(
+ target=FedMLCloudServerManager.stop_cloud_server, args=(run_id, server_id, args.agent_config))
+ stopping_process.start()
+
def _start_cloud_server(
self, args, run_id, request_json, edge_id=None,
use_local_process_as_cloud_server=False
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index 88b25f33ab..53a0aee151 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -292,7 +292,7 @@ def callback_exit_train(self, topic, payload):
server_id = request_json.get("server_id", None)
# Stop the job runner
- self._get_job_runner_manager().stop_job_runner(
+ self._get_job_runner_manager().complete_job_runner(
run_id, args=self.args, server_id=server_id, request_json=request_json,
run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 8ce9d0e102..6149e60939 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -361,8 +361,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
# Update the global deployment result mapping
self.slave_deployment_results_map[str(device_id)] = model_status
- logging.info("callback_deployment_result_message: topic {}, payload {}, result mapping {}.".format(
- topic, payload, self.slave_deployment_results_map))
+ logging.info("callback_deployment_result_message: topic {}, payload {}.".format(topic, payload))
request_json = self.request_json
if request_json is None:
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index b4c5b41d74..e7cf150040 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -100,9 +100,12 @@ def print_connected_info(self):
pass
def callback_deployment_result_message(self, topic=None, payload=None):
- logging.info(f"Received deployment result: {self}")
+ logging.info(f"Received deployment result")
FedMLDeployJobRunnerManager.get_instance().save_deployment_result(topic, payload)
+ def callback_exit_train(self, topic, payload):
+ pass
+
def callback_delete_deployment(self, topic, payload):
logging.info("[Master] callback_delete_deployment")
# Parse payload as the model message object.
From 48c0a4c96c5ab8c0ae08fc25391c0b57feb6a925 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 08:27:59 +0800
Subject: [PATCH 058/282] [CoreEngine] refactor the complete job callback.
---
.../master/base_master_protocol_manager.py | 14 +++++------
.../master_protocol_manager.py | 2 +-
.../scheduler_core/general_constants.py | 11 ++++++---
.../status_manager_protocols.py | 4 ++--
.../scheduler_core/task_protocol_manager.py | 24 -------------------
5 files changed, 18 insertions(+), 37 deletions(-)
delete mode 100755 python/fedml/computing/scheduler/scheduler_core/task_protocol_manager.py
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index 53a0aee151..f678389489 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -33,7 +33,7 @@ def __init__(self, args, agent_config=None):
self.agent_config = agent_config
self.topic_start_train = None
self.topic_stop_train = None
- self.topic_exit_train = None
+ self.topic_complete_job = None
self.topic_report_status = None
self.topic_ota_msg = None
self.topic_response_device_info = None
@@ -62,8 +62,8 @@ def generate_topics(self):
# The topi for stopping training
self.topic_stop_train = "mlops/flserver_agent_" + str(self.edge_id) + "/stop_train"
- # The topi for exiting training
- self.topic_exit_train = GeneralConstants.get_topic_exit_train(self.edge_id)
+ # The topic for completing job
+ self.topic_complete_job = GeneralConstants.get_topic_complete_job(self.edge_id)
# The topic for reporting current device status.
self.topic_report_status = "mlops/report_device_status"
@@ -93,7 +93,7 @@ def generate_topics(self):
self.subscribed_topics.clear()
self.add_subscribe_topic(self.topic_start_train)
self.add_subscribe_topic(self.topic_stop_train)
- self.add_subscribe_topic(self.topic_exit_train)
+ self.add_subscribe_topic(self.topic_complete_job)
self.add_subscribe_topic(self.topic_report_status)
self.add_subscribe_topic(self.topic_ota_msg)
self.add_subscribe_topic(self.topic_response_device_info)
@@ -108,7 +108,7 @@ def add_protocol_handler(self):
# Add the message listeners for all topics
self.add_message_listener(self.topic_start_train, self.callback_start_train)
self.add_message_listener(self.topic_stop_train, self.callback_stop_train)
- self.add_message_listener(self.topic_exit_train, self.callback_exit_train)
+ self.add_message_listener(self.topic_complete_job, self.callback_complete_job)
self.add_message_listener(self.topic_ota_msg, FedMLBaseMasterProtocolManager.callback_server_ota_msg)
self.add_message_listener(self.topic_report_status, self.callback_report_current_status)
self.add_message_listener(self.topic_response_device_info, self.callback_response_device_info)
@@ -281,7 +281,7 @@ def callback_stop_train(self, topic, payload, use_payload=None):
run_id, args=self.args, server_id=server_id, request_json=request_json,
run_as_cloud_agent=self.run_as_cloud_agent)
- def callback_exit_train(self, topic, payload):
+ def callback_complete_job(self, topic, payload):
# Parse the parameters.
request_json = json.loads(payload)
run_id = request_json.get("runId", None)
@@ -291,7 +291,7 @@ def callback_exit_train(self, topic, payload):
if server_id is None:
server_id = request_json.get("server_id", None)
- # Stop the job runner
+ # Complete the job runner
self._get_job_runner_manager().complete_job_runner(
run_id, args=self.args, server_id=server_id, request_json=request_json,
run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index e7cf150040..8a578cb7d2 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -103,7 +103,7 @@ def callback_deployment_result_message(self, topic=None, payload=None):
logging.info(f"Received deployment result")
FedMLDeployJobRunnerManager.get_instance().save_deployment_result(topic, payload)
- def callback_exit_train(self, topic, payload):
+ def callback_complete_job(self, topic, payload):
pass
def callback_delete_deployment(self, topic, payload):
diff --git a/python/fedml/computing/scheduler/scheduler_core/general_constants.py b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
index 347f157333..68c1a8e09d 100755
--- a/python/fedml/computing/scheduler/scheduler_core/general_constants.py
+++ b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
@@ -208,6 +208,11 @@ def get_ip_address(request_json, infer_host=None):
return ip
@staticmethod
- def get_topic_exit_train(server_id):
- topic_exit_train = f"status_center/flserver_agent_{server_id}/exit_train"
- return topic_exit_train
+ def get_topic_complete_job(server_id):
+ topic_complete_job = f"status_center/master_agent_{server_id}/complete_job"
+ return topic_complete_job
+
+ @staticmethod
+ def get_payload_complete_job(run_id, server_id):
+ payload_complete_job = {"runId": run_id, "serverId": server_id}
+ return payload_complete_job
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index 921632472b..e5dd312c80 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -67,9 +67,9 @@ def process_job_completed_status(self, master_id, status):
# self.stop_cloud_server()
# self.remove_listener_for_run_metrics(self.run_id)
# self.remove_listener_for_run_logs(self.run_id)
- payload_exit_train = {"runId": self.run_id, "serverId": master_id}
self.message_center.receive_message(
- GeneralConstants.get_topic_exit_train(master_id), json.dumps(payload_exit_train))
+ GeneralConstants.get_topic_complete_job(master_id),
+ json.dumps(GeneralConstants.get_payload_complete_job(self.run_id, master_id)))
if self.status_center.is_deployment_status_center and status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
self.report_deployment_status(self.run_id, GeneralConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
diff --git a/python/fedml/computing/scheduler/scheduler_core/task_protocol_manager.py b/python/fedml/computing/scheduler/scheduler_core/task_protocol_manager.py
deleted file mode 100755
index ddf4bb9b6e..0000000000
--- a/python/fedml/computing/scheduler/scheduler_core/task_protocol_manager.py
+++ /dev/null
@@ -1,24 +0,0 @@
-
-
-class TaskProtocolManager(object):
- def __init__(self):
- pass
-
- def log_metrics(self):
- # Build the message for logging metrics
-
- # Send the message to MQTT server
-
- pass
-
- def log_model(self):
- pass
-
- def log_artifacts_log(self):
- pass
-
- def log_artifacts(self):
- pass
-
-
-
From 525479399feae51cbfc9bdf66ce20e843694f10e Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 08:41:33 +0800
Subject: [PATCH 059/282] [CoreEngine] refactor the complete job callback.
---
.../scheduler/master/base_master_protocol_manager.py | 8 ++++----
.../computing/scheduler/master/master_protocol_manager.py | 7 +++++++
.../scheduler/model_scheduler/master_protocol_manager.py | 3 ---
3 files changed, 11 insertions(+), 7 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index f678389489..67507182a9 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -291,10 +291,10 @@ def callback_complete_job(self, topic, payload):
if server_id is None:
server_id = request_json.get("server_id", None)
- # Complete the job runner
- self._get_job_runner_manager().complete_job_runner(
- run_id, args=self.args, server_id=server_id, request_json=request_json,
- run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server)
+ self._process_job_complete_status(run_id, server_id, request_json)
+
+ def _process_job_complete_status(self, run_id, server_id, complete_payload):
+ pass
def callback_run_logs(self, topic, payload):
run_id = str(topic).split('/')[-1]
diff --git a/python/fedml/computing/scheduler/master/master_protocol_manager.py b/python/fedml/computing/scheduler/master/master_protocol_manager.py
index 5eef5914e7..ca9621e41d 100755
--- a/python/fedml/computing/scheduler/master/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/master_protocol_manager.py
@@ -34,3 +34,10 @@ def _init_extra_items(self):
# Override
def print_connected_info(self):
super().print_connected_info()
+
+ # Override
+ def _process_job_complete_status(self, run_id, server_id, complete_payload):
+ # Complete the job runner
+ self._get_job_runner_manager().complete_job_runner(
+ run_id, args=self.args, server_id=server_id, request_json=complete_payload,
+ run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index 8a578cb7d2..01165ff82e 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -103,9 +103,6 @@ def callback_deployment_result_message(self, topic=None, payload=None):
logging.info(f"Received deployment result")
FedMLDeployJobRunnerManager.get_instance().save_deployment_result(topic, payload)
- def callback_complete_job(self, topic, payload):
- pass
-
def callback_delete_deployment(self, topic, payload):
logging.info("[Master] callback_delete_deployment")
# Parse payload as the model message object.
From 86852c7cd1ff65c3540e07d0f965c84382ebd294 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 09:16:27 +0800
Subject: [PATCH 060/282] [CoreEngine] fixed the monitor issue.
---
python/fedml/computing/scheduler/comm_utils/job_monitor.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index 9bee76e780..8ae6e1c744 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -223,7 +223,7 @@ def monitor_replicas_number():
curr_version = fedml.get_env_version()
num_replica_url_path = "fedmlModelServer/api/v1/endpoint/replica-info"
mlops_prefix = fedml._get_backend_service()
- url = f"{mlops_prefix}{num_replica_url_path}"
+ url = f"{mlops_prefix}/{num_replica_url_path}"
cached_token = FedMLModelCache.get_instance().get_end_point_token_with_eid(endpoint_id)
if cached_token is None:
From 349493cefb4cf64605fec2c348874435339a1f30 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 09:45:10 +0800
Subject: [PATCH 061/282] [CoreEngine] update the serve example.
---
python/examples/launch/serve_mnist/fedml_model_config.yaml | 2 ++
1 file changed, 2 insertions(+)
diff --git a/python/examples/launch/serve_mnist/fedml_model_config.yaml b/python/examples/launch/serve_mnist/fedml_model_config.yaml
index 48254ccca4..f212dbb81d 100644
--- a/python/examples/launch/serve_mnist/fedml_model_config.yaml
+++ b/python/examples/launch/serve_mnist/fedml_model_config.yaml
@@ -1,6 +1,8 @@
workspace: "./"
entry_point: "mnist_serve_main.py"
+auto_detect_public_ip: true
+
data_cache_dir: ""
bootstrap: ""
From ba453d1b9f0259ca68cb640f97790e19ff1b3ebe Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 10:01:35 +0800
Subject: [PATCH 062/282] [CoreEngine] update the model master runner.
---
.../model_scheduler/device_model_inference.py | 56 +++++++++----------
.../model_scheduler/master_job_runner.py | 16 ++----
2 files changed, 33 insertions(+), 39 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index b8d85edd31..eb3088f327 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -26,35 +26,35 @@
pass
-# class Settings(BaseSettings):
-# redis_addr: str
-# redis_port: str
-# redis_password: str
-# end_point_name: str
-# model_name: str
-# model_version: str
-# model_infer_url: str
-# version: str
-# use_mqtt_inference: bool
-# use_worker_gateway: bool
-# ext_info: str
+class Settings(BaseSettings):
+ redis_addr: str
+ redis_port: str
+ redis_password: str
+ end_point_name: str
+ model_name: str
+ model_version: str
+ model_infer_url: str
+ version: str
+ use_mqtt_inference: bool
+ use_worker_gateway: bool
+ ext_info: str
+
+
+settings = Settings()
+
+# class settings:
+# redis_addr = "127.0.0.1"
+# redis_port = 6379
+# redis_password = "fedml_default"
+# end_point_name = ""
+# model_name = ""
+# model_version = ""
+# model_infer_url = "127.0.0.1"
+# version = "dev"
+# use_mqtt_inference = False
+# use_worker_gateway = False
+# ext_info = "2b34303961245c4f175f2236282d7a272c040b0904747579087f6a760112030109010c215d54505707140005190a051c347f365c4a430c020a7d39120e26032a78730f797f7c031f0901657e75"
#
-#
-# settings = Settings()
-
-class settings:
- redis_addr = "127.0.0.1"
- redis_port = 6379
- redis_password = "fedml_default"
- end_point_name = ""
- model_name = ""
- model_version = ""
- model_infer_url = "127.0.0.1"
- version = "dev"
- use_mqtt_inference = False
- use_worker_gateway = False
- ext_info = "2b34303961245c4f175f2236282d7a272c040b0904747579087f6a760112030109010c215d54505707140005190a051c347f365c4a430c020a7d39120e26032a78730f797f7c031f0901657e75"
-
api = FastAPI()
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 6149e60939..cecb9de7bc 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -115,7 +115,6 @@ def run_impl(
# start unified inference server
self.start_device_inference_gateway(
- run_id, end_point_name, model_id, model_name, model_version,
agent_config=self.agent_config, inference_port=inference_port)
# start inference monitor server
@@ -464,20 +463,16 @@ def process_deployment_result_message(self, topic=None, payload=None):
@staticmethod
def start_device_inference_gateway(
- run_id, end_point_name, model_id,
- model_name, model_version, inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
+ inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
agent_config=None, redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
):
# start unified inference server
- running_model_name = ServerConstants.get_running_model_name(end_point_name,
- model_name, model_version, run_id, model_id)
python_program = get_python_program()
master_port = os.getenv("FEDML_MASTER_PORT", None)
if master_port is not None:
inference_port = int(master_port)
if not ServerConstants.is_running_on_k8s():
- logging.info(f"start the model inference gateway, end point {run_id}, "
- f"model name {model_name} at port {inference_port}...")
+ logging.info(f"start the model inference gateway...")
use_mqtt_inference = os.getenv("FEDML_USE_MQTT_INFERENCE", "False")
use_mqtt_inference = True if use_mqtt_inference.lower() == 'true' else False
use_worker_gateway = os.getenv("FEDML_USE_WORKER_GATEWAY", "False")
@@ -501,8 +496,8 @@ def start_device_inference_gateway(
"USE_MQTT_INFERENCE={} USE_WORKER_GATEWAY={} EXT_INFO={} "
"{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
"--log-level critical".format(
- redis_addr, redis_port, redis_password, end_point_name,
- model_name, model_version, "", fedml.get_env_version(), use_mqtt_inference,
+ redis_addr, str(redis_port), redis_password, "",
+ "", "", "", fedml.get_env_version(), use_mqtt_inference,
use_worker_gateway, ext_info, python_program, inference_gw_cmd, str(inference_port),
fedml_base_dir),
should_capture_stdout=False, should_capture_stderr=False)
@@ -570,8 +565,7 @@ def recover_inference_and_monitor():
pass
FedMLDeployMasterJobRunner.start_device_inference_gateway(
- run_id, end_point_name, model_id, model_name, model_version, inference_port=inference_port,
- agent_config=agent_config)
+ inference_port=inference_port, agent_config=agent_config)
FedMLDeployMasterJobRunner.stop_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
From 14429adfd510b9b95c99bc4c7318c723accd2cb4 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 10:07:14 +0800
Subject: [PATCH 063/282] [CoreEngine] update the model master runner.
---
.../model_scheduler/master_job_runner.py | 19 +++++++++----------
1 file changed, 9 insertions(+), 10 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index cecb9de7bc..4d43d7c5c3 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -115,7 +115,7 @@ def run_impl(
# start unified inference server
self.start_device_inference_gateway(
- agent_config=self.agent_config, inference_port=inference_port)
+ inference_port=inference_port, agent_config=self.agent_config)
# start inference monitor server
self.stop_device_inference_monitor(
@@ -540,6 +540,14 @@ def stop_device_inference_monitor(run_id, end_point_name, model_id, model_name,
def recover_inference_and_monitor():
# noinspection PyBroadException
try:
+ agent_config = dict()
+ try:
+ agent_config["mqtt_config"], _, _, _ = MLOpsConfigs.fetch_all_configs()
+ except Exception as e:
+ pass
+
+ FedMLDeployMasterJobRunner.start_device_inference_gateway(agent_config=agent_config)
+
history_jobs = FedMLServerDataInterface.get_instance().get_history_jobs()
for job in history_jobs.job_list:
if job.running_json is None:
@@ -558,15 +566,6 @@ def recover_inference_and_monitor():
if not is_activated:
continue
- agent_config = dict()
- try:
- agent_config["mqtt_config"], _, _, _ = MLOpsConfigs.fetch_all_configs()
- except Exception as e:
- pass
-
- FedMLDeployMasterJobRunner.start_device_inference_gateway(
- inference_port=inference_port, agent_config=agent_config)
-
FedMLDeployMasterJobRunner.stop_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
FedMLDeployMasterJobRunner.start_device_inference_monitor(
From aa1df9922a4f50d9e6b2c372919163eacaca73aa Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 13:18:32 +0800
Subject: [PATCH 064/282] [CoreEngine] make the handshaking protocol work.
---
.../slave/base_slave_protocol_manager.py | 15 +++++
.../scheduler/slave/slave_protocol_manager.py | 56 +------------------
2 files changed, 16 insertions(+), 55 deletions(-)
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index 4ff931e6fd..1384e9906a 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -48,6 +48,7 @@ def __init__(self, args, agent_config=None):
self.topic_report_status = None
self.topic_ota_msg = None
self.topic_request_device_info = None
+ self.topic_request_device_info_from_mlops = None
self.topic_client_logout = None
self.topic_response_job_status = None
self.topic_report_device_status_in_job = None
@@ -87,6 +88,9 @@ def generate_topics(self):
# The topic for requesting device info from the client.
self.topic_request_device_info = "server/client/request_device_info/" + str(self.edge_id)
+ # The topic for requesting device info from mlops.
+ self.topic_request_device_info_from_mlops = f"deploy/mlops/slave_agent/request_device_info/{self.edge_id}"
+
# The topic for requesting device info from MLOps.
self.topic_client_logout = "mlops/client/logout/" + str(self.edge_id)
@@ -114,6 +118,7 @@ def generate_topics(self):
self.add_subscribe_topic(self.topic_report_status)
self.add_subscribe_topic(self.topic_ota_msg)
self.add_subscribe_topic(self.topic_request_device_info)
+ self.add_subscribe_topic(self.topic_request_device_info_from_mlops)
self.add_subscribe_topic(self.topic_client_logout)
self.add_subscribe_topic(self.topic_response_job_status)
self.add_subscribe_topic(self.topic_report_device_status_in_job)
@@ -132,6 +137,7 @@ def add_protocol_handler(self):
self.add_message_listener(self.topic_ota_msg, FedMLBaseSlaveProtocolManager.callback_client_ota_msg)
self.add_message_listener(self.topic_report_status, self.callback_report_current_status)
self.add_message_listener(self.topic_request_device_info, self.callback_report_device_info)
+ self.add_message_listener(self.topic_request_device_info_from_mlops, self.callback_request_device_info_from_mlops)
self.add_message_listener(self.topic_client_logout, self.callback_client_logout)
self.add_message_listener(self.topic_response_job_status, self.callback_response_job_status)
self.add_message_listener(self.topic_report_device_status_in_job, self.callback_response_device_status_in_job)
@@ -402,6 +408,15 @@ def callback_report_device_info(self, topic, payload):
response_payload["context"] = context
self.message_center.send_message(response_topic, json.dumps(response_payload), run_id=run_id)
+ def callback_request_device_info_from_mlops(self, topic, payload):
+ self.response_device_info_to_mlops(topic, payload)
+
+ def response_device_info_to_mlops(self, topic, payload):
+ response_topic = f"deploy/slave_agent/mlops/response_device_info"
+ response_payload = {"run_id": self.run_id, "slave_agent_device_id": self.edge_id,
+ "fedml_version": fedml.__version__}
+ self.message_center.send_message(response_topic, json.dumps(response_payload))
+
def callback_client_logout(self, topic, payload):
payload_json = json.loads(payload)
secret = payload_json.get("auth", None)
diff --git a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
index ef8dac8730..a1067a0d96 100755
--- a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
@@ -13,19 +13,11 @@ class FedMLLaunchSlaveProtocolManager(FedMLBaseSlaveProtocolManager):
def __init__(self, args, agent_config=None):
FedMLBaseSlaveProtocolManager.__init__(self, args, agent_config=agent_config)
- self.topic_request_deploy_slave_device_info_from_mlops = None
- self.topic_request_deploy_master_device_info_from_mlops = None
- self.topic_request_edge_device_info_from_mlops = None
# Override
def generate_topics(self):
super().generate_topics()
- # The topic for requesting device info from mlops.
- self.topic_request_edge_device_info_from_mlops = f"deploy/mlops/slave_agent/request_device_info/{self.edge_id}"
-
- self.add_subscribe_topic(self.topic_request_edge_device_info_from_mlops)
-
# Override
def add_protocol_handler(self):
super().add_protocol_handler()
@@ -106,54 +98,8 @@ def _init_extra_items(self):
os.environ["FEDML_DEPLOY_MASTER_ID"] = str(self.model_device_server_id)
os.environ["FEDML_DEPLOY_WORKER_IDS"] = str(self.model_device_client_edge_id_list)
- # Subscribe handshaking messages from MLOps.
- self.subscribe_handshaking_messages_from_mlops()
-
# Start the monitor process
self.args = copy.deepcopy(in_args)
self.mlops_metrics.stop_device_realtime_perf()
self.mlops_metrics.report_device_realtime_perf(self.args, self.args.agent_config["mqtt_config"])
- pass
-
- def callback_response_device_info_to_mlops(self, topic, payload):
- payload_json = json.loads(payload)
- server_id = payload_json.get("server_id", 0)
- run_id = payload_json.get("run_id", 0)
- listen_edge_id = str(topic).split("/")[-1]
- context = payload_json.get("context", None)
- response_topic = f"deploy/slave_agent/mlops/response_device_info"
- if self.mlops_metrics is not None and self.model_device_client_edge_id_list is not None and \
- self.model_device_server_id is not None:
- device_info_json = {
- "edge_id": listen_edge_id,
- "fedml_version": fedml.__version__,
- "user_id": self.args.user
- }
- salve_device_ids = list()
- for model_client_edge_id in self.model_device_client_edge_id_list:
- salve_device_ids.append(model_client_edge_id)
- response_payload = {"slave_device_id": self.model_device_client_edge_id_list[0],
- "slave_device_id_list": salve_device_ids,
- "master_device_id": self.model_device_server_id,
- "run_id": run_id, "edge_id": listen_edge_id,
- "edge_info": device_info_json}
- if context is not None:
- response_payload["context"] = context
- self.message_center.send_message(response_topic, json.dumps(response_payload), run_id=run_id)
-
- def subscribe_handshaking_messages_from_mlops(self):
- # The topic for requesting deployment master device info from mlops.
- self.topic_request_deploy_master_device_info_from_mlops = f"deploy/mlops/master_agent/request_device_info/{self.model_device_server_id}"
-
- # The topic for requesting deployment slave device info from mlops.
- self.topic_request_deploy_slave_device_info_from_mlops = f"deploy/mlops/slave_agent/request_device_info/{self.model_device_client_edge_id_list[0]}"
-
- self.add_subscribe_topic(self.topic_request_deploy_master_device_info_from_mlops)
- self.add_subscribe_topic(self.topic_request_deploy_slave_device_info_from_mlops)
-
- self.add_message_listener(
- self.topic_request_edge_device_info_from_mlops, self.callback_response_device_info_to_mlops)
- self.add_message_listener(
- self.topic_request_deploy_master_device_info_from_mlops, self.callback_response_device_info_to_mlops)
- self.add_message_listener(
- self.topic_request_deploy_slave_device_info_from_mlops, self.callback_response_device_info_to_mlops)
\ No newline at end of file
+ pass
\ No newline at end of file
From 94f408f186a2c4d889eb73ee445655e25eccdfd4 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 13:56:27 +0800
Subject: [PATCH 065/282] [CoreEngine] make the handshaking protocol work.
---
.../computing/scheduler/master/base_master_protocol_manager.py | 2 +-
.../computing/scheduler/slave/base_slave_protocol_manager.py | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index 67507182a9..cee91578dd 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -448,7 +448,7 @@ def response_device_info_to_mlops(self, topic, payload):
response_topic = f"deploy/master_agent/mlops/response_device_info"
if self.mlops_metrics is not None:
response_payload = {"run_id": self.run_id, "master_agent_device_id": self.edge_id,
- "fedml_version": fedml.__version__}
+ "fedml_version": fedml.__version__, "edge_id": self.edge_id}
self.mlops_metrics.report_json_message(response_topic, json.dumps(response_payload))
def init_job_task(self, request_json):
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index 1384e9906a..aa69d4482d 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -414,7 +414,7 @@ def callback_request_device_info_from_mlops(self, topic, payload):
def response_device_info_to_mlops(self, topic, payload):
response_topic = f"deploy/slave_agent/mlops/response_device_info"
response_payload = {"run_id": self.run_id, "slave_agent_device_id": self.edge_id,
- "fedml_version": fedml.__version__}
+ "fedml_version": fedml.__version__, "edge_id": self.edge_id}
self.message_center.send_message(response_topic, json.dumps(response_payload))
def callback_client_logout(self, topic, payload):
From 0726f66c6a3a3c3ba7263983741552803a6bb5b2 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 18:36:24 +0800
Subject: [PATCH 066/282] [CoreEngine] update the model master runner.
---
.../computing/scheduler/model_scheduler/master_job_runner.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 4d43d7c5c3..5991151e94 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -114,13 +114,13 @@ def run_impl(
message_center=self.message_center)
# start unified inference server
- self.start_device_inference_gateway(
+ self.inference_gateway_process = self.start_device_inference_gateway(
inference_port=inference_port, agent_config=self.agent_config)
# start inference monitor server
self.stop_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
- self.start_device_inference_monitor(
+ self.monitor_process = self.start_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
# Changed the status to "IDLE"
From 28984d54a8b3cb470438be98931bae50f7939e6b Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 18:57:20 +0800
Subject: [PATCH 067/282] [CoreEngine] update the model master runner.
---
.../scheduler/model_scheduler/master_job_runner.py | 11 ++++++-----
1 file changed, 6 insertions(+), 5 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 5991151e94..b0a15ed8ba 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -114,13 +114,13 @@ def run_impl(
message_center=self.message_center)
# start unified inference server
- self.inference_gateway_process = self.start_device_inference_gateway(
+ FedMLDeployMasterJobRunner.start_device_inference_gateway(
inference_port=inference_port, agent_config=self.agent_config)
# start inference monitor server
- self.stop_device_inference_monitor(
+ FedMLDeployMasterJobRunner.stop_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
- self.monitor_process = self.start_device_inference_monitor(
+ FedMLDeployMasterJobRunner.start_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
# Changed the status to "IDLE"
@@ -546,8 +546,6 @@ def recover_inference_and_monitor():
except Exception as e:
pass
- FedMLDeployMasterJobRunner.start_device_inference_gateway(agent_config=agent_config)
-
history_jobs = FedMLServerDataInterface.get_instance().get_history_jobs()
for job in history_jobs.job_list:
if job.running_json is None:
@@ -566,6 +564,9 @@ def recover_inference_and_monitor():
if not is_activated:
continue
+ FedMLDeployMasterJobRunner.start_device_inference_gateway(
+ inference_port=inference_port, agent_config=agent_config)
+
FedMLDeployMasterJobRunner.stop_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
FedMLDeployMasterJobRunner.start_device_inference_monitor(
From 310d43cbcde6d6fea8249b18ecf82e061816ce84 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 19:18:10 +0800
Subject: [PATCH 068/282] [CoreEngine] update the model master runner.
---
.../model_scheduler/master_job_runner.py | 101 ++++++++++++------
1 file changed, 71 insertions(+), 30 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index b0a15ed8ba..9b21237878 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -25,7 +25,6 @@
class FedMLDeployMasterJobRunner(FedMLBaseMasterJobRunner, FedMLDeployJobRunnerMsgSender, ABC):
-
default_redis_addr = "local"
default_redis_port = "6379"
default_redis_password = "fedml_default"
@@ -54,7 +53,7 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
self.deployment_result_queue = Queue()
# Override
- def _generate_job_runner_instance(self, args, run_id=None, request_json=None, agent_config=None, edge_id=None,):
+ def _generate_job_runner_instance(self, args, run_id=None, request_json=None, agent_config=None, edge_id=None, ):
return FedMLDeployMasterJobRunner(
args, run_id=run_id, request_json=request_json, agent_config=self.agent_config, edge_id=edge_id
)
@@ -65,10 +64,10 @@ def _generate_extend_queue_list(self):
# Override
def run_impl(
- self, edge_id_status_queue, edge_device_info_queue, run_metrics_queue,
- run_event_queue, run_artifacts_queue, run_logs_queue, edge_device_info_global_queue,
- run_extend_queue_list=None, sender_message_queue=None, listener_message_queue=None,
- status_center_queue=None
+ self, edge_id_status_queue, edge_device_info_queue, run_metrics_queue,
+ run_event_queue, run_artifacts_queue, run_logs_queue, edge_device_info_global_queue,
+ run_extend_queue_list=None, sender_message_queue=None, listener_message_queue=None,
+ status_center_queue=None
):
# Parse the model parameters.
run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
@@ -331,7 +330,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
elif run_operation == "UPDATE":
# Overwrite the json with the rollback version diff
rollback_version_diff = self.replica_controller.rollback_get_replica_version_diff(
- device_id_trigger=device_id, replica_no_trigger=replica_no)
+ device_id_trigger=device_id, replica_no_trigger=replica_no)
# Change the target version to the start version
self.replica_controller.rollback_setback_target_replica_version()
@@ -465,6 +464,15 @@ def process_deployment_result_message(self, topic=None, payload=None):
def start_device_inference_gateway(
inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
agent_config=None, redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
+ ):
+ from multiprocessing import Process
+ Process(target=FedMLDeployMasterJobRunner.start_device_inference_gateway_entry,
+ args=(inference_port, agent_config, redis_addr, redis_port, redis_password)).start()
+
+ @staticmethod
+ def start_device_inference_gateway_entry(
+ inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
+ agent_config=None, redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
):
# start unified inference server
python_program = get_python_program()
@@ -489,20 +497,32 @@ def start_device_inference_gateway(
agent_config["mqtt_config"]["MQTT_USER"] + connect_str +
agent_config["mqtt_config"]["MQTT_PWD"] + connect_str +
str(agent_config["mqtt_config"]["MQTT_KEEPALIVE"]), "FEDML@9999GREAT")
- inference_gateway_process = ServerConstants.exec_console_with_script(
- "REDIS_ADDR=\"{}\" REDIS_PORT=\"{}\" REDIS_PASSWORD=\"{}\" "
- "END_POINT_NAME=\"{}\" "
- "MODEL_NAME=\"{}\" MODEL_VERSION=\"{}\" MODEL_INFER_URL=\"{}\" VERSION=\"{}\" "
- "USE_MQTT_INFERENCE={} USE_WORKER_GATEWAY={} EXT_INFO={} "
- "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
- "--log-level critical".format(
- redis_addr, str(redis_port), redis_password, "",
- "", "", "", fedml.get_env_version(), use_mqtt_inference,
- use_worker_gateway, ext_info, python_program, inference_gw_cmd, str(inference_port),
- fedml_base_dir),
- should_capture_stdout=False, should_capture_stderr=False)
-
- return inference_gateway_process
+ python_program = get_python_program()
+ os.system("REDIS_ADDR=\"{}\" REDIS_PORT=\"{}\" REDIS_PASSWORD=\"{}\" "
+ "END_POINT_NAME=\"{}\" "
+ "MODEL_NAME=\"{}\" MODEL_VERSION=\"{}\" MODEL_INFER_URL=\"{}\" VERSION=\"{}\" "
+ "USE_MQTT_INFERENCE={} USE_WORKER_GATEWAY={} EXT_INFO={} "
+ "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
+ "--log-level critical".format(
+ redis_addr, str(redis_port), redis_password, "",
+ "", "", "", fedml.get_env_version(), use_mqtt_inference,
+ use_worker_gateway, ext_info, python_program, inference_gw_cmd, str(inference_port),
+ fedml_base_dir))
+
+ # inference_gateway_process = ServerConstants.exec_console_with_script(
+ # "REDIS_ADDR=\"{}\" REDIS_PORT=\"{}\" REDIS_PASSWORD=\"{}\" "
+ # "END_POINT_NAME=\"{}\" "
+ # "MODEL_NAME=\"{}\" MODEL_VERSION=\"{}\" MODEL_INFER_URL=\"{}\" VERSION=\"{}\" "
+ # "USE_MQTT_INFERENCE={} USE_WORKER_GATEWAY={} EXT_INFO={} "
+ # "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
+ # "--log-level critical".format(
+ # redis_addr, str(redis_port), redis_password, "",
+ # "", "", "", fedml.get_env_version(), use_mqtt_inference,
+ # use_worker_gateway, ext_info, python_program, inference_gw_cmd, str(inference_port),
+ # fedml_base_dir),
+ # should_capture_stdout=False, should_capture_stderr=False)
+ #
+ # return inference_gateway_process
return None
@@ -510,6 +530,16 @@ def start_device_inference_gateway(
def start_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version, check_stopped_event=True,
redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
+ ):
+ from multiprocessing import Process
+ Process(target=FedMLDeployMasterJobRunner.start_device_inference_monitor_entry,
+ args=(run_id, end_point_name, model_id, model_name, model_version, check_stopped_event,
+ redis_addr, redis_port, redis_password)).start()
+
+ @staticmethod
+ def start_device_inference_monitor_entry(
+ run_id, end_point_name, model_id, model_name, model_version, check_stopped_event=True,
+ redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
):
# start inference monitor server
# Will report the qps related metrics to the MLOps
@@ -520,14 +550,25 @@ def start_device_inference_monitor(
python_program = get_python_program()
running_model_name = ServerConstants.get_running_model_name(end_point_name,
model_name, model_version, run_id, model_id)
- monitor_process = ServerConstants.exec_console_with_shell_script_list(
- [python_program, monitor_file, "-v", fedml.get_env_version(), "-ep", run_id_str,
- "-epn", str(end_point_name), "-mi", str(model_id), "-mn", model_name,
- "-mv", model_version, "-iu", "infer_url", "-ra", redis_addr,
- "-rp", str(redis_port), "-rpw", redis_password],
- should_capture_stdout=False, should_capture_stderr=False
- )
- return monitor_process
+
+ os.system(f"{python_program} {monitor_file} -v {fedml.get_env_version()} -ep {run_id_str} "
+ f"-epn {end_point_name} -mi {model_id} -mn {model_name} -mv \"{model_version}\" "
+ f"-iu infer_url -ra {redis_addr} -rp {redis_port} -rpw redis_password")
+
+ # from fedml.computing.scheduler.model_scheduler.device_model_monitor import FedMLModelMetrics
+ # monitor_center = FedMLModelMetrics(
+ # run_id_str, end_point_name, model_id, model_name, model_version,
+ # "infer_url", redis_addr, redis_port, redis_password, version=fedml.get_env_version())
+ # monitor_center.start_monitoring_metrics_center()
+
+ # monitor_process = ServerConstants.exec_console_with_shell_script_list(
+ # [python_program, monitor_file, "-v", fedml.get_env_version(), "-ep", run_id_str,
+ # "-epn", str(end_point_name), "-mi", str(model_id), "-mn", model_name,
+ # "-mv", model_version, "-iu", "infer_url", "-ra", redis_addr,
+ # "-rp", str(redis_port), "-rpw", redis_password],
+ # should_capture_stdout=False, should_capture_stderr=False
+ # )
+ # return monitor_process
@staticmethod
def stop_device_inference_monitor(run_id, end_point_name, model_id, model_name, model_version):
@@ -779,7 +820,7 @@ def parse_model_run_params(running_json):
model_version = model_config["model_version"]
model_config_parameters = running_json.get("parameters", {})
- inference_port = model_config_parameters.get("server_internal_port", # Internal port is for the gateway
+ inference_port = model_config_parameters.get("server_internal_port", # Internal port is for the gateway
ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
inference_port_external = model_config_parameters.get("server_external_port", inference_port)
From f378d075d1d612f78a8340ef13eadf5566d03c68 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 19:37:50 +0800
Subject: [PATCH 069/282] [CoreEngine] update the model master runner.
---
.../model_scheduler/device_model_inference.py | 56 +++++++++----------
1 file changed, 28 insertions(+), 28 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index eb3088f327..b8d85edd31 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -26,35 +26,35 @@
pass
-class Settings(BaseSettings):
- redis_addr: str
- redis_port: str
- redis_password: str
- end_point_name: str
- model_name: str
- model_version: str
- model_infer_url: str
- version: str
- use_mqtt_inference: bool
- use_worker_gateway: bool
- ext_info: str
-
-
-settings = Settings()
-
-# class settings:
-# redis_addr = "127.0.0.1"
-# redis_port = 6379
-# redis_password = "fedml_default"
-# end_point_name = ""
-# model_name = ""
-# model_version = ""
-# model_infer_url = "127.0.0.1"
-# version = "dev"
-# use_mqtt_inference = False
-# use_worker_gateway = False
-# ext_info = "2b34303961245c4f175f2236282d7a272c040b0904747579087f6a760112030109010c215d54505707140005190a051c347f365c4a430c020a7d39120e26032a78730f797f7c031f0901657e75"
+# class Settings(BaseSettings):
+# redis_addr: str
+# redis_port: str
+# redis_password: str
+# end_point_name: str
+# model_name: str
+# model_version: str
+# model_infer_url: str
+# version: str
+# use_mqtt_inference: bool
+# use_worker_gateway: bool
+# ext_info: str
#
+#
+# settings = Settings()
+
+class settings:
+ redis_addr = "127.0.0.1"
+ redis_port = 6379
+ redis_password = "fedml_default"
+ end_point_name = ""
+ model_name = ""
+ model_version = ""
+ model_infer_url = "127.0.0.1"
+ version = "dev"
+ use_mqtt_inference = False
+ use_worker_gateway = False
+ ext_info = "2b34303961245c4f175f2236282d7a272c040b0904747579087f6a760112030109010c215d54505707140005190a051c347f365c4a430c020a7d39120e26032a78730f797f7c031f0901657e75"
+
api = FastAPI()
From 871cba9792972cf6bd8956d3cac1fd09b4b2f67f Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 19:40:51 +0800
Subject: [PATCH 070/282] [CoreEngine] update the model master runner.
---
.../computing/scheduler/model_scheduler/master_job_runner.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 9b21237878..2c48a4277e 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -553,7 +553,7 @@ def start_device_inference_monitor_entry(
os.system(f"{python_program} {monitor_file} -v {fedml.get_env_version()} -ep {run_id_str} "
f"-epn {end_point_name} -mi {model_id} -mn {model_name} -mv \"{model_version}\" "
- f"-iu infer_url -ra {redis_addr} -rp {redis_port} -rpw redis_password")
+ f"-iu infer_url -ra {redis_addr} -rp {redis_port} -rpw {redis_password}")
# from fedml.computing.scheduler.model_scheduler.device_model_monitor import FedMLModelMetrics
# monitor_center = FedMLModelMetrics(
From 9c9b20f0cfcf4f99572f025d14729b5c789e0cdf Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 20:14:01 +0800
Subject: [PATCH 071/282] [CoreEngine] not kill the subprocess when exiting the
model master runner.
---
.../master/base_master_job_runner.py | 5 +-
.../scheduler/master/server_constants.py | 5 +-
.../device_server_constants.py | 5 +-
.../model_scheduler/master_job_runner.py | 90 ++++++-------------
4 files changed, 37 insertions(+), 68 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner.py b/python/fedml/computing/scheduler/master/base_master_job_runner.py
index 1827de481d..18aa8a6eed 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner.py
@@ -111,10 +111,13 @@ def run(
if self.mlops_metrics is not None:
self.mlops_metrics.stop_sys_perf()
time.sleep(3)
- ServerConstants.cleanup_run_process(self.run_id)
+ self.cleanup_runner_process(self.run_id)
ServerConstants.cleanup_learning_process(self.run_id)
ServerConstants.cleanup_bootstrap_process(self.run_id)
+ def cleanup_runner_process(self, run_id):
+ ServerConstants.cleanup_run_process(run_id)
+
@debug
@abstractmethod
def run_impl(
diff --git a/python/fedml/computing/scheduler/master/server_constants.py b/python/fedml/computing/scheduler/master/server_constants.py
index 058c57e199..b835ba1bde 100644
--- a/python/fedml/computing/scheduler/master/server_constants.py
+++ b/python/fedml/computing/scheduler/master/server_constants.py
@@ -268,9 +268,10 @@ def get_dataset_metadata_url():
return get_dataset_metadata_url
@staticmethod
- def cleanup_run_process(run_id):
+ def cleanup_run_process(run_id, not_kill_subprocess=False):
RunProcessUtils.cleanup_run_process(
- run_id, ServerConstants.get_data_dir(), ServerConstants.LOCAL_RUNNER_INFO_DIR_NAME)
+ run_id, ServerConstants.get_data_dir(), ServerConstants.LOCAL_RUNNER_INFO_DIR_NAME,
+ not_kill_subprocess=not_kill_subprocess)
@staticmethod
def save_run_process(run_id, process_id):
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
index 86c7aac992..6b5b335863 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
@@ -295,9 +295,10 @@ def get_public_ip():
return ip
@staticmethod
- def cleanup_run_process(run_id):
+ def cleanup_run_process(run_id, not_kill_subprocess=False):
RunProcessUtils.cleanup_run_process(
- run_id, ServerConstants.get_data_dir(), ServerConstants.LOCAL_RUNNER_INFO_DIR_NAME)
+ run_id, ServerConstants.get_data_dir(), ServerConstants.LOCAL_RUNNER_INFO_DIR_NAME,
+ not_kill_subprocess=not_kill_subprocess)
@staticmethod
def save_run_process(run_id, process_id):
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 2c48a4277e..eef03d53f2 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -460,17 +460,11 @@ def process_deployment_result_message(self, topic=None, payload=None):
time.sleep(3)
self.trigger_completed_event()
- @staticmethod
- def start_device_inference_gateway(
- inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
- agent_config=None, redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
- ):
- from multiprocessing import Process
- Process(target=FedMLDeployMasterJobRunner.start_device_inference_gateway_entry,
- args=(inference_port, agent_config, redis_addr, redis_port, redis_password)).start()
+ def cleanup_runner_process(self, run_id):
+ ServerConstants.cleanup_run_process(run_id, not_kill_subprocess=True)
@staticmethod
- def start_device_inference_gateway_entry(
+ def start_device_inference_gateway(
inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
agent_config=None, redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
):
@@ -498,31 +492,22 @@ def start_device_inference_gateway_entry(
agent_config["mqtt_config"]["MQTT_PWD"] + connect_str +
str(agent_config["mqtt_config"]["MQTT_KEEPALIVE"]), "FEDML@9999GREAT")
python_program = get_python_program()
- os.system("REDIS_ADDR=\"{}\" REDIS_PORT=\"{}\" REDIS_PASSWORD=\"{}\" "
- "END_POINT_NAME=\"{}\" "
- "MODEL_NAME=\"{}\" MODEL_VERSION=\"{}\" MODEL_INFER_URL=\"{}\" VERSION=\"{}\" "
- "USE_MQTT_INFERENCE={} USE_WORKER_GATEWAY={} EXT_INFO={} "
- "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
- "--log-level critical".format(
- redis_addr, str(redis_port), redis_password, "",
- "", "", "", fedml.get_env_version(), use_mqtt_inference,
- use_worker_gateway, ext_info, python_program, inference_gw_cmd, str(inference_port),
- fedml_base_dir))
-
- # inference_gateway_process = ServerConstants.exec_console_with_script(
- # "REDIS_ADDR=\"{}\" REDIS_PORT=\"{}\" REDIS_PASSWORD=\"{}\" "
- # "END_POINT_NAME=\"{}\" "
- # "MODEL_NAME=\"{}\" MODEL_VERSION=\"{}\" MODEL_INFER_URL=\"{}\" VERSION=\"{}\" "
- # "USE_MQTT_INFERENCE={} USE_WORKER_GATEWAY={} EXT_INFO={} "
- # "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
- # "--log-level critical".format(
- # redis_addr, str(redis_port), redis_password, "",
- # "", "", "", fedml.get_env_version(), use_mqtt_inference,
- # use_worker_gateway, ext_info, python_program, inference_gw_cmd, str(inference_port),
- # fedml_base_dir),
- # should_capture_stdout=False, should_capture_stderr=False)
- #
- # return inference_gateway_process
+ inference_gateway_process = ServerConstants.exec_console_with_script(
+ "REDIS_ADDR=\"{}\" REDIS_PORT=\"{}\" REDIS_PASSWORD=\"{}\" "
+ "END_POINT_NAME=\"{}\" "
+ "MODEL_NAME=\"{}\" MODEL_VERSION=\"{}\" MODEL_INFER_URL=\"{}\" VERSION=\"{}\" "
+ "USE_MQTT_INFERENCE={} USE_WORKER_GATEWAY={} EXT_INFO={} "
+ "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
+ "--log-level critical".format(
+ redis_addr, str(redis_port), redis_password, "",
+ "", "", "", fedml.get_env_version(), use_mqtt_inference,
+ use_worker_gateway, ext_info, python_program, inference_gw_cmd, str(inference_port),
+ fedml_base_dir),
+ should_capture_stdout=False, should_capture_stderr=False)
+
+ return inference_gateway_process
+ else:
+ return inference_gateway_pids[0]
return None
@@ -530,16 +515,6 @@ def start_device_inference_gateway_entry(
def start_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version, check_stopped_event=True,
redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
- ):
- from multiprocessing import Process
- Process(target=FedMLDeployMasterJobRunner.start_device_inference_monitor_entry,
- args=(run_id, end_point_name, model_id, model_name, model_version, check_stopped_event,
- redis_addr, redis_port, redis_password)).start()
-
- @staticmethod
- def start_device_inference_monitor_entry(
- run_id, end_point_name, model_id, model_name, model_version, check_stopped_event=True,
- redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
):
# start inference monitor server
# Will report the qps related metrics to the MLOps
@@ -550,25 +525,14 @@ def start_device_inference_monitor_entry(
python_program = get_python_program()
running_model_name = ServerConstants.get_running_model_name(end_point_name,
model_name, model_version, run_id, model_id)
-
- os.system(f"{python_program} {monitor_file} -v {fedml.get_env_version()} -ep {run_id_str} "
- f"-epn {end_point_name} -mi {model_id} -mn {model_name} -mv \"{model_version}\" "
- f"-iu infer_url -ra {redis_addr} -rp {redis_port} -rpw {redis_password}")
-
- # from fedml.computing.scheduler.model_scheduler.device_model_monitor import FedMLModelMetrics
- # monitor_center = FedMLModelMetrics(
- # run_id_str, end_point_name, model_id, model_name, model_version,
- # "infer_url", redis_addr, redis_port, redis_password, version=fedml.get_env_version())
- # monitor_center.start_monitoring_metrics_center()
-
- # monitor_process = ServerConstants.exec_console_with_shell_script_list(
- # [python_program, monitor_file, "-v", fedml.get_env_version(), "-ep", run_id_str,
- # "-epn", str(end_point_name), "-mi", str(model_id), "-mn", model_name,
- # "-mv", model_version, "-iu", "infer_url", "-ra", redis_addr,
- # "-rp", str(redis_port), "-rpw", redis_password],
- # should_capture_stdout=False, should_capture_stderr=False
- # )
- # return monitor_process
+ monitor_process = ServerConstants.exec_console_with_shell_script_list(
+ [python_program, monitor_file, "-v", fedml.get_env_version(), "-ep", run_id_str,
+ "-epn", str(end_point_name), "-mi", str(model_id), "-mn", model_name,
+ "-mv", model_version, "-iu", "infer_url", "-ra", redis_addr,
+ "-rp", str(redis_port), "-rpw", redis_password],
+ should_capture_stdout=False, should_capture_stderr=False
+ )
+ return monitor_process
@staticmethod
def stop_device_inference_monitor(run_id, end_point_name, model_id, model_name, model_version):
From 17b053cf7243860791616e75abcab647528daac9 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 16 May 2024 20:19:05 +0800
Subject: [PATCH 072/282] [CoreEngine] not kill the subprocess when exiting the
model master runner.
---
.../scheduler/comm_utils/run_process_utils.py | 19 +++++++++++--------
1 file changed, 11 insertions(+), 8 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/run_process_utils.py b/python/fedml/computing/scheduler/comm_utils/run_process_utils.py
index e64e708fb5..05cc342e36 100644
--- a/python/fedml/computing/scheduler/comm_utils/run_process_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/run_process_utils.py
@@ -14,8 +14,10 @@ def get_run_process_prefix(prefix, run_id):
return f"{prefix}-run@{run_id}@pid@"
@staticmethod
- def cleanup_run_process(run_id, data_dir, info_dir,
- info_file_prefix=SchedulerConstants.RUN_PROCESS_TYPE_RUNNER_PROCESS):
+ def cleanup_run_process(
+ run_id, data_dir, info_dir,
+ info_file_prefix=SchedulerConstants.RUN_PROCESS_TYPE_RUNNER_PROCESS, not_kill_subprocess=False
+ ):
try:
local_pkg_data_dir = data_dir
run_process_dir = os.path.join(local_pkg_data_dir, info_dir)
@@ -43,12 +45,13 @@ def cleanup_run_process(run_id, data_dir, info_dir,
try:
process = psutil.Process(int(process_id))
- child_processes = process.children(recursive=True)
- for sub_process in child_processes:
- if platform.system() == 'Windows':
- os.system("taskkill /PID {} /T /F".format(sub_process.pid))
- else:
- os.kill(sub_process.pid, signal.SIGKILL)
+ if not not_kill_subprocess:
+ child_processes = process.children(recursive=True)
+ for sub_process in child_processes:
+ if platform.system() == 'Windows':
+ os.system("taskkill /PID {} /T /F".format(sub_process.pid))
+ else:
+ os.kill(sub_process.pid, signal.SIGKILL)
if process is not None:
if platform.system() == 'Windows':
From b8ec6163cd9df168d315f8f019e01d5b78cd2e9a Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Fri, 17 May 2024 21:34:25 +0800
Subject: [PATCH 073/282] [CoreEngine] fixed the issue that the gpu id is
released with no properly cases.
---
.../scheduler/comm_utils/job_monitor.py | 23 ++--
.../scheduler/scheduler_core/status_center.py | 76 ++-----------
.../status_manager_protocols.py | 101 ++++++++++++++----
3 files changed, 101 insertions(+), 99 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index 8ae6e1c744..bada84d96e 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -354,15 +354,15 @@ def monitor_slave_run_process_status(self):
# Check if all processes of the specific run are exited
# FIXME: Proactively release the gpu ids when the run processes have not even started yet as the docker
# image is being pulled
- run_process_list = client_constants.ClientConstants.get_learning_process_list(job.job_id)
- all_run_processes_exited = True if len(run_process_list) <= 0 else False
- if all_run_processes_exited:
- if not self.released_runs.get(str(job.job_id), False):
- self.released_runs[str(job.job_id)] = True
- # Release the gpu ids
- print(
- f"[run/device][{job.job_id}/{job.edge_id}] Release gpu resource when run processes has exited on monioring slave runs periodically.")
- JobRunnerUtils.get_instance().release_gpu_ids(job.job_id, job.edge_id)
+ # run_process_list = client_constants.ClientConstants.get_learning_process_list(job.job_id)
+ # all_run_processes_exited = True if len(run_process_list) <= 0 else False
+ # if all_run_processes_exited:
+ # if not self.released_runs.get(str(job.job_id), False):
+ # self.released_runs[str(job.job_id)] = True
+ # # Release the gpu ids
+ # print(
+ # f"[run/device][{job.job_id}/{job.edge_id}] Release gpu resource when run processes has exited on monioring slave runs periodically.")
+ # JobRunnerUtils.get_instance().release_gpu_ids(job.job_id, job.edge_id)
# Get the timeout threshold
timeout_threshold = None
@@ -381,8 +381,9 @@ def monitor_slave_run_process_status(self):
# If the run processes have exited but run status is not completed and
# timeout is out of the range, then release gpu ids and report failed status to the master agent.
- if all_run_processes_exited and not SchedulerConstants.is_run_completed(job.status) and \
- timeout_threshold is not None and timeout > timeout_threshold:
+ # if all_run_processes_exited and not SchedulerConstants.is_run_completed(job.status) and \
+ # timeout_threshold is not None and timeout > timeout_threshold:
+ if timeout_threshold is not None and timeout > timeout_threshold:
# Report failed status to the master agent
mlops.log_training_failed_status(
run_id=job.job_id, edge_id=job.edge_id, enable_broadcast=True)
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index 4a55dbb679..c0e1b6633a 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -1,8 +1,6 @@
import logging
import time
-from ..slave.client_constants import ClientConstants
-from ..master.server_constants import ServerConstants
from enum import Enum, unique
import multiprocessing
from multiprocessing import Process, Queue
@@ -11,7 +9,6 @@
from .message_center import FedMLMessageCenter
import traceback
from .status_manager_protocols import FedMLStatusManager
-from .compute_cache_manager import ComputeCacheManager
@unique
@@ -87,11 +84,6 @@ class FedMLStatusCenter(object):
def __init__(self, message_queue=None):
self.status_queue = message_queue
- self.job_status_in_slave = dict()
- self.entire_job_status = None
- self.job_status_in_master = dict()
- self.slave_devices_status = dict()
- self.master_devices_status = dict()
self.status_center_process = None
self.status_event = None
self.status_sender_message_center_queue = None
@@ -108,50 +100,6 @@ def __repr__(self):
attrs=" ".join("{}={!r}".format(k, v) for k, v in self.__dict__.items()),
)
- def add_job_status_in_slave(self, device_id, status):
- self.job_status_in_slave[device_id] = self._status_transition(status)
-
- def add_job_status_in_master(self, device_id, status):
- self.job_status_in_master[device_id] = self._status_transition(status)
-
- def set_entire_job_status(self, status):
- self.entire_job_status = status
-
- def add_slave_device_status(self, device_id, status):
- self.slave_devices_status[device_id] = self._status_transition(status)
-
- def add_master_device_status(self, device_id, status):
- self.master_devices_status[device_id] = self._status_transition(status)
-
- def get_job_status_in_slave(self, device_id):
- return self.job_status_in_slave.get(device_id, None)
-
- def get_job_status_in_master(self, device_id):
- return self.job_status_in_master.get(device_id, None)
-
- def get_entire_job_status(self):
- return self.entire_job_status
-
- def get_slave_device_status(self, device_id):
- return self.slave_devices_status.get(device_id, None)
-
- def get_master_device_status(self, device_id):
- return self.master_devices_status.get(device_id, None)
-
- def _status_transition(self, status):
- transition_status = status
- if self.entire_job_status is not None:
- if self.entire_job_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED or \
- self.entire_job_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
- if status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED or \
- status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or \
- status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED:
- transition_status = status
- else:
- transition_status = ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED
-
- return transition_status
-
def get_status_runner(self):
return None
@@ -205,16 +153,6 @@ def rebuild_message_center(self, message_center_queue):
def rebuild_status_center(self, status_queue):
pass
- @staticmethod
- def save_job_status(run_id, status):
- ComputeCacheManager.get_instance().set_redis_params()
- ComputeCacheManager.get_instance().get_status_cache().save_job_status(run_id, status)
-
- @staticmethod
- def save_device_status_in_job(run_id, device_id, status):
- ComputeCacheManager.get_instance().set_redis_params()
- ComputeCacheManager.get_instance().get_status_cache().save_device_status_in_job(run_id, device_id, status)
-
def run_status_dispatcher(self, status_event, status_queue,
sender_message_center_queue,
listener_message_center_queue):
@@ -272,6 +210,10 @@ def run_status_dispatcher(self, status_event, status_queue,
else:
status_manager_instances[status_entity.run_id].edge_id = status_entity.edge_id
+ # if the job status is completed then continue
+ if status_manager_instances[status_entity.run_id].is_job_completed():
+ continue
+
# Process the master and slave status.
if message_entity.topic.startswith(FedMLStatusCenter.TOPIC_MASTER_STATUS_PREFIX):
# Process the job status
@@ -279,7 +221,12 @@ def run_status_dispatcher(self, status_event, status_queue,
message_entity.topic, message_entity.payload)
# Save the job status
- FedMLStatusCenter.save_job_status(status_entity.run_id, self.get_entire_job_status())
+ status_manager_instances[status_entity.run_id].save_job_status()
+
+ # Popup the status manager instance when the job status is completed
+ if status_manager_instances[status_entity.run_id].is_job_completed():
+ status_manager_instances.pop(status_entity.run_id)
+ continue
elif message_entity.topic.startswith(FedMLStatusCenter.TOPIC_SLAVE_STATUS_PREFIX):
# Process the slave device status
@@ -287,8 +234,7 @@ def run_status_dispatcher(self, status_event, status_queue,
message_entity.topic, message_entity.payload)
# Save the device status in job
- FedMLStatusCenter.save_device_status_in_job(status_entity.run_id, status_entity.edge_id,
- self.get_job_status_in_slave(status_entity.edge_id))
+ status_manager_instances[status_entity.run_id].save_device_status_in_job(status_entity.edge_id)
except Exception as e:
if message_entity is not None:
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index e5dd312c80..272423f147 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -12,6 +12,7 @@
from ..master.server_data_interface import FedMLServerDataInterface
from .message_common import LogArgs
from .general_constants import GeneralConstants
+from ..scheduler_core.compute_cache_manager import ComputeCacheManager
class FedMLStatusManager(object):
@@ -33,6 +34,15 @@ def __init__(self, run_id=None, edge_id=None, server_id=None,
self.log_args = LogArgs(role="server", edge_id=self.edge_id,
server_id=self.server_id, log_file_dir=ServerConstants.get_log_file_dir())
+ self.job_status_in_slave = dict()
+ self.entire_job_status = None
+ self.job_status_in_master = dict()
+ self.slave_devices_status = dict()
+ self.master_devices_status = dict()
+ self.completed_job_status_list = [ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED,
+ ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED,
+ ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED]
+
def __repr__(self):
return "<{klass} @{id:x} {attrs}>".format(
klass=self.__class__.__name__,
@@ -40,6 +50,65 @@ def __repr__(self):
attrs=" ".join("{}={!r}".format(k, v) for k, v in self.__dict__.items()),
)
+ def add_job_status_in_slave(self, device_id, status):
+ self.job_status_in_slave[device_id] = self._status_transition(status)
+
+ def add_job_status_in_master(self, device_id, status):
+ self.job_status_in_master[device_id] = self._status_transition(status)
+
+ def set_entire_job_status(self, status):
+ self.entire_job_status = status
+
+ def add_slave_device_status(self, device_id, status):
+ self.slave_devices_status[device_id] = self._status_transition(status)
+
+ def add_master_device_status(self, run_id, device_id, status):
+ self.master_devices_status[device_id] = self._status_transition(status)
+
+ def get_job_status_in_slave(self, device_id):
+ return self.job_status_in_slave.get(device_id, None)
+
+ def get_job_status_in_master(self, device_id):
+ return self.job_status_in_master.get(device_id, None)
+
+ def get_entire_job_status(self):
+ return self.entire_job_status
+
+ def get_slave_device_status(self, device_id):
+ return self.slave_devices_status.get(device_id, None)
+
+ def get_master_device_status(self, device_id):
+ return self.master_devices_status.get(device_id, None)
+
+ def is_job_completed(self):
+ if self.entire_job_status and self.entire_job_status in self.completed_job_status_list:
+ return True
+ return False
+
+ def _status_transition(self, status):
+ transition_status = status
+ if self.entire_job_status is not None:
+ if self.entire_job_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED or \
+ self.entire_job_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
+ if status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED or \
+ status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or \
+ status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED:
+ transition_status = status
+ else:
+ transition_status = ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED
+
+ return transition_status
+
+ def save_job_status(self):
+ ComputeCacheManager.get_instance().set_redis_params()
+ ComputeCacheManager.get_instance().get_status_cache().save_job_status(
+ self.run_id, self.get_entire_job_status())
+
+ def save_device_status_in_job(self, device_id):
+ ComputeCacheManager.get_instance().set_redis_params()
+ ComputeCacheManager.get_instance().get_status_cache().save_device_status_in_job(
+ self.run_id, device_id, self.get_job_status_in_slave(device_id))
+
def process_job_completed_status(self, master_id, status):
# Stop the system performance monitor
try:
@@ -75,10 +144,8 @@ def process_job_completed_status(self, master_id, status):
self.report_deployment_status(self.run_id, GeneralConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
def process_job_exception_status(self, master_id, status):
- # Send the exception status to slave devices.
- self.report_exception_status(
- self.edge_id_list, run_id=self.run_id, server_id=master_id,
- status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
+ # Report exception job status
+ self.report_exception_status(status)
# Save the job status to local storage
FedMLServerDataInterface.get_instance().save_job_status(self.run_id, master_id, status, status)
@@ -113,9 +180,9 @@ def status_center_process_master_status(self, topic, payload):
def process_job_status_consensus(self, run_id, master_id, status):
# Set the master status in the job and entire job status
- self.status_center.set_entire_job_status(status)
- self.status_center.add_job_status_in_master(master_id, status)
- status = self.status_center.get_entire_job_status()
+ self.set_entire_job_status(status)
+ self.add_job_status_in_master(master_id, status)
+ status = self.get_entire_job_status()
# Set the device status based on the job status
edge_id_status_dict = self.client_agent_active_list.get(f"{run_id}", {})
@@ -152,8 +219,8 @@ def get_device_consensus_status_in_job(job_status, device_status):
return None
def get_device_consensus_status_in_current_device(self, edge_id, status):
- self.status_center.add_job_status_in_slave(edge_id, status)
- consensus_status = self.status_center.get_job_status_in_slave(edge_id)
+ self.add_job_status_in_slave(edge_id, status)
+ consensus_status = self.get_job_status_in_slave(edge_id)
consensus_status = ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED \
if consensus_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION else consensus_status
return consensus_status
@@ -275,25 +342,13 @@ def report_server_status(self, run_id, edge_id, server_id, status):
self.status_reporter.report_server_id_status(
run_id, status, edge_id=edge_id, server_id=server_id, server_agent_id=edge_id, update_db=False)
- def report_exception_status(
- self, edge_id_list, run_id=0, server_id=None, status=None, payload=None):
- if payload is None:
- payload_obj = {"runId": run_id, "edgeids": edge_id_list}
- if server_id is not None:
- payload_obj["serverId"] = server_id
- else:
- payload_obj = json.loads(payload)
- payload_obj["run_status"] = ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION if status is None else status
- topic_exception = "flserver_agent/" + str(self.edge_id) + "/stop_train"
- self.message_reporter.send_message(topic_exception, json.dumps(payload_obj))
+ def report_exception_status(self, status):
+ self.status_reporter.report_job_status(self.run_id, status)
def status_center_process_slave_status_to_master_in_slave_agent(self, topic, payload):
# Forward the status message to the sender queue of message center.
self.message_center.send_message(topic, payload)
- # Post the status message to the listener queue of message center
- #self.message_center.receive_message(GeneralConstants.MSG_TOPIC_REPORT_DEVICE_STATUS_IN_JOB, payload)
-
def status_center_process_slave_status_to_mlops_in_slave_agent(self, topic, payload):
# Forward the status message to message center.
self.message_center.send_message(topic, payload)
From 8975a30028b966e899872c9d53f2b3aaa17afeda Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Fri, 17 May 2024 22:47:16 +0800
Subject: [PATCH 074/282] [CoreEngine] change the training timeout.
---
python/fedml/computing/scheduler/comm_utils/constants.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/constants.py b/python/fedml/computing/scheduler/comm_utils/constants.py
index f89d5640ce..f3fcd4ed5a 100644
--- a/python/fedml/computing/scheduler/comm_utils/constants.py
+++ b/python/fedml/computing/scheduler/comm_utils/constants.py
@@ -83,7 +83,7 @@ class SchedulerConstants:
TRAIN_PROVISIONING_TIMEOUT = 60 * 25
TRAIN_STARTING_TIMEOUT = 60 * 15
TRAIN_STOPPING_TIMEOUT = 60 * 5
- TRAIN_RUNNING_TIMEOUT = 60 * 60 * 12
+ TRAIN_RUNNING_TIMEOUT = 60 * 60 * 24 * 2000
TRAIN_INIT_TIMEOUT = 60 * 5
PUBLIC_REDIS_PORT = 6379
From c26eaff6bb7ca0fb5bd1dad5bde9ad5410d23253 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Fri, 17 May 2024 13:37:00 -0700
Subject: [PATCH 075/282] Add timestamp in status payload
---
python/fedml/core/mlops/mlops_metrics.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/fedml/core/mlops/mlops_metrics.py b/python/fedml/core/mlops/mlops_metrics.py
index afa96f6870..2ae415fab2 100644
--- a/python/fedml/core/mlops/mlops_metrics.py
+++ b/python/fedml/core/mlops/mlops_metrics.py
@@ -7,7 +7,7 @@
import requests
import fedml
-from . import MLOpsConfigs
+from . import MLOpsConfigs, MLOpsUtils
from .mlops_device_perfs import MLOpsDevicePerfStats
from .mlops_job_perfs import MLOpsJobPerfStats
from ...computing.scheduler.master.server_constants import ServerConstants
@@ -221,6 +221,7 @@ def common_report_server_training_status(self, run_id, status, role=None, edge_i
if role is None:
role = "normal"
msg = {
+ "timestamp": MLOpsUtils.get_ntp_time(),
"run_id": run_id,
"edge_id": edge_id,
"status": status,
From 7b576cbdc56fb5a6d4946a34d86a042eb41b2cc5 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Fri, 17 May 2024 14:24:57 -0700
Subject: [PATCH 076/282] Fix Import
---
python/fedml/core/mlops/mlops_metrics.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/fedml/core/mlops/mlops_metrics.py b/python/fedml/core/mlops/mlops_metrics.py
index 2ae415fab2..3746f498b4 100644
--- a/python/fedml/core/mlops/mlops_metrics.py
+++ b/python/fedml/core/mlops/mlops_metrics.py
@@ -7,7 +7,8 @@
import requests
import fedml
-from . import MLOpsConfigs, MLOpsUtils
+from .mlops_utils import MLOpsUtils
+from .mlops_configs import MLOpsConfigs
from .mlops_device_perfs import MLOpsDevicePerfStats
from .mlops_job_perfs import MLOpsJobPerfStats
from ...computing.scheduler.master.server_constants import ServerConstants
From 3f40511877a060e8d25149af5aab9ed0f351ce0d Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Fri, 17 May 2024 17:46:59 -0700
Subject: [PATCH 077/282] Fix run logs cli command
---
python/fedml/api/modules/run.py | 11 ++++++-----
python/fedml/cli/modules/run.py | 12 ++++++------
.../scheduler/scheduler_entry/run_manager.py | 9 ++++-----
3 files changed, 16 insertions(+), 16 deletions(-)
diff --git a/python/fedml/api/modules/run.py b/python/fedml/api/modules/run.py
index 120a964316..cf50ce24b4 100644
--- a/python/fedml/api/modules/run.py
+++ b/python/fedml/api/modules/run.py
@@ -51,7 +51,7 @@ def start(platform: str, create_run_result: FedMLRunStartedModel, device_server:
run_start_result = FedMLRunManager.get_instance().start_run(platform=platform, create_run_result=create_run_result,
device_server=device_server, device_edges=device_edges,
- api_key=api_key,
+ api_key=get_api_key(),
feature_entry_point=feature_entry_point)
return run_start_result
@@ -79,7 +79,7 @@ def status(run_name: Optional[str], run_id: str, platform: str, api_key: str) ->
_authenticate_and_validate_platform(api_key, platform)
run_status = None
- run_list_obj = list_run(run_name=run_name, run_id=run_id, platform=platform, api_key=api_key)
+ run_list_obj = list_run(run_name=run_name, run_id=run_id, platform=platform, api_key=get_api_key())
if run_list_obj is not None:
if len(run_list_obj.run_list) > 1:
@@ -93,12 +93,13 @@ def status(run_name: Optional[str], run_id: str, platform: str, api_key: str) ->
# input: run_id, page_num, page_size, need_all_logs, platform, api_key
# return RunLogResult(run_status, total_log_lines, total_log_pages, log_line_list, run_logs)
def logs(run_id: str, page_num: int, page_size: int, need_all_logs: bool, platform: str, api_key: str) -> RunLogResult:
- _authenticate_and_validate_platform(api_key, platform)
+ api_key = authenticate(api_key)
+ validate_platform(platform)
if run_id is None:
raise Exception("Please specify run id.")
- _, run_status = status(run_name=None, run_id=run_id, platform=platform, api_key=get_api_key())
+ _, run_status = status(run_name=None, run_id=run_id, platform=platform, api_key=api_key)
total_log_nums, total_log_pages, log_line_list, run_logs = 0, 0, list(), None
@@ -110,7 +111,7 @@ def logs(run_id: str, page_num: int, page_size: int, need_all_logs: bool, platfo
user_api_key=api_key)
if run_logs is not None:
- total_log_pages, total_log_nums = run_logs.total_num, run_logs.total_pages
+ total_log_pages, total_log_nums = run_logs.total_pages, run_logs.total_num
_parse_logs(log_line_list, run_logs)
return RunLogResult(run_status=run_status, total_log_lines=total_log_nums, total_log_pages=total_log_pages,
diff --git a/python/fedml/cli/modules/run.py b/python/fedml/cli/modules/run.py
index b4e8a947fd..f2c24b445a 100644
--- a/python/fedml/cli/modules/run.py
+++ b/python/fedml/cli/modules/run.py
@@ -184,21 +184,21 @@ def status(platform, run_name, run_id, api_key, version):
"--page_num",
"-pn",
type=int,
- default=0,
+ default=1,
help="request page num for logs. --need_all_logs should be set to False if you want to use this option.",
)
@click.option(
"--page_size",
"-ps",
type=int,
- default=0,
+ default=10,
help="request page size for logs, --need_all_logs should be set to False if you want to use this option.",
)
@click.option(
"--need_all_logs",
"-a",
type=bool,
- default=True,
+ default=False,
help="boolean value representing if all logs are needed. Default to True",
)
def logs(platform, run_id, api_key, version, page_num, page_size, need_all_logs):
@@ -217,8 +217,8 @@ def logs(platform, run_id, api_key, version, page_num, page_size, need_all_logs)
return
# Show run log summary info
- log_head_table = PrettyTable(['Run ID', 'Total Log Lines', 'Log URL'])
- log_head_table.add_row([run_id, run_log_result.total_log_lines, run_logs.log_full_url])
+ log_head_table = PrettyTable(['Run ID', 'Printed Log Lines', 'Total Log Lines', 'Log URL'])
+ log_head_table.add_row([run_id, len(run_log_result.log_line_list), run_logs.total_num, run_logs.log_full_url])
click.echo("\nLogs summary info is as follows.")
print(log_head_table)
@@ -234,7 +234,7 @@ def logs(platform, run_id, api_key, version, page_num, page_size, need_all_logs)
if len(run_log_result.log_line_list) > 0:
click.echo("\nAll logs is as follows.")
for log_line in run_log_result.log_line_list:
- click.echo(log_line.rstrip('\n'))
+ click.echo(log_line)
def _print_run_table(run_list_obj):
diff --git a/python/fedml/computing/scheduler/scheduler_entry/run_manager.py b/python/fedml/computing/scheduler/scheduler_entry/run_manager.py
index 84fe109054..b91935e7b2 100755
--- a/python/fedml/computing/scheduler/scheduler_entry/run_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_entry/run_manager.py
@@ -162,10 +162,10 @@ def __init__(self, run_log_list_json):
self.log_devices = list()
for log_dev in log_devices_json:
self.log_devices.append(FedMLRunLogDeviceModel(log_dev))
- self.total_num = run_log_list_json.get("total_num", 0)
- self.total_pages = run_log_list_json.get("total_pages", 0)
- self.current_page = run_log_list_json.get("current_page", 0)
- self.log_lines = run_log_list_json.get("logs", [])
+ self.total_num = run_log_list_json.get("totalSize", 0)
+ self.total_pages = run_log_list_json.get("totalPages", 0)
+ self.current_page = run_log_list_json.get("pageNum", 0)
+ self.log_lines = run_log_list_json.get("logList", [])
class FedMLRunLogDeviceModel(object):
@@ -277,7 +277,6 @@ def get_run_logs(self, run_id: str, page_num: int, page_size: int, user_api_key:
run_log_list_result = None
run_logs_json = {
"apiKey": user_api_key,
- "edgeId": "-1",
"pageNum": page_num,
"pageSize": page_size,
"runId": run_id,
From 8c13ed2ad04b94bb291124c5c70b91c78cd50900 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Mon, 20 May 2024 17:38:46 +0800
Subject: [PATCH 078/282] [CoreEngine] make the server status work.
---
.../master_protocol_manager.py | 6 ++
.../scheduler_core/message_common.py | 2 +
.../scheduler/scheduler_core/status_center.py | 62 ++++++++++++-------
.../status_manager_protocols.py | 4 +-
4 files changed, 48 insertions(+), 26 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index 01165ff82e..09c2dd5d17 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -11,6 +11,7 @@
from .master_job_runner_manager import FedMLDeployJobRunnerManager
from ..scheduler_core.general_constants import GeneralConstants
from ..scheduler_core.endpoint_sync_protocol import FedMLEndpointSyncProtocol
+from ..scheduler_core.compute_cache_manager import ComputeCacheManager
class FedMLDeployMasterProtocolManager(FedMLBaseMasterProtocolManager):
@@ -135,6 +136,11 @@ def callback_delete_deployment(self, topic, payload):
model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_id,
model_msg_object.model_name, model_msg_object.model_version)
+ # Report the launch job status with killed status.
+ launch_job_id = ComputeCacheManager.get_instance().get_gpu_cache().get_endpoint_run_id_map(self.run_id)
+ if launch_job_id is not None:
+ self.status_reporter.report_server_id_status(launch_job_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_KILLED)
+
def callback_start_deployment(self, topic, payload):
# noinspection PyBroadException
try:
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_common.py b/python/fedml/computing/scheduler/scheduler_core/message_common.py
index 24449af3b5..13b99ff39d 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_common.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_common.py
@@ -49,6 +49,7 @@ def __init__(self, topic=None, payload=None, status_msg_body: dict = None):
self.payload = payload
self.run_id = None
self.edge_id = None
+ self.server_id = None
self.status = None
if status_msg_body is not None:
self.from_message_body(status_msg_body=status_msg_body)
@@ -61,6 +62,7 @@ def from_message_body(self, status_msg_body: dict = None):
self.run_id = payload_json.get("run_id", None)
self.run_id = payload_json.get("runId", None) if self.run_id is None else self.run_id
self.edge_id = payload_json.get("edge_id", None)
+ self.server_id = payload_json.get("server_id", None)
self.status = payload_json.get("status", None)
def get_message_body(self):
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index c0e1b6633a..fc1c726b5f 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -81,6 +81,7 @@ class FedMLStatusCenter(object):
TOPIC_SLAVE_JOB_LAUNCH_SUFFIX = "/start_train"
TOPIC_SLAVE_JOB_STOP_PREFIX = "flserver_agent/"
TOPIC_SLAVE_JOB_STOP_SUFFIX = "/stop_train"
+ ALLOWED_MAX_JOB_STATUS_CACHE_NUM = 1000
def __init__(self, message_queue=None):
self.status_queue = message_queue
@@ -203,38 +204,43 @@ def run_status_dispatcher(self, status_event, status_queue,
status_entity = FedMLStatusEntity(status_msg_body=message_body)
# Generate status manager instance
- if status_manager_instances.get(status_entity.run_id) is None:
- status_manager_instances[status_entity.run_id] = FedMLStatusManager(
- run_id=status_entity.run_id, edge_id=status_entity.edge_id, status_center=self,
+ run_id_str = str(status_entity.run_id)
+ run_id_int = int(status_entity.run_id)
+ if status_manager_instances.get(run_id_str) is None:
+ if len(status_manager_instances.keys()) >= FedMLStatusCenter.ALLOWED_MAX_JOB_STATUS_CACHE_NUM:
+ for iter_run_id, iter_status_mgr in status_manager_instances.items():
+ if iter_status_mgr.is_job_completed():
+ status_manager_instances.pop(iter_run_id)
+ break
+ status_manager_instances[run_id_str] = FedMLStatusManager(
+ run_id=run_id_int, edge_id=status_entity.edge_id,
+ server_id=status_entity.server_id, status_center=self,
message_center=message_center)
else:
- status_manager_instances[status_entity.run_id].edge_id = status_entity.edge_id
+ status_manager_instances[run_id_str].edge_id = status_entity.edge_id
+ if status_entity.server_id is None and status_entity.server_id != 0:
+ status_manager_instances[run_id_str].server_id = status_entity.server_id
# if the job status is completed then continue
- if status_manager_instances[status_entity.run_id].is_job_completed():
+ if status_manager_instances[run_id_str].is_job_completed():
continue
# Process the master and slave status.
if message_entity.topic.startswith(FedMLStatusCenter.TOPIC_MASTER_STATUS_PREFIX):
# Process the job status
- status_manager_instances[status_entity.run_id].status_center_process_master_status(
+ status_manager_instances[run_id_str].status_center_process_master_status(
message_entity.topic, message_entity.payload)
# Save the job status
- status_manager_instances[status_entity.run_id].save_job_status()
-
- # Popup the status manager instance when the job status is completed
- if status_manager_instances[status_entity.run_id].is_job_completed():
- status_manager_instances.pop(status_entity.run_id)
- continue
+ status_manager_instances[run_id_str].save_job_status()
elif message_entity.topic.startswith(FedMLStatusCenter.TOPIC_SLAVE_STATUS_PREFIX):
# Process the slave device status
- status_manager_instances[status_entity.run_id].status_center_process_slave_status(
+ status_manager_instances[run_id_str].status_center_process_slave_status(
message_entity.topic, message_entity.payload)
# Save the device status in job
- status_manager_instances[status_entity.run_id].save_device_status_in_job(status_entity.edge_id)
+ status_manager_instances[run_id_str].save_device_status_in_job(status_entity.edge_id)
except Exception as e:
if message_entity is not None:
@@ -295,40 +301,48 @@ def run_status_dispatcher_in_slave(self, status_event, status_queue,
status_entity = FedMLStatusEntity(status_msg_body=message_body)
# Generate status manager instance
- if status_manager_instances.get(status_entity.run_id) is None:
- status_manager_instances[status_entity.run_id] = FedMLStatusManager(
- run_id=status_entity.run_id, edge_id=status_entity.edge_id, status_center=self,
+ run_id_str = str(status_entity.run_id)
+ run_id_int = int(status_entity.run_id)
+ if status_manager_instances.get(run_id_str) is None:
+ if len(status_manager_instances.keys()) >= FedMLStatusCenter.ALLOWED_MAX_JOB_STATUS_CACHE_NUM:
+ for iter_run_id, iter_status_mgr in status_manager_instances.items():
+ if iter_status_mgr.is_job_completed():
+ status_manager_instances.pop(iter_run_id)
+ break
+
+ status_manager_instances[run_id_str] = FedMLStatusManager(
+ run_id=run_id_int, edge_id=status_entity.edge_id, status_center=self,
message_center=message_center)
else:
- status_manager_instances[status_entity.run_id].edge_id = status_entity.edge_id
+ status_manager_instances[run_id_str].edge_id = status_entity.edge_id
# Process the slave status
if message_entity.topic.startswith(FedMLStatusCenter.TOPIC_SLAVE_STATUS_PREFIX):
# Report the slave status to master
- status_manager_instances[status_entity.run_id]. \
+ status_manager_instances[run_id_str]. \
status_center_process_slave_status_to_master_in_slave_agent(
message_entity.topic, message_entity.payload
)
elif message_entity.topic.startswith(FedMLStatusCenter.TOPIC_SLAVE_STATUS_TO_MLOPS_PREFIX):
# Report slave status to mlops (Active/IDLE message)
- status_manager_instances[status_entity.run_id]. \
+ status_manager_instances[run_id_str]. \
status_center_process_slave_status_to_mlops_in_slave_agent(
message_entity.topic, message_entity.payload
)
elif (message_entity.topic.startswith(FedMLStatusCenter.TOPIC_SLAVE_JOB_LAUNCH_PREFIX) and
message_entity.topic.endswith(FedMLStatusCenter.TOPIC_SLAVE_JOB_LAUNCH_SUFFIX)):
# Async request the job status from master when launching the job
- job_launch_message_map[status_entity.run_id] = {"topic": message_entity.topic,
+ job_launch_message_map[run_id_str] = {"topic": message_entity.topic,
"payload": message_entity.payload}
- # status_manager_instances[status_entity.run_id]. \
+ # status_manager_instances[run_id_str]. \
# status_center_request_job_status_from_master_in_slave_agent(
# message_entity.topic, message_entity.payload
# )
elif (message_entity.topic.startswith(FedMLStatusCenter.TOPIC_SLAVE_JOB_STOP_PREFIX) and
message_entity.topic.endswith(FedMLStatusCenter.TOPIC_SLAVE_JOB_STOP_SUFFIX)):
# Cleanup when stopped the job
- if job_launch_message_map.get(status_entity.run_id, None) is not None:
- job_launch_message_map.pop(status_entity.run_id)
+ if job_launch_message_map.get(run_id_str, None) is not None:
+ job_launch_message_map.pop(run_id_str)
except Exception as e:
if message_entity is not None:
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index 272423f147..31afed463b 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -294,7 +294,7 @@ def process_device_status(self, run_id, edge_id, status):
fault_tolerance_rate=fault_tolerance_rate)
if status_to_report is not None:
logging.info(f"Run completed when processing edge status, will report status {status_to_report}")
- self.report_server_status(run_id, edge_id, server_id, status_to_report)
+ self.report_server_status(run_id, server_id, server_id, status_to_report)
def calculate_server_status(
self, run_id, total_edge_nums, number_of_failed_edges, number_of_finished_edges,
@@ -340,7 +340,7 @@ def parse_fault_tolerance_params(self, run_id):
def report_server_status(self, run_id, edge_id, server_id, status):
self.status_reporter.report_server_id_status(
- run_id, status, edge_id=edge_id, server_id=server_id, server_agent_id=edge_id, update_db=False)
+ run_id, status, edge_id=edge_id, server_id=server_id, server_agent_id=server_id, update_db=False)
def report_exception_status(self, status):
self.status_reporter.report_job_status(self.run_id, status)
From 98eff5585390047ff99e7c268abb92cc9cfd7f11 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Mon, 20 May 2024 17:40:26 +0800
Subject: [PATCH 079/282] [CoreEngine] make the server status work.
---
.../fedml/computing/scheduler/scheduler_core/status_center.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index fc1c726b5f..65258af2d9 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -309,7 +309,7 @@ def run_status_dispatcher_in_slave(self, status_event, status_queue,
if iter_status_mgr.is_job_completed():
status_manager_instances.pop(iter_run_id)
break
-
+
status_manager_instances[run_id_str] = FedMLStatusManager(
run_id=run_id_int, edge_id=status_entity.edge_id, status_center=self,
message_center=message_center)
From f037b32d4aca2142152aec3f03aa0ef1855616c8 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Mon, 20 May 2024 19:59:01 +0800
Subject: [PATCH 080/282] [CoreEngine] make the job stopping work.
---
.../master/base_master_job_runner.py | 12 +++++-------
.../master/base_master_protocol_manager.py | 19 +++++++++++--------
.../scheduler_base_job_runner_manager.py | 4 ++++
.../status_manager_protocols.py | 2 +-
.../slave/base_slave_protocol_manager.py | 15 +++++----------
python/fedml/core/mlops/mlops_metrics.py | 6 ++++--
6 files changed, 30 insertions(+), 28 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner.py b/python/fedml/computing/scheduler/master/base_master_job_runner.py
index 18aa8a6eed..9ebab258bb 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner.py
@@ -285,6 +285,10 @@ def run_server_job_impl(self, process_event, completed_event,
self.args.run_id = self.run_id
MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
+ self.status_reporter.report_server_id_status(
+ run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_RUNNING, edge_id=self.edge_id,
+ server_id=self.edge_id, server_agent_id=self.edge_id)
+
# get training params
private_local_data_dir = data_config.get("privateLocalData", "")
is_using_local_data = 0
@@ -562,7 +566,7 @@ def detect_edges_status(
return True, active_edge_info_dict, inactivate_edges
def report_exception_status(self, run_id):
- self.status_reporter.report_job_status(run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION)
+ self.mlops_metrics.report_job_status(run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION)
def callback_run_logs(self, topic, payload):
run_id = str(topic).split('/')[-1]
@@ -618,12 +622,6 @@ def send_training_request_to_edges(self, request_json, active_edge_info_dict=Non
f"request GPU count {request_num_gpus}"
logging.error(err_info)
- # Bug fix: This mqtt message needs to be sent so platform can clean up the failed run and change the
- # status from running to failed.
- self.mlops_metrics.report_server_training_status(
- run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id
- )
-
self.status_reporter.report_server_id_status(
run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
server_id=self.edge_id, server_agent_id=self.server_agent_id)
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index cee91578dd..46a6448269 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -267,19 +267,22 @@ def callback_stop_train(self, topic, payload, use_payload=None):
server_id = request_json.get("serverId", None)
if server_id is None:
server_id = request_json.get("server_id", None)
+ edge_ids = request_json.get("edgeids", None)
- # Broadcast the job status to all edges
- self.rebuild_status_center(self.get_status_queue())
- self.status_reporter.report_job_status(run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_KILLED)
+ # Stop the job runner
+ self._get_job_runner_manager().stop_job_runner(
+ run_id, args=self.args, server_id=server_id, request_json=request_json,
+ run_as_cloud_agent=self.run_as_cloud_agent)
# Cleanup the cached object
if self.running_request_json.get(run_id_str, None) is not None:
self.running_request_json.pop(run_id_str)
- # Stop the job runner
- self._get_job_runner_manager().stop_job_runner(
- run_id, args=self.args, server_id=server_id, request_json=request_json,
- run_as_cloud_agent=self.run_as_cloud_agent)
+ # Reset all edge status and server status
+ for iter_edge_id in edge_ids:
+ self.generate_status_report(run_id, iter_edge_id, server_agent_id=server_id).\
+ report_client_id_status(iter_edge_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_KILLED,
+ run_id=run_id, server_id=server_id)
def callback_complete_job(self, topic, payload):
# Parse the parameters.
@@ -536,7 +539,7 @@ def send_status_msg_to_edges(self, edge_id_list, run_id, server_id, context=None
self.send_status_check_msg(run_id, edge_id, self.edge_id, context=context)
def report_exception_status(self, run_id):
- self.status_reporter.report_job_status(run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION)
+ self.mlops_metrics.report_job_status(run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION)
@staticmethod
def get_start_train_topic_with_edge_id(edge_id):
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
index 77768da6c0..dcc4045699 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
@@ -39,6 +39,10 @@ def stop_job_runner(self, run_id):
if self.job_runners.get(run_id_str, None) is not None:
self.job_runners[run_id_str].trigger_stop_event()
+ def stop_all_job_runner(self):
+ for run_id, job_runner in self.job_runners.items():
+ job_runner.trigger_stop_event()
+
def complete_job_runner(self, run_id):
run_id_str = str(run_id)
if self.job_runners.get(run_id_str, None) is not None:
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index 31afed463b..96f5e4920f 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -343,7 +343,7 @@ def report_server_status(self, run_id, edge_id, server_id, status):
run_id, status, edge_id=edge_id, server_id=server_id, server_agent_id=server_id, update_db=False)
def report_exception_status(self, status):
- self.status_reporter.report_job_status(self.run_id, status)
+ self.message_reporter.report_job_status(self.run_id, status)
def status_center_process_slave_status_to_master_in_slave_agent(self, topic, payload):
# Forward the status message to the sender queue of message center.
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index aa69d4482d..a6d43936d2 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -57,7 +57,6 @@ def __init__(self, args, agent_config=None):
self.fl_topic_request_device_info = None
self.communication_mgr = None
self.subscribed_topics = list()
- self.job_runners = dict()
self.ota_upgrade = FedMLOtaUpgrade(edge_id=args.edge_id)
self.running_request_json = dict()
self.start_request_json = None
@@ -423,8 +422,7 @@ def callback_client_logout(self, topic, payload):
if secret is None or str(secret) != "246b1be6-0eeb-4b17-b118-7d74de1975d4":
return
logging.info("Received the logout request.")
- for runner in self.job_runners:
- runner.trigger_stop_event()
+ self._get_job_runner_manager().stop_all_job_runner()
self.disable_client_login = True
time.sleep(3)
os.system("fedml logout")
@@ -451,7 +449,7 @@ def callback_response_job_status(self, topic, payload):
# process the status
logging.info("process status in the job status callback.")
- self.process_status(run_id, job_status, edge_id)
+ self.process_status(run_id, job_status, edge_id, master_id=master_agent)
def callback_broadcasted_job_status(self, topic, payload):
# Parse the parameters
@@ -489,15 +487,14 @@ def generate_protocol_manager(self):
return message_status_runner
- def process_status(self, run_id, status, edge_id):
+ def process_status(self, run_id, status, edge_id, master_id=None):
run_id_str = str(run_id)
# Process the completed status
if status == GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or \
status == GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FAILED or \
status == GeneralConstants.MSG_MLOPS_CLIENT_STATUS_KILLED:
- if self.job_runners.get(run_id_str, None) is not None:
- self.job_runners[run_id_str].trigger_completed_event()
+ self._get_job_runner_manager().complete_job_runner(run_id)
# Stop the sys perf process
# noinspection PyBoardException
@@ -584,9 +581,7 @@ def get_all_run_process_list_map(self):
return run_process_dict
def stop_job(self, run_id):
- run_id_str = str(run_id)
- if self.job_runners.get(run_id_str, None) is not None:
- self.job_runners[run_id_str].trigger_stop_event()
+ self._get_job_runner_manager().stop_job_runner(run_id)
@staticmethod
def get_start_train_topic_with_edge_id(edge_id):
diff --git a/python/fedml/core/mlops/mlops_metrics.py b/python/fedml/core/mlops/mlops_metrics.py
index afa96f6870..c27a683759 100644
--- a/python/fedml/core/mlops/mlops_metrics.py
+++ b/python/fedml/core/mlops/mlops_metrics.py
@@ -185,9 +185,11 @@ def report_server_training_status(self, run_id, status, edge_id=0, role=None,
from ...computing.scheduler.master.server_data_interface import FedMLServerDataInterface
FedMLServerDataInterface.get_instance().save_job(run_id, self.edge_id, status, running_json)
- def report_job_status(self, run_id, status):
+ def report_job_status(self, run_id, status, master_id=None):
topic_name = f"master_agent/slave_agent/job_status/{run_id}"
- payload = {"run_id": run_id, "status": status}
+ payload = {"run_id": run_id, "status": status, "fedml_version": fedml.__version__}
+ if master_id is not None:
+ payload["master_agent"] = master_id
message_json = json.dumps(payload)
self.send_message(topic_name, message_json)
From a0664d87f54cb4d2e3faca683f4562bbdb8f5976 Mon Sep 17 00:00:00 2001
From: Ubuntu <cirrascale@QAIECC5.san01.cirrascale.net>
Date: Mon, 20 May 2024 22:55:59 +0000
Subject: [PATCH 081/282] Add support for GPU Utilization
---
.../comm_utils/gpu_utils/qualcomm_utils.py | 20 ++++++++++++-------
1 file changed, 13 insertions(+), 7 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
index 9c7ea21ea9..88114cf2ad 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
@@ -43,14 +43,16 @@ def get_gpu_cards() -> List[GPUCard]:
@staticmethod
def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memory: float) -> List[int]:
-
- if order != "memory":
+ gpu_cards: List[GPUCard] = QualcommNPUtil.get_gpu_cards()
+ gpu_cards = list(filter(lambda card: (card.memoryUtil < max_memory and card.load < max_load), gpu_cards))
+ if order == 'memory':
+ gpu_cards.sort(key=lambda card: float('inf') if math.isnan(card.memoryUtil) else card.memoryUtil, reverse=False)
+ elif order == 'load':
+ gpu_cards.sort(key=lambda card: float('inf') if math.isnan(card.memoryUtil) else card.load, reverse=False)
+ else:
raise NotImplementedError(f"Qualcomm utils doesn't have support to compute availability based on {order}. "
- f"Supported criteria: [memory]")
+ f"Supported criteria: [memory, load]")
- gpu_cards: List[GPUCard] = QualcommNPUtil.get_gpu_cards()
- gpu_cards = list(filter(lambda card: card.memoryUtil < max_memory, gpu_cards))
- gpu_cards.sort(key=lambda card: float('inf') if math.isnan(card.memoryUtil) else card.memoryUtil, reverse=False)
gpu_cards = gpu_cards[0:min(limit, len(gpu_cards))]
return list(map(lambda card: card.id, gpu_cards))
@@ -75,11 +77,14 @@ def get_docker_gpu_ids_by_container_name(container_name: str, docker_client: Doc
@staticmethod
def __convert(npu) -> GPUCard:
- # TODO (alaydshah): Add support for load, memoryUtil, temperature
+ # TODO (alaydshah): Add support for temperature
memory_total = npu.devData.resourceInfo.dramTotal / 1024
memory_free = npu.devData.resourceInfo.dramFree / 1024
memory_used = memory_total - memory_free
memory_utilized = float(memory_used) / float(memory_total)
+ nsp_free = npu.devData.resourceInfo.nspFree
+ nsp_total = npu.devData.resourceInfo.nspTotal
+ load = (nsp_total - nsp_free) / nsp_total
return GPUCard(
id=npu.qid,
@@ -91,6 +96,7 @@ def __convert(npu) -> GPUCard:
memoryFree=memory_free,
memoryUsed=memory_used,
memoryUtil=memory_utilized,
+ load=load,
)
@staticmethod
From ae06bf312bf7feb1467a0d481557f2bfa95bc1a4 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Tue, 21 May 2024 01:52:39 -0700
Subject: [PATCH 082/282] Add 10 minutes TTL Cache for config fetch
---
.../computing/scheduler/model_scheduler/modelops_configs.py | 2 ++
python/fedml/core/mlops/mlops_configs.py | 2 ++
2 files changed, 4 insertions(+)
diff --git a/python/fedml/computing/scheduler/model_scheduler/modelops_configs.py b/python/fedml/computing/scheduler/model_scheduler/modelops_configs.py
index e988c29a8a..719f3825c4 100644
--- a/python/fedml/computing/scheduler/model_scheduler/modelops_configs.py
+++ b/python/fedml/computing/scheduler/model_scheduler/modelops_configs.py
@@ -4,6 +4,7 @@
import certifi
import requests
+import cachetools.func
import fedml
from fedml.core.mlops.mlops_utils import MLOpsUtils
@@ -32,6 +33,7 @@ def get_instance(args):
return ModelOpsConfigs._config_instance
@staticmethod
+ @cachetools.func.ttl_cache(ttl=600)
def get_request_params():
url = fedml._get_backend_service()
url = "{}/fedmlOpsServer/configs/fetch".format(url)
diff --git a/python/fedml/core/mlops/mlops_configs.py b/python/fedml/core/mlops/mlops_configs.py
index c8c6422d6c..6c25c38128 100644
--- a/python/fedml/core/mlops/mlops_configs.py
+++ b/python/fedml/core/mlops/mlops_configs.py
@@ -4,6 +4,7 @@
import certifi
import requests
+import cachetools.func
import fedml
from fedml.core.mlops.mlops_utils import MLOpsUtils
@@ -41,6 +42,7 @@ def __init__(self):
pass
@staticmethod
+ @cachetools.func.ttl_cache(ttl=600)
def get_request_params():
url = fedml._get_backend_service()
url = f"{url}/fedmlOpsServer/configs/fetch"
From 3214f4a569e63a260000a14267e6e1f1147b7791 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Tue, 21 May 2024 20:38:01 +0800
Subject: [PATCH 083/282] [CoreEngine] fixed the issue that the endpoint status
is aborted when the deployment is fresh and failed.
---
.../master/base_master_protocol_manager.py | 9 +++--
.../scheduler/master/deploy_job_launcher.py | 12 +++++-
.../model_scheduler/master_job_runner.py | 6 ++-
.../master_protocol_manager.py | 10 ++++-
.../scheduler/scheduler_core/status_center.py | 5 ++-
.../slave/base_slave_protocol_manager.py | 40 -------------------
python/fedml/core/mlops/mlops_runtime_log.py | 2 +
7 files changed, 35 insertions(+), 49 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index 46a6448269..c95d73f4bf 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -284,6 +284,10 @@ def callback_stop_train(self, topic, payload, use_payload=None):
report_client_id_status(iter_edge_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_KILLED,
run_id=run_id, server_id=server_id)
+ # To be compatible to the previous version of edge devices, we just send the stopping train message to edges.
+ # Currently, the latest version of edge devices don't need to process the stopping train message.
+ self.send_training_stop_request_to_edges(edge_ids, payload=payload, run_id=run_id)
+
def callback_complete_job(self, topic, payload):
# Parse the parameters.
request_json = json.loads(payload)
@@ -508,13 +512,12 @@ def send_training_stop_request_to_edges(
self, edge_id_list, payload=None, run_id=0):
if payload is None:
payload_obj = {"runId": run_id, "edgeids": edge_id_list}
- else:
- payload_obj = json.loads(payload)
+ payload = json.dumps(payload_obj)
for edge_id in edge_id_list:
topic_stop_train = "flserver_agent/" + str(edge_id) + "/stop_train"
logging.info("stop_train: send topic " + topic_stop_train)
- self.message_center.send_message(topic_stop_train, json.dumps(payload_obj))
+ self.message_center.send_message(topic_stop_train, payload)
def send_training_stop_request_to_specific_edge(self, edge_id, payload):
topic_stop_train = "flserver_agent/" + str(edge_id) + "/stop_train"
diff --git a/python/fedml/computing/scheduler/master/deploy_job_launcher.py b/python/fedml/computing/scheduler/master/deploy_job_launcher.py
index e4af2a20be..359c6e641f 100755
--- a/python/fedml/computing/scheduler/master/deploy_job_launcher.py
+++ b/python/fedml/computing/scheduler/master/deploy_job_launcher.py
@@ -3,6 +3,7 @@
from fedml.computing.scheduler.model_scheduler import device_client_constants
from fedml.computing.scheduler.model_scheduler.device_model_cards import FedMLModelCards
from fedml.computing.scheduler.scheduler_entry.constants import Constants
+from fedml.computing.scheduler.scheduler_core.compute_cache_manager import ComputeCacheManager
class FedMLDeployJobLauncher:
@@ -40,6 +41,8 @@ def deploy_model(serving_devices, request_json, run_id):
"", random_list[1], None,
in_model_id=model_id, in_model_version=model_version,
endpoint_name=endpoint_name, endpoint_id=endpoint_id, run_id=run_id)
+ return endpoint_id
+ return None
def check_model_device_ready_and_deploy(self, request_json, run_id, master_device_id,
slave_device_id, run_edge_ids=None):
@@ -87,4 +90,11 @@ def check_model_device_ready_and_deploy(self, request_json, run_id, master_devic
serving_devices.extend(device_slave_ids)
# Start to deploy the model
- FedMLDeployJobLauncher.deploy_model(serving_devices, request_json, run_id=run_id)
+ endpoint_id = FedMLDeployJobLauncher.deploy_model(serving_devices, request_json, run_id=run_id)
+
+ # Save the relationship between run id and endpoint
+ ComputeCacheManager.get_instance().set_redis_params()
+ ComputeCacheManager.get_instance().get_gpu_cache().set_endpoint_run_id_map(
+ endpoint_id, run_id)
+
+
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index eef03d53f2..bf9cee3279 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -51,6 +51,7 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
self.deployed_replica_payload = None
self.slave_deployment_results_map = dict()
self.deployment_result_queue = Queue()
+ self.is_fresh_endpoint = True
# Override
def _generate_job_runner_instance(self, args, run_id=None, request_json=None, agent_config=None, edge_id=None, ):
@@ -75,6 +76,7 @@ def run_impl(
inference_end_point_id, use_gpu, memory_size, model_version, inference_port = \
FedMLDeployMasterJobRunner.parse_model_run_params(self.request_json)
self.run_id = run_id
+ self.is_fresh_endpoint = self.request_json.get("is_fresh_endpoint", True)
# Print request parameters.
logging.info("model deployment request: {}".format(self.request_json))
@@ -246,7 +248,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
f"{self.request_json}")
return
- logging.info(f"End point {end_point_id}; Device {device_id}; replica {replica_no}; "
+ logging.info(f"Endpoint {end_point_id}; Device {device_id}; replica {replica_no}; "
f"run_operation {run_operation} model status {model_status}.")
# OPTIONAL DEBUG PARAMS
@@ -280,7 +282,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
logging.error(f"Unsupported model status {model_status}.")
# Avoid endless loop, if the rollback also failed, we should report the failure to the MLOps
- if self.replica_controller.under_rollback:
+ if self.replica_controller.under_rollback or self.is_fresh_endpoint:
self.send_deployment_status(
end_point_id, end_point_name, payload_json["model_name"], "",
ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index 09c2dd5d17..d21ab44c6e 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -109,6 +109,10 @@ def callback_delete_deployment(self, topic, payload):
# Parse payload as the model message object.
model_msg_object = FedMLModelMsgObject(topic, payload)
+ # Get the launch job id
+ ComputeCacheManager.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+ launch_job_id = ComputeCacheManager.get_instance().get_gpu_cache().get_endpoint_run_id_map(self.run_id)
+
# Delete SQLite records
FedMLServerDataInterface.get_instance().delete_job_from_db(model_msg_object.run_id)
FedMLModelDatabase.get_instance().delete_deployment_result(
@@ -137,7 +141,6 @@ def callback_delete_deployment(self, topic, payload):
model_msg_object.model_name, model_msg_object.model_version)
# Report the launch job status with killed status.
- launch_job_id = ComputeCacheManager.get_instance().get_gpu_cache().get_endpoint_run_id_map(self.run_id)
if launch_job_id is not None:
self.status_reporter.report_server_id_status(launch_job_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_KILLED)
@@ -180,6 +183,11 @@ def callback_start_deployment(self, topic, payload):
# Set redis config
FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
+ # Query if the endpoint exists
+ endpoint_device_info = FedMLModelCache.get_instance(self.redis_addr, self.redis_port).get_end_point_device_info(
+ request_json["end_point_id"])
+ request_json["is_fresh_endpoint"] = True if endpoint_device_info is None else False
+
# Save the user setting (about replica number) of this run to Redis, if existed, update it
FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_user_setting_replica_num(
end_point_id=run_id, end_point_name=end_point_name, model_name=model_name, model_version=model_version,
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index 65258af2d9..4ababbc826 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -331,9 +331,10 @@ def run_status_dispatcher_in_slave(self, status_event, status_queue,
)
elif (message_entity.topic.startswith(FedMLStatusCenter.TOPIC_SLAVE_JOB_LAUNCH_PREFIX) and
message_entity.topic.endswith(FedMLStatusCenter.TOPIC_SLAVE_JOB_LAUNCH_SUFFIX)):
+ pass
# Async request the job status from master when launching the job
- job_launch_message_map[run_id_str] = {"topic": message_entity.topic,
- "payload": message_entity.payload}
+ # job_launch_message_map[run_id_str] = {"topic": message_entity.topic,
+ # "payload": message_entity.payload}
# status_manager_instances[run_id_str]. \
# status_center_request_job_status_from_master_in_slave_agent(
# message_entity.topic, message_entity.payload
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index a6d43936d2..de97684061 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -10,7 +10,6 @@
from ..comm_utils.constants import SchedulerConstants
from ..comm_utils.job_utils import JobRunnerUtils, DockerArgs
from ..comm_utils.run_process_utils import RunProcessUtils
-from ....core.mlops import MLOpsMetrics
from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
from ....core.mlops.mlops_configs import MLOpsConfigs
from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
@@ -44,7 +43,6 @@ def __init__(self, args, agent_config=None):
self.unique_device_id = args.unique_device_id
self.agent_config = agent_config
self.topic_start_train = None
- self.topic_stop_train = None
self.topic_report_status = None
self.topic_ota_msg = None
self.topic_request_device_info = None
@@ -53,7 +51,6 @@ def __init__(self, args, agent_config=None):
self.topic_response_job_status = None
self.topic_report_device_status_in_job = None
self.fl_topic_start_train = None
- self.fl_topic_stop_train = None
self.fl_topic_request_device_info = None
self.communication_mgr = None
self.subscribed_topics = list()
@@ -75,9 +72,6 @@ def generate_topics(self):
# The topic for stopping training
self.topic_start_train = "flserver_agent/" + str(self.edge_id) + "/start_train"
- # The topi for stopping training
- self.topic_stop_train = "flserver_agent/" + str(self.edge_id) + "/stop_train"
-
# The topic for reporting current device status.
self.topic_report_status = "mlops/report_device_status"
@@ -107,13 +101,11 @@ def generate_topics(self):
if self.general_edge_id is not None:
self.fl_topic_start_train = "flserver_agent/" + str(self.general_edge_id) + "/start_train"
- self.fl_topic_stop_train = "flserver_agent/" + str(self.general_edge_id) + "/stop_train"
self.fl_topic_request_device_info = "server/client/request_device_info/" + str(self.general_edge_id)
# Subscribe topics for starting train, stopping train and fetching client status.
self.subscribed_topics.clear()
self.add_subscribe_topic(self.topic_start_train)
- self.add_subscribe_topic(self.topic_stop_train)
self.add_subscribe_topic(self.topic_report_status)
self.add_subscribe_topic(self.topic_ota_msg)
self.add_subscribe_topic(self.topic_request_device_info)
@@ -123,7 +115,6 @@ def generate_topics(self):
self.add_subscribe_topic(self.topic_report_device_status_in_job)
if self.general_edge_id is not None:
self.add_subscribe_topic(self.fl_topic_start_train)
- self.add_subscribe_topic(self.fl_topic_stop_train)
self.add_subscribe_topic(self.fl_topic_request_device_info)
@abstractmethod
@@ -132,7 +123,6 @@ def add_protocol_handler(self):
# self.add_message_listener(self.topic_start_train, self.callback_start_train)
# Add the message listeners for all topics
self.add_message_listener(self.topic_start_train, self.callback_start_train)
- self.add_message_listener(self.topic_stop_train, self.callback_stop_train)
self.add_message_listener(self.topic_ota_msg, FedMLBaseSlaveProtocolManager.callback_client_ota_msg)
self.add_message_listener(self.topic_report_status, self.callback_report_current_status)
self.add_message_listener(self.topic_request_device_info, self.callback_report_device_info)
@@ -141,7 +131,6 @@ def add_protocol_handler(self):
self.add_message_listener(self.topic_response_job_status, self.callback_response_job_status)
self.add_message_listener(self.topic_report_device_status_in_job, self.callback_response_device_status_in_job)
self.add_message_listener(self.fl_topic_start_train, self.callback_start_train)
- self.add_message_listener(self.fl_topic_stop_train, self.callback_stop_train)
self.add_message_listener(self.fl_topic_request_device_info, self.callback_report_device_info)
@abstractmethod
@@ -295,27 +284,6 @@ def callback_start_train(self, topic, payload):
# Register the job launch message into the status center
self.register_job_launch_message(topic, payload)
- def callback_stop_train(self, topic, payload):
- # Parse the parameters.
- edge_id = str(topic).split("/")[-2]
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json.get("runId", None)
- run_id = request_json.get("id", None) if run_id is None else run_id
- run_status = request_json.get("run_status", GeneralConstants.MSG_MLOPS_CLIENT_STATUS_KILLED)
-
- # logging.info("Stop run with multiprocessing...")
- # Stop client with multiprocessing mode
- run_id_str = str(run_id)
- self._get_job_runner_manager().cleanup_containers_and_release_gpus(
- run_id, edge_id, SchedulerConstants.JOB_TASK_TYPE_TRAIN)
- self.sync_run_stop_status(run_status=run_status)
-
- # Register the job stopping message into the status center
- self.register_job_stop_message(topic, payload)
-
def callback_report_current_status(self, topic, payload):
logging.info(
f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
@@ -561,14 +529,6 @@ def remove_listener_job_status(self, run_id):
self.remove_message_listener(topic_job_status_from_master)
self.unsubscribe_msg(topic_job_status_from_master)
- def sync_run_stop_status(self, run_status=GeneralConstants.MSG_MLOPS_CLIENT_STATUS_KILLED):
- try:
- self.status_reporter.report_client_id_status(
- self.edge_id, run_status, server_id=self.server_id, run_id=self.run_id)
- except Exception as e:
- logging.error(f"Failed to sync run stop status with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
def get_all_run_process_list_map(self):
run_process_dict = dict()
all_runner_pid_dict = self._get_job_runner_manager().get_all_runner_pid_map()
diff --git a/python/fedml/core/mlops/mlops_runtime_log.py b/python/fedml/core/mlops/mlops_runtime_log.py
index 0bc4dc6b6c..0fc5db3d23 100644
--- a/python/fedml/core/mlops/mlops_runtime_log.py
+++ b/python/fedml/core/mlops/mlops_runtime_log.py
@@ -143,6 +143,8 @@ def __init__(self, args):
self.should_write_log_file = args.using_mlops
else:
self.should_write_log_file = False
+ if not hasattr(args, "log_file_dir"):
+ setattr(args, "log_file_dir", "./logs")
self.log_file_dir = args.log_file_dir
self.log_file = None
self.run_id = args.run_id
From bcf988144acbccd998d4facefa379f51e847d111 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Tue, 21 May 2024 20:58:04 +0800
Subject: [PATCH 084/282] [CoreEngine] save the relationship between endpoint
and run id.
---
.../computing/scheduler/master/deploy_job_launcher.py | 7 +------
.../scheduler/slave/base_slave_protocol_manager.py | 6 ++++++
2 files changed, 7 insertions(+), 6 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/deploy_job_launcher.py b/python/fedml/computing/scheduler/master/deploy_job_launcher.py
index 359c6e641f..50e4517547 100755
--- a/python/fedml/computing/scheduler/master/deploy_job_launcher.py
+++ b/python/fedml/computing/scheduler/master/deploy_job_launcher.py
@@ -3,7 +3,6 @@
from fedml.computing.scheduler.model_scheduler import device_client_constants
from fedml.computing.scheduler.model_scheduler.device_model_cards import FedMLModelCards
from fedml.computing.scheduler.scheduler_entry.constants import Constants
-from fedml.computing.scheduler.scheduler_core.compute_cache_manager import ComputeCacheManager
class FedMLDeployJobLauncher:
@@ -90,11 +89,7 @@ def check_model_device_ready_and_deploy(self, request_json, run_id, master_devic
serving_devices.extend(device_slave_ids)
# Start to deploy the model
- endpoint_id = FedMLDeployJobLauncher.deploy_model(serving_devices, request_json, run_id=run_id)
+ FedMLDeployJobLauncher.deploy_model(serving_devices, request_json, run_id=run_id)
- # Save the relationship between run id and endpoint
- ComputeCacheManager.get_instance().set_redis_params()
- ComputeCacheManager.get_instance().get_gpu_cache().set_endpoint_run_id_map(
- endpoint_id, run_id)
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index de97684061..447bd05cd9 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -257,6 +257,12 @@ def callback_start_train(self, topic, payload):
model_master_device_id=model_master_device_id,
model_slave_device_id=model_slave_device_id)
else:
+ # Save the relationship between run id and endpoint
+ ComputeCacheManager.get_instance().set_redis_params()
+ ComputeCacheManager.get_instance().get_gpu_cache().set_endpoint_run_id_map(
+ endpoint_id, run_id)
+
+ # Report the run status with finished status and return
self.generate_status_report(run_id, edge_id, server_agent_id=server_agent_id).report_client_id_status(
edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED, run_id=run_id)
return
From 1e69f68712a6439344126cc6a5b0dc82de59e179 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 22 May 2024 01:24:20 +0800
Subject: [PATCH 085/282] [CoreEngine] report the killed status when deleting
the deployment.
---
.../scheduler/model_scheduler/master_protocol_manager.py | 6 ++++--
1 file changed, 4 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index d21ab44c6e..b65d1bc8de 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -111,7 +111,7 @@ def callback_delete_deployment(self, topic, payload):
# Get the launch job id
ComputeCacheManager.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- launch_job_id = ComputeCacheManager.get_instance().get_gpu_cache().get_endpoint_run_id_map(self.run_id)
+ launch_job_id = ComputeCacheManager.get_instance().get_gpu_cache().get_endpoint_run_id_map(model_msg_object.run_id)
# Delete SQLite records
FedMLServerDataInterface.get_instance().delete_job_from_db(model_msg_object.run_id)
@@ -142,7 +142,9 @@ def callback_delete_deployment(self, topic, payload):
# Report the launch job status with killed status.
if launch_job_id is not None:
- self.status_reporter.report_server_id_status(launch_job_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_KILLED)
+ self.generate_status_report(model_msg_object.run_id, self.edge_id, server_agent_id=self.edge_id).\
+ report_server_id_status(launch_job_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_KILLED,
+ server_id=self.edge_id, server_agent_id=self.edge_id)
def callback_start_deployment(self, topic, payload):
# noinspection PyBroadException
From 2306ee366d8ffe574b1da14092f7271a76b1441c Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 22 May 2024 02:11:10 +0800
Subject: [PATCH 086/282] [CoreEngine] make the server status work.
---
.../fedml/computing/scheduler/scheduler_core/status_center.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index 4ababbc826..fa43fd649a 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -218,7 +218,7 @@ def run_status_dispatcher(self, status_event, status_queue,
message_center=message_center)
else:
status_manager_instances[run_id_str].edge_id = status_entity.edge_id
- if status_entity.server_id is None and status_entity.server_id != 0:
+ if status_entity.server_id is not None and status_entity.server_id != 0:
status_manager_instances[run_id_str].server_id = status_entity.server_id
# if the job status is completed then continue
From cd84d8209144e07d18e729c0d93287029a567e74 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 22 May 2024 02:19:23 +0800
Subject: [PATCH 087/282] [CoreEngine] make the server status work.
---
.../fedml/computing/scheduler/scheduler_core/status_center.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index fa43fd649a..97c2115e76 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -218,7 +218,7 @@ def run_status_dispatcher(self, status_event, status_queue,
message_center=message_center)
else:
status_manager_instances[run_id_str].edge_id = status_entity.edge_id
- if status_entity.server_id is not None and status_entity.server_id != 0:
+ if status_entity.server_id is not None and str(status_entity.server_id) != "0":
status_manager_instances[run_id_str].server_id = status_entity.server_id
# if the job status is completed then continue
From f7ab709a39af6981a3ae4d8985a060f92b937fdb Mon Sep 17 00:00:00 2001
From: Alay Dilipbhai Shah <alay11shah@gmail.com>
Date: Tue, 21 May 2024 16:29:09 -0700
Subject: [PATCH 088/282] Update setup.py
---
python/setup.py | 2 ++
1 file changed, 2 insertions(+)
diff --git a/python/setup.py b/python/setup.py
index fa425c98f7..0e314de29c 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -64,6 +64,8 @@ def finalize_options(self):
'uvicorn',
'wandb==0.13.2',
'wget',
+ # Need to pin this version due to breaking change released in python docker sdk
+ 'requests<2.32',
]
requirements_extra_mpi = [
From 649e42fa3f259cc892ca257b822aa844627da39d Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Tue, 21 May 2024 22:25:18 -0700
Subject: [PATCH 089/282] Remove Docker Client Timeout
---
.../fedml/computing/scheduler/comm_utils/container_utils.py | 4 ++--
python/fedml/computing/scheduler/comm_utils/job_utils.py | 2 +-
.../scheduler/model_scheduler/device_model_deployment.py | 4 ++--
3 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/container_utils.py b/python/fedml/computing/scheduler/comm_utils/container_utils.py
index f86e9fe1a2..2f5fa31fb5 100644
--- a/python/fedml/computing/scheduler/comm_utils/container_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/container_utils.py
@@ -26,7 +26,7 @@ def get_instance():
def get_docker_client(self):
try:
- client = docker.from_env(timeout=5, version="auto")
+ client = docker.from_env()
except Exception:
logging.error("Failed to connect to the docker daemon, please ensure that you have "
"installed Docker Desktop or Docker Engine, and the docker is running")
@@ -180,7 +180,7 @@ def get_container_rank_same_model(prefix: str):
running_model_name = hash("model_endpoint_id_{}_name_{}_model_id_{}_name_{}_ver_{}")
"""
try:
- client = docker.from_env(timeout=5, version="auto")
+ client = docker.from_env()
except Exception:
logging.error("Failed to connect to the docker daemon, please ensure that you have "
"installed Docker Desktop or Docker Engine, and the docker is running")
diff --git a/python/fedml/computing/scheduler/comm_utils/job_utils.py b/python/fedml/computing/scheduler/comm_utils/job_utils.py
index 08ce44d1dd..5b9a2c812a 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_utils.py
@@ -570,7 +570,7 @@ def get_run_container_name(run_id: int) -> str:
@staticmethod
def get_docker_client(docker_args: DockerArgs) -> DockerClient:
try:
- client = docker.from_env(timeout=5, version="auto")
+ client = docker.from_env()
if docker_args.username != "" and docker_args.registry != "":
client.login(username=docker_args.username, password=docker_args.password, registry=docker_args.registry)
except Exception as e:
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index f54965b599..1876373d25 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -210,7 +210,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
infer_host = "127.0.0.1"
try:
- client = docker.from_env(timeout=5, version="auto")
+ client = docker.from_env()
if enable_custom_image and docker_registry_user_name != "" and docker_registry_user_password != "" \
and docker_registry != "":
client.login(username=docker_registry_user_name, password=docker_registry_user_password,
@@ -467,7 +467,7 @@ def log_deployment_result(end_point_id, model_id, cmd_container_name, cmd_type,
logging.info(f"Attempt: {deploy_attempt} / {deploy_attempt_threshold} ...")
try:
- client = docker.from_env(timeout=5, version="auto")
+ client = docker.from_env()
except Exception:
logging.error("Failed to connect to the docker daemon, please ensure that you have "
"installed Docker Desktop or Docker Engine, and the docker is running")
From 8d9c8ed2876eee9dc14ff6f1ac30573c6a600c38 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 23 May 2024 02:21:44 +0800
Subject: [PATCH 090/282] [CoreEngine] change the edge status in the status
center.
---
.../status_manager_protocols.py | 23 +++++++++----------
1 file changed, 11 insertions(+), 12 deletions(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index 96f5e4920f..e045458db5 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -23,7 +23,7 @@ def __init__(self, run_id=None, edge_id=None, server_id=None,
self.edge_id = edge_id
self.server_id = server_id
self.edge_id_list = edge_id_list
- self.client_agent_active_list = dict()
+ self.edge_status_dict = None
self.running_scheduler_contract = running_scheduler_contract if running_scheduler_contract is not None else dict()
self.message_reporter = MLOpsMetrics()
self.message_reporter.set_messenger(message_center)
@@ -163,6 +163,8 @@ def status_center_process_master_status(self, topic, payload):
status = request_json["status"]
edge_id = request_json["edge_id"]
server_id = request_json.get("server_id", None)
+ if server_id is None or str(server_id) == "0":
+ server_id = self.server_id
run_id_str = str(run_id)
# Process the job status
@@ -185,8 +187,7 @@ def process_job_status_consensus(self, run_id, master_id, status):
status = self.get_entire_job_status()
# Set the device status based on the job status
- edge_id_status_dict = self.client_agent_active_list.get(f"{run_id}", {})
- for edge_id_item, edge_status_item in edge_id_status_dict.items():
+ for edge_id_item, edge_status_item in self.edge_status_dict.items():
if edge_id_item == "server":
continue
@@ -233,18 +234,17 @@ def status_center_process_slave_status(self, topic, payload):
init_edge_id_list = payload_json.get("init_all_edge_id_list", None)
init_server_id = payload_json.get("init_server_id", None)
- active_item_dict = self.client_agent_active_list.get(f"{run_id}", None)
- if active_item_dict is None:
- self.client_agent_active_list[f"{run_id}"] = dict()
+ if self.edge_status_dict is None:
+ self.edge_status_dict = dict()
if init_edge_id_list is not None:
- self.client_agent_active_list[f"{run_id}"][f"server"] = init_server_id
+ self.edge_status_dict[f"server"] = init_server_id
for edge_id_item in init_edge_id_list:
- self.client_agent_active_list[f"{run_id}"][f"{edge_id_item}"] = \
+ self.edge_status_dict[f"{edge_id_item}"] = \
ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE
if run_id is not None and edge_id is not None:
- self.client_agent_active_list[f"{run_id}"][f"{edge_id}"] = status
+ self.edge_status_dict[f"{edge_id}"] = status
self.process_device_status(run_id, edge_id, status)
@@ -252,12 +252,11 @@ def process_device_status(self, run_id, edge_id, status):
number_of_failed_edges = 0
number_of_finished_edges = 0
number_of_killed_edges = 0
- edge_id_status_dict = self.client_agent_active_list.get(f"{run_id}", {})
- server_id = edge_id_status_dict.get("server", 0)
+ server_id = self.edge_status_dict.get("server", 0)
enable_fault_tolerance, fault_tolerance_rate = self.parse_fault_tolerance_params(run_id)
running_edges_list = list()
edge_nums = 0
- for edge_id_item, status_item in edge_id_status_dict.items():
+ for edge_id_item, status_item in self.edge_status_dict.items():
if edge_id_item == "server":
continue
From 1162f6c18b266796ac958d766ab4790ce50a0b88 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 23 May 2024 15:13:23 +0800
Subject: [PATCH 091/282] [CoreEngine] forward the stopping request to the
cloud server.
---
.../master/base_master_protocol_manager.py | 17 ++++++++++-------
1 file changed, 10 insertions(+), 7 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index c95d73f4bf..1c4cbba4f4 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -264,23 +264,26 @@ def callback_stop_train(self, topic, payload, use_payload=None):
run_id = request_json.get("runId", None)
run_id = request_json.get("id", None) if run_id is None else run_id
run_id_str = str(run_id)
+ edge_ids = request_json.get("edgeids", None)
server_id = request_json.get("serverId", None)
if server_id is None:
server_id = request_json.get("server_id", None)
- edge_ids = request_json.get("edgeids", None)
-
- # Stop the job runner
- self._get_job_runner_manager().stop_job_runner(
- run_id, args=self.args, server_id=server_id, request_json=request_json,
- run_as_cloud_agent=self.run_as_cloud_agent)
+ server_agent_id = server_id
# Cleanup the cached object
if self.running_request_json.get(run_id_str, None) is not None:
self.running_request_json.pop(run_id_str)
+ # If it is the cloud agent, then forward the stopping request to the corresponding cloud server.
+ if self.run_as_cloud_agent:
+ server_agent_id = self.edge_id
+ topic_stop_train_to_cloud_server = f"mlops/flserver_agent_{server_id}/stop_train"
+ self.message_center.send_message(topic_stop_train_to_cloud_server, payload)
+ return
+
# Reset all edge status and server status
for iter_edge_id in edge_ids:
- self.generate_status_report(run_id, iter_edge_id, server_agent_id=server_id).\
+ self.generate_status_report(run_id, iter_edge_id, server_agent_id=server_agent_id).\
report_client_id_status(iter_edge_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_KILLED,
run_id=run_id, server_id=server_id)
From 92b7e162b66fa09b22a6af2dcfc22acf46ddf5cb Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 28 May 2024 17:46:52 +0000
Subject: [PATCH 092/282] [Deploy] Try to convert the gpu_topology value type
to int.
---
.../model_scheduler/device_replica_controller.py | 9 ++++++++-
1 file changed, 8 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_replica_controller.py b/python/fedml/computing/scheduler/model_scheduler/device_replica_controller.py
index 667d57c4f4..ea19efb8b6 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_replica_controller.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_replica_controller.py
@@ -67,7 +67,9 @@ def __init__(self, master_id, request_json: dict):
def calc_total_gpu_num(self):
total_gpu_num = 0
for device_id, gpu_num in self.devices_avail_gpus.items():
- total_gpu_num += gpu_num
+ if type(gpu_num) is not int:
+ logging.warning(f"The value in gpu_topology should be int, but got {type(gpu_num)}. Try to convert it.")
+ total_gpu_num += int(gpu_num)
return total_gpu_num
def init_id_replica_num(self):
@@ -77,6 +79,11 @@ def init_id_replica_num(self):
"""
id_replica_num = {}
for id, avail_num in self.devices_avail_gpus.items():
+ if type(avail_num) is not int:
+ logging.warning(f"The value in gpu_topology should be int, "
+ f"but got {type(avail_num)}. Try to convert it.")
+ avail_num = int(avail_num)
+
if avail_num % self.gpu_per_replica != 0:
raise ValueError("The number of gpus for each device should be divisible by gpu_per_replica")
id_replica_num[str(id)] = avail_num // self.gpu_per_replica
From 0a6eba9f6dfda93ed0df500e63b8b64bad5df44d Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 28 May 2024 22:48:38 +0000
Subject: [PATCH 093/282] [Deploy] Fix version diff function.
---
.../scheduler/model_scheduler/master_job_runner_manager.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
index 0bfc205b34..c761cd6d8f 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
@@ -64,4 +64,4 @@ def generate_request_json_with_replica_num_diff(run_id, edge_id, request_json):
@staticmethod
def generate_request_json_with_replica_version_diff(run_id, edge_id, request_json):
- return FedMLDeployMasterJobRunner.generate_request_json_with_replica_num_diff(run_id, edge_id, request_json)
+ return FedMLDeployMasterJobRunner.generate_request_json_with_replica_version_diff(run_id, edge_id, request_json)
From e8844d389b1aa7809df3ea3d8255c6ce83501e7b Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 28 May 2024 22:50:46 -0400
Subject: [PATCH 094/282] [Deploy] Fix timezone issue using pandas
---
.../scheduler/model_scheduler/autoscaler/autoscaler.py | 5 +++--
1 file changed, 3 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
index bb2b59e7d9..eb9f08b0eb 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
@@ -50,11 +50,11 @@ def filter_by_timestamp(cls,
filtered = metrics
if before_now_minutes:
less_than_ts = \
- str(pd.Timestamp.now() - pd.Timedelta(minutes=before_now_minutes))
+ str(pd.Timestamp.utcnow().replace(tzinfo=None) - pd.Timedelta(minutes=before_now_minutes))
filtered = metrics.query("'{}' <= {}".format(less_than_ts, "timestamp"))
if before_now_seconds:
less_than_ts = \
- str(pd.Timestamp.now() - pd.Timedelta(seconds=before_now_seconds))
+ str(pd.Timestamp.utcnow().replace(tzinfo=None) - pd.Timedelta(seconds=before_now_seconds))
filtered = metrics.query("'{}' <= {}".format(less_than_ts, "timestamp"))
return filtered
@@ -151,6 +151,7 @@ def scale_operation_query_concurrency(cls,
# Otherwise, we proceed as normal.
queries_num = period_data.shape[0]
+ logging.info(f"Detect {queries_num} of requests in {concurrent_query_policy.window_size_secs} seconds")
try:
# QSR: Queries per Second per Replica: (Number of Queries / Number of Current Replicas) / Window Size
From b58720cd93952f8ea95366af59cf160e882706aa Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 29 May 2024 19:15:37 +0800
Subject: [PATCH 095/282] [CoreEngine] In order to make the inference logs
work, we save the container inference logs to the single dir.
---
.../computing/scheduler/comm_utils/job_monitor.py | 11 ++++++++---
.../scheduler_core/scheduler_base_job_runner.py | 8 +++++++-
python/fedml/core/mlops/mlops_utils.py | 3 ++-
3 files changed, 17 insertions(+), 5 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index bada84d96e..a7d5214a02 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -48,6 +48,7 @@
class JobMonitor(Singleton):
ENDPOINT_CONTAINER_LOG_PREFIX = "endpoint"
TIME_INTERVAL_FOR_INFERENCE_ON_GATEWAY = 60 * 10
+ ENDPOINT_CONTAINER_LOG_SUBDIR = "monitor_endpoint_logs"
def __init__(self):
if not hasattr(self, "endpoint_unavailable_counter"):
@@ -1055,8 +1056,11 @@ def monitor_endpoint_logs(self):
model_version = model_config.get("model_version", None)
endpoint_name = endpoint_json.get("end_point_name", None)
+ log_file_dir = os.path.join(
+ device_client_constants.ClientConstants.get_log_file_dir(),
+ JobMonitor.ENDPOINT_CONTAINER_LOG_SUBDIR)
log_file_path, program_prefix = MLOpsLoggingUtils.build_log_file_path_with_run_params(
- job.job_id, int(job.edge_id), device_server_constants.ServerConstants.get_log_file_dir(), is_server=True,
+ job.job_id, int(job.edge_id), log_file_dir, is_server=False,
log_file_prefix=JobMonitor.ENDPOINT_CONTAINER_LOG_PREFIX,
)
@@ -1130,8 +1134,9 @@ def monitor_endpoint_logs(self):
nano_second_str = container_time.split(".")[1][:9]
t_datetime_obj = isoparse(container_time)
- if t_sec_offset is not None:
- t_datetime_obj = t_datetime_obj + datetime.timedelta(seconds=t_sec_offset)
+ # ISSUE: this will cause the timestamp is not correct.
+ #if t_sec_offset is not None:
+ # t_datetime_obj = t_datetime_obj + datetime.timedelta(seconds=t_sec_offset)
except Exception as e:
logging.error(f"Exception when parsing the container log time {e}")
t_datetime_obj = datetime.datetime.now()
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 69b69f4d4c..648ab18cf1 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -12,7 +12,7 @@
from ..comm_utils.constants import SchedulerConstants
from ..comm_utils.job_utils import JobRunnerUtils, DockerArgs
from ..scheduler_entry.constants import Constants
-from ....core.mlops import MLOpsMetrics
+from ....core.mlops import MLOpsMetrics, MLOpsRuntimeLogDaemon
from ....core.mlops.mlops_device_perfs import MLOpsDevicePerfStats
from ..comm_utils.yaml_utils import load_yaml_config
from .general_constants import GeneralConstants
@@ -449,10 +449,16 @@ def trigger_stop_event(self):
if self.run_process_event is not None:
self.run_process_event.set()
+ time.sleep(1)
+ MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, self.edge_id)
+
def trigger_completed_event(self):
if self.run_process_completed_event is not None:
self.run_process_completed_event.set()
+ time.sleep(1)
+ MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, self.edge_id)
+
def execute_job_task(self, unzip_package_path, entry_file_full_path, conf_file_full_path, dynamic_args_config,
fedml_config_object):
run_config = self.request_json["run_config"]
diff --git a/python/fedml/core/mlops/mlops_utils.py b/python/fedml/core/mlops/mlops_utils.py
index 1d6db23d02..8bde9e4299 100644
--- a/python/fedml/core/mlops/mlops_utils.py
+++ b/python/fedml/core/mlops/mlops_utils.py
@@ -150,10 +150,11 @@ def get_edge_id_from_args(args):
else:
edge_id = 0
else:
- if getattr(args, "client_id", None) is not None:
+ if getattr(args, "edge_id", None) is not None:
edge_id = args.edge_id
else:
edge_id = 0
+
return edge_id
@staticmethod
From 76250750358b57e572e78bb07fa03927bc460003 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Thu, 30 May 2024 07:29:38 +0000
Subject: [PATCH 096/282] [Deploy] Avoid re-download the same model serving
package.
---
.../model_scheduler/worker_job_runner.py | 116 ++++++++----------
1 file changed, 53 insertions(+), 63 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index 332dab2547..831064b591 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -146,10 +146,10 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
logging.info(f"[Worker] Received model deployment request from master for endpoint {run_id}.")
self.replica_handler = FedMLDeviceReplicaHandler(self.edge_id, self.request_json)
if self.replica_handler is not None:
- logging.info(f"=================Worker replica Handler ======================"
- f"Reconcile with num diff {self.replica_handler.replica_num_diff} "
- f"and version diff {self.replica_handler.replica_version_diff}."
- f"=============================================================")
+ logging.info("\n================= Worker replica Handler ======================\n"
+ f"Reconcile with num diff {self.replica_handler.replica_num_diff}\n"
+ f"and version diff {self.replica_handler.replica_version_diff}\n"
+ "===============================================================\n")
else:
logging.error(f"[Worker] Replica handler is None.")
return False
@@ -178,39 +178,13 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
logging.info("[Worker] No need to reconcile.")
return True
- logging.info(
- f"================Worker Reconcile Operations ======================\n"
- f" op: {op}; op num: {op_num}.\n"
- f"==================================================================\n")
-
- # If not rollback, download package from MLOps; otherwise, use the backup package
- if op != "rollback":
- logging.info("Download and unzip model to local...")
- unzip_package_path, _, _ = \
- self.update_local_fedml_config(run_id, model_config, model_config_parameters)
- if unzip_package_path is None:
- logging.info("Failed to update local fedml config.")
- self.check_runner_stop_event()
- self.status_reporter.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- is_from_model=True, run_id=run_id)
- return False
-
- if not os.path.exists(unzip_package_path):
- logging.info("Failed to unzip file.")
- self.check_runner_stop_event()
- self.status_reporter.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- is_from_model=True, run_id=run_id)
- return False
- else:
- logging.info("Try to use backup package to rollback...")
- # Find folder under "~/.fedml/fedml-model-client/fedml/model_packages \
- # /${end_point_id}_${end_point_name}_${model_name}_${model_version}"
- backup_folder_full_path = None
- models_root_dir = ClientConstants.get_model_package_dir()
+ logging.info("\n================ Worker Reconcile Operations ======================\n"
+ f" op: {op}; op num: {op_num}.\n"
+ "===================================================================\n")
+ if op == "rollback":
# Find the version (notified by master) to rollback
+ logging.info("Try to use backup package to rollback...")
version_diff_dict = self.request_json["replica_version_diff"][str(self.edge_id)]
version_rollback_to = None
for replica_no, rollback_ops in version_diff_dict.items():
@@ -222,39 +196,38 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
return False
model_version = version_rollback_to
- # Format the version to match the folder name
- model_version_formatted = version_rollback_to.replace(" ", "-")
- model_version_formatted = model_version_formatted.replace(":", "-")
-
- last_run_folder_sub_fd = f"{run_id}_{end_point_name}_{model_name}_{model_version_formatted}"
- for folder in os.listdir(models_root_dir):
- if last_run_folder_sub_fd in folder:
- backup_folder_full_path = os.path.join(models_root_dir, folder)
- break
- if backup_folder_full_path is None:
- logging.error(f"No backup folder found for run_id: {self.run_id} edge_id: {self.edge_id} "
- f"under {models_root_dir} with sub folder {last_run_folder_sub_fd}, rollback failed.")
- return False
+ # Construct the parent folder name for the package
+ model_version_formatted = model_version.replace(" ", "-")
+ model_version_formatted = model_version_formatted.replace(":", "-")
+ models_root_dir = ClientConstants.get_model_package_dir()
+ parent_fd = f"{run_id}_{end_point_name}_{model_name}_{model_version_formatted}"
- # Inside backup folder, find unzipped package with prefix unzip_fedml_run
- unzip_package_path_parent = None
- for folder in os.listdir(backup_folder_full_path):
- if folder.startswith("unzip_fedml_run"):
- unzip_package_path_parent = os.path.join(backup_folder_full_path, folder)
- break
-
- # Inside unzip folder, find the unzipped package, should be the only one
- unzip_package_path = None
- for folder in os.listdir(unzip_package_path_parent):
- if os.path.isdir(os.path.join(unzip_package_path_parent, folder)):
- unzip_package_path = os.path.join(unzip_package_path_parent, folder)
- break
+ # Check if the package is already downloaded
+ unzip_package_path = ""
+ if os.path.exists(os.path.join(models_root_dir, parent_fd)):
+ unzip_package_path = self.find_previous_downloaded_pkg(os.path.join(models_root_dir, parent_fd))
+ # Download the package if not found
+ if unzip_package_path == "":
+ logging.info("Download and unzip model to local...")
+ unzip_package_path, _, _ = \
+ self.update_local_fedml_config(run_id, model_config, model_config_parameters)
if unzip_package_path is None:
- logging.error(f"No unzipped package found for run_id: {self.run_id} edge_id: {self.edge_id} "
- f"under {backup_folder_full_path}, rollback failed.")
+ logging.info("Failed to update local fedml config.")
+ self.check_runner_stop_event()
+ self.status_reporter.report_client_id_status(
+ self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
+ is_from_model=True, run_id=run_id)
return False
+ if not os.path.exists(unzip_package_path):
+ logging.info("Failed to unzip file.")
+ self.check_runner_stop_event()
+ self.status_reporter.report_client_id_status(
+ self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
+ is_from_model=True, run_id=run_id)
+ return False
+
self.check_runner_stop_event()
running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
@@ -535,3 +508,20 @@ def build_dynamic_args(self, run_id, run_config, package_conf_object, base_dir):
# Override
def build_dynamic_constrain_variables(self, run_id, run_config):
pass
+
+ @staticmethod
+ def find_previous_downloaded_pkg(parent_dir) -> str:
+ unzip_fd = ""
+ res = ""
+
+ for folder in os.listdir(parent_dir):
+ if folder.startswith("unzip_fedml_run"):
+ unzip_fd = os.path.join(parent_dir, folder)
+ break
+
+ for folder in os.listdir(unzip_fd):
+ if os.path.isdir(os.path.join(unzip_fd, folder)):
+ res = os.path.join(unzip_fd, folder)
+ break
+
+ return res
From 9d8b0df6ae63c22f4ef95d330ff4b154db68b500 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Thu, 30 May 2024 12:22:59 -0700
Subject: [PATCH 097/282] Add inference gateway logs
---
.../model_scheduler/device_model_inference.py | 18 ++++-
.../model_scheduler/master_job_runner.py | 77 +++++++++++--------
.../master_job_runner_manager.py | 4 +-
.../master_protocol_manager.py | 2 +-
4 files changed, 65 insertions(+), 36 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index b8d85edd31..1b6d71ebb7 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -15,6 +15,7 @@
from fedml.computing.scheduler.model_scheduler.device_mqtt_inference_protocol import FedMLMqttInference
from fedml.computing.scheduler.model_scheduler.device_http_proxy_inference_protocol import FedMLHttpProxyInference
from fedml.computing.scheduler.comm_utils import sys_utils
+from fedml.core.mlops import MLOpsRuntimeLog, MLOpsRuntimeLogDaemon
try:
from pydantic import BaseSettings
@@ -56,6 +57,8 @@ class settings:
ext_info = "2b34303961245c4f175f2236282d7a272c040b0904747579087f6a760112030109010c215d54505707140005190a051c347f365c4a430c020a7d39120e26032a78730f797f7c031f0901657e75"
+logging_args = None
+
api = FastAPI()
@@ -229,7 +232,8 @@ def retrieve_info_by_endpoint_id(end_point_id, in_end_point_name=None, in_model_
model_name = ""
if in_end_point_name is not None:
end_point_name = in_end_point_name
- model_name = redis_key[len(f"{FedMLModelCache.FEDML_MODEL_DEPLOYMENT_STATUS_TAG}-{end_point_id}-{in_end_point_name}-"):]
+ model_name = redis_key[
+ len(f"{FedMLModelCache.FEDML_MODEL_DEPLOYMENT_STATUS_TAG}-{end_point_id}-{in_end_point_name}-"):]
else:
# e.g. FEDML_MODEL_DEPLOYMENT_STATUS--1234-dummy_endpoint_name-dummy_model_name
try:
@@ -366,8 +370,20 @@ def logging_inference_request(request, response):
logging.info("failed to log inference request and response to file.")
+def set_logging_args(args=None):
+ global logging_args
+ logging_args = args
+ if logging_args is not None:
+ # Force run id to 0, as the gateway is shared by all the runs.
+ setattr(args, "run_id", "0")
+ MLOpsRuntimeLog.get_instance(args).init_logs(log_level=logging.INFO)
+ MLOpsRuntimeLogDaemon.get_instance(args).start_log_processor(args.run_id, args.edge_id)
+ logging.info("start the log processor")
+
+
if __name__ == "__main__":
import uvicorn
+
port = 2203
logging.basicConfig(level=logging.INFO)
uvicorn.run(api, host="0.0.0.0", port=port, log_level="info")
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index bf9cee3279..5680b2ac6d 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -1,6 +1,7 @@
import copy
import json
import logging
+import multiprocessing
import os
import time
import queue
@@ -9,6 +10,7 @@
from multiprocessing import Queue
import fedml
+import uvicorn
from fedml.core.mlops import MLOpsRuntimeLog, MLOpsConfigs
from fedml.core.mlops.mlops_runtime_log import MLOpsFormatter
from .device_client_constants import ClientConstants
@@ -22,6 +24,7 @@
from ..master.base_master_job_runner import FedMLBaseMasterJobRunner
from .device_replica_controller import FedMLDeviceReplicaController
from .job_runner_msg_sender import FedMLDeployJobRunnerMsgSender
+from .device_model_inference import set_logging_args
class FedMLDeployMasterJobRunner(FedMLBaseMasterJobRunner, FedMLDeployJobRunnerMsgSender, ABC):
@@ -63,6 +66,11 @@ def _generate_job_runner_instance(self, args, run_id=None, request_json=None, ag
def _generate_extend_queue_list(self):
return [self.deployment_result_queue]
+ @staticmethod
+ def start_inference_gateway_server(inference_gw_cmd, port, args):
+ set_logging_args(args)
+ uvicorn.run(inference_gw_cmd, host="0.0.0.0", port=port, log_level="info")
+
# Override
def run_impl(
self, edge_id_status_queue, edge_device_info_queue, run_metrics_queue,
@@ -116,7 +124,7 @@ def run_impl(
# start unified inference server
FedMLDeployMasterJobRunner.start_device_inference_gateway(
- inference_port=inference_port, agent_config=self.agent_config)
+ args=self.args, inference_port=inference_port, agent_config=self.agent_config)
# start inference monitor server
FedMLDeployMasterJobRunner.stop_device_inference_monitor(
@@ -462,13 +470,14 @@ def process_deployment_result_message(self, topic=None, payload=None):
time.sleep(3)
self.trigger_completed_event()
+
def cleanup_runner_process(self, run_id):
ServerConstants.cleanup_run_process(run_id, not_kill_subprocess=True)
@staticmethod
def start_device_inference_gateway(
- inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
- agent_config=None, redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
+ args, inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT, agent_config=None,
+ redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
):
# start unified inference server
python_program = get_python_program()
@@ -477,35 +486,39 @@ def start_device_inference_gateway(
inference_port = int(master_port)
if not ServerConstants.is_running_on_k8s():
logging.info(f"start the model inference gateway...")
- use_mqtt_inference = os.getenv("FEDML_USE_MQTT_INFERENCE", "False")
- use_mqtt_inference = True if use_mqtt_inference.lower() == 'true' else False
- use_worker_gateway = os.getenv("FEDML_USE_WORKER_GATEWAY", "False")
- use_worker_gateway = True if use_worker_gateway.lower() == 'true' else False
+ # use_mqtt_inference = os.getenv("FEDML_USE_MQTT_INFERENCE", "False")
+ # use_mqtt_inference = True if use_mqtt_inference.lower() == 'true' else False
+ # use_worker_gateway = os.getenv("FEDML_USE_WORKER_GATEWAY", "False")
+ # use_worker_gateway = True if use_worker_gateway.lower() == 'true' else False
inference_gw_cmd = "fedml.computing.scheduler.model_scheduler.device_model_inference:api"
inference_gateway_pids = RunProcessUtils.get_pid_from_cmd_line(inference_gw_cmd)
if inference_gateway_pids is None or len(inference_gateway_pids) <= 0:
- cur_dir = os.path.dirname(__file__)
- fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- connect_str = "@FEDML@"
- ext_info = sys_utils.random1(
- agent_config["mqtt_config"]["BROKER_HOST"] + connect_str +
- str(agent_config["mqtt_config"]["BROKER_PORT"]) + connect_str +
- agent_config["mqtt_config"]["MQTT_USER"] + connect_str +
- agent_config["mqtt_config"]["MQTT_PWD"] + connect_str +
- str(agent_config["mqtt_config"]["MQTT_KEEPALIVE"]), "FEDML@9999GREAT")
- python_program = get_python_program()
- inference_gateway_process = ServerConstants.exec_console_with_script(
- "REDIS_ADDR=\"{}\" REDIS_PORT=\"{}\" REDIS_PASSWORD=\"{}\" "
- "END_POINT_NAME=\"{}\" "
- "MODEL_NAME=\"{}\" MODEL_VERSION=\"{}\" MODEL_INFER_URL=\"{}\" VERSION=\"{}\" "
- "USE_MQTT_INFERENCE={} USE_WORKER_GATEWAY={} EXT_INFO={} "
- "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
- "--log-level critical".format(
- redis_addr, str(redis_port), redis_password, "",
- "", "", "", fedml.get_env_version(), use_mqtt_inference,
- use_worker_gateway, ext_info, python_program, inference_gw_cmd, str(inference_port),
- fedml_base_dir),
- should_capture_stdout=False, should_capture_stderr=False)
+ # cur_dir = os.path.dirname(__file__)
+ # fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
+ # connect_str = "@FEDML@"
+ # ext_info = sys_utils.random1(
+ # agent_config["mqtt_config"]["BROKER_HOST"] + connect_str +
+ # str(agent_config["mqtt_config"]["BROKER_PORT"]) + connect_str +
+ # agent_config["mqtt_config"]["MQTT_USER"] + connect_str +
+ # agent_config["mqtt_config"]["MQTT_PWD"] + connect_str +
+ # str(agent_config["mqtt_config"]["MQTT_KEEPALIVE"]), "FEDML@9999GREAT")
+ # python_program = get_python_program()
+ inference_gateway_process = multiprocessing.Process(
+ target=FedMLDeployMasterJobRunner.start_inference_gateway_server, args=(inference_gw_cmd,
+ inference_port, args)
+ )
+ # inference_gateway_process = ServerConstants.exec_console_with_script(
+ # "REDIS_ADDR=\"{}\" REDIS_PORT=\"{}\" REDIS_PASSWORD=\"{}\" "
+ # "END_POINT_NAME=\"{}\" "
+ # "MODEL_NAME=\"{}\" MODEL_VERSION=\"{}\" MODEL_INFER_URL=\"{}\" VERSION=\"{}\" "
+ # "USE_MQTT_INFERENCE={} USE_WORKER_GATEWAY={} EXT_INFO={} "
+ # "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
+ # "--log-level critical".format(
+ # redis_addr, str(redis_port), redis_password, "",
+ # "", "", "", fedml.get_env_version(), use_mqtt_inference,
+ # use_worker_gateway, ext_info, python_program, inference_gw_cmd, str(inference_port),
+ # fedml_base_dir),
+ # should_capture_stdout=False, should_capture_stderr=False)
return inference_gateway_process
else:
@@ -544,7 +557,7 @@ def stop_device_inference_monitor(run_id, end_point_name, model_id, model_name,
model_id, model_name, model_version)
@staticmethod
- def recover_inference_and_monitor():
+ def recover_inference_and_monitor(args):
# noinspection PyBroadException
try:
agent_config = dict()
@@ -571,8 +584,7 @@ def recover_inference_and_monitor():
if not is_activated:
continue
- FedMLDeployMasterJobRunner.start_device_inference_gateway(
- inference_port=inference_port, agent_config=agent_config)
+ FedMLDeployMasterJobRunner.start_device_inference_gateway(args=args, inference_port=inference_port, agent_config=agent_config)
FedMLDeployMasterJobRunner.stop_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
@@ -807,3 +819,4 @@ def build_dynamic_args(self, run_id, run_config, package_conf_object, base_dir):
# Override
def build_dynamic_constrain_variables(self, run_id, run_config):
pass
+
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
index 0bfc205b34..7c700bb10f 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
@@ -55,8 +55,8 @@ def stop_device_inference_monitor(self, run_id, end_point_name, model_id, model_
run_id, end_point_name, model_id, model_name, model_version)
@staticmethod
- def recover_inference_and_monitor():
- FedMLDeployMasterJobRunner.recover_inference_and_monitor()
+ def recover_inference_and_monitor(args):
+ FedMLDeployMasterJobRunner.recover_inference_and_monitor(args=args)
@staticmethod
def generate_request_json_with_replica_num_diff(run_id, edge_id, request_json):
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index b65d1bc8de..8f77f609d0 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -82,7 +82,7 @@ def _init_extra_items(self):
except Exception as e:
pass
- FedMLDeployJobRunnerManager.recover_inference_and_monitor()
+ FedMLDeployJobRunnerManager.recover_inference_and_monitor(args = self.args)
# Override
def _process_connection_ready(self):
From 3fb45aac083d2ec7e341251ade6e0a18db986686 Mon Sep 17 00:00:00 2001
From: Ubuntu <cirrascale@QAIED41.san01.cirrascale.net>
Date: Mon, 3 Jun 2024 19:00:55 +0000
Subject: [PATCH 098/282] Make Inference Gateway Daemon Process
---
.../computing/scheduler/model_scheduler/master_job_runner.py | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 5680b2ac6d..4d5974237d 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -90,7 +90,7 @@ def run_impl(
logging.info("model deployment request: {}".format(self.request_json))
logging.info("send deployment stages...")
- # Generate the replica controller object.
+ # Generate the replica controller object
self.replica_controller = FedMLDeviceReplicaController(self.edge_id, self.request_json)
# Start the process to report system performance(cpu,memory,etc.) to MLOps
@@ -519,6 +519,8 @@ def start_device_inference_gateway(
# use_worker_gateway, ext_info, python_program, inference_gw_cmd, str(inference_port),
# fedml_base_dir),
# should_capture_stdout=False, should_capture_stderr=False)
+ inference_gateway_process.daemon = True
+ inference_gateway_process.start()
return inference_gateway_process
else:
From 8595e0f6a7693ba6cd043731d8bcf4c648288214 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Mon, 3 Jun 2024 18:25:05 -0400
Subject: [PATCH 099/282] Adding fail fast and timeout enforcement per request
policies.
---
.../debug/inference_timeout/config.yaml | 10 ++
.../debug/inference_timeout/src/serve_main.py | 32 ++++
.../scheduler/comm_utils/constants.py | 2 -
.../device_client_constants.py | 1 +
.../device_http_inference_protocol.py | 9 +-
.../model_scheduler/device_model_cache.py | 102 +++++++-----
.../model_scheduler/device_model_inference.py | 153 +++++++++++++-----
.../model_scheduler/master_job_runner.py | 4 +-
.../master_protocol_manager.py | 6 +-
.../customized_job_example/train_job.yaml | 4 +-
10 files changed, 226 insertions(+), 97 deletions(-)
create mode 100644 python/examples/deploy/debug/inference_timeout/config.yaml
create mode 100644 python/examples/deploy/debug/inference_timeout/src/serve_main.py
diff --git a/python/examples/deploy/debug/inference_timeout/config.yaml b/python/examples/deploy/debug/inference_timeout/config.yaml
new file mode 100644
index 0000000000..f6d2566e00
--- /dev/null
+++ b/python/examples/deploy/debug/inference_timeout/config.yaml
@@ -0,0 +1,10 @@
+workspace: "./src"
+entry_point: "serve_main.py"
+bootstrap: |
+ echo "Bootstrap start..."
+ sleep 5
+ echo "Bootstrap finished"
+auto_detect_public_ip: true
+use_gpu: true
+
+request_timeout_sec: 10
diff --git a/python/examples/deploy/debug/inference_timeout/src/serve_main.py b/python/examples/deploy/debug/inference_timeout/src/serve_main.py
new file mode 100644
index 0000000000..5884e41f85
--- /dev/null
+++ b/python/examples/deploy/debug/inference_timeout/src/serve_main.py
@@ -0,0 +1,32 @@
+from fedml.serving import FedMLPredictor
+from fedml.serving import FedMLInferenceRunner
+import uuid
+import torch
+
+# Calculate the number of elements
+num_elements = 1_073_741_824 // 4 # using integer division for whole elements
+
+
+class DummyPredictor(FedMLPredictor):
+ def __init__(self):
+ super().__init__()
+ # Create a tensor with these many elements
+ tensor = torch.empty(num_elements, dtype=torch.float32)
+
+ # Move the tensor to GPU
+ tensor_gpu = tensor.cuda()
+
+ # for debug
+ with open("/tmp/dummy_gpu_occupier.txt", "w") as f:
+ f.write("GPU is occupied")
+
+ self.worker_id = uuid.uuid4()
+
+ def predict(self, request):
+ return {f"AlohaV0From{self.worker_id}": request}
+
+
+if __name__ == "__main__":
+ predictor = DummyPredictor()
+ fedml_inference_runner = FedMLInferenceRunner(predictor)
+ fedml_inference_runner.run()
diff --git a/python/fedml/computing/scheduler/comm_utils/constants.py b/python/fedml/computing/scheduler/comm_utils/constants.py
index f3fcd4ed5a..22cb31de45 100644
--- a/python/fedml/computing/scheduler/comm_utils/constants.py
+++ b/python/fedml/computing/scheduler/comm_utils/constants.py
@@ -78,8 +78,6 @@ class SchedulerConstants:
ENDPOINT_INFERENCE_READY_TIMEOUT = 15
ENDPOINT_STATUS_CHECK_TIMEOUT = 60 * 3
- MQTT_INFERENCE_TIMEOUT = 60 * 6
-
TRAIN_PROVISIONING_TIMEOUT = 60 * 25
TRAIN_STARTING_TIMEOUT = 60 * 15
TRAIN_STOPPING_TIMEOUT = 60 * 5
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
index d2093569c3..7894f2c73e 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
@@ -95,6 +95,7 @@ class ClientConstants(object):
INFERENCE_ENGINE_TYPE_INT_DEFAULT = 2
INFERENCE_MODEL_VERSION = "1"
INFERENCE_INFERENCE_SERVER_VERSION = "v2"
+ INFERENCE_REQUEST_TIMEOUT = 30
MSG_MODELOPS_DEPLOYMENT_STATUS_INITIALIZING = "INITIALIZING"
MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYING = "DEPLOYING"
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
index e711a9e6a6..7e4c06ea5d 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
@@ -1,13 +1,12 @@
-import traceback
-from typing import Mapping
-from urllib.parse import urlparse
-
import httpx
+import traceback
from .device_client_constants import ClientConstants
-import requests
+
from fastapi.responses import Response
from fastapi.responses import StreamingResponse
+from urllib.parse import urlparse
+from typing import Mapping
class FedMLHttpInference:
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
index edcdf7d0f1..fca7b81d42 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
@@ -33,6 +33,8 @@ class FedMLModelCache(Singleton):
FEDML_KEY_COUNT_PER_SCAN = 1000
+ FEDML_PENDING_REQUESTS_COUNTER = "FEDML_PENDING_REQUESTS_COUNTER"
+
def __init__(self):
if not hasattr(self, "redis_pool"):
self.redis_pool = None
@@ -110,7 +112,7 @@ def set_user_setting_replica_num(self, end_point_id,
replica_num: int, enable_auto_scaling: bool = False,
scale_min: int = 0, scale_max: int = 0, state: str = "UNKNOWN",
target_queries_per_replica: int = 60, aggregation_window_size_seconds: int = 60,
- scale_down_delay_seconds: int = 120
+ scale_down_delay_seconds: int = 120, timeout_s: int = 30
) -> bool:
"""
Key: FEDML_MODEL_ENDPOINT_REPLICA_USER_SETTING_TAG--<end_point_id>
@@ -136,7 +138,8 @@ def set_user_setting_replica_num(self, end_point_id,
"scale_min": scale_min, "scale_max": scale_max, "state": state,
"target_queries_per_replica": target_queries_per_replica,
"aggregation_window_size_seconds": aggregation_window_size_seconds,
- "scale_down_delay_seconds": scale_down_delay_seconds
+ "scale_down_delay_seconds": scale_down_delay_seconds,
+ "request_timeout_sec": timeout_s
}
try:
self.redis_connection.set(self.get_user_setting_replica_num_key(end_point_id), json.dumps(replica_num_dict))
@@ -362,7 +365,7 @@ def get_idle_device(self, end_point_id, end_point_name,
if "model_status" in result_payload and result_payload["model_status"] == "DEPLOYED":
idle_device_list.append({"device_id": device_id, "end_point_id": end_point_id})
- logging.info(f"{len(idle_device_list)} devices has this model on it: {idle_device_list}")
+ logging.info(f"{len(idle_device_list)} devices this model has on it: {idle_device_list}")
if len(idle_device_list) <= 0:
return None, None
@@ -824,38 +827,37 @@ def get_monitor_metrics_key(self, end_point_id, end_point_name, model_name, mode
end_point_id, end_point_name, model_name, model_version)
def get_endpoint_metrics(self,
- endpoint_id,
+ end_point_id,
k_recent=None) -> List[Any]:
model_deployment_monitor_metrics = list()
try:
key_pattern = "{}*{}*".format(
self.FEDML_MODEL_DEPLOYMENT_MONITOR_TAG,
- endpoint_id)
- model_deployment_monitor_endpoint_keys = \
+ end_point_id)
+ model_deployment_monitor_endpoint_key = \
self.redis_connection.keys(pattern=key_pattern)
# Since the reply is a list, we need to make sure the list
# is non-empty otherwise the index will raise an error.
- if model_deployment_monitor_endpoint_keys:
+ if model_deployment_monitor_endpoint_key:
model_deployment_monitor_endpoint_key = \
- model_deployment_monitor_endpoint_keys[0]
- else:
- raise Exception("Function `get_endpoint_metrics` Key {} does not exist."
- .format(key_pattern))
- # Set start and end index depending on the size of the
- # list and the requested number of most recent records.
- num_records = self.redis_connection.llen(name=model_deployment_monitor_endpoint_key)
- # if k_most_recent is None, then fetch all by default.
- start, end = 0, -1
- # if k_most_recent is positive then fetch [-k_most_recent:]
- if k_recent and k_recent > 0:
- start = num_records - k_recent
- model_deployment_monitor_metrics = \
- self.redis_connection.lrange(
- name=model_deployment_monitor_endpoint_key,
- start=start,
- end=end)
- model_deployment_monitor_metrics = [
- json.loads(m) for m in model_deployment_monitor_metrics]
+ model_deployment_monitor_endpoint_key[0]
+
+ # Set start and end index depending on the size of the
+ # list and the requested number of most recent records.
+ num_records = self.redis_connection.llen(
+ name=model_deployment_monitor_endpoint_key)
+ # if k_most_recent is None, then fetch all by default.
+ start, end = 0, -1
+ # if k_most_recent is positive then fetch [-k_most_recent:]
+ if k_recent and k_recent > 0:
+ start = num_records - k_recent
+ model_deployment_monitor_metrics = \
+ self.redis_connection.lrange(
+ name=model_deployment_monitor_endpoint_key,
+ start=start,
+ end=end)
+ model_deployment_monitor_metrics = [
+ json.loads(m) for m in model_deployment_monitor_metrics]
except Exception as e:
logging.error(e)
@@ -868,24 +870,24 @@ def get_endpoint_replicas_results(self, endpoint_id) -> List[Any]:
key_pattern = "{}*{}*".format(
self.FEDML_MODEL_DEPLOYMENT_RESULT_TAG,
endpoint_id)
- model_deployment_result_key = \
+ model_deployment_result_keys = \
self.redis_connection.keys(pattern=key_pattern)
- if model_deployment_result_key:
+ if model_deployment_result_keys:
model_deployment_result_key = \
- model_deployment_result_key[0]
+ model_deployment_result_keys[0]
+ replicas_results = \
+ self.redis_connection.lrange(
+ name=model_deployment_result_key,
+ start=0,
+ end=-1)
+ # Format the result value to a properly formatted json.
+ for replica_idx, replica in enumerate(replicas_results):
+ replicas_results[replica_idx] = json.loads(replica)
+ replicas_results[replica_idx]["result"] = \
+ json.loads(replicas_results[replica_idx]["result"])
else:
raise Exception("Function `get_endpoint_replicas_results` Key {} does not exist."
.format(key_pattern))
- replicas_results = \
- self.redis_connection.lrange(
- name=model_deployment_result_key,
- start=0,
- end=-1)
-
- # Format the result value to a properly formatted json.
- for replica_idx, replica in enumerate(replicas_results):
- replicas_results[replica_idx] = json.loads(replica)
- replicas_results[replica_idx]["result"] = json.loads(replicas_results[replica_idx]["result"])
except Exception as e:
logging.error(e)
@@ -898,11 +900,13 @@ def get_endpoint_settings(self, endpoint_id) -> Dict:
key_pattern = "{}*{}*".format(
self.FEDML_MODEL_ENDPOINT_REPLICA_USER_SETTING_TAG,
endpoint_id)
- endpoint_settings = \
+ endpoint_settings_keys = \
self.redis_connection.keys(pattern=key_pattern)
- if endpoint_settings:
+ if endpoint_settings_keys:
endpoint_settings = \
- json.load(endpoint_settings[0])
+ json.load(endpoint_settings_keys[0])
+ if not isinstance(endpoint_settings, dict):
+ endpoint_settings = json.loads(endpoint_settings)
else:
raise Exception("Function `get_endpoint_settings` Key {} does not exist."
.format(key_pattern))
@@ -966,3 +970,17 @@ def delete_endpoint_scaling_down_decision_time(self, end_point_id) -> bool:
return bool(self.redis_connection.hdel(
self.FEDML_MODEL_ENDPOINT_SCALING_DOWN_DECISION_TIME_TAG,
end_point_id))
+
+ def get_pending_requests_counter(self) -> int:
+ if not self.redis_connection.exists(self.FEDML_PENDING_REQUESTS_COUNTER):
+ self.redis_connection.set(self.FEDML_PENDING_REQUESTS_COUNTER, 0)
+ return int(self.redis_connection.get(self.FEDML_PENDING_REQUESTS_COUNTER))
+
+ def update_pending_requests_counter(self, increase=False, decrease=False) -> int:
+ if not self.redis_connection.exists(self.FEDML_PENDING_REQUESTS_COUNTER):
+ self.redis_connection.set(self.FEDML_PENDING_REQUESTS_COUNTER, 0)
+ if increase:
+ self.redis_connection.incr(self.FEDML_PENDING_REQUESTS_COUNTER)
+ if decrease:
+ self.redis_connection.decr(self.FEDML_PENDING_REQUESTS_COUNTER)
+ return self.get_pending_requests_counter()
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index b8d85edd31..26c25bc09f 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -1,13 +1,16 @@
+import json
import logging
import time
import traceback
-from urllib.parse import urlparse
import os
+
+from urllib.parse import urlparse
from typing import Any, Mapping, MutableMapping, Union
from fastapi import FastAPI, Request, Response, status
from fastapi.responses import StreamingResponse
+from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants
from fedml.computing.scheduler.model_scheduler.device_http_inference_protocol import FedMLHttpInference
from fedml.computing.scheduler.model_scheduler.device_server_constants import ServerConstants
from fedml.computing.scheduler.model_scheduler.device_model_monitor import FedMLModelMetrics
@@ -26,23 +29,7 @@
pass
-# class Settings(BaseSettings):
-# redis_addr: str
-# redis_port: str
-# redis_password: str
-# end_point_name: str
-# model_name: str
-# model_version: str
-# model_infer_url: str
-# version: str
-# use_mqtt_inference: bool
-# use_worker_gateway: bool
-# ext_info: str
-#
-#
-# settings = Settings()
-
-class settings:
+class Settings:
redis_addr = "127.0.0.1"
redis_port = 6379
redis_password = "fedml_default"
@@ -58,10 +45,54 @@ class settings:
api = FastAPI()
+FEDML_MODEL_CACHE = FedMLModelCache.get_instance()
+FEDML_MODEL_CACHE.set_redis_params(
+ redis_addr=Settings.redis_addr,
+ redis_port=Settings.redis_port,
+ redis_password=Settings.redis_password)
+
+
+@api.middleware("http")
+async def auth_middleware(request: Request, call_next):
+
+ if "/inference" in request.url.path or "/api/v1/predict" in request.url.path:
+ try:
+ # Attempt to parse the JSON body.
+ request_json = await request.json()
+ except json.JSONDecodeError:
+ return Response("Invalid JSON.", status_code=status.HTTP_400_BAD_REQUEST)
+
+ # Get total pending requests.
+ pending_requests_num = FEDML_MODEL_CACHE.get_pending_requests_counter()
+ if pending_requests_num:
+ end_point_id = request_json.get("end_point_id", None)
+ # Fetch metrics of the past k=3 requests.
+ pask_k_metrics = FEDML_MODEL_CACHE.get_endpoint_metrics(
+ end_point_id=end_point_id,
+ k_recent=3)
+
+ # Get the request timeout from the endpoint settings.
+ request_timeout_s = FEDML_MODEL_CACHE.get_endpoint_settings(end_point_id) \
+ .get("request_timeout_s", ClientConstants.INFERENCE_REQUEST_TIMEOUT)
+
+ # Only proceed if the past k metrics collection is not empty.
+ if pask_k_metrics:
+ # Measure the average latency in seconds(!), hence the 0.001 multiplier.
+ past_k_latencies_sec = \
+ [float(j_obj["current_latency"]) * 0.001 for j_obj in pask_k_metrics]
+ mean_latency = sum(past_k_latencies_sec) / len(past_k_latencies_sec)
+
+ # If timeout threshold is exceeded then cancel and return time out error.
+ if (mean_latency * pending_requests_num) > request_timeout_s:
+ return Response("Request timed out.", status_code=status.HTTP_504_GATEWAY_TIMEOUT)
+
+ response = await call_next(request)
+ return response
+
@api.get('/')
async def root():
- return {'message': 'FedML Federated Inference Service!'}
+ return {'message': 'TensorOpera Inference Service!'}
@api.get('/ready')
@@ -141,6 +172,10 @@ async def _predict(
input_json,
header=None
) -> Union[MutableMapping[str, Any], Response, StreamingResponse]:
+
+ FEDML_MODEL_CACHE.update_pending_requests_counter(increase=True)
+ inference_response = {}
+
in_end_point_id = end_point_id
in_end_point_name = input_json.get("end_point_name", None)
in_model_name = input_json.get("model_name", None)
@@ -170,21 +205,26 @@ async def _predict(
if not is_endpoint_activated(in_end_point_id):
inference_response = {"error": True, "message": "endpoint is not activated."}
logging_inference_request(input_json, inference_response)
+ FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
return inference_response
# Found idle inference device
idle_device, end_point_id, model_id, model_name, model_version, inference_host, inference_output_url = \
found_idle_inference_device(in_end_point_id, in_end_point_name, in_model_name, in_model_version)
if idle_device is None or idle_device == "":
+ FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
return {"error": True, "error_code": status.HTTP_404_NOT_FOUND,
"message": "can not found active inference worker for this endpoint."}
# Start timing for model metrics
model_metrics = FedMLModelMetrics(end_point_id, in_end_point_name,
model_id, in_model_name, model_version,
- settings.model_infer_url,
- settings.redis_addr, settings.redis_port, settings.redis_password,
- version=settings.version)
+ Settings.model_infer_url,
+ Settings.redis_addr,
+ Settings.redis_port,
+ Settings.redis_password,
+ version=Settings.version)
+ # Setting time to the time before authentication and idle device discovery.
model_metrics.set_start_time(start_time)
# Send inference request to idle device
@@ -195,7 +235,12 @@ async def _predict(
input_list["stream"] = input_list.get("stream", stream_flag)
output_list = input_json.get("outputs", [])
inference_response = await send_inference_request(
- idle_device, end_point_id, inference_output_url, input_list, output_list, inference_type=in_return_type)
+ idle_device,
+ end_point_id,
+ inference_output_url,
+ input_list,
+ output_list,
+ inference_type=in_return_type)
# Calculate model metrics
try:
@@ -207,11 +252,12 @@ async def _predict(
pass
logging_inference_request(input_json, inference_response)
-
+ FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
return inference_response
else:
inference_response = {"error": True, "message": "token is not valid."}
logging_inference_request(input_json, inference_response)
+ FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
return inference_response
@@ -221,9 +267,7 @@ def retrieve_info_by_endpoint_id(end_point_id, in_end_point_name=None, in_model_
We allow missing end_point_name and model_name in the input parameters.
return end_point_name, model_name
"""
- FedMLModelCache.get_instance().set_redis_params(settings.redis_addr, settings.redis_port, settings.redis_password)
- redis_key = FedMLModelCache.get_instance(settings.redis_addr, settings.redis_port). \
- get_end_point_full_key_by_id(end_point_id)
+ redis_key = FEDML_MODEL_CACHE.get_end_point_full_key_by_id(end_point_id)
if redis_key is not None:
end_point_name = ""
model_name = ""
@@ -254,8 +298,7 @@ def found_idle_inference_device(end_point_id, end_point_name, in_model_name, in_
inference_output_url = ""
model_version = ""
# Found idle device (TODO: optimize the algorithm to search best device for inference)
- FedMLModelCache.get_instance().set_redis_params(settings.redis_addr, settings.redis_port, settings.redis_password)
- payload, idle_device = FedMLModelCache.get_instance(settings.redis_addr, settings.redis_port). \
+ payload, idle_device = FEDML_MODEL_CACHE.\
get_idle_device(end_point_id, end_point_name, in_model_name, in_model_version)
if payload is not None:
logging.info("found idle deployment result {}".format(payload))
@@ -273,8 +316,12 @@ def found_idle_inference_device(end_point_id, end_point_name, in_model_name, in_
return idle_device, end_point_id, model_id, model_name, model_version, inference_host, inference_output_url
-async def send_inference_request(idle_device, endpoint_id, inference_url, input_list, output_list,
+async def send_inference_request(idle_device, end_point_id, inference_url, input_list, output_list,
inference_type="default", has_public_ip=True):
+
+ request_timeout_sec = FEDML_MODEL_CACHE.get_endpoint_settings(end_point_id) \
+ .get("request_timeout_sec", ClientConstants.INFERENCE_REQUEST_TIMEOUT)
+
try:
http_infer_available = os.getenv("FEDML_INFERENCE_HTTP_AVAILABLE", True)
if not http_infer_available:
@@ -283,24 +330,35 @@ async def send_inference_request(idle_device, endpoint_id, inference_url, input_
if http_infer_available:
response_ok = await FedMLHttpInference.is_inference_ready(
- inference_url, timeout=os.getenv("FEDML_GATEWAY_HTTP_READY_TIMEOUT", 20))
+ inference_url,
+ timeout=request_timeout_sec)
if response_ok:
response_ok, inference_response = await FedMLHttpInference.run_http_inference_with_curl_request(
- inference_url, input_list, output_list, inference_type=inference_type)
+ inference_url,
+ input_list,
+ output_list,
+ inference_type=inference_type,
+ timeout=request_timeout_sec)
logging.info(f"Use http inference. return {response_ok}")
return inference_response
response_ok = await FedMLHttpProxyInference.is_inference_ready(
- inference_url, timeout=os.getenv("FEDML_GATEWAY_HTTP_PROXY_READY_TIMEOUT", 20))
+ inference_url,
+ timeout=request_timeout_sec)
if response_ok:
response_ok, inference_response = await FedMLHttpProxyInference.run_http_proxy_inference_with_request(
- endpoint_id, inference_url, input_list, output_list, inference_type=inference_type)
+ end_point_id,
+ inference_url,
+ input_list,
+ output_list,
+ inference_type=inference_type,
+ timeout=request_timeout_sec)
logging.info(f"Use http proxy inference. return {response_ok}")
return inference_response
if not has_public_ip:
connect_str = "@FEDML@"
- random_out = sys_utils.random2(settings.ext_info, "FEDML@9999GREAT")
+ random_out = sys_utils.random2(Settings.ext_info, "FEDML@9999GREAT")
config_list = random_out.split(connect_str)
agent_config = dict()
agent_config["mqtt_config"] = dict()
@@ -309,13 +367,24 @@ async def send_inference_request(idle_device, endpoint_id, inference_url, input_
agent_config["mqtt_config"]["MQTT_USER"] = config_list[2]
agent_config["mqtt_config"]["MQTT_PWD"] = config_list[3]
agent_config["mqtt_config"]["MQTT_KEEPALIVE"] = int(config_list[4])
- mqtt_inference = FedMLMqttInference(agent_config=agent_config, run_id=endpoint_id)
+ mqtt_inference = FedMLMqttInference(
+ agent_config=agent_config,
+ run_id=end_point_id)
response_ok = mqtt_inference.run_mqtt_health_check_with_request(
- idle_device, endpoint_id, inference_url)
+ idle_device,
+ end_point_id,
+ inference_url,
+ timeout=request_timeout_sec)
inference_response = {"error": True, "message": "Failed to use http, http-proxy and mqtt for inference."}
if response_ok:
response_ok, inference_response = mqtt_inference.run_mqtt_inference_with_request(
- idle_device, endpoint_id, inference_url, input_list, output_list, inference_type=inference_type)
+ idle_device,
+ end_point_id,
+ inference_url,
+ input_list,
+ output_list,
+ inference_type=inference_type,
+ timeout=request_timeout_sec)
logging.info(f"Use mqtt inference. return {response_ok}.")
return inference_response
@@ -332,22 +401,18 @@ def auth_request_token(end_point_id, end_point_name, model_name, token):
if token is None:
return False
- FedMLModelCache.get_instance().set_redis_params(settings.redis_addr, settings.redis_port, settings.redis_password)
- cached_token = FedMLModelCache.get_instance(settings.redis_addr, settings.redis_port). \
+ cached_token = FEDML_MODEL_CACHE.\
get_end_point_token(end_point_id, end_point_name, model_name)
if cached_token is not None and str(cached_token) == str(token):
return True
return False
-
def is_endpoint_activated(end_point_id):
if end_point_id is None:
return False
- FedMLModelCache.get_instance().set_redis_params(settings.redis_addr, settings.redis_port, settings.redis_password)
- activated = FedMLModelCache.get_instance(settings.redis_addr, settings.redis_port).get_end_point_activation(
- end_point_id)
+ activated = FEDML_MODEL_CACHE.get_end_point_activation(end_point_id)
return activated
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index bf9cee3279..cc1901de65 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -114,11 +114,11 @@ def run_impl(
ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYING,
message_center=self.message_center)
- # start unified inference server
+ # start unified inference gateway process if not started
FedMLDeployMasterJobRunner.start_device_inference_gateway(
inference_port=inference_port, agent_config=self.agent_config)
- # start inference monitor server
+ # start inference monitor process
FedMLDeployMasterJobRunner.stop_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
FedMLDeployMasterJobRunner.start_device_inference_monitor(
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index b65d1bc8de..668d1192ce 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -178,6 +178,9 @@ def callback_start_deployment(self, topic, payload):
aggregation_window_size_seconds = request_json.get("aggregation_window_size_seconds", 60)
scale_down_delay_seconds = request_json.get("scale_down_delay_seconds", 120)
+ model_config_parameters = request_json.get("parameters", {})
+ timeout_s = model_config_parameters.get("request_timeout_sec", 30)
+
inference_end_point_id = run_id
logging.info("[Master] received start deployment request for end point {}.".format(run_id))
@@ -197,7 +200,8 @@ def callback_start_deployment(self, topic, payload):
scale_min=scale_min, scale_max=scale_max, state="DEPLOYING",
aggregation_window_size_seconds=aggregation_window_size_seconds,
target_queries_per_replica=target_queries_per_replica,
- scale_down_delay_seconds=int(scale_down_delay_seconds)
+ scale_down_delay_seconds=int(scale_down_delay_seconds),
+ timeout_s=timeout_s
)
# Start log processor for current run
diff --git a/python/fedml/workflow/driver_example/customized_job_example/train_job.yaml b/python/fedml/workflow/driver_example/customized_job_example/train_job.yaml
index e0e8f0f3be..86c9df6594 100755
--- a/python/fedml/workflow/driver_example/customized_job_example/train_job.yaml
+++ b/python/fedml/workflow/driver_example/customized_job_example/train_job.yaml
@@ -25,10 +25,12 @@ bootstrap: |
pip install PyYAML==5.3.1 -i https://pypi.org/simple
pip install fedml==0.8.29
pip install -U typing_extensions -i https://pypi.org/simple
+ pip install -U pydantic
+ pip install -U fastapi
echo "Bootstrap finished."
computing:
- resource_type: RTX-4090 # e.g., A100-80G, please check the resource type list by "fedml show-resource-type" or visiting URL: https://open.fedml.ai/accelerator_resource_type
+ resource_type: A100-80GB-SXM # e.g., A100-80G, please check the resource type list by "fedml show-resource-type" or visiting URL: https://open.fedml.ai/accelerator_resource_type
minimum_num_gpus: 1 # minimum # of GPUs to provision
maximum_cost_per_hour: $10 # max cost per hour of all machines for your job
# device_type: GPU # GPU or CPU
From 9296884402f058f9fb64beca79aad6e5dea91596 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Mon, 3 Jun 2024 22:53:22 +0000
Subject: [PATCH 100/282] [Deploy] Fix config reading from redis.
---
.../scheduler/model_scheduler/device_model_cache.py | 7 +++++--
1 file changed, 5 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
index fca7b81d42..45a58c7ab9 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
@@ -900,11 +900,14 @@ def get_endpoint_settings(self, endpoint_id) -> Dict:
key_pattern = "{}*{}*".format(
self.FEDML_MODEL_ENDPOINT_REPLICA_USER_SETTING_TAG,
endpoint_id)
+
endpoint_settings_keys = \
self.redis_connection.keys(pattern=key_pattern)
- if endpoint_settings_keys:
+
+ if len(endpoint_settings_keys) > 0:
endpoint_settings = \
- json.load(endpoint_settings_keys[0])
+ self.redis_connection.get(endpoint_settings_keys[0])
+
if not isinstance(endpoint_settings, dict):
endpoint_settings = json.loads(endpoint_settings)
else:
From b1312e1db9131aec05213ef22bd48e397e708cf4 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Mon, 3 Jun 2024 17:25:58 -0700
Subject: [PATCH 101/282] Add global env file
---
python/fedml/__init__.py | 9 +++++++--
python/fedml/computing/scheduler/env/__init__.py | 3 +++
python/fedml/computing/scheduler/env/collect_env.py | 11 +++++++++++
.../scheduler/scheduler_core/message_center.py | 4 +++-
.../computing/scheduler/slave/client_constants.py | 8 +++++++-
python/setup.py | 1 +
6 files changed, 32 insertions(+), 4 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index 6b3ac3f61b..913457b5c7 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -1,5 +1,4 @@
import logging
-from copy import deepcopy
import multiprocess as multiprocessing
import os
@@ -9,7 +8,10 @@
import torch
import fedml
+import dotenv
+
from .computing.scheduler.env.collect_env import collect_env
+from fedml.computing.scheduler.env import get_env_file
from .constants import (
FEDML_BACKEND_SERVICE_URL_DEV,
FEDML_BACKEND_SERVICE_URL_LOCAL,
@@ -449,10 +451,13 @@ def _run_distributed():
def set_env_version(version):
- os.environ['FEDML_ENV_VERSION'] = version
+ env_file = get_env_file()
+ dotenv.load_dotenv(dotenv_path=env_file)
+ dotenv.set_key(env_file, "FEDML_ENV_VERSION", version)
def get_env_version():
+ dotenv.load_dotenv(dotenv_path=get_env_file())
return "release" if os.environ.get('FEDML_ENV_VERSION') is None else os.environ['FEDML_ENV_VERSION']
diff --git a/python/fedml/computing/scheduler/env/__init__.py b/python/fedml/computing/scheduler/env/__init__.py
index e69de29bb2..5bfeaa5509 100644
--- a/python/fedml/computing/scheduler/env/__init__.py
+++ b/python/fedml/computing/scheduler/env/__init__.py
@@ -0,0 +1,3 @@
+import os
+
+from collect_env import get_env_file
diff --git a/python/fedml/computing/scheduler/env/collect_env.py b/python/fedml/computing/scheduler/env/collect_env.py
index b2f7bd7f5e..ef471da3bc 100644
--- a/python/fedml/computing/scheduler/env/collect_env.py
+++ b/python/fedml/computing/scheduler/env/collect_env.py
@@ -4,6 +4,7 @@
import fedml
from fedml.computing.scheduler.comm_utils.hardware_utils import HardwareUtil
from fedml.computing.scheduler.slave.client_diagnosis import ClientDiagnosis
+from ..slave.client_constants import ClientConstants
def collect_env():
@@ -108,3 +109,13 @@ def collect_env():
except Exception as e:
print(f"The connection exception: {traceback.format_exc()}")
pass
+
+
+def get_env_file():
+ global_serivces_dir = ClientConstants.get_global_services_dir()
+ env_config_file = os.path.join(global_serivces_dir, ".env")
+ # Create file if not exists
+ if not os.path.exists(env_config_file):
+ with open(env_config_file, 'w') as f:
+ f.write("")
+ return env_config_file
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_center.py b/python/fedml/computing/scheduler/scheduler_core/message_center.py
index 869ed6e510..dbe11700a0 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_center.py
@@ -11,6 +11,7 @@
from os.path import expanduser
from fedml.core.distributed.communication.mqtt.mqtt_manager import MqttManager
+from ..slave.client_constants import ClientConstants
from ....core.mlops.mlops_metrics import MLOpsMetrics
from operator import methodcaller
from .message_common import FedMLMessageEntity, FedMLMessageRecord
@@ -466,7 +467,8 @@ class MessageCenterStoppedException(Exception):
class FedMLMessageCenterConstants:
def __init__(self):
+ global_services_dir = ClientConstants.get_global_services_dir()
self.home_dir = expanduser("~")
- self.message_center_dir = os.path.join(self.home_dir, ".fedml", "global_services", "message_center")
+ self.message_center_dir = os.path.join(global_services_dir, "message_center")
self.message_log_dir = os.path.join(self.message_center_dir, "logs")
os.makedirs(self.message_log_dir, exist_ok=True)
diff --git a/python/fedml/computing/scheduler/slave/client_constants.py b/python/fedml/computing/scheduler/slave/client_constants.py
index 2e15080541..e5b3d41846 100644
--- a/python/fedml/computing/scheduler/slave/client_constants.py
+++ b/python/fedml/computing/scheduler/slave/client_constants.py
@@ -153,6 +153,13 @@ def get_database_dir():
os.makedirs(database_dir, exist_ok=True)
return database_dir
+ @staticmethod
+ def get_global_services_dir():
+ home_dir = expanduser("~")
+ global_services_dir = os.path.join(home_dir, ".fedml", "global_services")
+ os.makedirs(global_services_dir, exist_ok=True)
+ return global_services_dir
+
@staticmethod
def cleanup_run_process(run_id):
RunProcessUtils.cleanup_run_process(
@@ -454,7 +461,6 @@ def remove_fedml_parent_pid_file():
f"Traceback: {traceback.format_exc()}")
pass
-
if __name__ == "__main__":
ignore = "*test*,abc*"
ignore = tuple(ignore.split(','))
diff --git a/python/setup.py b/python/setup.py
index 0e314de29c..e88788d1ff 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -66,6 +66,7 @@ def finalize_options(self):
'wget',
# Need to pin this version due to breaking change released in python docker sdk
'requests<2.32',
+ 'python-dotenv',
]
requirements_extra_mpi = [
From 19160a2e2eec75807605a7431eaa342d3ab1b009 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Mon, 3 Jun 2024 17:28:18 -0700
Subject: [PATCH 102/282] Nits
---
python/fedml/computing/scheduler/env/__init__.py | 2 --
1 file changed, 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/env/__init__.py b/python/fedml/computing/scheduler/env/__init__.py
index 5bfeaa5509..cc765f0979 100644
--- a/python/fedml/computing/scheduler/env/__init__.py
+++ b/python/fedml/computing/scheduler/env/__init__.py
@@ -1,3 +1 @@
-import os
-
from collect_env import get_env_file
From 1b2eefe4c140ce86eb500af8e62945f64cf4d52a Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Mon, 3 Jun 2024 17:46:06 -0700
Subject: [PATCH 103/282] Bug fix
---
python/fedml/computing/scheduler/env/__init__.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/env/__init__.py b/python/fedml/computing/scheduler/env/__init__.py
index cc765f0979..f157a33ec2 100644
--- a/python/fedml/computing/scheduler/env/__init__.py
+++ b/python/fedml/computing/scheduler/env/__init__.py
@@ -1 +1 @@
-from collect_env import get_env_file
+from .collect_env import get_env_file
From 3481aa8914636208ea7f152ff214a9619d212f71 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Mon, 3 Jun 2024 17:50:07 -0700
Subject: [PATCH 104/282] Write it to release by default
---
python/fedml/__init__.py | 6 ++++--
1 file changed, 4 insertions(+), 2 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index 913457b5c7..adde71a6d4 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -457,8 +457,10 @@ def set_env_version(version):
def get_env_version():
- dotenv.load_dotenv(dotenv_path=get_env_file())
- return "release" if os.environ.get('FEDML_ENV_VERSION') is None else os.environ['FEDML_ENV_VERSION']
+ env_file = get_env_file()
+ dotenv.load_dotenv(dotenv_path=env_file)
+ version = "release" if os.environ.get('FEDML_ENV_VERSION') is None else os.environ['FEDML_ENV_VERSION']
+ dotenv.set_key(env_file, "FEDML_ENV_VERSION", version)
def _get_backend_service():
From b2ea4d0bd44aaff5378de53dde774d8e5344f0bc Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Mon, 3 Jun 2024 17:51:05 -0700
Subject: [PATCH 105/282] Nit
---
python/fedml/__init__.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index adde71a6d4..a79fc7d60d 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -461,7 +461,7 @@ def get_env_version():
dotenv.load_dotenv(dotenv_path=env_file)
version = "release" if os.environ.get('FEDML_ENV_VERSION') is None else os.environ['FEDML_ENV_VERSION']
dotenv.set_key(env_file, "FEDML_ENV_VERSION", version)
-
+ return version
def _get_backend_service():
version = get_env_version()
From 21138dd06521a91c2abb598c833f1f09c37134c5 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Mon, 3 Jun 2024 17:51:18 -0700
Subject: [PATCH 106/282] Nit
---
python/fedml/__init__.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index a79fc7d60d..35658d4920 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -463,6 +463,7 @@ def get_env_version():
dotenv.set_key(env_file, "FEDML_ENV_VERSION", version)
return version
+
def _get_backend_service():
version = get_env_version()
# from inspect import getframeinfo, stack
@@ -517,7 +518,7 @@ def get_local_on_premise_platform_port():
def _get_local_s3_like_service_url():
return FEDML_S3_DOMAIN_LOCAL
-
+
from fedml import device
from fedml import data
From 1cc15525e2b81a22bd3589b1fc3501bd32f467da Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Tue, 4 Jun 2024 04:38:30 -0400
Subject: [PATCH 107/282] Hotfix mqtt timeout inference constant after
refactoring.
---
.../scheduler/model_scheduler/device_mqtt_inference_protocol.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_mqtt_inference_protocol.py b/python/fedml/computing/scheduler/model_scheduler/device_mqtt_inference_protocol.py
index b0bff261a4..1fac5a984b 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_mqtt_inference_protocol.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_mqtt_inference_protocol.py
@@ -105,7 +105,7 @@ def run_mqtt_inference_with_request(
only_do_health_check=only_do_health_check, timeout=timeout
)
- allowed_inference_timeout = SchedulerConstants.MQTT_INFERENCE_TIMEOUT if timeout is None else timeout
+ allowed_inference_timeout = timeout if timeout else -1
sleep_time_interval = 0.05
total_sleep_time = 0
while True:
From 8e0318321ac267f18106d2cdead745076ce3dd3b Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Tue, 4 Jun 2024 05:19:36 -0400
Subject: [PATCH 108/282] Improving pending requests counter robustness.
---
.../model_scheduler/device_model_cache.py | 6 +-
.../model_scheduler/device_model_inference.py | 160 +++++++++---------
2 files changed, 88 insertions(+), 78 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
index 45a58c7ab9..75cf4dbc2a 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
@@ -985,5 +985,9 @@ def update_pending_requests_counter(self, increase=False, decrease=False) -> int
if increase:
self.redis_connection.incr(self.FEDML_PENDING_REQUESTS_COUNTER)
if decrease:
- self.redis_connection.decr(self.FEDML_PENDING_REQUESTS_COUNTER)
+ # Making sure the counter never becomes negative!
+ if self.get_pending_requests_counter() < 0:
+ self.redis_connection.set(self.FEDML_PENDING_REQUESTS_COUNTER, 0)
+ else:
+ self.redis_connection.decr(self.FEDML_PENDING_REQUESTS_COUNTER)
return self.get_pending_requests_counter()
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 26c25bc09f..0e866c9626 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -173,92 +173,98 @@ async def _predict(
header=None
) -> Union[MutableMapping[str, Any], Response, StreamingResponse]:
+ # Always increase the pending requests counter on a new incoming request.
FEDML_MODEL_CACHE.update_pending_requests_counter(increase=True)
inference_response = {}
- in_end_point_id = end_point_id
- in_end_point_name = input_json.get("end_point_name", None)
- in_model_name = input_json.get("model_name", None)
- in_model_version = input_json.get("model_version", None)
- in_end_point_token = input_json.get("token", None)
- in_return_type = "default"
- if header is not None:
- in_return_type = header.get("Accept", "default")
-
- if in_model_version is None:
- in_model_version = "*" # * | latest | specific version
-
- start_time = time.time_ns()
-
- # Allow missing end_point_name and model_name in the input parameters.
- if in_model_name is None or in_end_point_name is None:
- ret_endpoint_name, ret_model_name = retrieve_info_by_endpoint_id(in_end_point_id, in_end_point_name)
- if in_model_name is None:
- in_model_name = ret_model_name
- if in_end_point_name is None:
- in_end_point_name = ret_endpoint_name
-
- # Authenticate request token
- inference_response = {}
- if auth_request_token(in_end_point_id, in_end_point_name, in_model_name, in_end_point_token):
- # Check the endpoint is activated
- if not is_endpoint_activated(in_end_point_id):
- inference_response = {"error": True, "message": "endpoint is not activated."}
+ try:
+ in_end_point_id = end_point_id
+ in_end_point_name = input_json.get("end_point_name", None)
+ in_model_name = input_json.get("model_name", None)
+ in_model_version = input_json.get("model_version", None)
+ in_end_point_token = input_json.get("token", None)
+ in_return_type = "default"
+ if header is not None:
+ in_return_type = header.get("Accept", "default")
+
+ if in_model_version is None:
+ in_model_version = "*" # * | latest | specific version
+
+ start_time = time.time_ns()
+
+ # Allow missing end_point_name and model_name in the input parameters.
+ if in_model_name is None or in_end_point_name is None:
+ ret_endpoint_name, ret_model_name = retrieve_info_by_endpoint_id(in_end_point_id, in_end_point_name)
+ if in_model_name is None:
+ in_model_name = ret_model_name
+ if in_end_point_name is None:
+ in_end_point_name = ret_endpoint_name
+
+ # Authenticate request token
+ if auth_request_token(in_end_point_id, in_end_point_name, in_model_name, in_end_point_token):
+ # Check the endpoint is activated
+ if not is_endpoint_activated(in_end_point_id):
+ inference_response = {"error": True, "message": "endpoint is not activated."}
+ logging_inference_request(input_json, inference_response)
+ FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
+ return inference_response
+
+ # Found idle inference device
+ idle_device, end_point_id, model_id, model_name, model_version, inference_host, inference_output_url = \
+ found_idle_inference_device(in_end_point_id, in_end_point_name, in_model_name, in_model_version)
+ if idle_device is None or idle_device == "":
+ FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
+ return {"error": True, "error_code": status.HTTP_404_NOT_FOUND,
+ "message": "can not found active inference worker for this endpoint."}
+
+ # Start timing for model metrics
+ model_metrics = FedMLModelMetrics(end_point_id, in_end_point_name,
+ model_id, in_model_name, model_version,
+ Settings.model_infer_url,
+ Settings.redis_addr,
+ Settings.redis_port,
+ Settings.redis_password,
+ version=Settings.version)
+ # Setting time to the time before authentication and idle device discovery.
+ model_metrics.set_start_time(start_time)
+
+ # Send inference request to idle device
+ logging.info("inference url {}.".format(inference_output_url))
+ if inference_output_url != "":
+ input_list = input_json.get("inputs", input_json)
+ stream_flag = input_json.get("stream", False)
+ input_list["stream"] = input_list.get("stream", stream_flag)
+ output_list = input_json.get("outputs", [])
+ inference_response = await send_inference_request(
+ idle_device,
+ end_point_id,
+ inference_output_url,
+ input_list,
+ output_list,
+ inference_type=in_return_type)
+
+ # Calculate model metrics
+ try:
+ model_metrics.calc_metrics(end_point_id, in_end_point_name,
+ model_id, model_name, model_version,
+ inference_output_url, idle_device)
+ except Exception as e:
+ logging.info("Calculate Inference Metrics Exception: {}".format(traceback.format_exc()))
+ pass
+
logging_inference_request(input_json, inference_response)
FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
return inference_response
-
- # Found idle inference device
- idle_device, end_point_id, model_id, model_name, model_version, inference_host, inference_output_url = \
- found_idle_inference_device(in_end_point_id, in_end_point_name, in_model_name, in_model_version)
- if idle_device is None or idle_device == "":
+ else:
+ inference_response = {"error": True, "message": "token is not valid."}
+ logging_inference_request(input_json, inference_response)
FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
- return {"error": True, "error_code": status.HTTP_404_NOT_FOUND,
- "message": "can not found active inference worker for this endpoint."}
-
- # Start timing for model metrics
- model_metrics = FedMLModelMetrics(end_point_id, in_end_point_name,
- model_id, in_model_name, model_version,
- Settings.model_infer_url,
- Settings.redis_addr,
- Settings.redis_port,
- Settings.redis_password,
- version=Settings.version)
- # Setting time to the time before authentication and idle device discovery.
- model_metrics.set_start_time(start_time)
-
- # Send inference request to idle device
- logging.info("inference url {}.".format(inference_output_url))
- if inference_output_url != "":
- input_list = input_json.get("inputs", input_json)
- stream_flag = input_json.get("stream", False)
- input_list["stream"] = input_list.get("stream", stream_flag)
- output_list = input_json.get("outputs", [])
- inference_response = await send_inference_request(
- idle_device,
- end_point_id,
- inference_output_url,
- input_list,
- output_list,
- inference_type=in_return_type)
+ return inference_response
- # Calculate model metrics
- try:
- model_metrics.calc_metrics(end_point_id, in_end_point_name,
- model_id, model_name, model_version,
- inference_output_url, idle_device)
- except Exception as e:
- logging.info("Calculate Inference Metrics Exception: {}".format(traceback.format_exc()))
- pass
-
- logging_inference_request(input_json, inference_response)
- FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
- return inference_response
- else:
- inference_response = {"error": True, "message": "token is not valid."}
- logging_inference_request(input_json, inference_response)
+ except Exception as e:
+ logging.error("Inference Exception: {}".format(traceback.format_exc()))
+ # Need to reduce the pending requests counter in whatever exception that may be raised.
FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
- return inference_response
def retrieve_info_by_endpoint_id(end_point_id, in_end_point_name=None, in_model_name=None,
From b0a55adc403dc4196204d12a596726d0ccdbb156 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Tue, 4 Jun 2024 08:11:20 -0400
Subject: [PATCH 109/282] Returning well formatted json messages in the case of
errored requests.
---
.../model_scheduler/device_model_inference.py | 10 +++++++---
1 file changed, 7 insertions(+), 3 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 0e866c9626..8192e7c300 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -8,7 +8,7 @@
from typing import Any, Mapping, MutableMapping, Union
from fastapi import FastAPI, Request, Response, status
-from fastapi.responses import StreamingResponse
+from fastapi.responses import StreamingResponse, JSONResponse
from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants
from fedml.computing.scheduler.model_scheduler.device_http_inference_protocol import FedMLHttpInference
@@ -60,7 +60,9 @@ async def auth_middleware(request: Request, call_next):
# Attempt to parse the JSON body.
request_json = await request.json()
except json.JSONDecodeError:
- return Response("Invalid JSON.", status_code=status.HTTP_400_BAD_REQUEST)
+ return JSONResponse(
+ {"error": True, "message": "Invalid JSON."},
+ status_code=status.HTTP_400_BAD_REQUEST)
# Get total pending requests.
pending_requests_num = FEDML_MODEL_CACHE.get_pending_requests_counter()
@@ -84,7 +86,9 @@ async def auth_middleware(request: Request, call_next):
# If timeout threshold is exceeded then cancel and return time out error.
if (mean_latency * pending_requests_num) > request_timeout_s:
- return Response("Request timed out.", status_code=status.HTTP_504_GATEWAY_TIMEOUT)
+ return JSONResponse(
+ {"error": True, "message": "Request timed out."},
+ status_code=status.HTTP_504_GATEWAY_TIMEOUT)
response = await call_next(request)
return response
From 2e5353609b278f9b4c906d99b6e1f38b179bd3b2 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Tue, 4 Jun 2024 11:59:44 -0700
Subject: [PATCH 110/282] Fix bug
---
python/fedml/__init__.py | 6 ++++--
1 file changed, 4 insertions(+), 2 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index 35658d4920..a41b3e56af 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -459,8 +459,10 @@ def set_env_version(version):
def get_env_version():
env_file = get_env_file()
dotenv.load_dotenv(dotenv_path=env_file)
- version = "release" if os.environ.get('FEDML_ENV_VERSION') is None else os.environ['FEDML_ENV_VERSION']
- dotenv.set_key(env_file, "FEDML_ENV_VERSION", version)
+ version = os.getenv('FEDML_ENV_VERSION')
+ if version is None:
+ version = "release"
+ set_env_version(version)
return version
From b5e4c252395c0ed316fa8d7b987381c0caeb738c Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Tue, 4 Jun 2024 12:24:37 -0700
Subject: [PATCH 111/282] Make env variables override system, abstract dotenv
api calls into functions
---
python/fedml/__init__.py | 12 +++++-------
python/fedml/computing/scheduler/env/__init__.py | 2 +-
.../fedml/computing/scheduler/env/collect_env.py | 16 ++++++++++++++--
3 files changed, 20 insertions(+), 10 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index a41b3e56af..c13a64566e 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -11,7 +11,7 @@
import dotenv
from .computing.scheduler.env.collect_env import collect_env
-from fedml.computing.scheduler.env import get_env_file
+from fedml.computing.scheduler.env import set_env_kv, load_env
from .constants import (
FEDML_BACKEND_SERVICE_URL_DEV,
FEDML_BACKEND_SERVICE_URL_LOCAL,
@@ -451,15 +451,13 @@ def _run_distributed():
def set_env_version(version):
- env_file = get_env_file()
- dotenv.load_dotenv(dotenv_path=env_file)
- dotenv.set_key(env_file, "FEDML_ENV_VERSION", version)
+ set_env_kv("FEDML_ENV_VERSION", version)
+ load_env()
def get_env_version():
- env_file = get_env_file()
- dotenv.load_dotenv(dotenv_path=env_file)
- version = os.getenv('FEDML_ENV_VERSION')
+ load_env()
+ version = os.getenv("FEDML_ENV_VERSION")
if version is None:
version = "release"
set_env_version(version)
diff --git a/python/fedml/computing/scheduler/env/__init__.py b/python/fedml/computing/scheduler/env/__init__.py
index f157a33ec2..0f71de6038 100644
--- a/python/fedml/computing/scheduler/env/__init__.py
+++ b/python/fedml/computing/scheduler/env/__init__.py
@@ -1 +1 @@
-from .collect_env import get_env_file
+from .collect_env import load_env, set_env_kv
diff --git a/python/fedml/computing/scheduler/env/collect_env.py b/python/fedml/computing/scheduler/env/collect_env.py
index ef471da3bc..84b903cdbb 100644
--- a/python/fedml/computing/scheduler/env/collect_env.py
+++ b/python/fedml/computing/scheduler/env/collect_env.py
@@ -2,6 +2,7 @@
import traceback
import fedml
+import dotenv
from fedml.computing.scheduler.comm_utils.hardware_utils import HardwareUtil
from fedml.computing.scheduler.slave.client_diagnosis import ClientDiagnosis
from ..slave.client_constants import ClientConstants
@@ -112,10 +113,21 @@ def collect_env():
def get_env_file():
- global_serivces_dir = ClientConstants.get_global_services_dir()
- env_config_file = os.path.join(global_serivces_dir, ".env")
+ global_services_dir = ClientConstants.get_global_services_dir()
+ env_config_file = os.path.join(global_services_dir, ".env")
# Create file if not exists
if not os.path.exists(env_config_file):
with open(env_config_file, 'w') as f:
f.write("")
return env_config_file
+
+
+def load_env():
+ env_config_file = get_env_file()
+ dotenv.load_dotenv(dotenv_path=env_config_file, override=True)
+
+
+def set_env_kv(key, value):
+ env_config_file = get_env_file()
+ dotenv.set_key(env_config_file, key, value)
+ load_env()
From e2430fca23c361b37c2ff96614ce155829915541 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Wed, 5 Jun 2024 11:18:30 -0400
Subject: [PATCH 112/282] Renaming endpoint_id key to end_point_id
---
.../scheduler/model_scheduler/autoscaler/autoscaler.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
index eb9f08b0eb..4cab1e133c 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
@@ -339,7 +339,7 @@ def scale_operation_endpoint(self,
# Fetch all metrics record from the database.
metrics = self.fedml_model_cache.get_endpoint_metrics(
- endpoint_id=endpoint_id)
+ end_point_id=endpoint_id)
# Default to nothing.
scale_op = ScaleOp.NO_OP
From 600905f283dfc9d57b8f315c6844e76cd4ab4f20 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Wed, 5 Jun 2024 19:04:19 +0000
Subject: [PATCH 113/282] [Deploy] Fix multi sub folder issue during
deployment.
---
.../model_scheduler/worker_job_runner.py | 15 ++++++++++++---
1 file changed, 12 insertions(+), 3 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index 831064b591..370ba57b49 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -205,7 +205,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
# Check if the package is already downloaded
unzip_package_path = ""
if os.path.exists(os.path.join(models_root_dir, parent_fd)):
- unzip_package_path = self.find_previous_downloaded_pkg(os.path.join(models_root_dir, parent_fd))
+ unzip_package_path = self.find_previous_downloaded_pkg(os.path.join(models_root_dir, parent_fd), model_name)
# Download the package if not found
if unzip_package_path == "":
@@ -510,7 +510,7 @@ def build_dynamic_constrain_variables(self, run_id, run_config):
pass
@staticmethod
- def find_previous_downloaded_pkg(parent_dir) -> str:
+ def find_previous_downloaded_pkg(parent_dir: str, model_name: str) -> str:
unzip_fd = ""
res = ""
@@ -519,8 +519,17 @@ def find_previous_downloaded_pkg(parent_dir) -> str:
unzip_fd = os.path.join(parent_dir, folder)
break
+ exact_matched = False
+
for folder in os.listdir(unzip_fd):
- if os.path.isdir(os.path.join(unzip_fd, folder)):
+ if folder == model_name:
+ res = os.path.join(unzip_fd, folder)
+ exact_matched = True
+ break
+
+ if not exact_matched:
+ # Use the first folder found
+ for folder in os.listdir(unzip_fd):
res = os.path.join(unzip_fd, folder)
break
From 2ce07f4c53c3670b7b88e41f9c9ed101834d7807 Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Wed, 5 Jun 2024 19:41:50 +0000
Subject: [PATCH 114/282] Optimize Inference
---
python/fedml/api/modules/device.py | 9 +-
.../computing/scheduler/env/collect_env.py | 1 +
.../model_scheduler/device_model_inference.py | 108 ++++++++----------
.../device_server_constants.py | 17 ++-
.../model_scheduler/master_job_runner.py | 33 +++---
.../computing/scheduler/slave/client_login.py | 2 -
python/fedml/core/mlops/mlops_configs.py | 4 +
7 files changed, 92 insertions(+), 82 deletions(-)
diff --git a/python/fedml/api/modules/device.py b/python/fedml/api/modules/device.py
index 84aa42e7b2..497fde9005 100644
--- a/python/fedml/api/modules/device.py
+++ b/python/fedml/api/modules/device.py
@@ -58,14 +58,15 @@ def _bind(
docker, docker_rank, infer_host,
redis_addr, redis_port, redis_password
):
+ fedml.load_env()
if os.getenv(ModuleConstants.ENV_FEDML_INFER_HOST) is None:
- os.environ[ModuleConstants.ENV_FEDML_INFER_HOST] = infer_host
+ fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_HOST, infer_host)
if os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_ADDR) is None:
- os.environ[ModuleConstants.ENV_FEDML_INFER_REDIS_ADDR] = redis_addr
+ fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_REDIS_ADDR, redis_addr)
if os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PORT) is None:
- os.environ[ModuleConstants.ENV_FEDML_INFER_REDIS_PORT] = redis_port
+ fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_REDIS_PORT, redis_port)
if os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD) is None:
- os.environ[ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD] = redis_password
+ fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD, redis_password)
url = fedml._get_backend_service()
platform_name = platform.system()
diff --git a/python/fedml/computing/scheduler/env/collect_env.py b/python/fedml/computing/scheduler/env/collect_env.py
index 84b903cdbb..da4d54e7a0 100644
--- a/python/fedml/computing/scheduler/env/collect_env.py
+++ b/python/fedml/computing/scheduler/env/collect_env.py
@@ -128,6 +128,7 @@ def load_env():
def set_env_kv(key, value):
+ os.environ[key] = value
env_config_file = get_env_file()
dotenv.set_key(env_config_file, key, value)
load_env()
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 1b6d71ebb7..d39ac0e4e7 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -1,3 +1,4 @@
+import argparse
import logging
import time
import traceback
@@ -8,52 +9,27 @@
from fastapi import FastAPI, Request, Response, status
from fastapi.responses import StreamingResponse
+import fedml
+from fedml.api.modules.constants import ModuleConstants
from fedml.computing.scheduler.model_scheduler.device_http_inference_protocol import FedMLHttpInference
from fedml.computing.scheduler.model_scheduler.device_server_constants import ServerConstants
from fedml.computing.scheduler.model_scheduler.device_model_monitor import FedMLModelMetrics
from fedml.computing.scheduler.model_scheduler.device_model_cache import FedMLModelCache
from fedml.computing.scheduler.model_scheduler.device_mqtt_inference_protocol import FedMLMqttInference
from fedml.computing.scheduler.model_scheduler.device_http_proxy_inference_protocol import FedMLHttpProxyInference
+from fedml.core.mlops.mlops_configs import MLOpsConfigs
from fedml.computing.scheduler.comm_utils import sys_utils
from fedml.core.mlops import MLOpsRuntimeLog, MLOpsRuntimeLogDaemon
-try:
- from pydantic import BaseSettings
-except Exception as e:
- pass
-try:
- from pydantic_settings import BaseSettings
-except Exception as e:
- pass
-
-
-# class Settings(BaseSettings):
-# redis_addr: str
-# redis_port: str
-# redis_password: str
-# end_point_name: str
-# model_name: str
-# model_version: str
-# model_infer_url: str
-# version: str
-# use_mqtt_inference: bool
-# use_worker_gateway: bool
-# ext_info: str
-#
-#
-# settings = Settings()
-
-class settings:
- redis_addr = "127.0.0.1"
- redis_port = 6379
- redis_password = "fedml_default"
- end_point_name = ""
- model_name = ""
- model_version = ""
- model_infer_url = "127.0.0.1"
- version = "dev"
- use_mqtt_inference = False
- use_worker_gateway = False
+
+class Settings:
+ fedml.load_env()
+ redis_addr = os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_ADDR)
+ redis_port = os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PORT)
+ redis_password = os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD)
+ model_infer_host = os.getenv(ModuleConstants.ENV_FEDML_INFER_HOST)
+ version = fedml.get_env_version()
+ mqtt_config = MLOpsConfigs.fetch_mqtt_config()
ext_info = "2b34303961245c4f175f2236282d7a272c040b0904747579087f6a760112030109010c215d54505707140005190a051c347f365c4a430c020a7d39120e26032a78730f797f7c031f0901657e75"
@@ -62,6 +38,11 @@ class settings:
api = FastAPI()
+@api.on_event("startup")
+async def startup_event():
+ configure_logging()
+
+
@api.get('/')
async def root():
return {'message': 'FedML Federated Inference Service!'}
@@ -185,9 +166,9 @@ async def _predict(
# Start timing for model metrics
model_metrics = FedMLModelMetrics(end_point_id, in_end_point_name,
model_id, in_model_name, model_version,
- settings.model_infer_url,
- settings.redis_addr, settings.redis_port, settings.redis_password,
- version=settings.version)
+ Settings.model_infer_host,
+ Settings.redis_addr, Settings.redis_port, Settings.redis_password,
+ version=Settings.version)
model_metrics.set_start_time(start_time)
# Send inference request to idle device
@@ -224,8 +205,8 @@ def retrieve_info_by_endpoint_id(end_point_id, in_end_point_name=None, in_model_
We allow missing end_point_name and model_name in the input parameters.
return end_point_name, model_name
"""
- FedMLModelCache.get_instance().set_redis_params(settings.redis_addr, settings.redis_port, settings.redis_password)
- redis_key = FedMLModelCache.get_instance(settings.redis_addr, settings.redis_port). \
+ FedMLModelCache.get_instance().set_redis_params(Settings.redis_addr, Settings.redis_port, Settings.redis_password)
+ redis_key = FedMLModelCache.get_instance(Settings.redis_addr, Settings.redis_port). \
get_end_point_full_key_by_id(end_point_id)
if redis_key is not None:
end_point_name = ""
@@ -258,8 +239,8 @@ def found_idle_inference_device(end_point_id, end_point_name, in_model_name, in_
inference_output_url = ""
model_version = ""
# Found idle device (TODO: optimize the algorithm to search best device for inference)
- FedMLModelCache.get_instance().set_redis_params(settings.redis_addr, settings.redis_port, settings.redis_password)
- payload, idle_device = FedMLModelCache.get_instance(settings.redis_addr, settings.redis_port). \
+ FedMLModelCache.get_instance().set_redis_params(Settings.redis_addr, Settings.redis_port, Settings.redis_password)
+ payload, idle_device = FedMLModelCache.get_instance(Settings.redis_addr, Settings.redis_port). \
get_idle_device(end_point_id, end_point_name, in_model_name, in_model_version)
if payload is not None:
logging.info("found idle deployment result {}".format(payload))
@@ -304,7 +285,7 @@ async def send_inference_request(idle_device, endpoint_id, inference_url, input_
if not has_public_ip:
connect_str = "@FEDML@"
- random_out = sys_utils.random2(settings.ext_info, "FEDML@9999GREAT")
+ random_out = sys_utils.random2(Settings.ext_info, "FEDML@9999GREAT")
config_list = random_out.split(connect_str)
agent_config = dict()
agent_config["mqtt_config"] = dict()
@@ -336,8 +317,8 @@ def auth_request_token(end_point_id, end_point_name, model_name, token):
if token is None:
return False
- FedMLModelCache.get_instance().set_redis_params(settings.redis_addr, settings.redis_port, settings.redis_password)
- cached_token = FedMLModelCache.get_instance(settings.redis_addr, settings.redis_port). \
+ FedMLModelCache.get_instance().set_redis_params(Settings.redis_addr, Settings.redis_port, Settings.redis_password)
+ cached_token = FedMLModelCache.get_instance(Settings.redis_addr, Settings.redis_port). \
get_end_point_token(end_point_id, end_point_name, model_name)
if cached_token is not None and str(cached_token) == str(token):
return True
@@ -349,8 +330,8 @@ def is_endpoint_activated(end_point_id):
if end_point_id is None:
return False
- FedMLModelCache.get_instance().set_redis_params(settings.redis_addr, settings.redis_port, settings.redis_password)
- activated = FedMLModelCache.get_instance(settings.redis_addr, settings.redis_port).get_end_point_activation(
+ FedMLModelCache.get_instance().set_redis_params(Settings.redis_addr, Settings.redis_port, Settings.redis_password)
+ activated = FedMLModelCache.get_instance(Settings.redis_addr, Settings.redis_port).get_end_point_activation(
end_point_id)
return activated
@@ -361,8 +342,6 @@ def logging_inference_request(request, response):
try:
log_dir = ServerConstants.get_log_file_dir()
- if not os.path.exists(log_dir):
- os.makedirs(log_dir, exist_ok=True)
inference_log_file = os.path.join(log_dir, "inference.log")
with open(inference_log_file, "a") as f:
f.writelines([f"request: {request}, response: {response}\n"])
@@ -370,15 +349,24 @@ def logging_inference_request(request, response):
logging.info("failed to log inference request and response to file.")
-def set_logging_args(args=None):
- global logging_args
- logging_args = args
- if logging_args is not None:
- # Force run id to 0, as the gateway is shared by all the runs.
- setattr(args, "run_id", "0")
- MLOpsRuntimeLog.get_instance(args).init_logs(log_level=logging.INFO)
- MLOpsRuntimeLogDaemon.get_instance(args).start_log_processor(args.run_id, args.edge_id)
- logging.info("start the log processor")
+def configure_logging():
+ parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ args = parser.parse_args([])
+
+ setattr(args, "log_file_dir", ServerConstants.get_log_file_dir())
+ setattr(args, "run_id", "inference_gateway")
+ setattr(args, "role", "server")
+ setattr(args, "using_mlops", True)
+ setattr(args, "config_version", fedml.get_env_version())
+
+ runner_info = ServerConstants.get_runner_infos()
+ if not (runner_info and "edge_id" in runner_info):
+ raise Exception("Inference gateway couldn't be started as edge_id couldn't be parsed from runner_infos.yaml")
+ setattr(args, "edge_id", runner_info.get("edge_id"))
+
+ MLOpsRuntimeLog.get_instance(args).init_logs(log_level=logging.INFO)
+ MLOpsRuntimeLogDaemon.get_instance(args).start_log_processor(args.run_id, args.edge_id)
+ logging.info("start the log processor for inference gateway")
if __name__ == "__main__":
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
index 6b5b335863..eb01fbb599 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
@@ -5,6 +5,7 @@
import subprocess
import sys
from os.path import expanduser
+from pathlib import Path
import psutil
import yaml
@@ -329,9 +330,23 @@ def save_bootstrap_process(run_id, process_id):
run_id, process_id, ServerConstants.get_data_dir(), ServerConstants.LOCAL_RUNNER_INFO_DIR_NAME,
info_file_prefix=SchedulerConstants.RUN_PROCESS_TYPE_BOOTSTRAP_PROCESS)
+ @staticmethod
+ def get_runner_infos():
+ local_pkg_data_dir = ServerConstants.get_data_dir()
+ os.makedirs(local_pkg_data_dir, exist_ok=True)
+ os.makedirs(os.path.join(local_pkg_data_dir, ServerConstants.LOCAL_RUNNER_INFO_DIR_NAME), exist_ok=True)
+
+ runner_info_file = os.path.join(local_pkg_data_dir, ServerConstants.LOCAL_RUNNER_INFO_DIR_NAME,
+ "runner_infos.yaml")
+ runner_info = {}
+ try:
+ runner_info = yaml.safe_load(Path(runner_info_file).read_text())
+ except Exception as e:
+ logging.error(f"Failed to parse runner info: {e}")
+ return runner_info
+
@staticmethod
def save_runner_infos(unique_device_id, edge_id, run_id=None):
- home_dir = expanduser("~")
local_pkg_data_dir = ServerConstants.get_data_dir()
os.makedirs(local_pkg_data_dir, exist_ok=True)
os.makedirs(os.path.join(local_pkg_data_dir, ServerConstants.LOCAL_RUNNER_INFO_DIR_NAME), exist_ok=True)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 4d5974237d..15841600ad 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -24,7 +24,6 @@
from ..master.base_master_job_runner import FedMLBaseMasterJobRunner
from .device_replica_controller import FedMLDeviceReplicaController
from .job_runner_msg_sender import FedMLDeployJobRunnerMsgSender
-from .device_model_inference import set_logging_args
class FedMLDeployMasterJobRunner(FedMLBaseMasterJobRunner, FedMLDeployJobRunnerMsgSender, ABC):
@@ -66,11 +65,6 @@ def _generate_job_runner_instance(self, args, run_id=None, request_json=None, ag
def _generate_extend_queue_list(self):
return [self.deployment_result_queue]
- @staticmethod
- def start_inference_gateway_server(inference_gw_cmd, port, args):
- set_logging_args(args)
- uvicorn.run(inference_gw_cmd, host="0.0.0.0", port=port, log_level="info")
-
# Override
def run_impl(
self, edge_id_status_queue, edge_device_info_queue, run_metrics_queue,
@@ -493,8 +487,8 @@ def start_device_inference_gateway(
inference_gw_cmd = "fedml.computing.scheduler.model_scheduler.device_model_inference:api"
inference_gateway_pids = RunProcessUtils.get_pid_from_cmd_line(inference_gw_cmd)
if inference_gateway_pids is None or len(inference_gateway_pids) <= 0:
- # cur_dir = os.path.dirname(__file__)
- # fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
+ cur_dir = os.path.dirname(__file__)
+ fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
# connect_str = "@FEDML@"
# ext_info = sys_utils.random1(
# agent_config["mqtt_config"]["BROKER_HOST"] + connect_str +
@@ -503,10 +497,19 @@ def start_device_inference_gateway(
# agent_config["mqtt_config"]["MQTT_PWD"] + connect_str +
# str(agent_config["mqtt_config"]["MQTT_KEEPALIVE"]), "FEDML@9999GREAT")
# python_program = get_python_program()
- inference_gateway_process = multiprocessing.Process(
- target=FedMLDeployMasterJobRunner.start_inference_gateway_server, args=(inference_gw_cmd,
- inference_port, args)
- )
+ # inference_gateway_process = multiprocessing.Process(
+ # target=FedMLDeployMasterJobRunner.start_inference_gateway_server, args=(inference_gw_cmd,
+ # inference_port, args)
+ # )
+ inference_gateway_process = ServerConstants.exec_console_with_script(f"{python_program} "
+ f"-m uvicorn {inference_gw_cmd} "
+ f"--host 0.0.0.0 "
+ f"--port {str(inference_port)} "
+ f"--reload --reload-delay 3 "
+ f"--reload-dir {fedml_base_dir} "
+ f"--log-level critical",
+ should_capture_stdout=False,
+ should_capture_stderr=False)
# inference_gateway_process = ServerConstants.exec_console_with_script(
# "REDIS_ADDR=\"{}\" REDIS_PORT=\"{}\" REDIS_PASSWORD=\"{}\" "
# "END_POINT_NAME=\"{}\" "
@@ -518,9 +521,9 @@ def start_device_inference_gateway(
# "", "", "", fedml.get_env_version(), use_mqtt_inference,
# use_worker_gateway, ext_info, python_program, inference_gw_cmd, str(inference_port),
# fedml_base_dir),
- # should_capture_stdout=False, should_capture_stderr=False)
- inference_gateway_process.daemon = True
- inference_gateway_process.start()
+ # )
+ # inference_gateway_process.daemon = True
+ # inference_gateway_process.start()
return inference_gateway_process
else:
diff --git a/python/fedml/computing/scheduler/slave/client_login.py b/python/fedml/computing/scheduler/slave/client_login.py
index 37a6dc8064..95c772a225 100755
--- a/python/fedml/computing/scheduler/slave/client_login.py
+++ b/python/fedml/computing/scheduler/slave/client_login.py
@@ -30,8 +30,6 @@ def logout():
if args.api_key == "":
args.api_key = args.user
- fedml.set_env_version("test")
-
if args.local_on_premise_platform_host != "127.0.0.1":
fedml.set_local_on_premise_platform_host(args.local_on_premise_platform_host)
if args.local_on_premise_platform_port != 80:
diff --git a/python/fedml/core/mlops/mlops_configs.py b/python/fedml/core/mlops/mlops_configs.py
index 6c25c38128..c1b46fd0dd 100644
--- a/python/fedml/core/mlops/mlops_configs.py
+++ b/python/fedml/core/mlops/mlops_configs.py
@@ -154,6 +154,10 @@ def fetch_all_configs():
fetched_configs[Configs.ML_OPS_CONFIG],
fetched_configs[Configs.DOCKER_CONFIG])
+ @staticmethod
+ def fetch_mqtt_config():
+ return MLOpsConfigs._fetch_configs({Configs.MQTT_CONFIG})
+
if __name__ == "__main__":
fedml.set_env_version("release")
From c1e37af1385f2746193658ff852f5b545afda13d Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Wed, 5 Jun 2024 20:02:57 +0000
Subject: [PATCH 115/282] Nit.
---
.../computing/scheduler/model_scheduler/worker_job_runner.py | 3 +++
1 file changed, 3 insertions(+)
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index 370ba57b49..348b760153 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -521,6 +521,9 @@ def find_previous_downloaded_pkg(parent_dir: str, model_name: str) -> str:
exact_matched = False
+ if unzip_fd == "":
+ return res
+
for folder in os.listdir(unzip_fd):
if folder == model_name:
res = os.path.join(unzip_fd, folder)
From a4b8ad23969efec0c51758a3068590b7c0152bb0 Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Wed, 5 Jun 2024 22:18:27 +0000
Subject: [PATCH 116/282] Pipe in Mqtt config directly instead of deserializing
object
---
.../model_scheduler/device_model_inference.py | 30 +++----------
.../model_scheduler/master_job_runner.py | 45 ++-----------------
.../master_job_runner_manager.py | 4 +-
.../master_protocol_manager.py | 2 +-
python/fedml/core/mlops/mlops_configs.py | 3 +-
5 files changed, 16 insertions(+), 68 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index f55eda9047..3d9db78a23 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -21,7 +21,6 @@
from fedml.computing.scheduler.model_scheduler.device_mqtt_inference_protocol import FedMLMqttInference
from fedml.computing.scheduler.model_scheduler.device_http_proxy_inference_protocol import FedMLHttpProxyInference
from fedml.core.mlops.mlops_configs import MLOpsConfigs
-from fedml.computing.scheduler.comm_utils import sys_utils
from fedml.core.mlops import MLOpsRuntimeLog, MLOpsRuntimeLogDaemon
@@ -33,11 +32,8 @@ class Settings:
model_infer_host = os.getenv(ModuleConstants.ENV_FEDML_INFER_HOST)
version = fedml.get_env_version()
mqtt_config = MLOpsConfigs.fetch_mqtt_config()
- ext_info = "2b34303961245c4f175f2236282d7a272c040b0904747579087f6a760112030109010c215d54505707140005190a051c347f365c4a430c020a7d39120e26032a78730f797f7c031f0901657e75"
-logging_args = None
-
api = FastAPI()
FEDML_MODEL_CACHE = FedMLModelCache.get_instance().set_redis_params(redis_addr=Settings.redis_addr,
@@ -47,7 +43,6 @@ class Settings:
@api.middleware("http")
async def auth_middleware(request: Request, call_next):
-
if "/inference" in request.url.path or "/api/v1/predict" in request.url.path:
try:
# Attempt to parse the JSON body.
@@ -138,7 +133,7 @@ async def predict_openai(end_point_id, request: Request):
try:
response = await _predict(end_point_id, input_json, header)
except Exception as e:
- response = {"error": True, "message": f"{traceback.format_exc()}"}
+ response = {"error": True, "message": f"{traceback.format_exc()}, exception {e}"}
return response
@@ -174,7 +169,6 @@ async def _predict(
input_json,
header=None
) -> Union[MutableMapping[str, Any], Response, StreamingResponse]:
-
# Always increase the pending requests counter on a new incoming request.
FEDML_MODEL_CACHE.update_pending_requests_counter(increase=True)
inference_response = {}
@@ -222,7 +216,7 @@ async def _predict(
# Start timing for model metrics
model_metrics = FedMLModelMetrics(end_point_id, in_end_point_name,
model_id, in_model_name, model_version,
- Settings.model_infer_url,
+ Settings.model_infer_host,
Settings.redis_addr,
Settings.redis_port,
Settings.redis_password,
@@ -269,7 +263,6 @@ async def _predict(
FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
-
def retrieve_info_by_endpoint_id(end_point_id, in_end_point_name=None, in_model_name=None,
in_model_version=None, enable_check=False):
"""
@@ -308,7 +301,7 @@ def found_idle_inference_device(end_point_id, end_point_name, in_model_name, in_
inference_output_url = ""
model_version = ""
# Found idle device (TODO: optimize the algorithm to search best device for inference)
- payload, idle_device = FEDML_MODEL_CACHE.\
+ payload, idle_device = FEDML_MODEL_CACHE. \
get_idle_device(end_point_id, end_point_name, in_model_name, in_model_version)
if payload is not None:
logging.info("found idle deployment result {}".format(payload))
@@ -328,7 +321,6 @@ def found_idle_inference_device(end_point_id, end_point_name, in_model_name, in_
async def send_inference_request(idle_device, end_point_id, inference_url, input_list, output_list,
inference_type="default", has_public_ip=True):
-
request_timeout_sec = FEDML_MODEL_CACHE.get_endpoint_settings(end_point_id) \
.get("request_timeout_sec", ClientConstants.INFERENCE_REQUEST_TIMEOUT)
@@ -367,16 +359,7 @@ async def send_inference_request(idle_device, end_point_id, inference_url, input
return inference_response
if not has_public_ip:
- connect_str = "@FEDML@"
- random_out = sys_utils.random2(Settings.ext_info, "FEDML@9999GREAT")
- config_list = random_out.split(connect_str)
- agent_config = dict()
- agent_config["mqtt_config"] = dict()
- agent_config["mqtt_config"]["BROKER_HOST"] = config_list[0]
- agent_config["mqtt_config"]["BROKER_PORT"] = int(config_list[1])
- agent_config["mqtt_config"]["MQTT_USER"] = config_list[2]
- agent_config["mqtt_config"]["MQTT_PWD"] = config_list[3]
- agent_config["mqtt_config"]["MQTT_KEEPALIVE"] = int(config_list[4])
+ agent_config = {"mqtt_config": Settings.mqtt_config}
mqtt_inference = FedMLMqttInference(
agent_config=agent_config,
run_id=end_point_id)
@@ -410,12 +393,13 @@ async def send_inference_request(idle_device, end_point_id, inference_url, input
def auth_request_token(end_point_id, end_point_name, model_name, token):
if token is None:
return False
- cached_token = FEDML_MODEL_CACHE.\
+ cached_token = FEDML_MODEL_CACHE. \
get_end_point_token(end_point_id, end_point_name, model_name)
if cached_token is not None and str(cached_token) == str(token):
return True
return False
+
def is_endpoint_activated(end_point_id):
if end_point_id is None:
return False
@@ -433,7 +417,7 @@ def logging_inference_request(request, response):
with open(inference_log_file, "a") as f:
f.writelines([f"request: {request}, response: {response}\n"])
except Exception as ex:
- logging.info("failed to log inference request and response to file.")
+ logging.info(f"failed to log inference request and response to file with exception {ex}")
def configure_logging():
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index bd40b71c0e..d8a81e016f 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -1,7 +1,6 @@
import copy
import json
import logging
-import multiprocessing
import os
import time
import queue
@@ -10,7 +9,6 @@
from multiprocessing import Queue
import fedml
-import uvicorn
from fedml.core.mlops import MLOpsRuntimeLog, MLOpsConfigs
from fedml.core.mlops.mlops_runtime_log import MLOpsFormatter
from .device_client_constants import ClientConstants
@@ -117,8 +115,7 @@ def run_impl(
message_center=self.message_center)
# start unified inference gateway process if not started
- FedMLDeployMasterJobRunner.start_device_inference_gateway(
- args=self.args, inference_port=inference_port, agent_config=self.agent_config)
+ FedMLDeployMasterJobRunner.start_device_inference_gateway(inference_port=inference_port)
# start inference monitor process
FedMLDeployMasterJobRunner.stop_device_inference_monitor(
@@ -469,10 +466,7 @@ def cleanup_runner_process(self, run_id):
ServerConstants.cleanup_run_process(run_id, not_kill_subprocess=True)
@staticmethod
- def start_device_inference_gateway(
- args, inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT, agent_config=None,
- redis_addr="localhost", redis_port=6379, redis_password="fedml_default"
- ):
+ def start_device_inference_gateway(inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT):
# start unified inference server
python_program = get_python_program()
master_port = os.getenv("FEDML_MASTER_PORT", None)
@@ -480,27 +474,11 @@ def start_device_inference_gateway(
inference_port = int(master_port)
if not ServerConstants.is_running_on_k8s():
logging.info(f"start the model inference gateway...")
- # use_mqtt_inference = os.getenv("FEDML_USE_MQTT_INFERENCE", "False")
- # use_mqtt_inference = True if use_mqtt_inference.lower() == 'true' else False
- # use_worker_gateway = os.getenv("FEDML_USE_WORKER_GATEWAY", "False")
- # use_worker_gateway = True if use_worker_gateway.lower() == 'true' else False
inference_gw_cmd = "fedml.computing.scheduler.model_scheduler.device_model_inference:api"
inference_gateway_pids = RunProcessUtils.get_pid_from_cmd_line(inference_gw_cmd)
if inference_gateway_pids is None or len(inference_gateway_pids) <= 0:
cur_dir = os.path.dirname(__file__)
fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- # connect_str = "@FEDML@"
- # ext_info = sys_utils.random1(
- # agent_config["mqtt_config"]["BROKER_HOST"] + connect_str +
- # str(agent_config["mqtt_config"]["BROKER_PORT"]) + connect_str +
- # agent_config["mqtt_config"]["MQTT_USER"] + connect_str +
- # agent_config["mqtt_config"]["MQTT_PWD"] + connect_str +
- # str(agent_config["mqtt_config"]["MQTT_KEEPALIVE"]), "FEDML@9999GREAT")
- # python_program = get_python_program()
- # inference_gateway_process = multiprocessing.Process(
- # target=FedMLDeployMasterJobRunner.start_inference_gateway_server, args=(inference_gw_cmd,
- # inference_port, args)
- # )
inference_gateway_process = ServerConstants.exec_console_with_script(f"{python_program} "
f"-m uvicorn {inference_gw_cmd} "
f"--host 0.0.0.0 "
@@ -510,21 +488,6 @@ def start_device_inference_gateway(
f"--log-level critical",
should_capture_stdout=False,
should_capture_stderr=False)
- # inference_gateway_process = ServerConstants.exec_console_with_script(
- # "REDIS_ADDR=\"{}\" REDIS_PORT=\"{}\" REDIS_PASSWORD=\"{}\" "
- # "END_POINT_NAME=\"{}\" "
- # "MODEL_NAME=\"{}\" MODEL_VERSION=\"{}\" MODEL_INFER_URL=\"{}\" VERSION=\"{}\" "
- # "USE_MQTT_INFERENCE={} USE_WORKER_GATEWAY={} EXT_INFO={} "
- # "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
- # "--log-level critical".format(
- # redis_addr, str(redis_port), redis_password, "",
- # "", "", "", fedml.get_env_version(), use_mqtt_inference,
- # use_worker_gateway, ext_info, python_program, inference_gw_cmd, str(inference_port),
- # fedml_base_dir),
- # )
- # inference_gateway_process.daemon = True
- # inference_gateway_process.start()
-
return inference_gateway_process
else:
return inference_gateway_pids[0]
@@ -562,7 +525,7 @@ def stop_device_inference_monitor(run_id, end_point_name, model_id, model_name,
model_id, model_name, model_version)
@staticmethod
- def recover_inference_and_monitor(args):
+ def recover_inference_and_monitor():
# noinspection PyBroadException
try:
agent_config = dict()
@@ -589,7 +552,7 @@ def recover_inference_and_monitor(args):
if not is_activated:
continue
- FedMLDeployMasterJobRunner.start_device_inference_gateway(args=args, inference_port=inference_port, agent_config=agent_config)
+ FedMLDeployMasterJobRunner.start_device_inference_gateway(inference_port=inference_port)
FedMLDeployMasterJobRunner.stop_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
index 02a6fde329..c761cd6d8f 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
@@ -55,8 +55,8 @@ def stop_device_inference_monitor(self, run_id, end_point_name, model_id, model_
run_id, end_point_name, model_id, model_name, model_version)
@staticmethod
- def recover_inference_and_monitor(args):
- FedMLDeployMasterJobRunner.recover_inference_and_monitor(args=args)
+ def recover_inference_and_monitor():
+ FedMLDeployMasterJobRunner.recover_inference_and_monitor()
@staticmethod
def generate_request_json_with_replica_num_diff(run_id, edge_id, request_json):
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index d90857c0ab..668d1192ce 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -82,7 +82,7 @@ def _init_extra_items(self):
except Exception as e:
pass
- FedMLDeployJobRunnerManager.recover_inference_and_monitor(args = self.args)
+ FedMLDeployJobRunnerManager.recover_inference_and_monitor()
# Override
def _process_connection_ready(self):
diff --git a/python/fedml/core/mlops/mlops_configs.py b/python/fedml/core/mlops/mlops_configs.py
index c1b46fd0dd..1ed2e0476d 100644
--- a/python/fedml/core/mlops/mlops_configs.py
+++ b/python/fedml/core/mlops/mlops_configs.py
@@ -156,7 +156,8 @@ def fetch_all_configs():
@staticmethod
def fetch_mqtt_config():
- return MLOpsConfigs._fetch_configs({Configs.MQTT_CONFIG})
+ fetched_config = MLOpsConfigs._fetch_configs({Configs.MQTT_CONFIG})
+ return fetched_config[Configs.MQTT_CONFIG]
if __name__ == "__main__":
From 0ad81ce9819ecf48ee678fd51d27b01a99741953 Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Wed, 5 Jun 2024 23:59:21 +0000
Subject: [PATCH 117/282] Nits
---
.../model_scheduler/device_model_inference.py | 2 +-
.../device_mqtt_inference_protocol.py | 1 -
.../model_scheduler/master_job_runner.py | 2 +-
.../fedml/core/mlops/mlops_runtime_log_daemon.py | 15 ++++++++++++---
4 files changed, 14 insertions(+), 6 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 3d9db78a23..111052faf1 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -442,7 +442,7 @@ def configure_logging():
if __name__ == "__main__":
import uvicorn
-
port = 2203
logging.basicConfig(level=logging.INFO)
+ configure_logging()
uvicorn.run(api, host="0.0.0.0", port=port, log_level="info")
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_mqtt_inference_protocol.py b/python/fedml/computing/scheduler/model_scheduler/device_mqtt_inference_protocol.py
index 1fac5a984b..9cd5c1e9a2 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_mqtt_inference_protocol.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_mqtt_inference_protocol.py
@@ -10,7 +10,6 @@
import asyncio
-from ..comm_utils.constants import SchedulerConstants
from ....core.distributed.communication.mqtt.mqtt_manager import MqttManager
from .device_http_inference_protocol import FedMLHttpInference
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index d8a81e016f..a10bd2c559 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -485,7 +485,7 @@ def start_device_inference_gateway(inference_port=ServerConstants.MODEL_INFERENC
f"--port {str(inference_port)} "
f"--reload --reload-delay 3 "
f"--reload-dir {fedml_base_dir} "
- f"--log-level critical",
+ f"--log-level info",
should_capture_stdout=False,
should_capture_stderr=False)
return inference_gateway_process
diff --git a/python/fedml/core/mlops/mlops_runtime_log_daemon.py b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
index 64bd982ae3..7791d8f4e5 100644
--- a/python/fedml/core/mlops/mlops_runtime_log_daemon.py
+++ b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
@@ -223,23 +223,32 @@ def __upload(self, log_upload_request) -> bool:
if cert_path is not None:
try:
requests.session().verify = cert_path
+ logging.info(f"FedMLDebug POST log to server. log_headers: {log_headers}, "
+ f"log_upload_request: {log_upload_request}, url: {self.log_server_url}")
+
# logging.info(f"FedMLDebug POST log to server. run_id {run_id}, device_id {device_id}")
response = requests.post(
self.log_server_url, json=log_upload_request, verify=True, headers=log_headers
)
+ logging.info(f"FedMLDebug POST log to server. response: {response}")
# logging.info(f"FedMLDebug POST log to server run_id {run_id}, device_id {device_id}. response.status_code: {response.status_code}")
except requests.exceptions.SSLError as err:
MLOpsConfigs.install_root_ca_file()
# logging.info(f"FedMLDebug POST log to server. run_id {run_id}, device_id {device_id}")
+ logging.info(f"FedMLDebug POST log to server. log_headers: {log_headers}, "
+ f"log_upload_request: {log_upload_request}, url: {self.log_server_url}")
+
response = requests.post(
self.log_server_url, json=log_upload_request, verify=True, headers=log_headers
)
+ logging.info(f"FedMLDebug POST log to server. response: {response}")
# logging.info(f"FedMLDebug POST log to server run_id {run_id}, device_id {device_id}. response.status_code: {response.status_code}")
else:
- # logging.info(f"FedMLDebug POST log to server. run_id {run_id}, device_id {device_id}")
+ logging.info(f"FedMLDebug POST log to server. log_headers: {log_headers}, "
+ f"log_upload_request: {log_upload_request}, url: {self.log_server_url}")
response = requests.post(self.log_server_url, headers=log_headers, json=log_upload_request)
- # logging.info(f"FedMLDebug POST log to server. run_id {run_id}, device_id {device_id}. response.status_code: {response.status_code}")
+ logging.info(f"FedMLDebug POST log to server. response: {response}")
if response.status_code != 200:
logging.error(f"Failed to upload log to server. run_id {self.run_id}, device_id {self.device_id}. "
f"response.status_code: {response.status_code}")
@@ -404,9 +413,9 @@ def __new__(cls, *args, **kwargs):
def __init__(self, in_args):
self.args = in_args
self.edge_id = MLOpsLoggingUtils.get_edge_id_from_args(self.args)
+ url = fedml._get_backend_service()
try:
if self.args.log_server_url is None or self.args.log_server_url == "":
- url = fedml._get_backend_service()
self.log_server_url = f"{url}/fedmlLogsServer/logs/update"
else:
self.log_server_url = self.args.log_server_url
From d71387651e813e93865fd2e864cb685c874a813c Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Thu, 6 Jun 2024 04:48:58 +0000
Subject: [PATCH 118/282] Fix bugs
---
.../model_scheduler/device_model_inference.py | 10 ++++++----
1 file changed, 6 insertions(+), 4 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 111052faf1..57f9d80208 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -25,6 +25,7 @@
class Settings:
+ server_name = "DEVICE_INFERENCE_GATEWAY"
fedml.load_env()
redis_addr = os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_ADDR)
redis_port = os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PORT)
@@ -425,7 +426,7 @@ def configure_logging():
args = parser.parse_args([])
setattr(args, "log_file_dir", ServerConstants.get_log_file_dir())
- setattr(args, "run_id", "inference_gateway")
+ setattr(args, "run_id", -1)
setattr(args, "role", "server")
setattr(args, "using_mlops", True)
setattr(args, "config_version", fedml.get_env_version())
@@ -433,10 +434,12 @@ def configure_logging():
runner_info = ServerConstants.get_runner_infos()
if not (runner_info and "edge_id" in runner_info):
raise Exception("Inference gateway couldn't be started as edge_id couldn't be parsed from runner_infos.yaml")
- setattr(args, "edge_id", runner_info.get("edge_id"))
+ setattr(args, "edge_id", int(runner_info.get("edge_id")))
MLOpsRuntimeLog.get_instance(args).init_logs(log_level=logging.INFO)
- MLOpsRuntimeLogDaemon.get_instance(args).start_log_processor(args.run_id, args.edge_id)
+ MLOpsRuntimeLogDaemon.get_instance(args).start_log_processor(log_run_id=args.run_id, log_device_id=args.edge_id,
+ log_source=Settings.server_name,
+ log_file_prefix=Settings.server_name)
logging.info("start the log processor for inference gateway")
@@ -444,5 +447,4 @@ def configure_logging():
import uvicorn
port = 2203
logging.basicConfig(level=logging.INFO)
- configure_logging()
uvicorn.run(api, host="0.0.0.0", port=port, log_level="info")
From 134f63e3adab71c976cfd012459fc7db6b744906 Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Thu, 6 Jun 2024 05:03:03 +0000
Subject: [PATCH 119/282] Remove info logging added for debugging
---
.../fedml/core/mlops/mlops_runtime_log_daemon.py | 15 ---------------
1 file changed, 15 deletions(-)
diff --git a/python/fedml/core/mlops/mlops_runtime_log_daemon.py b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
index 7791d8f4e5..ff06dc91b3 100644
--- a/python/fedml/core/mlops/mlops_runtime_log_daemon.py
+++ b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
@@ -223,32 +223,17 @@ def __upload(self, log_upload_request) -> bool:
if cert_path is not None:
try:
requests.session().verify = cert_path
- logging.info(f"FedMLDebug POST log to server. log_headers: {log_headers}, "
- f"log_upload_request: {log_upload_request}, url: {self.log_server_url}")
-
- # logging.info(f"FedMLDebug POST log to server. run_id {run_id}, device_id {device_id}")
response = requests.post(
self.log_server_url, json=log_upload_request, verify=True, headers=log_headers
)
- logging.info(f"FedMLDebug POST log to server. response: {response}")
- # logging.info(f"FedMLDebug POST log to server run_id {run_id}, device_id {device_id}. response.status_code: {response.status_code}")
except requests.exceptions.SSLError as err:
MLOpsConfigs.install_root_ca_file()
- # logging.info(f"FedMLDebug POST log to server. run_id {run_id}, device_id {device_id}")
- logging.info(f"FedMLDebug POST log to server. log_headers: {log_headers}, "
- f"log_upload_request: {log_upload_request}, url: {self.log_server_url}")
-
response = requests.post(
self.log_server_url, json=log_upload_request, verify=True, headers=log_headers
)
- logging.info(f"FedMLDebug POST log to server. response: {response}")
- # logging.info(f"FedMLDebug POST log to server run_id {run_id}, device_id {device_id}. response.status_code: {response.status_code}")
else:
- logging.info(f"FedMLDebug POST log to server. log_headers: {log_headers}, "
- f"log_upload_request: {log_upload_request}, url: {self.log_server_url}")
response = requests.post(self.log_server_url, headers=log_headers, json=log_upload_request)
- logging.info(f"FedMLDebug POST log to server. response: {response}")
if response.status_code != 200:
logging.error(f"Failed to upload log to server. run_id {self.run_id}, device_id {self.device_id}. "
f"response.status_code: {response.status_code}")
From fd446b0a1aed81b320da001295c3e44b232cbe4c Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Thu, 6 Jun 2024 05:49:01 +0000
Subject: [PATCH 120/282] Fix
---
.../scheduler/model_scheduler/device_model_inference.py | 7 ++++---
1 file changed, 4 insertions(+), 3 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 57f9d80208..7b3ac1d0bf 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -37,9 +37,10 @@ class Settings:
api = FastAPI()
-FEDML_MODEL_CACHE = FedMLModelCache.get_instance().set_redis_params(redis_addr=Settings.redis_addr,
- redis_port=Settings.redis_port,
- redis_password=Settings.redis_password)
+FEDML_MODEL_CACHE = FedMLModelCache.get_instance()
+FEDML_MODEL_CACHE.set_redis_params(redis_addr=Settings.redis_addr,
+ redis_port=Settings.redis_port,
+ redis_password=Settings.redis_password)
@api.middleware("http")
From 19abac107d31439ca1c3269b53414c92eff02c2f Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 6 Jun 2024 17:29:56 +0800
Subject: [PATCH 121/282] [CoreEngine] update the version and dependent libs.
---
python/fedml/__init__.py | 2 +-
python/setup.py | 5 ++++-
2 files changed, 5 insertions(+), 2 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index c13a64566e..21da84c9ab 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -36,7 +36,7 @@
_global_training_type = None
_global_comm_backend = None
-__version__ = "0.8.31"
+__version__ = "0.9.0"
# This is the deployment environment used for different roles (RD/PM/BD/Public Developers). Potential VALUE: local, dev, test, release
diff --git a/python/setup.py b/python/setup.py
index e88788d1ff..9651465d32 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -67,6 +67,9 @@ def finalize_options(self):
# Need to pin this version due to breaking change released in python docker sdk
'requests<2.32',
'python-dotenv',
+ 'protobuf>=3.20.2,<4.0dev',
+ 'typer<0.10.0,>=0.3.0',
+ 'fastapi-cli==0.0.1'
]
requirements_extra_mpi = [
@@ -123,7 +126,7 @@ def finalize_options(self):
setup(
name="fedml",
- version="0.8.31",
+ version="0.9.0",
author="FedML Team",
author_email="ch@fedml.ai",
description="A research and production integrated edge-cloud library for "
From 27ad2e7a21cf3f98ffbd9443a4db6f85218bbb92 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 6 Jun 2024 18:11:16 +0800
Subject: [PATCH 122/282] [CoreEngine] remove the deprecated files in the
scheduler.
---
.../master/server_runner_deprecated.py | 2775 -----------------
.../device_client_runner_deprecated.py | 1483 ---------
.../device_server_runner_deprecated.py | 2022 ------------
.../slave/client_runner_deprecated.py | 1872 -----------
4 files changed, 8152 deletions(-)
delete mode 100755 python/fedml/computing/scheduler/master/server_runner_deprecated.py
delete mode 100755 python/fedml/computing/scheduler/model_scheduler/device_client_runner_deprecated.py
delete mode 100755 python/fedml/computing/scheduler/model_scheduler/device_server_runner_deprecated.py
delete mode 100755 python/fedml/computing/scheduler/slave/client_runner_deprecated.py
diff --git a/python/fedml/computing/scheduler/master/server_runner_deprecated.py b/python/fedml/computing/scheduler/master/server_runner_deprecated.py
deleted file mode 100755
index 238349a3e4..0000000000
--- a/python/fedml/computing/scheduler/master/server_runner_deprecated.py
+++ /dev/null
@@ -1,2775 +0,0 @@
-import base64
-import copy
-import json
-import logging
-import platform
-import queue
-import sys
-
-import multiprocessing
-from multiprocessing import Process, Queue, Value, Array
-import os
-import shutil
-import stat
-import subprocess
-import threading
-
-import time
-import traceback
-import urllib
-import uuid
-import zipfile
-from os import listdir
-from urllib.parse import urljoin, urlparse
-
-import requests
-
-import fedml
-from ..comm_utils.job_cleanup import JobCleanup
-from ..scheduler_core.scheduler_matcher import SchedulerMatcher
-from ..comm_utils.constants import SchedulerConstants
-from ..comm_utils.job_utils import JobRunnerUtils
-from ..comm_utils.run_process_utils import RunProcessUtils
-from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
-
-from ....core.distributed.communication.mqtt.mqtt_manager import MqttManager
-from ..comm_utils.yaml_utils import load_yaml_config
-from ..slave.client_constants import ClientConstants
-from ..master.server_constants import ServerConstants
-
-from ....core.mlops.mlops_metrics import MLOpsMetrics
-
-from ....core.mlops.mlops_configs import MLOpsConfigs
-from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
-from ....core.mlops.mlops_status import MLOpsStatus
-from ..comm_utils.sys_utils import get_sys_runner_info, get_python_program
-from ..comm_utils import sys_utils
-from .server_data_interface import FedMLServerDataInterface
-from ....core.mlops.mlops_utils import MLOpsUtils
-from ..scheduler_entry.constants import Constants
-from ..model_scheduler.model_device_server import FedMLModelDeviceServerRunner
-from ..model_scheduler.device_model_cards import FedMLModelCards
-from ..model_scheduler import device_client_constants
-from ..scheduler_core.log_manager import LogsManager
-from ..scheduler_core.metrics_manager import MetricsManager
-from ..scheduler_core.master_api_daemon import MasterApiDaemon
-from fedml.utils.debugging import debug
-from ..scheduler_core.message_center import FedMLMessageCenter
-import ssl
-
-
-class RunnerError(Exception):
- """ Runner stopped. """
- pass
-
-
-class RunnerCompletedError(Exception):
- """ Runner completed. """
- pass
-
-
-class FedMLServerRunner(FedMLMessageCenter):
- FEDML_CLOUD_SERVER_PREFIX = "fedml-server-run-"
- debug_cloud_server = False
-
- def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id=0):
- super().__init__()
- self.master_api_daemon = None
- self.run_stop_process = None
- self.run_stop_process_map = dict()
- self.run_edge_id_status_queue_map = dict()
- self.run_metrics_queue_map = dict()
- self.run_events_queue_map = dict()
- self.run_artifacts_queue_map = dict()
- self.run_logs_queue_map = dict()
- self.async_check_timeout = 0
- self.enable_async_cluster = False
- self.origin_fedml_config_object = None
- self.package_type = SchedulerConstants.JOB_PACKAGE_TYPE_DEFAULT
- self.local_api_process = None
- self.run_process_event = None
- self.run_process_event_map = dict()
- self.run_process_completed_event = None
- self.run_process_completed_event_map = dict()
- self.run_process_event_map_for_stop = dict()
- self.edge_device_info_queue = None
- self.run_edge_device_info_queue_map = dict()
- self.run_edge_device_info_queue_map_for_stop = dict()
- self.run_edge_device_info_global_queue = None
- self.run_edge_device_info_global_queue_for_stop = None
- self.run_process = None
- self.run_process_map = dict()
- self.start_request_json = None
- self.server_docker_image = None
- self.cloud_server_name = None
- self.run_as_cloud_agent = False
- self.run_as_cloud_server = False
- self.run_as_edge_server_and_agent = False
- self.run_as_cloud_server_and_agent = False
- self.fedml_packages_base_dir = None
- self.fedml_packages_unzip_dir = None
- self.mqtt_mgr = None
- self.running_request_json = dict()
- self.run_id = run_id
- self.unique_device_id = None
- self.edge_id = edge_id
- self.server_agent_id = 0
- if request_json is not None:
- self.server_agent_id = request_json.get("server_id", 0)
- self.process = None
- self.args = args
- self.request_json = copy.deepcopy(request_json)
- self.version = args.version
- self.device_id = args.device_id
- self.cur_dir = os.path.split(os.path.realpath(__file__))[0]
- if args.current_running_dir is not None:
- self.cur_dir = args.current_running_dir
-
- image_version = self.version
- if image_version == "local":
- image_version = "dev"
- self.server_docker_base_image = "/fedml-device-image:" + image_version
-
- self.agent_config = agent_config
- self.fedml_data_base_package_dir = os.path.join("/", "fedml", "data")
- self.fedml_data_local_package_dir = os.path.join("/", "fedml", "fedml-package", "fedml", "data")
- self.fedml_data_dir = self.fedml_data_base_package_dir
- self.fedml_config_dir = os.path.join("/", "fedml", "conf")
-
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES = {
- "${FEDSYS.RUN_ID}": "",
- "${FEDSYS.PRIVATE_LOCAL_DATA}": "",
- "${FEDSYS.CLIENT_ID_LIST}": "",
- "${FEDSYS.SYNTHETIC_DATA_URL}": "",
- "${FEDSYS.IS_USING_LOCAL_DATA}": "",
- "${FEDSYS.CLIENT_NUM}": "",
- "${FEDSYS.CLIENT_INDEX}": "",
- "${FEDSYS.CLIENT_OBJECT_LIST}": "",
- "${FEDSYS.LOG_SERVER_URL}": "",
- }
-
- self.mlops_metrics = None
- self.client_agent_active_list = dict()
- self.server_active_list = dict()
- self.run_status = None
- self.ntp_offset = MLOpsUtils.get_ntp_offset()
- self.runner_list = dict()
- self.enable_simulation_cloud_agent = False
- self.use_local_process_as_cloud_server = False
-
- self.model_device_server = None
- self.run_model_device_ids = dict()
- self.run_edge_ids = dict()
- self.master_api_process = None
-
- self.subscribed_topics = list()
- self.user_name = None
- self.message_center = None
-
- def build_dynamic_constrain_variables(self, run_id, run_config):
- data_config = run_config.get("data_config", {})
- server_edge_id_list = self.request_json["edgeids"]
- is_using_local_data = 0
- private_data_dir = data_config.get("privateLocalData", "")
- synthetic_data_url = data_config.get("syntheticDataUrl", "")
- edges = self.request_json["edges"]
- # if private_data_dir is not None \
- # and len(str(private_data_dir).strip(' ')) > 0:
- # is_using_local_data = 1
- if private_data_dir is None or len(str(private_data_dir).strip(" ")) <= 0:
- params_config = run_config.get("parameters", None)
- private_data_dir = ServerConstants.get_data_dir()
- if synthetic_data_url is None or len(str(synthetic_data_url)) <= 0:
- synthetic_data_url = private_data_dir
-
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.RUN_ID}"] = run_id
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.PRIVATE_LOCAL_DATA}"] = private_data_dir.replace(" ", "")
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_ID_LIST}"] = str(server_edge_id_list).replace(" ", "")
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.SYNTHETIC_DATA_URL}"] = synthetic_data_url.replace(" ", "")
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.IS_USING_LOCAL_DATA}"] = str(is_using_local_data)
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_NUM}"] = len(server_edge_id_list)
- client_objects = str(json.dumps(edges))
- client_objects = client_objects.replace(" ", "").replace("\n", "").replace('"', '\\"')
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_OBJECT_LIST}"] = client_objects
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.LOG_SERVER_URL}"] = self.agent_config["ml_ops_config"][
- "LOG_SERVER_URL"
- ]
-
- def unzip_file(self, zip_file, unzip_file_path) -> str:
- unziped_file_name = ""
- if zipfile.is_zipfile(zip_file):
- with zipfile.ZipFile(zip_file, "r") as zipf:
- zipf.extractall(unzip_file_path)
- unziped_file_name = zipf.namelist()[0]
- else:
- raise Exception("Invalid zip file {}".format(zip_file))
-
- return unziped_file_name
-
- def package_download_progress(self, count, blksize, filesize):
- self.check_runner_stop_event()
-
- downloaded = count * blksize
- downloaded = filesize if downloaded > filesize else downloaded
- progress = (downloaded / filesize * 100) if filesize != 0 else 0
- progress_int = int(progress)
- downloaded_kb = format(downloaded / 1024, '.2f')
-
- # since this hook funtion is stateless, we need a state to avoid printing progress repeatly
- if count == 0:
- self.prev_download_progress = 0
- if progress_int != self.prev_download_progress and progress_int % 5 == 0:
- self.prev_download_progress = progress_int
- logging.info("package downloaded size {} KB, progress {}%".format(downloaded_kb, progress_int))
-
- def retrieve_and_unzip_package(self, package_name, package_url):
- local_package_path = ServerConstants.get_package_download_dir()
- os.makedirs(local_package_path, exist_ok=True)
- filename, filename_without_extension, file_extension = ServerConstants.get_filename_and_extension(package_url)
- local_package_file = os.path.join(local_package_path, f"fedml_run_{self.run_id}_{filename_without_extension}")
- if os.path.exists(local_package_file):
- os.remove(local_package_file)
- ssl._create_default_https_context = ssl._create_unverified_context
- urllib.request.urlretrieve(package_url, local_package_file,
- reporthook=self.package_download_progress)
- unzip_package_path = os.path.join(ClientConstants.get_package_unzip_dir(),
- f"unzip_fedml_run_{self.run_id}_{filename_without_extension}")
- try:
- shutil.rmtree(unzip_package_path, ignore_errors=True)
- except Exception as e:
- pass
-
- package_dir_name = self.unzip_file(local_package_file, unzip_package_path) # Using unziped folder name
- unzip_package_full_path = os.path.join(unzip_package_path, package_dir_name)
-
- logging.info("local_package_file {}, unzip_package_path {}, unzip file full path {}".format(
- local_package_file, unzip_package_path, unzip_package_full_path))
-
- return unzip_package_full_path
-
- def update_local_fedml_config(self, run_id, run_config):
- packages_config = run_config["packages_config"]
-
- # Copy config file from the client
- server_package_name = packages_config.get("server", None)
- server_package_url = packages_config.get("serverUrl", None)
- unzip_package_path = self.retrieve_and_unzip_package(server_package_name, server_package_url)
- self.fedml_packages_unzip_dir = unzip_package_path
- fedml_local_config_file = os.path.join(unzip_package_path, "conf", "fedml.yaml")
-
- # Load the above config to memory
- config_from_container = load_yaml_config(fedml_local_config_file)
- container_entry_file_config = config_from_container["entry_config"]
- container_dynamic_args_config = config_from_container["dynamic_args"]
- entry_file = container_entry_file_config["entry_file"]
- conf_file = container_entry_file_config["conf_file"]
- self.package_type = container_entry_file_config.get("package_type", SchedulerConstants.JOB_PACKAGE_TYPE_DEFAULT)
- full_conf_path = os.path.join(unzip_package_path, "fedml", "config", os.path.basename(conf_file))
-
- # Dynamically build constrain variable with realtime parameters from server
- self.build_dynamic_constrain_variables(run_id, run_config)
-
- # Update entry arguments value with constrain variable values with realtime parameters from server
- # currently we support the following constrain variables:
- # ${FEDSYS_RUN_ID}: a run id represented one entire Federated Learning flow
- # ${FEDSYS_PRIVATE_LOCAL_DATA}: private local data path in the Federated Learning client
- # ${FEDSYS_CLIENT_ID_LIST}: client list in one entire Federated Learning flow
- # ${FEDSYS_SYNTHETIC_DATA_URL}: synthetic data url from server,
- # if this value is not null, the client will download data from this URL to use it as
- # federated training data set
- # ${FEDSYS_IS_USING_LOCAL_DATA}: whether use private local data as federated training data set
- # container_dynamic_args_config["data_cache_dir"] = "${FEDSYS.PRIVATE_LOCAL_DATA}"
- for constrain_variable_key, constrain_variable_value in self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES.items():
- for argument_key, argument_value in container_dynamic_args_config.items():
- if argument_value is not None and str(argument_value).find(constrain_variable_key) == 0:
- replaced_argument_value = str(argument_value).replace(
- constrain_variable_key, str(constrain_variable_value)
- )
- container_dynamic_args_config[argument_key] = replaced_argument_value
-
- # Merge all container new config sections as new config dictionary
- package_conf_object = dict()
- package_conf_object["entry_config"] = container_entry_file_config
- package_conf_object["dynamic_args"] = container_dynamic_args_config
- package_conf_object["dynamic_args"]["config_version"] = self.args.config_version
- container_dynamic_args_config["mqtt_config_path"] = os.path.join(
- unzip_package_path, "fedml", "config", os.path.basename(container_dynamic_args_config["mqtt_config_path"])
- )
- container_dynamic_args_config["s3_config_path"] = os.path.join(
- unzip_package_path, "fedml", "config", os.path.basename(container_dynamic_args_config["s3_config_path"])
- )
- log_file_dir = ServerConstants.get_log_file_dir()
- os.makedirs(log_file_dir, exist_ok=True)
- package_conf_object["dynamic_args"]["log_file_dir"] = log_file_dir
-
- # Save new config dictionary to local file
- fedml_updated_config_file = os.path.join(unzip_package_path, "conf", "fedml.yaml")
- ServerConstants.generate_yaml_doc(package_conf_object, fedml_updated_config_file)
-
- # Build dynamic arguments and set arguments to fedml config object
- if not self.build_dynamic_args(run_id, run_config, package_conf_object, unzip_package_path):
- return None, None
-
- return unzip_package_path, package_conf_object
-
- def build_dynamic_args(self, run_id, run_config, package_conf_object, base_dir):
- fedml_conf_file = package_conf_object["entry_config"]["conf_file"]
- fedml_conf_file_processed = str(fedml_conf_file).replace('\\', os.sep).replace('/', os.sep)
- fedml_conf_path = os.path.join(base_dir, "fedml", "config",
- os.path.basename(fedml_conf_file_processed))
- fedml_conf_object = load_yaml_config(fedml_conf_path)
- self.origin_fedml_config_object = fedml_conf_object.copy()
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
-
- # Replace local fedml config objects with parameters from MLOps web
- parameters_object = run_config.get("parameters", None)
- if parameters_object is not None:
- for config_k, config_v in fedml_conf_object.items():
- parameter_v = parameters_object.get(config_k, None)
- if parameter_v is not None:
- fedml_conf_object[config_k] = parameter_v
- parameters_object.pop(config_k)
-
- for config_k, config_v in parameters_object.items():
- fedml_conf_object[config_k] = config_v
-
- package_dynamic_args = package_conf_object["dynamic_args"]
- if fedml_conf_object.get("comm_args", None) is not None:
- fedml_conf_object["comm_args"]["mqtt_config_path"] = package_dynamic_args["mqtt_config_path"]
- fedml_conf_object["comm_args"]["s3_config_path"] = package_dynamic_args["s3_config_path"]
- fedml_conf_object["common_args"]["using_mlops"] = True
- if fedml_conf_object.get("train_args", None) is not None:
- fedml_conf_object["train_args"]["run_id"] = package_dynamic_args["run_id"]
- fedml_conf_object["train_args"]["client_id_list"] = package_dynamic_args["client_id_list"]
- fedml_conf_object["train_args"]["client_num_in_total"] = int(package_dynamic_args["client_num_in_total"])
- fedml_conf_object["train_args"]["client_num_per_round"] = int(package_dynamic_args["client_num_in_total"])
- fedml_conf_object["train_args"]["server_id"] = self.edge_id
- fedml_conf_object["train_args"]["server_agent_id"] = self.request_json.get("cloud_agent_id", self.edge_id)
- fedml_conf_object["train_args"]["group_server_id_list"] = self.request_json.get("group_server_id_list",
- list())
- if fedml_conf_object.get("device_args", None) is not None:
- fedml_conf_object["device_args"]["worker_num"] = int(package_dynamic_args["client_num_in_total"])
- # fedml_conf_object["data_args"]["data_cache_dir"] = package_dynamic_args["data_cache_dir"]
- if fedml_conf_object.get("tracking_args", None) is not None:
- fedml_conf_object["tracking_args"]["log_file_dir"] = package_dynamic_args["log_file_dir"]
- fedml_conf_object["tracking_args"]["log_server_url"] = package_dynamic_args["log_server_url"]
-
- bootstrap_script_path = None
- env_args = fedml_conf_object.get("environment_args", None)
- if env_args is not None:
- bootstrap_script_file = env_args.get("bootstrap", None)
- if bootstrap_script_file is not None:
- bootstrap_script_file = str(bootstrap_script_file).replace('\\', os.sep).replace('/', os.sep)
- if platform.system() == 'Windows':
- bootstrap_script_file = bootstrap_script_file.rstrip('.sh') + '.bat'
- if bootstrap_script_file is not None:
- bootstrap_script_dir = os.path.join(base_dir, "fedml", os.path.dirname(bootstrap_script_file))
- bootstrap_script_path = os.path.join(
- bootstrap_script_dir, bootstrap_script_dir, os.path.basename(bootstrap_script_file)
- )
- # try:
- # os.makedirs(package_dynamic_args["data_cache_dir"], exist_ok=True)
- # except Exception as e:
- # pass
- fedml_conf_object["dynamic_args"] = package_dynamic_args
-
- ServerConstants.generate_yaml_doc(fedml_conf_object, fedml_conf_path)
-
- is_bootstrap_run_ok = True
- try:
- if bootstrap_script_path is not None:
- if os.path.exists(bootstrap_script_path):
- bootstrap_stat = os.stat(bootstrap_script_path)
- if platform.system() == 'Windows':
- os.chmod(bootstrap_script_path,
- bootstrap_stat.st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)
- bootstrap_scripts = "{}".format(bootstrap_script_path)
- else:
- os.chmod(bootstrap_script_path,
- bootstrap_stat.st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)
- bootstrap_scripts = "cd {}; ./{}".format(bootstrap_script_dir,
- os.path.basename(bootstrap_script_file))
- bootstrap_scripts = str(bootstrap_scripts).replace('\\', os.sep).replace('/', os.sep)
- logging.info("Bootstrap scripts are being executed...")
- shell_cmd_list = list()
- shell_cmd_list.append(bootstrap_scripts)
- process, error_list = ServerConstants.execute_commands_with_live_logs(
- shell_cmd_list, callback=self.callback_run_bootstrap)
-
- ret_code, out, err = process.returncode, None, None
- if ret_code is None or ret_code <= 0:
- if error_list is not None and len(error_list) > 0:
- is_bootstrap_run_ok = False
- else:
- if out is not None:
- out_str = sys_utils.decode_our_err_result(out)
- if out_str != "":
- logging.info("{}".format(out_str))
-
- sys_utils.log_return_info(bootstrap_script_file, 0)
-
- is_bootstrap_run_ok = True
- else:
- if err is not None:
- err_str = sys_utils.decode_our_err_result(err)
- if err_str != "":
- logging.error("{}".format(err_str))
-
- sys_utils.log_return_info(bootstrap_script_file, ret_code)
-
- is_bootstrap_run_ok = False
- except Exception as e:
- logging.error("Bootstrap scripts error: {}".format(traceback.format_exc()))
-
- is_bootstrap_run_ok = False
-
- return is_bootstrap_run_ok
-
- def callback_run_bootstrap(self, job_pid):
- ServerConstants.save_bootstrap_process(self.run_id, job_pid)
-
- @debug
- def run(
- self, process_event, completed_event, edge_id_status_queue=None,
- edge_device_info_queue=None, run_metrics_queue=None,
- run_event_queue=None, run_artifacts_queue=None, run_logs_queue=None,
- message_center_queue=None, edge_device_info_global_queue=None
- ):
- print(f"Server runner process id {os.getpid()}, run id {self.run_id}")
-
- if platform.system() != "Windows":
- os.setsid()
-
- os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
- os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
-
- self.run_process_event = process_event
- self.run_process_completed_event = completed_event
- try:
- MLOpsUtils.set_ntp_offset(self.ntp_offset)
-
- self.rebuild_message_center(message_center_queue)
-
- self.run_impl(edge_id_status_queue, edge_device_info_queue, run_metrics_queue,
- run_event_queue, run_artifacts_queue, run_logs_queue, edge_device_info_global_queue)
- except RunnerError:
- logging.info("Runner stopped.")
- self.mlops_metrics.report_server_id_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
- except RunnerCompletedError:
- logging.info("Runner completed.")
- except Exception as e:
- logging.error("Runner exits with exceptions. {}".format(traceback.format_exc()))
- self.mlops_metrics.report_server_id_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
- finally:
- logging.info("Release resources.")
- self._process_run_metrics_queue(run_metrics_queue)
- self._process_run_logs_queue(run_logs_queue)
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, self.edge_id)
- if self.mlops_metrics is not None:
- self.mlops_metrics.stop_sys_perf()
- time.sleep(3)
- ServerConstants.cleanup_run_process(self.run_id)
- ServerConstants.cleanup_learning_process(self.run_id)
- ServerConstants.cleanup_bootstrap_process(self.run_id)
-
- def check_runner_stop_event(self):
- if self.run_process_event is not None and self.run_process_event.is_set():
- logging.info("Received stopping event.")
- raise RunnerError("Runner stopped")
-
- if self.run_process_completed_event is not None and self.run_process_completed_event.is_set():
- logging.info("Received completed event.")
- raise RunnerCompletedError("Runner completed")
-
- def deploy_model(self, serving_devices, request_json, run_id):
- run_config = request_json["run_config"]
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
- job_type = job_yaml.get("job_type", None)
- job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
- if job_type == Constants.JOB_TASK_TYPE_DEPLOY or job_type == Constants.JOB_TASK_TYPE_SERVE:
- computing = job_yaml.get("computing", {})
- num_gpus = computing.get("minimum_num_gpus", 1)
- serving_args = run_params.get("serving_args", {})
- model_id = serving_args.get("model_id", None)
- model_name = serving_args.get("model_name", None)
- model_version = serving_args.get("model_version", None)
- model_storage_url = serving_args.get("model_storage_url", None)
- endpoint_name = serving_args.get("endpoint_name", None)
- endpoint_id = serving_args.get("endpoint_id", None)
- random = serving_args.get("random", "")
- random_out = sys_utils.random2(random, "FEDML@9999GREAT")
- random_list = random_out.split("FEDML@")
- device_type = device_client_constants.ClientConstants.login_role_list[
- device_client_constants.ClientConstants.LOGIN_MODE_FEDML_CLOUD_INDEX]
- FedMLModelCards.get_instance().deploy_model(
- model_name, device_type, json.dumps(serving_devices),
- "", random_list[1], None,
- in_model_id=model_id, in_model_version=model_version,
- endpoint_name=endpoint_name, endpoint_id=endpoint_id, run_id=run_id)
-
- @debug
- def run_impl(
- self, edge_id_status_queue, edge_device_info_queue, run_metrics_queue,
- run_event_queue, run_artifacts_queue, run_logs_queue, edge_device_info_global_queue
- ):
- run_id = self.request_json["runId"]
- run_config = self.request_json["run_config"]
- data_config = run_config["data_config"]
- edge_ids = self.request_json["edgeids"]
-
- self.check_runner_stop_event()
-
- self.run_id = run_id
- self.args.run_id = self.run_id
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
-
- # report server running status
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_STARTING, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- logging.info("Detect all status of Edge ids: " + str(edge_ids))
-
- status_ok, active_edge_info_dict, inactivate_edges = self.detect_edges_status(
- edge_device_info_queue, edge_device_info_global_queue=edge_device_info_global_queue,
- callback_when_edges_ready=self.send_training_request_to_edges)
- logging.info(f"Status OK: {status_ok}, Active edge info dict: {active_edge_info_dict}, "
- f"inactivate edges: {inactivate_edges}")
- if not status_ok:
- logging.error(f"Status of edge device is not OK. Active edge info dict: {active_edge_info_dict}, "
- f"Inactivate edges: {inactivate_edges}")
- return
-
- if not self.should_continue_run_job(run_id):
- if FedMLServerRunner.debug_cloud_server:
- while True:
- time.sleep(30)
- # Check if the run status is normal
- self.aggregate_run_status_metrics_logs(
- run_id, edge_ids, edge_id_status_queue, edge_device_info_queue,
- edge_device_info_global_queue,
- run_metrics_queue, run_logs_queue)
- return
-
- # Start the server job
- self._start_runner_process(run_id, self.request_json, is_server_job=True)
-
- # Check if the run status is normal
- self.aggregate_run_status_metrics_logs(
- run_id, edge_ids, edge_id_status_queue, edge_device_info_queue,
- edge_device_info_global_queue,
- run_metrics_queue, run_logs_queue)
-
- def aggregate_run_status_metrics_logs(
- self, run_id, edge_id_list, edge_id_status_queue, edge_device_info_queue,
- edge_device_info_global_queue, run_metrics_queue, run_logs_queue):
- total_sleep_seconds = 0
- sleep_seconds = 3
- allowed_status_check_sleep_seconds = 60 * 25
- server_id = self.edge_id
- normal_response_status_list = [
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE, ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_TRAINING, ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION, ClientConstants.MSG_MLOPS_CLIENT_STATUS_RUNNING
- ]
- edges_id_status_timeout_map = dict()
- number_of_failed_edges = 0
- number_of_finished_edges = 0
- number_of_killed_edges = 0
- running_edges_list = list()
- inactivate_edge_list = list()
- current_edge_id_status_map = dict()
-
- while True:
- self.check_runner_stop_event()
-
- # Process run metrics
- self._process_run_metrics_queue(run_metrics_queue)
-
- # Process run logs
- self._process_run_logs_queue(run_logs_queue)
-
- # Fetch edge id and status from the edge id status queue
- while True:
- try:
- queue_item = edge_id_status_queue.get(block=False, timeout=3)
- if queue_item is not None:
- current_edge_id_status_map.update(queue_item)
- except queue.Empty as e: # If queue is empty, then break loop
- break
-
- # Calc the total completed device number
- server_id = current_edge_id_status_map.get("server", 0)
- running_edges_list.clear()
- number_of_failed_edges = 0
- number_of_finished_edges = 0
- number_of_killed_edges = 0
- for edge_id_item, status_item in current_edge_id_status_map.items():
- if edge_id_item == "server":
- continue
-
- if status_item is None or status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED or \
- status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION:
- number_of_failed_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
- number_of_finished_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED:
- number_of_killed_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE or \
- status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE:
- continue
-
- running_edges_list.append(edge_id_item)
-
- # Process the no response edges and accumulate the counter.
- for edge_id_item in edge_id_list:
- status_dict = edges_id_status_timeout_map.get(str(edge_id_item))
- status_item = current_edge_id_status_map.get(str(edge_id_item))
- if status_item is None:
- continue
- if status_dict is None:
- status_dict = {"status": status_item, "count": 0}
- else:
- if status_item in normal_response_status_list:
- status_dict["count"] = 0
- else:
- status_dict["count"] += 1
- edges_id_status_timeout_map[str(edge_id_item)] = status_dict
-
- # If the completed device number is equal total device number, then break
- if len(running_edges_list) <= 0 and len(current_edge_id_status_map.keys()) == len(edge_id_list) + 1:
- break
-
- # Calc the timeout value to wait to device killed.
- self.check_runner_stop_event()
- time.sleep(sleep_seconds)
- total_sleep_seconds += sleep_seconds
- no_response_edge_ids = list()
- for no_res_edge, no_res_status in edges_id_status_timeout_map.items():
- if no_res_status.get("count") * sleep_seconds > allowed_status_check_sleep_seconds:
- no_response_edge_ids.append(no_res_edge)
-
- # If timeout, then report killed device status
- if len(no_response_edge_ids) > 0:
- for edge_id_item in no_response_edge_ids:
- self.mlops_metrics.report_client_id_status(
- edge_id_item, ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED,
- server_id=self.edge_id, run_id=self.run_id)
-
- # Check if we can get the response device info from edge devices
- # and set the inactive edges to killed status.
- self.check_runner_stop_event()
- given_edge_ids = list(set(edge_id_list) - set(inactivate_edge_list))
- status_ok, active_edge_info_dict, inactivate_edges = self.detect_edges_status(
- edge_device_info_queue, edge_device_info_global_queue=edge_device_info_global_queue,
- need_to_trigger_exception=False, status_timeout=60,
- given_edge_ids=given_edge_ids, callback_when_detecting=self.callback_when_detecting_on_aggregation,
- args_for_callback_when_detecting=(run_metrics_queue, run_logs_queue)
- )
- if not status_ok:
- inactivate_edge_list.extend(inactivate_edges)
- for edge_id_item in inactivate_edges:
- self.mlops_metrics.report_client_id_status(
- edge_id_item, ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE,
- server_id=self.edge_id, run_id=self.run_id)
-
- # Calc the final run status based on the completed device numbers and fault tolerance parameters.
- enable_fault_tolerance, fault_tolerance_rate = self.parse_fault_tolerance_params(run_id)
- running_edges_list = list(set(running_edges_list))
- status_to_report = self.calculate_server_status(
- run_id, len(edge_id_list), number_of_failed_edges, number_of_finished_edges,
- number_of_killed_edges, running_edges_list, enable_fault_tolerance=enable_fault_tolerance,
- fault_tolerance_rate=fault_tolerance_rate)
- if status_to_report is not None:
- logging.info(
- f"Run completed when aggregating status, metrics and logs, will report status {status_to_report}")
- self.mlops_metrics.report_server_id_status(
- self.run_id, status_to_report, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- def callback_when_detecting_on_aggregation(self, detecting_args):
- # Process run metrics
- self._process_run_metrics_queue(detecting_args[0])
-
- # Process run logs
- self._process_run_logs_queue(detecting_args[1])
-
- def _process_run_metrics_queue(self, run_metrics_queue):
- # Fetch metrics from the run metrics queue
- while True:
- try:
- metrics_item = run_metrics_queue.get(block=False, timeout=3)
- MetricsManager.get_instance().save_metrics(metrics_item)
- metric_json = json.loads(metrics_item)
- if metric_json.get("is_endpoint", False):
- metric_json().pop("is_endpoint")
- self.mlops_metrics.report_endpoint_metric({}, payload=json.dumps(metric_json))
- else:
- self.mlops_metrics.report_server_training_metric({}, payload=metrics_item)
- except queue.Empty as e: # If queue is empty, then break loop
- break
-
- def _process_run_logs_queue(self, run_logs_queue):
- # Fetch logs from the run logs queue
- while True:
- try:
- logs_item = run_logs_queue.get(block=False, timeout=3)
- LogsManager.save_logs(logs_item)
- except queue.Empty as e: # If queue is empty, then break loop
- break
-
- def run_server_job_impl(self, process_event, completed_event, edge_id_status_queue=None,
- edge_device_info_queue=None, run_metrics_queue=None,
- run_event_queue=None, run_artifacts_queue=None, run_logs_queue=None,
- message_center_queue=None, edge_device_info_global_queue=None):
- print(f"Server runner process id {os.getpid()}, run id {self.run_id}")
-
- if platform.system() != "Windows":
- os.setsid()
-
- os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
- os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
-
- self.run_process_event = process_event
- self.run_process_completed_event = completed_event
-
- MLOpsUtils.set_ntp_offset(self.ntp_offset)
-
- self.rebuild_message_center(message_center_queue)
-
- run_id = self.request_json["runId"]
- run_config = self.request_json["run_config"]
- data_config = run_config["data_config"]
- edge_ids = self.request_json["edgeids"]
-
- self.check_runner_stop_event()
-
- # get training params
- private_local_data_dir = data_config.get("privateLocalData", "")
- is_using_local_data = 0
- # if private_local_data_dir is not None and len(str(private_local_data_dir).strip(' ')) > 0:
- # is_using_local_data = 1
-
- # start a run according to the hyper-parameters
- # fedml_local_data_dir = self.cur_dir + "/fedml_data/run_" + run_id_str + "_edge_" + str(edge_id)
- fedml_local_data_dir = os.path.join(self.cur_dir, "fedml_data")
- fedml_local_config_dir = os.path.join(self.cur_dir, "fedml_config")
- if is_using_local_data:
- fedml_local_data_dir = private_local_data_dir
- self.fedml_data_dir = self.fedml_data_local_package_dir
-
- self.check_runner_stop_event()
-
- logging.info("download packages and run the bootstrap script...")
-
- # update local config with real time parameters from server and dynamically replace variables value
- unzip_package_path, fedml_config_object = self.update_local_fedml_config(run_id, run_config)
- if unzip_package_path is None or fedml_config_object is None:
- logging.info("failed to update local fedml config.")
- self.check_runner_stop_event()
- self.cleanup_run_when_starting_failed()
- self.send_training_stop_request_to_edges_when_exception(edge_ids, payload=self.start_request_json,
- run_id=run_id)
- return
-
- logging.info("cleanup the previous aggregation process and check downloaded packages...")
-
- entry_file_config = fedml_config_object["entry_config"]
- dynamic_args_config = fedml_config_object["dynamic_args"]
- entry_file = str(entry_file_config["entry_file"]).replace('\\', os.sep).replace('/', os.sep)
- entry_file = os.path.basename(entry_file)
- conf_file = entry_file_config["conf_file"]
- conf_file = str(conf_file).replace('\\', os.sep).replace('/', os.sep)
- ServerConstants.cleanup_learning_process(run_id)
- self.check_runner_stop_event()
- if not os.path.exists(unzip_package_path):
- logging.info("failed to unzip file.")
- self.check_runner_stop_event()
- self.cleanup_run_when_starting_failed()
- self.send_training_stop_request_to_edges_when_exception(edge_ids, payload=self.start_request_json,
- run_id=run_id)
- return
- os.chdir(os.path.join(unzip_package_path, "fedml"))
-
- self.check_runner_stop_event()
-
- logging.info("starting the server user process...")
-
- entry_file_full_path = os.path.join(unzip_package_path, "fedml", entry_file)
- conf_file_full_path = os.path.join(unzip_package_path, "fedml", conf_file)
- logging.info(" ")
- logging.info(" ")
- logging.info("====Your Run Logs Begin===")
- process, is_launch_task, error_list = self.execute_job_task(entry_file_full_path, conf_file_full_path, run_id)
- logging.info("====Your Run Logs End===")
- logging.info(" ")
- logging.info(" ")
-
- ret_code, out, err = process.returncode, None, None
- is_run_ok = sys_utils.is_runner_finished_normally(process.pid)
- if is_launch_task:
- is_run_ok = True
- if error_list is not None and len(error_list) > 0:
- is_run_ok = False
- if ret_code is None or ret_code <= 0:
- self.check_runner_stop_event()
-
- if is_run_ok:
- if out is not None:
- out_str = sys_utils.decode_our_err_result(out)
- if out_str != "":
- logging.info("{}".format(out_str))
-
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- if is_launch_task:
- sys_utils.log_return_info(f"job {run_id}", 0)
- else:
- sys_utils.log_return_info(entry_file, 0)
- else:
- is_run_ok = False
-
- if not is_run_ok:
- # If the run status is killed or finished, then return with the normal state.
- current_job = FedMLServerDataInterface.get_instance().get_job_by_id(run_id)
- if current_job is not None and (current_job.status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED or
- current_job.status == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED):
- return
-
- self.check_runner_stop_event()
-
- logging.error("failed to run the aggregation process...")
-
- if err is not None:
- err_str = sys_utils.decode_our_err_result(err)
- if err_str != "":
- logging.error("{}".format(err_str))
-
- if is_launch_task:
- sys_utils.log_return_info(f"job {run_id}", ret_code)
- else:
- sys_utils.log_return_info(entry_file, ret_code)
-
- self.send_training_stop_request_to_edges_when_exception(edge_ids, run_id=run_id)
-
- def init_job_task(self, request_json):
- run_id = request_json["runId"]
- run_config = request_json["run_config"]
- edge_ids = request_json["edgeids"]
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", None)
- server_id = request_json["server_id"]
- if self.run_as_cloud_agent:
- server_id = self.edge_id
-
- self.setup_listeners_for_edge_status(run_id, edge_ids, server_id)
- self.setup_listener_for_run_metrics(run_id)
- self.setup_listener_for_run_logs(run_id)
-
- def should_continue_run_job(self, run_id):
- run_config = self.request_json["run_config"]
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
- job_yaml_default_none = run_params.get("job_yaml", None)
- framework_type = job_yaml.get("framework_type", None)
- job_type = job_yaml.get("job_type", None)
- job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
- if job_yaml_default_none is not None:
- if job_type == Constants.JOB_TASK_TYPE_FEDERATE:
- return True
-
- if framework_type is None or framework_type != Constants.JOB_FRAMEWORK_TYPE_FEDML:
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_RUNNING, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
- return False
-
- return True
-
- def execute_job_task(self, entry_file_full_path, conf_file_full_path, run_id):
- run_config = self.request_json["run_config"]
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
- job_yaml_default_none = run_params.get("job_yaml", None)
- job_api_key = job_yaml.get("run_api_key", None)
- job_api_key = job_yaml.get("fedml_run_dynamic_params", None) if job_api_key is None else job_api_key
- assigned_gpu_ids = run_params.get("gpu_ids", None)
- framework_type = job_yaml.get("framework_type", None)
- job_type = job_yaml.get("job_type", None)
- job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
- conf_file_object = load_yaml_config(conf_file_full_path)
- entry_args_dict = conf_file_object.get("fedml_entry_args", {})
- entry_args = entry_args_dict.get("arg_items", None)
-
- executable_interpreter = ClientConstants.CLIENT_SHELL_PS \
- if platform.system() == ClientConstants.PLATFORM_WINDOWS else ClientConstants.CLIENT_SHELL_BASH
-
- if job_yaml_default_none is None:
- # Generate the job executing commands for previous federated learning (Compatibility)
- python_program = get_python_program()
- logging.info("Run the server: {} {} --cf {} --rank 0 --role server".format(
- python_program, entry_file_full_path, conf_file_full_path))
- entry_command = f"{python_program} {entry_file_full_path} --cf " \
- f"{conf_file_full_path} --rank 0 --role server"
- shell_cmd_list = [entry_command]
-
- # Run the job executing commands for previous federated learning (Compatibility)
- process, error_list = ClientConstants.execute_commands_with_live_logs(
- shell_cmd_list, callback=self.callback_start_fl_job, should_write_log_file=False)
- is_launch_task = False
- else:
- self.check_runner_stop_event()
-
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_RUNNING, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- # Generate the job executing commands
- job_executing_commands = JobRunnerUtils.generate_job_execute_commands(
- run_id=self.run_id, edge_id=self.edge_id, version=self.version, package_type=self.package_type,
- executable_interpreter=executable_interpreter, entry_file_full_path=entry_file_full_path,
- conf_file_object=conf_file_object, entry_args=entry_args, assigned_gpu_ids=assigned_gpu_ids,
- job_api_key=job_api_key, client_rank=0)
-
- # Run the job executing commands
- logging.info(f"Run the server job with job id {self.run_id}, device id {self.edge_id}.")
- process, error_list = ServerConstants.execute_commands_with_live_logs(
- job_executing_commands, callback=self.start_job_perf, error_processor=self.job_error_processor)
- is_launch_task = True
-
- return process, is_launch_task, error_list
-
- def callback_start_fl_job(self, job_pid):
- ServerConstants.save_learning_process(self.run_id, job_pid)
- self.mlops_metrics.report_sys_perf(
- self.args, self.agent_config["mqtt_config"], job_process_id=job_pid)
-
- def start_job_perf(self, job_pid):
- ServerConstants.save_learning_process(self.run_id, job_pid)
- self.mlops_metrics.report_job_perf(self.args, self.agent_config["mqtt_config"], job_pid)
-
- def job_error_processor(self, error_list):
- self.check_runner_stop_event()
-
- error_str = "\n".join(error_list)
- raise Exception(f"Error occurs when running the job... {error_str}")
-
- def process_job_status(self, run_id, edge_id, status):
- number_of_failed_edges = 0
- number_of_finished_edges = 0
- number_of_killed_edges = 0
- edge_id_status_dict = self.client_agent_active_list.get(f"{run_id}", {})
- server_id = edge_id_status_dict.get("server", 0)
- enable_fault_tolerance, fault_tolerance_rate = self.parse_fault_tolerance_params(run_id)
- running_edges_list = list()
- for edge_id_item, status_item in edge_id_status_dict.items():
- if edge_id_item == "server":
- continue
-
- if status_item is None or status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED or \
- status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION:
- number_of_failed_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
- number_of_finished_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED:
- number_of_killed_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE or \
- status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE:
- continue
-
- running_edges_list.append(edge_id_item)
-
- # Report client status
- edge_status = ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED if status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION else status
- self.mlops_metrics.report_client_training_status(edge_id, edge_status, run_id=run_id)
- self.mlops_metrics.report_client_device_status_to_web_ui(edge_id, edge_status, run_id=run_id)
-
- # Report server status based on the fault tolerance model and parameters
- edge_nums = len(edge_id_status_dict.keys()) - 1
- status_to_report = self.calculate_server_status(
- run_id, edge_nums, number_of_failed_edges, number_of_finished_edges, number_of_killed_edges,
- running_edges_list, enable_fault_tolerance=enable_fault_tolerance,
- fault_tolerance_rate=fault_tolerance_rate)
- if status_to_report is not None:
- logging.info(f"Run completed when processing edge status, will report status {status_to_report}")
- self.report_server_status(run_id, server_id, status_to_report)
-
- def calculate_server_status(
- self, run_id, total_edge_nums, number_of_failed_edges, number_of_finished_edges,
- number_of_killed_edges, running_edges_list, enable_fault_tolerance=False,
- fault_tolerance_rate=0.8
- ):
- # Report server status based on the fault tolerance model and parameters
- actual_failed_rate = number_of_failed_edges / total_edge_nums
- all_edges_run_completed = True if len(running_edges_list) <= 0 else False
- if all_edges_run_completed:
- status_to_report = None
- if enable_fault_tolerance:
- if actual_failed_rate >= fault_tolerance_rate:
- status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED
- self.send_training_stop_request_to_edges_when_exception(
- running_edges_list, run_id=run_id, status=status_to_report)
- return status_to_report
- else:
- if number_of_killed_edges == total_edge_nums:
- status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED
- else:
- status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED
- else:
- if number_of_failed_edges > 0:
- status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED
- elif number_of_finished_edges == total_edge_nums:
- status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED
- elif number_of_killed_edges == total_edge_nums:
- status_to_report = ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED
-
- return status_to_report
-
- def parse_fault_tolerance_params(self, run_id):
- run_json = self.running_request_json.get(str(run_id), None)
- if run_json is None:
- run_json = self.request_json
- run_config = run_json.get("run_config", {})
- run_params = run_config.get("parameters", {})
- common_args = run_params.get("common_args", {})
- enable_fault_tolerance = common_args.get("enable_fault_tolerance", False)
- fault_tolerance_rate = common_args.get("fault_tolerance_rate", 0)
- return enable_fault_tolerance, fault_tolerance_rate
-
- def report_server_status(self, run_id, server_id, status):
- self.mlops_metrics.report_server_id_status(run_id, status, edge_id=self.edge_id,
- server_id=server_id, server_agent_id=self.edge_id)
-
- def stop_run_when_starting_failed(self):
- edge_id_list = self.request_json["edgeids"]
- run_id = self.request_json.get("run_id", 0)
- logging.error("edge ids {}".format(str(edge_id_list)))
-
- payload = self.running_request_json.get(str(run_id))
- if payload is not None:
- self.send_training_stop_request_to_edges(edge_id_list, payload=json.dumps(payload), run_id=run_id)
-
- # logging.info("Stop run successfully when starting failed.")
-
- self.mlops_metrics.report_server_id_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- def cleanup_run_when_finished(self, should_send_server_id_status=True):
- # logging.info("Cleanup run successfully when finished.")
-
- self.mlops_metrics.report_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED, edge_id=self.edge_id
- )
-
- if should_send_server_id_status:
- self.mlops_metrics.report_server_id_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- pass
-
- time.sleep(1)
-
- ServerConstants.cleanup_learning_process(self.run_id)
- ServerConstants.cleanup_bootstrap_process(self.run_id)
-
- try:
- local_package_path = ServerConstants.get_package_download_dir()
- for package_file in listdir(local_package_path):
- if os.path.basename(package_file).startswith("run_" + str(self.run_id)):
- shutil.rmtree(os.path.join(local_package_path, package_file), ignore_errors=True)
- except Exception as e:
- pass
-
- def cleanup_run_when_starting_failed(
- self, status=ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, should_send_server_id_status=True):
- # logging.info("Cleanup run successfully when starting failed.")
-
- self.mlops_metrics.report_server_training_status(
- self.run_id, status, edge_id=self.edge_id)
-
- if should_send_server_id_status:
- self.mlops_metrics.report_server_id_status(
- self.run_id, status, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- pass
-
- time.sleep(1)
-
- ServerConstants.cleanup_learning_process(self.run_id)
- ServerConstants.cleanup_bootstrap_process(self.run_id)
-
- try:
- local_package_path = ServerConstants.get_package_download_dir()
- for package_file in listdir(local_package_path):
- if os.path.basename(package_file).startswith("run_" + str(self.run_id)):
- shutil.rmtree(os.path.join(local_package_path, package_file), ignore_errors=True)
- except Exception as e:
- pass
-
- def should_process_async_cluster(self):
- run_config = self.request_json.get("run_config", {})
- run_params = run_config.get("parameters", {})
- common_args = run_params.get("common_args", {})
- self.enable_async_cluster = common_args.get("enable_async_cluster", False)
- self.async_check_timeout = common_args.get("async_check_timeout", 0)
- if self.enable_async_cluster:
- return True, self.async_check_timeout
-
- return False, self.async_check_timeout
-
- @debug
- def detect_edges_status(
- self, edge_device_info_queue, edge_device_info_global_queue=None, callback_when_edges_ready=None, status_timeout=None,
- need_to_trigger_exception=True, status_check_context=None, given_edge_ids=None,
- callback_when_detecting=None, args_for_callback_when_detecting=None
- ):
- run_id = self.request_json["runId"]
- run_id_str = str(run_id)
- edge_id_list = self.request_json["edgeids"]
- if given_edge_ids is not None:
- edge_id_list = given_edge_ids
-
- # Init realtime status of all edges
- run_edges_realtime_status = dict()
- run_edges_realtime_status[run_id_str] = dict()
-
- edge_info_global_dict = dict()
- if edge_device_info_global_queue is not None:
- for edge_info_global in edge_device_info_global_queue:
- edge_info_id = edge_info_global.get("edge_id")
- edge_info_global_dict[edge_info_id] = edge_info_global
-
- # Send status message to all edges
- allowed_cache_edge_status_time = 60
- for edge_id in edge_id_list:
- # Check if the edge status was filled allowed_cache_edge_status_time seconds ago,
- # if so no more checking message would be sent.
- edge_info = edge_info_global_dict.get(edge_id, None)
- if edge_info is not None:
- timestamp = edge_info.get("timestamp", None)
- time_interval = time.time() - timestamp
- if time_interval <= allowed_cache_edge_status_time:
- continue
-
- self.send_status_check_msg(run_id, edge_id, self.edge_id, context=status_check_context)
- time.sleep(3)
-
- total_sleep_seconds = 0
- status_check_sleep_seconds = 10
- allowed_status_check_sleep_seconds = 60 * 2 if status_timeout is None else status_timeout
- allowed_status_check_sleep_seconds_for_async = 30
- inactivate_edges = list()
- active_edge_info_dict = dict()
- log_active_edge_info_flag = True
- while True:
- if callback_when_detecting is not None:
- callback_when_detecting(args_for_callback_when_detecting)
-
- # Fetch edge info from the edge status queue, which will be added to realtime status map
- while True:
- self.check_runner_stop_event()
-
- try:
- edge_info = edge_device_info_queue.get(block=False, timeout=1)
- if edge_info is not None:
- edge_id = edge_info.get("edge_id", None)
- if edge_id is not None:
- run_edges_realtime_status[run_id_str][edge_id] = edge_info
- except queue.Empty as e: # If queue is empty, then break loop
- break
-
- self.check_runner_stop_event()
-
- # Check all edges which don't send response status successfully
- # and retry to send the status checking message.
- active_edges_count = 0
- inactivate_edges.clear()
- active_edge_info_dict.clear()
- for edge_id in edge_id_list:
- edge_info_dict = run_edges_realtime_status.get(run_id_str, {})
- edge_info = edge_info_dict.get(edge_id, None)
- edge_info = edge_info_dict.get(str(edge_id), None) if edge_info is None else edge_info
- if edge_info is not None:
- active_edges_count += 1
- active_edge_info_dict[str(edge_id)] = edge_info
- else:
- # Check if the edge status was filled allowed_cache_edge_status_time seconds ago,
- # if so no more checking message would be sent.
- edge_info = edge_info_global_dict.get(edge_id, None)
- if edge_info is not None:
- timestamp = edge_info.get("timestamp", None)
- time_interval = time.time() - timestamp
- if time_interval <= allowed_cache_edge_status_time:
- active_edges_count += 1
- active_edge_info_dict[str(edge_id)] = edge_info
- continue
-
- inactivate_edges.append(edge_id)
- self.send_status_check_msg(run_id, edge_id, self.edge_id, context=status_check_context)
-
- # If all edges are ready then send the starting job message to them
- if active_edges_count == len(edge_id_list):
- if log_active_edge_info_flag:
- logging.debug(f"All edges are ready. Active edge id list is as follows. {active_edge_info_dict}")
- log_active_edge_info_flag = False
- if callback_when_edges_ready is not None:
- logging.info("All edges are ready. Start to process the callback function.")
- callback_when_edges_ready(active_edge_info_dict=active_edge_info_dict)
- else:
- logging.debug("All edges are ready. No callback function to process.")
- break
- else:
- logging.info(f"All edges are not ready. Active edge id list: {active_edge_info_dict}, "
- f"Inactive edge id list: {inactivate_edges}")
- log_active_edge_info_flag = True
-
- # Check if runner needs to stop and sleep specific time
- self.check_runner_stop_event()
- time.sleep(status_check_sleep_seconds)
- total_sleep_seconds += status_check_sleep_seconds
-
- # Check if the status response message has timed out to receive
- if total_sleep_seconds >= allowed_status_check_sleep_seconds:
- # If so, send failed message to MLOps and send exception message to all edges.
- logging.error(f"There are inactive edge devices. "
- f"Inactivate edge id list is as follows. {inactivate_edges}")
- if need_to_trigger_exception:
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.server_agent_id)
- self.send_training_stop_request_to_edges_when_exception(edge_id_list,
- payload=json.dumps(self.request_json),
- run_id=run_id)
- return False, active_edge_info_dict, inactivate_edges
-
- # If we enable the mode for async cluster, then sleep some time and send messages to all clients.
- if callback_when_edges_ready is not None:
- should_async, async_timeout = self.should_process_async_cluster()
- if should_async and total_sleep_seconds >= allowed_status_check_sleep_seconds_for_async:
- if async_timeout > allowed_status_check_sleep_seconds_for_async:
- time.sleep(async_timeout - allowed_status_check_sleep_seconds_for_async)
- self.send_training_request_to_edges()
- return True, active_edge_info_dict, inactivate_edges
-
- return True, active_edge_info_dict, inactivate_edges
-
- def send_status_check_msg(self, run_id, edge_id, server_id, context=None):
- topic_get_model_device_id = "server/client/request_device_info/" + str(edge_id)
- payload = {"server_id": server_id, "run_id": run_id}
- if context is not None:
- payload["context"] = context
- self.message_center.send_message(topic_get_model_device_id, json.dumps(payload))
-
- @debug
- def send_training_request_to_edges(self, active_edge_info_dict=None):
- run_id = self.request_json["runId"]
- edge_id_list = self.request_json["edgeids"]
- run_config = self.request_json.get("run_config", {})
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
- job_yaml_default_none = run_params.get("job_yaml", None)
- computing = job_yaml.get("computing", {})
- request_num_gpus = computing.get("minimum_num_gpus", None)
- job_gpu_id_list = self.request_json.get("job_gpu_id_list", None)
-
- logging.info("Send training request to Edge ids: " + str(edge_id_list))
-
- should_match_gpu = False
- if job_yaml_default_none is not None and request_num_gpus is not None and \
- int(request_num_gpus) > 0 and active_edge_info_dict is not None:
- should_match_gpu = True
- SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(active_edge_info_dict, show_gpu_list=True)
-
- # Match and assign gpus to each device
- assigned_gpu_num_dict, assigned_gpu_ids_dict = SchedulerMatcher.match_and_assign_gpu_resources_to_devices(
- request_num_gpus, edge_id_list, active_edge_info_dict, job_gpu_id_list=job_gpu_id_list)
- if assigned_gpu_num_dict is None or assigned_gpu_ids_dict is None:
- # If no resources available, send failed message to MLOps and send exception message to all edges.
- gpu_count, gpu_available_count = SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(
- active_edge_info_dict, should_print=True)
- err_info = f"No resources available." \
- f"Total available GPU count {gpu_available_count} is less than " \
- f"request GPU count {request_num_gpus}"
- logging.error(err_info)
-
- # Bug fix: This mqtt message needs to be sent so platform can clean up the failed run and change the
- # status from running to failed.
- self.mlops_metrics.report_server_training_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id
- )
-
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.server_agent_id)
- self.send_training_stop_request_to_edges_when_exception(edge_id_list,
- payload=json.dumps(self.request_json),
- run_id=run_id)
-
- serving_args = job_yaml.get("serving_args", {})
- endpoint_id = serving_args.get("endpoint_id", None)
- if endpoint_id is not None:
- fedml.mlops.log_endpoint_status(
- endpoint_id, device_client_constants.ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
- fedml.mlops.log_run_log_lines(
- endpoint_id, 0, [err_info],
- log_source=device_client_constants.ClientConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT
- )
- return
-
- # Generate master node addr and port
- master_node_addr, master_node_port = SchedulerMatcher.get_master_node_info(edge_id_list,
- active_edge_info_dict)
-
- # Generate new edge id list after matched
- edge_id_list = SchedulerMatcher.generate_new_edge_list_for_gpu_matching(assigned_gpu_num_dict)
- if len(edge_id_list) <= 0:
- gpu_count, gpu_available_count = SchedulerMatcher.parse_and_print_gpu_info_for_all_edges(
- active_edge_info_dict, should_print=True)
- logging.error(f"Request parameter for GPU num is invalid."
- f"Total available GPU count {gpu_available_count}."
- f"Request GPU num {request_num_gpus}")
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.server_agent_id)
- self.send_training_stop_request_to_edges_when_exception(edge_id_list,
- payload=json.dumps(self.request_json),
- run_id=run_id)
- return
-
- if should_match_gpu:
- # Report gpu num and related infos to MLOps.
- serving_args = job_yaml.get("serving_args", {})
- endpoint_id = serving_args.get("endpoint_id", None)
- if endpoint_id is not None:
- endpoint_info = list()
- for edge_id_item, gpu_num in assigned_gpu_num_dict.items():
- edge_info = active_edge_info_dict.get(str(edge_id_item), {})
- endpoint_info.append({
- "machine_id": edge_id_item, "endpoint_gpu_count": gpu_num,
- "master_deploy_id": edge_info.get("master_device_id", 0),
- "slave_deploy_id": edge_info.get("slave_device_id", 0)})
- topic_name = f"compute/mlops/endpoint"
- endpoint_info_json = {"endpoint_id": endpoint_id, "endpoint_info": endpoint_info}
- print(f"endpoint_info_json {endpoint_info_json}")
- self.message_center.send_message(topic_name, json.dumps(endpoint_info_json))
-
- client_rank = 1
- for edge_id in edge_id_list:
- topic_start_train = "flserver_agent/" + str(edge_id) + "/start_train"
- logging.info("start_train: send topic " + topic_start_train + " to client...")
- request_json = self.request_json
- request_json["client_rank"] = client_rank
- client_rank += 1
-
- if active_edge_info_dict is not None:
- edge_info = active_edge_info_dict.get(str(edge_id), {})
- model_master_device_id = edge_info.get("master_device_id", None)
- model_slave_device_id = edge_info.get("slave_device_id", None)
- model_slave_device_id_list = edge_info.get("slave_device_id_list", None)
-
- if should_match_gpu:
- request_json["scheduler_match_info"] = SchedulerMatcher.generate_match_info_for_scheduler(
- edge_id, edge_id_list, master_node_addr, master_node_port,
- assigned_gpu_num_dict, assigned_gpu_ids_dict,
- model_master_device_id=model_master_device_id,
- model_slave_device_id=model_slave_device_id,
- model_slave_device_id_list=model_slave_device_id_list
- )
-
- self.message_center.send_message(topic_start_train, json.dumps(request_json))
-
- def setup_listeners_for_edge_status(self, run_id, edge_ids, server_id):
- self.client_agent_active_list[f"{run_id}"] = dict()
- self.client_agent_active_list[f"{run_id}"][f"server"] = server_id
- for edge_id in edge_ids:
- self.client_agent_active_list[f"{run_id}"][f"{edge_id}"] = ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- edge_status_topic = "fl_client/flclient_agent_" + str(edge_id) + "/status"
- self.add_message_listener(edge_status_topic, self.callback_edge_status)
- self.subscribe_msg(edge_status_topic)
-
- def remove_listeners_for_edge_status(self, edge_ids=None):
- if edge_ids is None:
- edge_ids = self.request_json["edgeids"]
-
- for edge_id in edge_ids:
- edge_status_topic = "fl_client/flclient_agent_" + str(edge_id) + "/status"
- self.unsubscribe_msg(edge_status_topic)
-
- def setup_listener_for_run_metrics(self, run_id):
- metric_topic = f"fedml_slave/fedml_master/metrics/{run_id}"
- self.add_message_listener(metric_topic, self.callback_run_metrics)
- self.subscribe_msg(metric_topic)
-
- def remove_listener_for_run_metrics(self, run_id):
- metric_topic = f"fedml_slave/fedml_master/metrics/{run_id}"
- self.unsubscribe_msg(metric_topic)
-
- def setup_listener_for_run_logs(self, run_id):
- logs_topic = f"fedml_slave/fedml_master/logs/{run_id}"
- self.add_message_listener(logs_topic, self.callback_run_logs)
- self.subscribe_msg(logs_topic)
-
- def remove_listener_for_run_logs(self, run_id):
- logs_topic = f"fedml_slave/fedml_master/logs/{run_id}"
- self.unsubscribe_msg(logs_topic)
-
- def callback_run_logs(self, topic, payload):
- run_id = str(topic).split('/')[-1]
- run_id_str = str(run_id)
- if self.run_logs_queue_map.get(run_id_str) is None:
- self.run_logs_queue_map[run_id_str] = Queue()
- self.run_logs_queue_map[run_id_str].put(payload)
-
- def callback_run_metrics(self, topic, payload):
- print(f"callback_run_metrics topic {topic}, payload {payload}")
- run_id = str(topic).split('/')[-1]
- run_id_str = str(run_id)
- if self.run_metrics_queue_map.get(run_id_str) is None:
- self.run_metrics_queue_map[run_id_str] = Queue()
- self.run_metrics_queue_map[run_id_str].put(payload)
-
- def callback_edge_status(self, topic, payload):
- payload_json = json.loads(payload)
- run_id = payload_json.get("run_id", None)
- edge_id = payload_json.get("edge_id", None)
- status = payload_json.get("status", None)
- if run_id is not None and edge_id is not None:
- active_item_dict = self.client_agent_active_list.get(f"{run_id}", None)
- if active_item_dict is None:
- return
- self.client_agent_active_list[f"{run_id}"][f"{edge_id}"] = status
-
- if self.run_edge_id_status_queue_map.get(f"{run_id}") is None:
- self.run_edge_id_status_queue_map[f"{run_id}"] = Queue()
- self.run_edge_id_status_queue_map[f"{run_id}"].put(self.client_agent_active_list[f"{run_id}"])
-
- self.process_job_status(run_id, edge_id, status)
-
- def ota_upgrade(self, payload, request_json):
- run_id = request_json["runId"]
- force_ota = False
- ota_version = None
-
- try:
- run_config = request_json.get("run_config", None)
- parameters = run_config.get("parameters", None)
- common_args = parameters.get("common_args", None)
- force_ota = common_args.get("force_ota", False)
- ota_version = common_args.get("ota_version", None)
- except Exception as e:
- pass
-
- if force_ota and ota_version is not None:
- should_upgrade = True if ota_version != fedml.__version__ else False
- upgrade_version = ota_version
- else:
- try:
- fedml_is_latest_version, local_ver, remote_ver = sys_utils.check_fedml_is_latest_version(self.version)
- except Exception as e:
- return
-
- should_upgrade = False if fedml_is_latest_version else True
- upgrade_version = remote_ver
-
- if should_upgrade:
- job_obj = FedMLServerDataInterface.get_instance().get_job_by_id(run_id)
- if job_obj is None:
- FedMLServerDataInterface.get_instance(). \
- save_started_job(run_id, self.edge_id, time.time(),
- ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING,
- ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING,
- payload)
-
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
- logging.info(f"Upgrade to version {upgrade_version} ...")
-
- sys_utils.do_upgrade(self.version, upgrade_version)
-
- raise Exception("Restarting after upgraded...")
-
- def callback_start_train(self, topic=None, payload=None):
- print("callback_start_train: ")
- try:
- MLOpsConfigs.fetch_all_configs()
- except Exception as e:
- pass
-
- # [NOTES] Example Request JSON: https://fedml-inc.larksuite.com/wiki/ScnIwUif9iupbjkYS0LuBrd6sod#WjbEdhYrvogmlGxKTOGu98C6sSb
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
-
- # Process the log
- run_id = request_json["runId"]
- run_id_str = str(run_id)
- if self.run_as_edge_server_and_agent or self.enable_simulation_cloud_agent:
- # Start log processor for current run
- self.args.run_id = run_id
- self.args.edge_id = self.edge_id
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
- MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(
- run_id, self.edge_id, SchedulerConstants.get_log_source(request_json))
- logging.info("start the log processor.")
- elif self.run_as_cloud_agent:
- # Start log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(
- run_id, request_json.get("server_id", "0"), SchedulerConstants.get_log_source(request_json)
- )
- elif self.run_as_cloud_server:
- self.server_agent_id = request_json.get("cloud_agent_id", self.edge_id)
- run_id = request_json["runId"]
- run_id_str = str(run_id)
-
- # Start log processor for current run
- self.args.run_id = run_id
- MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(
- run_id, self.edge_id, SchedulerConstants.get_log_source(request_json))
-
- logging.info("callback_start_train payload: {}".format(payload))
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- # if not self.run_as_cloud_agent and not self.run_as_cloud_server:
- # self.ota_upgrade(payload, request_json)
-
- # report server running status
- if not self.run_as_cloud_server:
- self.mlops_metrics.report_server_id_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_STARTING, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- self.start_request_json = payload
- self.run_id = run_id
- ServerConstants.save_runner_infos(self.args.device_id + "." + self.args.os_name, self.edge_id, run_id=run_id)
-
- # Start server with multiprocessing mode
- self.request_json = request_json
- self.running_request_json[run_id_str] = request_json
- edge_id_list = request_json.get("edgeids", list())
- self.run_edge_ids[run_id_str] = edge_id_list
-
- logging.info("subscribe the client exception message.")
-
- if self.run_as_edge_server_and_agent or self.enable_simulation_cloud_agent:
- self.init_job_task(request_json)
-
- self.args.run_id = run_id
-
- self._start_runner_process(run_id, request_json)
-
- ServerConstants.save_run_process(run_id, self.run_process_map[run_id_str].pid)
- elif self.run_as_cloud_agent:
- self.init_job_task(request_json)
-
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=request_json, agent_config=self.agent_config
- )
- server_runner.run_as_cloud_agent = self.run_as_cloud_agent
- server_runner.start_request_json = json.dumps(request_json)
- self.run_process_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_event_map[run_id_str].clear()
- server_runner.run_process_event = self.run_process_event_map[run_id_str]
-
- if not self.use_local_process_as_cloud_server:
- self.run_process_map[run_id_str] = Process(target=server_runner.start_cloud_server_process_entry)
- self.run_process_map[run_id_str].start()
- else:
- message_bytes = json.dumps(self.request_json).encode("ascii")
- base64_bytes = base64.b64encode(message_bytes)
- runner_cmd_encoded = base64_bytes.decode("ascii")
- logging.info("runner_cmd_encoded: {}".format(runner_cmd_encoded))
-
- cloud_device_id = request_json.get("cloudServerDeviceId", "0")
-
- self.run_process_map[run_id_str] = Process(
- target=FedMLServerRunner.start_local_cloud_server,
- args=(run_id_str, self.args.user, self.version, cloud_device_id, runner_cmd_encoded))
- self.run_process_map[run_id_str].start()
- time.sleep(1)
-
- ServerConstants.save_run_process(run_id, self.run_process_map[run_id_str].pid)
- elif self.run_as_cloud_server:
- self.server_agent_id = request_json.get("cloud_agent_id", self.edge_id)
- self.start_request_json = json.dumps(request_json)
- run_id = request_json["runId"]
- run_id_str = str(run_id)
-
- self.init_job_task(request_json)
-
- self.args.run_id = run_id
-
- self._start_runner_process(run_id, request_json)
- # ServerConstants.save_run_process(run_id, self.run_process_map[run_id_str].pid)
-
- @staticmethod
- def start_local_cloud_server(run_id, user, version, cloud_device_id, runner_cmd_encoded):
- print(f"start cloud server, device id {cloud_device_id}, runner cmd {runner_cmd_encoded}")
- if not FedMLServerRunner.debug_cloud_server:
- pip_source_dir = os.path.dirname(__file__)
- login_cmd = os.path.join(pip_source_dir, "server_login.py")
- run_cmd = f"{get_python_program()} -W ignore {login_cmd} -t login -r cloud_server -u {str(user)} " \
- f"-v {version} -id {cloud_device_id} -rc {runner_cmd_encoded}"
- os.system(run_cmd)
-
- def _start_runner_process(self, run_id, request_json, is_server_job=False):
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=request_json, agent_config=self.agent_config
- )
- run_id_str = str(run_id)
- server_runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- server_runner.edge_id = self.edge_id
- server_runner.server_agent_id = self.server_agent_id
- server_runner.start_request_json = json.dumps(request_json)
- self.run_process_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_event_map[run_id_str].clear()
- server_runner.run_process_event = self.run_process_event_map[run_id_str]
- self.run_process_completed_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_completed_event_map[run_id_str].clear()
- server_runner.run_process_completed_event = self.run_process_completed_event_map[run_id_str]
- if self.run_edge_id_status_queue_map.get(run_id_str, None) is None:
- self.run_edge_id_status_queue_map[run_id_str] = Queue()
- if self.run_edge_device_info_queue_map.get(run_id_str, None) is None:
- self.run_edge_device_info_queue_map[run_id_str] = Queue()
- if self.run_metrics_queue_map.get(run_id_str, None) is None:
- self.run_metrics_queue_map[run_id_str] = Queue()
- if self.run_events_queue_map.get(run_id_str, None) is None:
- self.run_events_queue_map[run_id_str] = Queue()
- if self.run_artifacts_queue_map.get(run_id_str, None) is None:
- self.run_artifacts_queue_map[run_id_str] = Queue()
- if self.run_logs_queue_map.get(run_id_str, None) is None:
- self.run_logs_queue_map[run_id_str] = Queue()
- # if self.run_edge_device_info_global_queue is None:
- # self.run_edge_device_info_global_queue = Array('i', list())
- server_runner.edge_id_status_queue = self.run_edge_id_status_queue_map[run_id_str]
- server_runner.edge_device_info_queue = self.run_edge_device_info_queue_map[run_id_str]
- self.run_process_map[run_id_str] = Process(
- target=server_runner.run if not is_server_job else server_runner.run_server_job_impl, args=(
- self.run_process_event_map[run_id_str], self.run_process_completed_event_map[run_id_str],
- self.run_edge_id_status_queue_map[run_id_str], self.run_edge_device_info_queue_map[run_id_str],
- self.run_metrics_queue_map[run_id_str], self.run_events_queue_map[run_id_str],
- self.run_artifacts_queue_map[run_id_str], self.run_logs_queue_map[run_id_str],
- self.message_center.get_message_queue(),
- self.run_edge_device_info_global_queue
- )
- )
- self.run_process_map[run_id_str].start()
- ServerConstants.save_run_process(run_id, self.run_process_map[run_id_str].pid)
-
- def start_cloud_server_process_entry(self):
- try:
- self.start_cloud_server_process()
- except Exception as e:
- pass
-
- def start_cloud_server_process(self):
- run_config = self.request_json["run_config"]
- packages_config = run_config["packages_config"]
- self.start_cloud_server(packages_config)
-
- def start_cloud_server(self, packages_config):
- server_id = self.request_json["server_id"]
- self.cloud_server_name = FedMLServerRunner.FEDML_CLOUD_SERVER_PREFIX + str(self.run_id) + "-" + str(server_id)
- self.server_docker_image = (
- self.agent_config["docker_config"]["registry_server"]
- + self.agent_config["docker_config"]["registry_dir"]
- + self.server_docker_base_image
- )
-
- logging.info("docker image {}".format(self.server_docker_image))
- # logging.info("file_sys_driver {}".format(self.agent_config["docker_config"]["file_sys_driver"]))
-
- registry_secret_cmd = (
- "kubectl create namespace fedml-devops-aggregator-"
- + self.version
- + ";kubectl -n fedml-devops-aggregator-"
- + self.version
- + " delete secret secret-"
- + self.cloud_server_name
- + " ;kubectl create secret docker-registry secret-"
- + self.cloud_server_name
- + " --docker-server="
- + self.agent_config["docker_config"]["registry_server"]
- + " --docker-username="
- + self.agent_config["docker_config"]["user_name"]
- + " --docker-password=$(aws ecr-public get-login-password --region "
- + self.agent_config["docker_config"]["public_cloud_region"]
- + ")"
- + " --docker-email=fedml@fedml.ai -n fedml-devops-aggregator-"
- + self.version
- )
- logging.info("Create secret cmd: " + registry_secret_cmd)
- os.system(registry_secret_cmd)
-
- message_bytes = json.dumps(self.request_json).encode("ascii")
- base64_bytes = base64.b64encode(message_bytes)
- runner_cmd_encoded = base64_bytes.decode("ascii")
- logging.info("runner_cmd_encoded: {}".format(runner_cmd_encoded))
- # logging.info("runner_cmd_decoded: {}".format(base64.b64decode(runner_cmd_encoded).decode()))
- cur_dir = os.path.dirname(__file__)
- run_deployment_cmd = (
- "export FEDML_AGGREGATOR_NAME="
- + self.cloud_server_name
- + ";export FEDML_AGGREGATOR_SVC="
- + self.cloud_server_name
- + ";export FEDML_AGGREGATOR_VERSION="
- + self.version
- + ';export FEDML_AGGREGATOR_IMAGE_PATH="'
- + self.server_docker_image
- + '"'
- + ";export FEDML_CONF_ID="
- + self.cloud_server_name
- + ";export FEDML_DATA_PV_ID="
- + self.cloud_server_name
- + ";export FEDML_DATA_PVC_ID="
- + self.cloud_server_name
- + ";export FEDML_REGISTRY_SECRET_SUFFIX="
- + self.cloud_server_name
- + ";export FEDML_ACCOUNT_ID=0"
- + ";export FEDML_SERVER_DEVICE_ID="
- + self.request_json.get("cloudServerDeviceId", "0")
- + ";export FEDML_VERSION="
- + self.version
- + ";export FEDML_PACKAGE_NAME="
- + packages_config.get("server", "")
- + ";export FEDML_PACKAGE_URL="
- + packages_config.get("serverUrl", "")
- + ";export FEDML_RUNNER_CMD="
- + runner_cmd_encoded
- + ";envsubst < "
- + os.path.join(cur_dir, "templates", "fedml-server-deployment.yaml")
- + " | kubectl apply -f - "
- )
- logging.info("FedMLServerRunner.run with k8s: " + run_deployment_cmd)
- os.system(run_deployment_cmd)
-
- def stop_cloud_server(self):
- self.cloud_server_name = FedMLServerRunner.FEDML_CLOUD_SERVER_PREFIX + str(self.run_id) \
- + "-" + str(self.edge_id)
- self.server_docker_image = (
- self.agent_config["docker_config"]["registry_server"]
- + self.agent_config["docker_config"]["registry_dir"]
- + self.server_docker_base_image
- )
- delete_deployment_cmd = (
- "export FEDML_AGGREGATOR_NAME="
- + self.cloud_server_name
- + ";export FEDML_AGGREGATOR_SVC="
- + self.cloud_server_name
- + ";export FEDML_AGGREGATOR_VERSION="
- + self.version
- + ';export FEDML_AGGREGATOR_IMAGE_PATH="'
- + self.server_docker_image
- + '"'
- + ";export FEDML_CONF_ID="
- + self.cloud_server_name
- + ";export FEDML_DATA_PV_ID="
- + self.cloud_server_name
- + ";export FEDML_DATA_PVC_ID="
- + self.cloud_server_name
- + ";export FEDML_REGISTRY_SECRET_SUFFIX="
- + self.cloud_server_name
- + ";kubectl -n fedml-devops-aggregator-"
- + self.version
- + " delete deployment "
- + self.cloud_server_name
- + ";kubectl -n fedml-devops-aggregator-"
- + self.version
- + " delete svc "
- + self.cloud_server_name
- + ";kubectl -n fedml-devops-aggregator-"
- + self.version
- + " delete secret secret-"
- + self.cloud_server_name
- )
- logging.info("FedMLServerRunner.stop_run with k8s: " + delete_deployment_cmd)
- os.system(delete_deployment_cmd)
-
- def setup_message_center(self):
- if self.message_center is not None:
- return
-
- self.message_center = FedMLMessageCenter(agent_config=self.agent_config)
- self.message_center.start_sender()
-
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
- self.mlops_metrics.set_messenger(self.message_center)
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.edge_id = self.edge_id
- self.mlops_metrics.server_agent_id = self.server_agent_id
-
- def rebuild_message_center(self, message_center_queue):
- self.message_center = FedMLMessageCenter(message_queue=message_center_queue)
-
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
- self.mlops_metrics.set_messenger(self.message_center)
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.edge_id = self.edge_id
- self.mlops_metrics.server_agent_id = self.server_agent_id
-
- def release_message_center(self):
- try:
- if self.message_center is not None:
- self.message_center.stop()
- self.message_center = None
-
- except Exception as e:
- logging.error(
- f"Failed to release client mqtt manager with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- def send_training_stop_request_to_edges(
- self, edge_id_list, payload=None, run_id=0):
- if payload is None:
- payload_obj = {"runId": run_id, "edgeids": edge_id_list}
- else:
- payload_obj = json.loads(payload)
-
- for edge_id in edge_id_list:
- topic_stop_train = "flserver_agent/" + str(edge_id) + "/stop_train"
- logging.info("stop_train: send topic " + topic_stop_train)
- self.message_center.send_message(topic_stop_train, json.dumps(payload_obj))
-
- def send_training_stop_request_to_specific_edge(self, edge_id, payload):
- topic_stop_train = "flserver_agent/" + str(edge_id) + "/stop_train"
- logging.info("stop_train: send topic " + topic_stop_train)
- self.message_center.send_message(topic_stop_train, payload)
-
- def send_training_stop_request_to_cloud_server(self, edge_id, payload):
- topic_stop_train = "mlops/flserver_agent_" + str(edge_id) + "/stop_train"
- logging.info("stop_train: send topic " + topic_stop_train)
- self.message_center.send_message(topic_stop_train, payload)
-
- def send_training_stop_request_to_edges_when_exception(
- self, edge_id_list, payload=None, run_id=0, server_id=None, status=None):
- if payload is None:
- payload_obj = {"runId": run_id, "edgeids": edge_id_list}
- if server_id is not None:
- payload_obj["serverId"] = server_id
- else:
- payload_obj = json.loads(payload)
- payload_obj["run_status"] = ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION if status is None else status
- topic_stop_train = "flserver_agent/" + str(self.edge_id) + "/stop_train"
- self.callback_stop_train(topic_stop_train, json.dumps(payload_obj), use_payload=payload_obj)
-
- def callback_stop_train(self, topic, payload, use_payload=None):
- # logging.info("callback_stop_train: topic = %s, payload = %s" % (topic, payload))
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json.get("runId", None)
- if run_id is None:
- run_id = request_json.get("id", None)
-
- edge_id_list = request_json["edgeids"]
- server_id = request_json.get("serverId", None)
- if server_id is None:
- server_id = request_json.get("server_id", None)
-
- if run_id is None or server_id is None:
- logging.info("Json format is not correct!")
- return
-
- # logging.info("Stop run with multiprocessing.")
-
- # Stop server with multiprocessing mode
- run_id_str = str(run_id)
- stop_request_json = self.running_request_json.get(run_id_str, None)
- if stop_request_json is None:
- stop_request_json = request_json
- if use_payload is not None:
- stop_request_json = use_payload
-
- if self.run_process_event_map.get(run_id_str) is not None:
- self.run_process_event_map.get(run_id_str).set()
-
- if self.run_as_edge_server_and_agent or self.enable_simulation_cloud_agent:
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=stop_request_json, agent_config=self.agent_config,
- edge_id=self.edge_id
- )
- server_runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- self.run_process_event_map_for_stop[run_id_str] = multiprocessing.Event()
- if self.run_edge_id_status_queue_map.get(run_id_str, None) is None:
- self.run_edge_id_status_queue_map[run_id_str] = Queue()
- if self.run_edge_device_info_queue_map_for_stop.get(run_id_str, None) is None:
- self.run_edge_device_info_queue_map_for_stop[run_id_str] = Queue()
- # if self.run_edge_device_info_global_queue_for_stop is None:
- # self.run_edge_device_info_global_queue_for_stop = Array('i', list())
-
- self.run_stop_process_map[run_id_str] = Process(
- target=server_runner.run_stop, args=(
- self.run_process_event_map_for_stop[run_id_str],
- self.run_edge_id_status_queue_map[run_id_str],
- self.run_edge_device_info_queue_map_for_stop[run_id_str],
- self.run_edge_device_info_global_queue_for_stop,
- self.message_center.get_message_queue(),
- )
- )
- self.run_stop_process_map[run_id_str].start()
- elif self.run_as_cloud_agent:
- self.send_training_stop_request_to_cloud_server(server_id, payload)
- return
- elif self.run_as_cloud_server:
- # if not self.use_local_process_as_cloud_server:
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=stop_request_json, agent_config=self.agent_config,
- edge_id=server_id
- )
- server_runner.run_as_cloud_agent = self.run_as_cloud_agent
- self.run_process_event_map_for_stop[run_id_str] = multiprocessing.Event()
- if self.run_edge_id_status_queue_map.get(run_id_str, None) is None:
- self.run_edge_id_status_queue_map[run_id_str] = Queue()
- if self.run_edge_device_info_queue_map_for_stop.get(run_id_str, None) is None:
- self.run_edge_device_info_queue_map_for_stop[run_id_str] = Queue()
- # if self.run_edge_device_info_global_queue_for_stop is None:
- # self.run_edge_device_info_global_queue_for_stop = Array('i', list())
-
- self.run_stop_process_map[run_id_str] = Process(
- target=server_runner.run_stop, args=(
- self.run_process_event_map_for_stop[run_id_str],
- self.run_edge_id_status_queue_map[run_id_str],
- self.run_edge_device_info_queue_map_for_stop[run_id_str],
- self.run_edge_device_info_global_queue_for_stop,
- self.message_center.get_message_queue(),
- )
- )
- self.run_stop_process_map[run_id_str].start()
- return
-
- if self.running_request_json.get(run_id_str, None) is not None:
- self.running_request_json.pop(run_id_str)
-
- if self.run_process_map.get(run_id_str, None) is not None:
- self.run_process_map.pop(run_id_str)
-
- def run_stop(self, process_event, edge_id_status_queue, edge_device_info_queue,
- edge_device_info_global_queue, message_center_queue):
- if platform.system() != "Windows":
- os.setsid()
-
- os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
- os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
-
- self.run_process_event = process_event
- try:
- MLOpsUtils.set_ntp_offset(self.ntp_offset)
-
- self.rebuild_message_center(message_center_queue)
-
- self.run_stop_impl(edge_id_status_queue, edge_device_info_queue, edge_device_info_global_queue)
- except Exception as e:
- logging.error("Stop runner exits with exceptions. {}".format(traceback.format_exc()))
- finally:
- logging.info("Release resources.")
-
- def run_stop_impl(self, edge_id_status_queue, edge_device_info_queue, edge_device_info_global_queue):
- run_id_str = str(self.run_id)
- edge_id_list = self.request_json["edgeids"]
-
- # Detect running status of all edges
- status_ok, active_edge_info_dict, inactivate_edges = self.detect_edges_status(
- edge_device_info_queue, edge_device_info_global_queue=edge_device_info_global_queue,
- status_timeout=120, need_to_trigger_exception=False,
- status_check_context=SchedulerConstants.STATUS_CHECK_FRO_RUN_STOP_CONTEXT)
-
- # Send the training stopping request to running edges.
- for edge_id_item, _ in active_edge_info_dict.items():
- self.send_training_stop_request_to_specific_edge(edge_id_item, json.dumps(self.request_json))
- time.sleep(0.2)
- time.sleep(3)
-
- total_sleep_seconds = 0
- allowed_status_check_sleep_seconds = 60
- server_id = self.edge_id
- running_edges_list = list()
- current_edge_id_status_map = dict()
-
- while True:
- # Fetch edge id and status from the edge id status queue
- while True:
- try:
- queue_item = edge_id_status_queue.get(block=False, timeout=3)
- if queue_item is not None:
- current_edge_id_status_map.update(queue_item)
- except queue.Empty as e: # If queue is empty, then break loop
- break
-
- # Calc the total killed device number
- running_edges_list.clear()
- number_of_failed_edges = 0
- number_of_finished_edges = 0
- number_of_killed_edges = 0
- for edge_id_item, status_item in current_edge_id_status_map.items():
- if edge_id_item == "server":
- continue
-
- if status_item is None or status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED or \
- status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION:
- number_of_failed_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
- number_of_finished_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED:
- number_of_killed_edges += 1
- continue
-
- if status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE or \
- status_item == ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE:
- continue
-
- running_edges_list.append(edge_id_item)
-
- # If the killed device number is equal total device number, then break
- if len(running_edges_list) <= 0 and len(current_edge_id_status_map.keys()) == len(edge_id_list) + 1:
- break
-
- # Calc the timeout value to wait to device killed.
- time.sleep(3)
- total_sleep_seconds += 3
- if total_sleep_seconds < allowed_status_check_sleep_seconds:
- continue
-
- # If timeout, then report killed device status
- no_response_edges = list(set(edge_id_list) - set(running_edges_list))
- if len(no_response_edges) <= 0:
- break
- for edge_id_item in no_response_edges:
- self.mlops_metrics.report_client_id_status(
- edge_id_item, ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED,
- server_id=self.edge_id, run_id=self.run_id)
-
- if self.run_as_edge_server_and_agent or self.enable_simulation_cloud_agent:
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, self.edge_id)
- elif self.run_as_cloud_agent:
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, server_id)
-
- self.mlops_metrics.report_server_id_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED, edge_id=self.edge_id,
- server_id=self.edge_id, server_agent_id=self.edge_id)
-
- def set_run_status(self, run_id, status, running_request_json):
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=running_request_json, agent_config=self.agent_config
- )
- server_runner.edge_id = self.edge_id
- server_runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- server_runner.run_status = status
- server_runner.message_center = self.message_center
- server_runner.mlops_metrics = self.mlops_metrics
- server_runner.cleanup_client_with_status()
-
- def callback_runner_id_status(self, topic, payload):
- # logging.info("callback_runner_id_status: topic = %s, payload = %s" % (topic, payload))
- # logging.info(
- # f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- # )
-
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json["run_id"]
- status = request_json["status"]
- edge_id = request_json["edge_id"]
- server_id = request_json.get("server_id", None)
- run_id_str = str(run_id)
-
- if (
- status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED
- or status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED
- or status == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED
- ):
- completed_event = self.run_process_completed_event_map.get(run_id_str, None)
- if completed_event is not None:
- completed_event.set()
-
- FedMLServerDataInterface.get_instance().save_job_status(run_id, self.edge_id, status, status)
-
- # Stop server with multiprocessing mode
- running_request_json = self.running_request_json.get(run_id_str, None)
- if running_request_json is None:
- running_request_json = request_json
- if self.run_as_edge_server_and_agent or self.enable_simulation_cloud_agent:
- self.set_run_status(run_id, status, running_request_json)
-
- run_process = self.run_process_map.get(run_id_str, None)
- if run_process is not None:
- if run_process.pid is not None:
- RunProcessUtils.kill_process(run_process.pid)
-
- self.run_process_map.pop(run_id_str)
-
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- elif self.run_as_cloud_agent:
- pass
- elif self.run_as_cloud_server:
- self.set_run_status(run_id, status, running_request_json)
-
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- if self.use_local_process_as_cloud_server:
- # RunProcessUtils.kill_process(os.getpid())
- cloud_server_process = self.run_process_map.get(run_id_str, None)
- if cloud_server_process is not None:
- RunProcessUtils.kill_process(cloud_server_process.pid)
- else:
- self.stop_cloud_server()
-
- if self.run_process_map.get(run_id_str, None) is not None:
- self.run_process_map.pop(run_id_str)
-
- self.remove_listener_for_run_metrics(self.run_id)
- self.remove_listener_for_run_logs(self.run_id)
- elif (
- status == ServerConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION
- ):
- request_json = self.running_request_json.get(run_id_str, None)
- if request_json is not None:
- edge_id_list = request_json.get("edgeids", list())
- server_id = request_json.get("serverId", None)
- server_id = request_json.get("server_id", None) if server_id is None else server_id
- self.send_training_stop_request_to_edges_when_exception(
- edge_id_list, run_id=run_id, server_id=server_id,
- status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
-
- FedMLServerDataInterface.get_instance().save_job_status(run_id, self.edge_id, status, status)
- else:
- request_json = self.running_request_json.get(run_id_str, None)
- if request_json is None:
- request_json = self.start_request_json
- self.mlops_metrics.report_server_training_status(
- run_id, status, edge_id=self.edge_id, running_json=json.dumps(request_json))
-
- def cleanup_client_with_status(self):
- if self.run_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
- # logging.info("received to finished status.")
- self.cleanup_run_when_finished(should_send_server_id_status=False)
- elif self.run_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
- # logging.info("received to failed status.")
- self.cleanup_run_when_starting_failed(should_send_server_id_status=False)
- elif self.run_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED:
- # logging.info("received to failed status.")
- self.cleanup_run_when_starting_failed(
- status=self.run_status, should_send_server_id_status=False)
-
- def callback_report_current_status(self, topic, payload):
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- request_json = json.loads(payload)
- if self.run_as_edge_server_and_agent:
- self.send_agent_active_msg()
- elif self.run_as_cloud_agent:
- self.send_agent_active_msg()
- elif self.run_as_cloud_server:
- pass
-
- @staticmethod
- def process_ota_upgrade_msg():
- os.system("pip install -U fedml")
-
- def callback_server_ota_msg(self, topic, payload):
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- request_json = json.loads(payload)
- cmd = request_json["cmd"]
-
- if cmd == ServerConstants.FEDML_OTA_CMD_UPGRADE:
- try:
- self.process_ota_upgrade_msg()
- # Process(target=FedMLServerRunner.process_ota_upgrade_msg).start()
- raise Exception("After upgraded, restart runner...")
- except Exception as e:
- pass
- elif cmd == ServerConstants.FEDML_OTA_CMD_RESTART:
- raise Exception("Restart runner...")
-
- def callback_response_device_info(self, topic, payload):
- # Parse payload
- payload_json = json.loads(payload)
- run_id = payload_json.get("run_id", 0)
- context = payload_json.get("context", None)
- master_device_id = payload_json.get("master_device_id", 0)
- slave_device_id = payload_json.get("slave_device_id", 0)
- slave_device_id_list = payload_json.get("slave_device_id_list", 0)
- edge_id = payload_json.get("edge_id", 0)
- device_info = payload_json.get("edge_info", 0)
- device_info["master_device_id"] = master_device_id
- device_info["slave_device_id"] = slave_device_id
- device_info["slave_device_id_list"] = slave_device_id_list
- run_id_str = str(run_id)
-
- # Put device info into a multiprocessing queue so master runner checks if all edges are ready
- if context is None:
- if self.run_edge_device_info_queue_map.get(run_id_str, None) is None:
- self.run_edge_device_info_queue_map[run_id_str] = Queue()
- self.run_edge_device_info_queue_map[run_id_str].put(device_info)
-
- # if self.run_edge_device_info_global_queue is None:
- # self.run_edge_device_info_global_queue = Array('i', list())
- #
- # self.run_edge_device_info_global_queue[len(self.run_edge_device_info_global_queue)] = \
- # {"timestamp": time.time(), "edge_id": edge_id, "device_info": device_info}
-
- self.check_model_device_ready_and_deploy(run_id, master_device_id, slave_device_id,
- slave_device_id_list=slave_device_id_list)
- elif context == SchedulerConstants.STATUS_CHECK_FRO_RUN_STOP_CONTEXT:
- if self.run_edge_device_info_queue_map_for_stop.get(run_id_str, None) is None:
- self.run_edge_device_info_queue_map_for_stop[run_id_str] = Queue()
- self.run_edge_device_info_queue_map_for_stop[run_id_str].put(device_info)
-
- # if self.run_edge_device_info_global_queue_for_stop is None:
- # self.run_edge_device_info_global_queue_for_stop = Array('i', list())
- #
- # self.run_edge_device_info_global_queue_for_stop[len(self.run_edge_device_info_global_queue_for_stop)] = \
- # {"timestamp": time.time(), "edge_id": edge_id, "device_info": device_info}
-
- def check_model_device_ready_and_deploy(self, run_id, master_device_id, slave_device_id, slave_device_id_list=None):
- request_json = self.running_request_json.get(str(run_id), None)
- if request_json is None:
- return
- run_config = request_json["run_config"]
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
- job_type = job_yaml.get("job_type", None)
- job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
- if job_type != Constants.JOB_TASK_TYPE_DEPLOY and job_type != Constants.JOB_TASK_TYPE_SERVE:
- return
-
- # Init model device ids for each run
- run_id_str = str(run_id)
- if self.run_model_device_ids.get(run_id_str, None) is None:
- self.run_model_device_ids[run_id_str] = list()
-
- # Append master device and slave devices to the model devices map
- self.run_model_device_ids[run_id_str].append({"master_device_id": master_device_id,
- "slave_device_id": slave_device_id})
- model_device_ids = self.run_model_device_ids.get(run_id_str, None)
- if model_device_ids is None:
- return
-
- # Check if all model devices are ready
- if len(model_device_ids) != len(self.run_edge_ids.get(run_id_str, list())):
- return
-
- # Generate model master ids and model slave device ids
- device_master_ids = list()
- device_slave_ids = list()
- for device_ids in model_device_ids:
- model_master_id = device_ids.get("master_device_id")
- model_slave_id = device_ids.get("slave_device_id")
- device_master_ids.append(model_master_id)
- device_slave_ids.append(model_slave_id)
-
- if len(device_master_ids) <= 0:
- return
-
- # Generate serving devices for deploying
- serving_devices = list()
- serving_devices.append(device_master_ids[0])
- serving_devices.extend(device_slave_ids)
-
- # Start to deploy the model
- self.deploy_model(serving_devices, request_json, run_id=run_id)
-
- def callback_request_device_info_from_mlops(self, topic, payload):
- self.response_device_info_to_mlops(topic, payload)
-
- def response_device_info_to_mlops(self, topic, payload):
- response_topic = f"deploy/master_agent/mlops/response_device_info"
- payload_json = json.loads(payload)
- need_gpu_info = payload_json.get("need_gpu_info", False)
- if self.mlops_metrics is not None:
- if not need_gpu_info:
- response_payload = {
- "run_id": self.run_id,
- "master_agent_device_id": self.edge_id,
- "fedml_version": fedml.__version__
- }
- else:
- total_mem, free_mem, total_disk_size, free_disk_size, cup_utilization, cpu_cores, \
- gpu_cores_total, gpu_cores_available, sent_bytes, recv_bytes, gpu_available_ids = \
- sys_utils.get_sys_realtime_stats()
- gpu_available_ids = JobRunnerUtils.get_instance().get_available_gpu_id_list(self.edge_id)
- gpu_available_ids = JobRunnerUtils.trim_unavailable_gpu_ids(gpu_available_ids)
- gpu_cores_available = len(gpu_available_ids)
- response_payload = {
- "run_id": self.run_id,
- "master_agent_device_id": self.edge_id,
- "memoryTotal": round(total_mem * MLOpsUtils.BYTES_TO_GB, 2),
- "memoryAvailable": round(free_mem * MLOpsUtils.BYTES_TO_GB, 2),
- "diskSpaceTotal": round(total_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
- "diskSpaceAvailable": round(free_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
- "cpuUtilization": round(cup_utilization, 2),
- "cpuCores": cpu_cores,
- "gpuCoresTotal": gpu_cores_total,
- "gpuCoresAvailable": gpu_cores_available,
- "networkTraffic": sent_bytes + recv_bytes,
- "timestamp": int(MLOpsUtils.get_ntp_time()),
- "fedml_version": fedml.__version__
- }
- self.mlops_metrics.report_json_message(response_topic, json.dumps(response_payload))
-
- @staticmethod
- def get_device_id():
- device_file_path = os.path.join(ServerConstants.get_data_dir(), ServerConstants.LOCAL_RUNNER_INFO_DIR_NAME)
- file_for_device_id = os.path.join(device_file_path, "devices.id")
- if not os.path.exists(device_file_path):
- os.makedirs(device_file_path)
- elif os.path.exists(file_for_device_id):
- with open(file_for_device_id, 'r', encoding='utf-8') as f:
- device_id_from_file = f.readline()
- if device_id_from_file is not None and device_id_from_file != "":
- return device_id_from_file
-
- if platform.system() == "Darwin":
- cmd_get_serial_num = "system_profiler SPHardwareDataType | grep Serial | awk '{gsub(/ /,\"\")}{print}' " \
- "|awk -F':' '{print $2}' "
- device_id = os.popen(cmd_get_serial_num).read()
- device_id = device_id.replace('\n', '').replace(' ', '')
- if device_id is None or device_id == "":
- device_id = hex(uuid.getnode())
- else:
- device_id = "0x" + device_id
- else:
- if "nt" in os.name:
-
- def get_uuid():
- guid = ""
- try:
- cmd = "wmic csproduct get uuid"
- guid = str(subprocess.check_output(cmd))
- pos1 = guid.find("\\n") + 2
- guid = guid[pos1:-15]
- except Exception as ex:
- pass
- return str(guid)
-
- device_id = str(get_uuid())
- elif "posix" in os.name:
- device_id = sys_utils.get_device_id_in_docker()
- if device_id is None:
- device_id = hex(uuid.getnode())
- else:
- device_id = sys_utils.run_subprocess_open(
- "hal-get-property --udi /org/freedesktop/Hal/devices/computer --key system.hardware.uuid".split()
- )
- device_id = hex(device_id)
-
- if device_id is not None and device_id != "":
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
- else:
- device_id = hex(uuid.uuid4())
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
-
- return device_id
-
- def bind_account_and_device_id(self, url, account_id, device_id, os_name, api_key="", role=None):
- if role is None:
- role = "edge_server"
- if self.run_as_edge_server_and_agent:
- role = "edge_server"
- elif self.run_as_cloud_agent:
- role = "cloud_agent"
- elif self.run_as_cloud_server:
- role = "cloud_server"
-
- ip = requests.get('https://checkip.amazonaws.com').text.strip()
- fedml_ver, exec_path, os_ver, cpu_info, python_ver, torch_ver, mpi_installed, \
- cpu_usage, available_mem, total_mem, gpu_info, gpu_available_mem, gpu_total_mem, \
- gpu_count, gpu_vendor, cpu_count, gpu_device_name = get_sys_runner_info()
- host_name = sys_utils.get_host_name()
- json_params = {
- "accountid": account_id,
- "deviceid": device_id,
- "type": os_name,
- "state": ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE,
- "status": ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE,
- "processor": cpu_info,
- "core_type": cpu_info,
- "network": "",
- "role": role,
- "os_ver": os_ver,
- "memory": total_mem,
- "ip": ip,
- "api_key": api_key,
- "extra_infos": {"fedml_ver": fedml_ver, "exec_path": exec_path, "os_ver": os_ver,
- "cpu_info": cpu_info, "python_ver": python_ver, "torch_ver": torch_ver,
- "mpi_installed": mpi_installed, "cpu_usage": cpu_usage,
- "available_mem": available_mem, "total_mem": total_mem,
- "cpu_count": cpu_count, "gpu_count": 0, "host_name": host_name}
- }
- if gpu_count > 0:
- if gpu_total_mem is not None:
- json_params["gpu"] = gpu_info if gpu_info is not None else "" + ", Total GPU Memory: " + gpu_total_mem
- else:
- json_params["gpu"] = gpu_info if gpu_info is not None else ""
- json_params["extra_infos"]["gpu_info"] = gpu_info if gpu_info is not None else ""
- if gpu_available_mem is not None:
- json_params["extra_infos"]["gpu_available_mem"] = gpu_available_mem
- if gpu_total_mem is not None:
- json_params["extra_infos"]["gpu_total_mem"] = gpu_total_mem
-
- json_params["extra_infos"]["gpu_count"] = gpu_count
- json_params["extra_infos"]["gpu_vendor"] = gpu_vendor
- json_params["extra_infos"]["gpu_device_name"] = gpu_device_name
-
- gpu_available_id_list = sys_utils.get_available_gpu_id_list(limit=gpu_count)
- gpu_available_count = len(gpu_available_id_list) if gpu_available_id_list is not None else 0
- gpu_list = sys_utils.get_gpu_list()
- json_params["extra_infos"]["gpu_available_count"] = gpu_available_count
- json_params["extra_infos"]["gpu_available_id_list"] = gpu_available_id_list
- json_params["extra_infos"]["gpu_list"] = gpu_list
- else:
- json_params["gpu"] = "None"
- json_params["extra_infos"]["gpu_available_count"] = 0
- json_params["extra_infos"]["gpu_available_id_list"] = []
- json_params["extra_infos"]["gpu_list"] = []
-
- _, cert_path = MLOpsConfigs.get_request_params()
- if cert_path is not None:
- try:
- requests.session().verify = cert_path
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- except requests.exceptions.SSLError as err:
- MLOpsConfigs.install_root_ca_file()
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- else:
- response = requests.post(url, json=json_params, headers={"Connection": "close"})
- edge_id = -1
- user_name = None
- extra_url = None
- if response.status_code != 200:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- pass
- else:
- # print("url = {}, response = {}".format(url, response))
- status_code = response.json().get("code")
- if status_code == "SUCCESS":
- edge_id = response.json().get("data").get("id")
- user_name = response.json().get("data").get("userName", None)
- extra_url = response.json().get("data").get("url", None)
- if edge_id is None or edge_id <= 0:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- else:
- if status_code == SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR:
- raise SystemExit(SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR)
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- return -1, None, None
- return edge_id, user_name, extra_url
-
- def fetch_configs(self):
- return MLOpsConfigs.fetch_all_configs()
-
- def send_agent_active_msg(self):
- active_topic = "flserver_agent/active"
- status = MLOpsStatus.get_instance().get_server_agent_status(self.edge_id)
- if (
- status is not None
- and status != ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE
- and status != ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- ):
- return
-
- if self.run_as_cloud_agent:
- status = ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- else:
- try:
- current_job = FedMLServerDataInterface.get_instance().get_job_by_id(self.run_id)
- except Exception as e:
- current_job = None
- if current_job is None:
- if status is not None and status == ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE:
- status = ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- else:
- return
- else:
- status = ServerConstants.get_device_state_from_run_edge_state(current_job.status)
- active_msg = {"ID": self.edge_id, "status": status}
- MLOpsStatus.get_instance().set_server_agent_status(self.edge_id, status)
- if self.mqtt_mgr is not None:
- self.mqtt_mgr.send_message_json(active_topic, json.dumps(active_msg))
- else:
- self.send_message_json(active_topic, json.dumps(active_msg))
-
- def recover_start_train_msg_after_upgrading(self):
- try:
- current_job = FedMLServerDataInterface.get_instance().get_current_job()
- if current_job is not None and \
- current_job.status == ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING:
- logging.info("start training after upgrading.")
- server_agent_id = self.edge_id
- topic_start_train = "mlops/flserver_agent_" + str(server_agent_id) + "/start_train"
- self.callback_start_train(topic_start_train, current_job.running_json)
- except Exception as e:
- logging.info("recover starting train message after upgrading: {}".format(traceback.format_exc()))
-
- def on_agent_mqtt_connected(self, mqtt_client_object):
- # The MQTT message topic format is as follows: <sender>/<receiver>/<action>
-
- # Setup MQTT message listener for starting training
- server_agent_id = self.edge_id
- topic_start_train = "mlops/flserver_agent_" + str(server_agent_id) + "/start_train"
- self.add_message_listener(topic_start_train, self.callback_start_train)
- self.mqtt_mgr.add_message_listener(topic_start_train, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener for stopping training
- topic_stop_train = "mlops/flserver_agent_" + str(server_agent_id) + "/stop_train"
- self.add_message_listener(topic_stop_train, self.callback_stop_train)
- self.mqtt_mgr.add_message_listener(topic_stop_train, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener for server status switching
- topic_server_status = "fl_server/flserver_agent_" + str(server_agent_id) + "/status"
- self.add_message_listener(topic_server_status, self.callback_runner_id_status)
- self.mqtt_mgr.add_message_listener(topic_server_status, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to report current device status.
- topic_report_status = "mlops/report_device_status"
- self.add_message_listener(topic_report_status, self.callback_report_current_status)
- self.mqtt_mgr.add_message_listener(topic_report_status, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to OTA messages from the MLOps.
- topic_ota_msg = "mlops/flserver_agent_" + str(server_agent_id) + "/ota"
- self.add_message_listener(topic_ota_msg, self.callback_server_ota_msg)
- self.mqtt_mgr.add_message_listener(topic_ota_msg, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to request device info from the client.
- topic_response_device_info = "client/server/response_device_info/" + str(self.edge_id)
- self.add_message_listener(topic_response_device_info, self.callback_response_device_info)
- self.mqtt_mgr.add_message_listener(topic_response_device_info, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to request device info from MLOps.
- topic_request_device_info_from_mlops = f"deploy/mlops/master_agent/request_device_info/{self.edge_id}"
- self.add_message_listener(topic_request_device_info_from_mlops, self.callback_request_device_info_from_mlops)
- self.mqtt_mgr.add_message_listener(
- topic_request_device_info_from_mlops, self.listener_message_dispatch_center)
-
- # Subscribe topics for starting train, stopping train and fetching client status.
- mqtt_client_object.subscribe(topic_start_train, qos=2)
- mqtt_client_object.subscribe(topic_stop_train, qos=2)
- mqtt_client_object.subscribe(topic_server_status, qos=2)
- mqtt_client_object.subscribe(topic_report_status, qos=2)
- mqtt_client_object.subscribe(topic_ota_msg, qos=2)
- mqtt_client_object.subscribe(topic_response_device_info, qos=2)
- mqtt_client_object.subscribe(topic_request_device_info_from_mlops, qos=2)
-
- self.subscribed_topics.clear()
- self.subscribed_topics.append(topic_start_train)
- self.subscribed_topics.append(topic_stop_train)
- self.subscribed_topics.append(topic_server_status)
- self.subscribed_topics.append(topic_report_status)
- self.subscribed_topics.append(topic_ota_msg)
- self.subscribed_topics.append(topic_response_device_info)
- self.subscribed_topics.append(topic_request_device_info_from_mlops)
-
- # Broadcast the first active message.
- self.send_agent_active_msg()
-
- # Start the message center for listener
- self.start_listener(sender_message_queue=self.message_center.get_message_queue(),
- agent_config=self.agent_config)
-
- if self.run_as_cloud_server:
- # Start the FedML server
- message_bytes = self.args.runner_cmd.encode("ascii")
- base64_bytes = base64.b64decode(message_bytes)
- payload = base64_bytes.decode("ascii")
- self.receive_message_json(topic_start_train, payload)
-
- # Echo results
- MLOpsRuntimeLog.get_instance(self.args).enable_show_log_to_stdout()
- print("\nCongratulations, your device is connected to the FedML MLOps platform successfully!")
- print(
- "Your FedML Edge ID is " + str(self.edge_id) + ", unique device ID is "
- + str(self.unique_device_id)
- )
- MLOpsRuntimeLog.get_instance(self.args).enable_show_log_to_stdout(enable=True)
-
- def on_agent_mqtt_disconnected(self, mqtt_client_object):
- MLOpsStatus.get_instance().set_server_agent_status(
- self.edge_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE
- )
-
- def setup_agent_mqtt_connection(self, service_config):
- # Setup MQTT connection
- self.mqtt_mgr = MqttManager(
- service_config["mqtt_config"]["BROKER_HOST"],
- service_config["mqtt_config"]["BROKER_PORT"],
- service_config["mqtt_config"]["MQTT_USER"],
- service_config["mqtt_config"]["MQTT_PWD"],
- service_config["mqtt_config"]["MQTT_KEEPALIVE"],
- f"FedML_ServerAgent_Daemon_@{self.user_name}@_@{self.args.current_device_id}@_@{str(uuid.uuid4())}@",
- "flserver_agent/last_will_msg",
- json.dumps({"ID": self.edge_id, "status": ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE})
- )
-
- # Init local database
- FedMLServerDataInterface.get_instance().create_job_table()
-
- # Start the message center to process edge related messages.
- self.setup_message_center()
-
- server_api_cmd = "fedml.computing.scheduler.master.server_api:api"
- server_api_pids = RunProcessUtils.get_pid_from_cmd_line(server_api_cmd)
- if server_api_pids is None or len(server_api_pids) <= 0:
- # Start local API services
- cur_dir = os.path.dirname(__file__)
- fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- python_program = get_python_program()
- self.local_api_process = ServerConstants.exec_console_with_script(
- "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
- "--log-level critical".format(
- python_program, server_api_cmd, ServerConstants.LOCAL_SERVER_API_PORT,
- fedml_base_dir
- ),
- should_capture_stdout=False,
- should_capture_stderr=False
- )
- # if self.local_api_process is not None and self.local_api_process.pid is not None:
- # print(f"Server local API process id {self.local_api_process.pid}")
-
- # Setup MQTT connected listener
- self.mqtt_mgr.add_connected_listener(self.on_agent_mqtt_connected)
- self.mqtt_mgr.add_disconnected_listener(self.on_agent_mqtt_disconnected)
- self.mqtt_mgr.connect()
-
- # Report the IDLE status to MLOps
- self.mlops_metrics.report_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE, edge_id=self.edge_id)
- MLOpsStatus.get_instance().set_server_agent_status(
- self.edge_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- )
-
- # MLOpsRuntimeLogDaemon.get_instance(self.args).stop_all_log_processor()
-
- self.mlops_metrics.stop_device_realtime_perf()
- self.mlops_metrics.report_device_realtime_perf(self.args, service_config["mqtt_config"], is_client=False)
-
- if not self.run_as_cloud_server:
- self.recover_start_train_msg_after_upgrading()
-
- JobCleanup.get_instance().sync_data_on_startup(self.edge_id, is_client=False)
-
- self.master_api_daemon = MasterApiDaemon()
- self.master_api_process = Process(target=self.master_api_daemon.run)
- self.master_api_process.start()
-
- # if self.model_device_server is None:
- # self.model_device_server = FedMLModelDeviceServerRunner(self.args, self.args.current_device_id,
- # self.args.os_name, self.args.is_from_docker,
- # self.agent_config)
- # self.model_device_server.start()
-
- def start_agent_mqtt_loop(self):
- # Start MQTT message loop
- try:
- self.mqtt_mgr.loop_forever()
- except Exception as e:
- if str(e) == "Restarting after upgraded...":
- logging.info("Restarting after upgraded...")
- else:
- logging.info("Server tracing: {}".format(traceback.format_exc()))
-
- finally:
- login_exit_file = os.path.join(ServerConstants.get_log_file_dir(), "exited.log")
- with open(login_exit_file, "w") as f:
- f.writelines(f"{os.getpid()}.")
-
- self.stop_agent()
-
- time.sleep(5)
- sys_utils.cleanup_all_fedml_server_login_processes(
- ServerConstants.SERVER_LOGIN_PROGRAM, clean_process_group=False)
- sys.exit(1)
-
- def stop_agent(self):
- if self.run_process_event is not None:
- self.run_process_event.set()
-
- if self.mqtt_mgr is not None:
- try:
- for topic in self.subscribed_topics:
- self.mqtt_mgr.unsubscribe_msg(topic)
- except Exception as e:
- pass
-
- self.mqtt_mgr.loop_stop()
- self.mqtt_mgr.disconnect()
- self.release_message_center()
-
- def get_runner(self):
- runner = FedMLServerRunner(
- self.args, run_id=self.run_id, request_json=self.request_json,
- agent_config=self.agent_config
- )
- runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- runner.edge_id = self.edge_id
- runner.server_agent_id = self.server_agent_id
- runner.start_request_json = self.start_request_json
- runner.unique_device_id = self.unique_device_id
- runner.user_name = self.user_name
- runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- runner.run_as_cloud_agent = self.run_as_cloud_agent
- runner.run_as_cloud_server = self.run_as_cloud_server
- return runner
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_runner_deprecated.py b/python/fedml/computing/scheduler/model_scheduler/device_client_runner_deprecated.py
deleted file mode 100755
index 8bb03eebbd..0000000000
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_runner_deprecated.py
+++ /dev/null
@@ -1,1483 +0,0 @@
-import json
-import logging
-import multiprocessing
-import sys
-
-from multiprocessing import Process
-import os
-import platform
-import shutil
-import subprocess
-import threading
-
-import time
-import traceback
-import urllib
-import uuid
-import zipfile
-from urllib.parse import urlparse, urljoin
-
-import requests
-
-import yaml
-
-import fedml
-from fedml import mlops
-from fedml.computing.scheduler.model_scheduler.device_model_msg_object import FedMLModelMsgObject
-from fedml.computing.scheduler.scheduler_core.compute_cache_manager import ComputeCacheManager
-
-from fedml.computing.scheduler.scheduler_core.compute_utils import ComputeUtils
-from fedml.core.distributed.communication.s3.remote_storage import S3Storage
-from .device_model_cache import FedMLModelCache
-from ..comm_utils import sys_utils, security_utils
-
-from ..comm_utils.container_utils import ContainerUtils
-
-from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
-
-from ....core.distributed.communication.mqtt.mqtt_manager import MqttManager
-from ..comm_utils.yaml_utils import load_yaml_config
-from .device_client_constants import ClientConstants
-
-from ....core.mlops.mlops_metrics import MLOpsMetrics
-
-from ....core.mlops.mlops_configs import MLOpsConfigs
-from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
-from ....core.mlops.mlops_status import MLOpsStatus
-from ..comm_utils.sys_utils import get_sys_runner_info, get_python_program
-from .device_model_deployment import start_deployment, run_http_inference_with_curl_request
-from .device_client_data_interface import FedMLClientDataInterface
-from ....core.mlops.mlops_utils import MLOpsUtils
-from ..comm_utils.job_utils import JobRunnerUtils
-from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
-from .device_mqtt_inference_protocol import FedMLMqttInference
-from .device_model_db import FedMLModelDatabase
-from ..comm_utils.constants import SchedulerConstants
-from fedml.computing.scheduler.comm_utils.job_monitor import JobMonitor
-
-from .device_replica_handler import FedMLDeviceReplicaHandler
-
-from fedml.computing.scheduler.scheduler_core.endpoint_sync_protocol import FedMLEndpointSyncProtocol
-import ssl
-
-
-class RunnerError(Exception):
- """ Runner failed. """
- pass
-
-
-class RunnerCompletedError(Exception):
- """ Runner completed. """
- pass
-
-
-class FedMLClientRunner:
- FEDML_BOOTSTRAP_RUN_OK = "[FedML]Bootstrap Finished"
-
- def __init__(self, args, edge_id=0, request_json=None, agent_config=None, run_id=0):
- self.local_api_process = None
- self.run_process_event = None
- self.run_process_event_map = dict()
- self.run_process_completed_event = None
- self.run_process_completed_event_map = dict()
- self.run_inference_event_map = dict()
- self.run_inference_response_map = dict()
- self.run_process_map = dict()
- self.device_status = None
- self.current_training_status = None
- self.mqtt_mgr = None
- self.client_mqtt_mgr = None
- self.client_mqtt_is_connected = False
- self.client_mqtt_lock = None
- self.edge_id = edge_id
- self.run_id = run_id
- self.unique_device_id = None
- self.args = args
- self.request_json = request_json
- self.version = args.version
- self.device_id = args.device_id
- self.cur_dir = os.path.split(os.path.realpath(__file__))[0]
- if args.current_running_dir is not None:
- self.cur_dir = args.current_running_dir
- self.sudo_cmd = ""
- self.is_mac = False
- if platform.system() == "Darwin":
- self.is_mac = True
-
- self.agent_config = agent_config
- self.fedml_data_base_package_dir = os.path.join("/", "fedml", "data")
- self.fedml_data_local_package_dir = os.path.join("/", "fedml", "fedml-package", "fedml", "data")
- self.fedml_data_dir = self.fedml_data_base_package_dir
- self.fedml_config_dir = os.path.join("/", "fedml", "conf")
-
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES = {}
-
- self.mlops_metrics = None
- self.client_active_list = dict()
- self.infer_host = "127.0.0.1"
- self.redis_addr = "local"
- self.redis_port = "6379"
- self.redis_password = "fedml_default"
-
- self.model_runner_mapping = dict()
- self.ntp_offset = MLOpsUtils.get_ntp_offset()
- self.running_request_json = dict()
- self.endpoint_inference_runners = dict()
- self.mqtt_inference_obj = None
-
- self.subscribed_topics = list()
- self.user_name = None
-
- self.replica_handler = None
-
- def unzip_file(self, zip_file, unzip_file_path) -> str:
- unziped_file_name = ""
- if zipfile.is_zipfile(zip_file):
- with zipfile.ZipFile(zip_file, "r") as zipf:
- zipf.extractall(unzip_file_path)
- unziped_file_name = zipf.namelist()[0]
- else:
- raise Exception("Invalid zip file {}".format(zip_file))
-
- return unziped_file_name
-
- def retrieve_and_unzip_package(self, package_name, package_url):
- """
- Download the package from the url and unzip it to the local package directory
- ~/.fedml/fedml-model-client/fedml/model_packages/${end_point_id}_${end_point_name}_${model_name}_${model_version}
- Under this folder, there should be the zipped file and the unzipped folder.
- the zipped file starts with fedml_run_${end_point_id}_${end_point_name}_${model_name}_${model_version}
- """
- # Models root directory
- local_package_path = ClientConstants.get_model_package_dir()
- os.makedirs(local_package_path, exist_ok=True)
-
- # Specify this model directory using ${end_point_id}_${end_point_name}_${model_name}_${model_version}
- run_id = self.request_json["end_point_id"]
- end_point_name = self.request_json["end_point_name"]
- model_config = self.request_json["model_config"]
- model_name = model_config["model_name"]
- model_version = model_config["model_version"]
-
- model_version = model_version.replace(" ", "-") # Avoid using space for folder name
- model_version = model_version.replace(":", "-") # Since docker mount will conflict with ":"
-
- this_run_model_dir = f"{run_id}_{end_point_name}_{model_name}_{model_version}"
- this_run_model_full_path = os.path.join(local_package_path, this_run_model_dir)
- os.makedirs(this_run_model_full_path, exist_ok=True)
-
- # Download the zipped package, overwrite it even if it exists
- filename, filename_without_extension, file_extension = ClientConstants.get_filename_and_extension(package_url)
- local_package_file = os.path.join(this_run_model_full_path,
- f"fedml_run_{self.run_id}_{self.edge_id}_{filename_without_extension}")
- if os.path.exists(local_package_file):
- os.remove(local_package_file)
- logging.info("Download from package_url {}".format(package_url))
- ssl._create_default_https_context = ssl._create_unverified_context
- urllib.request.urlretrieve(package_url, local_package_file,
- reporthook=self.package_download_progress)
-
- # Unzip the package in the same folder, overwrite the unzipped folder even if it exists
- unzip_package_path = os.path.join(this_run_model_full_path,
- f"unzip_fedml_run_{self.run_id}_{self.edge_id}_{filename_without_extension}")
- try:
- shutil.rmtree(unzip_package_path, ignore_errors=True)
- except Exception as e:
- pass
- package_dir_name = self.unzip_file(local_package_file, unzip_package_path)
- unzip_package_full_path = os.path.join(unzip_package_path, package_dir_name)
- model_bin_file = os.path.join(unzip_package_path, "fedml_model.bin") # Will deprecated
- logging.info("local_package_file {}, unzip_package_path {}, unzip file full path {}".format(
- local_package_file, unzip_package_path, unzip_package_full_path))
-
- return unzip_package_full_path, model_bin_file
-
- def retrieve_binary_model_file(self, package_name, package_url):
- local_package_path = ClientConstants.get_model_package_dir()
- if not os.path.exists(local_package_path):
- os.makedirs(local_package_path, exist_ok=True)
- unzip_package_path = ClientConstants.get_model_dir()
- local_package_file = "{}".format(os.path.join(local_package_path, package_name))
- if os.path.exists(local_package_file):
- os.remove(local_package_file)
- urllib.request.urlretrieve(package_url, local_package_file,
- reporthook=self.package_download_progress)
-
- unzip_package_path = os.path.join(unzip_package_path, package_name)
- if not os.path.exists(unzip_package_path):
- os.makedirs(unzip_package_path, exist_ok=True)
- dst_model_file = os.path.join(unzip_package_path, package_name)
- if os.path.exists(local_package_file):
- shutil.copy(local_package_file, dst_model_file)
-
- return unzip_package_path, dst_model_file
-
- def package_download_progress(self, count, blksize, filesize):
- self.check_runner_stop_event()
-
- downloaded = count * blksize
- downloaded = filesize if downloaded > filesize else downloaded
- progress = (downloaded / filesize * 100) if filesize != 0 else 0
- progress_int = int(progress)
- downloaded_kb = format(downloaded / 1024, '.2f')
-
- # since this hook function is stateless, we need a state to avoid printing progress repeatedly
- if count == 0:
- self.prev_download_progress = 0
- if progress_int != self.prev_download_progress and progress_int % 5 == 0:
- self.prev_download_progress = progress_int
- logging.info("package downloaded size {} KB, progress {}%".format(downloaded_kb, progress_int))
-
- def build_dynamic_constrain_variables(self, run_id, run_config):
- pass
-
- def update_local_fedml_config(self, run_id, model_config, model_config_parameters):
- model_name = model_config["model_name"]
- model_storage_url = model_config["model_storage_url"]
-
- # Retrieve model package or model binary file.
- unzip_package_path, model_bin_file = self.retrieve_and_unzip_package(model_name, model_storage_url)
-
- # Load the config to memory
- fedml_local_config_file = os.path.join(unzip_package_path, "fedml_model_config.yaml")
-
- # Inject the config from UI to pkg yaml
- package_conf_object = model_config_parameters
-
- # Save the config to local
- with open(fedml_local_config_file, "w") as f:
- yaml.dump(package_conf_object, f)
-
- logging.info("The package_conf_object is {}".format(package_conf_object))
-
- return unzip_package_path, model_bin_file, package_conf_object
-
- def build_dynamic_args(self, run_config, package_conf_object, base_dir):
- pass
-
- def download_model_package(self, package_name, package_url):
- # Copy config file from the client
- unzip_package_path = self.retrieve_and_unzip_package(
- package_name, package_url
- )
-
- return unzip_package_path
-
- def run(self, process_event, completed_event):
- # print(f"Model worker runner process id {os.getpid()}, run id {self.run_id}")
-
- if platform.system() != "Windows":
- os.setsid()
-
- os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
- os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
-
- self.run_process_event = process_event
- self.run_process_completed_event = completed_event
- run_id = self.request_json.get("end_point_id")
-
- try:
- FedMLModelDatabase.get_instance().set_database_base_dir(ClientConstants.get_database_dir())
- FedMLModelDatabase.get_instance().create_table()
-
- MLOpsUtils.set_ntp_offset(self.ntp_offset)
- self.setup_client_mqtt_mgr()
-
- if not self.run_impl():
- logging.info(
- f"[endpoint/device][{run_id}/{self.edge_id}] "
- f"Failed to run the model deployment. run_impl return False.")
-
- # This if condition only happens when run_impl return False in a controllable way
- # Under this condition, the run_impl itself should have handled the cleanup
- # So no need to self.release_gpu_ids(run_id)
- except RunnerError:
- logging.error(
- f"[endpoint/device][{run_id}/{self.edge_id}] "
- f"Failed due to RunnerError {traceback.format_exc()}")
- self.release_gpu_ids(run_id)
-
- self.reset_devices_status(self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED)
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- except RunnerCompletedError:
- logging.error(
- f"[endpoint/device][{run_id}/{self.edge_id}] "
- f"Failed due to RunnerCompletedError {traceback.format_exc()}")
- self.release_gpu_ids(run_id)
-
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- except Exception as e:
- logging.error(
- f"[endpoint/device][{run_id}/{self.edge_id}] "
- f"Failed due to exception {traceback.format_exc()}")
-
- self.cleanup_run_when_starting_failed()
- self.mlops_metrics.client_send_exit_train_msg(
- run_id, self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
-
- self.release_gpu_ids(run_id)
-
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- time.sleep(2)
- sys.exit(1)
- finally:
- logging.info("[Worker] Release resources after deployment.")
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- if self.mlops_metrics is not None:
- self.mlops_metrics.stop_sys_perf()
- time.sleep(3)
- self.release_client_mqtt_mgr()
-
- def release_gpu_ids(self, run_id):
- JobRunnerUtils.get_instance().release_gpu_ids(run_id, self.edge_id)
-
- def check_runner_stop_event(self):
- if self.run_process_event.is_set():
- logging.info("Received stopping event.")
- raise RunnerError("Runner stopped")
-
- if self.run_process_completed_event is not None and self.run_process_completed_event.is_set():
- logging.info("Received completed event.")
- raise RunnerCompletedError("Runner completed")
-
- def run_impl(self):
- # Get deployment params
- run_id = self.request_json["end_point_id"]
- end_point_name = self.request_json["end_point_name"]
- device_ids = self.request_json["device_ids"]
- master_ip = self.request_json["master_node_ip"]
- model_config = self.request_json["model_config"]
- model_name = model_config["model_name"]
- model_id = model_config["model_id"]
- model_version = model_config["model_version"]
- model_config_parameters = self.request_json["parameters"]
- inference_port = model_config_parameters.get("worker_internal_port",
- ClientConstants.MODEL_INFERENCE_DEFAULT_PORT)
- inference_port_external = model_config_parameters.get("worker_external_port", inference_port)
- inference_engine = model_config_parameters.get("inference_engine",
- ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT)
- inference_end_point_id = run_id
-
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
-
- logging.info(f"[Worker] Received model deployment request from master for endpoint {run_id}.")
- if self.replica_handler is not None:
- logging.info(f"=================Worker replica Handler ======================"
- f"Reconcile with num diff {self.replica_handler.replica_num_diff} "
- f"and version diff {self.replica_handler.replica_version_diff}."
- f"=============================================================")
- else:
- logging.error(f"[Worker] Replica handler is None.")
- return False
-
- self.check_runner_stop_event()
-
- # Report the deployment status to mlops
- self.mlops_metrics.report_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_INITIALIZING,
- is_from_model=True, running_json=json.dumps(self.request_json), run_id=run_id)
- self.mlops_metrics.report_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_RUNNING,
- is_from_model=True, run_id=run_id)
-
- self.check_runner_stop_event()
-
- # Reconcile the replica number (op: add, remove)
- prev_rank, op, op_num = self.replica_handler.reconcile_num_replica()
-
- # Reconcile the replica version (op: update)
- replica_rank_to_update = []
- if not op:
- replica_rank_to_update, op = self.replica_handler.reconcile_replica_version()
-
- if not op:
- logging.info("[Worker] No need to reconcile.")
- return True
-
- logging.info(
- f"================Worker Reconcile Operations ======================\n"
- f" op: {op}; op num: {op_num}.\n"
- f"==================================================================\n")
-
- # If not rollback, download package from MLOps; otherwise, use the backup package
- if op != "rollback":
- logging.info("Download and unzip model to local...")
- unzip_package_path, _, _ = \
- self.update_local_fedml_config(run_id, model_config, model_config_parameters)
- if unzip_package_path is None:
- logging.info("Failed to update local fedml config.")
- self.check_runner_stop_event()
- self.cleanup_run_when_starting_failed()
- self.mlops_metrics.client_send_exit_train_msg(run_id, self.edge_id,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
- return False
-
- if not os.path.exists(unzip_package_path):
- logging.info("Failed to unzip file.")
- self.check_runner_stop_event()
- self.cleanup_run_when_starting_failed()
- self.mlops_metrics.client_send_exit_train_msg(run_id, self.edge_id,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
- return False
- else:
- logging.info("Try to use backup package to rollback...")
- # Find folder under "~/.fedml/fedml-model-client/fedml/model_packages \
- # /${end_point_id}_${end_point_name}_${model_name}_${model_version}"
- backup_folder_full_path = None
- models_root_dir = ClientConstants.get_model_package_dir()
-
- # Find the version (notified by master) to rollback
- version_diff_dict = self.request_json["replica_version_diff"][str(self.edge_id)]
- version_rollback_to = None
- for replica_no, rollback_ops in version_diff_dict.items():
- version_rollback_to = rollback_ops["new_version"] # Note that new_version is the version to rollback
- break
- if version_rollback_to is None:
- logging.error(f"No old version found for run_id: {self.run_id} "
- f"edge_id: {self.edge_id}, rollback failed. No old version found in request_json.")
- return False
- model_version = version_rollback_to
-
- # Format the version to match the folder name
- model_version_formatted = version_rollback_to.replace(" ", "-")
- model_version_formatted = model_version_formatted.replace(":", "-")
-
- last_run_folder_sub_fd = f"{run_id}_{end_point_name}_{model_name}_{model_version_formatted}"
- for folder in os.listdir(models_root_dir):
- if last_run_folder_sub_fd in folder:
- backup_folder_full_path = os.path.join(models_root_dir, folder)
- break
- if backup_folder_full_path is None:
- logging.error(f"No backup folder found for run_id: {self.run_id} edge_id: {self.edge_id} "
- f"under {models_root_dir} with sub folder {last_run_folder_sub_fd}, rollback failed.")
- return False
-
- # Inside backup folder, find unzipped package with prefix unzip_fedml_run
- unzip_package_path_parent = None
- for folder in os.listdir(backup_folder_full_path):
- if folder.startswith("unzip_fedml_run"):
- unzip_package_path_parent = os.path.join(backup_folder_full_path, folder)
- break
-
- # Inside unzip folder, find the unzipped package, should be the only one
- unzip_package_path = None
- for folder in os.listdir(unzip_package_path_parent):
- if os.path.isdir(os.path.join(unzip_package_path_parent, folder)):
- unzip_package_path = os.path.join(unzip_package_path_parent, folder)
- break
-
- if unzip_package_path is None:
- logging.error(f"No unzipped package found for run_id: {self.run_id} edge_id: {self.edge_id} "
- f"under {backup_folder_full_path}, rollback failed.")
- return False
-
- self.check_runner_stop_event()
-
- running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
- "", "", model_version, {}, {}
-
- if op == "add":
- worker_ip = self.get_ip_address(self.request_json)
- for rank in range(prev_rank + 1, prev_rank + 1 + op_num):
- try:
- running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
- start_deployment(
- end_point_id=inference_end_point_id, end_point_name=end_point_name, model_id=model_id,
- model_version=model_version, model_storage_local_path=unzip_package_path,
- inference_model_name=model_name, inference_engine=inference_engine,
- infer_host=worker_ip, master_ip=master_ip, edge_id=self.edge_id,
- master_device_id=device_ids[0], replica_rank=rank,
- gpu_per_replica=int(self.replica_handler.gpu_per_replica)
- )
- except Exception as e:
- inference_output_url = ""
- logging.error(f"[Worker] Exception at deployment: {traceback.format_exc()}")
-
- if inference_output_url == "":
- logging.error("[Worker] Failed to deploy the model.")
-
- # Release the gpu occupancy
- FedMLModelCache.get_instance().set_redis_params()
- replica_occupied_gpu_ids_str = FedMLModelCache.get_instance().get_replica_gpu_ids(
- run_id, end_point_name, model_name, self.edge_id, rank + 1)
- logging.info(f"Release gpu ids {replica_occupied_gpu_ids_str} for "
- f"failed deployment of replica no {rank + 1}.")
-
- if replica_occupied_gpu_ids_str is not None:
- replica_occupied_gpu_ids = json.loads(replica_occupied_gpu_ids_str)
- JobRunnerUtils.get_instance().release_partial_job_gpu(run_id,
- self.edge_id, replica_occupied_gpu_ids)
-
- # Send failed result back to master
- result_payload = self.send_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
- model_id, model_name, inference_output_url, inference_model_version, inference_port,
- inference_engine, model_metadata, model_config)
-
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.broadcast_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- is_from_model=True, run_id=self.run_id)
-
- self.mlops_metrics.client_send_exit_train_msg(
- run_id, self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
-
- return False
- else:
- # Send failed successful result back to master
- logging.info("Finished deployment, continue to send results to master...")
- result_payload = self.send_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
- model_id, model_name, inference_output_url, model_version, inference_port_external,
- inference_engine, model_metadata, model_config, replica_no=rank + 1)
-
- if inference_port_external != inference_port:
- # Save internal port to local db
- logging.info("inference_port_external {} != inference_port {}".format(
- inference_port_external, inference_port))
- result_payload = self.construct_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
- model_id, model_name, inference_output_url, model_version, inference_port,
- inference_engine, model_metadata, model_config, replica_no=rank + 1)
-
- FedMLModelDatabase.get_instance().set_deployment_result(
- run_id, end_point_name, model_name, model_version, self.edge_id,
- json.dumps(result_payload), replica_no=rank + 1)
-
- logging.info(f"Deploy replica {rank + 1} / {prev_rank + 1 + op_num} successfully.")
- time.sleep(5)
-
- time.sleep(1)
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.broadcast_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
- is_from_model=True, run_id=self.run_id)
- return True
- elif op == "remove":
- for rank_to_delete in range(prev_rank, prev_rank - op_num, -1):
- self.replica_handler.remove_replica(rank_to_delete)
-
- FedMLModelCache.get_instance().set_redis_params()
- replica_occupied_gpu_ids_str = FedMLModelCache.get_instance().get_replica_gpu_ids(
- run_id, end_point_name, model_name, self.edge_id, rank_to_delete + 1)
-
- replica_occupied_gpu_ids = json.loads(replica_occupied_gpu_ids_str)
-
- JobRunnerUtils.get_instance().release_partial_job_gpu(run_id, self.edge_id, replica_occupied_gpu_ids)
-
- FedMLModelDatabase.get_instance().delete_deployment_result_with_device_id_and_rank(
- run_id, end_point_name, model_name, self.edge_id, rank_to_delete)
-
- # Report the deletion msg to master
- result_payload = self.send_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DELETED,
- model_id, model_name, inference_output_url, model_version, inference_port_external,
- inference_engine, model_metadata, model_config, replica_no=rank_to_delete + 1)
-
- time.sleep(1)
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.broadcast_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
- is_from_model=True, run_id=self.run_id)
-
- # TODO: If delete all replica, then delete the job and related resources
- if rank_to_delete == 0:
- pass
- return True
- elif op == "update" or op == "rollback":
- # Update is combine of delete and add
- worker_ip = self.get_ip_address(self.request_json)
- for rank in replica_rank_to_update:
- # Delete a replica (container) if exists
- self.replica_handler.remove_replica(rank)
-
- FedMLModelCache.get_instance().set_redis_params()
- replica_occupied_gpu_ids_str = FedMLModelCache.get_instance().get_replica_gpu_ids(
- run_id, end_point_name, model_name, self.edge_id, rank + 1)
-
- replica_occupied_gpu_ids = json.loads(replica_occupied_gpu_ids_str)
- logging.info(f"Release gpu ids {replica_occupied_gpu_ids} for update / rollback.")
-
- # TODO (Raphael) check if this will allow another job to seize the gpu during high concurrency:
- try:
- JobRunnerUtils.get_instance().release_partial_job_gpu(
- run_id, self.edge_id, replica_occupied_gpu_ids)
- except Exception as e:
- if op == "rollback":
- pass
- else:
- logging.error(f"Failed to release gpu ids {replica_occupied_gpu_ids} for update.")
- return False
-
- # Delete the deployment result from local db
- FedMLModelDatabase.get_instance().delete_deployment_result_with_device_id_and_rank(
- run_id, end_point_name, model_name, self.edge_id, rank)
-
- logging.info(f"Delete replica with no {rank + 1} successfully.")
- time.sleep(1)
-
- # Add a replica (container)
- # TODO: Reduce the duplicated code
- logging.info(f"Start to deploy the model with replica no {rank + 1} ...")
- try:
- running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
- start_deployment(
- end_point_id=inference_end_point_id, end_point_name=end_point_name, model_id=model_id,
- model_version=model_version, model_storage_local_path=unzip_package_path,
- inference_model_name=model_name, inference_engine=inference_engine,
- infer_host=worker_ip, master_ip=master_ip, edge_id=self.edge_id,
- master_device_id=device_ids[0], replica_rank=rank,
- gpu_per_replica=int(self.replica_handler.gpu_per_replica)
- )
- except Exception as e:
- inference_output_url = ""
- logging.error(f"Exception at deployment: {traceback.format_exc()}")
-
- if inference_output_url == "":
- logging.error("Failed to deploy the model...")
-
- # If update failed, should release this replica's gpu
- FedMLModelCache.get_instance().set_redis_params()
- replica_occupied_gpu_ids_str = FedMLModelCache.get_instance().get_replica_gpu_ids(
- run_id, end_point_name, model_name, self.edge_id, rank + 1)
-
- replica_occupied_gpu_ids = json.loads(replica_occupied_gpu_ids_str)
-
- JobRunnerUtils.get_instance().release_partial_job_gpu(
- run_id, self.edge_id, replica_occupied_gpu_ids)
-
- result_payload = self.send_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
- model_id, model_name, inference_output_url, inference_model_version, inference_port,
- inference_engine, model_metadata, model_config)
-
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.broadcast_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- is_from_model=True, run_id=self.run_id)
-
- self.mlops_metrics.client_send_exit_train_msg(
- run_id, self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
-
- return False
- else:
- logging.info("Finished deployment, continue to send results to master...")
- result_payload = self.send_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
- model_id, model_name, inference_output_url, model_version, inference_port_external,
- inference_engine, model_metadata, model_config, replica_no=rank + 1)
-
- if inference_port_external != inference_port: # Save internal port to local db
- logging.info("inference_port_external {} != inference_port {}".format(
- inference_port_external, inference_port))
- result_payload = self.construct_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
- model_id, model_name, inference_output_url, model_version, inference_port,
- inference_engine, model_metadata, model_config, replica_no=rank + 1)
-
- FedMLModelDatabase.get_instance().set_deployment_result(
- run_id, end_point_name, model_name, model_version, self.edge_id,
- json.dumps(result_payload), replica_no=rank + 1)
-
- logging.info(f"Update replica with no {rank + 1} successfully. Op num {op_num}")
- time.sleep(5)
- time.sleep(1)
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.broadcast_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
- is_from_model=True, run_id=self.run_id)
- return True
-
- else:
- # The delete op will be handled by callback_delete_deployment
- logging.error(f"Unsupported op {op} with op num {op_num}")
- return False
-
- def construct_deployment_results(self, end_point_name, device_id, model_status,
- model_id, model_name, model_inference_url,
- model_version, inference_port, inference_engine,
- model_metadata, model_config, replica_no=1):
- deployment_results_payload = {"end_point_id": self.run_id, "end_point_name": end_point_name,
- "model_id": model_id, "model_name": model_name,
- "model_url": model_inference_url, "model_version": model_version,
- "port": inference_port,
- "inference_engine": inference_engine,
- "model_metadata": model_metadata,
- "model_config": model_config,
- "model_status": model_status,
- "inference_port": inference_port,
- "replica_no": replica_no,
- }
- return deployment_results_payload
-
- def construct_deployment_status(self, end_point_name, device_id,
- model_id, model_name, model_version,
- model_inference_url, model_status,
- inference_port=ClientConstants.MODEL_INFERENCE_DEFAULT_PORT,
- replica_no=1, # start from 1
- ):
- deployment_status_payload = {"end_point_id": self.run_id, "end_point_name": end_point_name,
- "device_id": device_id,
- "model_id": model_id, "model_name": model_name,
- "model_version": model_version,
- "model_url": model_inference_url, "model_status": model_status,
- "inference_port": inference_port,
- "replica_no": replica_no,
- }
- return deployment_status_payload
-
- def send_deployment_results(self, end_point_name, device_id, model_status,
- model_id, model_name, model_inference_url,
- model_version, inference_port, inference_engine,
- model_metadata, model_config, replica_no=1):
- deployment_results_topic = "model_device/model_device/return_deployment_result/{}/{}".format(
- self.run_id, device_id)
-
- deployment_results_payload = self.construct_deployment_results(
- end_point_name, device_id, model_status,
- model_id, model_name, model_inference_url,
- model_version, inference_port, inference_engine,
- model_metadata, model_config, replica_no=replica_no)
-
- logging.info("[client] send_deployment_results: topic {}, payload {}.".format(deployment_results_topic,
- deployment_results_payload))
- self.client_mqtt_mgr.send_message_json(deployment_results_topic, json.dumps(deployment_results_payload))
- return deployment_results_payload
-
- def send_deployment_status(self, end_point_name, device_id,
- model_id, model_name, model_version,
- model_inference_url, model_status,
- inference_port=ClientConstants.MODEL_INFERENCE_DEFAULT_PORT,
- replica_no=1, # start from 1
- ):
- # Deprecated
- pass
-
- def reset_devices_status(self, edge_id, status):
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.edge_id = edge_id
- self.mlops_metrics.broadcast_client_training_status(
- edge_id, status, is_from_model=True, run_id=self.run_id)
-
- def cleanup_run_when_starting_failed(self):
- logging.info("Cleanup run successfully when starting failed.")
-
- self.reset_devices_status(self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED)
-
- time.sleep(2)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- pass
-
- time.sleep(1)
-
- def cleanup_run_when_finished(self):
- logging.info("Cleanup run successfully when finished.")
-
- self.reset_devices_status(self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED)
-
- time.sleep(2)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- pass
-
- time.sleep(1)
-
- def on_client_mqtt_disconnected(self, mqtt_client_object):
- if self.client_mqtt_lock is None:
- self.client_mqtt_lock = threading.Lock()
-
- self.client_mqtt_lock.acquire()
- self.client_mqtt_is_connected = False
- self.client_mqtt_lock.release()
-
- def on_client_mqtt_connected(self, mqtt_client_object):
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
-
- self.mlops_metrics.set_messenger(self.client_mqtt_mgr)
- self.mlops_metrics.run_id = self.run_id
-
- if self.client_mqtt_lock is None:
- self.client_mqtt_lock = threading.Lock()
-
- self.client_mqtt_lock.acquire()
- self.client_mqtt_is_connected = True
- self.client_mqtt_lock.release()
-
- def setup_client_mqtt_mgr(self):
- if self.client_mqtt_mgr is not None:
- return
-
- if self.client_mqtt_lock is None:
- self.client_mqtt_lock = threading.Lock()
-
- self.client_mqtt_mgr = MqttManager(
- self.agent_config["mqtt_config"]["BROKER_HOST"],
- self.agent_config["mqtt_config"]["BROKER_PORT"],
- self.agent_config["mqtt_config"]["MQTT_USER"],
- self.agent_config["mqtt_config"]["MQTT_PWD"],
- self.agent_config["mqtt_config"]["MQTT_KEEPALIVE"],
- "FedML_ModelClientAgent_Metrics_@{}@_{}_{}_{}".format(self.user_name, self.args.current_device_id,
- str(os.getpid()),
- str(uuid.uuid4()))
- )
-
- self.client_mqtt_mgr.add_connected_listener(self.on_client_mqtt_connected)
- self.client_mqtt_mgr.add_disconnected_listener(self.on_client_mqtt_disconnected)
- self.client_mqtt_mgr.connect()
- self.client_mqtt_mgr.loop_start()
-
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
- self.mlops_metrics.set_messenger(self.client_mqtt_mgr)
- self.mlops_metrics.run_id = self.run_id
-
- def release_client_mqtt_mgr(self):
- try:
- if self.client_mqtt_mgr is not None:
- self.client_mqtt_mgr.loop_stop()
- self.client_mqtt_mgr.disconnect()
-
- self.client_mqtt_lock.acquire()
- if self.client_mqtt_mgr is not None:
- self.client_mqtt_is_connected = False
- self.client_mqtt_mgr = None
- self.client_mqtt_lock.release()
- except Exception:
- pass
-
- def ota_upgrade(self, payload, request_json):
- run_id = request_json["end_point_id"]
- force_ota = False
- ota_version = None
-
- try:
- parameters = request_json.get("parameters", None)
- common_args = parameters.get("common_args", None)
- force_ota = common_args.get("force_ota", False)
- ota_version = common_args.get("ota_version", None)
- except Exception as e:
- pass
-
- if force_ota and ota_version is not None:
- should_upgrade = True if ota_version != fedml.__version__ else False
- upgrade_version = ota_version
- else:
- try:
- fedml_is_latest_version, local_ver, remote_ver = sys_utils.check_fedml_is_latest_version(self.version)
- except Exception as e:
- return
-
- should_upgrade = False if fedml_is_latest_version else True
- upgrade_version = remote_ver
-
- if should_upgrade:
- FedMLClientDataInterface.get_instance(). \
- save_started_job(run_id, self.edge_id, time.time(),
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
- payload)
-
- logging.info(f"Upgrade to version {upgrade_version} ...")
-
- sys_utils.do_upgrade(self.version, upgrade_version)
-
- raise Exception("Restarting after upgraded...")
-
- def callback_start_deployment(self, topic, payload):
- # Get deployment params
- request_json = json.loads(payload)
- run_id = request_json["end_point_id"]
- inference_end_point_id = run_id
-
- try:
- MLOpsConfigs.fetch_all_configs()
- except Exception as e:
- pass
-
- # Start log processor for current run
- run_id = inference_end_point_id
- self.args.run_id = run_id
- self.args.edge_id = self.edge_id
- MLOpsRuntimeLog(args=self.args).init_logs()
- MLOpsRuntimeLogDaemon.get_instance(self.args).set_log_source(
- ClientConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT)
- MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(run_id, self.edge_id)
-
- # self.ota_upgrade(payload, request_json)
-
- # Start client with multiprocessing mode
- request_json["run_id"] = run_id
- run_id_str = str(run_id)
- self.request_json = request_json
- self.running_request_json[run_id_str] = request_json
- client_runner = FedMLClientRunner(
- self.args, edge_id=self.edge_id, request_json=request_json, agent_config=self.agent_config, run_id=run_id
- )
- client_runner.infer_host = self.get_ip_address(request_json)
- self.run_process_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_event_map[run_id_str].clear()
- client_runner.run_process_event = self.run_process_event_map[run_id_str]
- self.run_process_completed_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_completed_event_map[run_id_str].clear()
- client_runner.run_process_completed_event = self.run_process_completed_event_map[run_id_str]
- self.model_runner_mapping[run_id_str] = client_runner
-
- # Replica Handler will be init for every deployment
- replica_handler = FedMLDeviceReplicaHandler(self.edge_id, self.request_json)
- client_runner.replica_handler = replica_handler
-
- self.run_id = run_id
- self.run_process_map[run_id_str] = Process(target=client_runner.run, args=(
- self.run_process_event_map[run_id_str], self.run_process_completed_event_map[run_id_str]
- ))
-
- self.run_process_map[run_id_str].start()
- ClientConstants.save_run_process(run_id, self.run_process_map[run_id_str].pid)
- ClientConstants.save_runner_infos(self.args.device_id + "." + self.args.os_name, self.edge_id, run_id=run_id)
-
- def set_runner_stopped_event(self, run_id):
- run_id_str = str(run_id)
- client_runner = self.model_runner_mapping.get(run_id_str, None)
- if client_runner is not None:
- if client_runner.run_process_event is not None:
- client_runner.run_process_event.set()
- self.model_runner_mapping.pop(run_id_str)
-
- def set_runner_completed_event(self, run_id):
- run_id_str = str(run_id)
- client_runner = self.model_runner_mapping.get(run_id_str, None)
- if client_runner is not None:
- if client_runner.run_process_completed_event is not None:
- client_runner.run_process_completed_event.set()
- self.model_runner_mapping.pop(run_id_str)
-
- def callback_delete_deployment(self, topic, payload):
- logging.info("[Worker] callback_delete_deployment")
-
- # Parse payload as the model message object.
- model_msg_object = FedMLModelMsgObject(topic, payload)
-
- # Delete all replicas on this device
- try:
- ClientConstants.remove_deployment(
- model_msg_object.end_point_name, model_msg_object.model_name, model_msg_object.model_version,
- model_msg_object.run_id, model_msg_object.model_id, edge_id=self.edge_id)
- except Exception as e:
- logging.info(f"Exception when removing deployment {traceback.format_exc()}")
- pass
-
- self.set_runner_stopped_event(model_msg_object.run_id)
-
- logging.info(f"[endpoint/device][{model_msg_object.run_id}/{self.edge_id}] "
- f"Release gpu resource when the worker deployment deleted.")
- JobRunnerUtils.get_instance().release_gpu_ids(model_msg_object.run_id, self.edge_id)
-
- if self.running_request_json.get(str(model_msg_object.run_id)) is not None:
- try:
- self.running_request_json.pop(str(model_msg_object.run_id))
- except Exception as e:
- logging.error(f"Error when removing running_request_json: {traceback.format_exc()}")
- pass
-
- FedMLClientDataInterface.get_instance().delete_job_from_db(model_msg_object.run_id)
- FedMLModelDatabase.get_instance().delete_deployment_result_with_device_id(
- model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_name,
- self.edge_id)
-
- # Delete FEDML_GLOBAL_ENDPOINT_RUN_ID_MAP_TAG-${run_id} both in redis and local db
- ComputeCacheManager.get_instance().gpu_cache.delete_endpoint_run_id_map(str(model_msg_object.run_id))
-
- # Delete FEDML_EDGE_ID_MODEL_DEVICE_ID_MAP_TAG-${run_id} both in redis and local db
- ComputeCacheManager.get_instance().gpu_cache.delete_edge_model_id_map(str(model_msg_object.run_id))
-
- # Delete FEDML_GLOBAL_DEVICE_RUN_GPU_IDS_TAG-${run_id}-${device_id} both in redis and local db
- ComputeCacheManager.get_instance().gpu_cache.delete_device_run_gpu_ids(str(self.edge_id),
- str(model_msg_object.run_id))
-
- # Delete FEDML_GLOBAL_DEVICE_RUN_NUM_GPUS_TAG-${run_id}-${device_id} both in redis and local db
- ComputeCacheManager.get_instance().gpu_cache.delete_device_run_num_gpus(str(self.edge_id),
- str(model_msg_object.run_id))
-
- # Delete FEDML_MODEL_REPLICA_GPU_IDS_TAG-${run_id}-${end_point_name}-${model_name}-${device_id}-*
- FedMLModelCache.get_instance().set_redis_params()
- FedMLModelCache.get_instance().delete_all_replica_gpu_ids(model_msg_object.run_id,
- model_msg_object.end_point_name,
- model_msg_object.model_name, self.edge_id)
-
- def exit_run_with_exception_entry(self):
- try:
- self.setup_client_mqtt_mgr()
- self.exit_run_with_exception()
- except Exception as e:
- self.release_client_mqtt_mgr()
- sys.exit(1)
- finally:
- self.release_client_mqtt_mgr()
-
- def exit_run_with_exception(self):
- logging.info("Exit run successfully.")
-
- ClientConstants.cleanup_learning_process(self.run_id)
- ClientConstants.cleanup_run_process(self.run_id)
-
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- is_from_model=True, run_id=self.run_id)
-
- time.sleep(1)
-
- def callback_exit_train_with_exception(self, topic, payload):
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json.get("runId", None)
- if run_id is None:
- run_id = request_json.get("run_id", None)
- if run_id is None:
- run_id = request_json.get("id", None)
-
- if run_id is None:
- return
-
- # Stop client with multiprocessing mode
- self.request_json = request_json
- client_runner = FedMLClientRunner(
- self.args, edge_id=self.edge_id, request_json=request_json, agent_config=self.agent_config, run_id=run_id
- )
- try:
- Process(target=client_runner.exit_run_with_exception_entry).start()
- except Exception as e:
- pass
-
- def cleanup_client_with_status(self):
- self.setup_client_mqtt_mgr()
-
- if self.device_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED:
- self.cleanup_run_when_finished()
- elif self.device_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED:
- self.cleanup_run_when_starting_failed()
-
- self.release_client_mqtt_mgr()
-
- def callback_runner_id_status(self, topic, payload):
- # logging.info("callback_runner_id_status: topic = %s, payload = %s" % (topic, payload))
-
- request_json = json.loads(payload)
- run_id = request_json["run_id"]
- edge_id = request_json["edge_id"]
- status = request_json["status"]
-
- self.save_training_status(edge_id, status)
-
- if status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or \
- status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED:
- # Stop client with multiprocessing mode
- self.request_json = request_json
- client_runner = FedMLClientRunner(
- self.args,
- edge_id=self.edge_id,
- request_json=request_json,
- agent_config=self.agent_config,
- run_id=run_id,
- )
- client_runner.device_status = status
- status_process = Process(target=client_runner.cleanup_client_with_status)
- status_process.start()
- status_process.join(15)
-
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, edge_id)
-
- def callback_report_current_status(self, topic, payload):
- self.send_agent_active_msg()
-
- @staticmethod
- def process_ota_upgrade_msg():
- os.system("pip install -U fedml")
-
- def callback_client_ota_msg(self, topic, payload):
- request_json = json.loads(payload)
- cmd = request_json["cmd"]
-
- if cmd == ClientConstants.FEDML_OTA_CMD_UPGRADE:
- FedMLClientRunner.process_ota_upgrade_msg()
- # Process(target=FedMLClientRunner.process_ota_upgrade_msg).start()
- raise Exception("After upgraded, restart runner...")
- elif cmd == ClientConstants.FEDML_OTA_CMD_RESTART:
- raise Exception("Restart runner...")
-
- def save_training_status(self, edge_id, training_status):
- self.current_training_status = training_status
- ClientConstants.save_training_infos(edge_id, training_status)
-
- @staticmethod
- def get_device_id():
- device_file_path = os.path.join(ClientConstants.get_data_dir(),
- ClientConstants.LOCAL_RUNNER_INFO_DIR_NAME)
- file_for_device_id = os.path.join(device_file_path, "devices.id")
- if not os.path.exists(device_file_path):
- os.makedirs(device_file_path)
- elif os.path.exists(file_for_device_id):
- with open(file_for_device_id, 'r', encoding='utf-8') as f:
- device_id_from_file = f.readline()
- if device_id_from_file is not None and device_id_from_file != "":
- return device_id_from_file
-
- if platform.system() == "Darwin":
- cmd_get_serial_num = "system_profiler SPHardwareDataType | grep Serial | awk '{gsub(/ /,\"\")}{print}' " \
- "|awk -F':' '{print $2}' "
- device_id = os.popen(cmd_get_serial_num).read()
- device_id = device_id.replace('\n', '').replace(' ', '')
- if device_id is None or device_id == "":
- device_id = hex(uuid.getnode())
- else:
- device_id = "0x" + device_id
- else:
- if "nt" in os.name:
-
- def get_uuid():
- guid = ""
- try:
- cmd = "wmic csproduct get uuid"
- guid = str(subprocess.check_output(cmd))
- pos1 = guid.find("\\n") + 2
- guid = guid[pos1:-15]
- except Exception as ex:
- pass
- return str(guid)
-
- device_id = str(get_uuid())
- logging.info(device_id)
- elif "posix" in os.name:
- device_id = sys_utils.get_device_id_in_docker()
- if device_id is None:
- device_id = hex(uuid.getnode())
- else:
- device_id = sys_utils.run_subprocess_open(
- "hal-get-property --udi /org/freedesktop/Hal/devices/computer --key system.hardware.uuid".split()
- )
- device_id = hex(device_id)
-
- if device_id is not None and device_id != "":
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
- else:
- device_id = hex(uuid.uuid4())
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
-
- return device_id
-
- def get_ip_address(self, request_json):
- # OPTION 1: Use local ip
- ip = ClientConstants.get_local_ip()
-
- # OPTION 2: Auto detect public ip
- if "parameters" in request_json and \
- ClientConstants.AUTO_DETECT_PUBLIC_IP in request_json["parameters"] and \
- request_json["parameters"][ClientConstants.AUTO_DETECT_PUBLIC_IP]:
- ip = ClientConstants.get_public_ip()
- logging.info("Auto detect public ip for worker: " + ip)
-
- # OPTION 3: Use user indicated ip
- if self.infer_host is not None and self.infer_host != "127.0.0.1" and self.infer_host != "localhost":
- ip = self.infer_host
-
- return ip
-
- def bind_account_and_device_id(self, url, account_id, device_id, os_name, role="md.on_premise_device"):
- ip = requests.get('https://checkip.amazonaws.com').text.strip()
- fedml_ver, exec_path, os_ver, cpu_info, python_ver, torch_ver, mpi_installed, \
- cpu_usage, available_mem, total_mem, gpu_info, gpu_available_mem, gpu_total_mem, \
- gpu_count, gpu_vendor, cpu_count, gpu_device_name = get_sys_runner_info()
- host_name = sys_utils.get_host_name()
- json_params = {
- "accountid": account_id,
- "deviceid": device_id,
- "state": ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE,
- "status": ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE,
- "type": os_name,
- "processor": cpu_info,
- "core_type": cpu_info,
- "network": "",
- "role": role,
- "os_ver": os_ver,
- "memory": total_mem,
- "ip": ip,
- "extra_infos": {"fedml_ver": fedml_ver, "exec_path": exec_path, "os_ver": os_ver,
- "cpu_info": cpu_info, "python_ver": python_ver, "torch_ver": torch_ver,
- "mpi_installed": mpi_installed, "cpu_usage": cpu_usage,
- "available_mem": available_mem, "total_mem": total_mem,
- "cpu_count": cpu_count, "gpu_count": 0, "host_name": host_name}
- }
- if gpu_count > 0:
- if gpu_total_mem is not None:
- json_params["gpu"] = gpu_info if gpu_info is not None else "" + ", Total GPU Memory: " + gpu_total_mem
- else:
- json_params["gpu"] = gpu_info if gpu_info is not None else ""
- json_params["extra_infos"]["gpu_info"] = gpu_info if gpu_info is not None else ""
- if gpu_available_mem is not None:
- json_params["extra_infos"]["gpu_available_mem"] = gpu_available_mem
- if gpu_total_mem is not None:
- json_params["extra_infos"]["gpu_total_mem"] = gpu_total_mem
-
- json_params["extra_infos"]["gpu_count"] = gpu_count
- json_params["extra_infos"]["gpu_vendor"] = gpu_vendor
- json_params["extra_infos"]["gpu_device_name"] = gpu_device_name
-
- gpu_available_id_list = sys_utils.get_available_gpu_id_list(limit=gpu_count)
- gpu_available_count = len(gpu_available_id_list) if gpu_available_id_list is not None else 0
- gpu_list = sys_utils.get_gpu_list()
- json_params["extra_infos"]["gpu_available_count"] = gpu_available_count
- json_params["extra_infos"]["gpu_available_id_list"] = gpu_available_id_list
- json_params["extra_infos"]["gpu_list"] = gpu_list
- else:
- json_params["gpu"] = "None"
- json_params["extra_infos"]["gpu_available_count"] = 0
- json_params["extra_infos"]["gpu_available_id_list"] = []
- json_params["extra_infos"]["gpu_list"] = []
-
- _, cert_path = MLOpsConfigs.get_request_params()
- if cert_path is not None:
- try:
- requests.session().verify = cert_path
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- except requests.exceptions.SSLError as err:
- MLOpsConfigs.install_root_ca_file()
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- else:
- response = requests.post(url, json=json_params, headers={"Connection": "close"})
- edge_id = -1
- user_name = None
- extra_url = None
- if response.status_code != 200:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- pass
- else:
- # print("url = {}, response = {}".format(url, response))
- status_code = response.json().get("code")
- if status_code == "SUCCESS":
- edge_id = response.json().get("data").get("id")
- user_name = response.json().get("data").get("userName", None)
- extra_url = response.json().get("data").get("url", None)
- if edge_id is None or edge_id <= 0:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- else:
- if status_code == SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR:
- raise SystemExit(SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR)
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- return -1, None, None
- return edge_id, user_name, extra_url
-
- def fetch_configs(self):
- return MLOpsConfigs.fetch_all_configs()
-
- def send_agent_active_msg(self):
- active_topic = "flclient_agent/active"
- status = MLOpsStatus.get_instance().get_client_agent_status(self.edge_id)
- if (
- status is not None
- and status != ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE
- and status != ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE
- ):
- return
-
- try:
- current_job = FedMLClientDataInterface.get_instance().get_job_by_id(self.run_id)
- except Exception as e:
- current_job = None
- if current_job is None:
- if status is not None and status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE:
- status = ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE
- else:
- return
- else:
- status = ClientConstants.get_device_state_from_run_edge_state(current_job.status)
- active_msg = {"ID": self.edge_id, "status": status}
- MLOpsStatus.get_instance().set_client_agent_status(self.edge_id, status)
- self.mqtt_mgr.send_message_json(active_topic, json.dumps(active_msg))
-
- def recover_start_deployment_msg_after_upgrading(self):
- try:
- current_job = FedMLClientDataInterface.get_instance().get_current_job()
- if current_job is not None and \
- current_job.status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING:
- logging.info("start deployment after upgrading.")
- topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(self.edge_id))
- self.callback_start_deployment(topic_start_deployment, current_job.running_json)
- except Exception as e:
- logging.info("recover starting deployment message after upgrading: {}".format(traceback.format_exc()))
-
- def on_agent_mqtt_connected(self, mqtt_client_object):
- # The MQTT message topic format is as follows: <sender>/<receiver>/<action>
-
- # Setup MQTT message listener for starting deployment
- topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(self.edge_id))
- self.mqtt_mgr.add_message_listener(topic_start_deployment, self.callback_start_deployment)
-
- # Setup MQTT message listener for delete deployment
- topic_delete_deployment = "model_ops/model_device/delete_deployment/{}".format(str(self.edge_id))
- self.mqtt_mgr.add_message_listener(topic_delete_deployment, self.callback_delete_deployment)
-
- # Setup MQTT message listener for running failed
- topic_exit_train_with_exception = "flserver_agent/" + str(self.edge_id) + "/exit_train_with_exception"
- self.mqtt_mgr.add_message_listener(topic_exit_train_with_exception, self.callback_exit_train_with_exception)
-
- # Setup MQTT message listener for client status switching
- topic_client_status = "fl_client/flclient_agent_" + str(self.edge_id) + "/status"
- self.mqtt_mgr.add_message_listener(topic_client_status, self.callback_runner_id_status)
-
- # Setup MQTT message listener to report current device status.
- topic_report_status = "mlops/report_device_status"
- self.mqtt_mgr.add_message_listener(topic_report_status, self.callback_report_current_status)
-
- # Setup MQTT message listener to OTA messages from the MLOps.
- topic_ota_msg = "mlops/flclient_agent_" + str(self.edge_id) + "/ota"
- self.mqtt_mgr.add_message_listener(topic_ota_msg, self.callback_client_ota_msg)
-
- if self.mqtt_inference_obj is None:
- self.mqtt_inference_obj = FedMLMqttInference(agent_config=self.agent_config, mqtt_mgr=self.mqtt_mgr)
- self.mqtt_inference_obj.setup_listener_for_endpoint_inference_request(self.edge_id)
-
- # Subscribe topics for starting deployment, stopping deployment and fetching client status.
- mqtt_client_object.subscribe(topic_start_deployment, qos=2)
- mqtt_client_object.subscribe(topic_delete_deployment, qos=2)
- mqtt_client_object.subscribe(topic_client_status, qos=2)
- mqtt_client_object.subscribe(topic_report_status, qos=2)
- mqtt_client_object.subscribe(topic_exit_train_with_exception, qos=2)
- mqtt_client_object.subscribe(topic_ota_msg, qos=2)
-
- self.subscribed_topics.clear()
- self.subscribed_topics.append(topic_start_deployment)
- self.subscribed_topics.append(topic_delete_deployment)
- self.subscribed_topics.append(topic_client_status)
- self.subscribed_topics.append(topic_report_status)
- self.subscribed_topics.append(topic_exit_train_with_exception)
- self.subscribed_topics.append(topic_ota_msg)
-
- # Broadcast the first active message.
- self.send_agent_active_msg()
-
- # Echo results
- # print("\n\nCongratulations, your device is connected to the FedML MLOps platform successfully!")
- # print(
- # "Your FedML Edge ID is " + str(self.edge_id) + ", unique device ID is "
- # + str(self.unique_device_id)
- # + "\n"
- # )
-
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
-
- def on_agent_mqtt_disconnected(self, mqtt_client_object):
- MLOpsStatus.get_instance().set_client_agent_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE
- )
-
- try:
- if self.mqtt_inference_obj is not None:
- self.mqtt_inference_obj.remove_listener_for_endpoint_inference_request(self.edge_id)
- except Exception as e:
- pass
-
- def setup_agent_mqtt_connection(self, service_config):
- # Setup MQTT connection
- self.mqtt_mgr = MqttManager(
- service_config["mqtt_config"]["BROKER_HOST"],
- service_config["mqtt_config"]["BROKER_PORT"],
- service_config["mqtt_config"]["MQTT_USER"],
- service_config["mqtt_config"]["MQTT_PWD"],
- service_config["mqtt_config"]["MQTT_KEEPALIVE"],
- "FedML_ModelClientAgent_Daemon_@" + self.user_name + "@_" + self.args.current_device_id + str(uuid.uuid4()),
- "flclient_agent/last_will_msg",
- json.dumps({"ID": self.edge_id, "status": ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE})
- )
- self.agent_config = service_config
-
- # Init local database
- FedMLClientDataInterface.get_instance().create_job_table()
- try:
- FedMLModelDatabase.get_instance().set_database_base_dir(ClientConstants.get_database_dir())
- FedMLModelDatabase.get_instance().create_table()
- except Exception as e:
- pass
-
- client_api_cmd = "fedml.computing.scheduler.model_scheduler.device_client_api:api"
- client_api_pids = RunProcessUtils.get_pid_from_cmd_line(client_api_cmd)
- if client_api_pids is None or len(client_api_pids) <= 0:
- # Start local API services
- cur_dir = os.path.dirname(__file__)
- fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- python_program = get_python_program()
- self.local_api_process = ClientConstants.exec_console_with_script(
- "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
- "--log-level critical".format(
- python_program, client_api_cmd,
- ClientConstants.LOCAL_CLIENT_API_PORT, fedml_base_dir
- ),
- should_capture_stdout=False,
- should_capture_stderr=False
- )
- # if self.local_api_process is not None and self.local_api_process.pid is not None:
- # print(f"Model worker local API process id {self.local_api_process.pid}")
-
- # MLOpsRuntimeLogDaemon.get_instance(self.args).stop_all_log_processor()
-
- # Setup MQTT connected listener
- self.mqtt_mgr.add_connected_listener(self.on_agent_mqtt_connected)
- self.mqtt_mgr.add_disconnected_listener(self.on_agent_mqtt_disconnected)
- self.mqtt_mgr.connect()
-
- self.setup_client_mqtt_mgr()
- self.mlops_metrics.report_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE, is_from_model=True)
- MLOpsStatus.get_instance().set_client_agent_status(self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE)
-
- self.recover_start_deployment_msg_after_upgrading()
-
- def stop_agent(self):
- if self.run_process_event is not None:
- self.run_process_event.set()
-
- if self.mqtt_mgr is not None:
- try:
- for topic in self.subscribed_topics:
- self.mqtt_mgr.unsubscribe_msg(topic)
- except Exception as e:
- pass
-
- self.mqtt_mgr.loop_stop()
- self.mqtt_mgr.disconnect()
-
- self.release_client_mqtt_mgr()
-
- def start_agent_mqtt_loop(self, should_exit_sys=False):
- # Start MQTT message loop
- try:
- self.mqtt_mgr.loop_forever()
- except Exception as e:
- if str(e) == "Restarting after upgraded...":
- logging.info("Restarting after upgraded...")
- else:
- logging.info("Client tracing: {}".format(traceback.format_exc()))
- finally:
- self.stop_agent()
-
- if should_exit_sys:
- time.sleep(5)
- sys.exit(1)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_runner_deprecated.py b/python/fedml/computing/scheduler/model_scheduler/device_server_runner_deprecated.py
deleted file mode 100755
index 4bcac6d2db..0000000000
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_runner_deprecated.py
+++ /dev/null
@@ -1,2022 +0,0 @@
-import copy
-import json
-import logging
-import multiprocessing
-import platform
-import sys
-
-from multiprocessing import Process
-import os
-import shutil
-import subprocess
-import threading
-
-import time
-import traceback
-import urllib
-import uuid
-import zipfile
-from os import listdir
-
-import requests
-import torch
-
-import fedml
-from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
-from fedml.core.mlops.mlops_runtime_log import MLOpsFormatter
-
-from ..comm_utils import sys_utils
-from .device_server_data_interface import FedMLServerDataInterface
-from ..scheduler_core.endpoint_sync_protocol import FedMLEndpointSyncProtocol
-from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
-
-from ....core.distributed.communication.mqtt.mqtt_manager import MqttManager
-from ..comm_utils.yaml_utils import load_yaml_config
-from .device_client_constants import ClientConstants
-from .device_server_constants import ServerConstants
-
-from ....core.mlops.mlops_metrics import MLOpsMetrics
-
-from ....core.mlops.mlops_configs import MLOpsConfigs
-from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
-from ....core.mlops.mlops_status import MLOpsStatus
-from ..comm_utils.sys_utils import get_sys_runner_info, get_python_program
-from .device_model_cache import FedMLModelCache
-from .device_model_msg_object import FedMLModelMsgObject
-from ....core.mlops.mlops_utils import MLOpsUtils
-from ..comm_utils.constants import SchedulerConstants
-from .device_model_db import FedMLModelDatabase
-from .device_replica_controller import FedMLDeviceReplicaController
-
-
-class RunnerError(BaseException):
- """ Runner failed. """
- pass
-
-
-class RunnerCompletedError(Exception):
- """ Runner completed. """
- pass
-
-
-class FedMLServerRunner:
- FEDML_CLOUD_SERVER_PREFIX = "fedml-server-run-"
-
- def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id=0):
- self.inference_gateway_process = None
- self.local_api_process = None
- self.run_process_event = None
- self.run_process_event_map = dict()
- self.run_process_completed_event = None
- self.run_process_completed_event_map = dict()
- self.run_as_cloud_agent = False
- self.run_as_cloud_server = False
- self.run_as_edge_server_and_agent = False
- self.run_as_cloud_server_and_agent = False
- self.fedml_packages_base_dir = None
- self.fedml_packages_unzip_dir = None
- self.mqtt_mgr = None
- self.running_request_json = dict()
- self.run_id = run_id
- self.client_mqtt_mgr = None
- self.client_mqtt_is_connected = False
- self.client_mqtt_lock = None
- self.unique_device_id = None
- self.edge_id = edge_id
- self.server_agent_id = 0
- if request_json is not None:
- self.server_agent_id = request_json.get("server_id", 0)
- self.process = None
- self.args = args
- self.request_json = copy.deepcopy(request_json)
- self.version = args.version
- self.device_id = args.device_id
- self.cur_dir = os.path.split(os.path.realpath(__file__))[0]
- if args.current_running_dir is not None:
- self.cur_dir = args.current_running_dir
-
- self.agent_config = agent_config
- self.fedml_data_base_package_dir = os.path.join("/", "fedml", "data")
- self.fedml_data_local_package_dir = os.path.join("/", "fedml", "fedml-package", "fedml", "data")
- self.fedml_data_dir = self.fedml_data_base_package_dir
- self.fedml_config_dir = os.path.join("/", "fedml", "conf")
-
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES = {}
-
- self.mlops_metrics = None
- self.run_status = None
- self.infer_host = "127.0.0.1"
- self.redis_addr = "local"
- self.redis_port = "6379"
- self.redis_password = "fedml_default"
-
- self.slave_deployment_statuses_mapping = dict()
- self.slave_deployment_results_mapping = dict()
- self.slave_update_result_mapping = dict()
-
- self.model_runner_mapping = dict()
- self.ntp_offset = MLOpsUtils.get_ntp_offset()
-
- self.subscribed_topics = list()
- self.user_name = None
-
- self.replica_controller = None
- self.deployed_replica_payload = None
-
- self.autoscaler_launcher = None
-
- def build_dynamic_constrain_variables(self, run_id, run_config):
- pass
-
- def unzip_file(self, zip_file, unzip_file_path):
- unziped_file_name = ""
- if zipfile.is_zipfile(zip_file):
- with zipfile.ZipFile(zip_file, "r") as zipf:
- zipf.extractall(unzip_file_path)
- unziped_file_name = zipf.namelist()[0]
-
- return unziped_file_name
-
- def package_download_progress(self, count, blksize, filesize):
- self.check_runner_stop_event()
-
- downloaded = count * blksize
- downloaded = filesize if downloaded > filesize else downloaded
- progress = (downloaded / filesize * 100) if filesize != 0 else 0
- progress_int = int(progress)
- downloaded_kb = format(downloaded / 1024, '.2f')
-
- # since this hook function is stateless, we need a state to avoid printing progress repeatedly
- if count == 0:
- self.prev_download_progress = 0
- if progress_int != self.prev_download_progress and progress_int % 5 == 0:
- self.prev_download_progress = progress_int
- logging.info("package downloaded size {} KB, progress {}%".format(downloaded_kb, progress_int))
-
- def retrieve_and_unzip_package(self, package_name, package_url):
- local_package_path = ServerConstants.get_model_package_dir()
- if not os.path.exists(local_package_path):
- os.makedirs(local_package_path, exist_ok=True)
- local_package_file = "{}.zip".format(os.path.join(local_package_path, package_name))
- if os.path.exists(local_package_file):
- os.remove(local_package_file)
-
- # Download without renaming
- urllib.request.urlretrieve(package_url, filename=None, reporthook=self.package_download_progress)
-
- unzip_package_path = ServerConstants.get_model_dir()
- self.fedml_packages_base_dir = unzip_package_path
- try:
- shutil.rmtree(
- os.path.join(unzip_package_path, package_name), ignore_errors=True
- )
- except Exception as e:
- pass
- logging.info("local_package_file {}, unzip_package_path {}".format(
- local_package_file, unzip_package_path))
- package_name = self.unzip_file(local_package_file, unzip_package_path)
- unzip_package_path = os.path.join(unzip_package_path, package_name)
- return unzip_package_path
-
- def update_local_fedml_config(self, run_id, run_config):
- model_config = run_config
- model_name = model_config["model_name"]
- model_storage_url = model_config["model_storage_url"]
- scale_min = model_config.get("instance_scale_min", 0)
- scale_max = model_config.get("instance_scale_max", 0)
- inference_engine = model_config.get("inference_engine", 0)
- inference_end_point_id = run_id
-
- # Copy config file from the client
- unzip_package_path = self.retrieve_and_unzip_package(
- model_name, model_storage_url
- )
- fedml_local_config_file = os.path.join(unzip_package_path, "fedml_model_config.yaml")
-
- # Load the above config to memory
- package_conf_object = {}
- if os.path.exists(fedml_local_config_file):
- package_conf_object = load_yaml_config(fedml_local_config_file)
-
- return unzip_package_path, package_conf_object
-
- def get_usr_indicated_token(self, request_json) -> str:
- usr_indicated_token = ""
- if "parameters" in request_json and "authentication_token" in request_json["parameters"]:
- usr_indicated_token = request_json["parameters"]["authentication_token"]
- return usr_indicated_token
-
- def build_dynamic_args(self, run_config, package_conf_object, base_dir):
- pass
-
- def run(self, process_event, completed_event):
- # print(f"Model master runner process id {os.getpid()}, run id {self.run_id}")
-
- if platform.system() != "Windows":
- os.setsid()
-
- os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
- os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
-
- self.run_process_event = process_event
- self.run_process_completed_event = completed_event
- run_id = self.request_json.get("end_point_id")
-
- try:
- MLOpsUtils.set_ntp_offset(self.ntp_offset)
-
- self.setup_client_mqtt_mgr()
-
- self.run_impl()
- except RunnerError:
- logging.info("Runner stopped.")
- self.mlops_metrics.report_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_KILLED,
- is_from_model=True, edge_id=self.edge_id)
- except RunnerCompletedError:
- logging.info("Runner completed.")
- except Exception as e:
- logging.error("Runner exits with exceptions.")
- logging.error(traceback.format_exc())
- logging.error(e)
- self.mlops_metrics.report_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED,
- is_from_model=True, edge_id=self.edge_id)
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- if self.mlops_metrics is not None:
- self.mlops_metrics.stop_sys_perf()
- time.sleep(3)
- sys.exit(1)
- finally:
- logging.info("[Master] Deployment finished, release resources.")
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
- if self.mlops_metrics is not None:
- self.mlops_metrics.stop_sys_perf()
- time.sleep(3)
- if not self.run_as_cloud_server:
- self.release_client_mqtt_mgr()
-
- def parse_model_run_params(self, running_json):
- run_id = running_json["end_point_id"]
- end_point_name = running_json["end_point_name"]
- token = running_json["token"]
- user_id = running_json["user_id"]
- user_name = running_json["user_name"]
- device_ids = running_json["device_ids"]
- device_objs = running_json["device_objs"]
-
- model_config = running_json["model_config"]
- model_name = model_config["model_name"]
- model_id = model_config["model_id"]
- model_storage_url = model_config["model_storage_url"]
- scale_min = model_config.get("instance_scale_min", 0)
- scale_max = model_config.get("instance_scale_max", 0)
- inference_engine = model_config.get("inference_engine", 0)
- model_is_from_open = model_config["is_from_open"]
- inference_end_point_id = run_id
- use_gpu = "gpu" # TODO: Get GPU from device infos
- memory_size = "256m" # TODO: Get Memory size for each instance
- model_version = model_config["model_version"]
- model_config_parameters = running_json.get("parameters", {})
-
- inference_port = model_config_parameters.get("server_internal_port", # Internal port is for the gateway
- ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
- inference_port_external = model_config_parameters.get("server_external_port", inference_port)
-
- return run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
- model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
- inference_end_point_id, use_gpu, memory_size, model_version, inference_port
-
- def inference_run(self):
- # run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
- # model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
- # inference_end_point_id, use_gpu, memory_size, model_version, inference_port =
- # self.parse_model_run_params(self.request_json)
- #
- # inference_server = FedMLModelServingServer(self.args,
- # end_point_name,
- # model_name,
- # model_version,
- # inference_request=self.request_json)
- # inference_server.run()
- pass
-
- def run_impl(self):
- run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
- model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
- inference_end_point_id, use_gpu, memory_size, model_version, inference_port = self.parse_model_run_params(
- self.request_json)
-
- # TODO(Raphael): This measurement is for the host machine. Change to container's metrics
- self.mlops_metrics.report_sys_perf(self.args, self.agent_config["mqtt_config"], run_id=run_id)
-
- self.check_runner_stop_event()
-
- # Send stage: MODEL_DEPLOYMENT_STAGE4 = "ForwardRequest2Slave"
- self.send_deployment_stages(self.run_id, model_name, model_id,
- "",
- ServerConstants.MODEL_DEPLOYMENT_STAGE4["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE4["text"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE4["text"])
-
- self.args.run_id = self.run_id
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
-
- # Report server running status
- self.check_runner_stop_event()
- self.mlops_metrics.report_server_training_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_STARTING,
- is_from_model=True, running_json=json.dumps(self.request_json), edge_id=self.edge_id)
- self.send_deployment_status(self.run_id, end_point_name,
- model_name, "",
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYING)
-
- # Start unified inference gateway if it has not started
- self.start_device_inference_gateway(
- run_id, end_point_name, model_id, model_name, model_version, inference_port=inference_port)
-
- # (re)Start inference monitor server
- self.stop_device_inference_monitor(run_id, end_point_name, model_id, model_name, model_version)
- self.start_device_inference_monitor(run_id, end_point_name, model_id, model_name, model_version)
-
- # Changed the master's status to "IDLE"
- self.mlops_metrics.broadcast_server_training_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED,
- is_from_model=True, edge_id=self.edge_id)
-
- # Forward deployment request to slave devices
- self.check_runner_stop_event()
-
- # Handle "op:add" && "op:remove"
- devices_sent_add_or_remove_msg = self.send_deployment_start_request_to_edges()
-
- # Handle "op:update"
- try:
- devices_sent_update_remove_msg = self.send_first_scroll_update_msg()
-
- if len(devices_sent_add_or_remove_msg) == 0 and len(devices_sent_update_remove_msg) == 0:
- # No device is added, updated or removed
- logging.info("No device is added, updated or removed. No action needed for reconciliation.")
- ip = self.get_ip_address(self.request_json)
- master_port = os.getenv("FEDML_MASTER_PORT", None)
- if master_port is not None:
- inference_port = int(master_port)
- model_inference_port = inference_port
- if ip.startswith("http://") or ip.startswith("https://"):
- model_inference_url = "{}/api/v1/predict".format(ip)
- else:
- model_inference_url = "http://{}:{}/api/v1/predict".format(ip, model_inference_port)
-
- self.set_runner_completed_event(run_id)
-
- self.send_deployment_status(run_id, end_point_name,
- model_name,
- model_inference_url,
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED)
-
- # Set setting to "DEPLOYED" for autoscaling service reference
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- update_user_setting_replica_num(end_point_id=run_id, state="DEPLOYED")
-
- return
- except Exception as e:
- logging.error(f"Failed to send first scroll update message due to {e}.")
- logging.error(f"Exception traceback {traceback.format_exc()}.")
-
- logging.info("Start waiting for result callback from workers ...")
-
- while True:
- # Wait for all devices to finish the add / delete / update operation
- self.check_runner_stop_event()
- time.sleep(3)
-
- def check_runner_stop_event(self):
- if self.run_process_event is not None and self.run_process_event.is_set():
- logging.info("Received stopping event.")
- raise RunnerError("Runner stopped")
-
- if self.run_process_completed_event is not None and self.run_process_completed_event.is_set():
- logging.info("Received completed event.")
- raise RunnerCompletedError("Runner completed")
-
- def start_device_inference_gateway(
- self, run_id, end_point_name, model_id,
- model_name, model_version, inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT):
- # start unified inference server
- running_model_name = ServerConstants.get_running_model_name(end_point_name,
- model_name, model_version, run_id, model_id)
- python_program = get_python_program()
- master_port = os.getenv("FEDML_MASTER_PORT", None)
- if master_port is not None:
- inference_port = int(master_port)
- if not ServerConstants.is_running_on_k8s():
- logging.info(f"start the model inference gateway, end point {run_id}, "
- f"model name {model_name} at port {inference_port}...")
- self.check_runner_stop_event()
-
- use_mqtt_inference = os.getenv("FEDML_USE_MQTT_INFERENCE", "False")
- use_mqtt_inference = True if use_mqtt_inference.lower() == 'true' else False
- use_worker_gateway = os.getenv("FEDML_USE_WORKER_GATEWAY", "False")
- use_worker_gateway = True if use_worker_gateway.lower() == 'true' else False
- inference_gw_cmd = "fedml.computing.scheduler.model_scheduler.device_model_inference:api"
- inference_gateway_pids = RunProcessUtils.get_pid_from_cmd_line(inference_gw_cmd)
- if inference_gateway_pids is None or len(inference_gateway_pids) <= 0:
- cur_dir = os.path.dirname(__file__)
- fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- connect_str = "@FEDML@"
- ext_info = sys_utils.random1(
- self.agent_config["mqtt_config"]["BROKER_HOST"] + connect_str +
- str(self.agent_config["mqtt_config"]["BROKER_PORT"]) + connect_str +
- self.agent_config["mqtt_config"]["MQTT_USER"] + connect_str +
- self.agent_config["mqtt_config"]["MQTT_PWD"] + connect_str +
- str(self.agent_config["mqtt_config"]["MQTT_KEEPALIVE"]), "FEDML@9999GREAT")
- self.inference_gateway_process = ServerConstants.exec_console_with_script(
- "REDIS_ADDR=\"{}\" REDIS_PORT=\"{}\" REDIS_PASSWORD=\"{}\" "
- "END_POINT_NAME=\"{}\" "
- "MODEL_NAME=\"{}\" MODEL_VERSION=\"{}\" MODEL_INFER_URL=\"{}\" VERSION=\"{}\" "
- "USE_MQTT_INFERENCE={} USE_WORKER_GATEWAY={} EXT_INFO={} "
- "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
- "--log-level critical".format(
- self.redis_addr, self.redis_port, self.redis_password,
- end_point_name,
- model_name, model_version, "", self.args.version,
- use_mqtt_inference, use_worker_gateway, ext_info,
- python_program, inference_gw_cmd, str(inference_port), fedml_base_dir
- ),
- should_capture_stdout=False,
- should_capture_stderr=False
- )
-
- def start_device_inference_monitor(self, run_id, end_point_name,
- model_id, model_name, model_version, check_stopped_event=True):
- # start inference monitor server
- # Will report the qps related metrics to the MLOps
- logging.info(f"start the model inference monitor, end point {run_id}, model name {model_name}...")
- if check_stopped_event:
- self.check_runner_stop_event()
- run_id_str = str(run_id)
- pip_source_dir = os.path.dirname(__file__)
- monitor_file = os.path.join(pip_source_dir, "device_model_monitor.py")
- python_program = get_python_program()
- running_model_name = ServerConstants.get_running_model_name(end_point_name,
- model_name, model_version, run_id, model_id)
- self.monitor_process = ServerConstants.exec_console_with_shell_script_list(
- [
- python_program,
- monitor_file,
- "-v",
- self.args.version,
- "-ep",
- run_id_str,
- "-epn",
- str(end_point_name),
- "-mi",
- str(model_id),
- "-mn",
- model_name,
- "-mv",
- model_version,
- "-iu",
- "infer_url",
- "-ra",
- self.redis_addr,
- "-rp",
- self.redis_port,
- "-rpw",
- self.redis_password
- ],
- should_capture_stdout=False,
- should_capture_stderr=False
- )
-
- def stop_device_inference_monitor(self, run_id, end_point_name, model_id, model_name, model_version):
- # stop inference monitor server
- logging.info(f"stop the model inference monitor, end point {run_id}, model name {model_name}...")
- sys_utils.cleanup_model_monitor_processes(run_id, end_point_name,
- model_id, model_name, model_version)
-
- def cleanup_run_when_finished(self):
- logging.info("Cleanup run successfully when finished.")
-
- self.mlops_metrics.broadcast_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED,
- is_from_model=True, edge_id=self.edge_id
- )
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- pass
-
- time.sleep(1)
-
- try:
- local_package_path = ServerConstants.get_package_download_dir()
- for package_file in listdir(local_package_path):
- if os.path.basename(package_file).startswith("run_" + str(self.run_id)):
- shutil.rmtree(os.path.join(local_package_path, package_file), ignore_errors=True)
- except Exception as e:
- pass
-
- def cleanup_run_when_starting_failed(self):
- logging.info("Cleanup run successfully when starting failed.")
-
- self.mlops_metrics.broadcast_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED,
- is_from_model=True, edge_id=self.edge_id)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- pass
-
- time.sleep(1)
-
- try:
- local_package_path = ServerConstants.get_package_download_dir()
- for package_file in listdir(local_package_path):
- if os.path.basename(package_file).startswith("run_" + str(self.run_id)):
- shutil.rmtree(os.path.join(local_package_path, package_file), ignore_errors=True)
- except Exception as e:
- pass
-
- def cleanup_run_when_deploy_failed(self):
- topic = f"model_ops/model_device/delete_deployment/{self.edge_id}"
- self.callback_delete_deployment(topic, payload=json.dumps(self.request_json))
-
- def callback_deployment_result_message(self, topic=None, payload=None):
- """
- This method is called when a deployment result is received from a worker device.
- """
- # Save deployment result to local cache
- topic_splits = str(topic).split('/')
- device_id = topic_splits[-1]
- payload_json = json.loads(payload)
- end_point_id = payload_json["end_point_id"]
- end_point_name = payload_json["end_point_name"]
- model_id = payload_json["model_id"]
- model_name = payload_json["model_name"]
- model_version = payload_json["model_version"]
- model_status = payload_json["model_status"]
- replica_no = payload_json.get("replica_no", None) # "no" Idx start from 1
- run_id_str = str(end_point_id)
-
- # HotFix(Raphael): logging service cross talk
- # Change the handler since each handler need to write to different log files
- try:
- # Remove the existing file handler
- root_logger = logging.getLogger()
- for handler in root_logger.handlers:
- if isinstance(handler, logging.FileHandler):
- root_logger.removeHandler(handler)
-
- # Correct log path: ~/.fedml/fedml-model-server/fedml/logs/fedml-run-$rid-edge-$eid.log
- log_file = os.path.join(ServerConstants.get_log_file_dir(),
- f"fedml-run-{run_id_str}-edge-{self.edge_id}.log")
-
- filehandler = logging.FileHandler(log_file, "a")
-
- program_prefix = "FedML-Server @device-id-{}".format(self.edge_id)
- formatter = MLOpsFormatter(fmt="[" + program_prefix + "] [%(asctime)s] [%(levelname)s] "
- "[%(filename)s:%(lineno)d:%(funcName)s] %("
- "message)s")
-
- filehandler.setFormatter(formatter)
- root_logger.addHandler(filehandler)
- except Exception as e:
- logging.warning(f"Failed to change the logging handler due to {e}.")
-
- assert run_id_str in self.model_runner_mapping, (f"Run id {run_id_str} is not in the model runner mapping."
- f"Current mapping {self.model_runner_mapping}.")
-
- logging.info("========== callback_deployment_result_message ==========\n")
- # Identify the operation for this run (add, remove, update)
- if run_id_str not in self.running_request_json:
- logging.error(f"Run id {run_id_str} is not in the running request json.")
- return
-
- # The rolling update and scale out / in operation should not happen at the same time
- assert not ("replica_num_diff" in self.running_request_json[run_id_str] and
- len(self.running_request_json[run_id_str]["replica_num_diff"]) > 0 and
- "replica_version_diff" in self.running_request_json[run_id_str])
-
- if "replica_version_diff" in self.running_request_json[run_id_str]:
- run_operation = "UPDATE"
- elif "replica_num_diff" in self.running_request_json[run_id_str] and \
- len(self.running_request_json[run_id_str]["replica_num_diff"]) > 0:
- run_operation = "ADD_OR_REMOVE"
- else:
- logging.error(f"Unsupported operation for run id {run_id_str}. and request json "
- f"{self.running_request_json[run_id_str]}")
- return
-
- logging.info(f"End point {end_point_id}; Device {device_id}; replica {replica_no}; "
- f"run_operation {run_operation} model status {model_status}.")
-
- # OPTIONAL DEBUG PARAMS
- # this_run_controller = self.model_runner_mapping[run_id_str].replica_controller
- # logging.info(f"The current replica controller state is "
- # f"Total version diff num {this_run_controller.total_replica_version_diff_num}")
- # logging.info(f"self.request_json now {self.request_json}") # request_json will be deprecated
- # this_run_request_json = self.running_request_json.get(run_id_str, None)
- # logging.info(f"self.running_request_json now {this_run_request_json}")
-
- # Set redis + sqlite deployment result
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
-
- # Deal with different model status
- if model_status == ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DELETED:
- # remove
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- delete_deployment_result_with_device_id_and_replica_no(
- end_point_id, end_point_name, model_name, device_id, replica_no)
- elif model_status == ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED:
- # add or update or update-failed-rollback
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_deployment_result(end_point_id, end_point_name,
- model_name, model_version,
- device_id, payload, replica_no)
-
- # Note: To display the result in the UI, we need to save successful deployment result to the database
- self.model_runner_mapping[run_id_str].deployed_replica_payload = copy.deepcopy(payload_json)
- else:
- if model_status != ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED:
- logging.error(f"Unsupported model status {model_status}.")
-
- # Avoid endless loop, if the rollback also failed, we should report the failure to the MLOps
- if self.model_runner_mapping[run_id_str].replica_controller.under_rollback:
- self.send_deployment_status(
- end_point_id, end_point_name, payload_json["model_name"], "",
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
- return
-
- # Failure handler, send the rollback message to the worker devices only if it has not been rollback
- if run_operation == "ADD_OR_REMOVE":
- # During Scale out / in,
- # the worker that already been scaled out / in should be sent the rollback message
- rollback_dict = self.model_runner_mapping[run_id_str].replica_controller.rollback_add_or_remove_replica(
- device_id=device_id, replica_no=replica_no, op_type=run_operation
- )
- self.model_runner_mapping[run_id_str].replica_controller.under_rollback = True
-
- if rollback_dict is not None and len(rollback_dict) > 0:
- self.send_deployment_status(
- end_point_id, end_point_name, payload_json["model_name"], "",
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTING)
- self.send_rollback_add_remove_op(run_id_str, rollback_dict)
- return
- else:
- # This is the last worker that failed, so we should continue to "ABORTED" status
- model_config_parameters = self.running_request_json[run_id_str]["parameters"]
- inference_port = model_config_parameters.get("server_internal_port",
- ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
- inference_port_external = model_config_parameters.get("server_external_port", inference_port)
- ip = self.get_ip_address(self.running_request_json[run_id_str])
- if ip.startswith("http://") or ip.startswith("https://"):
- model_inference_url = "{}/inference/{}".format(ip, end_point_id)
- else:
- model_inference_url = "http://{}:{}/inference/{}".format(ip, inference_port_external,
- end_point_id)
-
- self.send_deployment_status(end_point_id, end_point_name,
- payload_json["model_name"],
- model_inference_url,
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTED)
-
- # For auto-scaling, should update the state to "DEPLOYED"
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- update_user_setting_replica_num(end_point_id=end_point_id, state="DEPLOYED")
-
- self.model_runner_mapping[run_id_str].replica_controller.under_rollback = False
-
- return
- elif run_operation == "UPDATE":
- # Overwrite the json with the rollback version diff
- rollback_version_diff = \
- self.model_runner_mapping[run_id_str].replica_controller.rollback_get_replica_version_diff(
- device_id_trigger=device_id, replica_no_trigger=replica_no)
-
- # Change the target version to the start version
- self.model_runner_mapping[run_id_str].replica_controller.rollback_setback_target_replica_version()
-
- self.running_request_json[run_id_str]["replica_version_diff"] = copy.deepcopy(rollback_version_diff)
-
- # Send the rollback message to the worker devices
- self.send_rollback_msg(run_id_str)
-
- # Set the deployment status to ABORTING
- self.send_deployment_status(
- end_point_id, end_point_name, payload_json["model_name"], "",
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTING)
-
- # TODO(Raphael): Check if resource left not cleaned up
- return
- else:
- logging.error(f"Unsupported operation {run_operation}.")
- return
-
- # Move to the next state (rolling update, finish the deployment, etc.)
- # Notify the replica number controller
- (self.model_runner_mapping[run_id_str].
- replica_controller.callback_update_curr_replica_num_state(device_id, replica_no, model_status))
-
- # Notify the replica version controller, which might trigger the next rolling update
- self.send_next_scroll_update_msg(run_id_str, device_id, replica_no)
-
- # Update the global deployment result mapping
- if run_id_str not in self.slave_deployment_results_mapping:
- self.slave_deployment_results_mapping[run_id_str] = dict()
- if str(device_id) not in self.slave_deployment_results_mapping[run_id_str]:
- self.slave_deployment_results_mapping[run_id_str][str(device_id)] = dict()
- self.slave_deployment_results_mapping[run_id_str][str(device_id)][str(replica_no)] = model_status
-
- logging.info("callback_deployment_result_message: topic {}, payload {}, result mapping {}.".format(
- topic, payload, self.slave_deployment_results_mapping[run_id_str]))
-
- request_json = self.running_request_json.get(run_id_str, None)
- if request_json is None:
- logging.error(f"The endpoint {end_point_id} is no longer running.")
- self.send_deployment_status(
- end_point_id, end_point_name, payload_json["model_name"], "",
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
- return
-
- # Wait for all replica-level's result, not device-level
- if (self.model_runner_mapping[run_id_str].replica_controller.is_all_replica_num_reconciled() and
- self.model_runner_mapping[run_id_str].replica_controller.is_all_replica_version_reconciled()):
- """
- When all the devices have finished the add / delete / update operation
- """
- # Generate one unified inference api
- # Note that here we use the gateway port instead of the inference port that is used by the slave device
- model_config_parameters = request_json["parameters"]
- inference_port = model_config_parameters.get("server_internal_port",
- ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
- inference_port_external = model_config_parameters.get("server_external_port", inference_port)
- ip = self.get_ip_address(request_json)
-
- if ip.startswith("http://") or ip.startswith("https://"):
- model_inference_url = "{}/inference/{}".format(ip, end_point_id)
- else:
- model_inference_url = "http://{}:{}/inference/{}".format(ip, inference_port_external, end_point_id)
-
- # Send stage: MODEL_DEPLOYMENT_STAGE5 = "StartInferenceIngress"
- self.send_deployment_stages(end_point_id, model_name, model_id,
- model_inference_url,
- ServerConstants.MODEL_DEPLOYMENT_STAGE5["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE5["text"],
- "inference url: {}".format(model_inference_url))
-
- # Send the result to MLOps
- if self.model_runner_mapping[run_id_str].deployed_replica_payload is not None:
- payload_json = self.model_runner_mapping[run_id_str].deployed_replica_payload
- model_slave_url = payload_json["model_url"]
- payload_json["model_url"] = model_inference_url
- payload_json["port"] = inference_port_external
- token = FedMLModelCache.get_instance(self.redis_addr, self.redis_port).get_end_point_token(
- end_point_id, end_point_name, model_name)
-
- model_metadata = payload_json["model_metadata"]
- model_inputs = model_metadata["inputs"]
- ret_inputs = list()
- if "type" in model_metadata and model_metadata["type"] == "default":
- payload_json["input_json"] = {"end_point_name": end_point_name,
- "model_name": model_name,
- "token": str(token),
- "inputs": model_inputs,
- "outputs": []}
- payload_json["output_json"] = model_metadata["outputs"]
- else:
- raise Exception(f"Unsupported model metadata type {model_metadata['type']}")
-
- self.send_deployment_results_with_payload(
- end_point_id, end_point_name, payload_json,
- self.model_runner_mapping[run_id_str].replica_controller.target_replica_ids)
-
- payload_json_saved = payload_json
- payload_json_saved["model_slave_url"] = model_slave_url
- FedMLServerDataInterface.get_instance().save_job_result(end_point_id, self.edge_id,
- json.dumps(payload_json_saved))
- else:
- # Arrive here because only contains remove ops, so we do not need to update the model metadata
- pass
-
- # For auto-scaling, should update the state to "DEPLOYED"
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- update_user_setting_replica_num(end_point_id=end_point_id, state="DEPLOYED")
-
- if self.model_runner_mapping[run_id_str].replica_controller.under_rollback:
- # If first time failed (Still might need rollback), then send failed message to the MLOps
- if not (FedMLModelCache.get_instance(self.redis_addr, self.redis_port).
- get_end_point_activation(end_point_id)):
- self.send_deployment_status(
- end_point_id, end_point_name, payload_json["model_name"], "",
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
- else:
- self.send_deployment_status(end_point_id, end_point_name,
- payload_json["model_name"],
- model_inference_url,
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_ABORTED)
- self.model_runner_mapping[run_id_str].replica_controller.under_rollback = False
- else:
- # Set the end point activation status to True, for scaling out / in and rolling update
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_activation(end_point_id, end_point_name, True)
-
- self.send_deployment_status(end_point_id, end_point_name,
- payload_json["model_name"],
- model_inference_url,
- ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED)
-
- self.slave_deployment_results_mapping[run_id_str] = dict()
-
- time.sleep(3)
- self.set_runner_completed_event(end_point_id)
-
- def callback_deployment_status_message(self, topic=None, payload=None):
- # [Deprecated] Merge the logic into callback_deployment_result_message
- logging.info("[Deprecated] callback_deployment_status_message: topic {}, payload {}.".format(
- topic, payload))
- pass
-
- def send_deployment_start_request_to_edges(self, in_request_json=None):
- if in_request_json is not None:
- self.request_json = in_request_json
-
- # Iterate through replica_num_diff, both add and replace should be sent to the edge devices
- if "replica_num_diff" not in self.request_json or self.request_json["replica_num_diff"] is None:
- return []
-
- edge_id_list = []
- for device_id in self.request_json["replica_num_diff"].keys():
- edge_id_list.append(device_id)
-
- self.request_json["master_node_ip"] = self.get_ip_address(self.request_json)
- should_added_devices = []
- for edge_id in edge_id_list:
- if edge_id == self.edge_id:
- continue
- should_added_devices.append(edge_id)
- # send start deployment request to each device
- self.send_deployment_start_request_to_edge(edge_id, self.request_json)
- return should_added_devices
-
- def send_deployment_start_request_to_edge(self, edge_id, res_json):
- topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(edge_id))
- logging.info("start_deployment: send topic " + topic_start_deployment + f" to client {edge_id}...")
- self.client_mqtt_mgr.send_message_json(topic_start_deployment, json.dumps(res_json))
-
- def get_ip_address(self, request_json):
- # OPTION 1: Use local ip
- ip = ServerConstants.get_local_ip()
-
- # OPTION 2: Auto detect public ip
- if "parameters" in request_json and \
- ServerConstants.AUTO_DETECT_PUBLIC_IP in request_json["parameters"] and \
- request_json["parameters"][ServerConstants.AUTO_DETECT_PUBLIC_IP]:
- ip = ServerConstants.get_public_ip()
-
- # OPTION 3: Use user indicated ip
- if self.infer_host is not None and self.infer_host != "127.0.0.1" and self.infer_host != "localhost":
- ip = self.infer_host
-
- return ip
-
- def send_deployment_delete_request_to_edges(self, payload, model_msg_object):
- edge_id_list_to_delete = model_msg_object.device_ids
-
- # Remove the model master node id from the list using index 0
- edge_id_list_to_delete = edge_id_list_to_delete[1:]
-
- logging.info("Device ids to be deleted: " + str(edge_id_list_to_delete))
-
- for edge_id in edge_id_list_to_delete:
- if edge_id == self.edge_id:
- continue
- # send delete deployment request to each model device
- topic_delete_deployment = "model_ops/model_device/delete_deployment/{}".format(str(edge_id))
- logging.info("delete_deployment: send topic " + topic_delete_deployment + " to client...")
- self.client_mqtt_mgr.send_message_json(topic_delete_deployment, payload)
-
- def ota_upgrade(self, payload, request_json):
- run_id = request_json["end_point_id"]
- force_ota = False
- ota_version = None
-
- try:
- parameters = request_json.get("parameters", None)
- common_args = parameters.get("common_args", None)
- force_ota = common_args.get("force_ota", False)
- ota_version = common_args.get("ota_version", None)
- except Exception as e:
- pass
-
- if force_ota and ota_version is not None:
- should_upgrade = True if ota_version != fedml.__version__ else False
- upgrade_version = ota_version
- else:
- try:
- fedml_is_latest_version, local_ver, remote_ver = sys_utils.check_fedml_is_latest_version(self.version)
- except Exception as e:
- return
-
- should_upgrade = False if fedml_is_latest_version else True
- upgrade_version = remote_ver
-
- if should_upgrade:
- job_obj = FedMLServerDataInterface.get_instance().get_job_by_id(run_id)
- if job_obj is None:
- FedMLServerDataInterface.get_instance(). \
- save_started_job(run_id, self.edge_id, time.time(),
- ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING,
- ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING,
- payload)
-
- logging.info(f"Upgrade to version {upgrade_version} ...")
-
- sys_utils.do_upgrade(self.version, upgrade_version)
-
- raise Exception("Restarting after upgraded...")
-
- def callback_start_deployment(self, topic, payload):
- try:
- MLOpsConfigs.fetch_all_configs()
- except Exception as e:
- pass
-
- # Get deployment params
- request_json = json.loads(payload)
- run_id = request_json["end_point_id"]
- end_point_name = request_json["end_point_name"]
- token = request_json["token"]
- user_id = request_json["user_id"]
- user_name = request_json["user_name"]
- device_ids = request_json["device_ids"]
- device_objs = request_json["device_objs"]
-
- model_config = request_json["model_config"]
- model_name = model_config["model_name"]
- model_version = model_config["model_version"]
- model_id = model_config["model_id"]
- model_storage_url = model_config["model_storage_url"]
- scale_min = model_config.get("instance_scale_min", 0)
- scale_max = model_config.get("instance_scale_max", 0)
- inference_engine = model_config.get("inference_engine", 0)
- enable_auto_scaling = request_json.get("enable_auto_scaling", False)
- desired_replica_num = request_json.get("desired_replica_num", 1)
-
- target_queries_per_replica = request_json.get("target_queries_per_replica", 10)
- aggregation_window_size_seconds = request_json.get("aggregation_window_size_seconds", 60)
- scale_down_delay_seconds = request_json.get("scale_down_delay_seconds", 120)
-
- inference_end_point_id = run_id
-
- logging.info("[Master] received start deployment request for end point {}.".format(run_id))
-
- # Set redis config
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
-
- # Save the user setting (about replica number) of this run to Redis, if existed, update it
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_user_setting_replica_num(
- end_point_id=run_id, end_point_name=end_point_name, model_name=model_name, model_version=model_version,
- replica_num=desired_replica_num, enable_auto_scaling=enable_auto_scaling,
- scale_min=scale_min, scale_max=scale_max, state="DEPLOYING",
- aggregation_window_size_seconds=aggregation_window_size_seconds,
- target_queries_per_replica=target_queries_per_replica,
- scale_down_delay_seconds=int(scale_down_delay_seconds)
- )
-
- # Start log processor for current run
- self.args.run_id = run_id
- self.args.edge_id = self.edge_id
- MLOpsRuntimeLog(args=self.args).init_logs()
- MLOpsRuntimeLogDaemon.get_instance(self.args).set_log_source(
- ServerConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT)
- MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(run_id, self.edge_id)
-
- # # Deprecated
- # self.ota_upgrade(payload, request_json)
-
- # Add additional parameters to the request_json
- run_id = inference_end_point_id
- self.args.run_id = run_id
- self.run_id = run_id
- request_json["run_id"] = run_id
- self.request_json = request_json
- run_id_str = str(run_id)
- self.running_request_json[run_id_str] = request_json
- self.request_json["master_node_ip"] = self.get_ip_address(self.request_json)
-
- # Set the target status of the devices to redis
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_device_info(request_json["end_point_id"], end_point_name, json.dumps(device_objs))
-
- # Setup Token
- usr_indicated_token = self.get_usr_indicated_token(request_json)
- if usr_indicated_token != "":
- logging.info(f"Change Token from{token} to {usr_indicated_token}")
- token = usr_indicated_token
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_token(run_id, end_point_name, model_name, token)
-
- self.subscribe_slave_devices_message(request_json)
-
- # Report stage to mlops: MODEL_DEPLOYMENT_STAGE1 = "Received"
- self.send_deployment_stages(self.run_id, model_name, model_id,
- "",
- ServerConstants.MODEL_DEPLOYMENT_STAGE1["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE1["text"],
- "Received request for endpoint {}".format(run_id))
-
- # Report stage to mlops: MODEL_DEPLOYMENT_STAGE2 = "Initializing"
- self.send_deployment_stages(self.run_id, model_name, model_id,
- "",
- ServerConstants.MODEL_DEPLOYMENT_STAGE2["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE2["text"])
-
- ServerConstants.save_runner_infos(self.args.device_id + "." + self.args.os_name, self.edge_id, run_id=run_id)
-
- if self.run_as_edge_server_and_agent:
- # Replica Controller is per deployment
- replica_controller = FedMLDeviceReplicaController(self.edge_id, self.request_json)
-
- # Prepare num diff
- new_request_with_num_diff = replica_controller.generate_diff_to_request_json()
- self.running_request_json[run_id_str] = new_request_with_num_diff
- request_json = new_request_with_num_diff
-
- # Listen to extra worker topics, especially when worker's replica remove to zero,
- # In this case, currently Java will NOT send those worker ids to the master, but still need to listen to it.
- if "replica_num_diff" in request_json and len(request_json["replica_num_diff"]) > 0:
- for device_id in request_json["replica_num_diff"].keys():
- # {"op": "remove", "curr_num": 1, "target_num": 0}
- if request_json["replica_num_diff"][device_id]["op"] == "remove" and \
- request_json["replica_num_diff"][device_id]["target_num"] == 0:
- self.subscribe_spec_device_message(run_id, device_id)
-
- # Prepare version diff
- new_request_with_version_diff = replica_controller.init_first_update_device_replica_mapping()
- self.running_request_json[run_id_str] = new_request_with_version_diff
- request_json = new_request_with_version_diff
-
- # Init the model runner
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=request_json, agent_config=self.agent_config
- )
- server_runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- server_runner.edge_id = self.edge_id
- server_runner.infer_host = self.infer_host
- server_runner.redis_addr = self.redis_addr
- server_runner.redis_port = self.redis_port
- server_runner.redis_password = self.redis_password
- server_runner.replica_controller = replica_controller
-
- logging.info(f"[Master] new request for id {run_id_str}")
- logging.info(f"[Master] model runner mapping before: {self.model_runner_mapping.items()}")
-
- self.run_process_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_event_map[run_id_str].clear()
- server_runner.run_process_event = self.run_process_event_map[run_id_str]
- self.run_process_completed_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_completed_event_map[run_id_str].clear()
- server_runner.run_process_completed_event = self.run_process_completed_event_map[run_id_str]
- self.model_runner_mapping[run_id_str] = server_runner
-
- logging.info(f"[Master] model runner mapping after: {self.model_runner_mapping.items()}")
-
- # This subprocess will copy the server_runner and run it, but they are not the same object
- server_process = Process(target=server_runner.run, args=(
- self.run_process_event_map[run_id_str], self.run_process_completed_event_map[run_id_str]
- ))
- server_process.start()
- ServerConstants.save_run_process(run_id, server_process.pid)
-
- # Send stage: MODEL_DEPLOYMENT_STAGE3 = "StartRunner"
- self.send_deployment_stages(self.run_id, model_name, model_id,
- "",
- ServerConstants.MODEL_DEPLOYMENT_STAGE3["index"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE3["text"],
- ServerConstants.MODEL_DEPLOYMENT_STAGE3["text"])
-
- def send_first_scroll_update_msg(self):
- """
- Replica-level rolling update.
- Delete the record of the replaced device and send the deployment msg to the devices
- """
- if "replica_version_diff" not in self.request_json or self.request_json["replica_version_diff"] is None:
- return []
-
- first_chunk_dict = self.request_json["replica_version_diff"]
-
- # Delete the record of the replaced device
- self.delete_device_replica_info_on_master(
- self.request_json["end_point_id"], self.request_json["end_point_name"],
- self.request_json["model_config"]["model_name"], first_chunk_dict)
-
- logging.info(f"Send the first scroll update msg to the device {first_chunk_dict} ")
-
- # Send the deployment msg to the devices, (we reuse the start_deployment msg)
- for edge_id in first_chunk_dict.keys():
- if edge_id == self.edge_id:
- continue
- # send start deployment request to each device
- self.send_deployment_start_request_to_edge(edge_id, self.request_json)
- return list(first_chunk_dict.keys())
-
- def send_rollback_msg(self, run_id_str):
- # Avoid using the old request_json
- self.delete_device_replica_info_on_master(
- self.running_request_json[run_id_str]["end_point_id"],
- self.running_request_json[run_id_str]["end_point_name"],
- self.running_request_json[run_id_str]["model_config"]["model_name"],
- self.running_request_json[run_id_str]["replica_version_diff"])
-
- # Send the deployment msg to the devices, (we reuse the start_deployment msg)
- for edge_id in self.running_request_json[run_id_str]["replica_version_diff"].keys():
- if edge_id == self.edge_id:
- continue
- # send start deployment request to each device
- self.send_deployment_start_request_to_edge(edge_id, self.running_request_json[run_id_str])
-
- def delete_device_replica_info_on_master(self, endpoint_id, endpoint_name, model_name, edge_id_replica_no_dict):
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- # Remove the record of the replaced device
- # [Deprecated] deployment status & device info
- # Delete the result in deployment result list in Redis / SQLite
- device_result_list = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_deployment_result_list(endpoint_id, endpoint_name, model_name)
-
- delete_device_result_list = []
- for device_result in device_result_list:
- device_result_dict = json.loads(device_result)
- if (str(device_result_dict["cache_device_id"]) in edge_id_replica_no_dict.keys() and
- str(device_result_dict["cache_replica_no"]) in
- edge_id_replica_no_dict[str(device_result_dict["cache_device_id"])]):
- delete_device_result_list.append(device_result)
-
- for delete_item in delete_device_result_list:
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).delete_deployment_result(
- delete_item, endpoint_id, endpoint_name, model_name
- )
-
- logging.info(f"Deleted the replica record on master: {edge_id_replica_no_dict}")
-
- def send_next_scroll_update_msg(self, run_id_str, device_id, replica_no):
- """
- Send the next scroll update msg to the devices if needed.
- If there is no need for the next scroll update, directly return.
- """
- if replica_no is None:
- return
-
- replica_controller = self.model_runner_mapping[run_id_str].replica_controller
-
- if replica_controller.total_replica_version_diff_num == 0:
- return
-
- if replica_controller.under_rollback:
- replica_controller.intermediate_replica_version[device_id][replica_no] = replica_controller.start_version
- return
-
- logging.info(f"Curr updating window: {replica_controller.curr_replica_updating_window} "
- f"Curr version diff num: {replica_controller.total_replica_version_diff_num}")
-
- replica_controller.callback_update_updating_window(device_id, replica_no)
-
- # Decide whether to send the next scroll update
- next_chunk_dict = replica_controller.get_next_chunk_devices_replica()
-
- if next_chunk_dict:
- logging.info(f"The next scroll update for end point {run_id_str} is {next_chunk_dict}")
- # Update curr updating window
- replica_controller.curr_replica_updating_window = copy.deepcopy(next_chunk_dict)
-
- # Use global deployment result mapping to decide whether to send the next scroll update
- self.running_request_json[run_id_str]["replica_version_diff"] = next_chunk_dict
-
- # Avoid using the old request_json
- self.delete_device_replica_info_on_master(
- self.running_request_json[run_id_str]["end_point_id"],
- self.running_request_json[run_id_str]["end_point_name"],
- self.running_request_json[run_id_str]["model_config"]["model_name"],
- next_chunk_dict)
-
- # Send the deployment msg to the devices, (we reuse the start_deployment msg)
- for edge_id in next_chunk_dict.keys():
- if edge_id == self.edge_id:
- continue
- # send start deployment request to each device
- self.send_deployment_start_request_to_edge(edge_id, self.running_request_json[run_id_str])
- return
-
- def send_rollback_add_remove_op(self, run_id, rollback_replica_dict):
- """
- This method is used when the original add op failed, we need to rollback by delete the existed replicas
- Input example:
- rollback_replica_dict = {'96684': {'curr_num': 2, 'op': 'remove', 'target_num': 1}}
- """
- existed_request_json = self.running_request_json[str(run_id)]
- updated_request_json = copy.deepcopy(existed_request_json)
-
- # Reverse the replica_num_diff
- updated_request_json["replica_num_diff"] = rollback_replica_dict
-
- self.send_deployment_start_request_to_edges(in_request_json=updated_request_json)
-
- def callback_activate_deployment(self, topic, payload):
- logging.info("callback_activate_deployment: topic = %s, payload = %s" % (topic, payload))
-
- # Parse payload as the model message object.
- model_msg_object = FedMLModelMsgObject(topic, payload)
-
- # Get the previous deployment status.
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- endpoint_status = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_end_point_status(model_msg_object.inference_end_point_id)
- if endpoint_status != ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED:
- return
-
- # Set end point as activated status
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_end_point_activation(
- model_msg_object.inference_end_point_id, model_msg_object.end_point_name, True)
-
- def callback_deactivate_deployment(self, topic, payload):
- logging.info("callback_deactivate_deployment: topic = %s, payload = %s" % (topic, payload))
-
- # Parse payload as the model message object.
- model_msg_object = FedMLModelMsgObject(topic, payload)
-
- # Get the endpoint status
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- endpoint_status = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_end_point_status(model_msg_object.inference_end_point_id)
- if endpoint_status != ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED:
- return
-
- # Set end point as deactivated status
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_end_point_activation(
- model_msg_object.inference_end_point_id, model_msg_object.model_name, False)
-
- def set_runner_stopped_event(self, run_id):
- run_id_str = str(run_id)
- server_runner = self.model_runner_mapping.get(run_id_str, None)
- if server_runner is not None:
- if server_runner.run_process_event is not None:
- server_runner.run_process_event.set()
- self.model_runner_mapping.pop(run_id_str)
-
- def set_runner_completed_event(self, run_id):
- run_id_str = str(run_id)
- server_runner = self.model_runner_mapping.get(run_id_str, None)
- if server_runner is not None:
- if server_runner.run_process_completed_event is not None:
- server_runner.run_process_completed_event.set()
- self.model_runner_mapping.pop(run_id_str)
-
- def callback_delete_deployment(self, topic, payload):
- logging.info("[Master] callback_delete_deployment")
- # Parse payload as the model message object.
- model_msg_object = FedMLModelMsgObject(topic, payload)
-
- # Delete SQLite records
- FedMLServerDataInterface.get_instance().delete_job_from_db(model_msg_object.run_id)
- FedMLModelDatabase.get_instance().delete_deployment_result(
- model_msg_object.run_id, model_msg_object.end_point_name, model_msg_object.model_name,
- model_version=model_msg_object.model_version)
- FedMLModelDatabase.get_instance().delete_deployment_run_info(
- end_point_id=model_msg_object.inference_end_point_id)
-
- # Delete Redis Records
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- set_end_point_activation(model_msg_object.inference_end_point_id,
- model_msg_object.end_point_name, False)
- FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- delete_end_point(model_msg_object.inference_end_point_id, model_msg_object.end_point_name,
- model_msg_object.model_name, model_msg_object.model_version)
-
- # Send delete deployment request to the edge devices
- self.send_deployment_delete_request_to_edges(payload, model_msg_object)
-
- # Stop processes on master
- self.set_runner_stopped_event(model_msg_object.run_id)
- self.stop_device_inference_monitor(model_msg_object.run_id, model_msg_object.end_point_name,
- model_msg_object.model_id, model_msg_object.model_name,
- model_msg_object.model_version)
-
- def send_deployment_results_with_payload(self, end_point_id, end_point_name, payload, replica_id_list=None):
- self.send_deployment_results(end_point_id, end_point_name,
- payload["model_name"], payload["model_url"],
- payload["model_version"], payload["port"],
- payload["inference_engine"],
- payload["model_metadata"],
- payload["model_config"],
- payload["input_json"],
- payload["output_json"],
- replica_id_list=replica_id_list)
-
- def send_deployment_results(self, end_point_id, end_point_name,
- model_name, model_inference_url,
- model_version, inference_port, inference_engine,
- model_metadata, model_config, input_json, output_json, replica_id_list=None):
- deployment_results_topic_prefix = "model_ops/model_device/return_deployment_result"
- deployment_results_topic = "{}/{}".format(deployment_results_topic_prefix, end_point_id)
- deployment_results_payload = {"end_point_id": end_point_id, "end_point_name": end_point_name,
- "model_name": model_name, "model_url": model_inference_url,
- "version": model_version, "port": inference_port,
- "inference_engine": inference_engine,
- "model_metadata": model_metadata,
- "model_config": model_config,
- "input_json": input_json,
- "output_json": output_json,
- "timestamp": int(format(time.time_ns() / 1000.0, '.0f')),
- "replica_ids": replica_id_list}
- logging.info(f"[Master] deployment_results_payload is sent to mlops: {deployment_results_payload}")
-
- self.client_mqtt_mgr.send_message_json(deployment_results_topic, json.dumps(deployment_results_payload))
- self.client_mqtt_mgr.send_message_json(deployment_results_topic_prefix, json.dumps(deployment_results_payload))
-
- def send_deployment_status(self, end_point_id, end_point_name, model_name, model_inference_url, model_status):
- deployment_status_topic_prefix = "model_ops/model_device/return_deployment_status"
- deployment_status_topic = "{}/{}".format(deployment_status_topic_prefix, end_point_id)
- deployment_status_payload = {"end_point_id": end_point_id, "end_point_name": end_point_name,
- "model_name": model_name,
- "model_url": model_inference_url,
- "model_status": model_status,
- "timestamp": int(format(time.time_ns() / 1000.0, '.0f'))}
- logging.info(f"[Master] deployment_status_payload is sent to mlops: {deployment_status_payload}")
-
- self.client_mqtt_mgr.send_message_json(deployment_status_topic, json.dumps(deployment_status_payload))
- self.client_mqtt_mgr.send_message_json(deployment_status_topic_prefix, json.dumps(deployment_status_payload))
-
- def send_deployment_stages(self, end_point_id, model_name, model_id, model_inference_url,
- model_stages_index, model_stages_title, model_stage_detail):
- deployment_stages_topic_prefix = "model_ops/model_device/return_deployment_stages"
- deployment_stages_topic = "{}/{}".format(deployment_stages_topic_prefix, end_point_id)
- deployment_stages_payload = {"model_name": model_name,
- "model_id": model_id,
- "model_url": model_inference_url,
- "end_point_id": end_point_id,
- "model_stage_index": model_stages_index,
- "model_stage_title": model_stages_title,
- "model_stage_detail": model_stage_detail,
- "timestamp": int(format(time.time_ns() / 1000.0, '.0f'))}
-
- self.client_mqtt_mgr.send_message_json(deployment_stages_topic, json.dumps(deployment_stages_payload))
- self.client_mqtt_mgr.send_message_json(deployment_stages_topic_prefix, json.dumps(deployment_stages_payload))
-
- logging.info(f"-------- Stages has been sent to mlops with stage {model_stages_index} and "
- f"payload {deployment_stages_payload}")
- time.sleep(2)
-
- def on_client_mqtt_disconnected(self, mqtt_client_object):
- if self.client_mqtt_lock is None:
- self.client_mqtt_lock = threading.Lock()
-
- self.client_mqtt_lock.acquire()
- self.client_mqtt_is_connected = False
- self.client_mqtt_lock.release()
-
- logging.info("on_client_mqtt_disconnected: {}.".format(self.client_mqtt_is_connected))
-
- def on_client_mqtt_connected(self, mqtt_client_object):
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
-
- self.mlops_metrics.set_messenger(self.client_mqtt_mgr)
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.edge_id = self.edge_id
- self.mlops_metrics.server_agent_id = self.server_agent_id
-
- if self.client_mqtt_lock is None:
- self.client_mqtt_lock = threading.Lock()
-
- self.client_mqtt_lock.acquire()
- self.client_mqtt_is_connected = True
- self.client_mqtt_lock.release()
-
- # logging.info("on_client_mqtt_connected: {}.".format(self.client_mqtt_is_connected))
-
- def setup_client_mqtt_mgr(self):
- if self.client_mqtt_mgr is not None:
- return
-
- if self.client_mqtt_lock is None:
- self.client_mqtt_lock = threading.Lock()
-
- # logging.info(
- # "server agent config: {},{}".format(
- # self.agent_config["mqtt_config"]["BROKER_HOST"], self.agent_config["mqtt_config"]["BROKER_PORT"]
- # )
- # )
-
- self.client_mqtt_mgr = MqttManager(
- self.agent_config["mqtt_config"]["BROKER_HOST"],
- self.agent_config["mqtt_config"]["BROKER_PORT"],
- self.agent_config["mqtt_config"]["MQTT_USER"],
- self.agent_config["mqtt_config"]["MQTT_PWD"],
- self.agent_config["mqtt_config"]["MQTT_KEEPALIVE"],
- "FedML_ModelServerAgent_Metrics_@{}@_{}_{}_{}".format(self.user_name, self.args.current_device_id,
- str(os.getpid()),
- str(uuid.uuid4()))
- )
- self.client_mqtt_mgr.add_connected_listener(self.on_client_mqtt_connected)
- self.client_mqtt_mgr.add_disconnected_listener(self.on_client_mqtt_disconnected)
- self.client_mqtt_mgr.connect()
- self.client_mqtt_mgr.loop_start()
-
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
- self.mlops_metrics.set_messenger(self.client_mqtt_mgr)
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.edge_id = self.edge_id
- self.mlops_metrics.server_agent_id = self.server_agent_id
-
- def release_client_mqtt_mgr(self):
- try:
- if self.client_mqtt_mgr is not None:
- self.client_mqtt_mgr.loop_stop()
- self.client_mqtt_mgr.disconnect()
-
- self.client_mqtt_lock.acquire()
- if self.client_mqtt_mgr is not None:
- self.client_mqtt_is_connected = False
- self.client_mqtt_mgr = None
- self.client_mqtt_lock.release()
- except Exception:
- pass
-
- def send_deployment_stop_request_to_edges(self, edge_id_list, payload):
- for edge_id in edge_id_list:
- topic_stop_deployment = "model_ops/model_device/stop_deployment/{}".format(str(self.edge_id))
- logging.info("stop_deployment: send topic " + topic_stop_deployment)
- self.client_mqtt_mgr.send_message_json(topic_stop_deployment, payload)
-
- def send_exit_train_with_exception_request_to_edges(self, edge_id_list, payload):
- for edge_id in edge_id_list:
- topic_exit_train = "flserver_agent/" + str(edge_id) + "/exit_train_with_exception"
- logging.info("exit_train_with_exception: send topic " + topic_exit_train)
- self.client_mqtt_mgr.send_message_json(topic_exit_train, payload)
-
- def exit_run_with_exception_entry(self):
- try:
- self.setup_client_mqtt_mgr()
- self.exit_run_with_exception()
- except Exception as e:
- self.release_client_mqtt_mgr()
- sys_utils.cleanup_all_fedml_server_login_processes(
- ServerConstants.SERVER_LOGIN_PROGRAM, clean_process_group=False)
- sys.exit(1)
- finally:
- self.release_client_mqtt_mgr()
-
- def exit_run_with_exception(self):
- logging.info("Exit run successfully.")
-
- ServerConstants.cleanup_learning_process(self.run_id)
- ServerConstants.cleanup_run_process(self.run_id)
-
- self.mlops_metrics.report_server_id_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED, edge_id=self.edge_id)
-
- time.sleep(1)
-
- def callback_exit_train_with_exception(self, topic, payload):
- # logging.info("callback_exit_train_with_exception: topic = %s, payload = %s" % (topic, payload))
-
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json.get("runId", None)
- if run_id is None:
- run_id = request_json.get("run_id", None)
- if run_id is None:
- run_id = request_json.get("id", None)
-
- if run_id is None:
- return
-
- edge_ids = request_json.get("edgeids", None)
-
- self.send_exit_train_with_exception_request_to_edges(edge_ids, payload)
-
- # Stop server with multiprocessing mode
- self.request_json = request_json
- server_runner = FedMLServerRunner(
- self.args, edge_id=self.edge_id, request_json=request_json, agent_config=self.agent_config, run_id=run_id
- )
- try:
- Process(target=server_runner.exit_run_with_exception_entry).start()
- except Exception as e:
- pass
-
- def callback_client_exit_train_with_exception(self, topic, payload):
- # logging.info("callback_client_exit_train_with_exception: topic = %s, payload = %s" % (topic, payload))
-
- request_json = json.loads(payload)
- run_id = request_json.get("run_id", None)
- edge_id = request_json.get("edge_id", None)
- if run_id is None:
- logging.info("callback_client_exit_train_with_exception run id is none")
- return
-
- job = FedMLServerDataInterface.get_instance().get_job_by_id(run_id)
- if job is not None and job.running_json is not None and job.running_json != "":
- job_json_obj = json.loads(job.running_json)
- edge_ids = job_json_obj.get("edgeids", None)
-
- self.mlops_metrics.broadcast_server_training_status(
- run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED,
- is_from_model=True, edge_id=edge_id)
-
- self.send_exit_train_with_exception_request_to_edges(edge_ids, job.running_json)
-
- self.exit_run_with_exception()
-
- def callback_runner_id_status(self, topic, payload):
- logging.info("callback_runner_id_status: topic = %s, payload = %s" % (topic, payload))
-
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json["run_id"]
- status = request_json["status"]
- edge_id = request_json["edge_id"]
- run_id_str = str(run_id)
-
- if (
- status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED
- or status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED
- ):
- # Stop server with multiprocessing mode
- stop_request_json = self.running_request_json.get(run_id_str, None)
- if stop_request_json is None:
- stop_request_json = request_json
- if self.run_as_edge_server_and_agent:
- server_runner = FedMLServerRunner(
- self.args, run_id=run_id, request_json=stop_request_json, agent_config=self.agent_config
- )
- server_runner.edge_id = self.edge_id
- server_runner.run_as_edge_server_and_agent = self.run_as_edge_server_and_agent
- server_runner.run_status = status
- status_process = Process(target=server_runner.cleanup_client_with_status)
- status_process.start()
- status_process.join(10)
-
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, self.edge_id)
-
- def cleanup_client_with_status(self):
- if self.run_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FINISHED:
- logging.info("received to finished status.")
- self.cleanup_run_when_finished()
- elif self.run_status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
- logging.info("received to failed status.")
- self.cleanup_run_when_starting_failed()
-
- def callback_report_current_status(self, topic, payload):
- request_json = json.loads(payload)
- if self.run_as_edge_server_and_agent:
- self.send_agent_active_msg()
- elif self.run_as_cloud_agent:
- self.send_agent_active_msg()
- elif self.run_as_cloud_server:
- pass
-
- @staticmethod
- def process_ota_upgrade_msg():
- os.system("pip install -U fedml")
-
- def callback_server_ota_msg(self, topic, payload):
- request_json = json.loads(payload)
- cmd = request_json["cmd"]
-
- if cmd == ServerConstants.FEDML_OTA_CMD_UPGRADE:
- try:
- self.process_ota_upgrade_msg()
- # Process(target=FedMLServerRunner.process_ota_upgrade_msg).start()
- raise Exception("After upgraded, restart runner...")
- except Exception as e:
- pass
- elif cmd == ServerConstants.FEDML_OTA_CMD_RESTART:
- raise Exception("Restart runner...")
-
- @staticmethod
- def get_device_id():
- device_file_path = os.path.join(ServerConstants.get_data_dir(), ServerConstants.LOCAL_RUNNER_INFO_DIR_NAME)
- file_for_device_id = os.path.join(device_file_path, "devices.id")
- if not os.path.exists(device_file_path):
- os.makedirs(device_file_path)
- elif os.path.exists(file_for_device_id):
- with open(file_for_device_id, 'r', encoding='utf-8') as f:
- device_id_from_file = f.readline()
- if device_id_from_file is not None and device_id_from_file != "":
- return device_id_from_file
-
- if platform.system() == "Darwin":
- cmd_get_serial_num = "system_profiler SPHardwareDataType | grep Serial | awk '{gsub(/ /,\"\")}{print}' " \
- "|awk -F':' '{print $2}' "
- device_id = os.popen(cmd_get_serial_num).read()
- device_id = device_id.replace('\n', '').replace(' ', '')
- if device_id is None or device_id == "":
- device_id = hex(uuid.getnode())
- else:
- device_id = "0x" + device_id
- else:
- if "nt" in os.name:
-
- def get_uuid():
- guid = ""
- try:
- cmd = "wmic csproduct get uuid"
- guid = str(subprocess.check_output(cmd))
- pos1 = guid.find("\\n") + 2
- guid = guid[pos1:-15]
- except Exception as ex:
- pass
- return str(guid)
-
- device_id = str(get_uuid())
- elif "posix" in os.name:
- device_id = sys_utils.get_device_id_in_docker()
- if device_id is None:
- device_id = hex(uuid.getnode())
- else:
- device_id = sys_utils.run_subprocess_open(
- "hal-get-property --udi /org/freedesktop/Hal/devices/computer --key system.hardware.uuid".split()
- )
- device_id = hex(device_id)
-
- if device_id is not None and device_id != "":
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
- else:
- device_id = hex(uuid.uuid4())
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
-
- return device_id
-
- def bind_account_and_device_id(self, url, account_id, device_id, os_name):
- role = ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_ON_PREMISE_MASTER_INDEX]
- if self.run_as_edge_server_and_agent:
- role = ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_ON_PREMISE_MASTER_INDEX]
- elif self.run_as_cloud_agent:
- role = ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_FEDML_CLOUD_MASTER_INDEX]
- elif self.run_as_cloud_server:
- role = ServerConstants.login_role_list[ServerConstants.LOGIN_MODE_INFERENCE_INSTANCE_INDEX]
-
- ip = requests.get('https://checkip.amazonaws.com').text.strip()
- fedml_ver, exec_path, os_ver, cpu_info, python_ver, torch_ver, mpi_installed, \
- cpu_usage, available_mem, total_mem, gpu_info, gpu_available_mem, gpu_total_mem, \
- gpu_count, gpu_vendor, cpu_count, gpu_device_name = get_sys_runner_info()
- host_name = sys_utils.get_host_name()
- json_params = {
- "accountid": account_id,
- "deviceid": device_id,
- "type": os_name,
- "state": ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE,
- "status": ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE,
- "processor": cpu_info,
- "core_type": cpu_info,
- "network": "",
- "role": role,
- "os_ver": os_ver,
- "memory": total_mem,
- "ip": ip,
- "extra_infos": {"fedml_ver": fedml_ver, "exec_path": exec_path, "os_ver": os_ver,
- "cpu_info": cpu_info, "python_ver": python_ver, "torch_ver": torch_ver,
- "mpi_installed": mpi_installed, "cpu_usage": cpu_usage,
- "available_mem": available_mem, "total_mem": total_mem,
- "cpu_count": cpu_count, "gpu_count": 0, "host_name": host_name}
- }
- if gpu_count > 0:
- if gpu_total_mem is not None:
- json_params["gpu"] = gpu_info if gpu_info is not None else "" + ", Total GPU Memory: " + gpu_total_mem
- else:
- json_params["gpu"] = gpu_info if gpu_info is not None else ""
- json_params["extra_infos"]["gpu_info"] = gpu_info if gpu_info is not None else ""
- if gpu_available_mem is not None:
- json_params["extra_infos"]["gpu_available_mem"] = gpu_available_mem
- if gpu_total_mem is not None:
- json_params["extra_infos"]["gpu_total_mem"] = gpu_total_mem
-
- json_params["extra_infos"]["gpu_count"] = gpu_count
- json_params["extra_infos"]["gpu_vendor"] = gpu_vendor
- json_params["extra_infos"]["gpu_device_name"] = gpu_device_name
-
- gpu_available_id_list = sys_utils.get_available_gpu_id_list(limit=gpu_count)
- gpu_available_count = len(gpu_available_id_list) if gpu_available_id_list is not None else 0
- gpu_list = sys_utils.get_gpu_list()
- json_params["extra_infos"]["gpu_available_count"] = gpu_available_count
- json_params["extra_infos"]["gpu_available_id_list"] = gpu_available_id_list
- json_params["extra_infos"]["gpu_list"] = gpu_list
- else:
- json_params["gpu"] = "None"
- json_params["extra_infos"]["gpu_available_count"] = 0
- json_params["extra_infos"]["gpu_available_id_list"] = []
- json_params["extra_infos"]["gpu_list"] = []
-
- _, cert_path = MLOpsConfigs.get_request_params()
- if cert_path is not None:
- try:
- requests.session().verify = cert_path
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- except requests.exceptions.SSLError as err:
- MLOpsConfigs.install_root_ca_file()
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- else:
- response = requests.post(url, json=json_params, headers={"Connection": "close"})
- edge_id = -1
- user_name = None
- extra_url = None
- if response.status_code != 200:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- pass
- else:
- # print("url = {}, response = {}".format(url, response))
- status_code = response.json().get("code")
- if status_code == "SUCCESS":
- edge_id = response.json().get("data").get("id")
- user_name = response.json().get("data").get("userName", None)
- extra_url = response.json().get("data").get("url", None)
- if edge_id is None or edge_id <= 0:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- else:
- if status_code == SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR:
- raise SystemExit(SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR)
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- return -1, None, None
- return edge_id, user_name, extra_url
-
- def fetch_configs(self):
- return MLOpsConfigs.fetch_all_configs()
-
- def send_agent_active_msg(self):
- active_topic = "flserver_agent/active"
- status = MLOpsStatus.get_instance().get_server_agent_status(self.edge_id)
- if (
- status is not None
- and status != ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE
- and status != ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- ):
- return
-
- status = ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- active_msg = {"ID": self.edge_id, "status": status}
- MLOpsStatus.get_instance().set_server_agent_status(self.edge_id, status)
- self.mqtt_mgr.send_message_json(active_topic, json.dumps(active_msg))
-
- def subscribe_slave_devices_message(self, request_json):
- if request_json is None:
- return
- run_id = request_json["run_id"]
- edge_id_list = request_json["device_ids"]
- for edge_id in edge_id_list:
- if str(edge_id) == str(self.edge_id):
- continue
-
- # subscribe deployment result message for each model device
- deployment_results_topic = "model_device/model_device/return_deployment_result/{}/{}".format(
- run_id, edge_id)
-
- self.mqtt_mgr.add_message_listener(deployment_results_topic, self.callback_deployment_result_message)
- self.mqtt_mgr.subscribe_msg(deployment_results_topic)
-
- def subscribe_spec_device_message(self, run_id, device_id):
- if device_id == self.edge_id:
- return
-
- # subscribe deployment result message for each model device
- deployment_results_topic = "model_device/model_device/return_deployment_result/{}/{}".format(
- run_id, device_id)
-
- self.mqtt_mgr.add_message_listener(deployment_results_topic, self.callback_deployment_result_message)
- self.mqtt_mgr.subscribe_msg(deployment_results_topic)
-
- def on_agent_mqtt_connected(self, mqtt_client_object):
- # The MQTT message topic format is as follows: <sender>/<receiver>/<action>
-
- # Setup MQTT message listener for starting deployment
- server_agent_id = self.edge_id
- topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(self.edge_id))
- self.mqtt_mgr.add_message_listener(topic_start_deployment, self.callback_start_deployment)
-
- # Setup MQTT message listener for activating deployment
- topic_activate_deployment = "model_ops/model_device/activate_deployment/{}".format(str(self.edge_id))
- self.mqtt_mgr.add_message_listener(topic_activate_deployment, self.callback_activate_deployment)
-
- # Setup MQTT message listener for deactivating deployment
- topic_deactivate_deployment = "model_ops/model_device/deactivate_deployment/{}".format(str(self.edge_id))
- self.mqtt_mgr.add_message_listener(topic_deactivate_deployment, self.callback_deactivate_deployment)
-
- # Setup MQTT message listener for delete deployment
- topic_delete_deployment = "model_ops/model_device/delete_deployment/{}".format(str(self.edge_id))
- self.mqtt_mgr.add_message_listener(topic_delete_deployment, self.callback_delete_deployment)
-
- # Setup MQTT message listener for server status switching
- topic_server_status = "fl_server/flserver_agent_" + str(server_agent_id) + "/status"
- self.mqtt_mgr.add_message_listener(topic_server_status, self.callback_runner_id_status)
-
- # Setup MQTT message listener to report current device status.
- topic_report_status = "mlops/report_device_status"
- self.mqtt_mgr.add_message_listener(topic_report_status, self.callback_report_current_status)
-
- # Setup MQTT message listener to OTA messages from the MLOps.
- topic_ota_msg = "mlops/flserver_agent_" + str(server_agent_id) + "/ota"
- self.mqtt_mgr.add_message_listener(topic_ota_msg, self.callback_server_ota_msg)
-
- # Subscribe topics for starting train, stopping train and fetching client status.
- mqtt_client_object.subscribe(topic_start_deployment, qos=2)
- mqtt_client_object.subscribe(topic_activate_deployment, qos=2)
- mqtt_client_object.subscribe(topic_deactivate_deployment, qos=2)
- mqtt_client_object.subscribe(topic_delete_deployment, qos=2)
- mqtt_client_object.subscribe(topic_server_status, qos=2)
- mqtt_client_object.subscribe(topic_report_status, qos=2)
- mqtt_client_object.subscribe(topic_ota_msg, qos=2)
-
- self.subscribed_topics.clear()
- self.subscribed_topics.append(topic_start_deployment)
- self.subscribed_topics.append(topic_activate_deployment)
- self.subscribed_topics.append(topic_deactivate_deployment)
- self.subscribed_topics.append(topic_delete_deployment)
- self.subscribed_topics.append(topic_server_status)
- self.subscribed_topics.append(topic_report_status)
- self.subscribed_topics.append(topic_ota_msg)
-
- self.endpoint_sync_protocol = FedMLEndpointSyncProtocol(agent_config=self.agent_config, mqtt_mgr=self.mqtt_mgr)
- self.endpoint_sync_protocol.setup_listener_for_sync_device_info(self.edge_id)
-
- # Broadcast the first active message.
- self.send_agent_active_msg()
-
- # Echo results
- # print("\n\nCongratulations, your device is connected to the FedML MLOps platform successfully!")
- # print(
- # "Your FedML Edge ID is " + str(self.edge_id) + ", unique device ID is "
- # + str(self.unique_device_id)
- # + "\n"
- # )
-
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
-
- def on_agent_mqtt_disconnected(self, mqtt_client_object):
- MLOpsStatus.get_instance().set_server_agent_status(
- self.edge_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE
- )
-
- def recover_inference_and_monitor(self):
- try:
- history_jobs = FedMLServerDataInterface.get_instance().get_history_jobs()
- for job in history_jobs.job_list:
- if job.running_json is None:
- continue
-
- if job.deployment_result == "":
- continue
-
- run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
- model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
- inference_end_point_id, use_gpu, memory_size, model_version, inference_port = \
- self.parse_model_run_params(json.loads(job.running_json))
-
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- is_activated = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_end_point_activation(run_id)
- if not is_activated:
- continue
-
- self.start_device_inference_gateway(run_id, end_point_name, model_id, model_name, model_version,
- inference_port=inference_port)
-
- self.stop_device_inference_monitor(run_id, end_point_name, model_id, model_name, model_version)
- self.start_device_inference_monitor(run_id, end_point_name, model_id, model_name, model_version)
- except Exception as e:
- logging.info("recover inference and monitor: {}".format(traceback.format_exc()))
-
- def recover_start_deployment_msg_after_upgrading(self):
- try:
- current_job = FedMLServerDataInterface.get_instance().get_current_job()
- if current_job is not None and \
- current_job.status == ServerConstants.MSG_MLOPS_SERVER_STATUS_UPGRADING:
- FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
- is_activated = FedMLModelCache.get_instance(self.redis_addr, self.redis_port). \
- get_end_point_activation(current_job.job_id)
- if not is_activated:
- return
- logging.info("start deployment after upgrading.")
- topic_start_deployment = "model_ops/model_device/start_deployment/{}".format(str(self.edge_id))
- self.callback_start_deployment(topic_start_deployment, current_job.running_json)
- except Exception as e:
- logging.info("recover starting deployment message after upgrading: {}".format(traceback.format_exc()))
-
- def setup_agent_mqtt_connection(self, service_config):
- # Setup MQTT connection
- self.mqtt_mgr = MqttManager(
- service_config["mqtt_config"]["BROKER_HOST"],
- service_config["mqtt_config"]["BROKER_PORT"],
- service_config["mqtt_config"]["MQTT_USER"],
- service_config["mqtt_config"]["MQTT_PWD"],
- service_config["mqtt_config"]["MQTT_KEEPALIVE"],
- "FedML_ModelServerAgent_Daemon_@" + self.user_name + "@_" + self.args.current_device_id + str(uuid.uuid4()),
- "flserver_agent/last_will_msg",
- json.dumps({"ID": self.edge_id, "status": ServerConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE})
- )
- self.agent_config = service_config
-
- # Init local database
- FedMLServerDataInterface.get_instance().create_job_table()
- try:
- FedMLModelDatabase.get_instance().set_database_base_dir(ServerConstants.get_database_dir())
- FedMLModelDatabase.get_instance().create_table()
- except Exception as e:
- pass
-
- server_api_cmd = "fedml.computing.scheduler.model_scheduler.device_server_api:api"
- server_api_pids = RunProcessUtils.get_pid_from_cmd_line(server_api_cmd)
- if server_api_pids is None or len(server_api_pids) <= 0:
- # Start local API services
- cur_dir = os.path.dirname(__file__)
- fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- python_program = get_python_program()
- self.local_api_process = ServerConstants.exec_console_with_script(
- "{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
- "--log-level critical".format(
- python_program, server_api_cmd, ServerConstants.LOCAL_SERVER_API_PORT,
- fedml_base_dir
- ),
- should_capture_stdout=False,
- should_capture_stderr=False
- )
- # if self.local_api_process is not None and self.local_api_process.pid is not None:
- # print(f"Model master local API process id {self.local_api_process.pid}")
-
- self.recover_inference_and_monitor()
-
- # MLOpsRuntimeLogDaemon.get_instance(self.args).stop_all_log_processor()
-
- # Setup MQTT connected listener
- self.mqtt_mgr.add_connected_listener(self.on_agent_mqtt_connected)
- self.mqtt_mgr.add_disconnected_listener(self.on_agent_mqtt_disconnected)
- self.mqtt_mgr.connect()
-
- self.setup_client_mqtt_mgr()
- self.mlops_metrics.report_server_training_status(
- self.run_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE,
- is_from_model=True, edge_id=self.edge_id)
- MLOpsStatus.get_instance().set_server_agent_status(
- self.edge_id, ServerConstants.MSG_MLOPS_SERVER_STATUS_IDLE
- )
-
- self.recover_start_deployment_msg_after_upgrading()
-
- def stop_agent(self):
- if self.run_process_event is not None:
- self.run_process_event.set()
-
- if self.mqtt_mgr is not None:
- try:
- for topic in self.subscribed_topics:
- self.mqtt_mgr.unsubscribe_msg(topic)
- except Exception as e:
- pass
-
- self.mqtt_mgr.loop_stop()
- self.mqtt_mgr.disconnect()
-
- self.release_client_mqtt_mgr()
-
- def start_agent_mqtt_loop(self, should_exit_sys=True):
- # Start MQTT message loop
- try:
- self.mqtt_mgr.loop_forever()
- except Exception as e:
- if str(e) == "Restarting after upgraded...":
- logging.info("Restarting after upgraded...")
- else:
- print("Server tracing: {}".format(traceback.format_exc()))
- finally:
- self.stop_agent()
- if should_exit_sys:
- pass
- """
- # Deprecated, will kill the process by the parent process.
- time.sleep(5)
- sys_utils.cleanup_all_fedml_server_login_processes(
- ServerConstants.SERVER_LOGIN_PROGRAM, clean_process_group=False)
- sys.exit(1)
- """
-
diff --git a/python/fedml/computing/scheduler/slave/client_runner_deprecated.py b/python/fedml/computing/scheduler/slave/client_runner_deprecated.py
deleted file mode 100755
index 79b5697728..0000000000
--- a/python/fedml/computing/scheduler/slave/client_runner_deprecated.py
+++ /dev/null
@@ -1,1872 +0,0 @@
-import json
-import logging
-import multiprocessing
-import sys
-
-from multiprocessing import Process
-import os
-import platform
-import shutil
-import subprocess
-import threading
-
-import time
-import traceback
-import urllib
-import uuid
-import zipfile
-from urllib.parse import urljoin, urlparse
-
-import requests
-
-import fedml
-from ..comm_utils.constants import SchedulerConstants
-from ..comm_utils.job_cleanup import JobCleanup
-from ..comm_utils.job_utils import JobRunnerUtils, DockerArgs
-from ..comm_utils.run_process_utils import RunProcessUtils
-from ..scheduler_entry.constants import Constants
-from ....core.mlops.mlops_device_perfs import MLOpsDevicePerfStats
-from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
-
-from ....core.distributed.communication.mqtt.mqtt_manager import MqttManager
-from ..comm_utils.yaml_utils import load_yaml_config
-from .client_constants import ClientConstants
-
-from ....core.mlops.mlops_metrics import MLOpsMetrics
-
-from ....core.mlops.mlops_configs import MLOpsConfigs
-from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
-from ....core.mlops.mlops_status import MLOpsStatus
-from ..comm_utils.sys_utils import get_sys_runner_info, get_python_program
-from .client_data_interface import FedMLClientDataInterface
-from ..comm_utils import sys_utils
-from ....core.mlops.mlops_utils import MLOpsUtils
-from ..model_scheduler.model_device_client import FedMLModelDeviceClientRunner
-from ..model_scheduler.model_device_server import FedMLModelDeviceServerRunner
-from ..comm_utils import security_utils
-from ..scheduler_core.compute_cache_manager import ComputeCacheManager
-from ..scheduler_core.message_center import FedMLMessageCenter
-import ssl
-
-
-class RunnerError(Exception):
- """ Runner stopped. """
- pass
-
-
-class RunnerCompletedError(Exception):
- """ Runner completed. """
- pass
-
-
-class FedMLClientRunner(FedMLMessageCenter):
-
- def __init__(self, args, edge_id=0, request_json=None, agent_config=None, run_id=0,
- cuda_visible_gpu_ids_str=None):
- super().__init__()
- self.model_device_server_id = None
- self.model_device_client_edge_id_list = None
- self.disable_client_login = False
- self.model_device_server = None
- self.model_device_client_list = None
- self.run_process_event = None
- self.run_process_event_map = dict()
- self.run_process_completed_event = None
- self.run_process_completed_event_map = dict()
- self.run_process = None
- self.run_process_map = dict()
- self.running_request_json = dict()
- self.local_api_process = None
- self.start_request_json = None
- self.device_status = None
- self.current_training_status = None
- self.mqtt_mgr = None
- self.edge_id = edge_id
- self.edge_user_name = None
- self.edge_extra_url = None
- self.run_id = run_id
- self.unique_device_id = None
- self.args = args
- self.request_json = request_json
- self.version = args.version
- self.device_id = args.device_id
- self.cur_dir = os.path.split(os.path.realpath(__file__))[0]
- if args.current_running_dir is not None:
- self.cur_dir = args.current_running_dir
- self.sudo_cmd = ""
- self.is_mac = False
- if platform.system() == "Darwin":
- self.is_mac = True
-
- self.agent_config = agent_config
- self.fedml_data_base_package_dir = os.path.join("/", "fedml", "data")
- self.fedml_data_local_package_dir = os.path.join("/", "fedml", "fedml-package", "fedml", "data")
- self.fedml_data_dir = self.fedml_data_base_package_dir
- self.fedml_config_dir = os.path.join("/", "fedml", "conf")
-
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES = {
- "${FEDSYS.RUN_ID}": "",
- "${FEDSYS.PRIVATE_LOCAL_DATA}": "",
- "${FEDSYS.CLIENT_ID_LIST}": "",
- "${FEDSYS.SYNTHETIC_DATA_URL}": "",
- "${FEDSYS.IS_USING_LOCAL_DATA}": "",
- "${FEDSYS.CLIENT_NUM}": "",
- "${FEDSYS.CLIENT_INDEX}": "",
- "${FEDSYS.CLIENT_OBJECT_LIST}": "",
- "${FEDSYS.LOG_SERVER_URL}": "",
- }
-
- self.mlops_metrics = None
- self.client_active_list = dict()
- self.ntp_offset = MLOpsUtils.get_ntp_offset()
- self.server_id = None
- self.computing_started_time = 0
- self.fedml_config_object = None
- self.package_type = SchedulerConstants.JOB_PACKAGE_TYPE_DEFAULT
- self.cuda_visible_gpu_ids_str = cuda_visible_gpu_ids_str
- # logging.info("Current directory of client agent: " + self.cur_dir)
- self.subscribed_topics = list()
- self.user_name = None
- self.general_edge_id = None
- self.message_center = None
-
- def __repr__(self):
- return "<{klass} @{id:x} {attrs}>".format(
- klass=self.__class__.__name__,
- id=id(self) & 0xFFFFFF,
- attrs=" ".join("{}={!r}".format(k, v) for k, v in self.__dict__.items()),
- )
-
- def copy_runner(self):
- copy_runner = FedMLClientRunner(self.args)
- copy_runner.disable_client_login = self.disable_client_login
- copy_runner.model_device_server = self.model_device_server
- copy_runner.model_device_client_list = self.model_device_client_list
- copy_runner.run_process_event = self.run_process_event
- copy_runner.run_process_event_map = self.run_process_event_map
- copy_runner.run_process_completed_event = self.run_process_completed_event
- copy_runner.run_process_completed_event_map = self.run_process_completed_event_map
- copy_runner.run_process = self.run_process
- copy_runner.run_process_map = self.run_process_map
- copy_runner.running_request_json = self.running_request_json
- copy_runner.local_api_process = self.local_api_process
- copy_runner.start_request_json = self.start_request_json
- copy_runner.device_status = self.device_status
- copy_runner.current_training_status = self.current_training_status
- copy_runner.mqtt_mgr = self.mqtt_mgr
- copy_runner.edge_id = self.edge_id
- copy_runner.edge_user_name = self.edge_user_name
- copy_runner.edge_extra_url = self.edge_extra_url
- copy_runner.run_id = self.run_id
- copy_runner.unique_device_id = self.unique_device_id
- copy_runner.args = self.args
- copy_runner.request_json = self.request_json
- copy_runner.version =self.version
- copy_runner.device_id = self.device_id
- copy_runner.cur_dir = self.cur_dir
- copy_runner.cur_dir = self.cur_dir
- copy_runner.sudo_cmd = self.sudo_cmd
- copy_runner.is_mac = self.is_mac
-
- copy_runner.agent_config = self.agent_config
- copy_runner.fedml_data_base_package_dir = self.fedml_data_base_package_dir
- copy_runner.fedml_data_local_package_dir = self.fedml_data_local_package_dir
- copy_runner.fedml_data_dir = self.fedml_data_dir
- copy_runner.fedml_config_dir = self.fedml_config_dir
-
- copy_runner.FEDML_DYNAMIC_CONSTRAIN_VARIABLES = self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES
-
- copy_runner.mlops_metrics = self.mlops_metrics
- copy_runner.client_active_list = self.client_active_list
- copy_runner.ntp_offset = self.ntp_offset
- copy_runner.server_id = self.server_id
- copy_runner.computing_started_time = self.computing_started_time
- copy_runner.fedml_config_object = self.fedml_config_object
- copy_runner.package_type = self.package_type
- copy_runner.cuda_visible_gpu_ids_str = self.cuda_visible_gpu_ids_str
- copy_runner.subscribed_topics = self.subscribed_topics
- copy_runner.user_name = self.user_name
- copy_runner.general_edge_id = self.general_edge_id
- copy_runner.message_center = self.message_center
-
- return copy_runner
-
- def build_dynamic_constrain_variables(self, run_id, run_config):
- data_config = run_config.get("data_config", {})
- server_edge_id_list = self.request_json["edgeids"]
- local_edge_id_list = list()
- local_edge_id_list.append(int(self.edge_id))
- is_using_local_data = 0
- private_data_dir = data_config.get("privateLocalData", "")
- synthetic_data_url = data_config.get("syntheticDataUrl", "")
- edges = self.request_json["edges"]
- # if private_data_dir is not None \
- # and len(str(private_data_dir).strip(' ')) > 0:
- # is_using_local_data = 1
- if private_data_dir is None or len(str(private_data_dir).strip(" ")) <= 0:
- params_config = run_config.get("parameters", None)
- private_data_dir = ClientConstants.get_data_dir()
- if synthetic_data_url is None or len(str(synthetic_data_url)) <= 0:
- synthetic_data_url = private_data_dir
-
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.RUN_ID}"] = run_id
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.PRIVATE_LOCAL_DATA}"] = private_data_dir.replace(" ", "")
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_ID_LIST}"] = str(local_edge_id_list).replace(" ", "")
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.SYNTHETIC_DATA_URL}"] = synthetic_data_url.replace(" ", "")
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.IS_USING_LOCAL_DATA}"] = str(is_using_local_data)
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_NUM}"] = len(server_edge_id_list)
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_INDEX}"] = 1
- for cur_index, id_value in enumerate(server_edge_id_list):
- if str(id_value) == str(self.edge_id):
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_INDEX}"] = cur_index + 1
- break
- client_objects = str(json.dumps(edges))
- client_objects = client_objects.replace(" ", "").replace("\n", "").replace('"', '\\"')
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.CLIENT_OBJECT_LIST}"] = client_objects
- self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES["${FEDSYS.LOG_SERVER_URL}"] = self.agent_config["ml_ops_config"][
- "LOG_SERVER_URL"
- ]
-
- def unzip_file(self, zip_file, unzip_file_path) -> str:
- if zipfile.is_zipfile(zip_file):
- with zipfile.ZipFile(zip_file, "r") as zipf:
- zipf.extractall(unzip_file_path)
- unzipped_file_name = zipf.namelist()[0]
- else:
- raise Exception("Invalid zip file {}".format(zip_file))
-
- return unzipped_file_name
-
- def package_download_progress(self, count, blksize, filesize):
- self.check_runner_stop_event()
-
- downloaded = count * blksize
- downloaded = filesize if downloaded > filesize else downloaded
- progress = (downloaded / filesize * 100) if filesize != 0 else 0
- progress_int = int(progress)
- downloaded_kb = format(downloaded / 1024, '.2f')
-
- # since this hook funtion is stateless, we need a state to avoid print progress repeatly
- if count == 0:
- self.prev_download_progress = 0
- if progress_int != self.prev_download_progress and progress_int % 5 == 0:
- self.prev_download_progress = progress_int
- logging.info("package downloaded size {} KB, progress {}%".format(downloaded_kb, progress_int))
-
- def retrieve_and_unzip_package(self, package_name, package_url):
- local_package_path = ClientConstants.get_package_download_dir()
- os.makedirs(local_package_path, exist_ok=True)
- filename, filename_without_extension, file_extension = ClientConstants.get_filename_and_extension(package_url)
- local_package_file = os.path.join(local_package_path, f"fedml_run_{self.run_id}_{filename_without_extension}")
- if os.path.exists(local_package_file):
- os.remove(local_package_file)
- ssl._create_default_https_context = ssl._create_unverified_context
- urllib.request.urlretrieve(package_url, local_package_file,
- reporthook=self.package_download_progress)
- unzip_package_path = os.path.join(ClientConstants.get_package_unzip_dir(),
- f"unzip_fedml_run_{self.run_id}_{filename_without_extension}")
- try:
- shutil.rmtree(unzip_package_path, ignore_errors=True)
- except Exception as e:
- logging.error(
- f"Failed to remove directory {unzip_package_path}, Exception: {e}, Traceback: {traceback.format_exc()}")
- pass
-
- package_dir_name = self.unzip_file(local_package_file, unzip_package_path) # Using unziped folder name
- unzip_package_full_path = os.path.join(unzip_package_path, package_dir_name)
-
- logging.info("local_package_file {}, unzip_package_path {}, unzip file full path {}".format(
- local_package_file, unzip_package_path, unzip_package_full_path))
-
- return unzip_package_full_path
-
- def update_local_fedml_config(self, run_id, run_config):
- packages_config = run_config["packages_config"]
-
- # Copy config file from the client
- unzip_package_path = self.retrieve_and_unzip_package(
- packages_config["linuxClient"], packages_config["linuxClientUrl"]
- )
- fedml_local_config_file = os.path.join(unzip_package_path, "conf", "fedml.yaml")
-
- # Load the above config to memory
- config_from_container = load_yaml_config(fedml_local_config_file)
- container_entry_file_config = config_from_container["entry_config"]
- container_dynamic_args_config = config_from_container["dynamic_args"]
- entry_file = container_entry_file_config["entry_file"]
- conf_file = container_entry_file_config["conf_file"]
- self.package_type = container_entry_file_config.get("package_type", SchedulerConstants.JOB_PACKAGE_TYPE_DEFAULT)
- full_conf_path = os.path.join(unzip_package_path, "fedml", "config", os.path.basename(conf_file))
-
- # Dynamically build constrain variable with realtime parameters from server
- self.build_dynamic_constrain_variables(run_id, run_config)
-
- # Update entry arguments value with constrain variable values with realtime parameters from server
- # currently we support the following constrain variables:
- # ${FEDSYS_RUN_ID}: a run id represented one entire Federated Learning flow
- # ${FEDSYS_PRIVATE_LOCAL_DATA}: private local data path in the Federated Learning client
- # ${FEDSYS_CLIENT_ID_LIST}: client list in one entire Federated Learning flow
- # ${FEDSYS_SYNTHETIC_DATA_URL}: synthetic data url from server,
- # if this value is not null, the client will download data from this URL to use it as
- # federated training data set
- # ${FEDSYS_IS_USING_LOCAL_DATA}: whether use private local data as federated training data set
- # container_dynamic_args_config["data_cache_dir"] = "${FEDSYS.PRIVATE_LOCAL_DATA}"
- for constrain_variable_key, constrain_variable_value in self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES.items():
- for argument_key, argument_value in container_dynamic_args_config.items():
- if argument_value is not None and str(argument_value).find(constrain_variable_key) == 0:
- replaced_argument_value = str(argument_value).replace(
- constrain_variable_key, str(constrain_variable_value)
- )
- container_dynamic_args_config[argument_key] = replaced_argument_value
-
- # Merge all container new config sections as new config dictionary
- package_conf_object = dict()
- package_conf_object["entry_config"] = container_entry_file_config
- package_conf_object["dynamic_args"] = container_dynamic_args_config
- package_conf_object["dynamic_args"]["config_version"] = self.args.config_version
- container_dynamic_args_config["mqtt_config_path"] = os.path.join(
- unzip_package_path, "fedml", "config", os.path.basename(container_dynamic_args_config["mqtt_config_path"])
- )
- container_dynamic_args_config["s3_config_path"] = os.path.join(
- unzip_package_path, "fedml", "config", os.path.basename(container_dynamic_args_config["s3_config_path"])
- )
- log_file_dir = ClientConstants.get_log_file_dir()
- os.makedirs(log_file_dir, exist_ok=True)
- package_conf_object["dynamic_args"]["log_file_dir"] = log_file_dir
-
- # Save new config dictionary to local file
- fedml_updated_config_file = os.path.join(unzip_package_path, "conf", "fedml.yaml")
- ClientConstants.generate_yaml_doc(package_conf_object, fedml_updated_config_file)
-
- # Build dynamic arguments and set arguments to fedml config object
- self.build_dynamic_args(run_id, run_config, package_conf_object, unzip_package_path)
- return unzip_package_path, package_conf_object
-
- def build_dynamic_args(self, run_id, run_config, package_conf_object, base_dir):
- fedml_conf_file = package_conf_object["entry_config"]["conf_file"]
- fedml_conf_file_processed = str(fedml_conf_file).replace('\\', os.sep).replace('/', os.sep)
- fedml_conf_path = os.path.join(base_dir, "fedml", "config",
- os.path.basename(fedml_conf_file_processed))
- fedml_conf_object = load_yaml_config(fedml_conf_path)
- run_params = run_config.get("parameters", {})
- job_yaml = run_params.get("job_yaml", {})
-
- # Replace local fedml config objects with parameters from MLOps web
- parameters_object = run_config.get("parameters", None)
- if parameters_object is not None:
- for config_k, config_v in fedml_conf_object.items():
- parameter_v = parameters_object.get(config_k, None)
- if parameter_v is not None:
- fedml_conf_object[config_k] = parameter_v
- parameters_object.pop(config_k)
-
- for config_k, config_v in parameters_object.items():
- fedml_conf_object[config_k] = config_v
-
- package_dynamic_args = package_conf_object["dynamic_args"]
- if fedml_conf_object.get("comm_args", None) is not None:
- fedml_conf_object["comm_args"]["mqtt_config_path"] = package_dynamic_args["mqtt_config_path"]
- fedml_conf_object["comm_args"]["s3_config_path"] = package_dynamic_args["s3_config_path"]
- fedml_conf_object["common_args"]["using_mlops"] = True
- if fedml_conf_object.get("train_args", None) is not None:
- fedml_conf_object["train_args"]["run_id"] = package_dynamic_args["run_id"]
- fedml_conf_object["train_args"]["client_id_list"] = package_dynamic_args["client_id_list"]
- fedml_conf_object["train_args"]["client_num_in_total"] = int(package_dynamic_args["client_num_in_total"])
- fedml_conf_object["train_args"]["client_num_per_round"] = int(package_dynamic_args["client_num_in_total"])
- fedml_conf_object["train_args"]["client_id"] = self.edge_id
- fedml_conf_object["train_args"]["server_id"] = self.request_json.get("server_id", "0")
- if fedml_conf_object.get("device_args", None) is not None:
- fedml_conf_object["device_args"]["worker_num"] = int(package_dynamic_args["client_num_in_total"])
- # fedml_conf_object["data_args"]["data_cache_dir"] = package_dynamic_args["data_cache_dir"]
- data_args = fedml_conf_object.get("data_args")
- if data_args is not None:
- data_cache_dir = fedml_conf_object["data_args"].get("data_cache_dir")
- if data_cache_dir is not None:
- data_cache_dir = os.path.join(data_cache_dir, str(self.edge_id))
- fedml_conf_object["data_args"]["data_cache_dir"] = data_cache_dir
- if fedml_conf_object.get("tracking_args", None) is not None:
- fedml_conf_object["tracking_args"]["log_file_dir"] = package_dynamic_args["log_file_dir"]
- fedml_conf_object["tracking_args"]["log_server_url"] = package_dynamic_args["log_server_url"]
-
- fedml_conf_object["dynamic_args"] = package_dynamic_args
- self.fedml_config_object = fedml_conf_object.copy()
- ClientConstants.generate_yaml_doc(fedml_conf_object, fedml_conf_path)
-
- def run_bootstrap_script(self, bootstrap_cmd_list, bootstrap_script_file):
- try:
- logging.info("Bootstrap commands are being executed...")
- process, error_list = ClientConstants.execute_commands_with_live_logs(bootstrap_cmd_list,
- callback=self.callback_run_bootstrap)
-
- ret_code, out, err = process.returncode, None, None
- if ret_code is None or ret_code <= 0:
- if error_list is not None and len(error_list) > 0:
- is_bootstrap_run_ok = False
- else:
- if out is not None:
- out_str = sys_utils.decode_our_err_result(out)
- if out_str != "":
- logging.info("{}".format(out_str))
-
- sys_utils.log_return_info(bootstrap_script_file, 0)
-
- is_bootstrap_run_ok = True
- else:
- if err is not None:
- err_str = sys_utils.decode_our_err_result(err)
- if err_str != "":
- logging.error("{}".format(err_str))
-
- sys_utils.log_return_info(bootstrap_script_file, ret_code)
-
- is_bootstrap_run_ok = False
- except Exception as e:
- logging.error(f"Bootstrap script error: Exception: {e}, Traceback: {traceback.format_exc()}")
- is_bootstrap_run_ok = False
- return is_bootstrap_run_ok
-
- def callback_run_bootstrap(self, job_pid):
- ClientConstants.save_bootstrap_process(self.run_id, job_pid)
-
- def run(self, process_event, completed_event, message_center_queue):
- print(f"Client runner process id {os.getpid()}, run id {self.run_id}")
-
- if platform.system() != "Windows":
- os.setsid()
-
- os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'
- os.environ.setdefault('PYTHONWARNINGS', 'ignore:semaphore_tracker:UserWarning')
-
- self.run_process_event = process_event
- self.run_process_completed_event = completed_event
- try:
- MLOpsUtils.set_ntp_offset(self.ntp_offset)
- self.rebuild_message_center(message_center_queue)
- self.run_impl()
- except RunnerError:
- logging.info("Runner stopped.")
- self.reset_devices_status(self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED)
- except RunnerCompletedError:
- logging.info("Runner completed.")
- except Exception as e:
- logging.error(f"Runner exited with errors. Exception: {e}, Traceback {traceback.format_exc()}")
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- server_id=self.server_id, run_id=self.run_id)
- finally:
- if self.mlops_metrics is not None:
- computing_ended_time = MLOpsUtils.get_ntp_time()
- self.mlops_metrics.report_edge_job_computing_cost(self.run_id, self.edge_id,
- self.computing_started_time, computing_ended_time,
- self.args.user, self.args.api_key)
- logging.info("Release resources.")
- self.cleanup_containers_and_release_gpus(self.run_id, self.edge_id)
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(self.run_id, self.edge_id)
- if self.mlops_metrics is not None:
- self.mlops_metrics.stop_sys_perf()
- time.sleep(3)
- ClientConstants.cleanup_learning_process(self.run_id)
- ClientConstants.cleanup_run_process(self.run_id)
-
- def check_runner_stop_event(self):
- if self.run_process_event.is_set():
- logging.info("Received stopping event.")
- raise RunnerError("Runner stopped")
-
- if self.run_process_completed_event.is_set():
- logging.info("Received completed event.")
- raise RunnerCompletedError("Runner completed")
-
- def run_impl(self):
- run_id = self.request_json["runId"]
- run_config = self.request_json["run_config"]
- data_config = run_config.get("data_config", {})
- packages_config = run_config["packages_config"]
-
- self.computing_started_time = MLOpsUtils.get_ntp_time()
- self.mlops_metrics.report_edge_job_computing_cost(run_id, self.edge_id,
- self.computing_started_time, 0,
- self.args.user, self.args.api_key)
-
- self.check_runner_stop_event()
-
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
-
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_INITIALIZING,
- running_json=self.start_request_json, run_id=run_id)
-
- # get training params
- private_local_data_dir = data_config.get("privateLocalData", "")
- is_using_local_data = 0
- # if private_local_data_dir is not None and len(str(private_local_data_dir).strip(' ')) > 0:
- # is_using_local_data = 1
-
- # start a run according to the hyper-parameters
- # fedml_local_data_dir = self.cur_dir + "/fedml_data/run_" + run_id_str + "_edge_" + str(edge_id)
- fedml_local_data_dir = os.path.join(self.cur_dir, "fedml_data")
- fedml_local_config_dir = os.path.join(self.cur_dir, "fedml_config")
- if is_using_local_data:
- fedml_local_data_dir = private_local_data_dir
- self.fedml_data_dir = self.fedml_data_local_package_dir
-
- self.check_runner_stop_event()
-
- logging.info("Download packages")
-
- # update local config with real time parameters from server and dynamically replace variables value
- unzip_package_path, fedml_config_object = self.update_local_fedml_config(run_id, run_config)
- # if unzip_package_path is None or fedml_config_object is None:
- # logging.info("failed to update local fedml config.")
- # self.check_runner_stop_event()
- # # Send failed msg when exceptions.
- # self.cleanup_run_when_starting_failed(status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION)
- # return
-
- logging.info("Check downloaded packages...")
-
- entry_file_config = fedml_config_object["entry_config"]
- dynamic_args_config = fedml_config_object["dynamic_args"]
- entry_file = str(entry_file_config["entry_file"]).replace('\\', os.sep).replace('/', os.sep)
- entry_file = os.path.basename(entry_file)
- conf_file = entry_file_config["conf_file"]
- conf_file = str(conf_file).replace('\\', os.sep).replace('/', os.sep)
- #####
- # ClientConstants.cleanup_learning_process(run_id)
- # ClientConstants.cleanup_bootstrap_process(run_id)
- #####
-
- if not os.path.exists(unzip_package_path):
- logging.info("failed to unzip file.")
- self.check_runner_stop_event()
- # Send failed msg when exceptions.
- self.cleanup_run_when_starting_failed(status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION)
- return
- os.chdir(os.path.join(unzip_package_path, "fedml"))
-
- self.check_runner_stop_event()
-
- logging.info("starting the user process...")
-
- entry_file_full_path = os.path.join(unzip_package_path, "fedml", entry_file)
- conf_file_full_path = os.path.join(unzip_package_path, "fedml", conf_file)
- logging.info("waiting the user process to finish...")
- logging.info(" ")
- logging.info(" ")
- logging.info("====Your Run Logs Begin===")
-
- process, is_launch_task, error_list = self.execute_job_task(unzip_package_path=unzip_package_path,
- entry_file_full_path=entry_file_full_path,
- conf_file_full_path=conf_file_full_path,
- dynamic_args_config=dynamic_args_config,
- fedml_config_object=self.fedml_config_object)
-
- logging.info("====Your Run Logs End===")
- logging.info(" ")
- logging.info(" ")
-
- ret_code, out, err = process.returncode if process else None, None, None
- is_run_ok = sys_utils.is_runner_finished_normally(process.pid)
- if is_launch_task:
- is_run_ok = True
- if error_list is not None and len(error_list) > 0:
- is_run_ok = False
- if ret_code is None or ret_code <= 0:
- self.check_runner_stop_event()
-
- if is_run_ok:
- if out is not None:
- out_str = sys_utils.decode_our_err_result(out)
- if out_str != "":
- logging.info("{}".format(out_str))
-
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
- server_id=self.server_id, run_id=run_id)
-
- if is_launch_task:
- sys_utils.log_return_info(f"job {run_id}", ret_code)
- else:
- sys_utils.log_return_info(entry_file, ret_code)
- else:
- is_run_ok = False
-
- if not is_run_ok:
- # If the run status is killed or finished, then return with the normal state.
- current_job = FedMLClientDataInterface.get_instance().get_job_by_id(run_id)
- if current_job is not None and (current_job.status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or
- current_job.status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED):
- return
-
- self.check_runner_stop_event()
-
- logging.error("failed to run the learning process...")
-
- if err is not None:
- err_str = sys_utils.decode_our_err_result(err)
- if err_str != "":
- logging.error("{}".format(err_str))
-
- if is_launch_task:
- sys_utils.log_return_info(f"job {run_id}", ret_code)
- else:
- sys_utils.log_return_info(entry_file, ret_code)
-
- # Send failed msg when exceptions.
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED,
- server_id=self.server_id, run_id=run_id)
-
- def execute_job_task(self, unzip_package_path, entry_file_full_path, conf_file_full_path, dynamic_args_config,
- fedml_config_object):
- run_config = self.request_json["run_config"]
- run_params = run_config.get("parameters", {})
- client_rank = self.request_json.get("client_rank", 1)
- job_yaml = run_params.get("job_yaml", {})
- job_yaml_default_none = run_params.get("job_yaml", None)
- job_api_key = job_yaml.get("run_api_key", None)
- job_api_key = job_yaml.get("fedml_run_dynamic_params", None) if job_api_key is None else job_api_key
- assigned_gpu_ids = run_params.get("gpu_ids", None)
- job_type = job_yaml.get("job_type", None)
- containerize = fedml_config_object.get("containerize", None)
- image_pull_policy = fedml_config_object.get("image_pull_policy", Constants.IMAGE_PULL_POLICY_ALWAYS)
- # TODO: Can we remove task_type?
- job_type = job_yaml.get("task_type", Constants.JOB_TASK_TYPE_TRAIN) if job_type is None else job_type
- conf_file_object = load_yaml_config(conf_file_full_path)
- entry_args_dict = conf_file_object.get("fedml_entry_args", {})
- entry_args = entry_args_dict.get("arg_items", None)
- scheduler_match_info = self.request_json.get("scheduler_match_info", {})
- if job_type == Constants.JOB_TASK_TYPE_TRAIN:
- containerize = True if containerize is None else containerize
-
- # Bootstrap Info
- bootstrap_script_path, bootstrap_script_dir, bootstrap_script_file = [None] * 3
- env_args = fedml_config_object.get("environment_args", None)
-
- if env_args is not None:
- bootstrap_script_file = env_args.get("bootstrap", None)
- if bootstrap_script_file is not None:
- bootstrap_script_file = str(bootstrap_script_file).replace('\\', os.sep).replace('/', os.sep)
- if platform.system() == 'Windows':
- bootstrap_script_file = bootstrap_script_file.rstrip('.sh') + '.bat'
- if bootstrap_script_file is not None:
- bootstrap_script_dir = os.path.join(unzip_package_path, "fedml",
- os.path.dirname(bootstrap_script_file))
- bootstrap_script_path = os.path.join(
- bootstrap_script_dir, bootstrap_script_dir, os.path.basename(bootstrap_script_file)
- )
-
- bootstrap_cmd_list = list()
- if bootstrap_script_path:
- logging.info("Bootstrap commands are being generated...")
- bootstrap_cmd_list = JobRunnerUtils.generate_bootstrap_commands(bootstrap_script_path=bootstrap_script_path,
- bootstrap_script_dir=bootstrap_script_dir,
- bootstrap_script_file=bootstrap_script_file)
- logging.info(f"Generated following Bootstrap commands: {bootstrap_cmd_list}")
-
- if not containerize:
- if len(bootstrap_cmd_list) and not (job_type == Constants.JOB_TASK_TYPE_DEPLOY or
- job_type == Constants.JOB_TASK_TYPE_SERVE):
- bootstrapping_successful = self.run_bootstrap_script(bootstrap_cmd_list=bootstrap_cmd_list,
- bootstrap_script_file=bootstrap_script_file)
-
- if not bootstrapping_successful:
- logging.info("failed to update local fedml config.")
- self.check_runner_stop_event()
- # Send failed msg when exceptions.
- self.cleanup_run_when_starting_failed(status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION)
- raise Exception(f"Failed to execute following bootstrap commands: {bootstrap_cmd_list}")
-
- logging.info("cleanup the previous learning process and bootstrap process...")
- ClientConstants.cleanup_learning_process(self.request_json["runId"])
- ClientConstants.cleanup_bootstrap_process(self.request_json["runId"])
-
- executable_interpreter = ClientConstants.CLIENT_SHELL_PS \
- if platform.system() == ClientConstants.PLATFORM_WINDOWS else ClientConstants.CLIENT_SHELL_BASH
-
- if job_yaml_default_none is None:
- # Generate the job executing commands for previous federated learning (Compatibility)
- python_program = get_python_program()
- logging.info("Run the client: {} {} --cf {} --rank {} --role client".format(
- python_program, entry_file_full_path, conf_file_full_path, str(dynamic_args_config.get("rank", 1))))
- rank = str(dynamic_args_config.get("rank", 1))
- entry_command = f"{python_program} {entry_file_full_path} --cf " \
- f"{conf_file_full_path} --rank {rank} --role client"
- shell_cmd_list = [entry_command]
-
- # Run the job executing commands for previous federated learning (Compatibility)
- process, error_list = ClientConstants.execute_commands_with_live_logs(
- shell_cmd_list, callback=self.callback_start_fl_job, should_write_log_file=False)
- is_launch_task = False
- else:
- self.check_runner_stop_event()
-
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_RUNNING, run_id=self.run_id)
-
- # Generate the job executing commands
- job_executing_commands = JobRunnerUtils.generate_job_execute_commands(
- self.run_id, self.edge_id, self.version,
- self.package_type, executable_interpreter, entry_file_full_path,
- conf_file_object, entry_args, assigned_gpu_ids,
- job_api_key, client_rank, scheduler_match_info=scheduler_match_info,
- cuda_visible_gpu_ids_str=self.cuda_visible_gpu_ids_str)
-
- if containerize is not None and containerize is True:
- docker_args = fedml_config_object.get("docker", {})
- docker_args = JobRunnerUtils.create_instance_from_dict(DockerArgs, docker_args)
- try:
- job_executing_commands = JobRunnerUtils.generate_launch_docker_command(docker_args=docker_args,
- run_id=self.run_id,
- edge_id=self.edge_id,
- unzip_package_path=unzip_package_path,
- executable_interpreter=executable_interpreter,
- entry_file_full_path=entry_file_full_path,
- bootstrap_cmd_list=bootstrap_cmd_list,
- cuda_visible_gpu_ids_str=self.cuda_visible_gpu_ids_str,
- image_pull_policy=image_pull_policy)
- except Exception as e:
- logging.error(f"Error occurred while generating containerized launch commands. "
- f"Exception: {e}, Traceback: {traceback.format_exc()}")
- return None, None, None
-
- if not job_executing_commands:
- raise Exception("Failed to generate docker execution command")
-
- # Run the job executing commands
- logging.info(f"Run the client job with job id {self.run_id}, device id {self.edge_id}.")
- process, error_list = ClientConstants.execute_commands_with_live_logs(
- job_executing_commands, callback=self.start_job_perf, error_processor=self.job_error_processor,
- should_write_log_file=False if job_type == Constants.JOB_TASK_TYPE_FEDERATE else True)
- is_launch_task = False if job_type == Constants.JOB_TASK_TYPE_FEDERATE else True
-
- return process, is_launch_task, error_list
-
- def callback_start_fl_job(self, job_pid):
- ClientConstants.save_learning_process(self.run_id, job_pid)
- self.mlops_metrics.report_sys_perf(
- self.args, self.agent_config["mqtt_config"], job_process_id=job_pid)
-
- def start_job_perf(self, job_pid):
- ClientConstants.save_learning_process(self.run_id, job_pid)
- self.mlops_metrics.report_job_perf(self.args, self.agent_config["mqtt_config"], job_pid)
-
- def job_error_processor(self, error_list):
- self.check_runner_stop_event()
-
- error_str = "\n".join(error_list)
- error_message = f"Error occurred when running the job... {error_str}"
- logging.error(error_message)
- raise Exception(error_message)
-
- def reset_devices_status(self, edge_id, status, should_send_client_id_status=True):
- self.mlops_metrics.run_id = self.run_id
- self.mlops_metrics.edge_id = edge_id
-
- if should_send_client_id_status:
- if status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED or \
- status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or \
- status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION:
- self.mlops_metrics.report_client_id_status(
- edge_id, status, server_id=self.server_id, run_id=self.run_id)
-
- def sync_run_stop_status(self, run_status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED):
- try:
- if self.run_process_event is not None:
- self.run_process_event.set()
-
- self.mlops_metrics.report_client_id_status(
- self.edge_id, run_status, server_id=self.server_id, run_id=self.run_id)
- except Exception as e:
- logging.error(f"Failed to sync run stop status with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- def cleanup_run_when_starting_failed(
- self, status=ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED, should_send_client_id_status=True):
- # logging.error("Cleanup run successfully when starting failed.")
-
- self.reset_devices_status(
- self.edge_id, status, should_send_client_id_status=should_send_client_id_status)
-
- time.sleep(2)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- logging.error(f"Failed to stop sys perf with Exception {ex}. Traceback: {traceback.format_exc()}")
- pass
-
- time.sleep(1)
-
- try:
- ClientConstants.cleanup_learning_process(self.run_id)
- ClientConstants.cleanup_bootstrap_process(self.run_id)
- ClientConstants.cleanup_run_process(self.run_id)
- except Exception as e:
- logging.error(
- f"Failed to cleanup run when starting failed with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- def cleanup_run_when_finished(self):
- # logging.info("Cleanup run successfully when finished.")
-
- self.reset_devices_status(self.edge_id,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
- should_send_client_id_status=False)
-
- time.sleep(2)
-
- try:
- self.mlops_metrics.stop_sys_perf()
- except Exception as ex:
- logging.error(f"Failed to stop sys perf with Exception {ex}. Traceback: {traceback.format_exc()}")
- pass
-
- time.sleep(1)
-
- try:
- ClientConstants.cleanup_learning_process(self.run_id)
- ClientConstants.cleanup_bootstrap_process(self.run_id)
- ClientConstants.cleanup_run_process(self.run_id)
- except Exception as e:
- logging.error(
- f"Failed to cleanup run when finished with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- def setup_message_center(self):
- if self.message_center is not None:
- return
-
- self.message_center = FedMLMessageCenter(agent_config=self.agent_config)
- self.message_center.start_sender()
-
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
- self.mlops_metrics.set_messenger(self.message_center)
- self.mlops_metrics.run_id = self.run_id
-
- def rebuild_message_center(self, message_center_queue):
- self.message_center = FedMLMessageCenter(message_queue=message_center_queue)
-
- if self.mlops_metrics is None:
- self.mlops_metrics = MLOpsMetrics()
- self.mlops_metrics.set_messenger(self.message_center)
- self.mlops_metrics.run_id = self.run_id
-
- def release_message_center(self):
- try:
- if self.message_center is not None:
- self.message_center.stop()
- self.message_center = None
-
- except Exception as e:
- logging.error(
- f"Failed to release client mqtt manager with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- def ota_upgrade(self, payload, request_json):
- run_id = request_json["runId"]
- force_ota = False
- ota_version = None
-
- try:
- run_config = request_json.get("run_config", None)
- parameters = run_config.get("parameters", None)
- common_args = parameters.get("common_args", None)
- force_ota = common_args.get("force_ota", False) if common_args is not None else False
- ota_version = common_args.get("ota_version", None) if common_args is not None else None
- except Exception as e:
- logging.error(
- f"Failed to get ota upgrade parameters with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- if force_ota and ota_version is not None:
- should_upgrade = True if ota_version != fedml.__version__ else False
- upgrade_version = ota_version
- else:
- try:
- fedml_is_latest_version, local_ver, remote_ver = sys_utils.check_fedml_is_latest_version(self.version)
- except Exception as e:
- logging.error(f"Failed to check fedml version with Exception {e}. Traceback: {traceback.format_exc()}")
- return
-
- should_upgrade = False if fedml_is_latest_version else True
- upgrade_version = remote_ver
-
- if should_upgrade:
- FedMLClientDataInterface.get_instance(). \
- save_started_job(run_id, self.edge_id, time.time(),
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
- ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING,
- payload)
- self.mlops_metrics.report_client_id_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING, run_id=run_id)
-
- logging.info(f"Upgrade to version {upgrade_version} ...")
-
- sys_utils.do_upgrade(self.version, upgrade_version)
- raise Exception("Restarting after upgraded...")
-
- def callback_start_train(self, topic, payload):
- # Get training params
-
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json["runId"]
-
- # Start log processor for current run
- train_edge_id = str(topic).split("/")[-2]
- self.args.run_id = run_id
- self.args.edge_id = train_edge_id
- MLOpsRuntimeLog.get_instance(self.args).init_logs(log_level=logging.INFO)
- MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(
- run_id, train_edge_id, log_source=SchedulerConstants.get_log_source(request_json))
- logging.info("start the log processor")
-
- try:
- MLOpsConfigs.fetch_all_configs()
- except Exception as e:
- logging.error(f"Failed to fetch all configs with Exception {e}. Traceback: {traceback.format_exc()}")
- pass
-
- if not FedMLClientDataInterface.get_instance().get_agent_status():
- request_json = json.loads(payload)
- run_id = request_json["runId"]
- logging.error(
- "FedMLDebug - Receive: topic ({}), payload ({}), but the client agent is disabled. {}".format(
- topic, payload, traceback.format_exc()
- )
- )
- # Send failed msg when exceptions.
- self.mlops_metrics.report_client_id_status(
- train_edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION, run_id=run_id,
- msg=f"the client agent {train_edge_id} is disabled")
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, train_edge_id)
- return
-
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- # Terminate previous process about starting or stopping run command
- logging.info("cleanup and save runner information")
- server_agent_id = request_json["cloud_agent_id"]
- ClientConstants.save_runner_infos(self.args.device_id + "." + self.args.os_name, train_edge_id, run_id=run_id)
-
- # OTA upgrade
- # self.ota_upgrade(payload, request_json)
-
- # Occupy GPUs
- scheduler_match_info = request_json.get("scheduler_match_info", {})
- matched_gpu_num = scheduler_match_info.get("matched_gpu_num", 0)
- model_master_device_id = scheduler_match_info.get("model_master_device_id", None)
- model_slave_device_id = scheduler_match_info.get("model_slave_device_id", None)
- model_slave_device_id_list = scheduler_match_info.get("model_slave_device_id_list", None)
- run_config = request_json.get("run_config", {})
- run_params = run_config.get("parameters", {})
- serving_args = run_params.get("serving_args", {})
- endpoint_id = serving_args.get("endpoint_id", None)
- job_yaml = run_params.get("job_yaml", {})
- job_type = job_yaml.get("job_type", SchedulerConstants.JOB_TASK_TYPE_TRAIN)
- cuda_visible_gpu_ids_str = None
- if not (job_type == SchedulerConstants.JOB_TASK_TYPE_SERVE or
- job_type == SchedulerConstants.JOB_TASK_TYPE_DEPLOY):
- cuda_visible_gpu_ids_str = JobRunnerUtils.get_instance().occupy_gpu_ids(
- run_id, matched_gpu_num, train_edge_id, inner_id=endpoint_id,
- model_master_device_id=model_master_device_id,
- model_slave_device_id=model_slave_device_id)
- logging.info(
- f"Run started, available gpu ids: {JobRunnerUtils.get_instance().get_available_gpu_id_list(train_edge_id)}")
-
- # Start server with multiprocessing mode
- self.request_json = request_json
- run_id_str = str(run_id)
- self.running_request_json[run_id_str] = request_json
- client_runner = FedMLClientRunner(
- self.args, edge_id=train_edge_id, request_json=request_json, agent_config=self.agent_config, run_id=run_id,
- cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str
- )
- client_runner.start_request_json = payload
- self.run_process_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_event_map[run_id_str].clear()
- client_runner.run_process_event = self.run_process_event_map[run_id_str]
- self.run_process_completed_event_map[run_id_str] = multiprocessing.Event()
- self.run_process_completed_event_map[run_id_str].clear()
- client_runner.run_process_completed_event = self.run_process_completed_event_map[run_id_str]
- client_runner.server_id = request_json.get("server_id", "0")
- logging.info("start the runner process.")
- self.run_process_map[run_id_str] = Process(target=client_runner.run, args=(
- self.run_process_event_map[run_id_str], self.run_process_completed_event_map[run_id_str],
- self.message_center.get_message_queue()))
- self.run_process_map[run_id_str].start()
- ClientConstants.save_run_process(run_id, self.run_process_map[run_id_str].pid)
-
- def callback_stop_train(self, topic, payload):
- # logging.info("callback_stop_train: topic = %s, payload = %s" % (topic, payload))
- # logging.info(
- # f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- # )
-
- train_edge_id = str(topic).split("/")[-2]
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json.get("runId", None)
- if run_id is None:
- run_id = request_json.get("id", None)
- run_status = request_json.get("run_status", ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED)
-
- # logging.info("Stop run with multiprocessing...")
-
- # Stop client with multiprocessing mode
- run_id_str = str(run_id)
- client_runner = FedMLClientRunner(
- self.args, edge_id=train_edge_id, request_json=request_json, agent_config=self.agent_config, run_id=run_id
- )
- self.cleanup_containers_and_release_gpus(run_id, train_edge_id)
- client_runner.run_process_event = self.run_process_event_map.get(run_id_str, None)
- client_runner.run_process = self.run_process_map.get(run_id_str, None)
- client_runner.message_center = self.message_center
- client_runner.mlops_metrics = self.mlops_metrics
- client_runner.sync_run_stop_status(run_status=run_status)
-
- def cleanup_containers_and_release_gpus(self, run_id, edge_id):
- job_type = JobRunnerUtils.get_job_type_from_run_id(run_id)
-
- if not job_type:
- logging.info(f"Failed to get job type from run id {run_id}. This is not an error as it would usually "
- f"happen when the job is not found in the database because job is already finished and "
- f"cleaned up. Exiting cleanup_containers_and_release_gpus.")
- return
-
- # Check if the job type is not "serve" or "deploy"
- if not (job_type == SchedulerConstants.JOB_TASK_TYPE_SERVE or
- job_type == SchedulerConstants.JOB_TASK_TYPE_DEPLOY):
-
- # Terminate the run docker container if exists
- container_name = JobRunnerUtils.get_run_container_name(run_id)
- docker_client = JobRunnerUtils.get_docker_client(DockerArgs())
- logging.info(f"Terminating the run docker container {container_name} if exists...")
- try:
- JobRunnerUtils.remove_run_container_if_exists(container_name, docker_client)
- except Exception as e:
- logging.error(f"Exception {e} occurred when terminating docker container. "
- f"Traceback: {traceback.format_exc()}")
-
- # Release the GPU ids and update the GPU availability in the persistent store
- JobRunnerUtils.get_instance().release_gpu_ids(run_id, edge_id)
-
- # Send mqtt message reporting the new gpu availability to the backend
- MLOpsDevicePerfStats.report_gpu_device_info(self.edge_id, mqtt_mgr=self.mqtt_mgr)
-
- def cleanup_client_with_status(self):
- if self.device_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED:
- # logging.info("received to finished status.")
- self.cleanup_run_when_finished()
- elif self.device_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED:
- # logging.error("received to failed status from the server agent")
- self.cleanup_run_when_starting_failed(should_send_client_id_status=False)
- elif self.device_status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED:
- # logging.error("received to failed status from the server agent")
- self.cleanup_run_when_starting_failed(status=self.device_status, should_send_client_id_status=False)
-
- def callback_runner_id_status(self, topic, payload):
- # logging.info("callback_runner_id_status: topic = %s, payload = %s" % (topic, payload))
- # logging.info(f"FedMLDebug - Receive: topic ({topic}), payload ({payload})")
- request_json = json.loads(payload)
- is_retain = request_json.get("is_retain", False)
- if is_retain:
- return
- run_id = request_json["run_id"]
- edge_id = str(topic).split("/")[-2].split('_')[-1]
- status = request_json["status"]
- run_id_str = str(run_id)
-
- self.save_training_status(
- edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED
- if status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_EXCEPTION else status)
-
- if status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED or \
- status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_FAILED or \
- status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_KILLED:
- completed_event = self.run_process_completed_event_map.get(run_id_str, None)
- if completed_event is not None:
- completed_event.set()
-
- # Stop client with multiprocessing mode
- client_runner = FedMLClientRunner(
- self.args,
- edge_id=edge_id,
- request_json=request_json,
- agent_config=self.agent_config,
- run_id=run_id,
- )
- client_runner.device_status = status
- client_runner.message_center = self.message_center
- client_runner.mlops_metrics = self.mlops_metrics
- client_runner.cleanup_client_with_status()
-
- running_json = self.running_request_json.get(run_id_str)
- if running_json is None:
- try:
- current_job = FedMLClientDataInterface.get_instance().get_job_by_id(run_id)
- running_json = json.loads(current_job.running_json)
- except Exception as e:
- logging.error(f"Failed to get running json with Exception {e}. Traceback: {traceback.format_exc()}")
-
- if running_json is not None:
- job_type = JobRunnerUtils.parse_job_type(running_json)
- if not SchedulerConstants.is_deploy_job(job_type):
- logging.info(f"[run/device][{run_id}/{edge_id}] Release gpu resource when run ended.")
- self.cleanup_containers_and_release_gpus(run_id, edge_id)
-
- run_process = self.run_process_map.get(run_id_str, None)
- if run_process is not None:
- if run_process.pid is not None:
- RunProcessUtils.kill_process(run_process.pid)
-
- # Terminate the run docker container if exists
- try:
- container_name = JobRunnerUtils.get_run_container_name(run_id)
- docker_client = JobRunnerUtils.get_docker_client(DockerArgs())
- logging.info(f"Terminating the run docker container {container_name} if exists...")
- JobRunnerUtils.remove_run_container_if_exists(container_name, docker_client)
- except Exception as e:
- logging.error(f"Error occurred when terminating docker container."
- f"Exception: {e}, Traceback: {traceback.format_exc()}.")
-
- self.run_process_map.pop(run_id_str)
-
- # Stop log processor for current run
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, edge_id)
-
- def callback_report_current_status(self, topic, payload):
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- self.send_agent_active_msg()
- if self.general_edge_id is not None:
- self.send_agent_active_msg(self.general_edge_id)
-
- @staticmethod
- def process_ota_upgrade_msg():
- os.system("pip install -U fedml")
-
- @staticmethod
- def callback_client_ota_msg(topic, payload):
- logging.info(
- f"FedMLDebug - Receive: topic ({topic}), payload ({payload})"
- )
-
- request_json = json.loads(payload)
- cmd = request_json["cmd"]
-
- if cmd == ClientConstants.FEDML_OTA_CMD_UPGRADE:
- FedMLClientRunner.process_ota_upgrade_msg()
- # Process(target=FedMLClientRunner.process_ota_upgrade_msg).start()
- raise Exception("After upgraded, restart runner...")
- elif cmd == ClientConstants.FEDML_OTA_CMD_RESTART:
- raise Exception("Restart runner...")
-
- def get_all_run_process_list_map(self):
- run_process_dict = dict()
- for run_id_str, process in self.run_process_map.items():
- cur_run_process_list = ClientConstants.get_learning_process_list(run_id_str)
- run_process_dict[run_id_str] = cur_run_process_list
-
- return run_process_dict
-
- def response_device_info_to_mlops(self, topic, payload):
- payload_json = json.loads(payload)
- server_id = payload_json.get("server_id", 0)
- run_id = payload_json.get("run_id", 0)
- listen_edge_id = str(topic).split("/")[-1]
- context = payload_json.get("context", None)
- need_gpu_info = payload_json.get("need_gpu_info", False)
- need_running_process_list = payload_json.get("need_running_process_list", False)
- response_topic = f"deploy/slave_agent/mlops/response_device_info"
- if self.mlops_metrics is not None and self.model_device_client_edge_id_list is not None and \
- self.model_device_server_id is not None:
- if not need_gpu_info:
- device_info_json = {
- "edge_id": listen_edge_id,
- "fedml_version": fedml.__version__,
- "user_id": self.args.user
- }
- else:
- total_mem, free_mem, total_disk_size, free_disk_size, cup_utilization, cpu_cores, gpu_cores_total, \
- gpu_cores_available, sent_bytes, recv_bytes, gpu_available_ids = sys_utils.get_sys_realtime_stats()
- host_ip = sys_utils.get_host_ip()
- host_port = sys_utils.get_available_port()
- gpu_available_ids = JobRunnerUtils.get_available_gpu_id_list(self.edge_id)
- gpu_available_ids = JobRunnerUtils.trim_unavailable_gpu_ids(gpu_available_ids)
- gpu_cores_available = len(gpu_available_ids)
- gpu_list = sys_utils.get_gpu_list()
- device_info_json = {
- "edge_id": listen_edge_id,
- "memoryTotal": round(total_mem * MLOpsUtils.BYTES_TO_GB, 2),
- "memoryAvailable": round(free_mem * MLOpsUtils.BYTES_TO_GB, 2),
- "diskSpaceTotal": round(total_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
- "diskSpaceAvailable": round(free_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
- "cpuUtilization": round(cup_utilization, 2),
- "cpuCores": cpu_cores,
- "gpuCoresTotal": gpu_cores_total,
- "gpuCoresAvailable": gpu_cores_available,
- "gpu_available_ids": gpu_available_ids,
- "gpu_list": gpu_list,
- "node_ip": host_ip,
- "node_port": host_port,
- "networkTraffic": sent_bytes + recv_bytes,
- "updateTime": int(MLOpsUtils.get_ntp_time()),
- "fedml_version": fedml.__version__,
- "user_id": self.args.user
- }
- if need_running_process_list:
- device_info_json["run_process_list_map"] = self.get_all_run_process_list_map()
- salve_device_ids = list()
- for model_client_edge_id in self.model_device_client_edge_id_list:
- salve_device_ids.append(model_client_edge_id)
- response_payload = {"slave_device_id": self.model_device_client_edge_id_list[0],
- "slave_device_id_list": salve_device_ids,
- "master_device_id": self.model_device_server_id,
- "run_id": run_id, "edge_id": listen_edge_id,
- "edge_info": device_info_json}
- if context is not None:
- response_payload["context"] = context
- self.message_center.send_message(response_topic, json.dumps(response_payload), run_id=run_id)
-
- def callback_report_device_info(self, topic, payload):
- payload_json = json.loads(payload)
- server_id = payload_json.get("server_id", 0)
- run_id = payload_json.get("run_id", 0)
- listen_edge_id = str(topic).split("/")[-1]
- context = payload_json.get("context", None)
- need_gpu_info = payload_json.get("need_gpu_info", False)
- need_running_process_list = payload_json.get("need_running_process_list", False)
- response_topic = f"client/server/response_device_info/{server_id}"
- if self.mlops_metrics is not None and self.model_device_client_edge_id_list is not None and \
- self.model_device_server_id is not None:
- if not need_gpu_info:
- device_info_json = {
- "edge_id": listen_edge_id,
- "fedml_version": fedml.__version__,
- "user_id": self.args.user
- }
- else:
- total_mem, free_mem, total_disk_size, free_disk_size, cup_utilization, cpu_cores, gpu_cores_total, \
- gpu_cores_available, sent_bytes, recv_bytes, gpu_available_ids = sys_utils.get_sys_realtime_stats()
- host_ip = sys_utils.get_host_ip()
- host_port = sys_utils.get_available_port()
- gpu_available_ids = JobRunnerUtils.get_available_gpu_id_list(self.edge_id)
- gpu_available_ids = JobRunnerUtils.trim_unavailable_gpu_ids(gpu_available_ids)
- gpu_cores_available = len(gpu_available_ids)
- gpu_list = sys_utils.get_gpu_list()
- device_info_json = {
- "edge_id": listen_edge_id,
- "memoryTotal": round(total_mem * MLOpsUtils.BYTES_TO_GB, 2),
- "memoryAvailable": round(free_mem * MLOpsUtils.BYTES_TO_GB, 2),
- "diskSpaceTotal": round(total_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
- "diskSpaceAvailable": round(free_disk_size * MLOpsUtils.BYTES_TO_GB, 2),
- "cpuUtilization": round(cup_utilization, 2),
- "cpuCores": cpu_cores,
- "gpuCoresTotal": gpu_cores_total,
- "gpuCoresAvailable": gpu_cores_available,
- "gpu_available_ids": gpu_available_ids,
- "gpu_list": gpu_list,
- "node_ip": host_ip,
- "node_port": host_port,
- "networkTraffic": sent_bytes + recv_bytes,
- "updateTime": int(MLOpsUtils.get_ntp_time()),
- "fedml_version": fedml.__version__,
- "user_id": self.args.user
- }
- if need_running_process_list:
- device_info_json["run_process_list_map"] = self.get_all_run_process_list_map()
- salve_device_ids = list()
- for model_client_edge_id in self.model_device_client_edge_id_list:
- salve_device_ids.append(model_client_edge_id)
- response_payload = {"slave_device_id": self.model_device_client_edge_id_list[0],
- "slave_device_id_list": salve_device_ids,
- "master_device_id": self.model_device_server_id,
- "run_id": run_id, "edge_id": listen_edge_id,
- "edge_info": device_info_json}
- if context is not None:
- response_payload["context"] = context
- self.message_center.send_message(response_topic, json.dumps(response_payload), run_id=run_id)
-
- def callback_client_logout(self, topic, payload):
- payload_json = json.loads(payload)
- secret = payload_json.get("auth", None)
- if secret is None or str(secret) != "246b1be6-0eeb-4b17-b118-7d74de1975d4":
- return
- logging.info("Received the logout request.")
- if self.run_process_event is not None:
- self.run_process_event.set()
- if self.run_process_completed_event is not None:
- self.run_process_completed_event.set()
- self.disable_client_login = True
- time.sleep(3)
- os.system("fedml logout")
-
- def save_training_status(self, edge_id, training_status):
- self.current_training_status = training_status
- ClientConstants.save_training_infos(edge_id, training_status)
-
- @staticmethod
- def get_gpu_machine_id():
- gpu_list = sys_utils.get_gpu_list()
- gpu_uuids = ""
- if len(gpu_list) > 0:
- for gpu in gpu_list:
- gpu_uuids += gpu.get("uuid", "")
- else:
- gpu_uuids = str(uuid.uuid4())
- device_id_combination = \
- f"{FedMLClientRunner.get_machine_id()}-{hex(uuid.getnode())}-{gpu_uuids}"
- device_id = security_utils.get_content_hash(device_id_combination)
- return device_id
-
- @staticmethod
- def get_device_id(use_machine_id=False):
- device_file_path = os.path.join(ClientConstants.get_data_dir(),
- ClientConstants.LOCAL_RUNNER_INFO_DIR_NAME)
- file_for_device_id = os.path.join(device_file_path, "devices.id")
- if not os.path.exists(device_file_path):
- os.makedirs(device_file_path, exist_ok=True)
- elif os.path.exists(file_for_device_id):
- with open(file_for_device_id, 'r', encoding='utf-8') as f:
- device_id_from_file = f.readline()
- if device_id_from_file is not None and device_id_from_file != "":
- return device_id_from_file
-
- if platform.system() == "Darwin":
- cmd_get_serial_num = "system_profiler SPHardwareDataType | grep Serial | awk '{gsub(/ /,\"\")}{print}' " \
- "|awk -F':' '{print $2}' "
- device_id = os.popen(cmd_get_serial_num).read()
- device_id = device_id.replace('\n', '').replace(' ', '')
- if device_id is None or device_id == "":
- if not use_machine_id:
- device_id = hex(uuid.getnode())
- else:
- device_id = FedMLClientRunner.get_gpu_machine_id()
- else:
- device_id = "0x" + device_id
- else:
- if "nt" in os.name:
-
- def get_uuid():
- guid = ""
- try:
- cmd = "wmic csproduct get uuid"
- guid = str(subprocess.check_output(cmd))
- pos1 = guid.find("\\n") + 2
- guid = guid[pos1:-15]
- except Exception as ex:
- logging.error(f"Failed to get uuid with Exception {ex}. Traceback: {traceback.format_exc()}")
- pass
- return str(guid)
-
- device_id = str(get_uuid())
- logging.info(device_id)
- elif "posix" in os.name:
- device_id = sys_utils.get_device_id_in_docker()
- if device_id is None:
- if not use_machine_id:
- device_id = hex(uuid.getnode())
- else:
- device_id = device_id = FedMLClientRunner.get_gpu_machine_id()
- else:
- device_id = sys_utils.run_subprocess_open(
- "hal-get-property --udi /org/freedesktop/Hal/devices/computer --key system.hardware.uuid".split()
- )
- device_id = hex(device_id)
-
- if device_id is not None and device_id != "":
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
- else:
- device_id = hex(uuid.uuid4())
- with open(file_for_device_id, 'w', encoding='utf-8') as f:
- f.write(device_id)
-
- return device_id
-
- @staticmethod
- def get_machine_id():
- try:
- import machineid
- return machineid.id().replace('\n', '').replace('\r\n', '').strip()
- except Exception as e:
- logging.error(f"Failed to get machine id with Exception {e}. Traceback: {traceback.format_exc()}")
- return hex(uuid.getnode())
-
- @staticmethod
- def bind_account_and_device_id(url, account_id, device_id, os_name, api_key="", role="client"):
- ip = requests.get('https://checkip.amazonaws.com').text.strip()
- fedml_ver, exec_path, os_ver, cpu_info, python_ver, torch_ver, mpi_installed, \
- cpu_usage, available_mem, total_mem, gpu_info, gpu_available_mem, gpu_total_mem, \
- gpu_count, gpu_vendor, cpu_count, gpu_device_name = get_sys_runner_info()
- host_name = sys_utils.get_host_name()
- json_params = {
- "accountid": account_id,
- "deviceid": device_id,
- "type": os_name,
- "state": ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE,
- "status": ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE,
- "processor": cpu_info,
- "core_type": cpu_info,
- "network": "",
- "role": role,
- "os_ver": os_ver,
- "memory": total_mem,
- "ip": ip,
- "api_key": api_key,
- "extra_infos": {"fedml_ver": fedml_ver, "exec_path": exec_path, "os_ver": os_ver,
- "cpu_info": cpu_info, "python_ver": python_ver, "torch_ver": torch_ver,
- "mpi_installed": mpi_installed, "cpu_usage": cpu_usage,
- "available_mem": available_mem, "total_mem": total_mem,
- "cpu_count": cpu_count, "gpu_count": 0, "host_name": host_name}
- }
- if gpu_count > 0:
- if gpu_total_mem is not None:
- json_params["gpu"] = gpu_info if gpu_info is not None else "" + ", Total GPU Memory: " + gpu_total_mem
- else:
- json_params["gpu"] = gpu_info if gpu_info is not None else ""
- json_params["extra_infos"]["gpu_info"] = gpu_info if gpu_info is not None else ""
- if gpu_available_mem is not None:
- json_params["extra_infos"]["gpu_available_mem"] = gpu_available_mem
- if gpu_total_mem is not None:
- json_params["extra_infos"]["gpu_total_mem"] = gpu_total_mem
-
- json_params["extra_infos"]["gpu_count"] = gpu_count
- json_params["extra_infos"]["gpu_vendor"] = gpu_vendor
- json_params["extra_infos"]["gpu_device_name"] = gpu_device_name
-
- gpu_available_id_list = sys_utils.get_available_gpu_id_list(limit=gpu_count)
- gpu_available_count = len(gpu_available_id_list) if gpu_available_id_list is not None else 0
- gpu_list = sys_utils.get_gpu_list()
- json_params["extra_infos"]["gpu_available_count"] = gpu_available_count
- json_params["extra_infos"]["gpu_available_id_list"] = gpu_available_id_list
- json_params["extra_infos"]["gpu_list"] = gpu_list
- else:
- json_params["gpu"] = "None"
- json_params["extra_infos"]["gpu_available_count"] = 0
- json_params["extra_infos"]["gpu_available_id_list"] = []
- json_params["extra_infos"]["gpu_list"] = []
-
- _, cert_path = MLOpsConfigs.get_request_params()
- if cert_path is not None:
- try:
- requests.session().verify = cert_path
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- except requests.exceptions.SSLError as err:
- logging.error(
- f"Failed to bind account and device id with error: {err}, traceback: {traceback.format_exc()}")
- MLOpsConfigs.install_root_ca_file()
- response = requests.post(
- url, json=json_params, verify=True,
- headers={"content-type": "application/json", "Connection": "close"}
- )
- else:
- response = requests.post(url, json=json_params, headers={"Connection": "close"})
- edge_id, user_name, extra_url, general_edge_id = -1, None, None, None
- if response.status_code != 200:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- pass
- else:
- # print("url = {}, response = {}".format(url, response))
- status_code = response.json().get("code")
- if status_code == "SUCCESS":
- edge_id = response.json().get("data").get("id")
- user_name = response.json().get("data").get("userName", None)
- extra_url = response.json().get("data").get("url", None)
- general_edge_id = response.json().get("data").get("general_edge_id", None)
- if edge_id is None or edge_id <= 0:
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- else:
- if status_code == SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR:
- raise SystemExit(SchedulerConstants.BINDING_ACCOUNT_NOT_EXIST_ERROR)
- print(f"Binding to MLOps with response.status_code = {response.status_code}, "
- f"response.content: {response.content}")
- return -1, None, None, None
- return edge_id, user_name, extra_url, general_edge_id
-
- def fetch_configs(self):
- return MLOpsConfigs.fetch_all_configs()
-
- def send_agent_active_msg(self, edge_id):
- active_topic = "flclient_agent/active"
- status = MLOpsStatus.get_instance().get_client_agent_status(edge_id)
- if (
- status is not None
- and status != ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE
- and status != ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE
- ):
- return
-
- try:
- current_job = FedMLClientDataInterface.get_instance().get_job_by_id(self.run_id)
- except Exception as e:
- logging.error(f"Failed to get current job with Exception {e}. Traceback: {traceback.format_exc()}")
- current_job = None
- if current_job is None:
- if status is not None and status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE:
- status = ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE
- else:
- return
- else:
- status = ClientConstants.get_device_state_from_run_edge_state(current_job.status)
- active_msg = {"ID": edge_id, "status": status}
- MLOpsStatus.get_instance().set_client_agent_status(edge_id, status)
- self.mqtt_mgr.send_message_json(active_topic, json.dumps(active_msg))
- logging.info(f"Send agent active msg {active_msg}")
-
- def recover_start_train_msg_after_upgrading(self):
- try:
- current_job = FedMLClientDataInterface.get_instance().get_current_job()
- if current_job is not None and \
- current_job.status == ClientConstants.MSG_MLOPS_CLIENT_STATUS_UPGRADING:
- logging.info("start training after upgrading.")
- topic_start_train = "flserver_agent/" + str(self.edge_id) + "/start_train"
- self.callback_start_train(topic_start_train, current_job.running_json)
- except Exception as e:
- logging.error(f"recover starting train message after upgrading failed with exception {e}, "
- f"Traceback {traceback.format_exc()}")
-
- def on_agent_mqtt_connected(self, mqtt_client_object):
- # The MQTT message topic format is as follows: <sender>/<receiver>/<action>
-
- # Setup MQTT message listener for starting training
- topic_start_train = "flserver_agent/" + str(self.edge_id) + "/start_train"
- self.add_message_listener(topic_start_train, self.callback_start_train)
- self.mqtt_mgr.add_message_listener(topic_start_train, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener for stopping training
- topic_stop_train = "flserver_agent/" + str(self.edge_id) + "/stop_train"
- self.add_message_listener(topic_stop_train, self.callback_stop_train)
- self.mqtt_mgr.add_message_listener(topic_stop_train, self.listener_message_dispatch_center)
-
-
- # Setup MQTT message listener for client status switching
- topic_client_status = "fl_client/flclient_agent_" + str(self.edge_id) + "/status"
- self.add_message_listener(topic_client_status, self.callback_runner_id_status)
- self.mqtt_mgr.add_message_listener(topic_client_status, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to report current device status.
- topic_report_status = "mlops/report_device_status"
- self.add_message_listener(topic_report_status, self.callback_report_current_status)
- self.mqtt_mgr.add_message_listener(topic_report_status, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to OTA messages from the MLOps.
- topic_ota_msg = "mlops/flclient_agent_" + str(self.edge_id) + "/ota"
- self.add_message_listener(topic_ota_msg, self.callback_client_ota_msg)
- self.mqtt_mgr.add_message_listener(topic_ota_msg, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to OTA messages from the MLOps.
- topic_request_device_info = "server/client/request_device_info/" + str(self.edge_id)
- self.add_message_listener(topic_request_device_info, self.callback_report_device_info)
- self.mqtt_mgr.add_message_listener(topic_request_device_info, self.listener_message_dispatch_center)
-
- topic_request_edge_device_info_from_mlops = f"deploy/mlops/slave_agent/request_device_info/{self.edge_id}"
- self.add_message_listener(topic_request_edge_device_info_from_mlops, self.response_device_info_to_mlops)
- self.mqtt_mgr.add_message_listener(topic_request_edge_device_info_from_mlops, self.listener_message_dispatch_center)
-
- topic_request_deploy_master_device_info_from_mlops = None
- if self.model_device_server_id is not None:
- topic_request_deploy_master_device_info_from_mlops = f"deploy/mlops/master_agent/request_device_info/{self.model_device_server_id}"
- self.add_message_listener(topic_request_deploy_master_device_info_from_mlops, self.response_device_info_to_mlops)
- self.mqtt_mgr.add_message_listener(topic_request_deploy_master_device_info_from_mlops, self.listener_message_dispatch_center)
-
- topic_request_deploy_slave_device_info_from_mlops = None
- if self.model_device_client_edge_id_list is not None and len(self.model_device_client_edge_id_list) > 0:
- topic_request_deploy_slave_device_info_from_mlops = f"deploy/mlops/slave_agent/request_device_info/{self.model_device_client_edge_id_list[0]}"
- self.add_message_listener(topic_request_deploy_slave_device_info_from_mlops, self.response_device_info_to_mlops)
- self.mqtt_mgr.add_message_listener(topic_request_deploy_slave_device_info_from_mlops, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to logout from MLOps.
- topic_client_logout = "mlops/client/logout/" + str(self.edge_id)
- self.add_message_listener(topic_client_logout, self.callback_client_logout)
- self.mqtt_mgr.add_message_listener(topic_client_logout, self.listener_message_dispatch_center)
-
- # Subscribe topics for starting train, stopping train and fetching client status.
- mqtt_client_object.subscribe(topic_start_train, qos=2)
- mqtt_client_object.subscribe(topic_stop_train, qos=2)
- mqtt_client_object.subscribe(topic_client_status, qos=2)
- mqtt_client_object.subscribe(topic_report_status, qos=2)
- mqtt_client_object.subscribe(topic_ota_msg, qos=2)
- mqtt_client_object.subscribe(topic_request_device_info, qos=2)
- mqtt_client_object.subscribe(topic_request_edge_device_info_from_mlops, qos=2)
- if topic_request_deploy_master_device_info_from_mlops is not None:
- mqtt_client_object.subscribe(topic_request_deploy_master_device_info_from_mlops, qos=2)
- if topic_request_deploy_slave_device_info_from_mlops is not None:
- mqtt_client_object.subscribe(topic_request_deploy_slave_device_info_from_mlops, qos=2)
- mqtt_client_object.subscribe(topic_client_logout, qos=2)
-
- self.subscribed_topics.clear()
- self.subscribed_topics.append(topic_start_train)
- self.subscribed_topics.append(topic_stop_train)
- self.subscribed_topics.append(topic_client_status)
- self.subscribed_topics.append(topic_report_status)
- self.subscribed_topics.append(topic_ota_msg)
- self.subscribed_topics.append(topic_request_device_info)
- self.subscribed_topics.append(topic_request_edge_device_info_from_mlops)
- if topic_request_deploy_master_device_info_from_mlops is not None:
- self.subscribed_topics.append(topic_request_deploy_master_device_info_from_mlops)
- if topic_request_deploy_slave_device_info_from_mlops is not None:
- self.subscribed_topics.append(topic_request_deploy_slave_device_info_from_mlops)
- self.subscribed_topics.append(topic_client_logout)
-
- # Subscribe the messages for federated learning.
- self.subscribe_fl_msgs()
-
- # Broadcast the first active message.
- self.send_agent_active_msg(self.edge_id)
- if self.general_edge_id is not None:
- self.send_agent_active_msg(self.general_edge_id)
-
- # Echo results
- MLOpsRuntimeLog.get_instance(self.args).enable_show_log_to_stdout()
- worker_deploy_id_list = [modeld_device_clint.edge_id for index, modeld_device_clint in
- enumerate(self.model_device_client_list)]
- print("\nCongratulations, your device is connected to the FedML MLOps platform successfully!")
- print(f"Your FedML Edge ID is {str(self.edge_id)}, unique device ID is {str(self.unique_device_id)}, "
- f"master deploy ID is {str(self.model_device_server.edge_id)}, "
- f"worker deploy ID is {worker_deploy_id_list}"
- )
- if self.edge_extra_url is not None and self.edge_extra_url != "":
- print(f"You may visit the following url to fill in more information with your device.\n"
- f"{self.edge_extra_url}")
- MLOpsRuntimeLog.get_instance(self.args).enable_show_log_to_stdout(enable=False)
-
- from fedml.core.mlops import sync_deploy_id
- sync_deploy_id(
- self.edge_id, self.model_device_server.edge_id, worker_deploy_id_list)
-
- # Start the message center for listener
- self.start_listener(sender_message_queue=self.message_center.get_message_queue(),
- agent_config=self.agent_config)
-
- def subscribe_fl_msgs(self):
- if self.general_edge_id is None:
- return
-
- # Setup MQTT message listener for starting training
- topic_start_train = "flserver_agent/" + str(self.general_edge_id) + "/start_train"
- self.add_message_listener(topic_start_train, self.callback_start_train)
- self.mqtt_mgr.add_message_listener(topic_start_train, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener for stopping training
- topic_stop_train = "flserver_agent/" + str(self.general_edge_id) + "/stop_train"
- self.add_message_listener(topic_stop_train, self.callback_stop_train)
- self.mqtt_mgr.add_message_listener(topic_stop_train, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener for client status switching
- topic_client_status = "fl_client/flclient_agent_" + str(self.general_edge_id) + "/status"
- self.add_message_listener(topic_client_status, self.callback_runner_id_status)
- self.mqtt_mgr.add_message_listener(topic_client_status, self.listener_message_dispatch_center)
-
- # Setup MQTT message listener to OTA messages from the MLOps.
- topic_request_device_info = "server/client/request_device_info/" + str(self.general_edge_id)
- self.add_message_listener(topic_request_device_info, self.callback_report_device_info)
- self.mqtt_mgr.add_message_listener(topic_request_device_info, self.listener_message_dispatch_center)
-
- topic_request_device_info_from_mlops = f"deploy/mlops/client_agent/request_device_info/{self.general_edge_id}"
- self.add_message_listener(topic_request_device_info_from_mlops, self.response_device_info_to_mlops)
- self.mqtt_mgr.add_message_listener(topic_request_device_info_from_mlops, self.listener_message_dispatch_center)
-
- # Subscribe topics for starting train, stopping train and fetching client status.
- self.mqtt_mgr.subscribe_msg(topic_start_train)
- self.mqtt_mgr.subscribe_msg(topic_stop_train)
- self.mqtt_mgr.subscribe_msg(topic_client_status)
- self.mqtt_mgr.subscribe_msg(topic_request_device_info)
- self.mqtt_mgr.subscribe_msg(topic_request_device_info_from_mlops)
-
- self.subscribed_topics.append(topic_start_train)
- self.subscribed_topics.append(topic_stop_train)
- self.subscribed_topics.append(topic_client_status)
- self.subscribed_topics.append(topic_request_device_info)
- self.subscribed_topics.append(topic_request_device_info_from_mlops)
-
- def on_agent_mqtt_disconnected(self, mqtt_client_object):
- MLOpsStatus.get_instance().set_client_agent_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE
- )
- pass
-
- def setup_agent_mqtt_connection(self, service_config):
- # Setup MQTT connection
- self.mqtt_mgr = MqttManager(
- service_config["mqtt_config"]["BROKER_HOST"],
- service_config["mqtt_config"]["BROKER_PORT"],
- service_config["mqtt_config"]["MQTT_USER"],
- service_config["mqtt_config"]["MQTT_PWD"],
- service_config["mqtt_config"]["MQTT_KEEPALIVE"],
- f"FedML_ClientAgent_Daemon_@{self.user_name}@_@{self.args.current_device_id}@_@{str(uuid.uuid4())}@",
- "flclient_agent/last_will_msg",
- json.dumps({"ID": self.edge_id, "status": ClientConstants.MSG_MLOPS_CLIENT_STATUS_OFFLINE})
- )
- self.agent_config = service_config
-
- # Init local database
- FedMLClientDataInterface.get_instance().create_job_table()
-
- # Start the message center to process edge related messages.
- self.setup_message_center()
-
- # Start local API services
- client_api_cmd = "fedml.computing.scheduler.slave.client_api:api"
- client_api_pids = RunProcessUtils.get_pid_from_cmd_line(client_api_cmd)
- if client_api_pids is None or len(client_api_pids) <= 0:
- python_program = get_python_program()
- cur_dir = os.path.dirname(__file__)
- fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- self.local_api_process = ClientConstants.exec_console_with_script(
- "{} -m uvicorn {} --host 0.0.0.0 --port {} "
- "--reload --reload-delay 3 --reload-dir {} --log-level critical".format(
- python_program, client_api_cmd, ClientConstants.LOCAL_CLIENT_API_PORT, fedml_base_dir),
- should_capture_stdout=False,
- should_capture_stderr=False
- )
- # if self.local_api_process is not None and self.local_api_process.pid is not None:
- # print(f"Client local API process id {self.local_api_process.pid}")
-
- # Setup MQTT connected listener
- self.mqtt_mgr.add_connected_listener(self.on_agent_mqtt_connected)
- self.mqtt_mgr.add_disconnected_listener(self.on_agent_mqtt_disconnected)
- self.mqtt_mgr.connect()
-
- # Report the IDLE status to MLOps
- self.mlops_metrics.report_client_training_status(
- self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE)
- MLOpsStatus.get_instance().set_client_agent_status(self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_IDLE)
-
- # MLOpsRuntimeLogDaemon.get_instance(self.args).stop_all_log_processor()
- self.recover_start_train_msg_after_upgrading()
-
- infer_host = os.getenv("FEDML_INFER_HOST", None)
- infer_redis_addr = os.getenv("FEDML_INFER_REDIS_ADDR", None)
- infer_redis_port = os.getenv("FEDML_INFER_REDIS_PORT", None)
- infer_redis_password = os.getenv("FEDML_INFER_REDIS_PASSWORD", None)
- model_client_num = os.getenv("FEDML_MODEL_WORKER_NUM", None)
- os.environ["FEDML_CURRENT_EDGE_ID"] = str(self.edge_id)
-
- if not ComputeCacheManager.get_instance().set_redis_params():
- os.environ["FEDML_DISABLE_REDIS_CONNECTION"] = "1"
-
- if self.model_device_client_edge_id_list is None:
- self.model_device_client_edge_id_list = list()
- if self.model_device_client_list is None:
- model_client_num = 1 if model_client_num is None else int(model_client_num)
- self.model_device_client_list = list()
- for client_index in range(model_client_num):
- model_device_client = FedMLModelDeviceClientRunner(
- self.args, f"{self.args.current_device_id}_{client_index + 1}", self.args.os_name,
- self.args.is_from_docker, self.agent_config)
- if infer_host is not None:
- model_device_client.infer_host = infer_host
- if infer_redis_addr is not None:
- model_device_client.redis_addr = infer_redis_addr
- if infer_redis_port is not None:
- model_device_client.redis_port = infer_redis_port
- if infer_redis_password is not None:
- model_device_client.redis_password = infer_redis_password
- model_device_client.start()
- self.model_device_client_list.append(model_device_client)
- self.model_device_client_edge_id_list.append(model_device_client.get_edge_id())
-
- if self.model_device_server is None:
- self.model_device_server = FedMLModelDeviceServerRunner(self.args, self.args.current_device_id,
- self.args.os_name, self.args.is_from_docker,
- self.agent_config)
- if infer_host is not None:
- self.model_device_server.infer_host = infer_host
- if infer_redis_addr is not None:
- self.model_device_server.redis_addr = infer_redis_addr
- if infer_redis_port is not None:
- self.model_device_server.redis_port = infer_redis_port
- if infer_redis_password is not None:
- self.model_device_server.redis_password = infer_redis_password
-
- self.model_device_server.start()
- self.model_device_server_id = self.model_device_server.get_edge_id()
-
- JobCleanup.get_instance().sync_data_on_startup(self.edge_id)
-
- os.environ["FEDML_DEPLOY_MASTER_ID"] = str(self.model_device_server.get_edge_id())
- os.environ["FEDML_DEPLOY_WORKER_IDS"] = str([client.get_edge_id() for client in self.model_device_client_list])
- self.mlops_metrics.stop_device_realtime_perf()
- self.mlops_metrics.report_device_realtime_perf(self.args, service_config["mqtt_config"])
-
- def start_agent_mqtt_loop(self):
- # Start MQTT message loop
- try:
- self.mqtt_mgr.loop_forever()
- except Exception as e:
- logging.error(f"Errors in the MQTT loop: Exception {e}, Traceback: {traceback.format_exc()}")
- if str(e) == "Restarting after upgraded...":
- logging.info("Restarting after upgraded...")
- else:
- logging.info("Client tracing: {}".format(traceback.format_exc()))
- finally:
- print("finally")
- login_exit_file = os.path.join(ClientConstants.get_log_file_dir(), "exited.log")
- with open(login_exit_file, "w") as f:
- f.writelines(f"{os.getpid()}.")
-
- self.stop_agent()
-
- time.sleep(5)
- sys_utils.cleanup_all_fedml_client_login_processes(
- ClientConstants.CLIENT_LOGIN_PROGRAM, clean_process_group=False)
- sys.exit(1)
-
- def stop_agent(self):
- if self.run_process_event is not None:
- self.run_process_event.set()
-
- if self.model_device_server is not None:
- self.model_device_server.stop()
- self.model_device_server = None
-
- if self.model_device_client_list is not None:
- for model_client in self.model_device_client_list:
- model_client.stop()
- self.model_device_client_list.clear()
- self.model_device_client_list = None
-
- if self.mqtt_mgr is not None:
- try:
- for topic in self.subscribed_topics:
- self.mqtt_mgr.unsubscribe_msg(topic)
- except Exception as e:
- logging.error(f"Unsubscribe topics error: {e}, Traceback: {traceback.format_exc()}")
- pass
-
- self.mqtt_mgr.loop_stop()
- self.mqtt_mgr.disconnect()
-
- self.release_message_center()
-
- def get_runner(self):
- runner = FedMLClientRunner(
- self.args, edge_id=self.edge_id, request_json=self.request_json,
- agent_config=self.agent_config, run_id=self.run_id,
- cuda_visible_gpu_ids_str=self.cuda_visible_gpu_ids_str
- )
- runner.edge_user_name = self.user_name
- runner.edge_extra_url = self.edge_extra_url
- runner.unique_device_id = self.unique_device_id
- runner.user_name = self.user_name
- runner.general_edge_id = self.general_edge_id
- runner.model_device_client_edge_id_list = self.model_device_client_edge_id_list
- runner.model_device_server_id = self.model_device_server_id
- return runner
From 493463e3b4002edb929df966cd1dd409b3a60522 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Thu, 6 Jun 2024 19:14:28 +0000
Subject: [PATCH 123/282] [Deploy] Recursively find the model serving package
folder
---
.../scheduler/comm_utils/file_utils.py | 13 ++++++
.../model_scheduler/worker_job_runner.py | 40 ++++++-------------
2 files changed, 25 insertions(+), 28 deletions(-)
create mode 100644 python/fedml/computing/scheduler/comm_utils/file_utils.py
diff --git a/python/fedml/computing/scheduler/comm_utils/file_utils.py b/python/fedml/computing/scheduler/comm_utils/file_utils.py
new file mode 100644
index 0000000000..1d8fc6ca83
--- /dev/null
+++ b/python/fedml/computing/scheduler/comm_utils/file_utils.py
@@ -0,0 +1,13 @@
+import os
+
+
+def find_file_inside_folder(folder_path, file_name):
+ """
+ Recursively search for a file inside a folder and its sub-folders.
+ return the full path of the file if found, otherwise return None.
+ """
+ for root, dirs, files in os.walk(folder_path):
+ if file_name in files:
+ return os.path.join(root, file_name)
+
+ return None
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index 348b760153..3c357e9dab 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -10,6 +10,7 @@
import yaml
from fedml.computing.scheduler.comm_utils.job_utils import JobRunnerUtils
from fedml.core.mlops import MLOpsRuntimeLog
+from fedml.computing.scheduler.comm_utils import file_utils
from .device_client_constants import ClientConstants
from .device_model_cache import FedMLModelCache
from ..scheduler_core.general_constants import GeneralConstants
@@ -205,7 +206,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
# Check if the package is already downloaded
unzip_package_path = ""
if os.path.exists(os.path.join(models_root_dir, parent_fd)):
- unzip_package_path = self.find_previous_downloaded_pkg(os.path.join(models_root_dir, parent_fd), model_name)
+ unzip_package_path = self.find_previous_downloaded_pkg(os.path.join(models_root_dir, parent_fd))
# Download the package if not found
if unzip_package_path == "":
@@ -510,30 +511,13 @@ def build_dynamic_constrain_variables(self, run_id, run_config):
pass
@staticmethod
- def find_previous_downloaded_pkg(parent_dir: str, model_name: str) -> str:
- unzip_fd = ""
- res = ""
-
- for folder in os.listdir(parent_dir):
- if folder.startswith("unzip_fedml_run"):
- unzip_fd = os.path.join(parent_dir, folder)
- break
-
- exact_matched = False
-
- if unzip_fd == "":
- return res
-
- for folder in os.listdir(unzip_fd):
- if folder == model_name:
- res = os.path.join(unzip_fd, folder)
- exact_matched = True
- break
-
- if not exact_matched:
- # Use the first folder found
- for folder in os.listdir(unzip_fd):
- res = os.path.join(unzip_fd, folder)
- break
-
- return res
+ def find_previous_downloaded_pkg(parent_dir: str) -> str:
+ """
+ Find a folder inside parent_dir that contains the fedml_model_config.yaml file.
+ """
+ res = file_utils.find_file_inside_folder(parent_dir, ClientConstants.MODEL_REQUIRED_MODEL_CONFIG_FILE)
+ if res is not None:
+ # return the parent folder of res
+ return os.path.dirname(res)
+ else:
+ return ""
From b4cb7c56a68f4a589c9c09e5092914be7dd2b423 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Thu, 6 Jun 2024 16:01:10 -0400
Subject: [PATCH 124/282] Making sure the unzipped file is a directory during
initial deployment.
---
.../scheduler_core/scheduler_base_job_runner.py | 9 ++++++---
1 file changed, 6 insertions(+), 3 deletions(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 648ab18cf1..5e7a71f25a 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -138,10 +138,13 @@ def get_client_id_list(self, server_edge_id_list):
@staticmethod
def unzip_file(zip_file, unzip_file_path) -> str:
+ unzipped_file_name = ""
if zipfile.is_zipfile(zip_file):
- with zipfile.ZipFile(zip_file, "r") as zipf:
+ with (zipfile.ZipFile(zip_file, "r") as zipf):
zipf.extractall(unzip_file_path)
- unzipped_file_name = zipf.namelist()[0]
+ # Make sure the unzipped file is a directory.
+ if zipf.namelist()[0].endswith("/"):
+ unzipped_file_name = zipf.namelist()[0]
else:
raise Exception("Invalid zip file {}".format(zip_file))
@@ -156,7 +159,7 @@ def package_download_progress(self, count, blksize, filesize):
progress_int = int(progress)
downloaded_kb = format(downloaded / 1024, '.2f')
- # since this hook funtion is stateless, we need a state to avoid print progress repeatly
+ # Since this hook function is stateless, we need a state to avoid print progress repeatedly.
if count == 0:
self.prev_download_progress = 0
if progress_int != self.prev_download_progress and progress_int % 5 == 0:
From f76d88ee5eb6e2c4d2e9a05be83c48de1a020659 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Thu, 6 Jun 2024 22:53:56 +0000
Subject: [PATCH 125/282] [Deploy] Hot fix grammar.
---
.../scheduler/scheduler_core/scheduler_base_job_runner.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 5e7a71f25a..6e0010f556 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -140,7 +140,7 @@ def get_client_id_list(self, server_edge_id_list):
def unzip_file(zip_file, unzip_file_path) -> str:
unzipped_file_name = ""
if zipfile.is_zipfile(zip_file):
- with (zipfile.ZipFile(zip_file, "r") as zipf):
+ with zipfile.ZipFile(zip_file, "r") as zipf:
zipf.extractall(unzip_file_path)
# Make sure the unzipped file is a directory.
if zipf.namelist()[0].endswith("/"):
From 4b1127052dff23ce58956918571d680ea9c7ba9f Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Thu, 6 Jun 2024 16:45:31 -0700
Subject: [PATCH 126/282] Hot fix to support local debugging
---
python/fedml/api/modules/device.py | 22 ++++++-------------
.../scheduler/comm_utils/constants.py | 6 +++++
.../model_scheduler/device_model_inference.py | 9 ++++----
3 files changed, 18 insertions(+), 19 deletions(-)
diff --git a/python/fedml/api/modules/device.py b/python/fedml/api/modules/device.py
index 497fde9005..77f7eb2424 100644
--- a/python/fedml/api/modules/device.py
+++ b/python/fedml/api/modules/device.py
@@ -7,6 +7,7 @@
import fedml
from fedml.api.modules.constants import ModuleConstants
from fedml.computing.scheduler.comm_utils import sys_utils
+from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
from fedml.computing.scheduler.master.server_constants import ServerConstants
from fedml.computing.scheduler.master.server_login import logout as server_logout
@@ -23,11 +24,6 @@ def bind(
device_id = "0"
os_name = ""
docker = None
- docker_rank = 1
- infer_host = "127.0.0.1"
- redis_addr = "local"
- redis_port = "6379"
- redis_password = "fedml_default"
role = ""
is_client = computing
is_server = server
@@ -47,26 +43,22 @@ def bind(
_bind(
userid, computing, server,
api_key, role, runner_cmd, device_id, os_name,
- docker, docker_rank, infer_host,
- redis_addr, redis_port, redis_password
- )
+ docker)
def _bind(
userid, computing, server,
api_key, role, runner_cmd, device_id, os_name,
- docker, docker_rank, infer_host,
- redis_addr, redis_port, redis_password
-):
+ docker):
fedml.load_env()
if os.getenv(ModuleConstants.ENV_FEDML_INFER_HOST) is None:
- fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_HOST, infer_host)
+ fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_HOST, SchedulerConstants.REDIS_INFER_HOST)
if os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_ADDR) is None:
- fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_REDIS_ADDR, redis_addr)
+ fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_REDIS_ADDR, SchedulerConstants.REDIS_ADDR)
if os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PORT) is None:
- fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_REDIS_PORT, redis_port)
+ fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_REDIS_PORT, SchedulerConstants.REDIS_PORT)
if os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD) is None:
- fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD, redis_password)
+ fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD, SchedulerConstants.REDIS_ADDR)
url = fedml._get_backend_service()
platform_name = platform.system()
diff --git a/python/fedml/computing/scheduler/comm_utils/constants.py b/python/fedml/computing/scheduler/comm_utils/constants.py
index 22cb31de45..67b9d8b14b 100644
--- a/python/fedml/computing/scheduler/comm_utils/constants.py
+++ b/python/fedml/computing/scheduler/comm_utils/constants.py
@@ -109,6 +109,12 @@ class SchedulerConstants:
IMAGE_PULL_POLICY_IF_NOT_PRESENT = "IfNotPresent"
IMAGE_PULL_POLICY_NEVER = "Never"
+ REDIS_INFER_HOST = "127.0.0.1"
+ REDIS_ADDR = "local"
+ REDIS_PORT = "6379"
+ REDIS_PASSWORD = "fedml_default"
+
+
@staticmethod
def get_log_source(run_json):
run_config = run_json.get("run_config", {})
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 7b3ac1d0bf..7bc7d6f097 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -13,6 +13,7 @@
import fedml
from fedml.api.modules.constants import ModuleConstants
+from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants
from fedml.computing.scheduler.model_scheduler.device_http_inference_protocol import FedMLHttpInference
from fedml.computing.scheduler.model_scheduler.device_server_constants import ServerConstants
@@ -27,10 +28,10 @@
class Settings:
server_name = "DEVICE_INFERENCE_GATEWAY"
fedml.load_env()
- redis_addr = os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_ADDR)
- redis_port = os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PORT)
- redis_password = os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD)
- model_infer_host = os.getenv(ModuleConstants.ENV_FEDML_INFER_HOST)
+ redis_addr = os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_ADDR, SchedulerConstants.REDIS_ADDR)
+ redis_port = os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PORT, SchedulerConstants.REDIS_PORT)
+ redis_password = os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD, SchedulerConstants.REDIS_PASSWORD)
+ model_infer_host = os.getenv(ModuleConstants.ENV_FEDML_INFER_HOST, SchedulerConstants.REDIS_INFER_HOST)
version = fedml.get_env_version()
mqtt_config = MLOpsConfigs.fetch_mqtt_config()
From 2de8c370bb35684121d8f512bb2f2aecf5624eec Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Thu, 6 Jun 2024 17:12:37 -0700
Subject: [PATCH 127/282] Bug fix
---
python/fedml/api/modules/device.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/api/modules/device.py b/python/fedml/api/modules/device.py
index 77f7eb2424..a853d538d0 100644
--- a/python/fedml/api/modules/device.py
+++ b/python/fedml/api/modules/device.py
@@ -58,7 +58,7 @@ def _bind(
if os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PORT) is None:
fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_REDIS_PORT, SchedulerConstants.REDIS_PORT)
if os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD) is None:
- fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD, SchedulerConstants.REDIS_ADDR)
+ fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD, SchedulerConstants.REDIS_PASSWORD)
url = fedml._get_backend_service()
platform_name = platform.system()
From 38bc898388a9cb458c8e2b8d90f3e653f12bdd9a Mon Sep 17 00:00:00 2001
From: bhargav191098 <bhargav3514@gmail.com>
Date: Thu, 6 Jun 2024 23:30:08 -0700
Subject: [PATCH 128/282] Adding sequential uploads & download using presigned
URL
---
python/fedml/api/modules/storage.py | 248 ++++++++++++++++--
.../scheduler/master/server_constants.py | 14 +
2 files changed, 242 insertions(+), 20 deletions(-)
diff --git a/python/fedml/api/modules/storage.py b/python/fedml/api/modules/storage.py
index 51f58539bf..cc93fd6f87 100644
--- a/python/fedml/api/modules/storage.py
+++ b/python/fedml/api/modules/storage.py
@@ -3,6 +3,13 @@
import shutil
import requests
+import math
+
+import requests.exceptions
+import tqdm
+import sys
+from concurrent.futures import ThreadPoolExecutor
+import concurrent.futures
from fedml.api.modules.utils import authenticate
from fedml.core.distributed.communication.s3.remote_storage import S3Storage
from fedml.core.mlops.mlops_configs import Configs, MLOpsConfigs
@@ -19,6 +26,7 @@ def __init__(self, data: dict):
self.updatedAt = data.get("updateTime", None)
self.size = _get_size(data.get("fileSize",None))
self.tag_list = data.get("tags", None)
+ self.download_url = data.get("fileUrl", None)
# Todo (alaydshah): Store service name in metadata
@@ -40,16 +48,16 @@ def upload(data_path, api_key, name, description, tag_list, service, show_progre
if not archive_path:
return FedMLResponse(code=ResponseCode.FAILURE, message=message)
- store = _get_storage_service(service)
name = os.path.splitext(os.path.basename(archive_path))[0] if name is None else name
file_name = name + ".zip"
dest_path = os.path.join(user_id, file_name)
file_size = os.path.getsize(archive_path)
- file_uploaded_url = store.upload_file_with_progress(src_local_path=archive_path, dest_s3_path=dest_path,
- show_progress=show_progress,
- out_progress_to_err=out_progress_to_err,
- progress_desc=progress_desc, metadata=metadata)
+ file_uploaded_url, message = _upload_multipart(api_key, file_name, archive_path, show_progress,
+ out_progress_to_err,
+ progress_desc, metadata)
+
+
os.remove(archive_path)
if not file_uploaded_url:
return FedMLResponse(code=ResponseCode.FAILURE, message=f"Failed to upload file: {archive_path}")
@@ -81,24 +89,36 @@ def download(data_name, api_key, service, dest_path, show_progress=True) -> FedM
if user_id is None:
return FedMLResponse(code=ResponseCode.FAILURE, message=message)
- store = _get_storage_service(service)
- zip_file_name = data_name + ".zip"
- key = os.path.join(user_id, zip_file_name)
- path_local = os.path.abspath(zip_file_name)
- dest_path = os.path.abspath(dest_path) if dest_path else data_name
- if store.download_file_with_progress(path_s3=key, path_local=path_local, show_progress=show_progress):
- try:
- shutil.unpack_archive(path_local, dest_path)
- os.remove(path_local)
- abs_dest_path = os.path.abspath(dest_path)
- return FedMLResponse(code=ResponseCode.SUCCESS, message=f"Successfully downloaded and unzipped data at "
- f"{abs_dest_path}", data=abs_dest_path)
- except Exception as e:
- error_message = f"Failed to unpack archive: {e}"
+ metadata_response = get_metadata(data_name, api_key)
+ if metadata_response.code == ResponseCode.SUCCESS:
+ metadata = metadata_response.data
+ if not metadata or not isinstance(metadata, StorageMetadata):
+ error_message = f"Unable to get the download URL"
+ logging.error(error_message)
+ return FedMLResponse(code=ResponseCode.FAILURE, message=error_message)
+ download_url = metadata.download_url
+ zip_file_name = data_name + ".zip"
+ path_local = os.path.abspath(zip_file_name)
+ dest_path = os.path.abspath(dest_path) if dest_path else data_name
+ if _download_using_presigned_url(download_url, zip_file_name, show_progress=show_progress):
+ try:
+ shutil.unpack_archive(path_local, dest_path)
+ os.remove(path_local)
+ abs_dest_path = os.path.abspath(dest_path)
+ return FedMLResponse(code=ResponseCode.SUCCESS, message=f"Successfully downloaded and unzipped data at "
+ f"{abs_dest_path}", data=abs_dest_path)
+ except Exception as e:
+ error_message = f"Failed to unpack archive: {e}"
+ logging.error(error_message)
+ return FedMLResponse(code=ResponseCode.FAILURE, message=error_message)
+
+ else:
+ error_message = "Failed to download data from source"
logging.error(error_message)
return FedMLResponse(code=ResponseCode.FAILURE, message=error_message)
+
else:
- error_message = f"Failed to download data: {data_name}"
+ error_message = "Unable to get the download URL"
logging.error(error_message)
return FedMLResponse(code=ResponseCode.FAILURE, message=error_message)
@@ -196,6 +216,194 @@ def delete(data_name, service, api_key=None) -> FedMLResponse:
logging.error(message, data_name, service)
return FedMLResponse(code=ResponseCode.FAILURE, message=message, data=False)
+def _get_num_chunks(file_size, max_chunk_size):
+ num_chunks = math.ceil(file_size / max_chunk_size)
+ return num_chunks
+
+
+def get_chunks(file_path, chunk_size):
+ with open(file_path, 'rb') as file:
+ while True:
+ chunk = file.read(chunk_size)
+ if not chunk:
+ break
+ yield chunk
+
+
+def _get_presigned_url(api_key, request_url, file_name, part_number=None):
+ cert_path = MLOpsConfigs.get_cert_path_with_version()
+ headers = ServerConstants.API_HEADERS
+ headers["Authorization"] = f"Bearer {api_key}"
+ params_dict = {'fileKey': file_name}
+ if part_number is not None:
+ params_dict['partNumber'] = part_number
+ if cert_path is None:
+ try:
+ requests.session().verify = cert_path
+ response = requests.get(request_url, verify=True, headers=headers, params=params_dict)
+ except requests.exceptions.SSLError as err:
+ MLOpsConfigs.install_root_ca_file()
+ response = requests.get(request_url, verify=True, headers=headers, params=params_dict)
+ else:
+ response = requests.get(request_url, verify=True, headers=headers, params=params_dict)
+ return response
+
+
+def _upload_part(url,part_data,session):
+ response = session.put(url,data=part_data,verify=True)
+ return response
+
+
+def _upload_chunk(presigned_url, chunk, part, pbar=None, max_retries=20,session=None):
+ for retry_attempt in range(max_retries):
+ try:
+ response = _upload_part(presigned_url,chunk,session)
+ except requests.exceptions.RequestException as e:
+ if retry_attempt < max_retries:
+ continue
+ else:
+ raise requests.exceptions.RequestException
+
+ if(pbar is not None):
+ pbar.update(chunk.__sizeof__())
+ return {'etag': response.headers['ETag'], 'partNumber': part}
+ raise requests.exceptions.RequestException
+
+def _process_post_response(response):
+ if response.status_code != 200:
+ message = (f"Failed to complete multipart upload with status code = {response.status_code}, "
+ f"response.content: {response.content}")
+ logging.error(message)
+ return None, message
+ else:
+ resp_data = response.json()
+ code = resp_data.get("code", None)
+ data_url = resp_data.get("data", None)
+
+ if code is None or data_url is None or code == "FAILURE":
+ message = resp_data.get("message", None)
+ message = (f"Failed to complete multipart upload with following message: {message}, "
+ f"response.content: {response.content}")
+ return None, message
+
+ return data_url, "Successfully uploaded the data! "
+
+def _complete_multipart_upload(api_key, file_key, part_info, upload_id):
+ complete_multipart_url = ServerConstants.get_complete_multipart_upload_url()
+ body_dict = {"fileKey": file_key, 'partETags': part_info, 'uploadId': upload_id}
+
+ cert_path = MLOpsConfigs.get_cert_path_with_version()
+ headers = ServerConstants.API_HEADERS
+ headers["Authorization"] = f"Bearer {api_key}"
+ if cert_path is None:
+ try:
+ requests.session().verify = cert_path
+ complete_multipart_response = requests.post(complete_multipart_url, json=body_dict, verify=True,
+ headers=headers)
+ except requests.exceptions.SSLError as err:
+ MLOpsConfigs.install_root_ca_file()
+ complete_multipart_response = requests.post(complete_multipart_url, json=body_dict, verify=True,
+ headers=headers)
+ else:
+ complete_multipart_response = requests.post(complete_multipart_url, json=body_dict, verify=True,
+ headers=headers)
+
+ return _process_post_response(complete_multipart_response)
+
+def _upload_multipart(api_key: str, file_key, archive_path, show_progress, out_progress_to_err,
+ progress_desc_text, metadata):
+ request_url = ServerConstants.get_presigned_multi_part_url()
+
+ file_size = os.path.getsize(archive_path)
+
+ max_chunk_size = 20 * 1024 * 1024
+
+ num_chunks = _get_num_chunks(file_size, max_chunk_size)
+
+ upload_id = ""
+ presigned_urls = []
+
+ presigned_url_response = _get_presigned_url(api_key, request_url, file_key, num_chunks)
+
+ if presigned_url_response.status_code != 200:
+ message = (f"Failed to get presigned URL with status code = {presigned_url_response.status_code}, "
+ f"response.content: {presigned_url_response.content}")
+ logging.error(message)
+ return None, message
+ else:
+ resp_data = presigned_url_response.json()
+ code = resp_data.get("code", None)
+ data = resp_data.get("data", None)
+
+ if code is None or data is None or code == "FAILURE":
+ message = resp_data.get("message", None)
+ message = (f"Failed getting presigned URL with following message: {message}, "
+ f"response.content: {presigned_url_response.content}")
+ return None, message
+
+ upload_id = data['uploadId']
+ presigned_urls = data['urls']
+
+ parts = []
+ chunks = get_chunks(archive_path, max_chunk_size)
+ part_info = []
+ chunk_count = 0
+ successful_chunks = 0
+
+ atomic_session = requests.session()
+ atomic_session.verify = MLOpsConfigs.get_cert_path_with_version()
+ with tqdm.tqdm(total=file_size, unit="B", unit_scale=True,
+ file=sys.stderr if out_progress_to_err else sys.stdout,
+ desc=progress_desc_text, leave=False) as pbar:
+ for part, chunk in enumerate(chunks, start=1):
+ presigned_url = presigned_urls[part - 1]
+ chunk_count += 1
+ # Upload chunk to presigned_url in a separate thread from the thread pool of 10 workers.
+ if show_progress:
+ try:
+ part_data = _upload_chunk(presigned_url=presigned_url, chunk=chunk, part=part,
+ pbar=pbar,session=atomic_session)
+ part_info.append(part_data)
+ successful_chunks += 1
+ except Exception as e:
+ return None, "unsuccessful"
+
+ else:
+ try:
+ part_data = _upload_chunk(presigned_url=presigned_url, chunk=chunk, part=part,
+ pbar=pbar,session=atomic_session)
+ part_info.append(part_data)
+ successful_chunks += 1
+ except Exception as e:
+ return None, "unsuccessful"
+
+ if successful_chunks == chunk_count:
+ return _complete_multipart_upload(api_key, file_key, part_info, upload_id)
+ else:
+ return None, "Unsuccessful!"
+
+
+def _download_using_presigned_url(url, fname, chunk_size=1024 * 1024, show_progress=True):
+ download_response = requests.get(url, verify=True, stream=True)
+ if download_response.status_code == 200:
+ total = int(download_response.headers.get('content-length', 0))
+ if show_progress:
+ with open(fname, 'wb') as file, tqdm.tqdm(
+ desc=fname,
+ total=total,
+ unit='B',
+ unit_scale=True,
+ unit_divisor=1024,
+ ) as bar:
+ for data in download_response.iter_content(chunk_size=chunk_size):
+ size = file.write(data)
+ bar.update(size)
+ else:
+ with open(fname, "wb") as file:
+ for data in download_response.iter_content(chunk_size=chunk_size):
+ size = file.write(data)
+ return True
+ return False
def _get_user_id_from_api_key(api_key: str) -> (str, str):
user_url = ServerConstants.get_user_url()
diff --git a/python/fedml/computing/scheduler/master/server_constants.py b/python/fedml/computing/scheduler/master/server_constants.py
index b835ba1bde..ebd8b2aef6 100644
--- a/python/fedml/computing/scheduler/master/server_constants.py
+++ b/python/fedml/computing/scheduler/master/server_constants.py
@@ -255,6 +255,20 @@ def get_dataset_url():
ServerConstants.get_mlops_url())
return create_dataset_url
+ @staticmethod
+ def get_presigned_multi_part_url():
+ get_presigned_multi_part_url = "{}/system/api/v1/cli/oss/multipart/presigned-url".format(
+ ServerConstants.get_mlops_url()
+ )
+ return get_presigned_multi_part_url
+
+ @staticmethod
+ def get_complete_multipart_upload_url():
+ complete_multipart_upload_url = "{}/system/api/v1/cli/oss/multipart/upload/complete".format(
+ ServerConstants.get_mlops_url()
+ )
+ return complete_multipart_upload_url
+
@staticmethod
def list_dataset_url():
list_dataset_url = "{}/fedmlOpsServer/api/v1/cli/dataset/list".format(
From aa62a94da7dc68100ed80a75714b8c6b3b60e57d Mon Sep 17 00:00:00 2001
From: bhargav191098 <bhargav3514@gmail.com>
Date: Thu, 6 Jun 2024 23:57:08 -0700
Subject: [PATCH 129/282] minor comments and some error handling
---
python/fedml/api/modules/storage.py | 12 +++++-------
1 file changed, 5 insertions(+), 7 deletions(-)
diff --git a/python/fedml/api/modules/storage.py b/python/fedml/api/modules/storage.py
index cc93fd6f87..e7d492c999 100644
--- a/python/fedml/api/modules/storage.py
+++ b/python/fedml/api/modules/storage.py
@@ -8,8 +8,6 @@
import requests.exceptions
import tqdm
import sys
-from concurrent.futures import ThreadPoolExecutor
-import concurrent.futures
from fedml.api.modules.utils import authenticate
from fedml.core.distributed.communication.s3.remote_storage import S3Storage
from fedml.core.mlops.mlops_configs import Configs, MLOpsConfigs
@@ -31,7 +29,7 @@ def __init__(self, data: dict):
# Todo (alaydshah): Store service name in metadata
# Todo (alaydshah): If data already exists, don't upload again. Instead suggest to use update command
-
+# Todo (bhargav) : Discuss and remove the service variable. Maybe needed sometime later.
def upload(data_path, api_key, name, description, tag_list, service, show_progress, out_progress_to_err, progress_desc,
metadata) -> FedMLResponse:
api_key = authenticate(api_key)
@@ -118,8 +116,7 @@ def download(data_name, api_key, service, dest_path, show_progress=True) -> FedM
return FedMLResponse(code=ResponseCode.FAILURE, message=error_message)
else:
- error_message = "Unable to get the download URL"
- logging.error(error_message)
+ error_message = metadata_response.message
return FedMLResponse(code=ResponseCode.FAILURE, message=error_message)
@@ -288,6 +285,7 @@ def _process_post_response(response):
return data_url, "Successfully uploaded the data! "
+
def _complete_multipart_upload(api_key, file_key, part_info, upload_id):
complete_multipart_url = ServerConstants.get_complete_multipart_upload_url()
body_dict = {"fileKey": file_key, 'partETags': part_info, 'uploadId': upload_id}
@@ -310,6 +308,7 @@ def _complete_multipart_upload(api_key, file_key, part_info, upload_id):
return _process_post_response(complete_multipart_response)
+
def _upload_multipart(api_key: str, file_key, archive_path, show_progress, out_progress_to_err,
progress_desc_text, metadata):
request_url = ServerConstants.get_presigned_multi_part_url()
@@ -349,7 +348,7 @@ def _upload_multipart(api_key: str, file_key, archive_path, show_progress, out_p
part_info = []
chunk_count = 0
successful_chunks = 0
-
+ #TODO: (bhargav191098) Using Thread pool and confirming openssl issue
atomic_session = requests.session()
atomic_session.verify = MLOpsConfigs.get_cert_path_with_version()
with tqdm.tqdm(total=file_size, unit="B", unit_scale=True,
@@ -358,7 +357,6 @@ def _upload_multipart(api_key: str, file_key, archive_path, show_progress, out_p
for part, chunk in enumerate(chunks, start=1):
presigned_url = presigned_urls[part - 1]
chunk_count += 1
- # Upload chunk to presigned_url in a separate thread from the thread pool of 10 workers.
if show_progress:
try:
part_data = _upload_chunk(presigned_url=presigned_url, chunk=chunk, part=part,
From 14bae990b9a610f7cdfb25f82355025b3d58b3e0 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Fri, 7 Jun 2024 16:20:34 +0800
Subject: [PATCH 130/282] =?UTF-8?q?[CoreEngine]=201.=20fixed=20the=20issue?=
=?UTF-8?q?=20that=20the=20fork=20method=20is=20not=20support=20in=20Windo?=
=?UTF-8?q?ws=20OS.=E2=80=A82.=20fixed=20the=20issue=20the=20sqlite=20path?=
=?UTF-8?q?=20is=20illegal=20=20in=20Windows=20OS.?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
python/fedml/__init__.py | 24 +++++++++++++------
.../scheduler/comm_utils/hardware_utils.py | 2 +-
.../model_scheduler/device_model_db.py | 6 ++++-
.../scheduler/scheduler_core/base_db.py | 6 ++++-
.../scheduler_base_protocol_manager.py | 4 +---
5 files changed, 29 insertions(+), 13 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index 21da84c9ab..c96d65adc5 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -1,4 +1,5 @@
import logging
+import platform
import multiprocess as multiprocessing
import os
@@ -92,13 +93,7 @@ def init(args=None, check_env=True, should_init_logs=True):
# Windows/Linux/MacOS compatability issues on multi-processing
# https://github.com/pytorch/pytorch/issues/3492
"""
- if multiprocessing.get_start_method() != "fork":
- # force all platforms (Windows/Linux/macOS) to use the same way (fork) for multiprocessing
- multiprocessing.set_start_method("fork", force=True)
-
- # if multiprocessing.get_start_method() != "spawn":
- # # force all platforms (Windows/Linux/MacOS) to use the same way (spawn) for multiprocessing
- # multiprocessing.set_start_method("spawn", force=True)
+ _init_multiprocessing()
"""
# https://stackoverflow.com/questions/53014306/error-15-initializing-libiomp5-dylib-but-found-libiomp5-dylib-already-initial
@@ -450,6 +445,21 @@ def _run_distributed():
pass
+def _init_multiprocessing():
+ """
+ # Windows/Linux/MacOS compatability issues on multi-processing
+ # https://github.com/pytorch/pytorch/issues/3492
+ """
+ if platform.system() == "Windows":
+ if multiprocessing.get_start_method() != "spawn":
+ # force all platforms (Windows/Linux/macOS) to use the same way (spawn) for multiprocessing
+ multiprocessing.set_start_method("spawn", force=True)
+ else:
+ if multiprocessing.get_start_method() != "fork":
+ # force all platforms (Windows/Linux/macOS) to use the same way (fork) for multiprocessing
+ multiprocessing.set_start_method("fork", force=True)
+
+
def set_env_version(version):
set_env_kv("FEDML_ENV_VERSION", version)
load_env()
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index 0062418631..e73809955e 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -27,7 +27,7 @@ def __get_util(cls) -> Optional[GPUCardUtil]:
except Exception as e:
pass
- logging.error("No GPU card detected")
+ # logging.error("No GPU card detected")
return None
@staticmethod
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_db.py b/python/fedml/computing/scheduler/model_scheduler/device_model_db.py
index 1f43f719f3..09573a1d1b 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_db.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_db.py
@@ -1,6 +1,7 @@
import json
import logging
import os
+import platform
import time
from fedml.computing.scheduler.model_scheduler.device_server_constants import ServerConstants
@@ -261,7 +262,10 @@ def open_job_db(self):
self.db_base_dir = ServerConstants.get_database_dir()
job_db_path = os.path.join(self.db_base_dir, FedMLModelDatabase.MODEL_DEPLOYMENT_DB)
- self.db_engine = create_engine('sqlite:////{}'.format(job_db_path), echo=False)
+ if platform.system() == "Windows":
+ self.db_engine = create_engine('sqlite:///{}'.format(job_db_path), echo=False)
+ else:
+ self.db_engine = create_engine('sqlite:////{}'.format(job_db_path), echo=False)
db_session_class = sessionmaker(bind=self.db_engine)
self.db_connection = db_session_class()
diff --git a/python/fedml/computing/scheduler/scheduler_core/base_db.py b/python/fedml/computing/scheduler/scheduler_core/base_db.py
index b827efacf7..dbb322cfae 100755
--- a/python/fedml/computing/scheduler/scheduler_core/base_db.py
+++ b/python/fedml/computing/scheduler/scheduler_core/base_db.py
@@ -1,5 +1,6 @@
import json
import os
+import platform
import time
from sqlalchemy import Column, String, TEXT, Integer, Float, create_engine, and_
@@ -25,7 +26,10 @@ def open_job_db(self):
if self.db_connection is not None:
return
- self.db_engine = create_engine('sqlite:////{}'.format(self.db_path), echo=False)
+ if platform.system() == "Windows":
+ self.db_engine = create_engine('sqlite:///{}'.format(self.db_path), echo=False)
+ else:
+ self.db_engine = create_engine('sqlite:////{}'.format(self.db_path), echo=False)
db_session_class = sessionmaker(bind=self.db_engine)
self.db_connection = db_session_class()
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
index 9bb8b7a7ec..19bb7e9882 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
@@ -46,9 +46,7 @@ def __init__(self, args, agent_config=None, is_master=False):
self.status_reporter = None
self.user_name = args.user_name
- if multiprocessing.get_start_method() != "fork":
- # force all platforms (Windows/Linux/macOS) to use the same way (fork) for multiprocessing
- multiprocessing.set_start_method("fork", force=True)
+ fedml._init_multiprocessing()
def generate_topics(self):
# generate the subscribed topics.
From 28ff0f3c1f1621f9994da08836a5920749b22fc6 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Fri, 7 Jun 2024 16:25:07 +0800
Subject: [PATCH 131/282] [CoreEngine] add the missed import.
---
.../fedml/computing/scheduler/scheduler_core/compute_gpu_db.py | 1 +
1 file changed, 1 insertion(+)
diff --git a/python/fedml/computing/scheduler/scheduler_core/compute_gpu_db.py b/python/fedml/computing/scheduler/scheduler_core/compute_gpu_db.py
index d50555d3c9..eb80c1424e 100755
--- a/python/fedml/computing/scheduler/scheduler_core/compute_gpu_db.py
+++ b/python/fedml/computing/scheduler/scheduler_core/compute_gpu_db.py
@@ -8,6 +8,7 @@
from fedml.core.common.singleton import Singleton
from .base_db import FedMLBaseDb
from .compute_utils import ComputeUtils
+from ..master.server_constants import ServerConstants
Base = declarative_base()
From c151831deb46e297e182f74cc0315491d97d535d Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Thu, 6 Jun 2024 17:03:46 -0400
Subject: [PATCH 132/282] Adding hash set for counting the number of pending
requests per endpoint.
---
.../model_scheduler/device_model_cache.py | 29 ++++++++++---------
.../model_scheduler/device_model_inference.py | 18 ++++++------
2 files changed, 24 insertions(+), 23 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
index 75cf4dbc2a..242501f2fa 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
@@ -974,20 +974,21 @@ def delete_endpoint_scaling_down_decision_time(self, end_point_id) -> bool:
self.FEDML_MODEL_ENDPOINT_SCALING_DOWN_DECISION_TIME_TAG,
end_point_id))
- def get_pending_requests_counter(self) -> int:
- if not self.redis_connection.exists(self.FEDML_PENDING_REQUESTS_COUNTER):
- self.redis_connection.set(self.FEDML_PENDING_REQUESTS_COUNTER, 0)
- return int(self.redis_connection.get(self.FEDML_PENDING_REQUESTS_COUNTER))
-
- def update_pending_requests_counter(self, increase=False, decrease=False) -> int:
- if not self.redis_connection.exists(self.FEDML_PENDING_REQUESTS_COUNTER):
- self.redis_connection.set(self.FEDML_PENDING_REQUESTS_COUNTER, 0)
+ def get_pending_requests_counter(self, end_point_id) -> int:
+ # If the endpoint does not exist inside the Hash collection, set its counter to 0.
+ if self.redis_connection.hexists(self.FEDML_PENDING_REQUESTS_COUNTER, end_point_id):
+ return int(self.redis_connection.hget(self.FEDML_PENDING_REQUESTS_COUNTER, end_point_id))
+ return 0
+
+ def update_pending_requests_counter(self, end_point_id, increase=False, decrease=False) -> int:
+ if not self.redis_connection.hexists(self.FEDML_PENDING_REQUESTS_COUNTER, end_point_id):
+ self.redis_connection.hset(self.FEDML_PENDING_REQUESTS_COUNTER, mapping={end_point_id: 0})
if increase:
- self.redis_connection.incr(self.FEDML_PENDING_REQUESTS_COUNTER)
+ self.redis_connection.hincrby(self.FEDML_PENDING_REQUESTS_COUNTER, end_point_id, 1)
if decrease:
+ # Careful on the negative, there is no native function for hash decreases.
+ self.redis_connection.hincrby(self.FEDML_PENDING_REQUESTS_COUNTER, end_point_id, -1)
# Making sure the counter never becomes negative!
- if self.get_pending_requests_counter() < 0:
- self.redis_connection.set(self.FEDML_PENDING_REQUESTS_COUNTER, 0)
- else:
- self.redis_connection.decr(self.FEDML_PENDING_REQUESTS_COUNTER)
- return self.get_pending_requests_counter()
+ if self.get_pending_requests_counter(end_point_id) < 0:
+ self.redis_connection.hset(self.FEDML_PENDING_REQUESTS_COUNTER, mapping={end_point_id: 0})
+ return self.get_pending_requests_counter(end_point_id)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 7bc7d6f097..c6e26ba53c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -55,10 +55,10 @@ async def auth_middleware(request: Request, call_next):
{"error": True, "message": "Invalid JSON."},
status_code=status.HTTP_400_BAD_REQUEST)
- # Get total pending requests.
- pending_requests_num = FEDML_MODEL_CACHE.get_pending_requests_counter()
+ # Get endpoint's total pending requests.
+ end_point_id = request_json.get("end_point_id", None)
+ pending_requests_num = FEDML_MODEL_CACHE.get_pending_requests_counter(end_point_id)
if pending_requests_num:
- end_point_id = request_json.get("end_point_id", None)
# Fetch metrics of the past k=3 requests.
pask_k_metrics = FEDML_MODEL_CACHE.get_endpoint_metrics(
end_point_id=end_point_id,
@@ -173,7 +173,7 @@ async def _predict(
header=None
) -> Union[MutableMapping[str, Any], Response, StreamingResponse]:
# Always increase the pending requests counter on a new incoming request.
- FEDML_MODEL_CACHE.update_pending_requests_counter(increase=True)
+ FEDML_MODEL_CACHE.update_pending_requests_counter(end_point_id, increase=True)
inference_response = {}
try:
@@ -205,14 +205,14 @@ async def _predict(
if not is_endpoint_activated(in_end_point_id):
inference_response = {"error": True, "message": "endpoint is not activated."}
logging_inference_request(input_json, inference_response)
- FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
+ FEDML_MODEL_CACHE.update_pending_requests_counter(end_point_id, decrease=True)
return inference_response
# Found idle inference device
idle_device, end_point_id, model_id, model_name, model_version, inference_host, inference_output_url = \
found_idle_inference_device(in_end_point_id, in_end_point_name, in_model_name, in_model_version)
if idle_device is None or idle_device == "":
- FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
+ FEDML_MODEL_CACHE.update_pending_requests_counter(end_point_id, decrease=True)
return {"error": True, "error_code": status.HTTP_404_NOT_FOUND,
"message": "can not found active inference worker for this endpoint."}
@@ -252,18 +252,18 @@ async def _predict(
pass
logging_inference_request(input_json, inference_response)
- FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
+ FEDML_MODEL_CACHE.update_pending_requests_counter(end_point_id, decrease=True)
return inference_response
else:
inference_response = {"error": True, "message": "token is not valid."}
logging_inference_request(input_json, inference_response)
- FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
+ FEDML_MODEL_CACHE.update_pending_requests_counter(end_point_id, decrease=True)
return inference_response
except Exception as e:
logging.error("Inference Exception: {}".format(traceback.format_exc()))
# Need to reduce the pending requests counter in whatever exception that may be raised.
- FEDML_MODEL_CACHE.update_pending_requests_counter(decrease=True)
+ FEDML_MODEL_CACHE.update_pending_requests_counter(end_point_id, decrease=True)
def retrieve_info_by_endpoint_id(end_point_id, in_end_point_name=None, in_model_name=None,
From c29cf1d6e6be0c231e6f2c3bd5e13e67d7431956 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Mon, 10 Jun 2024 19:58:00 +0000
Subject: [PATCH 133/282] [Deploy] Unified timeout key.
---
.../scheduler/model_scheduler/device_model_cache.py | 2 +-
.../scheduler/model_scheduler/device_model_inference.py | 5 +++--
.../scheduler/model_scheduler/device_server_constants.py | 3 +++
3 files changed, 7 insertions(+), 3 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
index 242501f2fa..30e4f460e6 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
@@ -139,7 +139,7 @@ def set_user_setting_replica_num(self, end_point_id,
"target_queries_per_replica": target_queries_per_replica,
"aggregation_window_size_seconds": aggregation_window_size_seconds,
"scale_down_delay_seconds": scale_down_delay_seconds,
- "request_timeout_sec": timeout_s
+ ServerConstants.INFERENCE_REQUEST_TIMEOUT_KEY: timeout_s
}
try:
self.redis_connection.set(self.get_user_setting_replica_num_key(end_point_id), json.dumps(replica_num_dict))
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index c6e26ba53c..d073533b72 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -66,7 +66,7 @@ async def auth_middleware(request: Request, call_next):
# Get the request timeout from the endpoint settings.
request_timeout_s = FEDML_MODEL_CACHE.get_endpoint_settings(end_point_id) \
- .get("request_timeout_s", ClientConstants.INFERENCE_REQUEST_TIMEOUT)
+ .get(ServerConstants.INFERENCE_REQUEST_TIMEOUT_KEY, ServerConstants.INFERENCE_REQUEST_TIMEOUT_DEFAULT)
# Only proceed if the past k metrics collection is not empty.
if pask_k_metrics:
@@ -76,7 +76,8 @@ async def auth_middleware(request: Request, call_next):
mean_latency = sum(past_k_latencies_sec) / len(past_k_latencies_sec)
# If timeout threshold is exceeded then cancel and return time out error.
- if (mean_latency * pending_requests_num) > request_timeout_s:
+ should_block = (mean_latency * pending_requests_num) > request_timeout_s
+ if should_block:
return JSONResponse(
{"error": True, "message": "Request timed out."},
status_code=status.HTTP_504_GATEWAY_TIMEOUT)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
index eb01fbb599..243c197b2f 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
@@ -104,6 +104,9 @@ class ServerConstants(object):
AUTO_DETECT_PUBLIC_IP = "auto_detect_public_ip"
MODEL_INFERENCE_DEFAULT_PORT = 2203
MODEL_CACHE_KEY_EXPIRE_TIME = 1 * 10
+
+ INFERENCE_REQUEST_TIMEOUT_KEY = "request_timeout_sec"
+ INFERENCE_REQUEST_TIMEOUT_DEFAULT = 30
# -----End-----
MODEL_DEPLOYMENT_STAGE1 = {"index": 1, "text": "ReceivedRequest"}
From c4a87149e3af296310f0a7ca04cd467e0bc9b06f Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 11 Jun 2024 00:06:14 +0000
Subject: [PATCH 134/282] [Deploy] Report worker's connectivity when it
finished.
---
.../scheduler/comm_utils/network_util.py | 16 +++++
.../device_client_constants.py | 5 ++
.../model_scheduler/device_model_inference.py | 60 +++++++++++--------
.../model_scheduler/master_job_runner.py | 8 ---
.../model_scheduler/worker_job_runner.py | 33 +++++++---
.../scheduler_core/general_constants.py | 16 ++---
6 files changed, 87 insertions(+), 51 deletions(-)
create mode 100644 python/fedml/computing/scheduler/comm_utils/network_util.py
diff --git a/python/fedml/computing/scheduler/comm_utils/network_util.py b/python/fedml/computing/scheduler/comm_utils/network_util.py
new file mode 100644
index 0000000000..13674840c5
--- /dev/null
+++ b/python/fedml/computing/scheduler/comm_utils/network_util.py
@@ -0,0 +1,16 @@
+import os
+from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants
+
+
+def return_this_device_connectivity_type() -> str:
+ """
+ Return -> "http" | "http_proxy" |"mqtt"
+ """
+ if os.environ.get(ClientConstants.ENV_CONNECTION_TYPE_KEY) == ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP:
+ return ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP
+ elif os.environ.get(ClientConstants.ENV_CONNECTION_TYPE_KEY) == ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP_PROXY:
+ return ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP_PROXY
+ elif os.environ.get(ClientConstants.ENV_CONNECTION_TYPE_KEY) == ClientConstants.WORKER_CONNECTIVITY_TYPE_MQTT:
+ return ClientConstants.WORKER_CONNECTIVITY_TYPE_MQTT
+ else:
+ return ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
index 7894f2c73e..d66c2f966a 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
@@ -97,6 +97,11 @@ class ClientConstants(object):
INFERENCE_INFERENCE_SERVER_VERSION = "v2"
INFERENCE_REQUEST_TIMEOUT = 30
+ ENV_CONNECTION_TYPE_KEY = "FEDML_CONNECTION_TYPE"
+ WORKER_CONNECTIVITY_TYPE_HTTP = "http"
+ WORKER_CONNECTIVITY_TYPE_HTTP_PROXY = "http_proxy"
+ WORKER_CONNECTIVITY_TYPE_MQTT = "mqtt"
+
MSG_MODELOPS_DEPLOYMENT_STATUS_INITIALIZING = "INITIALIZING"
MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYING = "DEPLOYING"
MSG_MODELOPS_DEPLOYMENT_STATUS_INFERRING = "INFERRING"
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index d073533b72..a9205ceb9a 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -210,7 +210,8 @@ async def _predict(
return inference_response
# Found idle inference device
- idle_device, end_point_id, model_id, model_name, model_version, inference_host, inference_output_url = \
+ idle_device, end_point_id, model_id, model_name, model_version, inference_host, inference_output_url,\
+ connectivity_type = \
found_idle_inference_device(in_end_point_id, in_end_point_name, in_model_name, in_model_version)
if idle_device is None or idle_device == "":
FEDML_MODEL_CACHE.update_pending_requests_counter(end_point_id, decrease=True)
@@ -235,13 +236,16 @@ async def _predict(
stream_flag = input_json.get("stream", False)
input_list["stream"] = input_list.get("stream", stream_flag)
output_list = input_json.get("outputs", [])
+
+ # main execution of redirecting the inference request to the idle device
inference_response = await send_inference_request(
idle_device,
end_point_id,
inference_output_url,
input_list,
output_list,
- inference_type=in_return_type)
+ inference_type=in_return_type,
+ connectivity_type=connectivity_type)
# Calculate model metrics
try:
@@ -304,11 +308,12 @@ def found_idle_inference_device(end_point_id, end_point_name, in_model_name, in_
inference_host = ""
inference_output_url = ""
model_version = ""
+ connectivity_type = ""
+
# Found idle device (TODO: optimize the algorithm to search best device for inference)
payload, idle_device = FEDML_MODEL_CACHE. \
get_idle_device(end_point_id, end_point_name, in_model_name, in_model_version)
if payload is not None:
- logging.info("found idle deployment result {}".format(payload))
deployment_result = payload
model_name = deployment_result["model_name"]
model_version = deployment_result["model_version"]
@@ -317,24 +322,25 @@ def found_idle_inference_device(end_point_id, end_point_name, in_model_name, in_
inference_output_url = deployment_result["model_url"]
url_parsed = urlparse(inference_output_url)
inference_host = url_parsed.hostname
+ connectivity_type = deployment_result.get("connectivity_type", ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP)
else:
logging.info("not found idle deployment result")
- return idle_device, end_point_id, model_id, model_name, model_version, inference_host, inference_output_url
+ res = (idle_device, end_point_id, model_id, model_name, model_version, inference_host, inference_output_url,
+ connectivity_type)
+ logging.info(f"found idle device with metrics: {res}")
+
+ return res
async def send_inference_request(idle_device, end_point_id, inference_url, input_list, output_list,
- inference_type="default", has_public_ip=True):
+ inference_type="default",
+ connectivity_type=ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP):
request_timeout_sec = FEDML_MODEL_CACHE.get_endpoint_settings(end_point_id) \
.get("request_timeout_sec", ClientConstants.INFERENCE_REQUEST_TIMEOUT)
try:
- http_infer_available = os.getenv("FEDML_INFERENCE_HTTP_AVAILABLE", True)
- if not http_infer_available:
- if http_infer_available == "False" or http_infer_available == "false":
- http_infer_available = False
-
- if http_infer_available:
+ if connectivity_type == ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP:
response_ok = await FedMLHttpInference.is_inference_ready(
inference_url,
timeout=request_timeout_sec)
@@ -347,22 +353,23 @@ async def send_inference_request(idle_device, end_point_id, inference_url, input
timeout=request_timeout_sec)
logging.info(f"Use http inference. return {response_ok}")
return inference_response
-
- response_ok = await FedMLHttpProxyInference.is_inference_ready(
- inference_url,
- timeout=request_timeout_sec)
- if response_ok:
- response_ok, inference_response = await FedMLHttpProxyInference.run_http_proxy_inference_with_request(
- end_point_id,
+ elif connectivity_type == ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP_PROXY:
+ logging.warning("Use http proxy inference.")
+ response_ok = await FedMLHttpProxyInference.is_inference_ready(
inference_url,
- input_list,
- output_list,
- inference_type=inference_type,
timeout=request_timeout_sec)
- logging.info(f"Use http proxy inference. return {response_ok}")
- return inference_response
-
- if not has_public_ip:
+ if response_ok:
+ response_ok, inference_response = await FedMLHttpProxyInference.run_http_proxy_inference_with_request(
+ end_point_id,
+ inference_url,
+ input_list,
+ output_list,
+ inference_type=inference_type,
+ timeout=request_timeout_sec)
+ logging.info(f"Use http proxy inference. return {response_ok}")
+ return inference_response
+ elif connectivity_type == ClientConstants.WORKER_CONNECTIVITY_TYPE_MQTT:
+ logging.warning("Use mqtt inference.")
agent_config = {"mqtt_config": Settings.mqtt_config}
mqtt_inference = FedMLMqttInference(
agent_config=agent_config,
@@ -385,7 +392,8 @@ async def send_inference_request(idle_device, end_point_id, inference_url, input
logging.info(f"Use mqtt inference. return {response_ok}.")
return inference_response
- return {"error": True, "message": "Failed to use http, http-proxy for inference, no response from replica."}
+ else:
+ return {"error": True, "message": "Failed to use http, http-proxy for inference, no response from replica."}
except Exception as e:
inference_response = {"error": True,
"message": f"Exception when using http, http-proxy and mqtt "
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index a10bd2c559..b9b9b4c356 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -250,14 +250,6 @@ def process_deployment_result_message(self, topic=None, payload=None):
logging.info(f"Endpoint {end_point_id}; Device {device_id}; replica {replica_no}; "
f"run_operation {run_operation} model status {model_status}.")
- # OPTIONAL DEBUG PARAMS
- # this_run_controller = self.model_runner_mapping[run_id_str].replica_controller
- # logging.info(f"The current replica controller state is "
- # f"Total version diff num {this_run_controller.total_replica_version_diff_num}")
- # logging.info(f"self.request_json now {self.request_json}") # request_json will be deprecated
- # this_run_request_json = self.request_json
- # logging.info(f"self.request_json now {this_run_request_json}")
-
# Set redis + sqlite deployment result
FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index 3c357e9dab..9e178228b2 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -9,6 +9,8 @@
from abc import ABC
import yaml
from fedml.computing.scheduler.comm_utils.job_utils import JobRunnerUtils
+from fedml.computing.scheduler.comm_utils.network_util import return_this_device_connectivity_type
+
from fedml.core.mlops import MLOpsRuntimeLog
from fedml.computing.scheduler.comm_utils import file_utils
from .device_client_constants import ClientConstants
@@ -234,8 +236,11 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
"", "", model_version, {}, {}
+ # ip and connectivity
+ worker_ip = GeneralConstants.get_ip_address(self.request_json)
+ connectivity = return_this_device_connectivity_type()
+
if op == "add":
- worker_ip = GeneralConstants.get_ip_address(self.request_json)
for rank in range(prev_rank + 1, prev_rank + 1 + op_num):
try:
running_model_name, inference_output_url, inference_model_version, model_metadata, model_config = \
@@ -269,7 +274,9 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
result_payload = self.send_deployment_results(
end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
model_id, model_name, inference_output_url, model_version, inference_port_external,
- inference_engine, model_metadata, model_config, replica_no=rank + 1)
+ inference_engine, model_metadata, model_config, replica_no=rank + 1,
+ connectivity=connectivity
+ )
if inference_port_external != inference_port:
# Save internal port to local db
@@ -278,7 +285,9 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
result_payload = self.construct_deployment_results(
end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
model_id, model_name, inference_output_url, model_version, inference_port,
- inference_engine, model_metadata, model_config, replica_no=rank + 1)
+ inference_engine, model_metadata, model_config, replica_no=rank + 1,
+ connectivity=connectivity
+ )
FedMLModelDatabase.get_instance().set_deployment_result(
run_id, end_point_name, model_name, model_version, self.edge_id,
@@ -326,7 +335,6 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
return True
elif op == "update" or op == "rollback":
# Update is combine of delete and add
- worker_ip = GeneralConstants.get_ip_address(self.request_json)
for rank in replica_rank_to_update:
# Delete a replica (container) if exists
self.replica_handler.remove_replica(rank)
@@ -402,7 +410,9 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
result_payload = self.send_deployment_results(
end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
model_id, model_name, inference_output_url, model_version, inference_port_external,
- inference_engine, model_metadata, model_config, replica_no=rank + 1)
+ inference_engine, model_metadata, model_config, replica_no=rank + 1,
+ connectivity=connectivity
+ )
if inference_port_external != inference_port: # Save internal port to local db
logging.info("inference_port_external {} != inference_port {}".format(
@@ -410,7 +420,9 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
result_payload = self.construct_deployment_results(
end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
model_id, model_name, inference_output_url, model_version, inference_port,
- inference_engine, model_metadata, model_config, replica_no=rank + 1)
+ inference_engine, model_metadata, model_config, replica_no=rank + 1,
+ connectivity=connectivity
+ )
FedMLModelDatabase.get_instance().set_deployment_result(
run_id, end_point_name, model_name, model_version, self.edge_id,
@@ -433,7 +445,8 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
def construct_deployment_results(self, end_point_name, device_id, model_status,
model_id, model_name, model_inference_url,
model_version, inference_port, inference_engine,
- model_metadata, model_config, replica_no=1):
+ model_metadata, model_config, replica_no=1,
+ connectivity=ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP):
deployment_results_payload = {"end_point_id": self.run_id, "end_point_name": end_point_name,
"model_id": model_id, "model_name": model_name,
"model_url": model_inference_url, "model_version": model_version,
@@ -444,6 +457,7 @@ def construct_deployment_results(self, end_point_name, device_id, model_status,
"model_status": model_status,
"inference_port": inference_port,
"replica_no": replica_no,
+ "connectivity_type": connectivity,
}
return deployment_results_payload
@@ -466,7 +480,8 @@ def construct_deployment_status(self, end_point_name, device_id,
def send_deployment_results(self, end_point_name, device_id, model_status,
model_id, model_name, model_inference_url,
model_version, inference_port, inference_engine,
- model_metadata, model_config, replica_no=1):
+ model_metadata, model_config, replica_no=1,
+ connectivity=ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP):
deployment_results_topic = "model_device/model_device/return_deployment_result/{}/{}".format(
self.run_id, device_id)
@@ -474,7 +489,7 @@ def send_deployment_results(self, end_point_name, device_id, model_status,
end_point_name, device_id, model_status,
model_id, model_name, model_inference_url,
model_version, inference_port, inference_engine,
- model_metadata, model_config, replica_no=replica_no)
+ model_metadata, model_config, replica_no=replica_no, connectivity=connectivity)
logging.info("[client] send_deployment_results: topic {}, payload {}.".format(deployment_results_topic,
deployment_results_payload))
diff --git a/python/fedml/computing/scheduler/scheduler_core/general_constants.py b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
index 68c1a8e09d..8c60b17bdf 100755
--- a/python/fedml/computing/scheduler/scheduler_core/general_constants.py
+++ b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
@@ -192,14 +192,14 @@ def get_public_ip():
@staticmethod
def get_ip_address(request_json, infer_host=None):
# OPTION 1: Use local ip
- ip = GeneralConstants.get_local_ip()
-
- # OPTION 2: Auto detect public ip
- if "parameters" in request_json and \
- GeneralConstants.CONFIG_KEY_AUTO_DETECT_PUBLIC_IP in request_json["parameters"] and \
- request_json["parameters"][GeneralConstants.CONFIG_KEY_AUTO_DETECT_PUBLIC_IP]:
- ip = GeneralConstants.get_public_ip()
- logging.info("Auto detect public ip for master: " + ip)
+ # ip = GeneralConstants.get_local_ip()
+ #
+ # # OPTION 2: Auto detect public ip
+ # if "parameters" in request_json and \
+ # GeneralConstants.CONFIG_KEY_AUTO_DETECT_PUBLIC_IP in request_json["parameters"] and \
+ # request_json["parameters"][GeneralConstants.CONFIG_KEY_AUTO_DETECT_PUBLIC_IP]:
+ ip = GeneralConstants.get_public_ip()
+ logging.info("Auto detect public ip for master: " + ip)
# OPTION 3: Use user indicated ip
if infer_host is not None and infer_host != "127.0.0.1" and infer_host != "localhost":
From ea03b600c9709ba951fdd47e757f07c522891b16 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Mon, 10 Jun 2024 17:21:10 -0700
Subject: [PATCH 135/282] [Deploy] Refactor the quick start example, use public
ip as default.
---
.../examples/deploy/quick_start/config.yaml | 21 +-
.../examples/deploy/quick_start/main_entry.py | 27 ++
.../deploy/quick_start/src/__init__.py | 0
.../deploy/quick_start/src/app/__init__.py | 0
.../quick_start/src/app/pipe/__init__.py | 0
.../quick_start/src/app/pipe/constants.py | 68 -----
.../src/app/pipe/instruct_pipeline.py | 261 ------------------
.../quick_start/src/config/bootstrap.sh | 14 -
.../deploy/quick_start/src/main_entry.py | 67 -----
python/fedml/api/modules/model.py | 3 +
.../device_client_runner_deprecated.py} | 0
.../model_scheduler/sample_model/README.md | 57 ----
.../sample_model/fedml_model.bin | Bin 1476451 -> 0 bytes
.../sample_model/fedml_model_config.yaml | 20 --
.../scheduler_core/general_constants.py | 16 +-
15 files changed, 42 insertions(+), 512 deletions(-)
create mode 100644 python/examples/deploy/quick_start/main_entry.py
delete mode 100644 python/examples/deploy/quick_start/src/__init__.py
delete mode 100644 python/examples/deploy/quick_start/src/app/__init__.py
delete mode 100644 python/examples/deploy/quick_start/src/app/pipe/__init__.py
delete mode 100644 python/examples/deploy/quick_start/src/app/pipe/constants.py
delete mode 100644 python/examples/deploy/quick_start/src/app/pipe/instruct_pipeline.py
delete mode 100644 python/examples/deploy/quick_start/src/config/bootstrap.sh
delete mode 100644 python/examples/deploy/quick_start/src/main_entry.py
rename python/{examples/deploy/quick_start/__init__.py => fedml/computing/scheduler/model_scheduler/device_client_runner_deprecated.py} (100%)
mode change 100644 => 100755
delete mode 100644 python/fedml/computing/scheduler/model_scheduler/sample_model/README.md
delete mode 100644 python/fedml/computing/scheduler/model_scheduler/sample_model/fedml_model.bin
delete mode 100644 python/fedml/computing/scheduler/model_scheduler/sample_model/fedml_model_config.yaml
diff --git a/python/examples/deploy/quick_start/config.yaml b/python/examples/deploy/quick_start/config.yaml
index 83479068e6..880ea92d2d 100644
--- a/python/examples/deploy/quick_start/config.yaml
+++ b/python/examples/deploy/quick_start/config.yaml
@@ -1,21 +1,8 @@
-workspace: "./src"
+workspace: "."
entry_point: "main_entry.py"
+
# If you want to install some packages
# Please write the command in the bootstrap.sh
bootstrap: |
- echo "Bootstrap start..."
- sh ./config/bootstrap.sh
- echo "Bootstrap finished"
-
-# If you do not have any GPU resource but want to serve the model
-# Try FedML® Nexus AI Platform, and Uncomment the following lines.
-# ------------------------------------------------------------
-computing:
- minimum_num_gpus: 1 # minimum # of GPUs to provision
- maximum_cost_per_hour: $3000 # max cost per hour for your job per gpu card
- #allow_cross_cloud_resources: true # true, false
- #device_type: CPU # options: GPU, CPU, hybrid
- resource_type: A100-80G # e.g., A100-80G,
- # please check the resource type list by "fedml show-resource-type"
- # or visiting URL: https://open.fedml.ai/accelerator_resource_type
-# ------------------------------------------------------------
+ echo "Install some packages..."
+ echo "Install finished!"
diff --git a/python/examples/deploy/quick_start/main_entry.py b/python/examples/deploy/quick_start/main_entry.py
new file mode 100644
index 0000000000..7c4fb910b0
--- /dev/null
+++ b/python/examples/deploy/quick_start/main_entry.py
@@ -0,0 +1,27 @@
+from fedml.serving import FedMLPredictor
+from fedml.serving import FedMLInferenceRunner
+
+
+class Bot(FedMLPredictor): # Inherit FedMLClientPredictor
+ def __init__(self):
+ super().__init__()
+
+ # --- Your model initialization code here ---
+
+ # -------------------------------------------
+
+ def predict(self, request: dict):
+ input_dict = request
+ question: str = input_dict.get("text", "").strip()
+
+ # --- Your model inference code here ---
+ response = "I do not know the answer to your question."
+ # ---------------------------------------
+
+ return {"generated_text": f"The answer to your question {question} is: {response}"}
+
+
+if __name__ == "__main__":
+ chatbot = Bot()
+ fedml_inference_runner = FedMLInferenceRunner(chatbot)
+ fedml_inference_runner.run()
diff --git a/python/examples/deploy/quick_start/src/__init__.py b/python/examples/deploy/quick_start/src/__init__.py
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/python/examples/deploy/quick_start/src/app/__init__.py b/python/examples/deploy/quick_start/src/app/__init__.py
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/python/examples/deploy/quick_start/src/app/pipe/__init__.py b/python/examples/deploy/quick_start/src/app/pipe/__init__.py
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/python/examples/deploy/quick_start/src/app/pipe/constants.py b/python/examples/deploy/quick_start/src/app/pipe/constants.py
deleted file mode 100644
index 811418bf5e..0000000000
--- a/python/examples/deploy/quick_start/src/app/pipe/constants.py
+++ /dev/null
@@ -1,68 +0,0 @@
-"""
-Adapted from https://github.com/databrickslabs/dolly/blob/master/training/consts.py
-"""
-
-# -----------------------------------------------------------------
-DEFAULT_MAX_SEQ_LENGTH = 1024
-IGNORE_INDEX = -100
-
-# -----------------------------------------------------------------
-MODEL_NAMES = [
- "EleutherAI/pythia-70m",
- "EleutherAI/pythia-160m",
- "EleutherAI/pythia-2.8b",
- "EleutherAI/pythia-6.9b",
- "EleutherAI/pythia-12b",
- "EleutherAI/gpt-j-6B",
- "databricks/dolly-v2-3b",
- "databricks/dolly-v2-7b",
- "databricks/dolly-v2-12b",
-]
-
-# -----------------------------------------------------------------
-INTRO_BLURB = (
- "Below is an instruction that describes a task. Write a response that appropriately completes the request."
-)
-
-INSTRUCTION_KEY = "### Instruction:"
-INPUT_KEY = "Input:"
-RESPONSE_KEY = "### Response:"
-END_KEY = "### End"
-RESPONSE_KEY_NL = f"{RESPONSE_KEY}\n"
-
-# This is a training prompt that does not contain an input string. The instruction by itself has enough information
-# to respond.For example, the instruction might ask for the year a historic figure was born.
-PROMPT_NO_INPUT_FORMAT = f"""{INTRO_BLURB}
-
-{INSTRUCTION_KEY}
-{{instruction}}
-
-{RESPONSE_KEY}
-{{response}}
-
-{END_KEY}"""
-
-# This is a training prompt that contains an input string that serves as context for the instruction. For example,
-# the input might be a passage from Wikipedia and the instruction is to extract some information from it.
-PROMPT_WITH_INPUT_FORMAT = f"""{INTRO_BLURB}
-
-{INSTRUCTION_KEY}
-{{instruction}}
-
-{INPUT_KEY}
-{{input}}
-
-{RESPONSE_KEY}
-{{response}}
-
-{END_KEY}"""
-
-# This is the prompt that is used for generating responses using an already trained model. It ends with the response
-# key, where the job of the model is to provide the completion that follows it (i.e. the response itself).
-PROMPT_FOR_GENERATION_FORMAT = f"""{INTRO_BLURB}
-
-{INSTRUCTION_KEY}
-{{instruction}}
-
-{RESPONSE_KEY}
-"""
\ No newline at end of file
diff --git a/python/examples/deploy/quick_start/src/app/pipe/instruct_pipeline.py b/python/examples/deploy/quick_start/src/app/pipe/instruct_pipeline.py
deleted file mode 100644
index edcc1a643b..0000000000
--- a/python/examples/deploy/quick_start/src/app/pipe/instruct_pipeline.py
+++ /dev/null
@@ -1,261 +0,0 @@
-"""
-Adapted from https://github.com/databrickslabs/dolly/blob/master/training/generate.py
-"""
-from typing import List, Optional, Tuple
-
-import logging
-import re
-
-import torch
-from transformers import (
- AutoModelForCausalLM,
- AutoTokenizer,
- Pipeline,
- PreTrainedModel,
- PreTrainedTokenizer,
-)
-from transformers.utils import is_tf_available
-
-if is_tf_available():
- import tensorflow as tf
-
-from .constants import END_KEY, PROMPT_FOR_GENERATION_FORMAT, RESPONSE_KEY
-
-logger = logging.getLogger(__name__)
-
-
-def load_model_tokenizer_for_generate(
- pretrained_model_name_or_path: str,
-) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
- """Loads the model and tokenizer so that it can be used for generating responses.
-
- Args:
- pretrained_model_name_or_path (str): name or path for model
-
- Returns:
- Tuple[PreTrainedModel, PreTrainedTokenizer]: model and tokenizer
- """
- tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, padding_side="left")
- model = AutoModelForCausalLM.from_pretrained(
- pretrained_model_name_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True
- )
- return model, tokenizer
-
-
-def get_special_token_id(tokenizer: PreTrainedTokenizer, key: str) -> int:
- """Gets the token ID for a given string that has been added to the tokenizer as a special token.
-
- When training, we configure the tokenizer so that the sequences like "### Instruction:" and "### End" are
- treated specially and converted to a single, new token. This retrieves the token ID each of these keys map to.
-
- Args:
- tokenizer (PreTrainedTokenizer): the tokenizer
- key (str): the key to convert to a single token
-
- Raises:
- ValueError: if more than one ID was generated
-
- Returns:
- int: the token ID for the given key
- """
- token_ids = tokenizer.encode(key)
- if len(token_ids) > 1:
- raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}")
- return token_ids[0]
-
-
-class InstructionTextGenerationPipeline(Pipeline):
- def __init__(
- self,
- *args,
- do_sample: bool = True,
- max_new_tokens: int = 256,
- top_p: float = 0.92,
- top_k: int = 0,
- **kwargs
- ):
- """Initialize the pipeline
-
- Args:
- do_sample (bool, optional): Whether to use sampling. Defaults to True.
- max_new_tokens (int, optional): Max new tokens after the prompt to generate. Defaults to 128.
- top_p (float, optional): If set to float < 1, only the smallest set of most probable tokens with
- probabilities that add up to top_p or higher are kept for generation. Defaults to 0.92.
- top_k (int, optional): The number of highest probability vocabulary tokens to keep for top-k-filtering.
- Defaults to 0.
- """
- super().__init__(
- *args,
- do_sample=do_sample,
- max_new_tokens=max_new_tokens,
- top_p=top_p,
- top_k=top_k,
- **kwargs
- )
-
- def _sanitize_parameters(
- self,
- return_full_text: bool = None,
- **generate_kwargs
- ):
- preprocess_params = {}
-
- # newer versions of the tokenizer configure the response key as a special token. newer versions still may
- # append a newline to yield a single token. find whatever token is configured for the response key.
- tokenizer_response_key = next(
- (token for token in self.tokenizer.additional_special_tokens if token.startswith(RESPONSE_KEY)), None
- )
-
- response_key_token_id = None
- end_key_token_id = None
- if tokenizer_response_key:
- try:
- response_key_token_id = get_special_token_id(self.tokenizer, tokenizer_response_key)
- end_key_token_id = get_special_token_id(self.tokenizer, END_KEY)
-
- # Ensure generation stops once it generates "### End"
- generate_kwargs["eos_token_id"] = end_key_token_id
- except ValueError:
- pass
-
- forward_params = generate_kwargs
- postprocess_params = {
- "response_key_token_id": response_key_token_id,
- "end_key_token_id": end_key_token_id
- }
-
- if return_full_text is not None:
- postprocess_params["return_full_text"] = return_full_text
-
- return preprocess_params, forward_params, postprocess_params
-
- def preprocess(self, instruction_text, **generate_kwargs):
- prompt_text = PROMPT_FOR_GENERATION_FORMAT.format(instruction=instruction_text)
- inputs = self.tokenizer(
- prompt_text,
- return_tensors="pt",
- )
- inputs["prompt_text"] = prompt_text
- inputs["instruction_text"] = instruction_text
- return inputs
-
- def _forward(self, model_inputs, **generate_kwargs):
- input_ids = model_inputs["input_ids"]
- attention_mask = model_inputs.get("attention_mask", None)
-
- if input_ids.shape[1] == 0:
- input_ids = None
- attention_mask = None
- in_b = 1
- else:
- in_b = input_ids.shape[0]
-
- generated_sequence = self.model.generate(
- input_ids=input_ids,
- attention_mask=attention_mask,
- pad_token_id=self.tokenizer.pad_token_id,
- **generate_kwargs,
- )
-
- out_b = generated_sequence.shape[0]
- if self.framework == "pt":
- generated_sequence = generated_sequence.reshape(in_b, out_b // in_b, *generated_sequence.shape[1:])
- elif self.framework == "tf":
- generated_sequence = tf.reshape(generated_sequence, (in_b, out_b // in_b, *generated_sequence.shape[1:]))
-
- instruction_text = model_inputs.pop("instruction_text")
- return {"generated_sequence": generated_sequence, "input_ids": input_ids, "instruction_text": instruction_text}
-
- def postprocess(
- self,
- model_outputs,
- response_key_token_id: Optional[int] = None,
- end_key_token_id: Optional[int] = None,
- return_full_text: bool = False
- ):
- generated_sequence: torch.Tensor = model_outputs["generated_sequence"][0]
- instruction_text = model_outputs["instruction_text"]
-
- generated_sequence: List[List[int]] = generated_sequence.tolist()
- records = []
- for sequence in generated_sequence:
-
- # The response will be set to this variable if we can identify it.
- decoded = None
-
- # If we have token IDs for the response and end, then we can find the tokens and only decode between them.
- if response_key_token_id and end_key_token_id:
- # Find where "### Response:" is first found in the generated tokens. Considering this is part of the
- # prompt, we should definitely find it. We will return the tokens found after this token.
- try:
- response_pos = sequence.index(response_key_token_id)
- except ValueError:
- logger.warning(f"Could not find response key {response_key_token_id} in: {sequence}")
- response_pos = None
-
- if response_pos:
- # Next find where "### End" is located. The model has been trained to end its responses with this
- # sequence (or actually, the token ID it maps to, since it is a special token). We may not find
- # this token, as the response could be truncated. If we don't find it then just return everything
- # to the end. Note that even though we set eos_token_id, we still see this token at the end.
- try:
- end_pos = sequence.index(end_key_token_id)
- except ValueError:
- end_pos = None
-
- decoded = self.tokenizer.decode(sequence[response_pos + 1: end_pos]).strip()
-
- if not decoded:
- # Otherwise we'll decode everything and use a regex to find the response and end.
-
- fully_decoded = self.tokenizer.decode(sequence)
-
- # The response appears after "### Response:". The model has been trained to append "### End" at the
- # end.
- m = re.search(r"#+\s*Response:\s*(.+?)#+\s*End", fully_decoded, flags=re.DOTALL)
-
- if m:
- decoded = m.group(1).strip()
- else:
- # The model might not generate the "### End" sequence before reaching the max tokens. In this case,
- # return everything after "### Response:".
- m = re.search(r"#+\s*Response:\s*(.+)", fully_decoded, flags=re.DOTALL)
- if m:
- decoded = m.group(1).strip()
- else:
- logger.warning(f"Failed to find response in:\n{fully_decoded}")
-
- # If the full text is requested, then append the decoded text to the original instruction.
- # This technically isn't the full text, as we format the instruction in the prompt the model has been
- # trained on, but to the client it will appear to be the full text.
- if return_full_text:
- decoded = f"{instruction_text}\n{decoded}"
-
- rec = {"generated_text": decoded}
-
- records.append(rec)
-
- return records
-
-
-def generate_response(
- instruction: str,
- *,
- model: PreTrainedModel,
- tokenizer: PreTrainedTokenizer,
- **kwargs,
-) -> str:
- """Given an instruction, uses the model and tokenizer to generate a response. This formats the instruction in
- the instruction format that the model was fine-tuned on.
-
- Args:
- instruction (str): _description_
- model (PreTrainedModel): the model to use
- tokenizer (PreTrainedTokenizer): the tokenizer to use
-
- Returns:
- str: response
- """
-
- generation_pipeline = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer, **kwargs)
- return generation_pipeline(instruction)[0]["generated_text"]
\ No newline at end of file
diff --git a/python/examples/deploy/quick_start/src/config/bootstrap.sh b/python/examples/deploy/quick_start/src/config/bootstrap.sh
deleted file mode 100644
index 950b749792..0000000000
--- a/python/examples/deploy/quick_start/src/config/bootstrap.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-### don't modify this part ###
-set -x
-##############################
-
-
-### please customize your script in this region ####
-pip install langchain
-pip install transformers
-pip install accelerate
-pip install "pydantic>=1.8.0,<2.0.0"
-
-### don't modify this part ###
-exit 0
-##############################
\ No newline at end of file
diff --git a/python/examples/deploy/quick_start/src/main_entry.py b/python/examples/deploy/quick_start/src/main_entry.py
deleted file mode 100644
index 82ff90155e..0000000000
--- a/python/examples/deploy/quick_start/src/main_entry.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import os
-from fedml.serving import FedMLPredictor
-from fedml.serving import FedMLInferenceRunner
-from langchain import PromptTemplate, LLMChain
-from langchain.llms import HuggingFacePipeline
-import torch
-from transformers import (
- AutoConfig,
- AutoModelForCausalLM,
- AutoTokenizer,
- TextGenerationPipeline,
-)
-
-class Chatbot(FedMLPredictor): # Inherit FedMLClientPredictor
- def __init__(self):
- super().__init__()
- PROMPT_FOR_GENERATION_FORMAT = f""""Below is an instruction that describes a task. Write a response that appropriately completes the request."
-
- ### Instruction:
- {{instruction}}
-
- ### Response:
- """
-
- prompt = PromptTemplate(
- input_variables=["instruction"],
- template=PROMPT_FOR_GENERATION_FORMAT
- )
-
- config = AutoConfig.from_pretrained("EleutherAI/pythia-70m")
- model = AutoModelForCausalLM.from_pretrained(
- "EleutherAI/pythia-70m",
- torch_dtype=torch.float32, # float 16 not supported on CPU
- trust_remote_code=True,
- device_map="auto"
- )
- tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-70m", device_map="auto")
-
- hf_pipeline = HuggingFacePipeline(
- pipeline=TextGenerationPipeline(
- model=model,
- tokenizer=tokenizer,
- return_full_text=True,
- task="text-generation",
- do_sample=True,
- max_new_tokens=256,
- top_p=0.92,
- top_k=0
- )
- )
- self.chatbot = LLMChain(llm=hf_pipeline, prompt=prompt, verbose=True)
-
- def predict(self, request:dict):
- input_dict = request
- question: str = input_dict.get("text", "").strip()
-
- if len(question) == 0:
- response_text = "<received empty input; no response generated.>"
- else:
- response_text = self.chatbot.predict(instruction=question)
-
- return {"generated_text": str(response_text)}
-
-if __name__ == "__main__":
- chatbot = Chatbot()
- fedml_inference_runner = FedMLInferenceRunner(chatbot)
- fedml_inference_runner.run()
\ No newline at end of file
diff --git a/python/fedml/api/modules/model.py b/python/fedml/api/modules/model.py
index ca5d0b95c1..a02e674f47 100644
--- a/python/fedml/api/modules/model.py
+++ b/python/fedml/api/modules/model.py
@@ -21,6 +21,9 @@ def create(name: str, model: str = None, model_config: str = None) -> bool:
return True
else:
return False
+ elif model.startswith("tutorial:quick_start"):
+ # ../../../python/examples/deploy/quick_start
+ return False
else:
# TODO: Support arbitrary model creation from GitHub / Nexus AI Job Store
click.echo("Model {} is not supported yet.".format(model))
diff --git a/python/examples/deploy/quick_start/__init__.py b/python/fedml/computing/scheduler/model_scheduler/device_client_runner_deprecated.py
old mode 100644
new mode 100755
similarity index 100%
rename from python/examples/deploy/quick_start/__init__.py
rename to python/fedml/computing/scheduler/model_scheduler/device_client_runner_deprecated.py
diff --git a/python/fedml/computing/scheduler/model_scheduler/sample_model/README.md b/python/fedml/computing/scheduler/model_scheduler/sample_model/README.md
deleted file mode 100644
index fcb51bd792..0000000000
--- a/python/fedml/computing/scheduler/model_scheduler/sample_model/README.md
+++ /dev/null
@@ -1,57 +0,0 @@
-## 1 Device Login:
-Login as fedml cloud device:
-```fedml model device login $user_id_or_api_key -c```
-
-Login as on premise device:
-```fedml model device login $user_id_or_api_key -p```
-
-
-## 2. Model Card:
-Create local model repository:
-```fedml model create -n $model_name```
-
-Delete local model repository:
-```fedml model delete -n $model_name -f $model_file_name```
-
-Add file to local model repository:
-```fedml model add -n $model_name -p $model_file_path```
-
-Remove file from local model repository:
-```fedml model remove -n $model_name -f $model_file_name```
-
-List model in the local model repository:
-```fedml model list -n $model_name```
-
-Build local model repository as zip model package:
-```fedml model package -n $model_name```
-
-Push local model repository to ModelOps(open.fedml.ai):
-```fedml model push -n $model_name -u $user_id_or_api_key```
-
-Pull remote model(ModelOps) to local model repository:
-```fedml model pull -n $model_name -u $user_id_or_api_key```
-
-
-## 3. Model Package:
-Create local model repository:
-```fedml model create -n $model_name```
-
-Delete local model repository:
-```fedml model delete -n $model_name -f $model_file_name```
-
-Add file to local model repository:
-```fedml model add -n $model_name -p $model_file_path```
-
-Remove file from local model repository:
-```fedml model remove -n $model_name -f $model_file_name```
-
-List model in the local model repository:
-```fedml model list -n $model_name```
-
-Build local model repository as zip model package:
-```fedml model package -n $model_name```
-
-## 4. Model Deploy:
-```
-fedml model deploy -n $model_name -dt $device_type(md.on_premise_device/md.fedml_cloud_device) -d $master_device_id -u $user_id_or_api_key -p $deployment_extra_params
-```
diff --git a/python/fedml/computing/scheduler/model_scheduler/sample_model/fedml_model.bin b/python/fedml/computing/scheduler/model_scheduler/sample_model/fedml_model.bin
deleted file mode 100644
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diff --git a/python/fedml/computing/scheduler/model_scheduler/sample_model/fedml_model_config.yaml b/python/fedml/computing/scheduler/model_scheduler/sample_model/fedml_model_config.yaml
deleted file mode 100644
index 491ed507d9..0000000000
--- a/python/fedml/computing/scheduler/model_scheduler/sample_model/fedml_model_config.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-{
- "platform": "onnxruntime",
- "max_batch_size": 1,
- "input_size": [[1,24], [1,2]],
- "input_types": ["int", "float"],
- "input": [
- {
- "name": "input",
- "data_type": "TYPE_FP32",
- "dims": []
- }
- ],
- "output": [
- {
- "name": "output",
- "data_type": "TYPE_FP32",
- "dims": []
- }
- ]
-}
\ No newline at end of file
diff --git a/python/fedml/computing/scheduler/scheduler_core/general_constants.py b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
index 68c1a8e09d..8c60b17bdf 100755
--- a/python/fedml/computing/scheduler/scheduler_core/general_constants.py
+++ b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
@@ -192,14 +192,14 @@ def get_public_ip():
@staticmethod
def get_ip_address(request_json, infer_host=None):
# OPTION 1: Use local ip
- ip = GeneralConstants.get_local_ip()
-
- # OPTION 2: Auto detect public ip
- if "parameters" in request_json and \
- GeneralConstants.CONFIG_KEY_AUTO_DETECT_PUBLIC_IP in request_json["parameters"] and \
- request_json["parameters"][GeneralConstants.CONFIG_KEY_AUTO_DETECT_PUBLIC_IP]:
- ip = GeneralConstants.get_public_ip()
- logging.info("Auto detect public ip for master: " + ip)
+ # ip = GeneralConstants.get_local_ip()
+ #
+ # # OPTION 2: Auto detect public ip
+ # if "parameters" in request_json and \
+ # GeneralConstants.CONFIG_KEY_AUTO_DETECT_PUBLIC_IP in request_json["parameters"] and \
+ # request_json["parameters"][GeneralConstants.CONFIG_KEY_AUTO_DETECT_PUBLIC_IP]:
+ ip = GeneralConstants.get_public_ip()
+ logging.info("Auto detect public ip for master: " + ip)
# OPTION 3: Use user indicated ip
if infer_host is not None and infer_host != "127.0.0.1" and infer_host != "localhost":
From 31d8e7c1a73fafcffd8d715f7e93d9d20561c82c Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 12 Jun 2024 02:16:44 +0800
Subject: [PATCH 136/282] [CoreEngine] Adjust the design of FedML Python Agent
to a decentralized architecture that supports Launch Master, Launch Slave,
Deploy Master, and Deploy Slave at the same time.
---
.../scheduler/master/base_master_agent.py | 26 +++++--
.../master/base_master_job_runner_manager.py | 22 ++++--
.../master/base_master_protocol_manager.py | 10 ++-
.../scheduler/master/cloud_server_manager.py | 4 ++
.../master/master_protocol_manager.py | 3 +-
.../scheduler_core/account_manager.py | 5 ++
.../scheduler_core/message_center.py | 13 +++-
.../scheduler_base_protocol_manager.py | 55 ++++++++++-----
.../scheduler/scheduler_core/status_center.py | 3 +
.../scheduler/slave/base_slave_agent.py | 25 +++++--
.../computing/scheduler/slave/client_login.py | 16 ++---
.../scheduler/slave/slave_protocol_manager.py | 13 ++--
.../scheduler/slave/united_agents.py | 67 +++++++++++++++++++
python/fedml/core/mlops/__init__.py | 16 +++--
14 files changed, 219 insertions(+), 59 deletions(-)
create mode 100755 python/fedml/computing/scheduler/slave/united_agents.py
diff --git a/python/fedml/computing/scheduler/master/base_master_agent.py b/python/fedml/computing/scheduler/master/base_master_agent.py
index 3aff523c24..b27ed9547a 100755
--- a/python/fedml/computing/scheduler/master/base_master_agent.py
+++ b/python/fedml/computing/scheduler/master/base_master_agent.py
@@ -23,7 +23,8 @@ def __init__(self):
def login(
self, user_id, api_key=None, device_id=None,
- os_name=None, role=None, runner_cmd=None
+ os_name=None, role=None, runner_cmd=None,
+ communication_manager=None, sender_message_queue=None, status_center_queue=None
):
# Login account
login_result = FedMLAccountManager.get_instance().login(
@@ -48,14 +49,18 @@ def login(
# Initialize the protocol manager
# noinspection PyBoardException
try:
- self._initialize_protocol_manager()
+ self._initialize_protocol_manager(
+ communication_manager=communication_manager,
+ sender_message_queue=sender_message_queue,
+ status_center_queue=status_center_queue)
except Exception as e:
FedMLAccountManager.write_login_failed_file(is_client=False)
self.protocol_mgr.stop()
raise e
# Start the protocol manager to process the messages from MLOps and slave agents.
- self.protocol_mgr.start()
+ if communication_manager is None:
+ self.protocol_mgr.start()
@staticmethod
def logout():
@@ -69,7 +74,11 @@ def _create_protocol_manager(self, role, login_result):
login_result, agent_config=login_result.agent_config)
self.protocol_mgr.run_as_edge_server_and_agent = True \
if role == FedMLAccountManager.ROLE_EDGE_SERVER else False
- self.protocol_mgr.run_as_cloud_agent = True if role == FedMLAccountManager.ROLE_CLOUD_AGENT else False
+ self.protocol_mgr.run_as_cloud_agent = True \
+ if role == FedMLAccountManager.ROLE_CLOUD_AGENT or role == FedMLAccountManager.ROLE_GPU_MASTER_SERVER \
+ else False
+ self.use_local_process_as_cloud_server = True \
+ if role == FedMLAccountManager.ROLE_GPU_MASTER_SERVER else self.use_local_process_as_cloud_server
self.protocol_mgr.run_as_cloud_server = True if role == FedMLAccountManager.ROLE_CLOUD_SERVER else False
self.protocol_mgr.args = login_result
self.protocol_mgr.edge_id = login_result.edge_id
@@ -79,12 +88,17 @@ def _create_protocol_manager(self, role, login_result):
self.protocol_mgr.enable_simulation_cloud_agent = self.enable_simulation_cloud_agent
self.protocol_mgr.use_local_process_as_cloud_server = self.use_local_process_as_cloud_server
- def _initialize_protocol_manager(self):
+ def _initialize_protocol_manager(
+ self, communication_manager=None, sender_message_queue=None, status_center_queue=None
+ ):
# Init local database
self._init_database()
# Initialize the master protocol
- self.protocol_mgr.initialize()
+ self.protocol_mgr.initialize(
+ communication_manager=communication_manager,
+ sender_message_queue=sender_message_queue,
+ status_center_queue=status_center_queue)
# Report the IDLE status to MLOps
self.mlops_metrics.report_server_training_status(
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
index 6831c9d034..08ef1d640e 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
@@ -1,10 +1,12 @@
import base64
import json
import logging
+import os
import time
from abc import ABC
from multiprocessing import Process
from .cloud_server_manager import FedMLCloudServerManager
+from ..comm_utils.run_process_utils import RunProcessUtils
from ..scheduler_core.scheduler_base_job_runner_manager import FedMLSchedulerBaseJobRunnerManager
@@ -38,26 +40,38 @@ def start_job_runner(
def stop_job_runner(
self, run_id, args=None, server_id=None, request_json=None,
- run_as_cloud_agent=False, run_as_cloud_server=False
+ run_as_cloud_agent=False, run_as_cloud_server=False,
+ use_local_process_as_cloud_server=False
):
super().stop_job_runner(run_id)
if run_as_cloud_agent or run_as_cloud_server:
stopping_process = Process(
- target=FedMLCloudServerManager.stop_cloud_server, args=(run_id, server_id, args.agent_config))
+ target=FedMLCloudServerManager.stop_cloud_server,
+ args=(run_id, server_id, args.agent_config))
stopping_process.start()
+ if run_as_cloud_server:
+ time.sleep(1)
+ RunProcessUtils.kill_process(os.getpid())
+
def complete_job_runner(
self, run_id, args=None, server_id=None, request_json=None,
- run_as_cloud_agent=False, run_as_cloud_server=False
+ run_as_cloud_agent=False, run_as_cloud_server=False,
+ use_local_process_as_cloud_server=False
):
super().complete_job_runner(run_id)
if run_as_cloud_agent or run_as_cloud_server:
stopping_process = Process(
- target=FedMLCloudServerManager.stop_cloud_server, args=(run_id, server_id, args.agent_config))
+ target=FedMLCloudServerManager.stop_cloud_server,
+ args=(run_id, server_id, args.agent_config))
stopping_process.start()
+ if run_as_cloud_server:
+ time.sleep(1)
+ RunProcessUtils.kill_process(os.getpid())
+
def _start_cloud_server(
self, args, run_id, request_json, edge_id=None,
use_local_process_as_cloud_server=False
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index 1c4cbba4f4..a6b47855c6 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -197,7 +197,7 @@ def callback_start_train(self, topic=None, payload=None):
self.run_edge_ids[run_id_str] = edge_id_list
# report server running status to master agent
- if not self.run_as_cloud_server:
+ if not self.run_as_cloud_server and not self.run_as_cloud_agent:
self.mlops_metrics.report_server_id_status(
run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_STARTING, edge_id=self.edge_id,
server_id=self.edge_id, server_agent_id=self.edge_id, running_json=payload)
@@ -390,6 +390,9 @@ def callback_request_job_status(self, topic, payload):
def callback_request_device_status_in_job(self, topic, payload):
self.response_device_status_in_job(topic, payload)
+ def process_extra_queues(self, extra_queues):
+ self.rebuild_status_center(extra_queues[0])
+
def generate_protocol_manager(self):
message_status_runner = self._generate_protocol_manager_instance(
self.args, agent_config=self.agent_config
@@ -476,6 +479,8 @@ def init_job_task(self, request_json):
self.setup_listener_for_run_logs(run_id)
def setup_listeners_for_edge_status(self, run_id, edge_ids, server_id):
+ if self.run_as_cloud_agent:
+ return
edge_status_topic = "fl_client/flclient_agent_" + str(server_id) + "/status"
payload = {"run_id": run_id, "init_all_edge_id_list": edge_ids, "init_server_id": server_id}
self.callback_edge_status(edge_status_topic, json.dumps(payload))
@@ -486,6 +491,9 @@ def setup_listeners_for_edge_status(self, run_id, edge_ids, server_id):
self.subscribe_msg(edge_status_topic)
def remove_listeners_for_edge_status(self, edge_ids=None):
+ if self.run_as_cloud_agent:
+ return
+
if edge_ids is None:
edge_ids = self.request_json["edgeids"]
diff --git a/python/fedml/computing/scheduler/master/cloud_server_manager.py b/python/fedml/computing/scheduler/master/cloud_server_manager.py
index 040a0f38a3..aa9c07e84f 100755
--- a/python/fedml/computing/scheduler/master/cloud_server_manager.py
+++ b/python/fedml/computing/scheduler/master/cloud_server_manager.py
@@ -2,6 +2,7 @@
import json
import logging
import os
+import platform
import traceback
import fedml
@@ -32,6 +33,9 @@ def __init__(self, args, run_id=None, edge_id=None, request_json=None, agent_con
@staticmethod
def start_local_cloud_server(user, version, cloud_device_id, runner_cmd_encoded):
+ if platform.system() != "Windows":
+ os.setsid()
+
print(f"start cloud server, device id {cloud_device_id}, runner cmd {runner_cmd_encoded}")
pip_source_dir = os.path.dirname(__file__)
login_cmd = os.path.join(pip_source_dir, "server_login.py")
diff --git a/python/fedml/computing/scheduler/master/master_protocol_manager.py b/python/fedml/computing/scheduler/master/master_protocol_manager.py
index ca9621e41d..eb8cde239f 100755
--- a/python/fedml/computing/scheduler/master/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/master_protocol_manager.py
@@ -40,4 +40,5 @@ def _process_job_complete_status(self, run_id, server_id, complete_payload):
# Complete the job runner
self._get_job_runner_manager().complete_job_runner(
run_id, args=self.args, server_id=server_id, request_json=complete_payload,
- run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server)
+ run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server,
+ use_local_process_as_cloud_server=self.use_local_process_as_cloud_server)
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
index 3491e102f6..20c5fcd842 100755
--- a/python/fedml/computing/scheduler/scheduler_core/account_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -25,6 +25,7 @@ class FedMLAccountManager(Singleton):
ROLE_CLOUD_SERVER = "cloud_server"
ROLE_EDGE_DEVICE = "client"
ROLE_GPU_PROVIDER = "gpu_supplier"
+ ROLE_GPU_MASTER_SERVER = "gpu_master_server"
ROLE_DEPLOY_MASTER_ON_PREM = "md.on_premise_device.master"
ROLE_DEPLOY_WORKER_ON_PREM = "md.on_premise_device"
@@ -33,6 +34,7 @@ class FedMLAccountManager(Singleton):
DEVICE_ID_SUFFIX_CLOUD_SERVER = ".Public.Server"
DEVICE_ID_SUFFIX_EDGE_DEVICE = ".Edge.Device"
DEVICE_ID_SUFFIX_GPU_PROVIDER = ".Edge.GPU.Supplier"
+ DEVICE_ID_SUFFIX_GPU_MASTER_SERVER = ".Edge.GPU.MasterServer"
DEVICE_ID_SUFFIX_DEPLOY = "MDA"
DEVICE_ID_SUFFIX_DEPLOY_MASTER_ON_PREM = ".OnPremise.Master.Device"
DEVICE_ID_SUFFIX_DEPLOY_WORKER_ON_PREM = ".OnPremise.Device"
@@ -144,6 +146,9 @@ def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None,
device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_EDGE_DEVICE
elif role == FedMLAccountManager.ROLE_GPU_PROVIDER:
device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_GPU_PROVIDER
+ elif role == FedMLAccountManager.ROLE_GPU_MASTER_SERVER:
+ device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_GPU_MASTER_SERVER
+ is_master = True
elif role == FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM:
device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_DEPLOY_MASTER_ON_PREM
is_master = True
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_center.py b/python/fedml/computing/scheduler/scheduler_core/message_center.py
index 869ed6e510..b716a8a373 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_center.py
@@ -19,6 +19,7 @@
class FedMLMessageCenter(object):
FUNC_SETUP_MESSAGE_CENTER = "setup_message_center"
FUNC_REBUILD_MESSAGE_CENTER = "rebuild_message_center"
+ FUNC_PROCESS_EXTRA_QUEUES = "process_extra_queues"
ENABLE_SAVE_MESSAGE_TO_FILE = True
PUBLISH_MESSAGE_RETRY_TIMEOUT = 60 * 1000.0
PUBLISH_MESSAGE_RETRY_COUNT = 3
@@ -295,7 +296,10 @@ def get_listener_message_queue(self):
def setup_listener_message_queue(self):
self.listener_message_queue = Queue()
- def start_listener(self, sender_message_queue=None, listener_message_queue=None, agent_config=None, message_center_name=None):
+ def start_listener(
+ self, sender_message_queue=None, listener_message_queue=None,
+ agent_config=None, message_center_name=None, extra_queues=None
+ ):
if self.listener_message_center_process is not None:
return
@@ -313,7 +317,7 @@ def start_listener(self, sender_message_queue=None, listener_message_queue=None,
target=message_runner.run_listener_dispatcher, args=(
self.listener_message_event, self.listener_message_queue,
self.listener_handler_funcs, sender_message_queue,
- message_center_name
+ message_center_name, extra_queues
)
)
self.listener_message_center_process.start()
@@ -349,7 +353,7 @@ def unsubscribe_msg(self, topic):
def run_listener_dispatcher(
self, message_event, message_queue, listener_funcs, sender_message_queue,
- message_center_name
+ message_center_name, extra_queues
):
self.listener_message_event = message_event
self.listener_message_queue = message_queue
@@ -363,6 +367,9 @@ def run_listener_dispatcher(
else:
methodcaller(FedMLMessageCenter.FUNC_REBUILD_MESSAGE_CENTER, sender_message_queue)(self)
+ if extra_queues is not None:
+ methodcaller(FedMLMessageCenter.FUNC_PROCESS_EXTRA_QUEUES, extra_queues)(self)
+
while True:
message_entity = None
try:
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
index 9bb8b7a7ec..9cc3544647 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
@@ -60,38 +60,51 @@ def add_protocol_handler(self):
# self.add_message_listener(self.topic_start_train, self.callback_start_train)
pass
- def initialize(self):
+ def initialize(
+ self, communication_manager=None, sender_message_queue=None, status_center_queue=None
+ ):
# Generate the message topics
self.generate_topics()
# Setup MQTT connection
- self.communication_mgr = MqttManager(
- self.agent_config["mqtt_config"]["BROKER_HOST"],
- self.agent_config["mqtt_config"]["BROKER_PORT"],
- self.agent_config["mqtt_config"]["MQTT_USER"],
- self.agent_config["mqtt_config"]["MQTT_PWD"],
- self.agent_config["mqtt_config"]["MQTT_KEEPALIVE"],
- f"FedML_Agent_Daemon_@{self.user_name}@_@{self.current_device_id}@_@{str(uuid.uuid4())}@",
- self.topic_last_will,
- json.dumps({"ID": self.edge_id, "status": GeneralConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE})
- )
+ if communication_manager is None:
+ self.communication_mgr = MqttManager(
+ self.agent_config["mqtt_config"]["BROKER_HOST"],
+ self.agent_config["mqtt_config"]["BROKER_PORT"],
+ self.agent_config["mqtt_config"]["MQTT_USER"],
+ self.agent_config["mqtt_config"]["MQTT_PWD"],
+ self.agent_config["mqtt_config"]["MQTT_KEEPALIVE"],
+ f"FedML_Agent_Daemon_@{self.user_name}@_@{self.current_device_id}@_@{str(uuid.uuid4())}@",
+ self.topic_last_will,
+ json.dumps({"ID": self.edge_id, "status": GeneralConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE})
+ )
+ else:
+ self.communication_mgr = communication_manager
# Add the message listeners for all topics
self.add_protocol_handler()
# Start the message center to process edge related messages.
- self.setup_message_center()
+ if sender_message_queue is None:
+ self.setup_message_center()
+ else:
+ self.rebuild_message_center(sender_message_queue)
# Setup the message listener queue
self.setup_listener_message_queue()
# Start the status center to process edge related status.
- self.start_status_listener_center()
+ if status_center_queue is None:
+ self.start_status_listener_center()
+ else:
+ self.set_status_queue(status_center_queue)
+ self.rebuild_status_center(status_center_queue)
# Start the message center for listener
self.start_listener(sender_message_queue=self.message_center.get_sender_message_queue(),
agent_config=self.agent_config,
- message_center_name=self.message_center_name)
+ message_center_name=self.message_center_name,
+ extra_queues=[self.get_status_queue()])
# Init extra items, e.g. database, recovery, etc.
self._init_extra_items()
@@ -99,11 +112,11 @@ def initialize(self):
# Setup MQTT connected listener
self.communication_mgr.add_connected_listener(self.on_agent_communication_connected)
self.communication_mgr.add_disconnected_listener(self.on_agent_communication_disconnected)
- self.communication_mgr.connect()
def start(self):
# Start MQTT message loop
try:
+ self.communication_mgr.connect()
self.communication_mgr.loop_forever()
except Exception as e:
if str(e) == "Restarting after upgraded...":
@@ -233,6 +246,9 @@ def rebuild_status_center(self, status_center_queue):
self.status_reporter.edge_id = self.edge_id
self.status_reporter.server_agent_id = self.server_agent_id
+ def process_extra_queues(self, extra_queues):
+ pass
+
def generate_status_report(self, run_id, edge_id, server_agent_id=None):
status_reporter = MLOpsMetrics()
status_reporter.set_messenger(self, send_message_func=self.send_status_message)
@@ -268,6 +284,15 @@ def get_status_runner(self):
return None
+ def get_get_protocol_communication_manager(self):
+ return self.communication_mgr
+
+ def get_protocol_sender_message_queue(self):
+ return self.message_center.get_sender_message_queue()
+
+ def get_get_protocol_status_center_queue(self):
+ return self.get_status_queue()
+
def send_agent_active_msg(self, edge_id):
active_msg = {"ID": edge_id, "status": GeneralConstants.MSG_MLOPS_SERVER_STATUS_IDLE}
self.message_center.send_message_json(self.topic_active, json.dumps(active_msg))
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index 97c2115e76..53dd73c1df 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -142,6 +142,9 @@ def send_status_message(self, topic, payload):
def get_status_queue(self):
return self.status_queue
+ def set_status_queue(self, status_queue):
+ self.status_queue = status_queue
+
def status_center_process_master_status(self, topic, payload):
pass
diff --git a/python/fedml/computing/scheduler/slave/base_slave_agent.py b/python/fedml/computing/scheduler/slave/base_slave_agent.py
index 01c0a39195..a8ac9fa1cb 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_agent.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_agent.py
@@ -24,7 +24,8 @@ def __init__(self):
def login(
self, userid, api_key=None, device_id=None,
- os_name=None, need_to_check_gpu=False, role=None
+ os_name=None, need_to_check_gpu=False, role=None,
+ communication_manager=None, sender_message_queue=None, status_center_queue=None
):
# Preprocess the login args
if need_to_check_gpu:
@@ -57,17 +58,21 @@ def login(
# Initialize the protocol manager
# noinspection PyBoardException
try:
- self._initialize_protocol_manager()
+ self._initialize_protocol_manager(
+ communication_manager=communication_manager,
+ sender_message_queue=sender_message_queue,
+ status_center_queue=status_center_queue)
except Exception as e:
FedMLAccountManager.write_login_failed_file(is_client=True)
self.protocol_mgr.stop()
raise e
+ return login_result
+
+ def start(self):
# Start the protocol manager to process the messages from MLOps and slave agents.
self.protocol_mgr.start()
- return login_result
-
@staticmethod
def logout():
GeneralConstants.cleanup_run_process(None)
@@ -84,12 +89,17 @@ def _create_protocol_manager(self, login_result):
self.protocol_mgr.user_name = login_result.user_name
self.protocol_mgr.agent_config = login_result.agent_config
- def _initialize_protocol_manager(self):
+ def _initialize_protocol_manager(
+ self, communication_manager=None, sender_message_queue=None, status_center_queue=None
+ ):
# Init local database
self._init_database()
# Initialize the master protocol
- self.protocol_mgr.initialize()
+ self.protocol_mgr.initialize(
+ communication_manager=communication_manager,
+ sender_message_queue=sender_message_queue,
+ status_center_queue=status_center_queue)
# Start the client API process
self._start_slave_api()
@@ -122,6 +132,9 @@ def _start_slave_api(self):
should_capture_stderr=False
)
+ def get_protocol_manager(self):
+ return self.protocol_mgr
+
@abstractmethod
def _get_log_file_dir(self):
pass
diff --git a/python/fedml/computing/scheduler/slave/client_login.py b/python/fedml/computing/scheduler/slave/client_login.py
index 37a6dc8064..7a1c759410 100755
--- a/python/fedml/computing/scheduler/slave/client_login.py
+++ b/python/fedml/computing/scheduler/slave/client_login.py
@@ -1,11 +1,11 @@
import argparse
import os
import fedml
-from fedml.computing.scheduler.slave.slave_agent import FedMLLaunchSlaveAgent
+from fedml.computing.scheduler.slave.united_agents import FedMLUnitedAgent
def logout():
- FedMLLaunchSlaveAgent.logout()
+ FedMLUnitedAgent.get_instance().logout()
if __name__ == "__main__":
@@ -18,6 +18,7 @@ def logout():
parser.add_argument("--version", "-v", type=str, default="release")
parser.add_argument("--local_server", "-ls", type=str, default="127.0.0.1")
parser.add_argument("--role", "-r", type=str, default="client")
+ parser.add_argument("--runner_cmd", "-rc", type=str, default="{}")
parser.add_argument("--device_id", "-id", type=str, default="0")
parser.add_argument("--os_name", "-os", type=str, default="")
parser.add_argument("--api_key", "-k", type=str, default="")
@@ -30,17 +31,16 @@ def logout():
if args.api_key == "":
args.api_key = args.user
- fedml.set_env_version("test")
-
if args.local_on_premise_platform_host != "127.0.0.1":
fedml.set_local_on_premise_platform_host(args.local_on_premise_platform_host)
if args.local_on_premise_platform_port != 80:
fedml.set_local_on_premise_platform_port(args.local_on_premise_platform_port)
fedml.set_env_version(args.version)
- slave_agent = FedMLLaunchSlaveAgent()
+ united_agents = FedMLUnitedAgent.get_instance()
if args.type == 'login':
- slave_agent.login(args.api_key, api_key=args.api_key, device_id=args.device_id,
- os_name=args.os_name, role=args.role)
+ united_agents.login(
+ args.api_key, api_key=args.api_key, device_id=args.device_id,
+ os_name=args.os_name, role=args.role, runner_cmd=args.runner_cmd)
else:
- FedMLLaunchSlaveAgent.logout()
+ united_agents.logout()
diff --git a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
index a1067a0d96..6e3cb2ebe1 100755
--- a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
@@ -1,7 +1,5 @@
import copy
-import json
import os
-import fedml
from ..comm_utils.job_cleanup import JobCleanup
from .base_slave_protocol_manager import FedMLBaseSlaveProtocolManager
from .launch_job_runner_manager import FedMLLaunchJobRunnerManager
@@ -34,7 +32,8 @@ def _get_job_runner_manager(self):
def _process_connection_ready(self):
from fedml.core.mlops import sync_deploy_id
sync_deploy_id(
- self.edge_id, self.model_device_server.edge_id, self.model_device_client_edge_id_list)
+ self.edge_id, self.model_device_server_id, self.model_device_client_edge_id_list,
+ message_center=self.message_center)
# Override
def _process_connection_lost(self):
@@ -73,9 +72,8 @@ def _init_extra_items(self):
model_device_client.redis_port = infer_redis_port
if infer_redis_password is not None:
model_device_client.redis_password = infer_redis_password
- model_device_client.start()
self.model_device_client_list.append(model_device_client)
- self.model_device_client_edge_id_list.append(model_device_client.get_edge_id())
+ self.model_device_client_edge_id_list.append(model_device_client.bind_device())
self.args = copy.deepcopy(in_args)
if self.model_device_server is None:
@@ -91,8 +89,7 @@ def _init_extra_items(self):
if infer_redis_password is not None:
self.model_device_server.redis_password = infer_redis_password
- self.model_device_server.start()
- self.model_device_server_id = self.model_device_server.get_edge_id()
+ self.model_device_server_id = self.model_device_server.bind_device()
# Save the deployed master and worker id list to the environment variable.
os.environ["FEDML_DEPLOY_MASTER_ID"] = str(self.model_device_server_id)
@@ -102,4 +99,4 @@ def _init_extra_items(self):
self.args = copy.deepcopy(in_args)
self.mlops_metrics.stop_device_realtime_perf()
self.mlops_metrics.report_device_realtime_perf(self.args, self.args.agent_config["mqtt_config"])
- pass
\ No newline at end of file
+ pass
diff --git a/python/fedml/computing/scheduler/slave/united_agents.py b/python/fedml/computing/scheduler/slave/united_agents.py
new file mode 100755
index 0000000000..e365de8860
--- /dev/null
+++ b/python/fedml/computing/scheduler/slave/united_agents.py
@@ -0,0 +1,67 @@
+
+from fedml.computing.scheduler.scheduler_core.account_manager import FedMLAccountManager
+from fedml.computing.scheduler.slave.slave_agent import FedMLLaunchSlaveAgent
+from fedml.computing.scheduler.master.master_agent import FedMLLaunchMasterAgent
+from fedml.computing.scheduler.model_scheduler.model_device_server import FedMLDeployMasterAgent
+from fedml.computing.scheduler.model_scheduler.model_device_client import FedMLDeployWorkerAgent
+from fedml.core.common.singleton import Singleton
+
+
+class FedMLUnitedAgent(Singleton):
+
+ @staticmethod
+ def get_instance():
+ return FedMLUnitedAgent()
+
+ def logout(self):
+ FedMLLaunchSlaveAgent.logout()
+
+ def login(self, userid, api_key=None, device_id=None,
+ os_name=None, need_to_check_gpu=False, role=None, runner_cmd=None):
+ # Create the launch master/slave and deploy master/slave agents.
+ launch_slave_agent = FedMLLaunchSlaveAgent()
+ launch_master_agent = FedMLLaunchMasterAgent()
+ deploy_slave_agent = FedMLDeployWorkerAgent()
+ deploy_master_agent = FedMLDeployMasterAgent()
+
+ # Login with the launch slave role
+ launch_slave_agent.login(
+ api_key, api_key=api_key, device_id=device_id,
+ os_name=os_name, role=role
+ )
+
+ # Get the communication manager, sender message queue and status center queue
+ shared_communication_mgr = launch_slave_agent.get_protocol_manager().get_get_protocol_communication_manager()
+ shared_sender_message_queue = launch_slave_agent.get_protocol_manager().get_protocol_sender_message_queue()
+ shared_status_center_queue = launch_slave_agent.get_protocol_manager().get_get_protocol_status_center_queue()
+
+ # Login with the launch master role based on the shared communication manager
+ launch_master_agent.login(
+ api_key, api_key=api_key, device_id=device_id,
+ os_name=os_name, runner_cmd=runner_cmd,
+ role=FedMLAccountManager.ROLE_GPU_MASTER_SERVER,
+ communication_manager=shared_communication_mgr,
+ sender_message_queue=shared_sender_message_queue,
+ status_center_queue=shared_status_center_queue
+ )
+
+ # Login with the deployment master role based on the shared communication manager
+ deploy_master_agent.login(
+ userid, api_key=api_key, device_id=device_id,
+ os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM,
+ communication_manager=shared_communication_mgr,
+ sender_message_queue=shared_sender_message_queue,
+ status_center_queue=shared_status_center_queue
+ )
+
+ # Login with the deployment slave role based on the shared communication manager
+ deploy_slave_agent.login(
+ userid, api_key=api_key, device_id=device_id,
+ os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM,
+ communication_manager=shared_communication_mgr,
+ sender_message_queue=shared_sender_message_queue,
+ status_center_queue=shared_status_center_queue
+ )
+
+ # Start the slave agent to connect to servers and loop forever.
+ launch_slave_agent.start()
diff --git a/python/fedml/core/mlops/__init__.py b/python/fedml/core/mlops/__init__.py
index 148427fe1f..121c8e26bb 100644
--- a/python/fedml/core/mlops/__init__.py
+++ b/python/fedml/core/mlops/__init__.py
@@ -1453,12 +1453,14 @@ def release_resources(run_id, device_id):
MLOpsConstants.MSG_TOPIC_LAUNCH_RELEASE_GPU_IDS, json.dumps(payload))
-def sync_deploy_id(device_id, master_deploy_id, worker_deploy_id_list):
- fedml_args = get_fedml_args()
-
- setup_log_mqtt_mgr()
-
+def sync_deploy_id(device_id, master_deploy_id, worker_deploy_id_list, message_center=None):
payload = {"device_id": device_id, "master_deploy_id": master_deploy_id, "worker_deploy_ids": worker_deploy_id_list}
- MLOpsStore.mlops_log_mqtt_mgr.send_message_json(
- MLOpsConstants.MSG_TOPIC_LAUNCH_SYNC_DEPLOY_IDS, json.dumps(payload))
+ if message_center is None:
+ fedml_args = get_fedml_args()
+ setup_log_mqtt_mgr()
+ MLOpsStore.mlops_log_mqtt_mgr.send_message_json(
+ MLOpsConstants.MSG_TOPIC_LAUNCH_SYNC_DEPLOY_IDS, json.dumps(payload))
+ else:
+ message_center.send_message( MLOpsConstants.MSG_TOPIC_LAUNCH_SYNC_DEPLOY_IDS, json.dumps(payload))
+
From edd148e42a8c2afdf194907cb7bd64804709c9ad Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 12 Jun 2024 02:45:48 +0800
Subject: [PATCH 137/282] [CoreEngine] Use the fork process on the MacOS and
linux to avoid the crash issue.
---
.../master/base_master_job_runner.py | 2 +-
.../scheduler_core/message_center.py | 5 +++--
.../scheduler_base_job_runner.py | 4 +++-
.../scheduler/scheduler_core/status_center.py | 4 +++-
.../scheduler/slave/base_slave_job_runner.py | 3 ++-
python/fedml/core/mlops/mlops_device_perfs.py | 19 ++++++++++---------
python/fedml/core/mlops/mlops_job_perfs.py | 5 +++--
.../core/mlops/mlops_runtime_log_daemon.py | 4 ++--
8 files changed, 27 insertions(+), 19 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner.py b/python/fedml/computing/scheduler/master/base_master_job_runner.py
index 9ebab258bb..1383c9058c 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner.py
@@ -425,7 +425,7 @@ def start_runner_process(
server_runner.edge_id_status_queue = self.run_edge_id_status_queue
server_runner.edge_device_info_queue = self.run_edge_device_info_queue
self.run_extend_queue_list = self._generate_extend_queue_list()
- self.run_process = Process(
+ self.run_process = fedml.get_multiprocessing_context().Process(
target=server_runner.run if not is_server_job else server_runner.run_server_job, args=(
self.run_process_event, self.run_process_completed_event, self.run_edge_id_status_queue,
self.run_edge_device_info_queue, self.run_metrics_queue, self.run_events_queue,
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_center.py b/python/fedml/computing/scheduler/scheduler_core/message_center.py
index f698d61816..74510c6939 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_center.py
@@ -10,6 +10,7 @@
import queue
from os.path import expanduser
+import fedml
from fedml.core.distributed.communication.mqtt.mqtt_manager import MqttManager
from ..slave.client_constants import ClientConstants
from ....core.mlops.mlops_metrics import MLOpsMetrics
@@ -137,7 +138,7 @@ def start_sender(self, message_center_name=None):
self.message_event.clear()
message_center = FedMLMessageCenter(agent_config=self.sender_agent_config,
sender_message_queue=self.sender_message_queue)
- self.message_center_process = Process(
+ self.message_center_process = fedml.get_multiprocessing_context().Process(
target=message_center.run_sender, args=(
self.message_event, self.sender_message_queue,
message_center_name
@@ -314,7 +315,7 @@ def start_listener(
self.listener_agent_config = agent_config
message_runner = self.get_message_runner()
message_runner.listener_agent_config = agent_config
- self.listener_message_center_process = Process(
+ self.listener_message_center_process = fedml.get_multiprocessing_context().Process(
target=message_runner.run_listener_dispatcher, args=(
self.listener_message_event, self.listener_message_queue,
self.listener_handler_funcs, sender_message_queue,
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 6e0010f556..44562c65f0 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -9,6 +9,8 @@
import traceback
import zipfile
import queue
+
+import fedml
from ..comm_utils.constants import SchedulerConstants
from ..comm_utils.job_utils import JobRunnerUtils, DockerArgs
from ..scheduler_entry.constants import Constants
@@ -209,7 +211,7 @@ def retrieve_and_unzip_package(self, package_name, package_url):
from multiprocessing import Process
completed_event = multiprocessing.Event()
info_queue = multiprocessing.Queue()
- download_process = Process(target=self.download_package_proc,
+ download_process = fedml.get_multiprocessing_context().Process(target=self.download_package_proc,
args=(package_url, local_package_file, completed_event, info_queue))
download_process.start()
allowed_block_download_time = 60
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index 53dd73c1df..0897f94c19 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -5,6 +5,8 @@
import multiprocessing
from multiprocessing import Process, Queue
import queue
+
+import fedml
from .message_common import FedMLMessageEntity, FedMLStatusEntity
from .message_center import FedMLMessageCenter
import traceback
@@ -114,7 +116,7 @@ def start_status_center(self, sender_message_center_queue=None,
self.status_runner = self.get_status_runner()
target_func = self.status_runner.run_status_dispatcher if not is_slave_agent else \
self.status_runner.run_status_dispatcher_in_slave
- self.status_center_process = Process(
+ self.status_center_process = fedml.get_multiprocessing_context().Process(
target=target_func, args=(
self.status_event, self.status_queue, self.status_sender_message_center_queue,
self.status_listener_message_center_queue
diff --git a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
index 5e530dbba7..a495a17dd0 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
@@ -7,6 +7,7 @@
import traceback
from abc import ABC, abstractmethod
+import fedml
from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
from .client_data_interface import FedMLClientDataInterface
@@ -259,7 +260,7 @@ def start_runner_process(
client_runner.server_id = request_json.get("server_id", "0")
self.run_extend_queue_list = self._generate_extend_queue_list()
logging.info("start the runner process.")
- self.run_process = Process(target=client_runner.run, args=(
+ self.run_process = fedml.get_multiprocessing_context().Process(target=client_runner.run, args=(
self.run_process_event, self.run_process_completed_event, self.run_extend_queue_list,
sender_message_queue, listener_message_queue, status_center_queue
))
diff --git a/python/fedml/core/mlops/mlops_device_perfs.py b/python/fedml/core/mlops/mlops_device_perfs.py
index 29183a6e78..13fca2e354 100644
--- a/python/fedml/core/mlops/mlops_device_perfs.py
+++ b/python/fedml/core/mlops/mlops_device_perfs.py
@@ -9,6 +9,7 @@
import multiprocessing
import psutil
+import fedml
from fedml.computing.scheduler.comm_utils import sys_utils
from .device_info_report_protocol import FedMLDeviceInfoReportProtocol
from .mlops_utils import MLOpsUtils
@@ -76,52 +77,52 @@ def setup_realtime_stats_process(self, sys_args):
self.device_realtime_stats_event.clear()
perf_stats.device_realtime_stats_event = self.device_realtime_stats_event
- self.device_realtime_stats_process = multiprocessing.Process(
+ self.device_realtime_stats_process = fedml.get_multiprocessing_context().Process(
target=perf_stats.report_device_realtime_stats_entry,
args=(self.device_realtime_stats_event, ROLE_DEVICE_INFO_REPORTER, self.is_client))
self.device_realtime_stats_process.start()
if self.enable_job_total_monitor:
- self.job_total_monitor_process = multiprocessing.Process(
+ self.job_total_monitor_process = fedml.get_multiprocessing_context().Process(
target=perf_stats.report_device_realtime_stats_entry,
args=(self.device_realtime_stats_event, ROLE_DEVICE_JOB_TOTAL_MONITOR, self.is_client))
self.job_total_monitor_process.start()
else:
if self.is_client:
- self.monitor_endpoint_master_process = multiprocessing.Process(
+ self.monitor_endpoint_master_process = fedml.get_multiprocessing_context().Process(
target=perf_stats.report_device_realtime_stats_entry,
args=(self.device_realtime_stats_event, ROLE_ENDPOINT_MASTER))
self.monitor_endpoint_master_process.start()
- self.monitor_run_slave_process = multiprocessing.Process(
+ self.monitor_run_slave_process = fedml.get_multiprocessing_context().Process(
target=perf_stats.report_device_realtime_stats_entry,
args=(self.device_realtime_stats_event, ROLE_RUN_SLAVE))
self.monitor_run_slave_process.start()
- self.monitor_endpoint_logs_process = multiprocessing.Process(
+ self.monitor_endpoint_logs_process = fedml.get_multiprocessing_context().Process(
target=perf_stats.report_device_realtime_stats_entry,
args=(self.device_realtime_stats_event, ROLE_ENDPOINT_LOGS))
self.monitor_endpoint_logs_process.start()
# Register auto-scaler process
- self.monitor_auto_scaler_process = multiprocessing.Process(
+ self.monitor_auto_scaler_process = fedml.get_multiprocessing_context().Process(
target=perf_stats.report_device_realtime_stats_entry,
args=(self.device_realtime_stats_event, ROLE_AUTO_SCALER))
self.monitor_auto_scaler_process.start()
# Register replica number report channel
- self.monitor_replica_num_process = multiprocessing.Process(
+ self.monitor_replica_num_process = fedml.get_multiprocessing_context().Process(
target=perf_stats.report_device_realtime_stats_entry,
args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_NUM))
self.monitor_replica_num_process.start()
# Register replica performance report channel
- self.monitor_replica_perf_process = multiprocessing.Process(
+ self.monitor_replica_perf_process = fedml.get_multiprocessing_context().Process(
target=perf_stats.report_device_realtime_stats_entry,
args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_PERF))
self.monitor_replica_perf_process.start()
else:
- self.monitor_run_master_process = multiprocessing.Process(
+ self.monitor_run_master_process = fedml.get_multiprocessing_context().Process(
target=perf_stats.report_device_realtime_stats_entry,
args=(self.device_realtime_stats_event, ROLE_RUN_MASTER))
self.monitor_run_master_process.start()
diff --git a/python/fedml/core/mlops/mlops_job_perfs.py b/python/fedml/core/mlops/mlops_job_perfs.py
index fe3d921558..b1f674a5c9 100644
--- a/python/fedml/core/mlops/mlops_job_perfs.py
+++ b/python/fedml/core/mlops/mlops_job_perfs.py
@@ -8,6 +8,7 @@
import multiprocess as multiprocessing
import psutil
+import fedml
from .mlops_utils import MLOpsUtils
from .system_stats import SysStats
from ...core.distributed.communication.mqtt.mqtt_manager import MqttManager
@@ -139,8 +140,8 @@ def setup_job_stats_process(self, sys_args):
perf_stats.job_stats_event = self.job_stats_event
perf_stats.job_process_id_map = self.job_process_id_map
- self.job_stats_process = multiprocessing.Process(target=perf_stats.report_job_stats_entry,
- args=(self.job_stats_event,))
+ self.job_stats_process = fedml.get_multiprocessing_context().Process(
+ target=perf_stats.report_job_stats_entry, args=(self.job_stats_event,))
self.job_stats_process.start()
def report_job_stats(self, sys_args):
diff --git a/python/fedml/core/mlops/mlops_runtime_log_daemon.py b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
index ff06dc91b3..88d49b7a22 100644
--- a/python/fedml/core/mlops/mlops_runtime_log_daemon.py
+++ b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
@@ -431,8 +431,8 @@ def start_log_processor(self, log_run_id, log_device_id, log_source=None, log_fi
self.log_process_event_map[event_map_id] = multiprocessing.Event()
self.log_process_event_map[event_map_id].clear()
log_processor.log_process_event = self.log_process_event_map[event_map_id]
- log_child_process = multiprocessing.Process(target=log_processor.log_process,
- args=(self.log_process_event_map[event_map_id],))
+ log_child_process = fedml.get_multiprocessing_context().Process(
+ target=log_processor.log_process, args=(self.log_process_event_map[event_map_id],))
# process = threading.Thread(target=log_processor.log_process)
# process.start()
if log_child_process is not None:
From fd5af7ec6ff09d2c7e1d4051a33758353f17e987 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 12 Jun 2024 02:47:53 +0800
Subject: [PATCH 138/282] [CoreEngine] Use the fork process on the MacOS and
linux to avoid the crash issue.
---
python/fedml/__init__.py | 11 +++++++++--
1 file changed, 9 insertions(+), 2 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index c96d65adc5..e5ba1b304b 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -452,14 +452,21 @@ def _init_multiprocessing():
"""
if platform.system() == "Windows":
if multiprocessing.get_start_method() != "spawn":
- # force all platforms (Windows/Linux/macOS) to use the same way (spawn) for multiprocessing
+ # force all platforms (Windows) to use the same way (spawn) for multiprocessing
multiprocessing.set_start_method("spawn", force=True)
else:
if multiprocessing.get_start_method() != "fork":
- # force all platforms (Windows/Linux/macOS) to use the same way (fork) for multiprocessing
+ # force all platforms (Linux/macOS) to use the same way (fork) for multiprocessing
multiprocessing.set_start_method("fork", force=True)
+def get_multiprocessing_context():
+ if platform.system() == "Windows":
+ return multiprocessing.get_context("spawn")
+ else:
+ return multiprocessing.get_context("fork")
+
+
def set_env_version(version):
set_env_kv("FEDML_ENV_VERSION", version)
load_env()
From 4a9622c439f4368a4111490aef8722145825c659 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Tue, 11 Jun 2024 15:53:08 -0400
Subject: [PATCH 139/282] Adding default http connectivity type constant.
Fixing minor typos and reducing condition checks.
---
.../scheduler/comm_utils/network_util.py | 16 +++++++++-------
.../device_client_constants.py | 1 +
.../model_scheduler/device_model_cache.py | 10 +++++++---
.../model_scheduler/device_model_inference.py | 19 ++++++++++---------
.../model_scheduler/worker_job_runner.py | 4 ++--
5 files changed, 29 insertions(+), 21 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/network_util.py b/python/fedml/computing/scheduler/comm_utils/network_util.py
index 13674840c5..48e478f23f 100644
--- a/python/fedml/computing/scheduler/comm_utils/network_util.py
+++ b/python/fedml/computing/scheduler/comm_utils/network_util.py
@@ -6,11 +6,13 @@ def return_this_device_connectivity_type() -> str:
"""
Return -> "http" | "http_proxy" |"mqtt"
"""
- if os.environ.get(ClientConstants.ENV_CONNECTION_TYPE_KEY) == ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP:
- return ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP
- elif os.environ.get(ClientConstants.ENV_CONNECTION_TYPE_KEY) == ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP_PROXY:
- return ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP_PROXY
- elif os.environ.get(ClientConstants.ENV_CONNECTION_TYPE_KEY) == ClientConstants.WORKER_CONNECTIVITY_TYPE_MQTT:
- return ClientConstants.WORKER_CONNECTIVITY_TYPE_MQTT
+ # Get the environmental variable's value and convert to lower case.
+ env_conn_type = os.getenv(ClientConstants.ENV_CONNECTION_TYPE_KEY, "").lower()
+ if env_conn_type in [
+ ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP,
+ ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP_PROXY,
+ ClientConstants.WORKER_CONNECTIVITY_TYPE_MQTT
+ ]:
+ return env_conn_type
else:
- return ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP
+ return ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
index d66c2f966a..2c06189d2e 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
@@ -101,6 +101,7 @@ class ClientConstants(object):
WORKER_CONNECTIVITY_TYPE_HTTP = "http"
WORKER_CONNECTIVITY_TYPE_HTTP_PROXY = "http_proxy"
WORKER_CONNECTIVITY_TYPE_MQTT = "mqtt"
+ WORKER_CONNECTIVITY_TYPE_DEFAULT = WORKER_CONNECTIVITY_TYPE_HTTP
MSG_MODELOPS_DEPLOYMENT_STATUS_INITIALIZING = "INITIALIZING"
MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYING = "DEPLOYING"
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
index 30e4f460e6..6c90944277 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
@@ -344,9 +344,13 @@ def get_result_item_info(self, result_item):
result_payload = result_item_json["result"]
return device_id, replica_no, result_payload
- def get_idle_device(self, end_point_id, end_point_name,
- model_name, model_version,
- check_end_point_status=True, limit_specific_model_version=False):
+ def get_idle_device(self,
+ end_point_id,
+ end_point_name,
+ model_name,
+ model_version,
+ check_end_point_status=True,
+ limit_specific_model_version=False):
# Deprecated the model status logic, query directly from the deployment result list
idle_device_list = list()
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index a9205ceb9a..3aeec67932 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -313,16 +313,17 @@ def found_idle_inference_device(end_point_id, end_point_name, in_model_name, in_
# Found idle device (TODO: optimize the algorithm to search best device for inference)
payload, idle_device = FEDML_MODEL_CACHE. \
get_idle_device(end_point_id, end_point_name, in_model_name, in_model_version)
- if payload is not None:
- deployment_result = payload
- model_name = deployment_result["model_name"]
- model_version = deployment_result["model_version"]
- model_id = deployment_result["model_id"]
- end_point_id = deployment_result["end_point_id"]
- inference_output_url = deployment_result["model_url"]
+ if payload:
+ model_name = payload["model_name"]
+ model_version = payload["model_version"]
+ model_id = payload["model_id"]
+ end_point_id = payload["end_point_id"]
+ inference_output_url = payload["model_url"]
+ connectivity_type = \
+ payload.get("connectivity_type",
+ ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT)
url_parsed = urlparse(inference_output_url)
inference_host = url_parsed.hostname
- connectivity_type = deployment_result.get("connectivity_type", ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP)
else:
logging.info("not found idle deployment result")
@@ -335,7 +336,7 @@ def found_idle_inference_device(end_point_id, end_point_name, in_model_name, in_
async def send_inference_request(idle_device, end_point_id, inference_url, input_list, output_list,
inference_type="default",
- connectivity_type=ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP):
+ connectivity_type=ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT):
request_timeout_sec = FEDML_MODEL_CACHE.get_endpoint_settings(end_point_id) \
.get("request_timeout_sec", ClientConstants.INFERENCE_REQUEST_TIMEOUT)
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index 9e178228b2..ef65e37904 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -446,7 +446,7 @@ def construct_deployment_results(self, end_point_name, device_id, model_status,
model_id, model_name, model_inference_url,
model_version, inference_port, inference_engine,
model_metadata, model_config, replica_no=1,
- connectivity=ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP):
+ connectivity=ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT):
deployment_results_payload = {"end_point_id": self.run_id, "end_point_name": end_point_name,
"model_id": model_id, "model_name": model_name,
"model_url": model_inference_url, "model_version": model_version,
@@ -481,7 +481,7 @@ def send_deployment_results(self, end_point_name, device_id, model_status,
model_id, model_name, model_inference_url,
model_version, inference_port, inference_engine,
model_metadata, model_config, replica_no=1,
- connectivity=ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP):
+ connectivity=ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT):
deployment_results_topic = "model_device/model_device/return_deployment_result/{}/{}".format(
self.run_id, device_id)
From 653fe660df5a79db4375b4f7311290fc32d9cdac Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 12 Jun 2024 05:05:31 +0800
Subject: [PATCH 140/282] [CoreEngine] make the multiprocess work on windows,
linux and mac.
---
python/fedml/__init__.py | 7 ++
.../master/base_master_job_runner.py | 45 ++++----
.../model_scheduler/master_job_runner.py | 3 +-
.../scheduler_core/message_center.py | 47 +++++---
.../scheduler_base_job_runner.py | 12 ++-
.../scheduler/scheduler_core/status_center.py | 22 ++--
.../scheduler/slave/base_slave_job_runner.py | 16 ++-
python/fedml/core/mlops/mlops_device_perfs.py | 100 +++++++++++++-----
python/fedml/core/mlops/mlops_job_perfs.py | 10 +-
.../core/mlops/mlops_runtime_log_daemon.py | 9 +-
10 files changed, 190 insertions(+), 81 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index e5ba1b304b..bf07838e56 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -467,6 +467,13 @@ def get_multiprocessing_context():
return multiprocessing.get_context("fork")
+def get_process(target=None, args=None):
+ if platform.system() == "Windows":
+ return multiprocessing.Process(target=target, args=args)
+ else:
+ return multiprocessing.get_context("fork").Process(target=target, args=args)
+
+
def set_env_version(version):
set_env_kv("FEDML_ENV_VERSION", version)
load_env()
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner.py b/python/fedml/computing/scheduler/master/base_master_job_runner.py
index 1383c9058c..fe2d426af4 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner.py
@@ -19,7 +19,6 @@
from fedml.utils.debugging import debug
from ..scheduler_core.status_center import JobStatus
from ..scheduler_core.compute_cache_manager import ComputeCacheManager
-from multiprocessing import Process, Queue
from ..scheduler_core.general_constants import GeneralConstants
from ..scheduler_core.scheduler_base_job_runner import FedMLSchedulerBaseJobRunner, RunnerError, RunnerCompletedError
from abc import ABC, abstractmethod
@@ -43,13 +42,13 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
is_master_runner=True
)
- self.run_edge_id_status_queue = Queue()
- self.run_metrics_queue = Queue()
- self.run_events_queue = Queue()
- self.run_artifacts_queue = Queue()
- self.run_logs_queue = Queue()
- self.run_edge_device_info_queue = Queue()
- self.run_edge_device_info_global_queue = Queue()
+ self.run_edge_id_status_queue = multiprocessing.Manager().Queue(-1)
+ self.run_metrics_queue = multiprocessing.Manager().Queue(-1)
+ self.run_events_queue = multiprocessing.Manager().Queue(-1)
+ self.run_artifacts_queue = multiprocessing.Manager().Queue(-1)
+ self.run_logs_queue = multiprocessing.Manager().Queue(-1)
+ self.run_edge_device_info_queue = multiprocessing.Manager().Queue(-1)
+ self.run_edge_device_info_global_queue = multiprocessing.Manager().Queue(-1)
self.run_extend_queue_list = None
self.async_check_timeout = 0
self.enable_async_cluster = False
@@ -425,14 +424,24 @@ def start_runner_process(
server_runner.edge_id_status_queue = self.run_edge_id_status_queue
server_runner.edge_device_info_queue = self.run_edge_device_info_queue
self.run_extend_queue_list = self._generate_extend_queue_list()
- self.run_process = fedml.get_multiprocessing_context().Process(
- target=server_runner.run if not is_server_job else server_runner.run_server_job, args=(
- self.run_process_event, self.run_process_completed_event, self.run_edge_id_status_queue,
- self.run_edge_device_info_queue, self.run_metrics_queue, self.run_events_queue,
- self.run_artifacts_queue, self.run_logs_queue, self.run_edge_device_info_global_queue,
- self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue
+ if platform.system() == "Windows":
+ self.run_process = multiprocessing.Process(
+ target=server_runner.run if not is_server_job else server_runner.run_server_job, args=(
+ self.run_process_event, self.run_process_completed_event, self.run_edge_id_status_queue,
+ self.run_edge_device_info_queue, self.run_metrics_queue, self.run_events_queue,
+ self.run_artifacts_queue, self.run_logs_queue, self.run_edge_device_info_global_queue,
+ self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue
+ )
+ )
+ else:
+ self.run_process = fedml.get_process(
+ target=server_runner.run if not is_server_job else server_runner.run_server_job, args=(
+ self.run_process_event, self.run_process_completed_event, self.run_edge_id_status_queue,
+ self.run_edge_device_info_queue, self.run_metrics_queue, self.run_events_queue,
+ self.run_artifacts_queue, self.run_logs_queue, self.run_edge_device_info_global_queue,
+ self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue
+ )
)
- )
self.run_process.start()
ServerConstants.save_run_process(run_id, self.run_process.pid)
return self.run_process
@@ -444,7 +453,7 @@ def put_run_edge_device_info_to_queue(self, run_id, edge_id, device_info):
if int(edge_id) in edge_ids or str(edge_id) in edge_ids:
run_id_str = str(run_id)
if self.run_edge_device_info_queue is None:
- self.run_edge_device_info_queue = Queue()
+ self.run_edge_device_info_queue = multiprocessing.Manager().Queue(-1)
self.run_edge_device_info_queue.put(device_info)
def should_continue_run_job(self, run_id):
@@ -572,7 +581,7 @@ def callback_run_logs(self, topic, payload):
run_id = str(topic).split('/')[-1]
run_id_str = str(run_id)
if self.run_logs_queue is None:
- self.run_logs_queue = Queue()
+ self.run_logs_queue = multiprocessing.Manager().Queue(-1)
self.run_logs_queue.put(payload)
def callback_run_metrics(self, topic, payload):
@@ -580,7 +589,7 @@ def callback_run_metrics(self, topic, payload):
run_id = str(topic).split('/')[-1]
run_id_str = str(run_id)
if self.run_metrics_queue is None:
- self.run_metrics_queue = Queue()
+ self.run_metrics_queue = multiprocessing.Manager().Queue(-1)
self.run_metrics_queue.put(payload)
# def send_training_request_to_edges(self, active_edge_info_dict):
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index a10bd2c559..e504ded561 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -6,7 +6,6 @@
import queue
import traceback
from abc import ABC
-from multiprocessing import Queue
import fedml
from fedml.core.mlops import MLOpsRuntimeLog, MLOpsConfigs
@@ -50,7 +49,7 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
self.replica_controller = None
self.deployed_replica_payload = None
self.slave_deployment_results_map = dict()
- self.deployment_result_queue = Queue()
+ self.deployment_result_queue = multiprocessing.Manager().Queue(-1)
self.is_fresh_endpoint = True
# Override
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_center.py b/python/fedml/computing/scheduler/scheduler_core/message_center.py
index 74510c6939..2bfa3b514f 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_center.py
@@ -1,12 +1,12 @@
import json
import logging
import os
+import platform
import threading
import time
import traceback
import uuid
import multiprocessing
-from multiprocessing import Process, Queue
import queue
from os.path import expanduser
@@ -133,17 +133,25 @@ def get_sender_message_queue(self):
return self.sender_message_queue
def start_sender(self, message_center_name=None):
- self.sender_message_queue = Queue()
+ self.sender_message_queue = multiprocessing.Manager().Queue(-1)
self.message_event = multiprocessing.Event()
self.message_event.clear()
message_center = FedMLMessageCenter(agent_config=self.sender_agent_config,
sender_message_queue=self.sender_message_queue)
- self.message_center_process = fedml.get_multiprocessing_context().Process(
- target=message_center.run_sender, args=(
- self.message_event, self.sender_message_queue,
- message_center_name
+ if platform.system() == "Windows":
+ self.message_center_process = multiprocessing.Process(
+ target=message_center.run_sender, args=(
+ self.message_event, self.sender_message_queue,
+ message_center_name
+ )
+ )
+ else:
+ self.message_center_process = fedml.get_process(
+ target=message_center.run_sender, args=(
+ self.message_event, self.sender_message_queue,
+ message_center_name
+ )
)
- )
self.message_center_process.start()
def stop(self):
@@ -296,7 +304,7 @@ def get_listener_message_queue(self):
return self.listener_message_queue
def setup_listener_message_queue(self):
- self.listener_message_queue = Queue()
+ self.listener_message_queue = multiprocessing.Manager().Queue(-1)
def start_listener(
self, sender_message_queue=None, listener_message_queue=None,
@@ -307,7 +315,7 @@ def start_listener(
if listener_message_queue is None:
if self.listener_message_queue is None:
- self.listener_message_queue = Queue()
+ self.listener_message_queue = multiprocessing.Manager().Queue(-1)
else:
self.listener_message_queue = listener_message_queue
self.listener_message_event = multiprocessing.Event()
@@ -315,13 +323,22 @@ def start_listener(
self.listener_agent_config = agent_config
message_runner = self.get_message_runner()
message_runner.listener_agent_config = agent_config
- self.listener_message_center_process = fedml.get_multiprocessing_context().Process(
- target=message_runner.run_listener_dispatcher, args=(
- self.listener_message_event, self.listener_message_queue,
- self.listener_handler_funcs, sender_message_queue,
- message_center_name, extra_queues
+ if platform.system() == "Windows":
+ self.listener_message_center_process = multiprocessing.Process(
+ target=message_runner.run_listener_dispatcher, args=(
+ self.listener_message_event, self.listener_message_queue,
+ self.listener_handler_funcs, sender_message_queue,
+ message_center_name, extra_queues
+ )
+ )
+ else:
+ self.listener_message_center_process = fedml.get_process(
+ target=message_runner.run_listener_dispatcher, args=(
+ self.listener_message_event, self.listener_message_queue,
+ self.listener_handler_funcs, sender_message_queue,
+ message_center_name, extra_queues
+ )
)
- )
self.listener_message_center_process.start()
def check_listener_message_stop_event(self):
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 44562c65f0..80de5c5b18 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -210,9 +210,15 @@ def retrieve_and_unzip_package(self, package_name, package_url):
# Open a process to download the package so that we can avoid the request is blocked and check the timeout.
from multiprocessing import Process
completed_event = multiprocessing.Event()
- info_queue = multiprocessing.Queue()
- download_process = fedml.get_multiprocessing_context().Process(target=self.download_package_proc,
- args=(package_url, local_package_file, completed_event, info_queue))
+ info_queue = multiprocessing.Manager().Queue(-1)
+ if platform.system() == "Windows":
+ download_process = multiprocessing.Process(
+ target=self.download_package_proc,
+ args=(package_url, local_package_file, completed_event, info_queue))
+ else:
+ download_process = fedml.get_process(
+ target=self.download_package_proc,
+ args=(package_url, local_package_file, completed_event, info_queue))
download_process.start()
allowed_block_download_time = 60
download_finished = False
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index 0897f94c19..7e0cf1f98f 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -1,9 +1,9 @@
import logging
+import platform
import time
from enum import Enum, unique
import multiprocessing
-from multiprocessing import Process, Queue
import queue
import fedml
@@ -108,7 +108,7 @@ def get_status_runner(self):
def start_status_center(self, sender_message_center_queue=None,
listener_message_center_queue=None, is_slave_agent=False):
- self.status_queue = Queue()
+ self.status_queue = multiprocessing.Manager().Queue(-1)
self.status_event = multiprocessing.Event()
self.status_event.clear()
self.status_sender_message_center_queue = sender_message_center_queue
@@ -116,12 +116,20 @@ def start_status_center(self, sender_message_center_queue=None,
self.status_runner = self.get_status_runner()
target_func = self.status_runner.run_status_dispatcher if not is_slave_agent else \
self.status_runner.run_status_dispatcher_in_slave
- self.status_center_process = fedml.get_multiprocessing_context().Process(
- target=target_func, args=(
- self.status_event, self.status_queue, self.status_sender_message_center_queue,
- self.status_listener_message_center_queue
+ if platform.system() == "Windows":
+ self.status_center_process = multiprocessing.Process(
+ target=target_func, args=(
+ self.status_event, self.status_queue, self.status_sender_message_center_queue,
+ self.status_listener_message_center_queue
+ )
+ )
+ else:
+ self.status_center_process = fedml.get_process(
+ target=target_func, args=(
+ self.status_event, self.status_queue, self.status_sender_message_center_queue,
+ self.status_listener_message_center_queue
+ )
)
- )
self.status_center_process.start()
diff --git a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
index a495a17dd0..8876fc7e39 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
@@ -260,9 +260,17 @@ def start_runner_process(
client_runner.server_id = request_json.get("server_id", "0")
self.run_extend_queue_list = self._generate_extend_queue_list()
logging.info("start the runner process.")
- self.run_process = fedml.get_multiprocessing_context().Process(target=client_runner.run, args=(
- self.run_process_event, self.run_process_completed_event, self.run_extend_queue_list,
- sender_message_queue, listener_message_queue, status_center_queue
- ))
+
+ if platform.system() == "Windows":
+ self.run_process = multiprocessing.Process(
+ target=client_runner.run, args=(
+ self.run_process_event, self.run_process_completed_event, self.run_extend_queue_list,
+ sender_message_queue, listener_message_queue, status_center_queue
+ ))
+ else:
+ self.run_process = fedml.get_process(target=client_runner.run, args=(
+ self.run_process_event, self.run_process_completed_event, self.run_extend_queue_list,
+ sender_message_queue, listener_message_queue, status_center_queue
+ ))
self.run_process.start()
return self.run_process
diff --git a/python/fedml/core/mlops/mlops_device_perfs.py b/python/fedml/core/mlops/mlops_device_perfs.py
index 13fca2e354..d0f1f3898f 100644
--- a/python/fedml/core/mlops/mlops_device_perfs.py
+++ b/python/fedml/core/mlops/mlops_device_perfs.py
@@ -1,6 +1,7 @@
import json
import logging
import os
+import platform
import time
import traceback
import uuid
@@ -77,54 +78,99 @@ def setup_realtime_stats_process(self, sys_args):
self.device_realtime_stats_event.clear()
perf_stats.device_realtime_stats_event = self.device_realtime_stats_event
- self.device_realtime_stats_process = fedml.get_multiprocessing_context().Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_DEVICE_INFO_REPORTER, self.is_client))
+ if platform.system() == "Windows":
+ self.device_realtime_stats_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_DEVICE_INFO_REPORTER, self.is_client))
+ else:
+ self.device_realtime_stats_process = fedml.get_process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_DEVICE_INFO_REPORTER, self.is_client))
self.device_realtime_stats_process.start()
if self.enable_job_total_monitor:
- self.job_total_monitor_process = fedml.get_multiprocessing_context().Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_DEVICE_JOB_TOTAL_MONITOR, self.is_client))
+ if platform.system() == "Windows":
+ self.job_total_monitor_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_DEVICE_JOB_TOTAL_MONITOR, self.is_client))
+ else:
+ self.job_total_monitor_process = fedml.get_process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_DEVICE_JOB_TOTAL_MONITOR, self.is_client))
self.job_total_monitor_process.start()
else:
if self.is_client:
- self.monitor_endpoint_master_process = fedml.get_multiprocessing_context().Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_MASTER))
+ if platform.system() == "Windows":
+ self.monitor_endpoint_master_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_MASTER))
+ else:
+ self.monitor_endpoint_master_process = fedml.get_process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_MASTER))
self.monitor_endpoint_master_process.start()
- self.monitor_run_slave_process = fedml.get_multiprocessing_context().Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_RUN_SLAVE))
+ if platform.system() == "Windows":
+ self.monitor_run_slave_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_RUN_SLAVE))
+ else:
+ self.monitor_run_slave_process = fedml.get_process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_RUN_SLAVE))
self.monitor_run_slave_process.start()
- self.monitor_endpoint_logs_process = fedml.get_multiprocessing_context().Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_LOGS))
+ if platform.system() == "Windows":
+ self.monitor_endpoint_logs_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_LOGS))
+ else:
+ self.monitor_endpoint_logs_process = fedml.get_process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_LOGS))
self.monitor_endpoint_logs_process.start()
# Register auto-scaler process
- self.monitor_auto_scaler_process = fedml.get_multiprocessing_context().Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_AUTO_SCALER))
+ if platform.system() == "Windows":
+ self.monitor_auto_scaler_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_AUTO_SCALER))
+ else:
+ self.monitor_auto_scaler_process = fedml.get_process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_AUTO_SCALER))
self.monitor_auto_scaler_process.start()
# Register replica number report channel
- self.monitor_replica_num_process = fedml.get_multiprocessing_context().Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_NUM))
+ if platform.system() == "Windows":
+ self.monitor_replica_num_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_NUM))
+ else:
+ self.monitor_replica_num_process = fedml.get_process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_NUM))
self.monitor_replica_num_process.start()
# Register replica performance report channel
- self.monitor_replica_perf_process = fedml.get_multiprocessing_context().Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_PERF))
+ if platform.system() == "Windows":
+ self.monitor_replica_perf_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_PERF))
+ else:
+ self.monitor_replica_perf_process = fedml.get_process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_PERF))
self.monitor_replica_perf_process.start()
else:
- self.monitor_run_master_process = fedml.get_multiprocessing_context().Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_RUN_MASTER))
+ if platform.system() == "Windows":
+ self.monitor_run_master_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_RUN_MASTER))
+ else:
+ self.monitor_run_master_process = fedml.get_process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_RUN_MASTER))
self.monitor_run_master_process.start()
def report_device_realtime_stats_entry(self, sys_event, role, is_client=False):
diff --git a/python/fedml/core/mlops/mlops_job_perfs.py b/python/fedml/core/mlops/mlops_job_perfs.py
index b1f674a5c9..e834ed4a0c 100644
--- a/python/fedml/core/mlops/mlops_job_perfs.py
+++ b/python/fedml/core/mlops/mlops_job_perfs.py
@@ -1,6 +1,7 @@
import json
import logging
import os
+import platform
import time
import traceback
import uuid
@@ -139,9 +140,12 @@ def setup_job_stats_process(self, sys_args):
self.job_stats_event.clear()
perf_stats.job_stats_event = self.job_stats_event
perf_stats.job_process_id_map = self.job_process_id_map
-
- self.job_stats_process = fedml.get_multiprocessing_context().Process(
- target=perf_stats.report_job_stats_entry, args=(self.job_stats_event,))
+ if platform.system() == "Windows":
+ self.job_stats_process = multiprocessing.Process(
+ target=perf_stats.report_job_stats_entry, args=(self.job_stats_event,))
+ else:
+ self.job_stats_process = fedml.get_process(
+ target=perf_stats.report_job_stats_entry, args=(self.job_stats_event,))
self.job_stats_process.start()
def report_job_stats(self, sys_args):
diff --git a/python/fedml/core/mlops/mlops_runtime_log_daemon.py b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
index 88d49b7a22..f72d88cfea 100644
--- a/python/fedml/core/mlops/mlops_runtime_log_daemon.py
+++ b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
@@ -1,6 +1,7 @@
import argparse
import logging
import os
+import platform
import shutil
import threading
import time
@@ -431,8 +432,12 @@ def start_log_processor(self, log_run_id, log_device_id, log_source=None, log_fi
self.log_process_event_map[event_map_id] = multiprocessing.Event()
self.log_process_event_map[event_map_id].clear()
log_processor.log_process_event = self.log_process_event_map[event_map_id]
- log_child_process = fedml.get_multiprocessing_context().Process(
- target=log_processor.log_process, args=(self.log_process_event_map[event_map_id],))
+ if platform.system() == "Windows":
+ log_child_process = multiprocessing.Process(
+ target=log_processor.log_process, args=(self.log_process_event_map[event_map_id],))
+ else:
+ log_child_process = fedml.get_process(
+ target=log_processor.log_process, args=(self.log_process_event_map[event_map_id],))
# process = threading.Thread(target=log_processor.log_process)
# process.start()
if log_child_process is not None:
From 23d88fc7dcfdbe9f9b319a08b72b39f0c58fdbb3 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 11 Jun 2024 11:48:20 -0700
Subject: [PATCH 141/282] [Deploy] Remove unnecessary logic.
---
.../device_model_deployment.py | 232 +-----------------
.../model_scheduler/master_job_runner.py | 1 -
.../model_scheduler/worker_job_runner.py | 16 +-
3 files changed, 10 insertions(+), 239 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 1876373d25..5d3ba9873d 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -1,12 +1,13 @@
+import fedml
+
import logging
import os
-import pickle
-import platform
import shutil
import time
import traceback
import yaml
import datetime
+import docker
import requests
import torch
@@ -15,27 +16,18 @@
import collections.abc
-import fedml
from fedml.computing.scheduler.comm_utils import sys_utils, security_utils
-from fedml.computing.scheduler.comm_utils.container_utils import ContainerUtils
from fedml.computing.scheduler.comm_utils.hardware_utils import HardwareUtil
from fedml.computing.scheduler.comm_utils.job_utils import JobRunnerUtils
-
-for type_name in collections.abc.__all__:
- setattr(collections, type_name, getattr(collections.abc, type_name))
-
from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants
-import io
-
-import docker
-from ..scheduler_core.compute_cache_manager import ComputeCacheManager
+from fedml.computing.scheduler.model_scheduler.device_model_cache import FedMLModelCache
from ..scheduler_core.compute_utils import ComputeUtils
from ..comm_utils.container_utils import ContainerUtils
-
from .device_http_inference_protocol import FedMLHttpInference
-from fedml.computing.scheduler.model_scheduler.device_model_cache import FedMLModelCache
+for type_name in collections.abc.__all__:
+ setattr(collections, type_name, getattr(collections.abc, type_name))
no_real_gpu_allocation = None
@@ -432,8 +424,6 @@ def should_exit_logs(end_point_id, model_id, cmd_type, model_name, inference_eng
if cmd_type == ClientConstants.CMD_TYPE_RUN_DEFAULT_SERVER:
# TODO: Exited Quickly if the container is Exited or Removed
# If the container has exited, return True, means we should exit the logs
- # container_name = "{}".format(ClientConstants.FEDML_DEFAULT_SERVER_CONTAINER_NAME_PREFIX) + "__" + \
- # security_utils.get_content_hash(model_name)
try:
inference_output_url, model_version, model_metadata, model_config = \
get_model_info(model_name, inference_engine, inference_port, infer_host,
@@ -554,8 +544,6 @@ def log_deployment_result(end_point_id, model_id, cmd_container_name, cmd_type,
def is_client_inference_container_ready(infer_url_host, inference_http_port, inference_model_name, local_infer_url,
inference_type="default", model_version="", request_input_example=None):
- # logging.info(f"Inference type: {inference_type}, infer_url_host {infer_url_host}, \
- # inference_http_port: {inference_http_port}, local_infer_url {local_infer_url}")
if inference_type == "default":
default_client_container_ready_url = "http://{}:{}/ready".format("0.0.0.0", inference_http_port)
@@ -631,211 +619,5 @@ def run_http_inference_with_curl_request(inference_url, inference_input_list, in
inference_type=inference_type, engine_type=engine_type, timeout=timeout)
-def convert_model_to_onnx(
- torch_model, output_path: str, dummy_input_list, input_size: int, input_is_tensor=True
-) -> None:
- from collections import OrderedDict
- import torch
- from torch.onnx import TrainingMode
-
- torch.onnx.export(torch_model, # model being run
- dummy_input_list if input_is_tensor else tuple(dummy_input_list),
- # model input (or a tuple for multiple inputs)
- f=output_path, # where to save the model (can be a file or file-like object)
- export_params=True, # store the trained parameter weights inside the model file
- opset_version=11, # the ONNX version to export the model to
- do_constant_folding=False, # whether to execute constant folding for optimization
- input_names=["input1", "input2"],
- # the model's input names
- output_names=['output'], # the model's output names
- training=TrainingMode.EVAL,
- verbose=True,
- dynamic_axes={"input1": {0: "batch_size"},
- "input2": {0: "batch_size"},
- "output": {0: "batch_size"}}
- )
-
-
-def test_start_triton_server(model_serving_dir):
- sudo_prefix = "sudo "
- sys_name = platform.system()
- if sys_name == "Darwin":
- sudo_prefix = ""
- gpu_attach_cmd = ""
-
- triton_server_container_name = "{}".format(ClientConstants.FEDML_TRITON_SERVER_CONTAINER_NAME_PREFIX)
- triton_server_cmd = "{}docker stop {}; {}docker rm {}; {}docker run --name {} {} -p{}:8000 " \
- "-p{}:8001 -p{}:8002 " \
- "--shm-size {} " \
- "-v {}:/models {} " \
- "bash -c \"pip install transformers && tritonserver --strict-model-config=false " \
- "--model-control-mode=poll --repository-poll-secs={} " \
- "--model-repository=/models\" ".format(sudo_prefix, triton_server_container_name,
- sudo_prefix, triton_server_container_name,
- sudo_prefix, triton_server_container_name,
- gpu_attach_cmd,
- ClientConstants.INFERENCE_HTTP_PORT,
- ClientConstants.INFERENCE_GRPC_PORT,
- 8002,
- "4096m",
- model_serving_dir,
- ClientConstants.INFERENCE_SERVER_IMAGE,
- ClientConstants.FEDML_MODEL_SERVING_REPO_SCAN_INTERVAL)
- logging.info("Run triton inference server: {}".format(triton_server_cmd))
- triton_server_process = ClientConstants.exec_console_with_script(triton_server_cmd,
- should_capture_stdout=False,
- should_capture_stderr=False,
- no_sys_out_err=True)
-
-
-def test_convert_pytorch_model_to_onnx(model_net_file, model_bin_file, model_name, model_in_params):
- torch_model = torch.jit.load(model_net_file)
- with open(model_bin_file, 'rb') as model_pkl_file:
- model_state_dict = pickle.load(model_pkl_file)
- torch_model.load_state_dict(model_state_dict)
- torch_model.eval()
-
- input_size = model_in_params["input_size"]
- input_types = model_in_params["input_types"]
-
- dummy_input_list = []
- for index, input_i in enumerate(input_size):
- if input_types[index] == "int":
- this_input = torch.tensor(torch.randint(0, 1, input_i))
- else:
- this_input = torch.tensor(torch.zeros(input_i))
- dummy_input_list.append(this_input)
-
- onnx_model_dir = os.path.join(ClientConstants.get_model_cache_dir(),
- ClientConstants.FEDML_CONVERTED_MODEL_DIR_NAME,
- model_name, ClientConstants.INFERENCE_MODEL_VERSION)
- if not os.path.exists(onnx_model_dir):
- os.makedirs(onnx_model_dir, exist_ok=True)
- onnx_model_path = os.path.join(onnx_model_dir, "model.onnx")
-
- convert_model_to_onnx(torch_model, onnx_model_path, dummy_input_list, input_size,
- input_is_tensor=True)
-
- model_serving_dir = os.path.join(ClientConstants.get_model_cache_dir(),
- ClientConstants.FEDML_CONVERTED_MODEL_DIR_NAME)
- return model_serving_dir
-
-
-def start_gpu_model_load_process():
- from multiprocessing import Process
- import time
- process = Process(target=load_gpu_model_to_cpu_device)
- process.start()
- while True:
- time.sleep(1)
-
-
-def load_gpu_model_to_cpu_device():
- import pickle
- import io
- import torch
-
- class CPU_Unpickler(pickle.Unpickler):
- def find_class(self, module, name):
- if module == 'torch.storage' and name == '_load_from_bytes':
- return lambda b: torch.load(io.BytesIO(b), map_location='cpu')
- else:
- return super().find_class(module, name)
-
- model_file = "/home/fedml/.fedml/fedml-client/fedml/models/theta_rec_auc_81_single_label/theta_rec_auc_81_single_label"
- with open(model_file, "rb") as model_pkl_file:
- if not torch.cuda.is_available():
- model = CPU_Unpickler(model_pkl_file).load()
- if model is None:
- print("Failed to load gpu model to cpu device")
- else:
- print("Succeeded to load gpu model to cpu device")
-
-
if __name__ == "__main__":
- start_gpu_model_load_process()
-
- model_serving_dir = test_convert_pytorch_model_to_onnx("./sample-open-training-model-net",
- "./sample-open-training-model",
- "rec-model",
- {"input_size": [[1, 24], [1, 2]],
- "input_types": ["int", "float"]})
-
- test_start_triton_server(model_serving_dir)
-
- # input_data = {"model_version": "v0-Sun Feb 05 12:17:16 GMT 2023",
- # "model_name": "model_414_45_open-model-test_v0-Sun-Feb-05-12-17-16-GMT-2023",
- # # "data": "file:///Users/alexliang/fedml_data/mnist-image.png",
- # "data": "https://raw.githubusercontent.com/niyazed/triton-mnist-example/master/images/sample_image.png",
- # "end_point_id": 414, "model_id": 45, "token": "a09a18a14c4c4d89a8d5f9515704c073"}
- #
- # data_list = list()
- # data_list.append(input_data["data"])
- # run_http_inference_with_lib_http_api_with_image_data(input_data["model_name"],
- # 5001, 1, data_list, "")
- #
- #
- # class LogisticRegression(torch.nn.Module):
- # def __init__(self, input_dim, output_dim):
- # super(LogisticRegression, self).__init__()
- # self.linear = torch.nn.Linear(input_dim, output_dim)
- #
- # def forward(self, x):
- # outputs = torch.sigmoid(self.linear(x))
- # return outputs
- #
- #
- # model = LogisticRegression(28 * 28, 10)
- # checkpoint = {'model': model}
- # model_net_file = "/Users/alexliang/fedml-client/fedml/models/open-model-test/model-net.pt"
- # torch.save(checkpoint, model_net_file)
- #
- # with open("/Users/alexliang/fedml-client/fedml/models/open-model-test/open-model-test", 'rb') as model_pkl_file:
- # model_params = pickle.load(model_pkl_file)
- # # torch.save(model_params, "/Users/alexliang/fedml-client/fedml/models/open-model-test/a.pt")
- # # model = torch.load("/Users/alexliang/fedml-client/fedml/models/open-model-test/a.pt")
- # loaded_checkpoint = torch.load(model_net_file)
- # loaded_model = loaded_checkpoint["model"]
- # loaded_model.load_state_dict(model_params)
- # for parameter in loaded_model.parameters():
- # parameter.requires_grad = False
- # loaded_model.eval()
- # input_names = {"x": 0}
- # convert_model_to_onnx(loaded_model, "/Users/alexliang/fedml-client/fedml/models/open-model-test/a.onnx",
- # input_names, 28 * 28)
-
- # parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
- # parser.add_argument("--cf", "-c", help="config file")
- # parser.add_argument("--role", "-r", type=str, default="client", help="role")
- # parser.add_argument("--model_storage_local_path", "-url", type=str, default="/home/ubuntu",
- # help="model storage local path")
- # parser.add_argument("--inference_model_name", "-n", type=str, default="fedml-model",
- # help="inference model name")
- # parser.add_argument("--inference_engine", "-engine", type=str, default="ONNX", help="inference engine")
- # parser.add_argument("--inference_http_port", "-http", type=int, default=8000, help="inference http port")
- # parser.add_argument("--inference_grpc_port", "-gprc", type=int, default=8001, help="inference grpc port")
- # parser.add_argument("--inference_metric_port", "-metric", type=int, default=8002, help="inference metric port")
- # parser.add_argument("--inference_use_gpu", "-gpu", type=str, default="gpu", help="inference use gpu")
- # parser.add_argument("--inference_memory_size", "-mem", type=str, default="256m", help="inference memory size")
- # parser.add_argument("--inference_convertor_image", "-convertor", type=str,
- # default=ClientConstants.INFERENCE_CONVERTOR_IMAGE, help="inference convertor image")
- # parser.add_argument("--inference_server_image", "-server", type=str,
- # default=ClientConstants.INFERENCE_SERVER_IMAGE, help="inference server image")
- # args = parser.parse_args()
- # args.user = args.user
- #
- # pip_source_dir = os.path.dirname(__file__)
- # __running_model_name, __inference_output_url, __model_version, __model_metadata, __model_config = \
- # start_deployment(
- # args.model_storage_local_path,
- # args.inference_model_name,
- # args.inference_engine,
- # args.inference_http_port,
- # args.inference_grpc_port,
- # args.inference_metric_port,
- # args.inference_use_gpu,
- # args.inference_memory_size,
- # args.inference_convertor_image,
- # args.inference_server_image)
- # print("Model deployment results, running model name: {}, url: {}, model metadata: {}, model config: {}".format(
- # __running_model_name, __inference_output_url, __model_metadata, __model_config))
+ pass
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index b9b9b4c356..ef2c01c49d 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -453,7 +453,6 @@ def process_deployment_result_message(self, topic=None, payload=None):
time.sleep(3)
self.trigger_completed_event()
-
def cleanup_runner_process(self, run_id):
ServerConstants.cleanup_run_process(run_id, not_kill_subprocess=True)
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index ef65e37904..8100707386 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -294,9 +294,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
json.dumps(result_payload), replica_no=rank + 1)
logging.info(f"Deploy replica {rank + 1} / {prev_rank + 1 + op_num} successfully.")
- time.sleep(5)
- time.sleep(1)
self.status_reporter.run_id = self.run_id
self.status_reporter.report_client_id_status(
self.edge_id, ClientConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED,
@@ -348,7 +346,8 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
# TODO (Raphael) check if this will allow another job to seize the gpu during high concurrency:
try:
- JobRunnerUtils.get_instance().release_partial_job_gpu(run_id, self.edge_id, replica_occupied_gpu_ids)
+ JobRunnerUtils.get_instance().release_partial_job_gpu(
+ run_id, self.edge_id, replica_occupied_gpu_ids)
except Exception as e:
if op == "rollback":
pass
@@ -395,7 +394,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
JobRunnerUtils.get_instance().release_partial_job_gpu(
run_id, self.edge_id, replica_occupied_gpu_ids)
- result_payload = self.send_deployment_results(
+ self.send_deployment_results(
end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
model_id, model_name, inference_output_url, inference_model_version, inference_port,
inference_engine, model_metadata, model_config)
@@ -496,15 +495,6 @@ def send_deployment_results(self, end_point_name, device_id, model_status,
self.message_center.send_message_json(deployment_results_topic, json.dumps(deployment_results_payload))
return deployment_results_payload
- def send_deployment_status(self, end_point_name, device_id,
- model_id, model_name, model_version,
- model_inference_url, model_status,
- inference_port=ClientConstants.MODEL_INFERENCE_DEFAULT_PORT,
- replica_no=1, # start from 1
- ):
- # Deprecated
- pass
-
def reset_devices_status(self, edge_id, status):
self.status_reporter.run_id = self.run_id
self.status_reporter.edge_id = edge_id
From e0ad9b5bef5bcea1eaefe3458a3d6b49aa399d46 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 11 Jun 2024 12:15:22 -0700
Subject: [PATCH 142/282] [Deploy] Remove unnecessary logic; Rename readiness
check function; Forbidden user level control of host post.
---
.../device_model_deployment.py | 150 +++++-------------
1 file changed, 40 insertions(+), 110 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 5d3ba9873d..edd2ebea9a 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -68,6 +68,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
num_gpus = gpu_per_replica
gpu_ids, gpu_attach_cmd = None, ""
+ # Concatenate the model name
running_model_name = ClientConstants.get_running_model_name(
end_point_name, inference_model_name, model_version, end_point_id, model_id, edge_id=edge_id)
@@ -77,6 +78,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
config = yaml.safe_load(file)
# Resource related
+ inference_type = "default"
use_gpu = config.get('use_gpu', True)
num_gpus_frm_yml = config.get('num_gpus', None)
if not use_gpu:
@@ -85,9 +87,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
if num_gpus_frm_yml is not None:
num_gpus = int(num_gpus_frm_yml)
usr_indicated_wait_time = config.get('deploy_timeout', 900)
- usr_indicated_worker_port = config.get('worker_port', "")
- if usr_indicated_worker_port == "":
- usr_indicated_worker_port = os.environ.get("FEDML_WORKER_PORT", "")
+ usr_indicated_retry_cnt = max(int(usr_indicated_wait_time) // 10, 1)
shm_size = config.get('shm_size', None)
storage_opt = config.get('storage_opt', None)
tmpfs = config.get('tmpfs', None)
@@ -96,17 +96,6 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
cpus = int(cpus)
memory = config.get('memory', None)
- if usr_indicated_worker_port == "":
- usr_indicated_worker_port = None
- else:
- usr_indicated_worker_port = int(usr_indicated_worker_port)
-
- worker_port_env = os.environ.get("FEDML_WORKER_PORT", "")
- worker_port_from_config = config.get('worker_port', "")
- logging.info(f"usr_indicated_worker_port {usr_indicated_worker_port}, worker port env {worker_port_env}, "
- f"worker port from config {worker_port_from_config}")
-
- usr_indicated_retry_cnt = max(int(usr_indicated_wait_time) // 10, 1)
inference_image_name = config.get('inference_image_name',
ClientConstants.INFERENCE_SERVER_CUSTOME_IMAGE)
image_pull_policy = config.get('image_pull_policy', SchedulerConstants.IMAGE_PULL_POLICY_IF_NOT_PRESENT)
@@ -144,6 +133,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
# If using customized image, then bootstrap + job will be the entry point
enable_custom_image = config.get("enable_custom_image", False)
+ # inference_type = "custom"
customized_image_entry_cmd = \
"/bin/bash /home/fedml/models_serving/fedml-deploy-bootstrap-entry-auto-gen.sh"
@@ -151,18 +141,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
docker_registry_user_password = config.get("docker_registry_user_password", "")
docker_registry = config.get("docker_registry", "")
- port_inside_container = int(config.get("port_inside_container", 2345))
- use_triton = config.get("use_triton", False)
- if use_triton:
- inference_type = "triton"
- else:
- inference_type = "default"
-
- # Config check
- if src_code_dir == "":
- raise Exception("Please indicate source_code_dir in the fedml_model_config.yaml")
- if relative_entry == "":
- logging.warning("You missed main_entry in the fedml_model_config.yaml")
+ port_inside_container = int(config.get("port", 2345))
# Request the GPU ids for the deployment
if num_gpus > 0:
@@ -175,22 +154,10 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
end_point_id, end_point_name, inference_model_name, edge_id, replica_rank+1, gpu_ids)
logging.info("GPU ids allocated: {}".format(gpu_ids))
+ # Create the model serving dir if not exists
model_serving_dir = ClientConstants.get_model_serving_dir()
if not os.path.exists(model_serving_dir):
os.makedirs(model_serving_dir, exist_ok=True)
- converted_model_path = os.path.join(model_storage_local_path, ClientConstants.FEDML_CONVERTED_MODEL_DIR_NAME)
- if os.path.exists(converted_model_path):
- model_file_list = os.listdir(converted_model_path)
- for model_file in model_file_list:
- src_model_file = os.path.join(converted_model_path, model_file)
- dst_model_file = os.path.join(model_serving_dir, model_file)
- if os.path.isdir(src_model_file):
- if not os.path.exists(dst_model_file):
- shutil.copytree(src_model_file, dst_model_file, copy_function=shutil.copy,
- ignore_dangling_symlinks=True)
- else:
- if not os.path.exists(dst_model_file):
- shutil.copyfile(src_model_file, dst_model_file)
if inference_engine != ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT:
raise Exception(f"inference engine {inference_engine} is not supported")
@@ -228,13 +195,12 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
logging.info(f"Start pulling the inference image {inference_image_name}... with policy {image_pull_policy}")
ContainerUtils.get_instance().pull_image_with_policy(image_pull_policy, inference_image_name)
- volumns = []
+ volumes = []
binds = {}
environment = {}
# data_cache_dir mounting
- assert type(data_cache_dir_input) == dict or type(data_cache_dir_input) == str
- if type(data_cache_dir_input) == str:
+ if isinstance(data_cache_dir_input, str):
# In this case, we mount to the same folder, if it has ~, we replace it with /home/fedml
src_data_cache_dir, dst_data_cache_dir = "", ""
if data_cache_dir_input != "":
@@ -253,28 +219,30 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
if type(src_data_cache_dir) == str and src_data_cache_dir != "":
logging.info("Start copying the data cache to the container...")
if os.path.exists(src_data_cache_dir):
- volumns.append(src_data_cache_dir)
+ volumes.append(src_data_cache_dir)
binds[src_data_cache_dir] = {
"bind": dst_data_cache_dir,
"mode": "rw"
}
environment["DATA_CACHE_FOLDER"] = dst_data_cache_dir
- else:
+ elif isinstance(data_cache_dir_input, dict):
for k, v in data_cache_dir_input.items():
if os.path.exists(k):
- volumns.append(v)
+ volumes.append(v)
binds[k] = {
"bind": v,
"mode": "rw"
}
else:
logging.warning(f"{k} does not exist, skip mounting it to the container")
- logging.info(f"Data cache mount: {volumns}, {binds}")
+ logging.info(f"Data cache mount: {volumes}, {binds}")
+ else:
+ logging.warning("data_cache_dir_input is not a string or a dictionary, skip mounting it to the container")
# Default mounting
if not enable_custom_image or (enable_custom_image and relative_entry != ""):
logging.info("Start copying the source code to the container...")
- volumns.append(src_code_dir)
+ volumes.append(src_code_dir)
binds[src_code_dir] = {
"bind": dst_model_serving_dir,
"mode": "rw"
@@ -284,7 +252,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
host_config_dict = {
"binds": binds,
"port_bindings": {
- port_inside_container: usr_indicated_worker_port
+ port_inside_container: None
},
"shm_size": shm_size,
"storage_opt": storage_opt,
@@ -312,7 +280,6 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
if not enable_custom_image:
# For some image, the default user is root. Unified to fedml.
environment["HOME"] = "/home/fedml"
-
environment["BOOTSTRAP_DIR"] = dst_bootstrap_dir
environment["FEDML_CURRENT_RUN_ID"] = end_point_id
environment["FEDML_CURRENT_EDGE_ID"] = edge_id
@@ -326,12 +293,13 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
for key in extra_envs:
environment[key] = extra_envs[key]
+ # Create the container
try:
host_config = client.api.create_host_config(**host_config_dict)
new_container = client.api.create_container(
image=inference_image_name,
name=default_server_container_name,
- volumes=volumns,
+ volumes=volumes,
ports=[port_inside_container], # port open inside the container
environment=environment,
host_config=host_config,
@@ -349,22 +317,18 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
while True:
cnt += 1
try:
- if usr_indicated_worker_port is not None:
- inference_http_port = usr_indicated_worker_port
- break
- else:
- # Find the random port
- port_info = client.api.port(new_container.get("Id"), port_inside_container)
- inference_http_port = port_info[0]["HostPort"]
- logging.info("inference_http_port: {}".format(inference_http_port))
- break
+ # Find the random port
+ port_info = client.api.port(new_container.get("Id"), port_inside_container)
+ inference_http_port = port_info[0]["HostPort"]
+ logging.info("host port allocated: {}".format(inference_http_port))
+ break
except:
if cnt >= 5:
raise Exception("Failed to get the port allocation")
time.sleep(3)
# Logging the info from the container when starting
- log_deployment_result(end_point_id, model_id, default_server_container_name,
+ log_deployment_output(end_point_id, model_id, default_server_container_name,
ClientConstants.CMD_TYPE_RUN_DEFAULT_SERVER,
inference_model_name, inference_engine, inference_http_port, inference_type,
retry_interval=10, deploy_attempt_threshold=usr_indicated_retry_cnt,
@@ -373,9 +337,8 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
# Return the running model name and the inference output url
inference_output_url, running_model_version, ret_model_metadata, ret_model_config = \
- get_model_info(inference_model_name, inference_engine, inference_http_port,
- infer_host, False, inference_type, request_input_example=request_input_example,
- enable_custom_image=enable_custom_image)
+ check_container_readiness(inference_http_port=inference_http_port, infer_host=infer_host,
+ request_input_example=request_input_example)
if inference_output_url == "":
return running_model_name, "", None, None, None
@@ -426,9 +389,8 @@ def should_exit_logs(end_point_id, model_id, cmd_type, model_name, inference_eng
# If the container has exited, return True, means we should exit the logs
try:
inference_output_url, model_version, model_metadata, model_config = \
- get_model_info(model_name, inference_engine, inference_port, infer_host,
- inference_type=inference_type, request_input_example=request_input_example,
- enable_custom_image=enable_custom_image)
+ check_container_readiness(inference_http_port=inference_port, infer_host=infer_host,
+ request_input_example=request_input_example)
if inference_output_url != "":
logging.info("Log test for deploying model successfully, inference url: {}, "
"model metadata: {}, model config: {}".
@@ -443,7 +405,7 @@ def should_exit_logs(end_point_id, model_id, cmd_type, model_name, inference_eng
return False
-def log_deployment_result(end_point_id, model_id, cmd_container_name, cmd_type,
+def log_deployment_output(end_point_id, model_id, cmd_container_name, cmd_type,
inference_model_name, inference_engine,
inference_http_port, inference_type="default",
retry_interval=10, deploy_attempt_threshold=10,
@@ -542,10 +504,10 @@ def log_deployment_result(end_point_id, model_id, cmd_container_name, cmd_type,
time.sleep(retry_interval)
-def is_client_inference_container_ready(infer_url_host, inference_http_port, inference_model_name, local_infer_url,
- inference_type="default", model_version="", request_input_example=None):
+def is_client_inference_container_ready(infer_url_host, inference_http_port, readiness_check_type="default",
+ readiness_check_cmd=None, request_input_example=None):
- if inference_type == "default":
+ if readiness_check_type == "default":
default_client_container_ready_url = "http://{}:{}/ready".format("0.0.0.0", inference_http_port)
response = None
try:
@@ -555,7 +517,7 @@ def is_client_inference_container_ready(infer_url_host, inference_http_port, inf
if not response or response.status_code != 200:
return "", "", {}, {}
- # Report the deployed model info
+ # Construct the model metadata (input and output)
model_metadata = {}
if request_input_example is not None and len(request_input_example) > 0:
model_metadata["inputs"] = request_input_example
@@ -563,51 +525,19 @@ def is_client_inference_container_ready(infer_url_host, inference_http_port, inf
model_metadata["inputs"] = {"text": "What is a good cure for hiccups?"}
model_metadata["outputs"] = []
model_metadata["type"] = "default"
+
return "http://{}:{}/predict".format(infer_url_host, inference_http_port), None, model_metadata, None
else:
- triton_server_url = "{}:{}".format(infer_url_host, inference_http_port)
- if model_version == "" or model_version is None:
- model_version = ClientConstants.INFERENCE_MODEL_VERSION
- logging.info(
- f"triton_server_url: {triton_server_url} model_version: {model_version} model_name: {inference_model_name}")
- triton_client = http_client.InferenceServerClient(url=triton_server_url, verbose=False)
- if not triton_client.is_model_ready(
- model_name=inference_model_name, model_version=model_version
- ):
- return "", model_version, {}, {}
- logging.info(f"Model {inference_model_name} is ready, start to get model metadata...")
- model_metadata = triton_client.get_model_metadata(model_name=inference_model_name, model_version=model_version)
- model_config = triton_client.get_model_config(model_name=inference_model_name, model_version=model_version)
- version_list = model_metadata.get("versions", None)
- if version_list is not None and len(version_list) > 0:
- model_version = version_list[0]
- else:
- model_version = ClientConstants.INFERENCE_MODEL_VERSION
-
- inference_output_url = "http://{}:{}/{}/models/{}/versions/{}/infer".format(infer_url_host,
- inference_http_port,
- ClientConstants.INFERENCE_INFERENCE_SERVER_VERSION,
- inference_model_name,
- model_version)
-
- return inference_output_url, model_version, model_metadata, model_config
-
-
-def get_model_info(model_name, inference_engine, inference_http_port, infer_host="127.0.0.1", is_hg_model=False,
- inference_type="default", request_input_example=None, enable_custom_image=False):
- if model_name is None:
+ # TODO(Raphael): Support arbitrary readiness check command
+ logging.error(f"Unknown readiness check type: {readiness_check_type}")
return "", "", {}, {}
- local_infer_url = "{}:{}".format(infer_host, inference_http_port)
-
- if is_hg_model:
- inference_model_name = "{}_{}_inference".format(model_name, str(inference_engine))
- else:
- inference_model_name = model_name
+def check_container_readiness(inference_http_port, infer_host="127.0.0.1", request_input_example=None,
+ readiness_check_type="default", readiness_check_cmd=None):
response_from_client_container = is_client_inference_container_ready(
- infer_host, inference_http_port, inference_model_name, local_infer_url,
- inference_type, model_version="", request_input_example=request_input_example)
+ infer_host, inference_http_port, readiness_check_type, readiness_check_cmd,
+ request_input_example=request_input_example)
return response_from_client_container
From 64e8c779c61edfecf7ca8e638b6b54ff31d7983b Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 11 Jun 2024 16:29:37 -0700
Subject: [PATCH 143/282] [Deploy] Nit
---
.../computing/scheduler/model_scheduler/device_model_cards.py | 1 -
1 file changed, 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cards.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cards.py
index 8feb757a63..c2f11a2917 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cards.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cards.py
@@ -14,7 +14,6 @@
from fedml.core.common.singleton import Singleton
from fedml.computing.scheduler.model_scheduler.modelops_configs import ModelOpsConfigs
-from fedml.computing.scheduler.model_scheduler.device_model_deployment import get_model_info
from fedml.computing.scheduler.model_scheduler.device_server_constants import ServerConstants
from fedml.computing.scheduler.model_scheduler.device_model_object import FedMLModelList, FedMLEndpointDetail
from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants
From 9194f8424f77008b49a48908ee72f19fe59ba23d Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 11 Jun 2024 16:42:46 -0700
Subject: [PATCH 144/282] [Deploy] Hide unnecessary log.
---
.../scheduler/model_scheduler/device_model_cache.py | 8 ++++----
.../scheduler/model_scheduler/device_model_inference.py | 6 +++---
2 files changed, 7 insertions(+), 7 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
index 6c90944277..c941c42102 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
@@ -369,7 +369,7 @@ def get_idle_device(self,
if "model_status" in result_payload and result_payload["model_status"] == "DEPLOYED":
idle_device_list.append({"device_id": device_id, "end_point_id": end_point_id})
- logging.info(f"{len(idle_device_list)} devices this model has on it: {idle_device_list}")
+ logging.debug(f"{len(idle_device_list)} devices this model has on it: {idle_device_list}")
if len(idle_device_list) <= 0:
return None, None
@@ -398,7 +398,7 @@ def get_idle_device(self,
logging.info("Inference Device selection Failed:")
logging.info(e)
- logging.info(f"Using Round Robin, the device index is {selected_device_index}")
+ logging.debug(f"Using Round Robin, the device index is {selected_device_index}")
idle_device_dict = idle_device_list[selected_device_index]
# Note that within the same endpoint_id, there could be one device with multiple same models
@@ -411,7 +411,7 @@ def get_idle_device(self,
# Find deployment result from the target idle device.
try:
for result_item in result_list:
- logging.info("enter the for loop")
+ logging.debug("enter the for loop")
device_id, _, result_payload = self.get_result_item_info(result_item)
found_end_point_id = result_payload["end_point_id"]
found_end_point_name = result_payload["end_point_name"]
@@ -425,7 +425,7 @@ def get_idle_device(self,
if same_model_device_rank > 0:
same_model_device_rank -= 1
continue
- logging.info(f"The chosen device is {device_id}")
+ logging.debug(f"The chosen device is {device_id}")
return result_payload, device_id
except Exception as e:
logging.info(str(e))
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 3aeec67932..ba13006245 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -230,7 +230,7 @@ async def _predict(
model_metrics.set_start_time(start_time)
# Send inference request to idle device
- logging.info("inference url {}.".format(inference_output_url))
+ logging.debug("inference url {}.".format(inference_output_url))
if inference_output_url != "":
input_list = input_json.get("inputs", input_json)
stream_flag = input_json.get("stream", False)
@@ -329,7 +329,7 @@ def found_idle_inference_device(end_point_id, end_point_name, in_model_name, in_
res = (idle_device, end_point_id, model_id, model_name, model_version, inference_host, inference_output_url,
connectivity_type)
- logging.info(f"found idle device with metrics: {res}")
+ logging.debug(f"found idle device with metrics: {res}")
return res
@@ -352,7 +352,7 @@ async def send_inference_request(idle_device, end_point_id, inference_url, input
output_list,
inference_type=inference_type,
timeout=request_timeout_sec)
- logging.info(f"Use http inference. return {response_ok}")
+ logging.debug(f"Use http inference. return {response_ok}")
return inference_response
elif connectivity_type == ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP_PROXY:
logging.warning("Use http proxy inference.")
From 243be07831c7ffd078203f402efae339ed0b58a3 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 11 Jun 2024 17:50:30 -0700
Subject: [PATCH 145/282] [Deploy] Read port info from env.
---
.../scheduler/model_scheduler/device_client_constants.py | 1 +
.../scheduler/model_scheduler/device_server_constants.py | 1 +
.../scheduler/model_scheduler/master_job_runner.py | 6 +++---
.../scheduler/model_scheduler/worker_protocol_manager.py | 8 +++++++-
4 files changed, 12 insertions(+), 4 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
index 2c06189d2e..f1e7dea91f 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
@@ -74,6 +74,7 @@ class ClientConstants(object):
K8S_DEPLOYMENT_SLAVE_MOUNT_HOME_DIR = "/home/fedml/fedml-client"
LOCAL_CLIENT_API_PORT = 22030
+ ENV_CLIENT_PROXY_PORT_KEY = "FEDML_WORKER_INFERENCE_PROXY_PORT"
INFERENCE_HTTP_PORT = 8000
INFERENCE_GRPC_PORT = 8001
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
index 243c197b2f..a868d03b41 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
@@ -103,6 +103,7 @@ class ServerConstants(object):
AUTO_DETECT_PUBLIC_IP = "auto_detect_public_ip"
MODEL_INFERENCE_DEFAULT_PORT = 2203
+ ENV_MASTER_INFERENCE_PORT_KEY = "FEDML_MASTER_INFERENCE_GATEWAY_PORT"
MODEL_CACHE_KEY_EXPIRE_TIME = 1 * 10
INFERENCE_REQUEST_TIMEOUT_KEY = "request_timeout_sec"
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index ef2c01c49d..d7565d7647 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -460,9 +460,9 @@ def cleanup_runner_process(self, run_id):
def start_device_inference_gateway(inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT):
# start unified inference server
python_program = get_python_program()
- master_port = os.getenv("FEDML_MASTER_PORT", None)
- if master_port is not None:
- inference_port = int(master_port)
+ master_port_frm_env = os.getenv(ServerConstants.MODEL_INFERENCE_DEFAULT_PORT, None)
+ if master_port_frm_env is not None:
+ inference_port = int(master_port_frm_env)
if not ServerConstants.is_running_on_k8s():
logging.info(f"start the model inference gateway...")
inference_gw_cmd = "fedml.computing.scheduler.model_scheduler.device_model_inference:api"
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
index f9bc70452d..ee59f87441 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
@@ -79,6 +79,12 @@ def _init_extra_items(self):
client_api_cmd = "fedml.computing.scheduler.model_scheduler.device_client_api:api"
client_api_pids = RunProcessUtils.get_pid_from_cmd_line(client_api_cmd)
+
+ worker_proxy_port = ClientConstants.LOCAL_CLIENT_API_PORT
+ worker_proxy_port_frm_env = os.environ.get(ClientConstants.ENV_CLIENT_PROXY_PORT_KEY, None)
+ if worker_proxy_port_frm_env is not None:
+ worker_proxy_port = int(worker_proxy_port_frm_env)
+
if client_api_pids is None or len(client_api_pids) <= 0:
# Start local API services
cur_dir = os.path.dirname(__file__)
@@ -88,7 +94,7 @@ def _init_extra_items(self):
"{} -m uvicorn {} --host 0.0.0.0 --port {} --reload --reload-delay 3 --reload-dir {} "
"--log-level critical".format(
python_program, client_api_cmd,
- ClientConstants.LOCAL_CLIENT_API_PORT, fedml_base_dir
+ worker_proxy_port, fedml_base_dir
),
should_capture_stdout=False,
should_capture_stderr=False
From 0b2349983b5fabe50666d0f845cc2e94433fdf91 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 12 Jun 2024 23:08:06 +0800
Subject: [PATCH 146/282] [CoreEngine] make the status center work in the
united agents.
---
.../scheduler/comm_utils/sys_utils.py | 21 +++++++++++++
.../scheduler/master/base_master_agent.py | 3 ++
.../model_scheduler/master_job_runner.py | 1 +
.../scheduler_base_protocol_manager.py | 2 +-
.../scheduler/slave/united_agents.py | 31 +++++++++++--------
5 files changed, 44 insertions(+), 14 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/sys_utils.py b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
index aaa37bc4db..065482c23b 100644
--- a/python/fedml/computing/scheduler/comm_utils/sys_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
@@ -114,6 +114,8 @@ def get_sys_runner_info():
except:
pass
+ enable_simulation_gpu, simulation_gpu_count = get_simulation_gpu_env()
+
if enable_simulation_gpu:
gpu_count = simulation_gpu_count
gpu_total_mem = "80G"
@@ -128,9 +130,26 @@ def get_sys_runner_info():
gpu_count, gpu_vendor, cpu_count, gpu_device_name
+def get_simulation_gpu_env():
+ _enable_simulation_gpu = enable_simulation_gpu
+ _simulation_gpu_count = simulation_gpu_count
+
+ env_enable_simulation_gpu = os.getenv("FEDML_ENABLE_SIMULATION_GPU", None)
+ if env_enable_simulation_gpu is not None:
+ _enable_simulation_gpu = True if env_enable_simulation_gpu == "1" or env_enable_simulation_gpu == 1 else False
+
+ env_simulation_gpu_count = os.getenv("FEDML_SIMULATION_GPU_COUNT", None)
+ if env_simulation_gpu_count is not None:
+ _simulation_gpu_count = int(env_simulation_gpu_count)
+
+ return _enable_simulation_gpu, _simulation_gpu_count
+
+
# GPU list: [GPU(ID, uuid, load, memoryTotal, memoryUsed, memoryFree, driver,
# gpu_name, serial, display_mode, display_active, temperature)]
def get_gpu_list():
+ enable_simulation_gpu, simulation_gpu_count = get_simulation_gpu_env()
+
if enable_simulation_gpu:
ret_gpu_list = [
{'ID': 0, 'uuid': 'GPU-dab987f0-be09-294a-96d6-f9afeef49877', 'load': 1.0,
@@ -184,6 +203,8 @@ def get_gpu_list():
def get_available_gpu_id_list(limit=1) -> List[int]:
+ enable_simulation_gpu, simulation_gpu_count = get_simulation_gpu_env()
+
if enable_simulation_gpu:
available_gpu_ids = [0, 1, 2, 3, 4, 5, 6, 7]
if simulation_gpu_count > 8:
diff --git a/python/fedml/computing/scheduler/master/base_master_agent.py b/python/fedml/computing/scheduler/master/base_master_agent.py
index b27ed9547a..d6c6420cf3 100755
--- a/python/fedml/computing/scheduler/master/base_master_agent.py
+++ b/python/fedml/computing/scheduler/master/base_master_agent.py
@@ -123,6 +123,9 @@ def _init_logs(self, agent_args, edge_id):
in_args.server_agent_id = edge_id
MLOpsRuntimeLog.get_instance(in_args).init_logs()
+ def get_protocol_manager(self):
+ return self.protocol_mgr
+
@abstractmethod
def _get_log_file_dir(self):
pass
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index e504ded561..61cce1b39c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -1,6 +1,7 @@
import copy
import json
import logging
+import multiprocessing
import os
import time
import queue
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
index bd843eecc2..f80508a509 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
@@ -288,7 +288,7 @@ def get_get_protocol_communication_manager(self):
def get_protocol_sender_message_queue(self):
return self.message_center.get_sender_message_queue()
- def get_get_protocol_status_center_queue(self):
+ def get_protocol_status_center_queue(self):
return self.get_status_queue()
def send_agent_active_msg(self, edge_id):
diff --git a/python/fedml/computing/scheduler/slave/united_agents.py b/python/fedml/computing/scheduler/slave/united_agents.py
index e365de8860..7135925ec8 100755
--- a/python/fedml/computing/scheduler/slave/united_agents.py
+++ b/python/fedml/computing/scheduler/slave/united_agents.py
@@ -25,42 +25,47 @@ def login(self, userid, api_key=None, device_id=None,
deploy_master_agent = FedMLDeployMasterAgent()
# Login with the launch slave role
- launch_slave_agent.login(
+ login_result = launch_slave_agent.login(
api_key, api_key=api_key, device_id=device_id,
os_name=os_name, role=role
)
- # Get the communication manager, sender message queue and status center queue
+ # Get the communication manager, sender message queue
shared_communication_mgr = launch_slave_agent.get_protocol_manager().get_get_protocol_communication_manager()
shared_sender_message_queue = launch_slave_agent.get_protocol_manager().get_protocol_sender_message_queue()
- shared_status_center_queue = launch_slave_agent.get_protocol_manager().get_get_protocol_status_center_queue()
- # Login with the launch master role based on the shared communication manager
+ # Login with the launch master role based on
+ # the shared communication manager, sender message center
launch_master_agent.login(
- api_key, api_key=api_key, device_id=device_id,
+ api_key, api_key=api_key, device_id=login_result.device_id,
os_name=os_name, runner_cmd=runner_cmd,
role=FedMLAccountManager.ROLE_GPU_MASTER_SERVER,
communication_manager=shared_communication_mgr,
- sender_message_queue=shared_sender_message_queue,
- status_center_queue=shared_status_center_queue
+ sender_message_queue=shared_sender_message_queue
)
- # Login with the deployment master role based on the shared communication manager
+ # Get the status center queue
+ shared_slave_status_center_queue = launch_slave_agent.get_protocol_manager().get_protocol_status_center_queue()
+ shared_master_status_center_queue = launch_master_agent.get_protocol_manager().get_protocol_status_center_queue()
+
+ # Login with the deployment master role based on
+ # the shared communication manager, sender message center, status center
deploy_master_agent.login(
- userid, api_key=api_key, device_id=device_id,
+ userid, api_key=api_key, device_id=login_result.device_id,
os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM,
communication_manager=shared_communication_mgr,
sender_message_queue=shared_sender_message_queue,
- status_center_queue=shared_status_center_queue
+ status_center_queue=shared_master_status_center_queue
)
- # Login with the deployment slave role based on the shared communication manager
+ # Login with the deployment slave role based on
+ # the shared communication manager, sender message center, status center
deploy_slave_agent.login(
- userid, api_key=api_key, device_id=device_id,
+ userid, api_key=api_key, device_id=login_result.device_id,
os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM,
communication_manager=shared_communication_mgr,
sender_message_queue=shared_sender_message_queue,
- status_center_queue=shared_status_center_queue
+ status_center_queue=shared_slave_status_center_queue
)
# Start the slave agent to connect to servers and loop forever.
From 3a034717f7ebc43ff035e73cc49c13ea1c2e7d79 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Wed, 12 Jun 2024 12:04:36 -0700
Subject: [PATCH 147/282] [Deploy] Nit.
---
.../computing/scheduler/model_scheduler/master_job_runner.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index d7565d7647..67a3e8bb82 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -460,7 +460,7 @@ def cleanup_runner_process(self, run_id):
def start_device_inference_gateway(inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT):
# start unified inference server
python_program = get_python_program()
- master_port_frm_env = os.getenv(ServerConstants.MODEL_INFERENCE_DEFAULT_PORT, None)
+ master_port_frm_env = os.getenv(ServerConstants.ENV_MASTER_INFERENCE_PORT_KEY, None)
if master_port_frm_env is not None:
inference_port = int(master_port_frm_env)
if not ServerConstants.is_running_on_k8s():
From f0dd29e04fa600339c0efb74526694d4dee2842e Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Wed, 12 Jun 2024 12:12:25 -0700
Subject: [PATCH 148/282] [Deploy] Nit.
---
.../scheduler/model_scheduler/master_job_runner.py | 8 ++------
1 file changed, 2 insertions(+), 6 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 67a3e8bb82..5f82a6c046 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -369,12 +369,8 @@ def process_deployment_result_message(self, topic=None, payload=None):
"""
When all the devices have finished the add / delete / update operation
"""
- # Generate one unified inference api
- # Note that here we use the gateway port instead of the inference port that is used by the slave device
- model_config_parameters = request_json["parameters"]
- inference_port = model_config_parameters.get("server_internal_port",
- ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
- inference_port_external = model_config_parameters.get("server_external_port", inference_port)
+ inference_port_external = os.environ.get(ServerConstants.ENV_MASTER_INFERENCE_PORT_KEY,
+ ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
ip = GeneralConstants.get_ip_address(request_json)
if ip.startswith("http://") or ip.startswith("https://"):
From 21a8a4c9d97e712f029f0e7abe39e0b5e56954a2 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Wed, 12 Jun 2024 12:18:58 -0700
Subject: [PATCH 149/282] [Deploy] Change few more places relate to gateway
port.
---
.../scheduler/model_scheduler/master_job_runner.py | 13 ++++++-------
1 file changed, 6 insertions(+), 7 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 5f82a6c046..50d902b933 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -144,7 +144,8 @@ def run_impl(
# No device is added, updated or removed
logging.info("No device is added, updated or removed. No action needed for reconciliation.")
ip = GeneralConstants.get_ip_address(self.request_json)
- master_port = os.getenv("FEDML_MASTER_PORT", None)
+ master_port = os.environ.get(ServerConstants.ENV_MASTER_INFERENCE_PORT_KEY,
+ ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
if master_port is not None:
inference_port = int(master_port)
model_inference_port = inference_port
@@ -299,9 +300,8 @@ def process_deployment_result_message(self, topic=None, payload=None):
else:
# This is the last worker that failed, so we should continue to "ABORTED" status
model_config_parameters = self.request_json["parameters"]
- inference_port = model_config_parameters.get("server_internal_port",
- ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
- inference_port_external = model_config_parameters.get("server_external_port", inference_port)
+ inference_port_external = os.environ.get(ServerConstants.ENV_MASTER_INFERENCE_PORT_KEY,
+ ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
ip = GeneralConstants.get_ip_address(self.request_json)
if ip.startswith("http://") or ip.startswith("https://"):
model_inference_url = "{}/inference/{}".format(ip, end_point_id)
@@ -753,9 +753,8 @@ def parse_model_run_params(running_json):
model_version = model_config["model_version"]
model_config_parameters = running_json.get("parameters", {})
- inference_port = model_config_parameters.get("server_internal_port", # Internal port is for the gateway
- ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
- inference_port_external = model_config_parameters.get("server_external_port", inference_port)
+ inference_port = int(os.environ.get(ServerConstants.ENV_MASTER_INFERENCE_PORT_KEY,
+ ServerConstants.MODEL_INFERENCE_DEFAULT_PORT))
return run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
From e7e974d24f510a47e2ee5e9df1a6161665fffa1e Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Wed, 12 Jun 2024 15:29:53 -0700
Subject: [PATCH 150/282] [Deploy] Write port info into env file.
---
python/fedml/api/__init__.py | 12 ++++++----
python/fedml/api/modules/device.py | 13 ++++++++---
python/fedml/cli/modules/login.py | 22 +++++++++++++++++--
.../device_client_constants.py | 8 +++++++
.../device_server_constants.py | 9 ++++++++
.../model_scheduler/master_job_runner.py | 22 +++++++------------
.../worker_protocol_manager.py | 3 +--
7 files changed, 64 insertions(+), 25 deletions(-)
diff --git a/python/fedml/api/__init__.py b/python/fedml/api/__init__.py
index 3e75b987d6..f753e4255b 100755
--- a/python/fedml/api/__init__.py
+++ b/python/fedml/api/__init__.py
@@ -24,6 +24,8 @@
from fedml.computing.scheduler.scheduler_entry.cluster_manager import FedMLClusterModelList
from fedml.computing.scheduler.scheduler_entry.run_manager import FedMLRunStartedModel, FedMLGpuDevices, \
FedMLRunModelList, FeatureEntryPoint
+from fedml.computing.scheduler.model_scheduler.device_server_constants import ServerConstants
+from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants
def fedml_login(api_key: str = None):
@@ -209,16 +211,18 @@ def fedml_build(platform, type, source_folder, entry_point, config_folder, dest_
return build.build(platform, type, source_folder, entry_point, config_folder, dest_folder, ignore)
-def login(api_key, computing, server, supplier):
- device_bind(api_key, computing, server, supplier)
+def login(api_key, computing, server, supplier,
+ master_inference_gateway_port: int = ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
+ worker_inference_proxy_port: int = ClientConstants.LOCAL_CLIENT_API_PORT):
+ device_bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port)
def logout(computing, server):
device_unbind(computing, server)
-def device_bind(api_key, computing, server, supplier):
- device.bind(api_key, computing, server, supplier)
+def device_bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port):
+ device.bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port)
def device_unbind(computing, server):
diff --git a/python/fedml/api/modules/device.py b/python/fedml/api/modules/device.py
index a853d538d0..14591147a6 100644
--- a/python/fedml/api/modules/device.py
+++ b/python/fedml/api/modules/device.py
@@ -10,14 +10,18 @@
from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
from fedml.computing.scheduler.master.server_constants import ServerConstants
+from fedml.computing.scheduler.model_scheduler.device_server_constants import ServerConstants as DeviceServerConstants
from fedml.computing.scheduler.master.server_login import logout as server_logout
from fedml.computing.scheduler.slave.client_constants import ClientConstants
+from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants as DeviceClientConstants
from fedml.computing.scheduler.slave.client_login import logout as client_logout
from fedml.computing.scheduler.scheduler_entry.resource_manager import FedMLResourceManager
def bind(
- api_key, computing, server, supplier
+ api_key, computing, server, supplier,
+ master_inference_gateway_port=DeviceServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
+ worker_inference_proxy_port=DeviceClientConstants.LOCAL_CLIENT_API_PORT
):
userid = api_key
runner_cmd = "{}"
@@ -43,13 +47,13 @@ def bind(
_bind(
userid, computing, server,
api_key, role, runner_cmd, device_id, os_name,
- docker)
+ docker, master_inference_gateway_port, worker_inference_proxy_port)
def _bind(
userid, computing, server,
api_key, role, runner_cmd, device_id, os_name,
- docker):
+ docker, master_inference_gateway_port, worker_inference_proxy_port):
fedml.load_env()
if os.getenv(ModuleConstants.ENV_FEDML_INFER_HOST) is None:
fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_HOST, SchedulerConstants.REDIS_INFER_HOST)
@@ -60,6 +64,9 @@ def _bind(
if os.getenv(ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD) is None:
fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_REDIS_PASSWORD, SchedulerConstants.REDIS_PASSWORD)
+ fedml.set_env_kv(DeviceServerConstants.ENV_MASTER_INFERENCE_PORT_KEY, str(master_inference_gateway_port))
+ fedml.set_env_kv(DeviceClientConstants.ENV_CLIENT_PROXY_PORT_KEY, str(worker_inference_proxy_port))
+
url = fedml._get_backend_service()
platform_name = platform.system()
docker_config_text = None
diff --git a/python/fedml/cli/modules/login.py b/python/fedml/cli/modules/login.py
index f2e4d76322..f3c982f456 100644
--- a/python/fedml/cli/modules/login.py
+++ b/python/fedml/cli/modules/login.py
@@ -4,6 +4,8 @@
import fedml.api
from fedml.api.modules.utils import authenticate
+from fedml.computing.scheduler.model_scheduler.device_server_constants import ServerConstants
+from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants
@click.command("login", help="Login the FedML® Nexus AI Platform")
@@ -51,9 +53,25 @@
default=80,
help="The port for local on-premise Nexus AI Platform.",
)
+@click.option(
+ "--master_inference_gateway_port",
+ "-mgp",
+ type=int,
+ default=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
+ help="The port for master inference gateway.",
+)
+@click.option(
+ "--worker_inference_proxy_port",
+ "-wpp",
+ type=int,
+ default=ClientConstants.LOCAL_CLIENT_API_PORT,
+ help="The port for worker inference proxy.",
+)
def fedml_login(
api_key, version, compute_node, server, provider, deploy_worker_num,
- local_on_premise_platform, local_on_premise_platform_port):
+ local_on_premise_platform, local_on_premise_platform_port,
+ master_inference_gateway_port, worker_inference_proxy_port
+):
fedml.set_env_version(version)
fedml.set_local_on_premise_platform_host(local_on_premise_platform)
fedml.set_local_on_premise_platform_port(local_on_premise_platform_port)
@@ -66,4 +84,4 @@ def fedml_login(
print(f"Maybe you are using account id to login, we will try to login with account {api_key}.")
pass
os.environ["FEDML_MODEL_WORKER_NUM"] = str(deploy_worker_num)
- fedml.api.login(api_key, compute_node, server, provider)
+ fedml.api.login(api_key, compute_node, server, provider, master_inference_gateway_port, worker_inference_proxy_port)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
index f1e7dea91f..fdcbdf0a34 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
@@ -458,6 +458,14 @@ def get_public_ip():
logging.info("Failed to get public ip: {}".format(e))
return ip
+ @staticmethod
+ def get_inference_worker_proxy_port() -> int:
+ # Use dotenv to load the environment variables
+ fedml.load_env()
+ worker_proxy_port = int(os.getenv(ClientConstants.ENV_CLIENT_PROXY_PORT_KEY,
+ default=ClientConstants.LOCAL_CLIENT_API_PORT))
+ return worker_proxy_port
+
@staticmethod
def check_process_is_running(process_id):
for proc in psutil.process_iter():
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
index a868d03b41..a5048c26a6 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
@@ -349,6 +349,15 @@ def get_runner_infos():
logging.error(f"Failed to parse runner info: {e}")
return runner_info
+ @staticmethod
+ def get_inference_master_gateway_port():
+ # Use dotenv to load the environment variables
+ fedml.load_env()
+ master_inference_port = int(os.getenv(ServerConstants.ENV_MASTER_INFERENCE_PORT_KEY,
+ default=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT))
+ return master_inference_port
+
+
@staticmethod
def save_runner_infos(unique_device_id, edge_id, run_id=None):
local_pkg_data_dir = ServerConstants.get_data_dir()
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 50d902b933..eff26684b7 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -115,7 +115,7 @@ def run_impl(
message_center=self.message_center)
# start unified inference gateway process if not started
- FedMLDeployMasterJobRunner.start_device_inference_gateway(inference_port=inference_port)
+ FedMLDeployMasterJobRunner.start_device_inference_gateway()
# start inference monitor process
FedMLDeployMasterJobRunner.stop_device_inference_monitor(
@@ -144,8 +144,7 @@ def run_impl(
# No device is added, updated or removed
logging.info("No device is added, updated or removed. No action needed for reconciliation.")
ip = GeneralConstants.get_ip_address(self.request_json)
- master_port = os.environ.get(ServerConstants.ENV_MASTER_INFERENCE_PORT_KEY,
- ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
+ master_port = ServerConstants.get_inference_master_gateway_port()
if master_port is not None:
inference_port = int(master_port)
model_inference_port = inference_port
@@ -300,8 +299,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
else:
# This is the last worker that failed, so we should continue to "ABORTED" status
model_config_parameters = self.request_json["parameters"]
- inference_port_external = os.environ.get(ServerConstants.ENV_MASTER_INFERENCE_PORT_KEY,
- ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
+ inference_port_external = ServerConstants.get_inference_master_gateway_port()
ip = GeneralConstants.get_ip_address(self.request_json)
if ip.startswith("http://") or ip.startswith("https://"):
model_inference_url = "{}/inference/{}".format(ip, end_point_id)
@@ -369,8 +367,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
"""
When all the devices have finished the add / delete / update operation
"""
- inference_port_external = os.environ.get(ServerConstants.ENV_MASTER_INFERENCE_PORT_KEY,
- ServerConstants.MODEL_INFERENCE_DEFAULT_PORT)
+ inference_port_external = ServerConstants.get_inference_master_gateway_port()
ip = GeneralConstants.get_ip_address(request_json)
if ip.startswith("http://") or ip.startswith("https://"):
@@ -453,12 +450,10 @@ def cleanup_runner_process(self, run_id):
ServerConstants.cleanup_run_process(run_id, not_kill_subprocess=True)
@staticmethod
- def start_device_inference_gateway(inference_port=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT):
+ def start_device_inference_gateway():
# start unified inference server
python_program = get_python_program()
- master_port_frm_env = os.getenv(ServerConstants.ENV_MASTER_INFERENCE_PORT_KEY, None)
- if master_port_frm_env is not None:
- inference_port = int(master_port_frm_env)
+ inference_port = ServerConstants.get_inference_master_gateway_port()
if not ServerConstants.is_running_on_k8s():
logging.info(f"start the model inference gateway...")
inference_gw_cmd = "fedml.computing.scheduler.model_scheduler.device_model_inference:api"
@@ -539,7 +534,7 @@ def recover_inference_and_monitor():
if not is_activated:
continue
- FedMLDeployMasterJobRunner.start_device_inference_gateway(inference_port=inference_port)
+ FedMLDeployMasterJobRunner.start_device_inference_gateway()
FedMLDeployMasterJobRunner.stop_device_inference_monitor(
run_id, end_point_name, model_id, model_name, model_version)
@@ -753,8 +748,7 @@ def parse_model_run_params(running_json):
model_version = model_config["model_version"]
model_config_parameters = running_json.get("parameters", {})
- inference_port = int(os.environ.get(ServerConstants.ENV_MASTER_INFERENCE_PORT_KEY,
- ServerConstants.MODEL_INFERENCE_DEFAULT_PORT))
+ inference_port = ServerConstants.get_inference_master_gateway_port()
return run_id, end_point_name, token, user_id, user_name, device_ids, device_objs, model_config, model_name, \
model_id, model_storage_url, scale_min, scale_max, inference_engine, model_is_from_open, \
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
index ee59f87441..cdfa43c33b 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
@@ -80,8 +80,7 @@ def _init_extra_items(self):
client_api_cmd = "fedml.computing.scheduler.model_scheduler.device_client_api:api"
client_api_pids = RunProcessUtils.get_pid_from_cmd_line(client_api_cmd)
- worker_proxy_port = ClientConstants.LOCAL_CLIENT_API_PORT
- worker_proxy_port_frm_env = os.environ.get(ClientConstants.ENV_CLIENT_PROXY_PORT_KEY, None)
+ worker_proxy_port = ClientConstants.get_inference_worker_proxy_port()
if worker_proxy_port_frm_env is not None:
worker_proxy_port = int(worker_proxy_port_frm_env)
From 9c8ce99c41e6bf8df8f38fe88a6f782141d3a19e Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Wed, 12 Jun 2024 15:33:47 -0700
Subject: [PATCH 151/282] [Deploy] Nit.
---
.../scheduler/model_scheduler/worker_protocol_manager.py | 2 --
1 file changed, 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
index cdfa43c33b..b1d0bebc47 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
@@ -81,8 +81,6 @@ def _init_extra_items(self):
client_api_pids = RunProcessUtils.get_pid_from_cmd_line(client_api_cmd)
worker_proxy_port = ClientConstants.get_inference_worker_proxy_port()
- if worker_proxy_port_frm_env is not None:
- worker_proxy_port = int(worker_proxy_port_frm_env)
if client_api_pids is None or len(client_api_pids) <= 0:
# Start local API services
From 505103f9f05106712de4ea7078441526ee33b9f7 Mon Sep 17 00:00:00 2001
From: bhargav191098 <bhargav3514@gmail.com>
Date: Thu, 13 Jun 2024 17:08:51 -0700
Subject: [PATCH 152/282] removing zip from upload
---
python/fedml/api/modules/storage.py | 45 ++++++++++++++++++++---------
1 file changed, 31 insertions(+), 14 deletions(-)
diff --git a/python/fedml/api/modules/storage.py b/python/fedml/api/modules/storage.py
index e7d492c999..a928b325b2 100644
--- a/python/fedml/api/modules/storage.py
+++ b/python/fedml/api/modules/storage.py
@@ -38,27 +38,42 @@ def upload(data_path, api_key, name, description, tag_list, service, show_progre
if user_id is None:
return FedMLResponse(code=ResponseCode.FAILURE, message=message)
+
+ data_type = _get_data_type(data_path)
- if(not _check_data_path(data_path)):
+ if(data_type == "invalid"):
return FedMLResponse(code=ResponseCode.FAILURE,message="Invalid data path")
- archive_path, message = _archive_data(data_path)
- if not archive_path:
+ if(data_type == "dir"):
+ to_upload_path, message = _archive_data(data_path)
+ name = os.path.splitext(os.path.basename(to_upload_path))[0] if name is None else name
+ file_name = name + ".zip"
+ else:
+ to_upload_path = data_path
+ base_name = os.path.basename(to_upload_path)
+ given_extension = os.path.splitext(name)[1]
+ if given_extension is None or given_extension == "":
+ given_extension = os.path.splitext(base_name)[1]
+ name = base_name if name is None else name + given_extension
+ file_name = name
+
+ if not to_upload_path:
return FedMLResponse(code=ResponseCode.FAILURE, message=message)
- name = os.path.splitext(os.path.basename(archive_path))[0] if name is None else name
- file_name = name + ".zip"
+
dest_path = os.path.join(user_id, file_name)
- file_size = os.path.getsize(archive_path)
+ file_size = os.path.getsize(to_upload_path)
- file_uploaded_url, message = _upload_multipart(api_key, file_name, archive_path, show_progress,
+ file_uploaded_url, message = _upload_multipart(api_key, file_name, to_upload_path, show_progress,
out_progress_to_err,
progress_desc, metadata)
-
- os.remove(archive_path)
+ if(data_type == "dir"):
+ os.remove(to_upload_path)
if not file_uploaded_url:
- return FedMLResponse(code=ResponseCode.FAILURE, message=f"Failed to upload file: {archive_path}")
+ return FedMLResponse(code=ResponseCode.FAILURE, message=f"Failed to upload file: {to_upload_path}")
+
+ print("url: ",file_uploaded_url)
json_data = {
"datasetName": name,
@@ -438,10 +453,12 @@ def _get_storage_service(service):
else:
raise NotImplementedError(f"Service {service} not implemented")
-def _check_data_path(data_path):
- if os.path.isdir(data_path) or os.path.isfile(data_path):
- return True
- return False
+def _get_data_type(data_path):
+ if os.path.isdir(data_path):
+ return "dir"
+ elif os.path.isfile(data_path):
+ return "file"
+ return "invalid"
def _archive_data(data_path: str) -> (str, str):
From 03c58a2a42d8b43b3adf6331b38e38de92cc69d2 Mon Sep 17 00:00:00 2001
From: bhargav191098 <bhargav3514@gmail.com>
Date: Thu, 13 Jun 2024 17:32:36 -0700
Subject: [PATCH 153/282] changes in the download to support files
---
python/fedml/api/modules/storage.py | 23 ++++++++++++++++++-----
python/fedml/cli/modules/storage.py | 2 +-
2 files changed, 19 insertions(+), 6 deletions(-)
diff --git a/python/fedml/api/modules/storage.py b/python/fedml/api/modules/storage.py
index a928b325b2..94031c163e 100644
--- a/python/fedml/api/modules/storage.py
+++ b/python/fedml/api/modules/storage.py
@@ -110,13 +110,26 @@ def download(data_name, api_key, service, dest_path, show_progress=True) -> FedM
logging.error(error_message)
return FedMLResponse(code=ResponseCode.FAILURE, message=error_message)
download_url = metadata.download_url
- zip_file_name = data_name + ".zip"
- path_local = os.path.abspath(zip_file_name)
+ given_extension = os.path.splitext(data_name)[1]
+ is_file = True
+ if(given_extension is None or given_extension ==""):
+ is_file = False
+
+ if not is_file:
+ download_file_name = data_name + ".zip"
+ else:
+ download_file_name = data_name
+ path_local = os.path.abspath(download_file_name)
dest_path = os.path.abspath(dest_path) if dest_path else data_name
- if _download_using_presigned_url(download_url, zip_file_name, show_progress=show_progress):
+ if _download_using_presigned_url(download_url, download_file_name, show_progress=show_progress):
try:
- shutil.unpack_archive(path_local, dest_path)
- os.remove(path_local)
+ if not is_file:
+ shutil.unpack_archive(path_local, dest_path)
+ os.remove(path_local)
+ else:
+ if not os.path.exists(dest_path):
+ os.makedirs(dest_path)
+ shutil.move(path_local,dest_path)
abs_dest_path = os.path.abspath(dest_path)
return FedMLResponse(code=ResponseCode.SUCCESS, message=f"Successfully downloaded and unzipped data at "
f"{abs_dest_path}", data=abs_dest_path)
diff --git a/python/fedml/cli/modules/storage.py b/python/fedml/cli/modules/storage.py
index af75cda85f..7e060fc12e 100644
--- a/python/fedml/cli/modules/storage.py
+++ b/python/fedml/cli/modules/storage.py
@@ -47,7 +47,7 @@ def validate_argument(ctx, param, value):
@click.help_option("--help", "-h")
@click.argument("data_path", nargs=1, callback=validate_argument)
@click.option("--name", "-n", type=str, help="Name your data to store. If not provided, the name will be the same as "
- "the data file or directory name.")
+ "the data file or directory name. For files, extension need not be mentioned!")
@click.option("--description", "-d", type=str, help="Add description to your data to store. If not provided, "
"the description will be empty.")
@click.option("--user_metadata", "-um", type=str, help="User-defined metadata in the form of a dictionary, for instance, "
From cb7da7009f13fdf0191ba7710fdb0b100d90796f Mon Sep 17 00:00:00 2001
From: bhargav191098 <bhargav3514@gmail.com>
Date: Thu, 13 Jun 2024 17:38:05 -0700
Subject: [PATCH 154/282] print statement removal
---
python/fedml/api/modules/storage.py | 2 --
1 file changed, 2 deletions(-)
diff --git a/python/fedml/api/modules/storage.py b/python/fedml/api/modules/storage.py
index 94031c163e..2d10ff2588 100644
--- a/python/fedml/api/modules/storage.py
+++ b/python/fedml/api/modules/storage.py
@@ -73,8 +73,6 @@ def upload(data_path, api_key, name, description, tag_list, service, show_progre
if not file_uploaded_url:
return FedMLResponse(code=ResponseCode.FAILURE, message=f"Failed to upload file: {to_upload_path}")
- print("url: ",file_uploaded_url)
-
json_data = {
"datasetName": name,
"description": description,
From 394906ecf03fe2e221bfba4a7a46c87105d26a35 Mon Sep 17 00:00:00 2001
From: bhargav191098 <bhargav3514@gmail.com>
Date: Fri, 14 Jun 2024 12:33:23 -0700
Subject: [PATCH 155/282] name issue
---
python/fedml/api/modules/storage.py | 13 +++++++++----
1 file changed, 9 insertions(+), 4 deletions(-)
diff --git a/python/fedml/api/modules/storage.py b/python/fedml/api/modules/storage.py
index 2d10ff2588..3e4219775d 100644
--- a/python/fedml/api/modules/storage.py
+++ b/python/fedml/api/modules/storage.py
@@ -51,10 +51,15 @@ def upload(data_path, api_key, name, description, tag_list, service, show_progre
else:
to_upload_path = data_path
base_name = os.path.basename(to_upload_path)
- given_extension = os.path.splitext(name)[1]
- if given_extension is None or given_extension == "":
- given_extension = os.path.splitext(base_name)[1]
- name = base_name if name is None else name + given_extension
+ file_extension = os.path.splitext(base_name)[1]
+ given_extension = None
+ if name is not None:
+ given_extension = os.path.splitext(name)[1]
+ if given_extension is None or given_extension == "":
+ name = name + file_extension
+ else:
+ name = base_name
+
file_name = name
if not to_upload_path:
From 2170797de1235e78f9a92722b495cb01af8d92c2 Mon Sep 17 00:00:00 2001
From: bhargav191098 <bhargav3514@gmail.com>
Date: Fri, 14 Jun 2024 18:13:08 -0700
Subject: [PATCH 156/282] \Adding Enum for data type
---
python/fedml/api/modules/storage.py | 15 ++++++++++-----
1 file changed, 10 insertions(+), 5 deletions(-)
diff --git a/python/fedml/api/modules/storage.py b/python/fedml/api/modules/storage.py
index 3e4219775d..0729c09edc 100644
--- a/python/fedml/api/modules/storage.py
+++ b/python/fedml/api/modules/storage.py
@@ -4,6 +4,7 @@
import requests
import math
+from enum import Enum, unique
import requests.exceptions
import tqdm
@@ -26,6 +27,10 @@ def __init__(self, data: dict):
self.tag_list = data.get("tags", None)
self.download_url = data.get("fileUrl", None)
+class DataType(Enum):
+ FILE = "file"
+ DIRECTORY = "directory"
+ INVALID = "invalid"
# Todo (alaydshah): Store service name in metadata
# Todo (alaydshah): If data already exists, don't upload again. Instead suggest to use update command
@@ -41,10 +46,10 @@ def upload(data_path, api_key, name, description, tag_list, service, show_progre
data_type = _get_data_type(data_path)
- if(data_type == "invalid"):
+ if(data_type == DataType.INVALID):
return FedMLResponse(code=ResponseCode.FAILURE,message="Invalid data path")
- if(data_type == "dir"):
+ if(data_type == DataType.DIRECTORY):
to_upload_path, message = _archive_data(data_path)
name = os.path.splitext(os.path.basename(to_upload_path))[0] if name is None else name
file_name = name + ".zip"
@@ -471,10 +476,10 @@ def _get_storage_service(service):
def _get_data_type(data_path):
if os.path.isdir(data_path):
- return "dir"
+ return DataType.DIRECTORY
elif os.path.isfile(data_path):
- return "file"
- return "invalid"
+ return DataType.FILE
+ return DataType.INVALID
def _archive_data(data_path: str) -> (str, str):
From 5fb5ed43d42f54b0c47e9a0ae802bcab29197052 Mon Sep 17 00:00:00 2001
From: bhargav191098 <bhargav3514@gmail.com>
Date: Fri, 14 Jun 2024 18:32:05 -0700
Subject: [PATCH 157/282] adding user_id to bucket path
---
python/fedml/api/modules/storage.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/api/modules/storage.py b/python/fedml/api/modules/storage.py
index 0729c09edc..33e781be08 100644
--- a/python/fedml/api/modules/storage.py
+++ b/python/fedml/api/modules/storage.py
@@ -70,11 +70,11 @@ def upload(data_path, api_key, name, description, tag_list, service, show_progre
if not to_upload_path:
return FedMLResponse(code=ResponseCode.FAILURE, message=message)
-
+ #TODO(bhargav191098) - Better done on the backend. Remove and pass file_name once completed on backend.
dest_path = os.path.join(user_id, file_name)
file_size = os.path.getsize(to_upload_path)
- file_uploaded_url, message = _upload_multipart(api_key, file_name, to_upload_path, show_progress,
+ file_uploaded_url, message = _upload_multipart(api_key, dest_path, to_upload_path, show_progress,
out_progress_to_err,
progress_desc, metadata)
From a1af6151947309306a1425b86e54e9e2175ffed6 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Mon, 17 Jun 2024 14:47:10 +0800
Subject: [PATCH 158/282] [CoreEngine] refactor to support to pass the
communication manager, status center and message center to agents.
---
.../scheduler/comm_utils/job_monitor.py | 12 ++-
.../scheduler/master/base_master_agent.py | 13 +++
.../master/base_master_job_runner.py | 20 ++---
.../master/base_master_job_runner_manager.py | 84 ++++++++++++++++---
.../master/base_master_protocol_manager.py | 29 +++++--
.../scheduler/master/cloud_server_manager.py | 18 +++-
.../master/master_protocol_manager.py | 4 +
.../model_scheduler/master_job_runner.py | 2 +-
.../scheduler_core/message_center.py | 27 +++++-
.../scheduler_base_job_runner.py | 2 +-
.../scheduler_base_protocol_manager.py | 9 +-
.../scheduler/scheduler_core/status_center.py | 5 +-
.../scheduler/slave/base_slave_agent.py | 1 +
.../scheduler/slave/client_data_interface.py | 9 ++
.../scheduler/slave/united_agents.py | 11 +--
15 files changed, 200 insertions(+), 46 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index a7d5214a02..916883fd0f 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -166,7 +166,7 @@ def autoscaler_reconcile_after_interval(self):
# Get cached token for authorization of autoscale request
cached_token = fedml_model_cache.get_end_point_token(e_id, e_name, model_name)
if cached_token is None:
- logging.error(f"Failed to get the cached token for endpoint {e_id}.")
+ # logging.error(f"Failed to get the cached token for endpoint {e_id}.")
return
req_header = {
@@ -228,7 +228,7 @@ def monitor_replicas_number():
cached_token = FedMLModelCache.get_instance().get_end_point_token_with_eid(endpoint_id)
if cached_token is None:
- logging.error(f"Failed to get the cached token for endpoint {endpoint_id}.")
+ # logging.error(f"Failed to get the cached token for endpoint {endpoint_id}.")
return
req_header = {
@@ -338,6 +338,10 @@ def monitor_replicas_perf(edge_id, mqtt_mgr=None):
def monitor_slave_run_process_status(self):
try:
count = 0
+ try:
+ client_data_interface.FedMLClientDataInterface.get_instance().create_job_table()
+ except Exception as e:
+ pass
job_list = client_data_interface.FedMLClientDataInterface.get_instance().get_jobs_from_db()
for job in job_list.job_list:
count += 1
@@ -447,6 +451,10 @@ def monitor_master_run_process_status(self, server_id, device_info_reporter=None
try:
ComputeCacheManager.get_instance().set_redis_params()
count = 0
+ try:
+ server_data_interface.FedMLServerDataInterface.get_instance().create_job_table()
+ except Exception as e:
+ pass
job_list = server_data_interface.FedMLServerDataInterface.get_instance().get_jobs_from_db()
for job in job_list.job_list:
count += 1
diff --git a/python/fedml/computing/scheduler/master/base_master_agent.py b/python/fedml/computing/scheduler/master/base_master_agent.py
index d6c6420cf3..39898b5d40 100755
--- a/python/fedml/computing/scheduler/master/base_master_agent.py
+++ b/python/fedml/computing/scheduler/master/base_master_agent.py
@@ -95,6 +95,7 @@ def _initialize_protocol_manager(
self._init_database()
# Initialize the master protocol
+ self.protocol_mgr.set_parent_agent(self)
self.protocol_mgr.initialize(
communication_manager=communication_manager,
sender_message_queue=sender_message_queue,
@@ -141,3 +142,15 @@ def _init_database(self):
@abstractmethod
def _generate_protocol_manager_instance(self, args, agent_config=None):
return None
+
+ def start_master_server_instance(self, payload):
+ self.protocol_mgr.start_master_server_instance(payload)
+
+ def generate_agent_instance(self):
+ return FedMLBaseMasterAgent()
+
+ def process_job_complete_status(self, run_id, topic, payload):
+ if topic in self.protocol_mgr.get_subscribed_topics():
+ message_handler = self.protocol_mgr.get_listener_handler(topic)
+ if message_handler is not None:
+ message_handler(topic, payload)
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner.py b/python/fedml/computing/scheduler/master/base_master_job_runner.py
index fe2d426af4..1072e6b045 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner.py
@@ -42,13 +42,13 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
is_master_runner=True
)
- self.run_edge_id_status_queue = multiprocessing.Manager().Queue(-1)
- self.run_metrics_queue = multiprocessing.Manager().Queue(-1)
- self.run_events_queue = multiprocessing.Manager().Queue(-1)
- self.run_artifacts_queue = multiprocessing.Manager().Queue(-1)
- self.run_logs_queue = multiprocessing.Manager().Queue(-1)
- self.run_edge_device_info_queue = multiprocessing.Manager().Queue(-1)
- self.run_edge_device_info_global_queue = multiprocessing.Manager().Queue(-1)
+ self.run_edge_id_status_queue = multiprocessing.Queue()
+ self.run_metrics_queue = multiprocessing.Queue()
+ self.run_events_queue = multiprocessing.Queue()
+ self.run_artifacts_queue = multiprocessing.Queue()
+ self.run_logs_queue = multiprocessing.Queue()
+ self.run_edge_device_info_queue = multiprocessing.Queue()
+ self.run_edge_device_info_global_queue = multiprocessing.Queue()
self.run_extend_queue_list = None
self.async_check_timeout = 0
self.enable_async_cluster = False
@@ -453,7 +453,7 @@ def put_run_edge_device_info_to_queue(self, run_id, edge_id, device_info):
if int(edge_id) in edge_ids or str(edge_id) in edge_ids:
run_id_str = str(run_id)
if self.run_edge_device_info_queue is None:
- self.run_edge_device_info_queue = multiprocessing.Manager().Queue(-1)
+ self.run_edge_device_info_queue = multiprocessing.Queue()
self.run_edge_device_info_queue.put(device_info)
def should_continue_run_job(self, run_id):
@@ -581,7 +581,7 @@ def callback_run_logs(self, topic, payload):
run_id = str(topic).split('/')[-1]
run_id_str = str(run_id)
if self.run_logs_queue is None:
- self.run_logs_queue = multiprocessing.Manager().Queue(-1)
+ self.run_logs_queue = multiprocessing.Queue()
self.run_logs_queue.put(payload)
def callback_run_metrics(self, topic, payload):
@@ -589,7 +589,7 @@ def callback_run_metrics(self, topic, payload):
run_id = str(topic).split('/')[-1]
run_id_str = str(run_id)
if self.run_metrics_queue is None:
- self.run_metrics_queue = multiprocessing.Manager().Queue(-1)
+ self.run_metrics_queue = multiprocessing.Queue()
self.run_metrics_queue.put(payload)
# def send_training_request_to_edges(self, active_edge_info_dict):
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
index 08ef1d640e..dfaf29b5de 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
@@ -1,29 +1,40 @@
import base64
import json
import logging
+import multiprocessing
import os
+import platform
import time
from abc import ABC
from multiprocessing import Process
+
+import fedml
from .cloud_server_manager import FedMLCloudServerManager
from ..comm_utils.run_process_utils import RunProcessUtils
from ..scheduler_core.scheduler_base_job_runner_manager import FedMLSchedulerBaseJobRunnerManager
+from ..scheduler_core.account_manager import FedMLAccountManager
class FedMLBaseMasterJobRunnerManager(FedMLSchedulerBaseJobRunnerManager, ABC):
def __init__(self):
FedMLSchedulerBaseJobRunnerManager.__init__(self)
+ if not hasattr(self, "master_agent_instance_map"):
+ self.master_agent_instance_map = dict()
# Override
def start_job_runner(
self, run_id, request_json, args=None, edge_id=None, is_server_job=False,
sender_message_queue=None, listener_message_queue=None, status_center_queue=None,
- should_start_cloud_server=False, use_local_process_as_cloud_server=False,
- cuda_visible_gpu_ids_str=None
+ communication_manager=None, master_agent_instance=None, should_start_cloud_server=False,
+ use_local_process_as_cloud_server=False, cuda_visible_gpu_ids_str=None
):
if should_start_cloud_server:
- self._start_cloud_server(args, run_id, request_json, edge_id=edge_id,
- use_local_process_as_cloud_server=use_local_process_as_cloud_server)
+ self._start_cloud_server(
+ args, run_id, request_json, edge_id=edge_id,
+ use_local_process_as_cloud_server=use_local_process_as_cloud_server,
+ sender_message_queue=sender_message_queue, listener_message_queue=listener_message_queue,
+ status_center_queue=status_center_queue, communication_manager=communication_manager,
+ master_agent_instance=master_agent_instance)
return
run_id_str = str(run_id)
@@ -51,9 +62,14 @@ def stop_job_runner(
args=(run_id, server_id, args.agent_config))
stopping_process.start()
+ run_id_str = str(run_id)
+ if self.master_agent_instance_map.get(run_id_str, None) is not None:
+ self.master_agent_instance_map.pop(run_id_str)
+
if run_as_cloud_server:
time.sleep(1)
- RunProcessUtils.kill_process(os.getpid())
+ RunProcessUtils.kill_process(self.cloud_run_process_map[run_id_str].pid)
+ #RunProcessUtils.kill_process(os.getpid())
def complete_job_runner(
self, run_id, args=None, server_id=None, request_json=None,
@@ -68,13 +84,20 @@ def complete_job_runner(
args=(run_id, server_id, args.agent_config))
stopping_process.start()
+ run_id_str = str(run_id)
+ if self.master_agent_instance_map.get(run_id_str, None) is not None:
+ self.master_agent_instance_map.pop(run_id_str)
+
if run_as_cloud_server:
time.sleep(1)
RunProcessUtils.kill_process(os.getpid())
def _start_cloud_server(
self, args, run_id, request_json, edge_id=None,
- use_local_process_as_cloud_server=False
+ use_local_process_as_cloud_server=False,
+ sender_message_queue=None, listener_message_queue=None,
+ status_center_queue=None, communication_manager=None,
+ master_agent_instance=None
):
run_id_str = str(run_id)
cloud_server_mgr = FedMLCloudServerManager(
@@ -85,19 +108,47 @@ def _start_cloud_server(
self.cloud_run_process_map[run_id_str] = Process(target=cloud_server_mgr.start_cloud_server_process_entry)
self.cloud_run_process_map[run_id_str].start()
else:
+ cloud_device_id = request_json.get("cloudServerDeviceId", "0")
message_bytes = json.dumps(request_json).encode("ascii")
base64_bytes = base64.b64encode(message_bytes)
- runner_cmd_encoded = base64_bytes.decode("ascii")
- cloud_device_id = request_json.get("cloudServerDeviceId", "0")
+ payload = base64_bytes.decode("ascii")
+
+ logging.info("start the master server: {}".format(payload))
- logging.info("runner_cmd_encoded: {}".format(runner_cmd_encoded))
+ if platform.system() == "Windows":
+ self.run_process = multiprocessing.Process(
+ target=cloud_server_mgr.start_local_master_server,
+ args=(args.account_id, args.api_key, args.os_name, args.version,
+ cloud_device_id, run_id, payload,
+ communication_manager, sender_message_queue,
+ status_center_queue, master_agent_instance))
+ else:
+ self.cloud_run_process_map[run_id_str] = fedml.get_process(
+ target=cloud_server_mgr.start_local_master_server,
+ args=(args.account_id, args.api_key, args.os_name, args.version,
+ cloud_device_id, run_id, payload,
+ communication_manager, sender_message_queue,
+ status_center_queue, master_agent_instance))
- self.cloud_run_process_map[run_id_str] = Process(
- target=cloud_server_mgr.start_local_cloud_server,
- args=(args.account_id, args.version, cloud_device_id, runner_cmd_encoded))
self.cloud_run_process_map[run_id_str].start()
time.sleep(1)
+ def start_local_master_server(
+ self, user, api_key, os_name, version, cloud_device_id, run_id, payload,
+ communication_manager=None, sender_message_queue=None, status_center_queue=None,
+ master_agent_instance=None
+ ):
+ if master_agent_instance is None:
+ return
+ master_agent_instance.login(
+ user, api_key=api_key, device_id=cloud_device_id, os_name=os_name,
+ role=FedMLAccountManager.ROLE_CLOUD_SERVER,
+ communication_manager=None,
+ sender_message_queue=None,
+ status_center_queue=None)
+ self.master_agent_instance_map[str(run_id)] = master_agent_instance
+ master_agent_instance.start_master_server_instance(payload)
+
def callback_run_logs(self, run_id, topic, payload):
run_id_str = str(run_id)
if self.job_runners.get(run_id_str, None) is not None:
@@ -107,3 +158,12 @@ def callback_run_metrics(self, run_id, topic, payload):
run_id_str = str(run_id)
if self.job_runners.get(run_id_str, None) is not None:
self.job_runners[run_id_str].callback_run_metrics(topic, payload)
+
+ def callback_proxy_unknown_messages(self, run_id, topic, payload):
+ run_id_str = str(run_id)
+ master_agent = self.master_agent_instance_map.get(run_id_str, None)
+ if master_agent is None:
+ return
+ master_agent.process_job_complete_status(run_id, topic, payload)
+
+
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index a6b47855c6..2d00e442a0 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -2,6 +2,8 @@
import base64
import json
import logging
+import time
+
import fedml
from ..comm_utils.constants import SchedulerConstants
from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
@@ -212,7 +214,8 @@ def callback_start_train(self, topic=None, payload=None):
run_id, request_json, args=self.args, edge_id=self.edge_id,
sender_message_queue=self.message_center.get_sender_message_queue(),
listener_message_queue=self.get_listener_message_queue(),
- status_center_queue=self.get_status_queue()
+ status_center_queue=self.get_status_queue(),
+ communication_manager=self.get_listener_communication_manager()
)
process = self._get_job_runner_manager().get_runner_process(run_id)
@@ -227,16 +230,22 @@ def callback_start_train(self, topic=None, payload=None):
run_id, request_json, args=self.args, edge_id=self.edge_id,
sender_message_queue=self.message_center.get_sender_message_queue(),
listener_message_queue=self.get_listener_message_queue(),
- status_center_queue=self.get_status_queue(), should_start_cloud_server=True,
+ status_center_queue=self.get_status_queue(),
+ communication_manager=self.get_listener_communication_manager(),
+ master_agent_instance=self.generate_agent_instance(),
+ should_start_cloud_server=True,
use_local_process_as_cloud_server=self.use_local_process_as_cloud_server
)
process = self._get_job_runner_manager().get_runner_process(run_id, is_cloud_server=True)
if process is not None:
GeneralConstants.save_run_process(run_id, process.pid, is_master=True)
+
+ self.send_status_msg_to_edges(edge_id_list, run_id, request_json.get("server_id"))
elif self.run_as_cloud_server:
self.server_agent_id = request_json.get("cloud_agent_id", self.edge_id)
self.start_request_json = json.dumps(request_json)
+ server_id = request_json.get("server_id", self.edge_id)
run_id = request_json["runId"]
run_id_str = str(run_id)
@@ -248,11 +257,10 @@ def callback_start_train(self, topic=None, payload=None):
run_id, request_json, args=self.args, edge_id=self.edge_id,
sender_message_queue=self.message_center.get_sender_message_queue(),
listener_message_queue=self.get_listener_message_queue(),
- status_center_queue=self.get_status_queue()
+ status_center_queue=self.get_status_queue(),
+ communication_manager=self.get_listener_communication_manager()
)
- self.send_status_msg_to_edges(edge_id_list, run_id, self.edge_id)
-
def callback_stop_train(self, topic, payload, use_payload=None):
# Print the payload
logging.info(
@@ -390,6 +398,9 @@ def callback_request_job_status(self, topic, payload):
def callback_request_device_status_in_job(self, topic, payload):
self.response_device_status_in_job(topic, payload)
+ def callback_proxy_unknown_messages(self, run_id, topic, payload):
+ self._get_job_runner_manager().callback_proxy_unknown_messages(run_id, topic, payload)
+
def process_extra_queues(self, extra_queues):
self.rebuild_status_center(extra_queues[0])
@@ -550,7 +561,7 @@ def send_status_check_msg(self, run_id, edge_id, server_id, context=None):
def send_status_msg_to_edges(self, edge_id_list, run_id, server_id, context=None):
# Send status message to all edges
for edge_id in edge_id_list:
- self.send_status_check_msg(run_id, edge_id, self.edge_id, context=context)
+ self.send_status_check_msg(run_id, edge_id, server_id, context=context)
def report_exception_status(self, run_id):
self.mlops_metrics.report_job_status(run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION)
@@ -562,3 +573,9 @@ def get_start_train_topic_with_edge_id(edge_id):
@abstractmethod
def _generate_protocol_manager_instance(self, args, agent_config=None):
return None
+
+ def start_master_server_instance(self, payload):
+ super().on_agent_communication_connected(None)
+
+ self.receive_message_json(self.topic_start_train, payload)
+
diff --git a/python/fedml/computing/scheduler/master/cloud_server_manager.py b/python/fedml/computing/scheduler/master/cloud_server_manager.py
index aa9c07e84f..9c35f233fd 100755
--- a/python/fedml/computing/scheduler/master/cloud_server_manager.py
+++ b/python/fedml/computing/scheduler/master/cloud_server_manager.py
@@ -7,6 +7,7 @@
import fedml
from fedml.computing.scheduler.comm_utils.sys_utils import get_python_program
+from fedml.computing.scheduler.scheduler_core.account_manager import FedMLAccountManager
class FedMLCloudServerManager:
@@ -32,7 +33,7 @@ def __init__(self, args, run_id=None, edge_id=None, request_json=None, agent_con
self.cloud_server_name = None
@staticmethod
- def start_local_cloud_server(user, version, cloud_device_id, runner_cmd_encoded):
+ def start_local_cloud_server(user, api_key, os_name, version, cloud_device_id, runner_cmd_encoded):
if platform.system() != "Windows":
os.setsid()
@@ -40,9 +41,22 @@ def start_local_cloud_server(user, version, cloud_device_id, runner_cmd_encoded)
pip_source_dir = os.path.dirname(__file__)
login_cmd = os.path.join(pip_source_dir, "server_login.py")
run_cmd = f"{get_python_program()} -W ignore {login_cmd} -t login -r cloud_server -u {str(user)} " \
- f"-v {version} -id {cloud_device_id} -rc {runner_cmd_encoded}"
+ f"-k {api_key} -v {version} -id {cloud_device_id} -rc {runner_cmd_encoded}"
os.system(run_cmd)
+ def start_local_master_server(
+ self, user, api_key, os_name, version, cloud_device_id, run_id, payload,
+ communication_manager=None, sender_message_queue=None, status_center_queue=None,
+ master_agent_instance=None
+ ):
+ if platform.system() != "Windows":
+ os.setsid()
+
+ master_agent_instance.login(
+ user, api_key=api_key, device_id=cloud_device_id, os_name=os_name,
+ role=FedMLAccountManager.ROLE_CLOUD_SERVER, runner_cmd=payload,
+ communication_manager=None, sender_message_queue=None, status_center_queue=None)
+
def start_cloud_server_process_entry(self):
try:
self.start_cloud_server_process()
diff --git a/python/fedml/computing/scheduler/master/master_protocol_manager.py b/python/fedml/computing/scheduler/master/master_protocol_manager.py
index eb8cde239f..c941502b9c 100755
--- a/python/fedml/computing/scheduler/master/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/master_protocol_manager.py
@@ -42,3 +42,7 @@ def _process_job_complete_status(self, run_id, server_id, complete_payload):
run_id, args=self.args, server_id=server_id, request_json=complete_payload,
run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server,
use_local_process_as_cloud_server=self.use_local_process_as_cloud_server)
+
+ def generate_agent_instance(self):
+ from .master_agent import FedMLLaunchMasterAgent
+ return FedMLLaunchMasterAgent()
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 61cce1b39c..e32e7421f6 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -50,7 +50,7 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
self.replica_controller = None
self.deployed_replica_payload = None
self.slave_deployment_results_map = dict()
- self.deployment_result_queue = multiprocessing.Manager().Queue(-1)
+ self.deployment_result_queue = multiprocessing.Queue()
self.is_fresh_endpoint = True
# Override
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_center.py b/python/fedml/computing/scheduler/scheduler_core/message_center.py
index 2bfa3b514f..9229b2c0da 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_center.py
@@ -133,7 +133,7 @@ def get_sender_message_queue(self):
return self.sender_message_queue
def start_sender(self, message_center_name=None):
- self.sender_message_queue = multiprocessing.Manager().Queue(-1)
+ self.sender_message_queue = multiprocessing.Queue()
self.message_event = multiprocessing.Event()
self.message_event.clear()
message_center = FedMLMessageCenter(agent_config=self.sender_agent_config,
@@ -258,10 +258,16 @@ def run_sender(self, message_event, message_queue, message_center_name):
self.release_sender_mqtt_mgr()
+ def get_protocol_communication_manager(self):
+ return None
+
def setup_listener_mqtt_mgr(self):
if self.listener_mqtt_mgr is not None:
return
+ # self.listener_mqtt_mgr = self.get_protocol_communication_manager()
+ # return
+
self.listener_mqtt_mgr = MqttManager(
self.listener_agent_config["mqtt_config"]["BROKER_HOST"],
self.listener_agent_config["mqtt_config"]["BROKER_PORT"],
@@ -274,7 +280,11 @@ def setup_listener_mqtt_mgr(self):
self.listener_mqtt_mgr.connect()
self.listener_mqtt_mgr.loop_start()
+ def get_listener_communication_manager(self):
+ return self.listener_mqtt_mgr
+
def release_listener_mqtt_mgr(self):
+ #return
try:
if self.listener_mqtt_mgr is not None:
self.listener_mqtt_mgr.loop_stop()
@@ -297,6 +307,9 @@ def remove_message_listener(self, topic):
self.listener_topics.remove(topic)
self.listener_handler_funcs.pop(topic)
+ def get_listener_handler(self, topic):
+ return self.listener_handler_funcs.get(topic)
+
def get_message_runner(self):
return None
@@ -304,7 +317,7 @@ def get_listener_message_queue(self):
return self.listener_message_queue
def setup_listener_message_queue(self):
- self.listener_message_queue = multiprocessing.Manager().Queue(-1)
+ self.listener_message_queue = multiprocessing.Queue()
def start_listener(
self, sender_message_queue=None, listener_message_queue=None,
@@ -315,13 +328,14 @@ def start_listener(
if listener_message_queue is None:
if self.listener_message_queue is None:
- self.listener_message_queue = multiprocessing.Manager().Queue(-1)
+ self.listener_message_queue = multiprocessing.Queue()
else:
self.listener_message_queue = listener_message_queue
self.listener_message_event = multiprocessing.Event()
self.listener_message_event.clear()
self.listener_agent_config = agent_config
- message_runner = self.get_message_runner()
+ # message_runner = self.get_message_runner()
+ message_runner = self
message_runner.listener_agent_config = agent_config
if platform.system() == "Windows":
self.listener_message_center_process = multiprocessing.Process(
@@ -427,6 +441,11 @@ def run_listener_dispatcher(
message_handler_func_name = self.listener_handler_funcs.get(message_entity.topic, None)
if message_handler_func_name is not None:
methodcaller(message_handler_func_name, message_entity.topic, message_entity.payload)(self)
+ else:
+ if hasattr(self, "callback_proxy_unknown_messages") and \
+ self.callback_proxy_unknown_messages is not None:
+ self.callback_proxy_unknown_messages(
+ message_entity.run_id, message_entity.topic, message_entity.payload)
except Exception as e:
if message_entity is not None:
logging.info(
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 80de5c5b18..ffaee555af 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -210,7 +210,7 @@ def retrieve_and_unzip_package(self, package_name, package_url):
# Open a process to download the package so that we can avoid the request is blocked and check the timeout.
from multiprocessing import Process
completed_event = multiprocessing.Event()
- info_queue = multiprocessing.Manager().Queue(-1)
+ info_queue = multiprocessing.Queue()
if platform.system() == "Windows":
download_process = multiprocessing.Process(
target=self.download_package_proc,
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
index f80508a509..80d67f33cb 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
@@ -45,6 +45,7 @@ def __init__(self, args, agent_config=None, is_master=False):
self.mlops_metrics = None
self.status_reporter = None
self.user_name = args.user_name
+ self.parent_agent = None
fedml._init_multiprocessing()
@@ -282,7 +283,7 @@ def get_status_runner(self):
return None
- def get_get_protocol_communication_manager(self):
+ def get_protocol_communication_manager(self):
return self.communication_mgr
def get_protocol_sender_message_queue(self):
@@ -291,6 +292,12 @@ def get_protocol_sender_message_queue(self):
def get_protocol_status_center_queue(self):
return self.get_status_queue()
+ def get_subscribed_topics(self):
+ return self.subscribed_topics
+
def send_agent_active_msg(self, edge_id):
active_msg = {"ID": edge_id, "status": GeneralConstants.MSG_MLOPS_SERVER_STATUS_IDLE}
self.message_center.send_message_json(self.topic_active, json.dumps(active_msg))
+
+ def set_parent_agent(self, parent_agent):
+ self.parent_agent = parent_agent
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index 7e0cf1f98f..2a7a76c2ca 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -108,12 +108,13 @@ def get_status_runner(self):
def start_status_center(self, sender_message_center_queue=None,
listener_message_center_queue=None, is_slave_agent=False):
- self.status_queue = multiprocessing.Manager().Queue(-1)
+ self.status_queue = multiprocessing.Queue()
self.status_event = multiprocessing.Event()
self.status_event.clear()
self.status_sender_message_center_queue = sender_message_center_queue
self.status_listener_message_center_queue = listener_message_center_queue
- self.status_runner = self.get_status_runner()
+ #self.status_runner = self.get_status_runner()
+ self.status_runner = self
target_func = self.status_runner.run_status_dispatcher if not is_slave_agent else \
self.status_runner.run_status_dispatcher_in_slave
if platform.system() == "Windows":
diff --git a/python/fedml/computing/scheduler/slave/base_slave_agent.py b/python/fedml/computing/scheduler/slave/base_slave_agent.py
index a8ac9fa1cb..fed10fa039 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_agent.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_agent.py
@@ -96,6 +96,7 @@ def _initialize_protocol_manager(
self._init_database()
# Initialize the master protocol
+ self.protocol_mgr.set_parent_agent(self)
self.protocol_mgr.initialize(
communication_manager=communication_manager,
sender_message_queue=sender_message_queue,
diff --git a/python/fedml/computing/scheduler/slave/client_data_interface.py b/python/fedml/computing/scheduler/slave/client_data_interface.py
index 0e9e84381a..74bf7a64a3 100755
--- a/python/fedml/computing/scheduler/slave/client_data_interface.py
+++ b/python/fedml/computing/scheduler/slave/client_data_interface.py
@@ -343,6 +343,15 @@ def handle_database_compatibility(self):
self.close_job_db()
+ def check_if_table_exist(self, current_db_cursor):
+ results = current_db_cursor.execute("select * from sqlite_master where type='table' and name='jobs';")
+ if results is None:
+ return False
+ result_len = 0
+ for row in results:
+ result_len += 1
+ return False if result_len == 0 else True
+
def get_agent_status(self, edge_id=0):
self.open_job_db()
enabled = 1
diff --git a/python/fedml/computing/scheduler/slave/united_agents.py b/python/fedml/computing/scheduler/slave/united_agents.py
index 7135925ec8..64ba3c5465 100755
--- a/python/fedml/computing/scheduler/slave/united_agents.py
+++ b/python/fedml/computing/scheduler/slave/united_agents.py
@@ -31,8 +31,8 @@ def login(self, userid, api_key=None, device_id=None,
)
# Get the communication manager, sender message queue
- shared_communication_mgr = launch_slave_agent.get_protocol_manager().get_get_protocol_communication_manager()
- shared_sender_message_queue = launch_slave_agent.get_protocol_manager().get_protocol_sender_message_queue()
+ shared_communication_mgr = launch_slave_agent.get_protocol_manager().get_protocol_communication_manager()
+ shared_slave_sender_message_queue = launch_slave_agent.get_protocol_manager().get_protocol_sender_message_queue()
# Login with the launch master role based on
# the shared communication manager, sender message center
@@ -41,12 +41,13 @@ def login(self, userid, api_key=None, device_id=None,
os_name=os_name, runner_cmd=runner_cmd,
role=FedMLAccountManager.ROLE_GPU_MASTER_SERVER,
communication_manager=shared_communication_mgr,
- sender_message_queue=shared_sender_message_queue
+ sender_message_queue=None
)
# Get the status center queue
shared_slave_status_center_queue = launch_slave_agent.get_protocol_manager().get_protocol_status_center_queue()
shared_master_status_center_queue = launch_master_agent.get_protocol_manager().get_protocol_status_center_queue()
+ shared_master_sender_message_queue = launch_master_agent.get_protocol_manager().get_protocol_sender_message_queue()
# Login with the deployment master role based on
# the shared communication manager, sender message center, status center
@@ -54,7 +55,7 @@ def login(self, userid, api_key=None, device_id=None,
userid, api_key=api_key, device_id=login_result.device_id,
os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM,
communication_manager=shared_communication_mgr,
- sender_message_queue=shared_sender_message_queue,
+ sender_message_queue=shared_master_sender_message_queue,
status_center_queue=shared_master_status_center_queue
)
@@ -64,7 +65,7 @@ def login(self, userid, api_key=None, device_id=None,
userid, api_key=api_key, device_id=login_result.device_id,
os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM,
communication_manager=shared_communication_mgr,
- sender_message_queue=shared_sender_message_queue,
+ sender_message_queue=shared_slave_sender_message_queue,
status_center_queue=shared_slave_status_center_queue
)
From 6e8788c3300f40002d5cd06f18f0f247a9d33422 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Mon, 17 Jun 2024 14:54:54 +0800
Subject: [PATCH 159/282] [CoreEngine] refactor to support to pass the
communication manager, status center and message center to agents.
---
.../scheduler/master/base_master_job_runner_manager.py | 3 +--
1 file changed, 1 insertion(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
index dfaf29b5de..b67f0e1e21 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
@@ -69,7 +69,6 @@ def stop_job_runner(
if run_as_cloud_server:
time.sleep(1)
RunProcessUtils.kill_process(self.cloud_run_process_map[run_id_str].pid)
- #RunProcessUtils.kill_process(os.getpid())
def complete_job_runner(
self, run_id, args=None, server_id=None, request_json=None,
@@ -90,7 +89,7 @@ def complete_job_runner(
if run_as_cloud_server:
time.sleep(1)
- RunProcessUtils.kill_process(os.getpid())
+ RunProcessUtils.kill_process(self.cloud_run_process_map[run_id_str].pid)
def _start_cloud_server(
self, args, run_id, request_json, edge_id=None,
From 78e310c2fcf94aa828573dc577da434c861b6651 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Mon, 17 Jun 2024 21:02:32 +0800
Subject: [PATCH 160/282] [CoreEngine] stop the status center, message center
and other processes when the job is completed in the master server.
---
python/fedml/api/api_test.py | 6 +--
.../scheduler/comm_utils/run_process_utils.py | 4 +-
.../scheduler/master/base_master_agent.py | 17 ++++++--
.../master/base_master_job_runner_manager.py | 25 +++++------
.../master/base_master_protocol_manager.py | 5 +--
.../scheduler/master/cloud_server_manager.py | 6 ++-
.../scheduler/master/server_login.py | 1 +
.../scheduler_core/message_center.py | 37 +++++++++++-----
.../scheduler_base_protocol_manager.py | 43 ++++++++++++++++---
.../scheduler/scheduler_core/status_center.py | 41 ++++++++++++------
.../status_manager_protocols.py | 3 ++
.../scheduler/slave/base_slave_agent.py | 12 ++++--
.../slave/base_slave_protocol_manager.py | 3 --
.../scheduler/slave/united_agents.py | 8 +++-
14 files changed, 149 insertions(+), 62 deletions(-)
diff --git a/python/fedml/api/api_test.py b/python/fedml/api/api_test.py
index 5a01a76448..1aa5ac3767 100755
--- a/python/fedml/api/api_test.py
+++ b/python/fedml/api/api_test.py
@@ -4,9 +4,9 @@
import fedml
# Login
-fedml.set_env_version("test")
+fedml.set_env_version("local")
fedml.set_local_on_premise_platform_port(18080)
-error_code, error_msg = fedml.api.fedml_login(api_key="")
+error_code, error_msg = fedml.api.fedml_login(api_key="1316b93c82da40ce90113a2ed12f0b14")
if error_code != 0:
print("API Key is invalid!")
exit(1)
@@ -19,7 +19,7 @@
# Launch job
launch_result_list = list()
-for i in range(0, 10):
+for i in range(0, 1):
launch_result = fedml.api.launch_job(yaml_file)
launch_result_list.append(launch_result)
# launch_result = fedml.api.launch_job_on_cluster(yaml_file, "alex-cluster")
diff --git a/python/fedml/computing/scheduler/comm_utils/run_process_utils.py b/python/fedml/computing/scheduler/comm_utils/run_process_utils.py
index 05cc342e36..6dd575f307 100644
--- a/python/fedml/computing/scheduler/comm_utils/run_process_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/run_process_utils.py
@@ -135,13 +135,15 @@ def save_run_process(run_id, process_id, data_dir, info_dir,
pass
@staticmethod
- def kill_process(process_id):
+ def kill_process(process_id, exclude_current_pid=False):
try:
process = psutil.Process(process_id)
if process is None:
return
child_processes = process.children(recursive=True)
for sub_process in child_processes:
+ if exclude_current_pid and sub_process.pid == os.getpid():
+ continue
if platform.system() == 'Windows':
os.system("taskkill /PID {} /T /F".format(sub_process.pid))
else:
diff --git a/python/fedml/computing/scheduler/master/base_master_agent.py b/python/fedml/computing/scheduler/master/base_master_agent.py
index 39898b5d40..4fb3a5e755 100755
--- a/python/fedml/computing/scheduler/master/base_master_agent.py
+++ b/python/fedml/computing/scheduler/master/base_master_agent.py
@@ -24,7 +24,8 @@ def __init__(self):
def login(
self, user_id, api_key=None, device_id=None,
os_name=None, role=None, runner_cmd=None,
- communication_manager=None, sender_message_queue=None, status_center_queue=None
+ communication_manager=None, sender_message_queue=None,
+ status_center_queue=None, sender_message_event=None
):
# Login account
login_result = FedMLAccountManager.get_instance().login(
@@ -52,7 +53,8 @@ def login(
self._initialize_protocol_manager(
communication_manager=communication_manager,
sender_message_queue=sender_message_queue,
- status_center_queue=status_center_queue)
+ status_center_queue=status_center_queue,
+ sender_message_event=sender_message_event)
except Exception as e:
FedMLAccountManager.write_login_failed_file(is_client=False)
self.protocol_mgr.stop()
@@ -67,6 +69,9 @@ def logout():
GeneralConstants.cleanup_run_process(None, is_master=True)
sys_utils.cleanup_all_fedml_server_api_processes()
+ def stop(self, kill_process=False):
+ self.protocol_mgr.stop(kill_process=kill_process)
+
def _create_protocol_manager(self, role, login_result):
if self.protocol_mgr is not None:
return
@@ -89,7 +94,8 @@ def _create_protocol_manager(self, role, login_result):
self.protocol_mgr.use_local_process_as_cloud_server = self.use_local_process_as_cloud_server
def _initialize_protocol_manager(
- self, communication_manager=None, sender_message_queue=None, status_center_queue=None
+ self, communication_manager=None, sender_message_queue=None,
+ status_center_queue=None, sender_message_event=None
):
# Init local database
self._init_database()
@@ -99,7 +105,8 @@ def _initialize_protocol_manager(
self.protocol_mgr.initialize(
communication_manager=communication_manager,
sender_message_queue=sender_message_queue,
- status_center_queue=status_center_queue)
+ status_center_queue=status_center_queue,
+ sender_message_event=sender_message_event)
# Report the IDLE status to MLOps
self.mlops_metrics.report_server_training_status(
@@ -150,6 +157,8 @@ def generate_agent_instance(self):
return FedMLBaseMasterAgent()
def process_job_complete_status(self, run_id, topic, payload):
+ if self.protocol_mgr is None:
+ return
if topic in self.protocol_mgr.get_subscribed_topics():
message_handler = self.protocol_mgr.get_listener_handler(topic)
if message_handler is not None:
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
index b67f0e1e21..664fb4671e 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
@@ -57,13 +57,15 @@ def stop_job_runner(
super().stop_job_runner(run_id)
if run_as_cloud_agent or run_as_cloud_server:
- stopping_process = Process(
- target=FedMLCloudServerManager.stop_cloud_server,
- args=(run_id, server_id, args.agent_config))
- stopping_process.start()
+ if not use_local_process_as_cloud_server:
+ stopping_process = Process(
+ target=FedMLCloudServerManager.stop_cloud_server,
+ args=(run_id, server_id, args.agent_config))
+ stopping_process.start()
run_id_str = str(run_id)
if self.master_agent_instance_map.get(run_id_str, None) is not None:
+ self.master_agent_instance_map.get(run_id_str).stop()
self.master_agent_instance_map.pop(run_id_str)
if run_as_cloud_server:
@@ -78,19 +80,17 @@ def complete_job_runner(
super().complete_job_runner(run_id)
if run_as_cloud_agent or run_as_cloud_server:
- stopping_process = Process(
- target=FedMLCloudServerManager.stop_cloud_server,
- args=(run_id, server_id, args.agent_config))
- stopping_process.start()
+ if not use_local_process_as_cloud_server:
+ stopping_process = Process(
+ target=FedMLCloudServerManager.stop_cloud_server,
+ args=(run_id, server_id, args.agent_config))
+ stopping_process.start()
run_id_str = str(run_id)
if self.master_agent_instance_map.get(run_id_str, None) is not None:
+ self.master_agent_instance_map.get(run_id_str).stop(kill_process=True)
self.master_agent_instance_map.pop(run_id_str)
- if run_as_cloud_server:
- time.sleep(1)
- RunProcessUtils.kill_process(self.cloud_run_process_map[run_id_str].pid)
-
def _start_cloud_server(
self, args, run_id, request_json, edge_id=None,
use_local_process_as_cloud_server=False,
@@ -111,6 +111,7 @@ def _start_cloud_server(
message_bytes = json.dumps(request_json).encode("ascii")
base64_bytes = base64.b64encode(message_bytes)
payload = base64_bytes.decode("ascii")
+ self.master_agent_instance_map[str(run_id)] = master_agent_instance
logging.info("start the master server: {}".format(payload))
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index 2d00e442a0..2f8a4c5838 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -2,7 +2,6 @@
import base64
import json
import logging
-import time
import fedml
from ..comm_utils.constants import SchedulerConstants
@@ -240,8 +239,6 @@ def callback_start_train(self, topic=None, payload=None):
process = self._get_job_runner_manager().get_runner_process(run_id, is_cloud_server=True)
if process is not None:
GeneralConstants.save_run_process(run_id, process.pid, is_master=True)
-
- self.send_status_msg_to_edges(edge_id_list, run_id, request_json.get("server_id"))
elif self.run_as_cloud_server:
self.server_agent_id = request_json.get("cloud_agent_id", self.edge_id)
self.start_request_json = json.dumps(request_json)
@@ -261,6 +258,8 @@ def callback_start_train(self, topic=None, payload=None):
communication_manager=self.get_listener_communication_manager()
)
+ self.send_status_msg_to_edges(edge_id_list, run_id, server_id)
+
def callback_stop_train(self, topic, payload, use_payload=None):
# Print the payload
logging.info(
diff --git a/python/fedml/computing/scheduler/master/cloud_server_manager.py b/python/fedml/computing/scheduler/master/cloud_server_manager.py
index 9c35f233fd..0aabaf5dbf 100755
--- a/python/fedml/computing/scheduler/master/cloud_server_manager.py
+++ b/python/fedml/computing/scheduler/master/cloud_server_manager.py
@@ -49,13 +49,17 @@ def start_local_master_server(
communication_manager=None, sender_message_queue=None, status_center_queue=None,
master_agent_instance=None
):
+ logging.info(f"Local master server pid: {os.getpid()}")
if platform.system() != "Windows":
os.setsid()
master_agent_instance.login(
user, api_key=api_key, device_id=cloud_device_id, os_name=os_name,
role=FedMLAccountManager.ROLE_CLOUD_SERVER, runner_cmd=payload,
- communication_manager=None, sender_message_queue=None, status_center_queue=None)
+ communication_manager=None, sender_message_queue=None,
+ status_center_queue=None)
+
+ master_agent_instance.stop()
def start_cloud_server_process_entry(self):
try:
diff --git a/python/fedml/computing/scheduler/master/server_login.py b/python/fedml/computing/scheduler/master/server_login.py
index 8dd0696bc8..be7b73103f 100755
--- a/python/fedml/computing/scheduler/master/server_login.py
+++ b/python/fedml/computing/scheduler/master/server_login.py
@@ -41,4 +41,5 @@ def logout():
master_agent.login(args.api_key, api_key=args.api_key, device_id=args.device_id,
os_name=args.os_name, role=args.role, runner_cmd=args.runner_cmd)
else:
+ master_agent.stop()
master_agent.logout()
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_center.py b/python/fedml/computing/scheduler/scheduler_core/message_center.py
index 9229b2c0da..3ceafe976b 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_center.py
@@ -29,11 +29,12 @@ class FedMLMessageCenter(object):
MESSAGE_SENT_SUCCESS_RECORDS_FILE = "message-sent-success-records.log"
MESSAGE_RECEIVED_RECORDS_FILE = "message-received-records.log"
- def __init__(self, agent_config=None, sender_message_queue=None, listener_message_queue=None):
+ def __init__(self, agent_config=None, sender_message_queue=None,
+ listener_message_queue=None, sender_message_event=None):
self.sender_agent_config = agent_config
self.listener_agent_config = agent_config
self.sender_message_queue = sender_message_queue
- self.message_event = None
+ self.message_event = sender_message_event
self.message_center_process = None
self.sender_mqtt_mgr = None
self.sender_mlops_metrics = None
@@ -132,6 +133,9 @@ def release_sender_mqtt_mgr(self):
def get_sender_message_queue(self):
return self.sender_message_queue
+ def get_sender_message_event(self):
+ return self.message_event
+
def start_sender(self, message_center_name=None):
self.sender_message_queue = multiprocessing.Queue()
self.message_event = multiprocessing.Event()
@@ -154,7 +158,7 @@ def start_sender(self, message_center_name=None):
)
self.message_center_process.start()
- def stop(self):
+ def stop_message_center(self):
if self.message_event is not None:
self.message_event.set()
@@ -166,6 +170,10 @@ def check_message_stop_event(self):
logging.info("Received message center stopping event.")
raise MessageCenterStoppedException("Message center stopped (for sender)")
+ if self.listener_message_event is not None and self.listener_message_event.is_set():
+ logging.info("Received message center stopping event.")
+ raise MessageCenterStoppedException("Message center stopped (for listener)")
+
def send_message(self, topic, payload, run_id=None):
message_entity = FedMLMessageEntity(topic=topic, payload=payload, run_id=run_id)
self.sender_message_queue.put(message_entity.get_message_body())
@@ -204,6 +212,9 @@ def retry_sending_undelivered_message(self):
self.save_message_record(message_entity.run_id, message_entity.device_id, sent_message_record)
def run_sender(self, message_event, message_queue, message_center_name):
+ if platform.system() != "Windows":
+ os.setsid()
+
self.message_event = message_event
self.sender_message_queue = message_queue
self.message_center_name = message_center_name
@@ -321,7 +332,7 @@ def setup_listener_message_queue(self):
def start_listener(
self, sender_message_queue=None, listener_message_queue=None,
- agent_config=None, message_center_name=None, extra_queues=None
+ sender_message_event=None, agent_config=None, message_center_name=None, extra_queues=None
):
if self.listener_message_center_process is not None:
return
@@ -342,7 +353,7 @@ def start_listener(
target=message_runner.run_listener_dispatcher, args=(
self.listener_message_event, self.listener_message_queue,
self.listener_handler_funcs, sender_message_queue,
- message_center_name, extra_queues
+ sender_message_event, message_center_name, extra_queues
)
)
else:
@@ -350,7 +361,7 @@ def start_listener(
target=message_runner.run_listener_dispatcher, args=(
self.listener_message_event, self.listener_message_queue,
self.listener_handler_funcs, sender_message_queue,
- message_center_name, extra_queues
+ sender_message_event, message_center_name, extra_queues
)
)
self.listener_message_center_process.start()
@@ -385,13 +396,19 @@ def unsubscribe_msg(self, topic):
self.listener_mqtt_mgr.unsubscribe_msg(topic)
def run_listener_dispatcher(
- self, message_event, message_queue, listener_funcs, sender_message_queue,
+ self, listener_message_event, listener_message_queue,
+ listener_funcs, sender_message_queue, sender_message_event,
message_center_name, extra_queues
):
- self.listener_message_event = message_event
- self.listener_message_queue = message_queue
+ if platform.system() != "Windows":
+ os.setsid()
+
+ self.listener_message_event = listener_message_event
+ self.listener_message_queue = listener_message_queue
self.listener_handler_funcs = listener_funcs
self.message_center_name = message_center_name
+ self.sender_message_queue = sender_message_queue
+ self.message_event = sender_message_event
self.setup_listener_mqtt_mgr()
@@ -417,7 +434,7 @@ def run_listener_dispatcher(
# Get the message from the queue
try:
- message_body = message_queue.get(block=False, timeout=0.1)
+ message_body = listener_message_queue.get(block=False, timeout=0.1)
except queue.Empty as e: # If queue is empty, then break loop
message_body = None
if message_body is None:
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
index 80d67f33cb..833fa1edc0 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
@@ -2,11 +2,13 @@
import json
import logging
import multiprocessing
+import os
import sys
import time
import traceback
import uuid
import fedml
+from ..comm_utils.run_process_utils import RunProcessUtils
from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
from ....core.distributed.communication.mqtt.mqtt_manager import MqttManager
from ....core.mlops.mlops_metrics import MLOpsMetrics
@@ -60,7 +62,8 @@ def add_protocol_handler(self):
pass
def initialize(
- self, communication_manager=None, sender_message_queue=None, status_center_queue=None
+ self, communication_manager=None, sender_message_queue=None,
+ status_center_queue=None, sender_message_event=None
):
# Generate the message topics
self.generate_topics()
@@ -86,6 +89,7 @@ def initialize(
# Start the message center to process edge related messages.
if sender_message_queue is None:
self.setup_message_center()
+ sender_message_event = self.message_center.get_sender_message_event()
else:
self.rebuild_message_center(sender_message_queue)
@@ -94,13 +98,14 @@ def initialize(
# Start the status center to process edge related status.
if status_center_queue is None:
- self.start_status_listener_center()
+ self.start_status_listener_center(sender_message_event=sender_message_event)
else:
self.set_status_queue(status_center_queue)
self.rebuild_status_center(status_center_queue)
# Start the message center for listener
self.start_listener(sender_message_queue=self.message_center.get_sender_message_queue(),
+ sender_message_event=sender_message_event,
agent_config=self.agent_config,
message_center_name=self.message_center_name,
extra_queues=[self.get_status_queue()])
@@ -124,6 +129,8 @@ def start(self):
logging.info("Server tracing: {}".format(traceback.format_exc()))
finally:
+ logging.info(f"Protocol manager is about to exit, pid: {os.getpid()}")
+
FedMLAccountManager.write_login_failed_file(is_client=not self.is_master_agent)
self.stop()
@@ -134,7 +141,7 @@ def start(self):
clean_process_group=False)
sys.exit(1)
- def stop(self):
+ def stop(self, kill_process=False):
if self.communication_mgr is not None:
# noinspection PyBroadException
try:
@@ -146,7 +153,9 @@ def stop(self):
self.communication_mgr.loop_stop()
self.communication_mgr.disconnect()
- self.release_message_center()
+ if kill_process:
+ self.release_message_center()
+ RunProcessUtils.kill_process(os.getppid(), exclude_current_pid=True)
@abstractmethod
def _init_extra_items(self):
@@ -210,20 +219,37 @@ def rebuild_message_center(self, message_center_queue):
def release_message_center(self):
try:
+ self.stop_message_center()
+
if self.message_center is not None:
- self.message_center.stop()
+ self.message_center.stop_message_center()
self.message_center = None
except Exception as e:
logging.error(
- f"Failed to release slave communication manager with Exception {e}. "
+ f"Failed to release the message center with Exception {e}. "
f"Traceback: {traceback.format_exc()}")
pass
- def start_status_listener_center(self):
+ def release_status_center(self):
+ try:
+ self.stop_status_center()
+
+ if self.status_center is not None:
+ self.status_center.stop_status_center()
+ self.status_center = None
+
+ except Exception as e:
+ logging.error(
+ f"Failed to release the status center with Exception {e}. "
+ f"Traceback: {traceback.format_exc()}")
+ pass
+
+ def start_status_listener_center(self, sender_message_event=None):
self.start_status_center(
sender_message_center_queue=self.message_center.get_sender_message_queue(),
listener_message_center_queue=self.get_listener_message_queue(),
+ sender_message_event=sender_message_event,
is_slave_agent=not self.is_master_agent
)
@@ -289,6 +315,9 @@ def get_protocol_communication_manager(self):
def get_protocol_sender_message_queue(self):
return self.message_center.get_sender_message_queue()
+ def get_protocol_sender_message_event(self):
+ return self.message_center.get_sender_message_event()
+
def get_protocol_status_center_queue(self):
return self.get_status_queue()
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index 2a7a76c2ca..b2949af291 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -1,4 +1,5 @@
import logging
+import os
import platform
import time
@@ -107,7 +108,9 @@ def get_status_runner(self):
return None
def start_status_center(self, sender_message_center_queue=None,
- listener_message_center_queue=None, is_slave_agent=False):
+ listener_message_center_queue=None,
+ sender_message_event=None,
+ is_slave_agent=False):
self.status_queue = multiprocessing.Queue()
self.status_event = multiprocessing.Event()
self.status_event.clear()
@@ -121,20 +124,24 @@ def start_status_center(self, sender_message_center_queue=None,
self.status_center_process = multiprocessing.Process(
target=target_func, args=(
self.status_event, self.status_queue, self.status_sender_message_center_queue,
- self.status_listener_message_center_queue
+ self.status_listener_message_center_queue, sender_message_event
)
)
else:
self.status_center_process = fedml.get_process(
target=target_func, args=(
self.status_event, self.status_queue, self.status_sender_message_center_queue,
- self.status_listener_message_center_queue
+ self.status_listener_message_center_queue, sender_message_event
)
)
self.status_center_process.start()
- def check_message_stop_event(self):
+ def stop_status_center(self):
+ if self.status_event is not None:
+ self.status_event.set()
+
+ def check_status_stop_event(self):
if self.status_event is not None and self.status_event.is_set():
logging.info("Received status center stopping event.")
raise StatusCenterStoppedException("Status center stopped (for sender)")
@@ -170,7 +177,11 @@ def rebuild_status_center(self, status_queue):
def run_status_dispatcher(self, status_event, status_queue,
sender_message_center_queue,
- listener_message_center_queue):
+ listener_message_center_queue,
+ sender_message_event):
+ if platform.system() != "Windows":
+ os.setsid()
+
# Save the parameters
self.status_event = status_event
self.status_queue = status_queue
@@ -183,10 +194,11 @@ def run_status_dispatcher(self, status_event, status_queue,
self.rebuild_message_center(sender_message_center_queue)
message_center = FedMLMessageCenter(
sender_message_queue=sender_message_center_queue,
- listener_message_queue=listener_message_center_queue
+ listener_message_queue=listener_message_center_queue,
+ sender_message_event=sender_message_event
)
- if sender_message_center_queue is not None:
+ if status_queue is not None:
self.rebuild_status_center(status_queue)
# Init status manager instances
@@ -197,7 +209,7 @@ def run_status_dispatcher(self, status_event, status_queue,
# Check if we should stop status dispatcher
try:
- self.check_message_stop_event()
+ self.check_status_stop_event()
except StatusCenterStoppedException as e:
break
@@ -266,7 +278,11 @@ def run_status_dispatcher(self, status_event, status_queue,
def run_status_dispatcher_in_slave(self, status_event, status_queue,
sender_message_center_queue,
- listener_message_center_queue):
+ listener_message_center_queue,
+ sender_message_event):
+ if platform.system() != "Windows":
+ os.setsid()
+
# Save the parameters
self.status_event = status_event
self.status_queue = status_queue
@@ -279,10 +295,11 @@ def run_status_dispatcher_in_slave(self, status_event, status_queue,
self.rebuild_message_center(sender_message_center_queue)
message_center = FedMLMessageCenter(
sender_message_queue=sender_message_center_queue,
- listener_message_queue=listener_message_center_queue
+ listener_message_queue=listener_message_center_queue,
+ sender_message_event=sender_message_event
)
- if sender_message_center_queue is not None:
+ if status_queue is not None:
self.rebuild_status_center(status_queue)
# Init status manager instances
@@ -294,7 +311,7 @@ def run_status_dispatcher_in_slave(self, status_event, status_queue,
# Check if we should stop status dispatcher
try:
- self.check_message_stop_event()
+ self.check_status_stop_event()
except StatusCenterStoppedException as e:
break
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index e045458db5..fec19bed70 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -143,6 +143,9 @@ def process_job_completed_status(self, master_id, status):
if self.status_center.is_deployment_status_center and status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
self.report_deployment_status(self.run_id, GeneralConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
+ self.message_center.stop_message_center()
+ self.status_center.stop_status_center()
+
def process_job_exception_status(self, master_id, status):
# Report exception job status
self.report_exception_status(status)
diff --git a/python/fedml/computing/scheduler/slave/base_slave_agent.py b/python/fedml/computing/scheduler/slave/base_slave_agent.py
index fed10fa039..58a79aae88 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_agent.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_agent.py
@@ -25,7 +25,8 @@ def __init__(self):
def login(
self, userid, api_key=None, device_id=None,
os_name=None, need_to_check_gpu=False, role=None,
- communication_manager=None, sender_message_queue=None, status_center_queue=None
+ communication_manager=None, sender_message_queue=None,
+ status_center_queue=None, sender_message_event=None
):
# Preprocess the login args
if need_to_check_gpu:
@@ -61,7 +62,8 @@ def login(
self._initialize_protocol_manager(
communication_manager=communication_manager,
sender_message_queue=sender_message_queue,
- status_center_queue=status_center_queue)
+ status_center_queue=status_center_queue,
+ sender_message_event=sender_message_event)
except Exception as e:
FedMLAccountManager.write_login_failed_file(is_client=True)
self.protocol_mgr.stop()
@@ -90,7 +92,8 @@ def _create_protocol_manager(self, login_result):
self.protocol_mgr.agent_config = login_result.agent_config
def _initialize_protocol_manager(
- self, communication_manager=None, sender_message_queue=None, status_center_queue=None
+ self, communication_manager=None, sender_message_queue=None,
+ status_center_queue=None, sender_message_event=None
):
# Init local database
self._init_database()
@@ -100,7 +103,8 @@ def _initialize_protocol_manager(
self.protocol_mgr.initialize(
communication_manager=communication_manager,
sender_message_queue=sender_message_queue,
- status_center_queue=status_center_queue)
+ status_center_queue=status_center_queue,
+ sender_message_event=sender_message_event)
# Start the client API process
self._start_slave_api()
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index 447bd05cd9..49aad618c1 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -148,12 +148,9 @@ def add_subscribe_topic(self, topic):
def stop(self):
if self.model_device_server is not None:
- self.model_device_server.stop()
self.model_device_server = None
if self.model_device_client_list is not None:
- for model_client in self.model_device_client_list:
- model_client.stop()
self.model_device_client_list.clear()
self.model_device_client_list = None
diff --git a/python/fedml/computing/scheduler/slave/united_agents.py b/python/fedml/computing/scheduler/slave/united_agents.py
index 64ba3c5465..7aef66290d 100755
--- a/python/fedml/computing/scheduler/slave/united_agents.py
+++ b/python/fedml/computing/scheduler/slave/united_agents.py
@@ -33,6 +33,7 @@ def login(self, userid, api_key=None, device_id=None,
# Get the communication manager, sender message queue
shared_communication_mgr = launch_slave_agent.get_protocol_manager().get_protocol_communication_manager()
shared_slave_sender_message_queue = launch_slave_agent.get_protocol_manager().get_protocol_sender_message_queue()
+ shared_slave_sender_message_event = launch_slave_agent.get_protocol_manager().get_protocol_sender_message_event()
# Login with the launch master role based on
# the shared communication manager, sender message center
@@ -48,6 +49,7 @@ def login(self, userid, api_key=None, device_id=None,
shared_slave_status_center_queue = launch_slave_agent.get_protocol_manager().get_protocol_status_center_queue()
shared_master_status_center_queue = launch_master_agent.get_protocol_manager().get_protocol_status_center_queue()
shared_master_sender_message_queue = launch_master_agent.get_protocol_manager().get_protocol_sender_message_queue()
+ shared_master_sender_message_event = launch_master_agent.get_protocol_manager().get_protocol_sender_message_event()
# Login with the deployment master role based on
# the shared communication manager, sender message center, status center
@@ -56,7 +58,8 @@ def login(self, userid, api_key=None, device_id=None,
os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM,
communication_manager=shared_communication_mgr,
sender_message_queue=shared_master_sender_message_queue,
- status_center_queue=shared_master_status_center_queue
+ status_center_queue=shared_master_status_center_queue,
+ sender_message_event=shared_master_sender_message_event
)
# Login with the deployment slave role based on
@@ -66,7 +69,8 @@ def login(self, userid, api_key=None, device_id=None,
os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM,
communication_manager=shared_communication_mgr,
sender_message_queue=shared_slave_sender_message_queue,
- status_center_queue=shared_slave_status_center_queue
+ status_center_queue=shared_slave_status_center_queue,
+ sender_message_event=shared_slave_sender_message_event
)
# Start the slave agent to connect to servers and loop forever.
From aecafb80f9d6731b6b15e4cfca7b15035b82cf84 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Mon, 17 Jun 2024 14:39:16 -0700
Subject: [PATCH 161/282] Fix compatibility by limiting numpy latest version.
---
python/setup.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/setup.py b/python/setup.py
index 9651465d32..4757c10a17 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -40,7 +40,7 @@ def finalize_options(self):
'multiprocess',
'networkx<3.0',
'ntplib',
- 'numpy>=1.21',
+ 'numpy<2.0.0',
'onnx',
'paho-mqtt<2.0.0',
'pandas',
From 1af78e732658c2f3af7f26670b9fda29c1cb0361 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Tue, 18 Jun 2024 11:58:30 +0800
Subject: [PATCH 162/282] [CoreEngine] replace the queue with the managed queue
to avoid the multiprocessing lock problem.
---
.../master/base_master_job_runner.py | 20 +++++++++----------
.../model_scheduler/master_job_runner.py | 2 +-
.../scheduler_core/message_center.py | 6 +++---
.../scheduler_base_job_runner.py | 7 ++++---
.../scheduler/scheduler_core/status_center.py | 2 +-
5 files changed, 19 insertions(+), 18 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner.py b/python/fedml/computing/scheduler/master/base_master_job_runner.py
index 1072e6b045..9a77c2ba82 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner.py
@@ -42,13 +42,13 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
is_master_runner=True
)
- self.run_edge_id_status_queue = multiprocessing.Queue()
- self.run_metrics_queue = multiprocessing.Queue()
- self.run_events_queue = multiprocessing.Queue()
- self.run_artifacts_queue = multiprocessing.Queue()
- self.run_logs_queue = multiprocessing.Queue()
- self.run_edge_device_info_queue = multiprocessing.Queue()
- self.run_edge_device_info_global_queue = multiprocessing.Queue()
+ self.run_edge_id_status_queue = multiprocessing.Manager().Queue()
+ self.run_metrics_queue = multiprocessing.Manager().Queue()
+ self.run_events_queue = multiprocessing.Manager().Queue()
+ self.run_artifacts_queue = multiprocessing.Manager().Queue()
+ self.run_logs_queue = multiprocessing.Manager().Queue()
+ self.run_edge_device_info_queue = multiprocessing.Manager().Queue()
+ self.run_edge_device_info_global_queue = multiprocessing.Manager().Queue()
self.run_extend_queue_list = None
self.async_check_timeout = 0
self.enable_async_cluster = False
@@ -453,7 +453,7 @@ def put_run_edge_device_info_to_queue(self, run_id, edge_id, device_info):
if int(edge_id) in edge_ids or str(edge_id) in edge_ids:
run_id_str = str(run_id)
if self.run_edge_device_info_queue is None:
- self.run_edge_device_info_queue = multiprocessing.Queue()
+ self.run_edge_device_info_queue = multiprocessing.Manager().Queue()
self.run_edge_device_info_queue.put(device_info)
def should_continue_run_job(self, run_id):
@@ -581,7 +581,7 @@ def callback_run_logs(self, topic, payload):
run_id = str(topic).split('/')[-1]
run_id_str = str(run_id)
if self.run_logs_queue is None:
- self.run_logs_queue = multiprocessing.Queue()
+ self.run_logs_queue = multiprocessing.Manager().Queue()
self.run_logs_queue.put(payload)
def callback_run_metrics(self, topic, payload):
@@ -589,7 +589,7 @@ def callback_run_metrics(self, topic, payload):
run_id = str(topic).split('/')[-1]
run_id_str = str(run_id)
if self.run_metrics_queue is None:
- self.run_metrics_queue = multiprocessing.Queue()
+ self.run_metrics_queue = multiprocessing.Manager().Queue()
self.run_metrics_queue.put(payload)
# def send_training_request_to_edges(self, active_edge_info_dict):
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index e32e7421f6..9854dad5f6 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -50,7 +50,7 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
self.replica_controller = None
self.deployed_replica_payload = None
self.slave_deployment_results_map = dict()
- self.deployment_result_queue = multiprocessing.Queue()
+ self.deployment_result_queue = multiprocessing.Manager().Queue()
self.is_fresh_endpoint = True
# Override
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_center.py b/python/fedml/computing/scheduler/scheduler_core/message_center.py
index 3ceafe976b..aeac1a3855 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_center.py
@@ -137,7 +137,7 @@ def get_sender_message_event(self):
return self.message_event
def start_sender(self, message_center_name=None):
- self.sender_message_queue = multiprocessing.Queue()
+ self.sender_message_queue = multiprocessing.Manager().Queue()
self.message_event = multiprocessing.Event()
self.message_event.clear()
message_center = FedMLMessageCenter(agent_config=self.sender_agent_config,
@@ -328,7 +328,7 @@ def get_listener_message_queue(self):
return self.listener_message_queue
def setup_listener_message_queue(self):
- self.listener_message_queue = multiprocessing.Queue()
+ self.listener_message_queue = multiprocessing.Manager().Queue()
def start_listener(
self, sender_message_queue=None, listener_message_queue=None,
@@ -339,7 +339,7 @@ def start_listener(
if listener_message_queue is None:
if self.listener_message_queue is None:
- self.listener_message_queue = multiprocessing.Queue()
+ self.listener_message_queue = multiprocessing.Manager().Queue()
else:
self.listener_message_queue = listener_message_queue
self.listener_message_event = multiprocessing.Event()
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index ffaee555af..30df7f1905 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -75,6 +75,7 @@ def __init__(self, args, edge_id=0, request_json=None, agent_config=None, run_id
self.user_name = None
self.general_edge_id = None
self.message_center = None
+ self.status_center = None
self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES = {
"${FEDSYS.RUN_ID}": "",
"${FEDSYS.PRIVATE_LOCAL_DATA}": "",
@@ -210,7 +211,7 @@ def retrieve_and_unzip_package(self, package_name, package_url):
# Open a process to download the package so that we can avoid the request is blocked and check the timeout.
from multiprocessing import Process
completed_event = multiprocessing.Event()
- info_queue = multiprocessing.Queue()
+ info_queue = multiprocessing.Manager().Queue()
if platform.system() == "Windows":
download_process = multiprocessing.Process(
target=self.download_package_proc,
@@ -648,8 +649,8 @@ def rebuild_message_status_center(self, sender_message_queue, listener_message_q
self.mlops_metrics.set_messenger(self.message_center)
self.mlops_metrics.run_id = self.run_id
- status_center = FedMLStatusCenter.rebuild_status_center_from_queue(status_queue)
+ self.status_center = FedMLStatusCenter.rebuild_status_center_from_queue(status_queue)
if self.status_reporter is None:
self.status_reporter = MLOpsMetrics()
- self.status_reporter.set_messenger(status_center)
+ self.status_reporter.set_messenger(self.status_center)
self.status_reporter.run_id = self.run_id
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index b2949af291..76ba9857c6 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -111,7 +111,7 @@ def start_status_center(self, sender_message_center_queue=None,
listener_message_center_queue=None,
sender_message_event=None,
is_slave_agent=False):
- self.status_queue = multiprocessing.Queue()
+ self.status_queue = multiprocessing.Manager().Queue()
self.status_event = multiprocessing.Event()
self.status_event.clear()
self.status_sender_message_center_queue = sender_message_center_queue
From 89219fb3c20972ff94badc76f8e90d71592e5647 Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Tue, 18 Jun 2024 07:00:43 +0000
Subject: [PATCH 163/282] Workaround device mapping inconsistency
---
.../comm_utils/gpu_utils/gpu_utils.py | 1 +
.../comm_utils/gpu_utils/qualcomm_utils.py | 36 +++++++++++++------
.../scheduler/comm_utils/hardware_utils.py | 2 ++
.../scheduler_core/account_manager.py | 2 +-
4 files changed, 29 insertions(+), 12 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
index bc7a3b8216..b48a3e85b7 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/gpu_utils.py
@@ -27,6 +27,7 @@ class GPUCard:
memoryUsed: float
memoryUtil: float
load: Optional[float] = 0.0
+ device_path: Optional[str] = ""
uuid: Optional[str] = ""
display_mode: Optional[str] = ""
display_active: Optional[str] = ""
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
index 88114cf2ad..13131e362d 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/qualcomm_utils.py
@@ -26,19 +26,22 @@ def detect_gpu_card_type(cls) -> Optional[GPUCardType]:
@staticmethod
def get_gpu_cards() -> List[GPUCard]:
- from qaicrt import Util, QIDList, QDevInfo, QStatus
+ return list(QualcommNPUtil.__get_gpu_cards().values())
- cards = []
+ @staticmethod
+ def __get_gpu_cards() -> Dict[int, GPUCard]:
+ from qaicrt import Util, QIDList, QDevInfo, QStatus
+ cards = dict()
util = Util()
status, card_list = util.getDeviceIds()
if status.value == 0:
for card in card_list:
status, card_info = util.getDeviceInfo(card)
if status.value == 0 and card_info.devStatus.value == 1:
- cards.append(QualcommNPUtil.__convert(card_info))
-
+ gpu_card = QualcommNPUtil.__convert(card_info)
+ cards[gpu_card.id] = gpu_card
else:
- logging.error("Qualcomm Card Status not Healthy")
+ logging.error("Qualcomm Cards Status not Healthy")
return cards
@staticmethod
@@ -58,11 +61,21 @@ def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memo
@staticmethod
def get_docker_gpu_device_mapping(gpu_ids: Optional[List[int]], num_gpus: int = 0) -> Optional[Dict]:
- if gpu_ids is not None and len(gpu_ids):
- return {
- "devices": [f"{QualcommNPUtil.NPU_CARD_PATH}{gpu_id}:{QualcommNPUtil.NPU_CARD_PATH}{gpu_id}" for gpu_id
- in gpu_ids]}
- return None
+ if gpu_ids is None or not len(gpu_ids):
+ return None
+
+ devices = []
+ gpu_cards = QualcommNPUtil.__get_gpu_cards()
+
+ for gpu_id in gpu_ids:
+ if not (gpu_id in gpu_cards and gpu_cards[gpu_id].device_path):
+ logging.error("Failed to get gpu device mapping for docker")
+ break
+ else:
+ device_path = gpu_cards[gpu_id].device_path
+ devices.append(f"{device_path}:{device_path}")
+
+ return {"devices": devices} if len(devices) == len(gpu_ids) else None
@staticmethod
def get_docker_gpu_ids_by_container_name(container_name: str, docker_client: DockerClient) -> List[int]:
@@ -87,7 +100,8 @@ def __convert(npu) -> GPUCard:
load = (nsp_total - nsp_free) / nsp_total
return GPUCard(
- id=npu.qid,
+ id=npu.mhiId,
+ device_path=npu.name,
name=npu.pciInfo.devicename,
driver=npu.devData.fwQCImageVersionString,
serial=npu.devData.serial,
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index e73809955e..c876948145 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -60,5 +60,7 @@ def get_docker_gpu_ids_by_container_name(container_name: str, docker_client: Doc
if __name__ == "__main__":
gpus = HardwareUtil.get_gpus()
get_available_gpu_cards = HardwareUtil.get_available_gpu_ids(limit=len(gpus))
+ device_mapping = HardwareUtil.get_docker_gpu_device_mapping(get_available_gpu_cards, len(get_available_gpu_cards))
print(gpus)
print(get_available_gpu_cards)
+ print(device_mapping)
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
index 3491e102f6..3b80511d12 100755
--- a/python/fedml/computing/scheduler/scheduler_core/account_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -266,7 +266,7 @@ def get_uuid():
if not use_machine_id:
device_id = hex(uuid.getnode())
else:
- device_id = device_id = FedMLAccountManager.get_gpu_machine_id()
+ device_id = FedMLAccountManager.get_gpu_machine_id()
else:
device_id = sys_utils.run_subprocess_open(
"hal-get-property --udi /org/freedesktop/Hal/devices/computer --key system.hardware.uuid".split()
From 1d5a05db71ba3943cb42eea0836fabc181af7ac6 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 18 Jun 2024 18:23:40 -0700
Subject: [PATCH 164/282] [Deploy][Autoscale] Bug fix: continue the for loop if
no scale op.
---
python/fedml/computing/scheduler/comm_utils/job_monitor.py | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index a7d5214a02..97a4cb6ebc 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -148,7 +148,7 @@ def autoscaler_reconcile_after_interval(self):
if current_replicas == new_replicas:
# Basically the autoscaler decided that no scaling operation should take place.
logging.info(f"No scaling operation for endpoint {e_id}.")
- return
+ continue
# Should scale in / out
curr_version = fedml.get_env_version()
@@ -159,7 +159,7 @@ def autoscaler_reconcile_after_interval(self):
mlops_prefix = "https://open-test.fedml.ai/"
else:
logging.error(f"Do not support the version {curr_version}.")
- return
+ continue
autoscale_url_path = "fedmlModelServer/api/v1/endpoint/auto-scale"
url = f"{mlops_prefix}{autoscale_url_path}"
@@ -167,7 +167,7 @@ def autoscaler_reconcile_after_interval(self):
cached_token = fedml_model_cache.get_end_point_token(e_id, e_name, model_name)
if cached_token is None:
logging.error(f"Failed to get the cached token for endpoint {e_id}.")
- return
+ continue
req_header = {
"Authorization": f"Bearer {cached_token}"
From 31c57e01d426a82127fd3cff2ae45ee36f6bbe14 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Tue, 18 Jun 2024 21:32:41 -0400
Subject: [PATCH 165/282] Polishing the autoscaler real test.
---
.../test/scaling_algorithm_real_test.py | 64 +++++--------------
1 file changed, 15 insertions(+), 49 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/scaling_algorithm_real_test.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/scaling_algorithm_real_test.py
index 34721d9002..0fae77c3f3 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/scaling_algorithm_real_test.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/scaling_algorithm_real_test.py
@@ -2,9 +2,10 @@
import logging
from collections import namedtuple
-from fedml.computing.scheduler.model_scheduler.autoscaler.autoscaler import Autoscaler, ReactivePolicy
+from fedml.computing.scheduler.model_scheduler.autoscaler.autoscaler import Autoscaler
from fedml.core.mlops.mlops_runtime_log import MLOpsRuntimeLog
from fedml.computing.scheduler.model_scheduler.device_model_cache import FedMLModelCache
+from fedml.computing.scheduler.model_scheduler.autoscaler.policies import ConcurrentQueryPolicy
if __name__ == "__main__":
@@ -18,9 +19,6 @@
parser.add_argument('--redis_addr', default="local")
parser.add_argument('--redis_port', default=6379)
parser.add_argument('--redis_password', default="fedml_default")
- parser.add_argument('--metric',
- default="latency",
- help="Either latency or qps")
args = parser.parse_args()
fedml_model_cache = FedMLModelCache.get_instance()
@@ -32,50 +30,18 @@
# Init the autoscaler
autoscaler = Autoscaler(args.redis_addr, args.redis_port, args.redis_password)
- latency_reactive_policy_default = {
- "metric": "latency",
- "ewm_mins": 15,
- "ewm_alpha": 0.5,
- "ub_threshold": 0.5,
- "lb_threshold": 0.99,
- "triggering_value": 1.6561916828471053
+ autoscaling_policy_config = {
+ "current_replicas": 1,
+ "min_replicas": 1,
+ "max_replicas": 3,
+ "queries_per_replica": 2,
+ "window_size_secs": 60,
+ "scaledown_delay_secs": 120,
}
- qps_reactive_policy_default = {
- "metric": "qps",
- "ewm_mins": 15,
- "ewm_alpha": 0.5,
- "ub_threshold": 2,
- "lb_threshold": 0.5
- }
- policy_config = latency_reactive_policy_default \
- if args.metric == "latency" else qps_reactive_policy_default
- autoscaling_policy = ReactivePolicy(**policy_config)
-
- for endpoint_settings in endpoints_settings_list:
- endpoint_state = endpoint_settings["state"]
- if endpoint_state == "DEPLOYED" and endpoint_settings["enable_auto_scaling"]:
-
- e_id, e_name, model_name = \
- endpoint_settings["endpoint_id"], \
- endpoint_settings["endpoint_name"], \
- endpoint_settings["model_name"]
- logging.info(f"Querying the autoscaler for endpoint {e_id} with user settings {endpoint_settings}.")
-
- # For every endpoint we just update the policy configuration.
- autoscaling_policy.min_replicas = endpoint_settings["scale_min"]
- autoscaling_policy.max_replicas = endpoint_settings["scale_max"]
- # We retrieve a list of replicas for every endpoint. The number
- # of running replicas is the length of that list.
- current_replicas = len(fedml_model_cache.get_endpoint_replicas_results(e_id))
- autoscaling_policy.current_replicas = current_replicas
- logging.info(f"Endpoint {e_id} autoscaling policy: {autoscaling_policy}.")
-
- scale_op = autoscaler.scale_operation_endpoint(
- autoscaling_policy,
- str(e_id))
-
- new_replicas = current_replicas + scale_op.value
+ autoscaling_policy = ConcurrentQueryPolicy(**autoscaling_policy_config)
- logging.info(f"Scaling operation {scale_op.value} for endpoint {e_id} .")
- logging.info(f"New Replicas {new_replicas} for endpoint {e_id} .")
- logging.info(f"Current Replicas {current_replicas} for endpoint {e_id} .")
+ e_id = 1821952311
+ scale_op = autoscaler.scale_operation_endpoint(
+ autoscaling_policy,
+ str(e_id))
+ logging.info(f"Scaling operation {scale_op.value} for endpoint {e_id} .")
From 4cb53fe55f5a4e748af0daaf27eb53773cdde2d6 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Tue, 18 Jun 2024 21:37:12 -0400
Subject: [PATCH 166/282] Replacing e_id.
---
.../autoscaler/test/scaling_algorithm_real_test.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/scaling_algorithm_real_test.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/scaling_algorithm_real_test.py
index 0fae77c3f3..78a1231abf 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/scaling_algorithm_real_test.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/test/scaling_algorithm_real_test.py
@@ -40,7 +40,8 @@
}
autoscaling_policy = ConcurrentQueryPolicy(**autoscaling_policy_config)
- e_id = 1821952311
+ # Please replace the `e_id` below with a proper e_id value.
+ e_id = 1111
scale_op = autoscaler.scale_operation_endpoint(
autoscaling_policy,
str(e_id))
From 1422fa10d649d55e484ad5844ed9006fcd8bb38e Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 19 Jun 2024 15:24:03 +0800
Subject: [PATCH 167/282] [CoreEngine] check the nil pointer and update the
numpy version.
---
.../status_manager_protocols.py | 21 ++++++++++---------
python/setup.py | 6 +++---
2 files changed, 14 insertions(+), 13 deletions(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index fec19bed70..ac3d8c3cb3 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -190,16 +190,17 @@ def process_job_status_consensus(self, run_id, master_id, status):
status = self.get_entire_job_status()
# Set the device status based on the job status
- for edge_id_item, edge_status_item in self.edge_status_dict.items():
- if edge_id_item == "server":
- continue
-
- # Calc the device status based on the job status
- consensus_device_status = FedMLStatusManager.get_device_consensus_status_in_job(
- status, edge_status_item)
- if consensus_device_status is not None:
- self.message_reporter.report_client_training_status(
- edge_id_item, consensus_device_status, run_id=run_id, update_db=False)
+ if self.edge_status_dict is not None:
+ for edge_id_item, edge_status_item in self.edge_status_dict.items():
+ if edge_id_item == "server":
+ continue
+
+ # Calc the device status based on the job status
+ consensus_device_status = FedMLStatusManager.get_device_consensus_status_in_job(
+ status, edge_status_item)
+ if consensus_device_status is not None:
+ self.message_reporter.report_client_training_status(
+ edge_id_item, consensus_device_status, run_id=run_id, update_db=False)
# Save the job status to local storage
FedMLServerDataInterface.get_instance().save_job_status(run_id, master_id, status, status)
diff --git a/python/setup.py b/python/setup.py
index 9651465d32..f00c0b4335 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -20,7 +20,7 @@ def finalize_options(self):
requirements = [
'GPUtil',
- 'PyYAML',
+ 'PyYAML==5.3.1',
'aiohttp>=3.8.1',
'attrdict',
'attrs',
@@ -40,7 +40,7 @@ def finalize_options(self):
'multiprocess',
'networkx<3.0',
'ntplib',
- 'numpy>=1.21',
+ 'numpy>=1.21,<2.0',
'onnx',
'paho-mqtt<2.0.0',
'pandas',
@@ -126,7 +126,7 @@ def finalize_options(self):
setup(
name="fedml",
- version="0.9.0",
+ version="0.8.31b23",
author="FedML Team",
author_email="ch@fedml.ai",
description="A research and production integrated edge-cloud library for "
From 158eb9c1d281e520f364cdec0602b4b9b417836b Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 19 Jun 2024 15:41:32 +0800
Subject: [PATCH 168/282] [CoreEngine] remove the deprecated action runners.
---
.../build_wheels_and_releases.yml-backup | 0
.../{ => deprecated}/full_e2e_test.yml-bakcup | 0
.github/workflows/{ => deprecated}/runner.md | 0
...oke_test_cross_device_mnn_server_linux.yml | 0
...ke_test_cross_silo_fedavg_attack_linux.yml | 0
...smoke_test_cross_silo_fedavg_cdp_linux.yml | 0
...e_test_cross_silo_fedavg_defense_linux.yml | 0
...smoke_test_cross_silo_fedavg_ldp_linux.yml | 0
.../smoke_test_cross_silo_ho_linux.yml | 0
.../smoke_test_cross_silo_ho_win.yml | 0
...moke_test_cross_silo_lightsecagg_linux.yml | 0
.../smoke_test_cross_silo_lightsecagg_win.yml | 0
.../smoke_test_flow_linux.yml | 0
.../smoke_test_ml_engines_linux_jax.yml | 0
.../smoke_test_ml_engines_linux_mxnet.yml | 0
.../smoke_test_ml_engines_linux_tf.yml | 0
.../smoke_test_ml_engines_win.yml | 0
.../smoke_test_pip_cli_sp_linux.yml | 0
.../smoke_test_pip_cli_sp_win.yml | 0
.../{ => deprecated}/smoke_test_security.yml | 0
.../smoke_test_simulation_mpi_linux.yml | 0
.../federate/quick_start/octopus/dump.rdb | Bin 0 -> 7248 bytes
.../federate/quick_start/octopus/nohup.out | 3032 +++++++++++++++++
23 files changed, 3032 insertions(+)
rename .github/workflows/{ => deprecated}/build_wheels_and_releases.yml-backup (100%)
rename .github/workflows/{ => deprecated}/full_e2e_test.yml-bakcup (100%)
rename .github/workflows/{ => deprecated}/runner.md (100%)
rename .github/workflows/{ => deprecated}/smoke_test_cross_device_mnn_server_linux.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_cross_silo_fedavg_attack_linux.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_cross_silo_fedavg_cdp_linux.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_cross_silo_fedavg_defense_linux.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_cross_silo_fedavg_ldp_linux.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_cross_silo_ho_linux.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_cross_silo_ho_win.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_cross_silo_lightsecagg_linux.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_cross_silo_lightsecagg_win.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_flow_linux.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_ml_engines_linux_jax.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_ml_engines_linux_mxnet.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_ml_engines_linux_tf.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_ml_engines_win.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_pip_cli_sp_linux.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_pip_cli_sp_win.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_security.yml (100%)
rename .github/workflows/{ => deprecated}/smoke_test_simulation_mpi_linux.yml (100%)
create mode 100644 python/examples/federate/quick_start/octopus/dump.rdb
create mode 100644 python/examples/federate/quick_start/octopus/nohup.out
diff --git a/.github/workflows/build_wheels_and_releases.yml-backup b/.github/workflows/deprecated/build_wheels_and_releases.yml-backup
similarity index 100%
rename from .github/workflows/build_wheels_and_releases.yml-backup
rename to .github/workflows/deprecated/build_wheels_and_releases.yml-backup
diff --git a/.github/workflows/full_e2e_test.yml-bakcup b/.github/workflows/deprecated/full_e2e_test.yml-bakcup
similarity index 100%
rename from .github/workflows/full_e2e_test.yml-bakcup
rename to .github/workflows/deprecated/full_e2e_test.yml-bakcup
diff --git a/.github/workflows/runner.md b/.github/workflows/deprecated/runner.md
similarity index 100%
rename from .github/workflows/runner.md
rename to .github/workflows/deprecated/runner.md
diff --git a/.github/workflows/smoke_test_cross_device_mnn_server_linux.yml b/.github/workflows/deprecated/smoke_test_cross_device_mnn_server_linux.yml
similarity index 100%
rename from .github/workflows/smoke_test_cross_device_mnn_server_linux.yml
rename to .github/workflows/deprecated/smoke_test_cross_device_mnn_server_linux.yml
diff --git a/.github/workflows/smoke_test_cross_silo_fedavg_attack_linux.yml b/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_attack_linux.yml
similarity index 100%
rename from .github/workflows/smoke_test_cross_silo_fedavg_attack_linux.yml
rename to .github/workflows/deprecated/smoke_test_cross_silo_fedavg_attack_linux.yml
diff --git a/.github/workflows/smoke_test_cross_silo_fedavg_cdp_linux.yml b/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_cdp_linux.yml
similarity index 100%
rename from .github/workflows/smoke_test_cross_silo_fedavg_cdp_linux.yml
rename to .github/workflows/deprecated/smoke_test_cross_silo_fedavg_cdp_linux.yml
diff --git a/.github/workflows/smoke_test_cross_silo_fedavg_defense_linux.yml b/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_defense_linux.yml
similarity index 100%
rename from .github/workflows/smoke_test_cross_silo_fedavg_defense_linux.yml
rename to .github/workflows/deprecated/smoke_test_cross_silo_fedavg_defense_linux.yml
diff --git a/.github/workflows/smoke_test_cross_silo_fedavg_ldp_linux.yml b/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_ldp_linux.yml
similarity index 100%
rename from .github/workflows/smoke_test_cross_silo_fedavg_ldp_linux.yml
rename to .github/workflows/deprecated/smoke_test_cross_silo_fedavg_ldp_linux.yml
diff --git a/.github/workflows/smoke_test_cross_silo_ho_linux.yml b/.github/workflows/deprecated/smoke_test_cross_silo_ho_linux.yml
similarity index 100%
rename from .github/workflows/smoke_test_cross_silo_ho_linux.yml
rename to .github/workflows/deprecated/smoke_test_cross_silo_ho_linux.yml
diff --git a/.github/workflows/smoke_test_cross_silo_ho_win.yml b/.github/workflows/deprecated/smoke_test_cross_silo_ho_win.yml
similarity index 100%
rename from .github/workflows/smoke_test_cross_silo_ho_win.yml
rename to .github/workflows/deprecated/smoke_test_cross_silo_ho_win.yml
diff --git a/.github/workflows/smoke_test_cross_silo_lightsecagg_linux.yml b/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_linux.yml
similarity index 100%
rename from .github/workflows/smoke_test_cross_silo_lightsecagg_linux.yml
rename to .github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_linux.yml
diff --git a/.github/workflows/smoke_test_cross_silo_lightsecagg_win.yml b/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_win.yml
similarity index 100%
rename from .github/workflows/smoke_test_cross_silo_lightsecagg_win.yml
rename to .github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_win.yml
diff --git a/.github/workflows/smoke_test_flow_linux.yml b/.github/workflows/deprecated/smoke_test_flow_linux.yml
similarity index 100%
rename from .github/workflows/smoke_test_flow_linux.yml
rename to .github/workflows/deprecated/smoke_test_flow_linux.yml
diff --git a/.github/workflows/smoke_test_ml_engines_linux_jax.yml b/.github/workflows/deprecated/smoke_test_ml_engines_linux_jax.yml
similarity index 100%
rename from .github/workflows/smoke_test_ml_engines_linux_jax.yml
rename to .github/workflows/deprecated/smoke_test_ml_engines_linux_jax.yml
diff --git a/.github/workflows/smoke_test_ml_engines_linux_mxnet.yml b/.github/workflows/deprecated/smoke_test_ml_engines_linux_mxnet.yml
similarity index 100%
rename from .github/workflows/smoke_test_ml_engines_linux_mxnet.yml
rename to .github/workflows/deprecated/smoke_test_ml_engines_linux_mxnet.yml
diff --git a/.github/workflows/smoke_test_ml_engines_linux_tf.yml b/.github/workflows/deprecated/smoke_test_ml_engines_linux_tf.yml
similarity index 100%
rename from .github/workflows/smoke_test_ml_engines_linux_tf.yml
rename to .github/workflows/deprecated/smoke_test_ml_engines_linux_tf.yml
diff --git a/.github/workflows/smoke_test_ml_engines_win.yml b/.github/workflows/deprecated/smoke_test_ml_engines_win.yml
similarity index 100%
rename from .github/workflows/smoke_test_ml_engines_win.yml
rename to .github/workflows/deprecated/smoke_test_ml_engines_win.yml
diff --git a/.github/workflows/smoke_test_pip_cli_sp_linux.yml b/.github/workflows/deprecated/smoke_test_pip_cli_sp_linux.yml
similarity index 100%
rename from .github/workflows/smoke_test_pip_cli_sp_linux.yml
rename to .github/workflows/deprecated/smoke_test_pip_cli_sp_linux.yml
diff --git a/.github/workflows/smoke_test_pip_cli_sp_win.yml b/.github/workflows/deprecated/smoke_test_pip_cli_sp_win.yml
similarity index 100%
rename from .github/workflows/smoke_test_pip_cli_sp_win.yml
rename to .github/workflows/deprecated/smoke_test_pip_cli_sp_win.yml
diff --git a/.github/workflows/smoke_test_security.yml b/.github/workflows/deprecated/smoke_test_security.yml
similarity index 100%
rename from .github/workflows/smoke_test_security.yml
rename to .github/workflows/deprecated/smoke_test_security.yml
diff --git a/.github/workflows/smoke_test_simulation_mpi_linux.yml b/.github/workflows/deprecated/smoke_test_simulation_mpi_linux.yml
similarity index 100%
rename from .github/workflows/smoke_test_simulation_mpi_linux.yml
rename to .github/workflows/deprecated/smoke_test_simulation_mpi_linux.yml
diff --git a/python/examples/federate/quick_start/octopus/dump.rdb b/python/examples/federate/quick_start/octopus/dump.rdb
new file mode 100644
index 0000000000000000000000000000000000000000..f31af5cb4fd49f4a377eb444135610e4915c5730
GIT binary patch
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diff --git a/python/examples/federate/quick_start/octopus/nohup.out b/python/examples/federate/quick_start/octopus/nohup.out
new file mode 100644
index 0000000000..2350761958
--- /dev/null
+++ b/python/examples/federate/quick_start/octopus/nohup.out
@@ -0,0 +1,3032 @@
+58896:C 27 May 2024 16:58:15.551 # oO0OoO0OoO0Oo Redis is starting oO0OoO0OoO0Oo
+58896:C 27 May 2024 16:58:15.551 # Redis version=7.0.11, bits=64, commit=00000000, modified=0, pid=58896, just started
+58896:C 27 May 2024 16:58:15.551 # Warning: no config file specified, using the default config. In order to specify a config file use redis-server /path/to/redis.conf
+58896:M 27 May 2024 16:58:15.551 * monotonic clock: POSIX clock_gettime
+58896:M 27 May 2024 16:58:15.552 * Running mode=standalone, port=6379.
+58896:M 27 May 2024 16:58:15.552 # WARNING: The TCP backlog setting of 511 cannot be enforced because kern.ipc.somaxconn is set to the lower value of 128.
+58896:M 27 May 2024 16:58:15.552 # Server initialized
+58896:M 27 May 2024 16:58:15.552 * Ready to accept connections
+58896:M 27 May 2024 16:58:22.918 * DB saved on disk
+58896:M 27 May 2024 17:58:23.012 * 1 changes in 3600 seconds. Saving...
+58896:M 27 May 2024 17:58:23.013 * Background saving started by pid 65644
+65644:C 27 May 2024 17:58:23.020 * DB saved on disk
+65644:C 27 May 2024 17:58:23.021 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 27 May 2024 17:58:23.114 * Background saving terminated with success
+58896:M 27 May 2024 19:13:18.626 * 1 changes in 3600 seconds. Saving...
+58896:M 27 May 2024 19:13:18.736 * Background saving started by pid 72278
+72278:C 27 May 2024 19:13:18.746 * DB saved on disk
+72278:C 27 May 2024 19:13:18.746 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 27 May 2024 19:13:18.838 * Background saving terminated with success
+58896:M 28 May 2024 14:08:25.606 * 1 changes in 3600 seconds. Saving...
+58896:M 28 May 2024 14:08:25.608 * Background saving started by pid 78120
+78120:C 28 May 2024 14:08:25.615 * DB saved on disk
+78120:C 28 May 2024 14:08:25.616 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 28 May 2024 14:08:25.709 * Background saving terminated with success
+58896:M 28 May 2024 15:19:26.423 * 1 changes in 3600 seconds. Saving...
+58896:M 28 May 2024 15:19:26.539 * Background saving started by pid 84225
+84225:C 28 May 2024 15:19:26.545 * DB saved on disk
+84225:C 28 May 2024 15:19:26.546 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 28 May 2024 15:19:26.642 * Background saving terminated with success
+58896:M 29 May 2024 17:57:07.834 * 1 changes in 3600 seconds. Saving...
+58896:M 29 May 2024 17:57:07.835 * Background saving started by pid 4206
+4206:C 29 May 2024 17:57:07.849 * DB saved on disk
+4206:C 29 May 2024 17:57:07.850 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 29 May 2024 17:57:07.935 * Background saving terminated with success
+58896:M 29 May 2024 18:57:08.006 * 1 changes in 3600 seconds. Saving...
+58896:M 29 May 2024 18:57:08.008 * Background saving started by pid 11453
+11453:C 29 May 2024 18:57:08.021 * DB saved on disk
+11453:C 29 May 2024 18:57:08.022 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 29 May 2024 18:57:08.109 * Background saving terminated with success
+58896:M 29 May 2024 19:21:58.973 * 100 changes in 300 seconds. Saving...
+58896:M 29 May 2024 19:21:58.973 * Background saving started by pid 19535
+19535:C 29 May 2024 19:21:58.981 * DB saved on disk
+19535:C 29 May 2024 19:21:58.981 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 29 May 2024 19:21:59.074 * Background saving terminated with success
+58896:M 29 May 2024 19:27:40.433 * DB saved on disk
+58896:M 29 May 2024 20:39:51.202 * 1 changes in 3600 seconds. Saving...
+58896:M 29 May 2024 20:39:51.203 * Background saving started by pid 21314
+21314:C 29 May 2024 20:39:51.314 * DB saved on disk
+21314:C 29 May 2024 20:39:51.315 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 29 May 2024 20:39:51.407 * Background saving terminated with success
+58896:M 29 May 2024 22:49:24.515 * 1 changes in 3600 seconds. Saving...
+58896:M 29 May 2024 22:49:24.516 * Background saving started by pid 25814
+25814:C 29 May 2024 22:49:24.587 * DB saved on disk
+25814:C 29 May 2024 22:49:24.587 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 29 May 2024 22:49:24.617 * Background saving terminated with success
+58896:M 29 May 2024 23:08:52.971 * 100 changes in 300 seconds. Saving...
+58896:M 29 May 2024 23:08:52.972 * Background saving started by pid 28739
+28739:C 29 May 2024 23:08:52.978 * DB saved on disk
+28739:C 29 May 2024 23:08:52.978 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 29 May 2024 23:08:53.073 * Background saving terminated with success
+58896:M 30 May 2024 00:09:08.434 * 1 changes in 3600 seconds. Saving...
+58896:M 30 May 2024 00:09:08.435 * Background saving started by pid 30988
+30988:C 30 May 2024 00:09:08.569 * DB saved on disk
+30988:C 30 May 2024 00:09:08.569 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 30 May 2024 00:09:08.638 * Background saving terminated with success
+58896:M 30 May 2024 12:51:39.719 * 1 changes in 3600 seconds. Saving...
+58896:M 30 May 2024 12:51:39.720 * Background saving started by pid 41021
+41021:C 30 May 2024 12:51:39.729 * DB saved on disk
+41021:C 30 May 2024 12:51:39.729 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 30 May 2024 12:51:39.821 * Background saving terminated with success
+58896:M 30 May 2024 12:56:40.011 * 100 changes in 300 seconds. Saving...
+58896:M 30 May 2024 12:56:40.017 * Background saving started by pid 41850
+41850:C 30 May 2024 12:56:40.033 * DB saved on disk
+41850:C 30 May 2024 12:56:40.034 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 30 May 2024 12:56:40.120 * Background saving terminated with success
+58896:M 30 May 2024 13:16:33.022 * 100 changes in 300 seconds. Saving...
+58896:M 30 May 2024 13:16:33.138 * Background saving started by pid 42823
+42823:C 30 May 2024 13:16:33.145 * DB saved on disk
+42823:C 30 May 2024 13:16:33.145 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 30 May 2024 13:16:33.240 * Background saving terminated with success
+58896:M 30 May 2024 15:25:24.413 * 1 changes in 3600 seconds. Saving...
+58896:M 30 May 2024 15:25:24.413 * Background saving started by pid 48206
+48206:C 30 May 2024 15:25:24.425 * DB saved on disk
+48206:C 30 May 2024 15:25:24.426 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 30 May 2024 15:25:24.513 * Background saving terminated with success
+58896:M 30 May 2024 15:30:25.016 * 100 changes in 300 seconds. Saving...
+58896:M 30 May 2024 15:30:25.017 * Background saving started by pid 48800
+48800:C 30 May 2024 15:30:25.029 * DB saved on disk
+48800:C 30 May 2024 15:30:25.031 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 30 May 2024 15:30:25.119 * Background saving terminated with success
+58896:M 30 May 2024 15:35:26.012 * 100 changes in 300 seconds. Saving...
+58896:M 30 May 2024 15:35:26.014 * Background saving started by pid 49390
+49390:C 30 May 2024 15:35:26.025 * DB saved on disk
+49390:C 30 May 2024 15:35:26.025 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 30 May 2024 15:35:26.115 * Background saving terminated with success
+58896:M 30 May 2024 16:42:51.523 * 1 changes in 3600 seconds. Saving...
+58896:M 30 May 2024 16:42:51.646 * Background saving started by pid 65592
+65592:C 30 May 2024 16:42:51.655 * DB saved on disk
+65592:C 30 May 2024 16:42:51.655 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 30 May 2024 16:42:51.747 * Background saving terminated with success
+58896:M 30 May 2024 17:15:29.037 * 100 changes in 300 seconds. Saving...
+58896:M 30 May 2024 17:15:29.039 * Background saving started by pid 69523
+69523:C 30 May 2024 17:15:29.050 * DB saved on disk
+69523:C 30 May 2024 17:15:29.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 30 May 2024 17:15:29.142 * Background saving terminated with success
+58896:M 30 May 2024 18:15:30.060 * 1 changes in 3600 seconds. Saving...
+58896:M 30 May 2024 18:15:30.063 * Background saving started by pid 84706
+84706:C 30 May 2024 18:15:30.075 * DB saved on disk
+84706:C 30 May 2024 18:15:30.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 30 May 2024 18:15:30.167 * Background saving terminated with success
+58896:M 03 Jun 2024 18:06:53.699 * 1 changes in 3600 seconds. Saving...
+58896:M 03 Jun 2024 18:06:53.703 * Background saving started by pid 90870
+90870:C 03 Jun 2024 18:06:53.713 * DB saved on disk
+90870:C 03 Jun 2024 18:06:53.714 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 03 Jun 2024 18:06:53.803 * Background saving terminated with success
+58896:M 03 Jun 2024 18:11:54.075 * 100 changes in 300 seconds. Saving...
+58896:M 03 Jun 2024 18:11:54.078 * Background saving started by pid 91526
+91526:C 03 Jun 2024 18:11:54.087 * DB saved on disk
+91526:C 03 Jun 2024 18:11:54.087 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 03 Jun 2024 18:11:54.180 * Background saving terminated with success
+58896:M 03 Jun 2024 18:18:46.019 * 100 changes in 300 seconds. Saving...
+58896:M 03 Jun 2024 18:18:46.024 * Background saving started by pid 92286
+92286:C 03 Jun 2024 18:18:46.034 * DB saved on disk
+92286:C 03 Jun 2024 18:18:46.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 03 Jun 2024 18:18:46.126 * Background saving terminated with success
+58896:M 03 Jun 2024 18:24:50.037 * 100 changes in 300 seconds. Saving...
+58896:M 03 Jun 2024 18:24:50.038 * Background saving started by pid 93124
+93124:C 03 Jun 2024 18:24:50.050 * DB saved on disk
+93124:C 03 Jun 2024 18:24:50.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 03 Jun 2024 18:24:50.140 * Background saving terminated with success
+58896:M 03 Jun 2024 18:29:51.049 * 100 changes in 300 seconds. Saving...
+58896:M 03 Jun 2024 18:29:51.051 * Background saving started by pid 93844
+93844:C 03 Jun 2024 18:29:51.063 * DB saved on disk
+93844:C 03 Jun 2024 18:29:51.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 03 Jun 2024 18:29:51.153 * Background saving terminated with success
+58896:M 03 Jun 2024 18:34:52.071 * 100 changes in 300 seconds. Saving...
+58896:M 03 Jun 2024 18:34:52.073 * Background saving started by pid 94768
+94768:C 03 Jun 2024 18:34:52.088 * DB saved on disk
+94768:C 03 Jun 2024 18:34:52.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 03 Jun 2024 18:34:52.174 * Background saving terminated with success
+58896:M 03 Jun 2024 18:48:44.981 * 100 changes in 300 seconds. Saving...
+58896:M 03 Jun 2024 18:48:44.982 * Background saving started by pid 96629
+96629:C 03 Jun 2024 18:48:45.005 * DB saved on disk
+96629:C 03 Jun 2024 18:48:45.006 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 03 Jun 2024 18:48:45.083 * Background saving terminated with success
+58896:M 03 Jun 2024 23:49:36.949 * 1 changes in 3600 seconds. Saving...
+58896:M 03 Jun 2024 23:49:36.968 * Background saving started by pid 97783
+97783:C 03 Jun 2024 23:49:37.370 * DB saved on disk
+97783:C 03 Jun 2024 23:49:37.436 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 03 Jun 2024 23:49:37.493 * Background saving terminated with success
+58896:M 04 Jun 2024 14:48:17.992 * 1 changes in 3600 seconds. Saving...
+58896:M 04 Jun 2024 14:48:18.002 * Background saving started by pid 19353
+19353:C 04 Jun 2024 14:48:18.012 * DB saved on disk
+19353:C 04 Jun 2024 14:48:18.012 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 04 Jun 2024 14:48:18.109 * Background saving terminated with success
+58896:M 04 Jun 2024 14:53:19.081 * 100 changes in 300 seconds. Saving...
+58896:M 04 Jun 2024 14:53:19.082 * Background saving started by pid 19823
+19823:C 04 Jun 2024 14:53:19.087 * DB saved on disk
+19823:C 04 Jun 2024 14:53:19.088 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 04 Jun 2024 14:53:19.184 * Background saving terminated with success
+58896:M 04 Jun 2024 15:17:23.645 * 100 changes in 300 seconds. Saving...
+58896:M 04 Jun 2024 15:17:23.646 * Background saving started by pid 23721
+23721:C 04 Jun 2024 15:17:23.656 * DB saved on disk
+23721:C 04 Jun 2024 15:17:23.656 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 04 Jun 2024 15:17:23.748 * Background saving terminated with success
+58896:M 05 Jun 2024 15:06:53.989 * 1 changes in 3600 seconds. Saving...
+58896:M 05 Jun 2024 15:06:53.991 * Background saving started by pid 74889
+74889:C 05 Jun 2024 15:06:54.001 * DB saved on disk
+74889:C 05 Jun 2024 15:06:54.001 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 15:06:54.091 * Background saving terminated with success
+58896:M 05 Jun 2024 15:11:55.053 * 100 changes in 300 seconds. Saving...
+58896:M 05 Jun 2024 15:11:55.057 * Background saving started by pid 75549
+75549:C 05 Jun 2024 15:11:55.084 * DB saved on disk
+75549:C 05 Jun 2024 15:11:55.084 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 15:11:55.158 * Background saving terminated with success
+58896:M 05 Jun 2024 15:16:56.075 * 100 changes in 300 seconds. Saving...
+58896:M 05 Jun 2024 15:16:56.078 * Background saving started by pid 76399
+76399:C 05 Jun 2024 15:16:56.095 * DB saved on disk
+76399:C 05 Jun 2024 15:16:56.097 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 15:16:56.180 * Background saving terminated with success
+58896:M 05 Jun 2024 15:51:48.859 * 100 changes in 300 seconds. Saving...
+58896:M 05 Jun 2024 15:51:48.861 * Background saving started by pid 79566
+79566:C 05 Jun 2024 15:51:48.871 * DB saved on disk
+79566:C 05 Jun 2024 15:51:48.871 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 15:51:48.963 * Background saving terminated with success
+58896:M 05 Jun 2024 16:17:59.180 * 100 changes in 300 seconds. Saving...
+58896:M 05 Jun 2024 16:17:59.183 * Background saving started by pid 82108
+82108:C 05 Jun 2024 16:17:59.192 * DB saved on disk
+82108:C 05 Jun 2024 16:17:59.193 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 16:17:59.284 * Background saving terminated with success
+58896:M 05 Jun 2024 16:35:17.999 * 100 changes in 300 seconds. Saving...
+58896:M 05 Jun 2024 16:35:18.002 * Background saving started by pid 83723
+83723:C 05 Jun 2024 16:35:18.010 * DB saved on disk
+83723:C 05 Jun 2024 16:35:18.011 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 16:35:18.103 * Background saving terminated with success
+58896:M 05 Jun 2024 16:43:22.260 * 100 changes in 300 seconds. Saving...
+58896:M 05 Jun 2024 16:43:22.261 * Background saving started by pid 84583
+84583:C 05 Jun 2024 16:43:22.275 * DB saved on disk
+84583:C 05 Jun 2024 16:43:22.278 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 16:43:22.362 * Background saving terminated with success
+58896:M 05 Jun 2024 16:48:23.046 * 100 changes in 300 seconds. Saving...
+58896:M 05 Jun 2024 16:48:23.047 * Background saving started by pid 85131
+85131:C 05 Jun 2024 16:48:23.065 * DB saved on disk
+85131:C 05 Jun 2024 16:48:23.066 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 16:48:23.148 * Background saving terminated with success
+58896:M 05 Jun 2024 17:24:20.810 * 100 changes in 300 seconds. Saving...
+58896:M 05 Jun 2024 17:24:20.813 * Background saving started by pid 90105
+90105:C 05 Jun 2024 17:24:20.823 * DB saved on disk
+90105:C 05 Jun 2024 17:24:20.826 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 17:24:20.914 * Background saving terminated with success
+58896:M 05 Jun 2024 17:38:07.895 * 100 changes in 300 seconds. Saving...
+58896:M 05 Jun 2024 17:38:07.898 * Background saving started by pid 91506
+91506:C 05 Jun 2024 17:38:07.907 * DB saved on disk
+91506:C 05 Jun 2024 17:38:07.907 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 17:38:07.998 * Background saving terminated with success
+58896:M 05 Jun 2024 17:43:08.028 * 100 changes in 300 seconds. Saving...
+58896:M 05 Jun 2024 17:43:08.031 * Background saving started by pid 92110
+92110:C 05 Jun 2024 17:43:08.047 * DB saved on disk
+92110:C 05 Jun 2024 17:43:08.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 17:43:08.133 * Background saving terminated with success
+58896:M 05 Jun 2024 17:48:09.040 * 100 changes in 300 seconds. Saving...
+58896:M 05 Jun 2024 17:48:09.042 * Background saving started by pid 92684
+92684:C 05 Jun 2024 17:48:09.056 * DB saved on disk
+92684:C 05 Jun 2024 17:48:09.057 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 17:48:09.144 * Background saving terminated with success
+58896:M 05 Jun 2024 17:53:10.043 * 100 changes in 300 seconds. Saving...
+58896:M 05 Jun 2024 17:53:10.045 * Background saving started by pid 93293
+93293:C 05 Jun 2024 17:53:10.054 * DB saved on disk
+93293:C 05 Jun 2024 17:53:10.056 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 17:53:10.146 * Background saving terminated with success
+58896:M 05 Jun 2024 17:58:11.081 * 100 changes in 300 seconds. Saving...
+58896:M 05 Jun 2024 17:58:11.083 * Background saving started by pid 93757
+93757:C 05 Jun 2024 17:58:11.096 * DB saved on disk
+93757:C 05 Jun 2024 17:58:11.097 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 05 Jun 2024 17:58:11.186 * Background saving terminated with success
+58896:M 07 Jun 2024 16:39:41.351 * 1 changes in 3600 seconds. Saving...
+58896:M 07 Jun 2024 16:39:41.353 * Background saving started by pid 27460
+27460:C 07 Jun 2024 16:39:41.369 * DB saved on disk
+27460:C 07 Jun 2024 16:39:41.370 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 16:39:41.454 * Background saving terminated with success
+58896:M 07 Jun 2024 16:44:42.066 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 16:44:42.071 * Background saving started by pid 28358
+28358:C 07 Jun 2024 16:44:42.082 * DB saved on disk
+28358:C 07 Jun 2024 16:44:42.083 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 16:44:42.173 * Background saving terminated with success
+58896:M 07 Jun 2024 17:16:01.732 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 17:16:01.734 * Background saving started by pid 33049
+33049:C 07 Jun 2024 17:16:01.746 * DB saved on disk
+33049:C 07 Jun 2024 17:16:01.746 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 17:16:01.835 * Background saving terminated with success
+58896:M 07 Jun 2024 17:21:02.052 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 17:21:02.059 * Background saving started by pid 33638
+33638:C 07 Jun 2024 17:21:02.070 * DB saved on disk
+33638:C 07 Jun 2024 17:21:02.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 17:21:02.161 * Background saving terminated with success
+58896:M 07 Jun 2024 17:33:38.484 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 17:33:38.486 * Background saving started by pid 35103
+35103:C 07 Jun 2024 17:33:38.495 * DB saved on disk
+35103:C 07 Jun 2024 17:33:38.495 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 17:33:38.587 * Background saving terminated with success
+58896:M 07 Jun 2024 17:38:39.030 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 17:38:39.031 * Background saving started by pid 35753
+35753:C 07 Jun 2024 17:38:39.044 * DB saved on disk
+35753:C 07 Jun 2024 17:38:39.045 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 17:38:39.132 * Background saving terminated with success
+58896:M 07 Jun 2024 17:43:40.049 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 17:43:40.051 * Background saving started by pid 36373
+36373:C 07 Jun 2024 17:43:40.062 * DB saved on disk
+36373:C 07 Jun 2024 17:43:40.062 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 17:43:40.152 * Background saving terminated with success
+58896:M 07 Jun 2024 17:49:19.866 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 17:49:19.867 * Background saving started by pid 36987
+36987:C 07 Jun 2024 17:49:19.874 * DB saved on disk
+36987:C 07 Jun 2024 17:49:19.875 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 17:49:19.967 * Background saving terminated with success
+58896:M 07 Jun 2024 17:54:20.070 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 17:54:20.074 * Background saving started by pid 37622
+37622:C 07 Jun 2024 17:54:20.087 * DB saved on disk
+37622:C 07 Jun 2024 17:54:20.088 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 17:54:20.178 * Background saving terminated with success
+58896:M 07 Jun 2024 18:00:52.446 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 18:00:52.448 * Background saving started by pid 38338
+38338:C 07 Jun 2024 18:00:52.458 * DB saved on disk
+38338:C 07 Jun 2024 18:00:52.460 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 18:00:52.548 * Background saving terminated with success
+58896:M 07 Jun 2024 18:05:53.016 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 18:05:53.018 * Background saving started by pid 39003
+39003:C 07 Jun 2024 18:05:53.032 * DB saved on disk
+39003:C 07 Jun 2024 18:05:53.033 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 18:05:53.119 * Background saving terminated with success
+58896:M 07 Jun 2024 18:10:54.046 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 18:10:54.047 * Background saving started by pid 39675
+39675:C 07 Jun 2024 18:10:54.071 * DB saved on disk
+39675:C 07 Jun 2024 18:10:54.073 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 18:10:54.149 * Background saving terminated with success
+58896:M 07 Jun 2024 18:22:32.800 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 18:22:32.801 * Background saving started by pid 40966
+40966:C 07 Jun 2024 18:22:32.812 * DB saved on disk
+40966:C 07 Jun 2024 18:22:32.813 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 18:22:32.904 * Background saving terminated with success
+58896:M 07 Jun 2024 18:27:33.013 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 18:27:33.013 * Background saving started by pid 41713
+41713:C 07 Jun 2024 18:27:33.026 * DB saved on disk
+41713:C 07 Jun 2024 18:27:33.028 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 18:27:33.115 * Background saving terminated with success
+58896:M 07 Jun 2024 18:32:34.020 * 100 changes in 300 seconds. Saving...
+58896:M 07 Jun 2024 18:32:34.022 * Background saving started by pid 42366
+42366:C 07 Jun 2024 18:32:34.039 * DB saved on disk
+42366:C 07 Jun 2024 18:32:34.041 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 07 Jun 2024 18:32:34.124 * Background saving terminated with success
+58896:M 09 Jun 2024 01:21:17.009 * 1 changes in 3600 seconds. Saving...
+58896:M 09 Jun 2024 01:21:17.010 * Background saving started by pid 51967
+51967:C 09 Jun 2024 01:21:17.025 * DB saved on disk
+51967:C 09 Jun 2024 01:21:17.025 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 09 Jun 2024 01:21:17.111 * Background saving terminated with success
+58896:M 09 Jun 2024 01:26:18.067 * 100 changes in 300 seconds. Saving...
+58896:M 09 Jun 2024 01:26:18.068 * Background saving started by pid 52613
+52613:C 09 Jun 2024 01:26:18.076 * DB saved on disk
+52613:C 09 Jun 2024 01:26:18.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 09 Jun 2024 01:26:18.169 * Background saving terminated with success
+58896:M 09 Jun 2024 01:31:19.072 * 100 changes in 300 seconds. Saving...
+58896:M 09 Jun 2024 01:31:19.074 * Background saving started by pid 53131
+53131:C 09 Jun 2024 01:31:19.092 * DB saved on disk
+53131:C 09 Jun 2024 01:31:19.094 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 09 Jun 2024 01:31:19.176 * Background saving terminated with success
+58896:M 09 Jun 2024 01:36:20.080 * 100 changes in 300 seconds. Saving...
+58896:M 09 Jun 2024 01:36:20.083 * Background saving started by pid 53704
+53704:C 09 Jun 2024 01:36:20.094 * DB saved on disk
+53704:C 09 Jun 2024 01:36:20.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 09 Jun 2024 01:36:20.185 * Background saving terminated with success
+58896:M 09 Jun 2024 01:41:21.074 * 100 changes in 300 seconds. Saving...
+58896:M 09 Jun 2024 01:41:21.076 * Background saving started by pid 54385
+54385:C 09 Jun 2024 01:41:21.090 * DB saved on disk
+54385:C 09 Jun 2024 01:41:21.091 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 09 Jun 2024 01:41:21.179 * Background saving terminated with success
+58896:M 09 Jun 2024 01:46:22.080 * 100 changes in 300 seconds. Saving...
+58896:M 09 Jun 2024 01:46:22.083 * Background saving started by pid 54916
+54916:C 09 Jun 2024 01:46:22.102 * DB saved on disk
+54916:C 09 Jun 2024 01:46:22.102 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 09 Jun 2024 01:46:22.184 * Background saving terminated with success
+58896:M 09 Jun 2024 01:51:23.049 * 100 changes in 300 seconds. Saving...
+58896:M 09 Jun 2024 01:51:23.052 * Background saving started by pid 55511
+55511:C 09 Jun 2024 01:51:23.064 * DB saved on disk
+55511:C 09 Jun 2024 01:51:23.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 09 Jun 2024 01:51:23.152 * Background saving terminated with success
+58896:M 09 Jun 2024 01:59:40.782 * 100 changes in 300 seconds. Saving...
+58896:M 09 Jun 2024 01:59:40.783 * Background saving started by pid 56315
+56315:C 09 Jun 2024 01:59:40.795 * DB saved on disk
+56315:C 09 Jun 2024 01:59:40.795 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 09 Jun 2024 01:59:40.883 * Background saving terminated with success
+58896:M 09 Jun 2024 02:04:41.092 * 100 changes in 300 seconds. Saving...
+58896:M 09 Jun 2024 02:04:41.096 * Background saving started by pid 56862
+56862:C 09 Jun 2024 02:04:41.111 * DB saved on disk
+56862:C 09 Jun 2024 02:04:41.112 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 09 Jun 2024 02:04:41.198 * Background saving terminated with success
+58896:M 11 Jun 2024 13:19:27.398 * 1 changes in 3600 seconds. Saving...
+58896:M 11 Jun 2024 13:19:27.402 * Background saving started by pid 99129
+99129:C 11 Jun 2024 13:19:27.435 * DB saved on disk
+99129:C 11 Jun 2024 13:19:27.437 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 13:19:27.503 * Background saving terminated with success
+58896:M 11 Jun 2024 13:24:28.020 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 13:24:28.021 * Background saving started by pid 222
+222:C 11 Jun 2024 13:24:28.037 * DB saved on disk
+222:C 11 Jun 2024 13:24:28.038 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 13:24:28.127 * Background saving terminated with success
+58896:M 11 Jun 2024 13:29:29.094 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 13:29:29.096 * Background saving started by pid 1314
+1314:C 11 Jun 2024 13:29:29.108 * DB saved on disk
+1314:C 11 Jun 2024 13:29:29.114 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 13:29:29.197 * Background saving terminated with success
+58896:M 11 Jun 2024 13:34:30.038 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 13:34:30.042 * Background saving started by pid 1927
+1927:C 11 Jun 2024 13:34:30.054 * DB saved on disk
+1927:C 11 Jun 2024 13:34:30.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 13:34:30.144 * Background saving terminated with success
+58896:M 11 Jun 2024 13:39:31.059 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 13:39:31.063 * Background saving started by pid 2516
+2516:C 11 Jun 2024 13:39:31.100 * DB saved on disk
+2516:C 11 Jun 2024 13:39:31.102 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 13:39:31.164 * Background saving terminated with success
+58896:M 11 Jun 2024 13:44:32.023 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 13:44:32.024 * Background saving started by pid 3067
+3067:C 11 Jun 2024 13:44:32.033 * DB saved on disk
+3067:C 11 Jun 2024 13:44:32.033 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 13:44:32.125 * Background saving terminated with success
+58896:M 11 Jun 2024 13:51:40.611 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 13:51:40.614 * Background saving started by pid 3795
+3795:C 11 Jun 2024 13:51:40.627 * DB saved on disk
+3795:C 11 Jun 2024 13:51:40.628 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 13:51:40.715 * Background saving terminated with success
+58896:M 11 Jun 2024 13:56:41.066 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 13:56:41.069 * Background saving started by pid 4441
+4441:C 11 Jun 2024 13:56:41.091 * DB saved on disk
+4441:C 11 Jun 2024 13:56:41.095 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 13:56:41.170 * Background saving terminated with success
+58896:M 11 Jun 2024 14:01:42.031 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 14:01:42.035 * Background saving started by pid 5198
+5198:C 11 Jun 2024 14:01:42.058 * DB saved on disk
+5198:C 11 Jun 2024 14:01:42.058 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 14:01:42.137 * Background saving terminated with success
+58896:M 11 Jun 2024 14:08:57.215 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 14:08:57.217 * Background saving started by pid 5893
+5893:C 11 Jun 2024 14:08:57.230 * DB saved on disk
+5893:C 11 Jun 2024 14:08:57.232 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 14:08:57.319 * Background saving terminated with success
+58896:M 11 Jun 2024 14:13:58.030 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 14:13:58.034 * Background saving started by pid 6544
+6544:C 11 Jun 2024 14:13:58.061 * DB saved on disk
+6544:C 11 Jun 2024 14:13:58.063 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 14:13:58.142 * Background saving terminated with success
+58896:M 11 Jun 2024 14:18:59.057 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 14:18:59.060 * Background saving started by pid 7250
+7250:C 11 Jun 2024 14:18:59.074 * DB saved on disk
+7250:C 11 Jun 2024 14:18:59.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 14:18:59.164 * Background saving terminated with success
+58896:M 11 Jun 2024 14:24:00.000 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 14:24:00.003 * Background saving started by pid 7777
+7777:C 11 Jun 2024 14:24:00.017 * DB saved on disk
+7777:C 11 Jun 2024 14:24:00.018 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 14:24:00.105 * Background saving terminated with success
+58896:M 11 Jun 2024 14:29:01.003 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 14:29:01.005 * Background saving started by pid 9446
+9446:C 11 Jun 2024 14:29:01.022 * DB saved on disk
+9446:C 11 Jun 2024 14:29:01.023 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 14:29:01.106 * Background saving terminated with success
+58896:M 11 Jun 2024 14:38:46.573 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 14:38:46.575 * Background saving started by pid 16553
+16553:C 11 Jun 2024 14:38:46.587 * DB saved on disk
+16553:C 11 Jun 2024 14:38:46.587 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 14:38:46.677 * Background saving terminated with success
+58896:M 11 Jun 2024 14:43:47.042 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 14:43:47.046 * Background saving started by pid 18767
+18767:C 11 Jun 2024 14:43:47.064 * DB saved on disk
+18767:C 11 Jun 2024 14:43:47.065 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 14:43:47.149 * Background saving terminated with success
+58896:M 11 Jun 2024 14:48:48.071 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 14:48:48.072 * Background saving started by pid 19508
+19508:C 11 Jun 2024 14:48:48.105 * DB saved on disk
+19508:C 11 Jun 2024 14:48:48.106 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 14:48:48.173 * Background saving terminated with success
+58896:M 11 Jun 2024 15:55:55.546 * 1 changes in 3600 seconds. Saving...
+58896:M 11 Jun 2024 15:55:55.665 * Background saving started by pid 20200
+20200:C 11 Jun 2024 15:55:55.673 * DB saved on disk
+20200:C 11 Jun 2024 15:55:55.674 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 15:55:55.766 * Background saving terminated with success
+58896:M 11 Jun 2024 16:55:56.028 * 1 changes in 3600 seconds. Saving...
+58896:M 11 Jun 2024 16:55:56.029 * Background saving started by pid 26736
+26736:C 11 Jun 2024 16:55:56.039 * DB saved on disk
+26736:C 11 Jun 2024 16:55:56.041 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 16:55:56.130 * Background saving terminated with success
+58896:M 11 Jun 2024 17:00:57.094 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 17:00:57.096 * Background saving started by pid 27696
+27696:C 11 Jun 2024 17:00:57.110 * DB saved on disk
+27696:C 11 Jun 2024 17:00:57.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 17:00:57.197 * Background saving terminated with success
+58896:M 11 Jun 2024 17:05:58.022 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 17:05:58.028 * Background saving started by pid 28649
+28649:C 11 Jun 2024 17:05:58.040 * DB saved on disk
+28649:C 11 Jun 2024 17:05:58.042 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 17:05:58.130 * Background saving terminated with success
+58896:M 11 Jun 2024 17:10:59.011 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 17:10:59.013 * Background saving started by pid 29517
+29517:C 11 Jun 2024 17:10:59.028 * DB saved on disk
+29517:C 11 Jun 2024 17:10:59.029 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 17:10:59.114 * Background saving terminated with success
+58896:M 11 Jun 2024 17:16:00.031 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 17:16:00.033 * Background saving started by pid 30399
+30399:C 11 Jun 2024 17:16:00.044 * DB saved on disk
+30399:C 11 Jun 2024 17:16:00.045 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 17:16:00.134 * Background saving terminated with success
+58896:M 11 Jun 2024 17:21:01.099 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 17:21:01.102 * Background saving started by pid 34058
+34058:C 11 Jun 2024 17:21:01.136 * DB saved on disk
+34058:C 11 Jun 2024 17:21:01.136 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 17:21:01.204 * Background saving terminated with success
+58896:M 11 Jun 2024 17:26:02.077 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 17:26:02.078 * Background saving started by pid 35339
+35339:C 11 Jun 2024 17:26:02.092 * DB saved on disk
+35339:C 11 Jun 2024 17:26:02.093 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 17:26:02.179 * Background saving terminated with success
+58896:M 11 Jun 2024 17:31:03.024 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 17:31:03.027 * Background saving started by pid 36164
+36164:C 11 Jun 2024 17:31:03.044 * DB saved on disk
+36164:C 11 Jun 2024 17:31:03.046 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 17:31:03.129 * Background saving terminated with success
+58896:M 11 Jun 2024 17:36:04.080 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 17:36:04.083 * Background saving started by pid 37017
+37017:C 11 Jun 2024 17:36:04.100 * DB saved on disk
+37017:C 11 Jun 2024 17:36:04.100 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 17:36:04.184 * Background saving terminated with success
+58896:M 11 Jun 2024 17:41:05.070 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 17:41:05.071 * Background saving started by pid 37887
+37887:C 11 Jun 2024 17:41:05.096 * DB saved on disk
+37887:C 11 Jun 2024 17:41:05.097 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 17:41:05.173 * Background saving terminated with success
+58896:M 11 Jun 2024 17:46:06.098 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 17:46:06.099 * Background saving started by pid 38777
+38777:C 11 Jun 2024 17:46:06.110 * DB saved on disk
+38777:C 11 Jun 2024 17:46:06.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 17:46:06.200 * Background saving terminated with success
+58896:M 11 Jun 2024 17:51:07.052 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 17:51:07.054 * Background saving started by pid 39630
+39630:C 11 Jun 2024 17:51:07.065 * DB saved on disk
+39630:C 11 Jun 2024 17:51:07.065 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 17:51:07.156 * Background saving terminated with success
+58896:M 11 Jun 2024 17:56:08.017 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 17:56:08.020 * Background saving started by pid 40590
+40590:C 11 Jun 2024 17:56:08.031 * DB saved on disk
+40590:C 11 Jun 2024 17:56:08.032 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 17:56:08.122 * Background saving terminated with success
+58896:M 11 Jun 2024 18:01:09.041 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 18:01:09.044 * Background saving started by pid 41552
+41552:C 11 Jun 2024 18:01:09.054 * DB saved on disk
+41552:C 11 Jun 2024 18:01:09.056 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 18:01:09.145 * Background saving terminated with success
+58896:M 11 Jun 2024 18:06:10.031 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 18:06:10.034 * Background saving started by pid 42635
+42635:C 11 Jun 2024 18:06:10.047 * DB saved on disk
+42635:C 11 Jun 2024 18:06:10.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 18:06:10.136 * Background saving terminated with success
+58896:M 11 Jun 2024 18:11:11.043 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 18:11:11.046 * Background saving started by pid 43579
+43579:C 11 Jun 2024 18:11:11.061 * DB saved on disk
+43579:C 11 Jun 2024 18:11:11.062 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 18:11:11.147 * Background saving terminated with success
+58896:M 11 Jun 2024 18:16:12.053 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 18:16:12.056 * Background saving started by pid 44500
+44500:C 11 Jun 2024 18:16:12.068 * DB saved on disk
+44500:C 11 Jun 2024 18:16:12.068 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 18:16:12.157 * Background saving terminated with success
+58896:M 11 Jun 2024 18:21:13.023 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 18:21:13.027 * Background saving started by pid 45459
+45459:C 11 Jun 2024 18:21:13.054 * DB saved on disk
+45459:C 11 Jun 2024 18:21:13.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 18:21:13.128 * Background saving terminated with success
+58896:M 11 Jun 2024 18:26:14.079 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 18:26:14.080 * Background saving started by pid 46446
+46446:C 11 Jun 2024 18:26:14.095 * DB saved on disk
+46446:C 11 Jun 2024 18:26:14.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 18:26:14.181 * Background saving terminated with success
+58896:M 11 Jun 2024 18:31:15.020 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 18:31:15.023 * Background saving started by pid 47369
+47369:C 11 Jun 2024 18:31:15.033 * DB saved on disk
+47369:C 11 Jun 2024 18:31:15.033 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 18:31:15.125 * Background saving terminated with success
+58896:M 11 Jun 2024 18:44:26.665 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 18:44:26.668 * Background saving started by pid 47820
+47820:C 11 Jun 2024 18:44:26.794 * DB saved on disk
+47820:C 11 Jun 2024 18:44:26.795 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 18:44:26.870 * Background saving terminated with success
+58896:M 11 Jun 2024 18:51:12.584 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 18:51:12.586 * Background saving started by pid 47950
+47950:C 11 Jun 2024 18:51:12.599 * DB saved on disk
+47950:C 11 Jun 2024 18:51:12.600 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 18:51:12.689 * Background saving terminated with success
+58896:M 11 Jun 2024 19:02:27.776 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 19:02:27.778 * Background saving started by pid 48131
+48131:C 11 Jun 2024 19:02:27.802 * DB saved on disk
+48131:C 11 Jun 2024 19:02:27.804 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 19:02:27.881 * Background saving terminated with success
+58896:M 11 Jun 2024 19:07:28.043 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 19:07:28.045 * Background saving started by pid 48889
+48889:C 11 Jun 2024 19:07:28.056 * DB saved on disk
+48889:C 11 Jun 2024 19:07:28.063 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 19:07:28.147 * Background saving terminated with success
+58896:M 11 Jun 2024 19:12:29.059 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 19:12:29.061 * Background saving started by pid 49675
+49675:C 11 Jun 2024 19:12:29.074 * DB saved on disk
+49675:C 11 Jun 2024 19:12:29.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 19:12:29.165 * Background saving terminated with success
+58896:M 11 Jun 2024 19:17:30.038 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 19:17:30.039 * Background saving started by pid 50454
+50454:C 11 Jun 2024 19:17:30.048 * DB saved on disk
+50454:C 11 Jun 2024 19:17:30.049 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 19:17:30.141 * Background saving terminated with success
+58896:M 11 Jun 2024 19:22:31.015 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 19:22:31.019 * Background saving started by pid 51066
+51066:C 11 Jun 2024 19:22:31.034 * DB saved on disk
+51066:C 11 Jun 2024 19:22:31.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 19:22:31.121 * Background saving terminated with success
+58896:M 11 Jun 2024 19:27:32.083 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 19:27:32.086 * Background saving started by pid 51963
+51963:C 11 Jun 2024 19:27:32.100 * DB saved on disk
+51963:C 11 Jun 2024 19:27:32.101 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 19:27:32.187 * Background saving terminated with success
+58896:M 11 Jun 2024 19:32:33.008 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 19:32:33.010 * Background saving started by pid 52753
+52753:C 11 Jun 2024 19:32:33.021 * DB saved on disk
+52753:C 11 Jun 2024 19:32:33.021 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 19:32:33.111 * Background saving terminated with success
+58896:M 11 Jun 2024 19:37:34.032 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 19:37:34.034 * Background saving started by pid 54020
+54020:C 11 Jun 2024 19:37:34.047 * DB saved on disk
+54020:C 11 Jun 2024 19:37:34.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 19:37:34.135 * Background saving terminated with success
+58896:M 11 Jun 2024 19:42:35.021 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 19:42:35.025 * Background saving started by pid 55025
+55025:C 11 Jun 2024 19:42:35.055 * DB saved on disk
+55025:C 11 Jun 2024 19:42:35.056 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 19:42:35.126 * Background saving terminated with success
+58896:M 11 Jun 2024 19:47:36.075 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 19:47:36.077 * Background saving started by pid 55926
+55926:C 11 Jun 2024 19:47:36.091 * DB saved on disk
+55926:C 11 Jun 2024 19:47:36.092 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 19:47:36.178 * Background saving terminated with success
+58896:M 11 Jun 2024 19:52:37.054 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 19:52:37.061 * Background saving started by pid 56735
+56735:C 11 Jun 2024 19:52:37.078 * DB saved on disk
+56735:C 11 Jun 2024 19:52:37.078 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 19:52:37.163 * Background saving terminated with success
+58896:M 11 Jun 2024 19:57:38.075 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 19:57:38.077 * Background saving started by pid 57603
+57603:C 11 Jun 2024 19:57:38.092 * DB saved on disk
+57603:C 11 Jun 2024 19:57:38.092 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 19:57:38.178 * Background saving terminated with success
+58896:M 11 Jun 2024 20:02:39.013 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 20:02:39.015 * Background saving started by pid 58545
+58545:C 11 Jun 2024 20:02:39.028 * DB saved on disk
+58545:C 11 Jun 2024 20:02:39.029 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 20:02:39.116 * Background saving terminated with success
+58896:M 11 Jun 2024 20:07:40.055 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 20:07:40.057 * Background saving started by pid 59368
+59368:C 11 Jun 2024 20:07:40.077 * DB saved on disk
+59368:C 11 Jun 2024 20:07:40.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 20:07:40.159 * Background saving terminated with success
+58896:M 11 Jun 2024 20:12:41.003 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 20:12:41.005 * Background saving started by pid 60133
+60133:C 11 Jun 2024 20:12:41.021 * DB saved on disk
+60133:C 11 Jun 2024 20:12:41.022 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 20:12:41.107 * Background saving terminated with success
+58896:M 11 Jun 2024 20:17:42.079 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 20:17:42.081 * Background saving started by pid 60949
+60949:C 11 Jun 2024 20:17:42.091 * DB saved on disk
+60949:C 11 Jun 2024 20:17:42.093 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 20:17:42.181 * Background saving terminated with success
+58896:M 11 Jun 2024 20:22:43.066 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 20:22:43.067 * Background saving started by pid 61718
+61718:C 11 Jun 2024 20:22:43.077 * DB saved on disk
+61718:C 11 Jun 2024 20:22:43.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 20:22:43.167 * Background saving terminated with success
+58896:M 11 Jun 2024 20:27:44.036 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 20:27:44.037 * Background saving started by pid 62501
+62501:C 11 Jun 2024 20:27:44.047 * DB saved on disk
+62501:C 11 Jun 2024 20:27:44.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 20:27:44.138 * Background saving terminated with success
+58896:M 11 Jun 2024 20:32:45.098 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 20:32:45.099 * Background saving started by pid 63324
+63324:C 11 Jun 2024 20:32:45.112 * DB saved on disk
+63324:C 11 Jun 2024 20:32:45.113 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 20:32:45.199 * Background saving terminated with success
+58896:M 11 Jun 2024 20:37:46.011 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 20:37:46.014 * Background saving started by pid 64283
+64283:C 11 Jun 2024 20:37:46.023 * DB saved on disk
+64283:C 11 Jun 2024 20:37:46.024 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 20:37:46.115 * Background saving terminated with success
+58896:M 11 Jun 2024 20:42:47.025 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 20:42:47.028 * Background saving started by pid 65146
+65146:C 11 Jun 2024 20:42:47.036 * DB saved on disk
+65146:C 11 Jun 2024 20:42:47.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 20:42:47.129 * Background saving terminated with success
+58896:M 11 Jun 2024 20:47:48.097 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 20:47:48.099 * Background saving started by pid 65963
+65963:C 11 Jun 2024 20:47:48.114 * DB saved on disk
+65963:C 11 Jun 2024 20:47:48.114 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 20:47:48.200 * Background saving terminated with success
+58896:M 11 Jun 2024 20:52:49.080 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 20:52:49.081 * Background saving started by pid 66721
+66721:C 11 Jun 2024 20:52:49.089 * DB saved on disk
+66721:C 11 Jun 2024 20:52:49.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 20:52:49.182 * Background saving terminated with success
+58896:M 11 Jun 2024 20:57:50.072 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 20:57:50.074 * Background saving started by pid 67533
+67533:C 11 Jun 2024 20:57:50.086 * DB saved on disk
+67533:C 11 Jun 2024 20:57:50.087 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 20:57:50.175 * Background saving terminated with success
+58896:M 11 Jun 2024 21:02:51.093 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 21:02:51.096 * Background saving started by pid 68492
+68492:C 11 Jun 2024 21:02:51.104 * DB saved on disk
+68492:C 11 Jun 2024 21:02:51.105 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 21:02:51.197 * Background saving terminated with success
+58896:M 11 Jun 2024 21:07:52.069 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 21:07:52.071 * Background saving started by pid 69301
+69301:C 11 Jun 2024 21:07:52.080 * DB saved on disk
+69301:C 11 Jun 2024 21:07:52.081 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 21:07:52.172 * Background saving terminated with success
+58896:M 11 Jun 2024 21:12:53.015 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 21:12:53.016 * Background saving started by pid 70023
+70023:C 11 Jun 2024 21:12:53.026 * DB saved on disk
+70023:C 11 Jun 2024 21:12:53.027 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 21:12:53.117 * Background saving terminated with success
+58896:M 11 Jun 2024 21:17:54.036 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 21:17:54.039 * Background saving started by pid 70808
+70808:C 11 Jun 2024 21:17:54.049 * DB saved on disk
+70808:C 11 Jun 2024 21:17:54.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 21:17:54.139 * Background saving terminated with success
+58896:M 11 Jun 2024 21:22:55.017 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 21:22:55.020 * Background saving started by pid 71619
+71619:C 11 Jun 2024 21:22:55.034 * DB saved on disk
+71619:C 11 Jun 2024 21:22:55.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 21:22:55.122 * Background saving terminated with success
+58896:M 11 Jun 2024 21:27:56.099 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 21:27:56.101 * Background saving started by pid 72347
+72347:C 11 Jun 2024 21:27:56.110 * DB saved on disk
+72347:C 11 Jun 2024 21:27:56.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 21:27:56.204 * Background saving terminated with success
+58896:M 11 Jun 2024 21:32:57.085 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 21:32:57.087 * Background saving started by pid 73084
+73084:C 11 Jun 2024 21:32:57.097 * DB saved on disk
+73084:C 11 Jun 2024 21:32:57.098 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 21:32:57.190 * Background saving terminated with success
+58896:M 11 Jun 2024 21:37:58.093 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 21:37:58.096 * Background saving started by pid 73831
+73831:C 11 Jun 2024 21:37:58.111 * DB saved on disk
+73831:C 11 Jun 2024 21:37:58.112 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 21:37:58.198 * Background saving terminated with success
+58896:M 11 Jun 2024 21:42:59.011 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 21:42:59.013 * Background saving started by pid 74560
+74560:C 11 Jun 2024 21:42:59.029 * DB saved on disk
+74560:C 11 Jun 2024 21:42:59.032 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 21:42:59.114 * Background saving terminated with success
+58896:M 11 Jun 2024 21:48:00.014 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 21:48:00.016 * Background saving started by pid 75300
+75300:C 11 Jun 2024 21:48:00.033 * DB saved on disk
+75300:C 11 Jun 2024 21:48:00.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 21:48:00.118 * Background saving terminated with success
+58896:M 11 Jun 2024 21:53:01.092 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 21:53:01.094 * Background saving started by pid 76026
+76026:C 11 Jun 2024 21:53:01.107 * DB saved on disk
+76026:C 11 Jun 2024 21:53:01.109 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 21:53:01.197 * Background saving terminated with success
+58896:M 11 Jun 2024 21:58:02.084 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 21:58:02.087 * Background saving started by pid 76774
+76774:C 11 Jun 2024 21:58:02.099 * DB saved on disk
+76774:C 11 Jun 2024 21:58:02.102 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 21:58:02.188 * Background saving terminated with success
+58896:M 11 Jun 2024 22:03:03.053 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 22:03:03.057 * Background saving started by pid 77506
+77506:C 11 Jun 2024 22:03:03.079 * DB saved on disk
+77506:C 11 Jun 2024 22:03:03.080 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 22:03:03.158 * Background saving terminated with success
+58896:M 11 Jun 2024 22:08:04.061 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 22:08:04.065 * Background saving started by pid 78241
+78241:C 11 Jun 2024 22:08:04.080 * DB saved on disk
+78241:C 11 Jun 2024 22:08:04.081 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 22:08:04.168 * Background saving terminated with success
+58896:M 11 Jun 2024 22:13:05.038 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 22:13:05.038 * Background saving started by pid 78971
+78971:C 11 Jun 2024 22:13:05.049 * DB saved on disk
+78971:C 11 Jun 2024 22:13:05.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 22:13:05.141 * Background saving terminated with success
+58896:M 11 Jun 2024 22:18:06.009 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 22:18:06.012 * Background saving started by pid 79712
+79712:C 11 Jun 2024 22:18:06.031 * DB saved on disk
+79712:C 11 Jun 2024 22:18:06.031 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 22:18:06.115 * Background saving terminated with success
+58896:M 11 Jun 2024 22:23:07.017 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 22:23:07.018 * Background saving started by pid 80463
+80463:C 11 Jun 2024 22:23:07.030 * DB saved on disk
+80463:C 11 Jun 2024 22:23:07.031 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 22:23:07.121 * Background saving terminated with success
+58896:M 11 Jun 2024 22:28:08.091 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 22:28:08.095 * Background saving started by pid 81195
+81195:C 11 Jun 2024 22:28:08.126 * DB saved on disk
+81195:C 11 Jun 2024 22:28:08.127 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 22:28:08.197 * Background saving terminated with success
+58896:M 11 Jun 2024 22:33:09.069 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 22:33:09.073 * Background saving started by pid 81924
+81924:C 11 Jun 2024 22:33:09.090 * DB saved on disk
+81924:C 11 Jun 2024 22:33:09.091 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 22:33:09.176 * Background saving terminated with success
+58896:M 11 Jun 2024 22:38:10.051 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 22:38:10.056 * Background saving started by pid 82688
+82688:C 11 Jun 2024 22:38:10.073 * DB saved on disk
+82688:C 11 Jun 2024 22:38:10.074 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 22:38:10.158 * Background saving terminated with success
+58896:M 11 Jun 2024 22:43:11.058 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 22:43:11.062 * Background saving started by pid 83419
+83419:C 11 Jun 2024 22:43:11.085 * DB saved on disk
+83419:C 11 Jun 2024 22:43:11.086 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 22:43:11.165 * Background saving terminated with success
+58896:M 11 Jun 2024 22:48:12.050 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 22:48:12.055 * Background saving started by pid 84242
+84242:C 11 Jun 2024 22:48:12.071 * DB saved on disk
+84242:C 11 Jun 2024 22:48:12.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 22:48:12.156 * Background saving terminated with success
+58896:M 11 Jun 2024 22:53:13.061 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 22:53:13.064 * Background saving started by pid 84972
+84972:C 11 Jun 2024 22:53:13.080 * DB saved on disk
+84972:C 11 Jun 2024 22:53:13.082 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 22:53:13.167 * Background saving terminated with success
+58896:M 11 Jun 2024 22:58:14.056 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 22:58:14.059 * Background saving started by pid 85710
+85710:C 11 Jun 2024 22:58:14.075 * DB saved on disk
+85710:C 11 Jun 2024 22:58:14.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 22:58:14.161 * Background saving terminated with success
+58896:M 11 Jun 2024 23:03:15.013 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 23:03:15.014 * Background saving started by pid 86445
+86445:C 11 Jun 2024 23:03:15.027 * DB saved on disk
+86445:C 11 Jun 2024 23:03:15.031 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 23:03:15.116 * Background saving terminated with success
+58896:M 11 Jun 2024 23:08:16.036 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 23:08:16.038 * Background saving started by pid 87172
+87172:C 11 Jun 2024 23:08:16.052 * DB saved on disk
+87172:C 11 Jun 2024 23:08:16.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 23:08:16.138 * Background saving terminated with success
+58896:M 11 Jun 2024 23:13:17.001 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 23:13:17.006 * Background saving started by pid 87901
+87901:C 11 Jun 2024 23:13:17.020 * DB saved on disk
+87901:C 11 Jun 2024 23:13:17.021 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 23:13:17.109 * Background saving terminated with success
+58896:M 11 Jun 2024 23:18:18.101 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 23:18:18.103 * Background saving started by pid 88646
+88646:C 11 Jun 2024 23:18:18.115 * DB saved on disk
+88646:C 11 Jun 2024 23:18:18.116 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 23:18:18.205 * Background saving terminated with success
+58896:M 11 Jun 2024 23:23:19.087 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 23:23:19.090 * Background saving started by pid 89375
+89375:C 11 Jun 2024 23:23:19.101 * DB saved on disk
+89375:C 11 Jun 2024 23:23:19.103 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 23:23:19.192 * Background saving terminated with success
+58896:M 11 Jun 2024 23:28:20.011 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 23:28:20.013 * Background saving started by pid 90105
+90105:C 11 Jun 2024 23:28:20.027 * DB saved on disk
+90105:C 11 Jun 2024 23:28:20.028 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 23:28:20.115 * Background saving terminated with success
+58896:M 11 Jun 2024 23:33:21.067 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 23:33:21.069 * Background saving started by pid 90839
+90839:C 11 Jun 2024 23:33:21.079 * DB saved on disk
+90839:C 11 Jun 2024 23:33:21.080 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 23:33:21.170 * Background saving terminated with success
+58896:M 11 Jun 2024 23:38:22.003 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 23:38:22.008 * Background saving started by pid 91662
+91662:C 11 Jun 2024 23:38:22.019 * DB saved on disk
+91662:C 11 Jun 2024 23:38:22.019 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 23:38:22.111 * Background saving terminated with success
+58896:M 11 Jun 2024 23:43:23.059 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 23:43:23.063 * Background saving started by pid 92434
+92434:C 11 Jun 2024 23:43:23.075 * DB saved on disk
+92434:C 11 Jun 2024 23:43:23.075 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 23:43:23.165 * Background saving terminated with success
+58896:M 11 Jun 2024 23:48:24.037 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 23:48:24.038 * Background saving started by pid 93164
+93164:C 11 Jun 2024 23:48:24.049 * DB saved on disk
+93164:C 11 Jun 2024 23:48:24.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 23:48:24.142 * Background saving terminated with success
+58896:M 11 Jun 2024 23:53:25.018 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 23:53:25.021 * Background saving started by pid 93965
+93965:C 11 Jun 2024 23:53:25.035 * DB saved on disk
+93965:C 11 Jun 2024 23:53:25.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 23:53:25.123 * Background saving terminated with success
+58896:M 11 Jun 2024 23:58:26.009 * 100 changes in 300 seconds. Saving...
+58896:M 11 Jun 2024 23:58:26.011 * Background saving started by pid 94758
+94758:C 11 Jun 2024 23:58:26.025 * DB saved on disk
+94758:C 11 Jun 2024 23:58:26.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 11 Jun 2024 23:58:26.112 * Background saving terminated with success
+58896:M 12 Jun 2024 00:03:27.039 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 00:03:27.041 * Background saving started by pid 95491
+95491:C 12 Jun 2024 00:03:27.055 * DB saved on disk
+95491:C 12 Jun 2024 00:03:27.057 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 00:03:27.142 * Background saving terminated with success
+58896:M 12 Jun 2024 00:08:28.034 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 00:08:28.035 * Background saving started by pid 96284
+96284:C 12 Jun 2024 00:08:28.051 * DB saved on disk
+96284:C 12 Jun 2024 00:08:28.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 00:08:28.136 * Background saving terminated with success
+58896:M 12 Jun 2024 00:13:29.015 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 00:13:29.019 * Background saving started by pid 97696
+97696:C 12 Jun 2024 00:13:29.029 * DB saved on disk
+97696:C 12 Jun 2024 00:13:29.030 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 00:13:29.120 * Background saving terminated with success
+58896:M 12 Jun 2024 00:18:30.052 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 00:18:30.055 * Background saving started by pid 98669
+98669:C 12 Jun 2024 00:18:30.072 * DB saved on disk
+98669:C 12 Jun 2024 00:18:30.073 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 00:18:30.157 * Background saving terminated with success
+58896:M 12 Jun 2024 00:23:31.067 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 00:23:31.069 * Background saving started by pid 99666
+99666:C 12 Jun 2024 00:23:31.085 * DB saved on disk
+99666:C 12 Jun 2024 00:23:31.087 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 00:23:31.169 * Background saving terminated with success
+58896:M 12 Jun 2024 00:28:32.087 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 00:28:32.093 * Background saving started by pid 831
+831:C 12 Jun 2024 00:28:32.110 * DB saved on disk
+831:C 12 Jun 2024 00:28:32.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 00:28:32.194 * Background saving terminated with success
+58896:M 12 Jun 2024 00:33:33.077 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 00:33:33.079 * Background saving started by pid 1642
+1642:C 12 Jun 2024 00:33:33.099 * DB saved on disk
+1642:C 12 Jun 2024 00:33:33.100 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 00:33:33.181 * Background saving terminated with success
+58896:M 12 Jun 2024 00:38:34.030 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 00:38:34.032 * Background saving started by pid 2590
+2590:C 12 Jun 2024 00:38:34.045 * DB saved on disk
+2590:C 12 Jun 2024 00:38:34.046 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 00:38:34.134 * Background saving terminated with success
+58896:M 12 Jun 2024 00:43:35.021 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 00:43:35.024 * Background saving started by pid 3742
+3742:C 12 Jun 2024 00:43:35.037 * DB saved on disk
+3742:C 12 Jun 2024 00:43:35.038 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 00:43:35.125 * Background saving terminated with success
+58896:M 12 Jun 2024 00:48:36.015 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 00:48:36.018 * Background saving started by pid 4666
+4666:C 12 Jun 2024 00:48:36.030 * DB saved on disk
+4666:C 12 Jun 2024 00:48:36.031 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 00:48:36.119 * Background saving terminated with success
+58896:M 12 Jun 2024 00:53:37.025 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 00:53:37.027 * Background saving started by pid 5691
+5691:C 12 Jun 2024 00:53:37.042 * DB saved on disk
+5691:C 12 Jun 2024 00:53:37.042 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 00:53:37.129 * Background saving terminated with success
+58896:M 12 Jun 2024 00:58:38.050 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 00:58:38.054 * Background saving started by pid 6620
+6620:C 12 Jun 2024 00:58:38.065 * DB saved on disk
+6620:C 12 Jun 2024 00:58:38.066 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 00:58:38.155 * Background saving terminated with success
+58896:M 12 Jun 2024 01:03:39.073 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 01:03:39.075 * Background saving started by pid 7624
+7624:C 12 Jun 2024 01:03:39.101 * DB saved on disk
+7624:C 12 Jun 2024 01:03:39.114 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 01:03:39.176 * Background saving terminated with success
+58896:M 12 Jun 2024 01:08:40.009 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 01:08:40.012 * Background saving started by pid 8631
+8631:C 12 Jun 2024 01:08:40.025 * DB saved on disk
+8631:C 12 Jun 2024 01:08:40.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 01:08:40.113 * Background saving terminated with success
+58896:M 12 Jun 2024 01:20:43.620 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 01:20:43.625 * Background saving started by pid 10740
+10740:C 12 Jun 2024 01:20:43.650 * DB saved on disk
+10740:C 12 Jun 2024 01:20:43.650 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 01:20:43.727 * Background saving terminated with success
+58896:M 12 Jun 2024 01:25:44.086 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 01:25:44.089 * Background saving started by pid 11660
+11660:C 12 Jun 2024 01:25:44.101 * DB saved on disk
+11660:C 12 Jun 2024 01:25:44.102 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 01:25:44.190 * Background saving terminated with success
+58896:M 12 Jun 2024 01:30:45.057 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 01:30:45.059 * Background saving started by pid 12738
+12738:C 12 Jun 2024 01:30:45.066 * DB saved on disk
+12738:C 12 Jun 2024 01:30:45.066 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 01:30:45.159 * Background saving terminated with success
+58896:M 12 Jun 2024 01:35:46.087 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 01:35:46.088 * Background saving started by pid 13711
+13711:C 12 Jun 2024 01:35:46.099 * DB saved on disk
+13711:C 12 Jun 2024 01:35:46.099 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 01:35:46.189 * Background saving terminated with success
+58896:M 12 Jun 2024 01:40:47.061 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 01:40:47.062 * Background saving started by pid 14674
+14674:C 12 Jun 2024 01:40:47.069 * DB saved on disk
+14674:C 12 Jun 2024 01:40:47.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 01:40:47.162 * Background saving terminated with success
+58896:M 12 Jun 2024 01:45:48.078 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 01:45:48.082 * Background saving started by pid 18780
+18780:C 12 Jun 2024 01:45:48.103 * DB saved on disk
+18780:C 12 Jun 2024 01:45:48.105 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 01:45:48.184 * Background saving terminated with success
+58896:M 12 Jun 2024 01:50:49.025 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 01:50:49.026 * Background saving started by pid 19686
+19686:C 12 Jun 2024 01:50:49.038 * DB saved on disk
+19686:C 12 Jun 2024 01:50:49.039 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 01:50:49.127 * Background saving terminated with success
+58896:M 12 Jun 2024 01:56:34.702 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 01:56:34.702 * Background saving started by pid 20738
+20738:C 12 Jun 2024 01:56:34.741 * DB saved on disk
+20738:C 12 Jun 2024 01:56:34.744 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 01:56:34.803 * Background saving terminated with success
+58896:M 12 Jun 2024 02:01:35.007 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 02:01:35.008 * Background saving started by pid 21912
+21912:C 12 Jun 2024 02:01:35.017 * DB saved on disk
+21912:C 12 Jun 2024 02:01:35.018 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 02:01:35.108 * Background saving terminated with success
+58896:M 12 Jun 2024 02:06:36.095 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 02:06:36.097 * Background saving started by pid 23306
+23306:C 12 Jun 2024 02:06:36.103 * DB saved on disk
+23306:C 12 Jun 2024 02:06:36.104 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 02:06:36.197 * Background saving terminated with success
+58896:M 12 Jun 2024 02:11:37.037 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 02:11:37.038 * Background saving started by pid 24623
+24623:C 12 Jun 2024 02:11:37.049 * DB saved on disk
+24623:C 12 Jun 2024 02:11:37.049 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 02:11:37.138 * Background saving terminated with success
+58896:M 12 Jun 2024 02:16:38.032 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 02:16:38.035 * Background saving started by pid 28935
+28935:C 12 Jun 2024 02:16:38.056 * DB saved on disk
+28935:C 12 Jun 2024 02:16:38.060 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 02:16:38.136 * Background saving terminated with success
+58896:M 12 Jun 2024 02:21:39.065 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 02:21:39.068 * Background saving started by pid 30316
+30316:C 12 Jun 2024 02:21:39.089 * DB saved on disk
+30316:C 12 Jun 2024 02:21:39.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 02:21:39.169 * Background saving terminated with success
+58896:M 12 Jun 2024 02:26:40.088 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 02:26:40.092 * Background saving started by pid 31108
+31108:C 12 Jun 2024 02:26:40.115 * DB saved on disk
+31108:C 12 Jun 2024 02:26:40.116 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 02:26:40.192 * Background saving terminated with success
+58896:M 12 Jun 2024 02:31:41.089 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 02:31:41.090 * Background saving started by pid 32709
+32709:C 12 Jun 2024 02:31:41.115 * DB saved on disk
+32709:C 12 Jun 2024 02:31:41.116 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 02:31:41.191 * Background saving terminated with success
+58896:M 12 Jun 2024 02:34:11.210 * DB saved on disk
+58896:M 12 Jun 2024 02:39:12.090 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 02:39:12.090 * Background saving started by pid 34729
+34729:C 12 Jun 2024 02:39:12.098 * DB saved on disk
+34729:C 12 Jun 2024 02:39:12.099 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 02:39:12.191 * Background saving terminated with success
+58896:M 12 Jun 2024 02:44:13.030 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 02:44:13.031 * Background saving started by pid 36552
+36552:C 12 Jun 2024 02:44:13.039 * DB saved on disk
+36552:C 12 Jun 2024 02:44:13.040 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 02:44:13.132 * Background saving terminated with success
+58896:M 12 Jun 2024 02:49:14.003 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 02:49:14.007 * Background saving started by pid 40406
+40406:C 12 Jun 2024 02:49:14.016 * DB saved on disk
+40406:C 12 Jun 2024 02:49:14.017 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 02:49:14.108 * Background saving terminated with success
+58896:M 12 Jun 2024 02:54:15.052 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 02:54:15.053 * Background saving started by pid 41373
+41373:C 12 Jun 2024 02:54:15.068 * DB saved on disk
+41373:C 12 Jun 2024 02:54:15.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 02:54:15.155 * Background saving terminated with success
+58896:M 12 Jun 2024 02:59:16.098 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 02:59:16.100 * Background saving started by pid 42324
+42324:C 12 Jun 2024 02:59:16.108 * DB saved on disk
+42324:C 12 Jun 2024 02:59:16.109 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 02:59:16.201 * Background saving terminated with success
+58896:M 12 Jun 2024 03:04:17.012 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 03:04:17.015 * Background saving started by pid 43225
+43225:C 12 Jun 2024 03:04:17.025 * DB saved on disk
+43225:C 12 Jun 2024 03:04:17.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 03:04:17.116 * Background saving terminated with success
+58896:M 12 Jun 2024 03:04:34.446 * DB saved on disk
+58896:M 12 Jun 2024 03:09:35.045 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 03:09:35.048 * Background saving started by pid 44410
+44410:C 12 Jun 2024 03:09:35.055 * DB saved on disk
+44410:C 12 Jun 2024 03:09:35.057 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 03:09:35.148 * Background saving terminated with success
+58896:M 12 Jun 2024 03:14:36.016 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 03:14:36.019 * Background saving started by pid 46540
+46540:C 12 Jun 2024 03:14:36.031 * DB saved on disk
+46540:C 12 Jun 2024 03:14:36.033 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 03:14:36.119 * Background saving terminated with success
+58896:M 12 Jun 2024 03:19:37.037 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 03:19:37.041 * Background saving started by pid 47386
+47386:C 12 Jun 2024 03:19:37.055 * DB saved on disk
+47386:C 12 Jun 2024 03:19:37.058 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 03:19:37.143 * Background saving terminated with success
+58896:M 12 Jun 2024 03:24:38.035 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 03:24:38.042 * Background saving started by pid 48201
+48201:C 12 Jun 2024 03:24:38.063 * DB saved on disk
+48201:C 12 Jun 2024 03:24:38.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 03:24:38.147 * Background saving terminated with success
+58896:M 12 Jun 2024 03:29:39.031 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 03:29:39.035 * Background saving started by pid 49082
+49082:C 12 Jun 2024 03:29:39.051 * DB saved on disk
+49082:C 12 Jun 2024 03:29:39.052 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 03:29:39.136 * Background saving terminated with success
+58896:M 12 Jun 2024 03:34:40.043 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 03:34:40.046 * Background saving started by pid 53122
+53122:C 12 Jun 2024 03:34:40.058 * DB saved on disk
+53122:C 12 Jun 2024 03:34:40.059 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 03:34:40.147 * Background saving terminated with success
+58896:M 12 Jun 2024 03:39:41.032 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 03:39:41.035 * Background saving started by pid 54267
+54267:C 12 Jun 2024 03:39:41.056 * DB saved on disk
+54267:C 12 Jun 2024 03:39:41.057 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 03:39:41.137 * Background saving terminated with success
+58896:M 12 Jun 2024 03:44:42.005 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 03:44:42.006 * Background saving started by pid 55054
+55054:C 12 Jun 2024 03:44:42.022 * DB saved on disk
+55054:C 12 Jun 2024 03:44:42.024 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 03:44:42.107 * Background saving terminated with success
+58896:M 12 Jun 2024 03:49:43.092 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 03:49:43.094 * Background saving started by pid 55938
+55938:C 12 Jun 2024 03:49:43.104 * DB saved on disk
+55938:C 12 Jun 2024 03:49:43.105 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 03:49:43.195 * Background saving terminated with success
+58896:M 12 Jun 2024 03:54:44.085 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 03:54:44.087 * Background saving started by pid 56777
+56777:C 12 Jun 2024 03:54:44.105 * DB saved on disk
+56777:C 12 Jun 2024 03:54:44.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 03:54:44.189 * Background saving terminated with success
+58896:M 12 Jun 2024 03:59:45.049 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 03:59:45.050 * Background saving started by pid 57643
+57643:C 12 Jun 2024 03:59:45.059 * DB saved on disk
+57643:C 12 Jun 2024 03:59:45.061 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 03:59:45.151 * Background saving terminated with success
+58896:M 12 Jun 2024 04:04:46.023 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 04:04:46.027 * Background saving started by pid 58638
+58638:C 12 Jun 2024 04:04:46.049 * DB saved on disk
+58638:C 12 Jun 2024 04:04:46.049 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 04:04:46.129 * Background saving terminated with success
+58896:M 12 Jun 2024 04:09:47.069 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 04:09:47.071 * Background saving started by pid 59417
+59417:C 12 Jun 2024 04:09:47.089 * DB saved on disk
+59417:C 12 Jun 2024 04:09:47.090 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 04:09:47.173 * Background saving terminated with success
+58896:M 12 Jun 2024 04:14:48.091 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 04:14:48.092 * Background saving started by pid 60190
+60190:C 12 Jun 2024 04:14:48.100 * DB saved on disk
+60190:C 12 Jun 2024 04:14:48.105 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 04:14:48.193 * Background saving terminated with success
+58896:M 12 Jun 2024 04:19:49.050 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 04:19:49.052 * Background saving started by pid 61120
+61120:C 12 Jun 2024 04:19:49.060 * DB saved on disk
+61120:C 12 Jun 2024 04:19:49.060 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 04:19:49.153 * Background saving terminated with success
+58896:M 12 Jun 2024 04:24:50.028 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 04:24:50.032 * Background saving started by pid 61993
+61993:C 12 Jun 2024 04:24:50.051 * DB saved on disk
+61993:C 12 Jun 2024 04:24:50.052 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 04:24:50.134 * Background saving terminated with success
+58896:M 12 Jun 2024 04:29:51.054 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 04:29:51.058 * Background saving started by pid 62790
+62790:C 12 Jun 2024 04:29:51.077 * DB saved on disk
+62790:C 12 Jun 2024 04:29:51.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 04:29:51.159 * Background saving terminated with success
+58896:M 12 Jun 2024 04:34:52.067 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 04:34:52.069 * Background saving started by pid 63597
+63597:C 12 Jun 2024 04:34:52.077 * DB saved on disk
+63597:C 12 Jun 2024 04:34:52.078 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 04:34:52.170 * Background saving terminated with success
+58896:M 12 Jun 2024 04:39:53.088 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 04:39:53.089 * Background saving started by pid 64519
+64519:C 12 Jun 2024 04:39:53.098 * DB saved on disk
+64519:C 12 Jun 2024 04:39:53.099 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 04:39:53.190 * Background saving terminated with success
+58896:M 12 Jun 2024 04:44:54.089 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 04:44:54.091 * Background saving started by pid 65472
+65472:C 12 Jun 2024 04:44:54.099 * DB saved on disk
+65472:C 12 Jun 2024 04:44:54.100 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 04:44:54.193 * Background saving terminated with success
+58896:M 12 Jun 2024 04:49:55.090 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 04:49:55.091 * Background saving started by pid 66430
+66430:C 12 Jun 2024 04:49:55.102 * DB saved on disk
+66430:C 12 Jun 2024 04:49:55.104 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 04:49:55.192 * Background saving terminated with success
+58896:M 12 Jun 2024 04:54:56.019 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 04:54:56.022 * Background saving started by pid 67514
+67514:C 12 Jun 2024 04:54:56.034 * DB saved on disk
+67514:C 12 Jun 2024 04:54:56.039 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 04:54:56.124 * Background saving terminated with success
+58896:M 12 Jun 2024 04:59:57.000 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 04:59:57.001 * Background saving started by pid 68698
+68698:C 12 Jun 2024 04:59:57.010 * DB saved on disk
+68698:C 12 Jun 2024 04:59:57.012 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 04:59:57.102 * Background saving terminated with success
+58896:M 12 Jun 2024 05:04:58.100 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 05:04:58.102 * Background saving started by pid 74173
+74173:C 12 Jun 2024 05:04:58.117 * DB saved on disk
+74173:C 12 Jun 2024 05:04:58.117 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 05:04:58.204 * Background saving terminated with success
+58896:M 12 Jun 2024 05:09:59.003 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 05:09:59.005 * Background saving started by pid 77013
+77013:C 12 Jun 2024 05:09:59.012 * DB saved on disk
+77013:C 12 Jun 2024 05:09:59.013 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 05:09:59.106 * Background saving terminated with success
+58896:M 12 Jun 2024 05:15:00.056 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 05:15:00.058 * Background saving started by pid 77983
+77983:C 12 Jun 2024 05:15:00.070 * DB saved on disk
+77983:C 12 Jun 2024 05:15:00.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 05:15:00.159 * Background saving terminated with success
+58896:M 12 Jun 2024 05:20:01.096 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 05:20:01.103 * Background saving started by pid 79011
+79011:C 12 Jun 2024 05:20:01.122 * DB saved on disk
+79011:C 12 Jun 2024 05:20:01.132 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 05:20:01.204 * Background saving terminated with success
+58896:M 12 Jun 2024 05:25:02.097 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 05:25:02.099 * Background saving started by pid 80216
+80216:C 12 Jun 2024 05:25:02.113 * DB saved on disk
+80216:C 12 Jun 2024 05:25:02.113 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 05:25:02.200 * Background saving terminated with success
+58896:M 12 Jun 2024 05:30:03.063 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 05:30:03.068 * Background saving started by pid 81376
+81376:C 12 Jun 2024 05:30:03.083 * DB saved on disk
+81376:C 12 Jun 2024 05:30:03.088 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 05:30:03.169 * Background saving terminated with success
+58896:M 12 Jun 2024 05:35:04.075 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 05:35:04.078 * Background saving started by pid 82164
+82164:C 12 Jun 2024 05:35:04.088 * DB saved on disk
+82164:C 12 Jun 2024 05:35:04.088 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 05:35:04.179 * Background saving terminated with success
+58896:M 12 Jun 2024 05:40:05.085 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 05:40:05.087 * Background saving started by pid 83362
+83362:C 12 Jun 2024 05:40:05.113 * DB saved on disk
+83362:C 12 Jun 2024 05:40:05.116 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 05:40:05.189 * Background saving terminated with success
+58896:M 12 Jun 2024 05:45:06.011 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 05:45:06.012 * Background saving started by pid 84525
+84525:C 12 Jun 2024 05:45:06.021 * DB saved on disk
+84525:C 12 Jun 2024 05:45:06.022 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 05:45:06.114 * Background saving terminated with success
+58896:M 12 Jun 2024 05:50:07.081 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 05:50:07.082 * Background saving started by pid 85679
+85679:C 12 Jun 2024 05:50:07.095 * DB saved on disk
+85679:C 12 Jun 2024 05:50:07.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 05:50:07.184 * Background saving terminated with success
+58896:M 12 Jun 2024 06:09:17.678 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 06:09:17.799 * Background saving started by pid 86522
+86522:C 12 Jun 2024 06:09:17.807 * DB saved on disk
+86522:C 12 Jun 2024 06:09:17.807 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 06:09:17.913 * Background saving terminated with success
+58896:M 12 Jun 2024 07:23:10.681 * 1 changes in 3600 seconds. Saving...
+58896:M 12 Jun 2024 07:23:10.685 * Background saving started by pid 86688
+86688:C 12 Jun 2024 07:23:10.799 * DB saved on disk
+86688:C 12 Jun 2024 07:23:10.800 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 07:23:10.887 * Background saving terminated with success
+58896:M 12 Jun 2024 08:28:27.701 * 1 changes in 3600 seconds. Saving...
+58896:M 12 Jun 2024 08:28:27.709 * Background saving started by pid 86783
+86783:C 12 Jun 2024 08:28:27.805 * DB saved on disk
+86783:C 12 Jun 2024 08:28:27.806 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 08:28:27.810 * Background saving terminated with success
+58896:M 12 Jun 2024 08:37:48.107 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 08:37:48.109 * Background saving started by pid 86998
+86998:C 12 Jun 2024 08:37:48.124 * DB saved on disk
+86998:C 12 Jun 2024 08:37:48.125 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 08:37:48.210 * Background saving terminated with success
+58896:M 12 Jun 2024 08:45:00.378 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 08:45:00.441 * Background saving started by pid 88055
+88055:C 12 Jun 2024 08:45:00.998 * DB saved on disk
+88055:C 12 Jun 2024 08:45:01.006 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 08:45:01.057 * Background saving terminated with success
+58896:M 12 Jun 2024 09:03:18.658 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 09:03:18.662 * Background saving started by pid 88847
+88847:C 12 Jun 2024 09:03:18.782 * DB saved on disk
+88847:C 12 Jun 2024 09:03:18.783 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 09:03:18.865 * Background saving terminated with success
+58896:M 12 Jun 2024 09:20:51.679 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 09:20:51.682 * Background saving started by pid 89079
+89079:C 12 Jun 2024 09:20:51.805 * DB saved on disk
+89079:C 12 Jun 2024 09:20:51.806 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 09:20:51.885 * Background saving terminated with success
+58896:M 12 Jun 2024 09:36:57.986 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 09:36:57.986 * Background saving started by pid 89273
+89273:C 12 Jun 2024 09:36:57.994 * DB saved on disk
+89273:C 12 Jun 2024 09:36:57.994 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 09:36:58.089 * Background saving terminated with success
+58896:M 12 Jun 2024 09:52:59.001 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 09:52:59.002 * Background saving started by pid 89459
+89459:C 12 Jun 2024 09:52:59.013 * DB saved on disk
+89459:C 12 Jun 2024 09:52:59.014 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 09:52:59.103 * Background saving terminated with success
+58896:M 12 Jun 2024 10:09:08.619 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 10:09:08.624 * Background saving started by pid 89697
+89697:C 12 Jun 2024 10:09:08.738 * DB saved on disk
+89697:C 12 Jun 2024 10:09:08.739 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 10:09:08.831 * Background saving terminated with success
+58896:M 12 Jun 2024 10:27:48.637 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 10:27:48.640 * Background saving started by pid 90312
+90312:C 12 Jun 2024 10:27:48.754 * DB saved on disk
+90312:C 12 Jun 2024 10:27:48.754 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 10:27:48.843 * Background saving terminated with success
+58896:M 12 Jun 2024 10:45:08.649 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 10:45:08.765 * Background saving started by pid 90689
+90689:C 12 Jun 2024 10:45:08.781 * DB saved on disk
+90689:C 12 Jun 2024 10:45:08.781 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 10:45:08.872 * Background saving terminated with success
+58896:M 12 Jun 2024 11:04:19.611 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 11:04:19.731 * Background saving started by pid 91384
+91384:C 12 Jun 2024 11:04:19.738 * DB saved on disk
+91384:C 12 Jun 2024 11:04:19.739 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 11:04:19.837 * Background saving terminated with success
+58896:M 12 Jun 2024 11:13:15.115 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 11:13:15.119 * Background saving started by pid 91575
+91575:C 12 Jun 2024 11:13:15.137 * DB saved on disk
+91575:C 12 Jun 2024 11:13:15.138 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 11:13:15.222 * Background saving terminated with success
+58896:M 12 Jun 2024 12:18:47.632 * 1 changes in 3600 seconds. Saving...
+58896:M 12 Jun 2024 12:18:47.635 * Background saving started by pid 91694
+91694:C 12 Jun 2024 12:18:47.756 * DB saved on disk
+91694:C 12 Jun 2024 12:18:47.757 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 12:18:47.839 * Background saving terminated with success
+58896:M 12 Jun 2024 12:39:26.809 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 12:39:26.811 * Background saving started by pid 92013
+92013:C 12 Jun 2024 12:39:26.824 * DB saved on disk
+92013:C 12 Jun 2024 12:39:26.825 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 12:39:26.912 * Background saving terminated with success
+58896:M 12 Jun 2024 12:44:27.044 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 12:44:27.045 * Background saving started by pid 93150
+93150:C 12 Jun 2024 12:44:27.052 * DB saved on disk
+93150:C 12 Jun 2024 12:44:27.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 12:44:27.146 * Background saving terminated with success
+58896:M 12 Jun 2024 12:49:28.034 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 12:49:28.036 * Background saving started by pid 94288
+94288:C 12 Jun 2024 12:49:28.048 * DB saved on disk
+94288:C 12 Jun 2024 12:49:28.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 12:49:28.137 * Background saving terminated with success
+58896:M 12 Jun 2024 12:54:29.012 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 12:54:29.015 * Background saving started by pid 95469
+95469:C 12 Jun 2024 12:54:29.028 * DB saved on disk
+95469:C 12 Jun 2024 12:54:29.028 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 12:54:29.116 * Background saving terminated with success
+58896:M 12 Jun 2024 12:59:30.022 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 12:59:30.023 * Background saving started by pid 96610
+96610:C 12 Jun 2024 12:59:30.041 * DB saved on disk
+96610:C 12 Jun 2024 12:59:30.041 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 12:59:30.124 * Background saving terminated with success
+58896:M 12 Jun 2024 13:04:31.035 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 13:04:31.036 * Background saving started by pid 97725
+97725:C 12 Jun 2024 13:04:31.047 * DB saved on disk
+97725:C 12 Jun 2024 13:04:31.049 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 13:04:31.138 * Background saving terminated with success
+58896:M 12 Jun 2024 13:09:32.020 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 13:09:32.022 * Background saving started by pid 98866
+98866:C 12 Jun 2024 13:09:32.038 * DB saved on disk
+98866:C 12 Jun 2024 13:09:32.039 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 13:09:32.123 * Background saving terminated with success
+58896:M 12 Jun 2024 13:14:33.004 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 13:14:33.005 * Background saving started by pid 103
+103:C 12 Jun 2024 13:14:33.013 * DB saved on disk
+103:C 12 Jun 2024 13:14:33.014 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 13:14:33.106 * Background saving terminated with success
+58896:M 12 Jun 2024 13:19:34.047 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 13:19:34.048 * Background saving started by pid 1450
+1450:C 12 Jun 2024 13:19:34.058 * DB saved on disk
+1450:C 12 Jun 2024 13:19:34.058 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 13:19:34.149 * Background saving terminated with success
+58896:M 12 Jun 2024 13:24:35.060 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 13:24:35.063 * Background saving started by pid 2598
+2598:C 12 Jun 2024 13:24:35.072 * DB saved on disk
+2598:C 12 Jun 2024 13:24:35.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 13:24:35.165 * Background saving terminated with success
+58896:M 12 Jun 2024 13:29:36.006 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 13:29:36.009 * Background saving started by pid 3725
+3725:C 12 Jun 2024 13:29:36.019 * DB saved on disk
+3725:C 12 Jun 2024 13:29:36.020 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 13:29:36.110 * Background saving terminated with success
+58896:M 12 Jun 2024 13:34:37.016 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 13:34:37.017 * Background saving started by pid 4859
+4859:C 12 Jun 2024 13:34:37.036 * DB saved on disk
+4859:C 12 Jun 2024 13:34:37.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 13:34:37.118 * Background saving terminated with success
+58896:M 12 Jun 2024 13:52:39.660 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 13:52:39.663 * Background saving started by pid 5136
+5136:C 12 Jun 2024 13:52:39.783 * DB saved on disk
+5136:C 12 Jun 2024 13:52:39.783 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 13:52:39.867 * Background saving terminated with success
+58896:M 12 Jun 2024 14:18:00.088 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 14:18:00.089 * Background saving started by pid 5336
+5336:C 12 Jun 2024 14:18:00.100 * DB saved on disk
+5336:C 12 Jun 2024 14:18:00.101 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 14:18:00.192 * Background saving terminated with success
+58896:M 12 Jun 2024 14:23:39.578 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 14:23:39.579 * Background saving started by pid 5595
+5595:C 12 Jun 2024 14:23:39.589 * DB saved on disk
+5595:C 12 Jun 2024 14:23:39.590 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 14:23:39.686 * Background saving terminated with success
+58896:M 12 Jun 2024 14:28:40.051 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 14:28:40.063 * Background saving started by pid 6818
+6818:C 12 Jun 2024 14:28:40.079 * DB saved on disk
+6818:C 12 Jun 2024 14:28:40.080 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 14:28:40.165 * Background saving terminated with success
+58896:M 12 Jun 2024 14:33:41.100 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 14:33:41.101 * Background saving started by pid 7974
+7974:C 12 Jun 2024 14:33:41.111 * DB saved on disk
+7974:C 12 Jun 2024 14:33:41.112 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 14:33:41.202 * Background saving terminated with success
+58896:M 12 Jun 2024 14:39:15.833 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 14:39:15.834 * Background saving started by pid 9245
+9245:C 12 Jun 2024 14:39:15.843 * DB saved on disk
+9245:C 12 Jun 2024 14:39:15.843 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 14:39:15.936 * Background saving terminated with success
+58896:M 12 Jun 2024 14:44:16.080 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 14:44:16.083 * Background saving started by pid 10377
+10377:C 12 Jun 2024 14:44:16.090 * DB saved on disk
+10377:C 12 Jun 2024 14:44:16.091 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 14:44:16.184 * Background saving terminated with success
+58896:M 12 Jun 2024 14:49:17.004 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 14:49:17.006 * Background saving started by pid 11532
+11532:C 12 Jun 2024 14:49:17.020 * DB saved on disk
+11532:C 12 Jun 2024 14:49:17.020 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 14:49:17.107 * Background saving terminated with success
+58896:M 12 Jun 2024 14:54:18.054 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 14:54:18.055 * Background saving started by pid 12728
+12728:C 12 Jun 2024 14:54:18.071 * DB saved on disk
+12728:C 12 Jun 2024 14:54:18.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 14:54:18.156 * Background saving terminated with success
+58896:M 12 Jun 2024 14:59:19.060 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 14:59:19.063 * Background saving started by pid 13955
+13955:C 12 Jun 2024 14:59:19.095 * DB saved on disk
+13955:C 12 Jun 2024 14:59:19.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 14:59:19.164 * Background saving terminated with success
+58896:M 12 Jun 2024 15:04:50.041 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 15:04:50.046 * Background saving started by pid 14209
+14209:C 12 Jun 2024 15:04:50.161 * DB saved on disk
+14209:C 12 Jun 2024 15:04:50.162 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 15:04:50.249 * Background saving terminated with success
+58896:M 12 Jun 2024 15:16:01.278 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 15:16:01.280 * Background saving started by pid 14412
+14412:C 12 Jun 2024 15:16:01.296 * DB saved on disk
+14412:C 12 Jun 2024 15:16:01.297 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 15:16:01.382 * Background saving terminated with success
+58896:M 12 Jun 2024 15:21:42.305 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 15:21:42.308 * Background saving started by pid 15693
+15693:C 12 Jun 2024 15:21:42.425 * DB saved on disk
+15693:C 12 Jun 2024 15:21:42.426 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 15:21:42.512 * Background saving terminated with success
+58896:M 12 Jun 2024 15:26:43.091 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 15:26:43.093 * Background saving started by pid 15892
+15892:C 12 Jun 2024 15:26:43.108 * DB saved on disk
+15892:C 12 Jun 2024 15:26:43.109 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 15:26:43.208 * Background saving terminated with success
+58896:M 12 Jun 2024 15:32:08.470 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 15:32:08.473 * Background saving started by pid 16107
+16107:C 12 Jun 2024 15:32:08.586 * DB saved on disk
+16107:C 12 Jun 2024 15:32:08.587 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 15:32:08.675 * Background saving terminated with success
+58896:M 12 Jun 2024 15:37:51.306 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 15:37:51.311 * Background saving started by pid 16417
+16417:C 12 Jun 2024 15:37:51.431 * DB saved on disk
+16417:C 12 Jun 2024 15:37:51.432 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 15:37:51.515 * Background saving terminated with success
+58896:M 12 Jun 2024 15:43:24.501 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 15:43:24.505 * Background saving started by pid 16735
+16735:C 12 Jun 2024 15:43:24.619 * DB saved on disk
+16735:C 12 Jun 2024 15:43:24.620 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 15:43:24.709 * Background saving terminated with success
+58896:M 12 Jun 2024 15:49:17.280 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 15:49:17.282 * Background saving started by pid 17067
+17067:C 12 Jun 2024 15:49:17.403 * DB saved on disk
+17067:C 12 Jun 2024 15:49:17.403 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 15:49:17.486 * Background saving terminated with success
+58896:M 12 Jun 2024 15:58:31.612 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 15:58:31.612 * Background saving started by pid 17349
+17349:C 12 Jun 2024 15:58:31.624 * DB saved on disk
+17349:C 12 Jun 2024 15:58:31.625 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 15:58:31.713 * Background saving terminated with success
+58896:M 12 Jun 2024 16:07:32.858 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 16:07:32.859 * Background saving started by pid 17568
+17568:C 12 Jun 2024 16:07:32.873 * DB saved on disk
+17568:C 12 Jun 2024 16:07:32.874 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 16:07:32.961 * Background saving terminated with success
+58896:M 12 Jun 2024 16:26:11.532 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 16:26:11.537 * Background saving started by pid 17835
+17835:C 12 Jun 2024 16:26:11.655 * DB saved on disk
+17835:C 12 Jun 2024 16:26:11.655 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 16:26:11.739 * Background saving terminated with success
+58896:M 12 Jun 2024 16:50:57.768 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 16:50:57.771 * Background saving started by pid 18014
+18014:C 12 Jun 2024 16:50:57.787 * DB saved on disk
+18014:C 12 Jun 2024 16:50:57.789 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 16:50:57.873 * Background saving terminated with success
+58896:M 12 Jun 2024 17:50:58.003 * 1 changes in 3600 seconds. Saving...
+58896:M 12 Jun 2024 17:50:58.005 * Background saving started by pid 18164
+18164:C 12 Jun 2024 17:50:58.024 * DB saved on disk
+18164:C 12 Jun 2024 17:50:58.025 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 17:50:58.107 * Background saving terminated with success
+58896:M 12 Jun 2024 18:04:59.651 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 18:04:59.655 * Background saving started by pid 18427
+18427:C 12 Jun 2024 18:04:59.667 * DB saved on disk
+18427:C 12 Jun 2024 18:04:59.667 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 18:04:59.756 * Background saving terminated with success
+58896:M 12 Jun 2024 18:10:00.077 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 18:10:00.079 * Background saving started by pid 19644
+19644:C 12 Jun 2024 18:10:00.091 * DB saved on disk
+19644:C 12 Jun 2024 18:10:00.092 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 18:10:00.180 * Background saving terminated with success
+58896:M 12 Jun 2024 18:15:01.093 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 18:15:01.095 * Background saving started by pid 20794
+20794:C 12 Jun 2024 18:15:01.115 * DB saved on disk
+20794:C 12 Jun 2024 18:15:01.116 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 18:15:01.197 * Background saving terminated with success
+58896:M 12 Jun 2024 18:20:02.078 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 18:20:02.079 * Background saving started by pid 21958
+21958:C 12 Jun 2024 18:20:02.088 * DB saved on disk
+21958:C 12 Jun 2024 18:20:02.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 18:20:02.181 * Background saving terminated with success
+58896:M 12 Jun 2024 18:25:03.029 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 18:25:03.030 * Background saving started by pid 23368
+23368:C 12 Jun 2024 18:25:03.037 * DB saved on disk
+23368:C 12 Jun 2024 18:25:03.039 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 18:25:03.131 * Background saving terminated with success
+58896:M 12 Jun 2024 18:30:04.044 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 18:30:04.046 * Background saving started by pid 24145
+24145:C 12 Jun 2024 18:30:04.054 * DB saved on disk
+24145:C 12 Jun 2024 18:30:04.054 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 18:30:04.147 * Background saving terminated with success
+58896:M 12 Jun 2024 18:35:05.081 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 18:35:05.085 * Background saving started by pid 24948
+24948:C 12 Jun 2024 18:35:05.096 * DB saved on disk
+24948:C 12 Jun 2024 18:35:05.097 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 18:35:05.188 * Background saving terminated with success
+58896:M 12 Jun 2024 18:40:06.059 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 18:40:06.060 * Background saving started by pid 26487
+26487:C 12 Jun 2024 18:40:06.072 * DB saved on disk
+26487:C 12 Jun 2024 18:40:06.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 18:40:06.160 * Background saving terminated with success
+58896:M 12 Jun 2024 18:45:07.016 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 18:45:07.019 * Background saving started by pid 27478
+27478:C 12 Jun 2024 18:45:07.030 * DB saved on disk
+27478:C 12 Jun 2024 18:45:07.033 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 18:45:07.120 * Background saving terminated with success
+58896:M 12 Jun 2024 18:50:08.096 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 18:50:08.100 * Background saving started by pid 28255
+28255:C 12 Jun 2024 18:50:08.118 * DB saved on disk
+28255:C 12 Jun 2024 18:50:08.120 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 18:50:08.202 * Background saving terminated with success
+58896:M 12 Jun 2024 18:55:09.066 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 18:55:09.067 * Background saving started by pid 29026
+29026:C 12 Jun 2024 18:55:09.077 * DB saved on disk
+29026:C 12 Jun 2024 18:55:09.078 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 18:55:09.169 * Background saving terminated with success
+58896:M 12 Jun 2024 19:00:10.054 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 19:00:10.060 * Background saving started by pid 29846
+29846:C 12 Jun 2024 19:00:10.077 * DB saved on disk
+29846:C 12 Jun 2024 19:00:10.078 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 19:00:10.162 * Background saving terminated with success
+58896:M 12 Jun 2024 19:05:11.091 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 19:05:11.093 * Background saving started by pid 30656
+30656:C 12 Jun 2024 19:05:11.106 * DB saved on disk
+30656:C 12 Jun 2024 19:05:11.107 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 19:05:11.195 * Background saving terminated with success
+58896:M 12 Jun 2024 19:10:12.069 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 19:10:12.070 * Background saving started by pid 31436
+31436:C 12 Jun 2024 19:10:12.082 * DB saved on disk
+31436:C 12 Jun 2024 19:10:12.083 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 19:10:12.172 * Background saving terminated with success
+58896:M 12 Jun 2024 19:15:13.031 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 19:15:13.032 * Background saving started by pid 32456
+32456:C 12 Jun 2024 19:15:13.045 * DB saved on disk
+32456:C 12 Jun 2024 19:15:13.046 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 19:15:13.133 * Background saving terminated with success
+58896:M 12 Jun 2024 19:20:14.035 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 19:20:14.036 * Background saving started by pid 33746
+33746:C 12 Jun 2024 19:20:14.045 * DB saved on disk
+33746:C 12 Jun 2024 19:20:14.046 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 19:20:14.137 * Background saving terminated with success
+58896:M 12 Jun 2024 19:25:15.038 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 19:25:15.041 * Background saving started by pid 34925
+34925:C 12 Jun 2024 19:25:15.057 * DB saved on disk
+34925:C 12 Jun 2024 19:25:15.057 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 19:25:15.142 * Background saving terminated with success
+58896:M 12 Jun 2024 19:30:16.003 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 19:30:16.008 * Background saving started by pid 36180
+36180:C 12 Jun 2024 19:30:16.026 * DB saved on disk
+36180:C 12 Jun 2024 19:30:16.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 19:30:16.110 * Background saving terminated with success
+58896:M 12 Jun 2024 19:30:50.487 * DB saved on disk
+58896:M 12 Jun 2024 19:35:51.066 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 19:35:51.070 * Background saving started by pid 37695
+37695:C 12 Jun 2024 19:35:51.097 * DB saved on disk
+37695:C 12 Jun 2024 19:35:51.098 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 19:35:51.171 * Background saving terminated with success
+58896:M 12 Jun 2024 19:40:52.077 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 19:40:52.078 * Background saving started by pid 38605
+38605:C 12 Jun 2024 19:40:52.091 * DB saved on disk
+38605:C 12 Jun 2024 19:40:52.092 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 19:40:52.180 * Background saving terminated with success
+58896:M 12 Jun 2024 19:45:53.092 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 19:45:53.093 * Background saving started by pid 39738
+39738:C 12 Jun 2024 19:45:53.101 * DB saved on disk
+39738:C 12 Jun 2024 19:45:53.101 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 19:45:53.194 * Background saving terminated with success
+58896:M 12 Jun 2024 19:50:54.028 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 19:50:54.031 * Background saving started by pid 40670
+40670:C 12 Jun 2024 19:50:54.042 * DB saved on disk
+40670:C 12 Jun 2024 19:50:54.043 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 19:50:54.133 * Background saving terminated with success
+58896:M 12 Jun 2024 19:55:55.096 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 19:55:55.099 * Background saving started by pid 43457
+43457:C 12 Jun 2024 19:55:55.107 * DB saved on disk
+43457:C 12 Jun 2024 19:55:55.107 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 19:55:55.199 * Background saving terminated with success
+58896:M 12 Jun 2024 20:00:56.029 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 20:00:56.031 * Background saving started by pid 44453
+44453:C 12 Jun 2024 20:00:56.037 * DB saved on disk
+44453:C 12 Jun 2024 20:00:56.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 20:00:56.133 * Background saving terminated with success
+58896:M 12 Jun 2024 20:05:57.013 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 20:05:57.016 * Background saving started by pid 45420
+45420:C 12 Jun 2024 20:05:57.025 * DB saved on disk
+45420:C 12 Jun 2024 20:05:57.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 20:05:57.117 * Background saving terminated with success
+58896:M 12 Jun 2024 20:10:58.035 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 20:10:58.037 * Background saving started by pid 46445
+46445:C 12 Jun 2024 20:10:58.045 * DB saved on disk
+46445:C 12 Jun 2024 20:10:58.045 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 20:10:58.138 * Background saving terminated with success
+58896:M 12 Jun 2024 20:15:59.030 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 20:15:59.032 * Background saving started by pid 47438
+47438:C 12 Jun 2024 20:15:59.041 * DB saved on disk
+47438:C 12 Jun 2024 20:15:59.042 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 20:15:59.133 * Background saving terminated with success
+58896:M 12 Jun 2024 20:21:00.034 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 20:21:00.036 * Background saving started by pid 48364
+48364:C 12 Jun 2024 20:21:00.043 * DB saved on disk
+48364:C 12 Jun 2024 20:21:00.046 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 20:21:00.137 * Background saving terminated with success
+58896:M 12 Jun 2024 20:26:01.085 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 20:26:01.086 * Background saving started by pid 49394
+49394:C 12 Jun 2024 20:26:01.094 * DB saved on disk
+49394:C 12 Jun 2024 20:26:01.095 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 20:26:01.188 * Background saving terminated with success
+58896:M 12 Jun 2024 20:31:02.083 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 20:31:02.088 * Background saving started by pid 50479
+50479:C 12 Jun 2024 20:31:02.109 * DB saved on disk
+50479:C 12 Jun 2024 20:31:02.110 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 20:31:02.190 * Background saving terminated with success
+58896:M 12 Jun 2024 20:36:03.042 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 20:36:03.043 * Background saving started by pid 51442
+51442:C 12 Jun 2024 20:36:03.065 * DB saved on disk
+51442:C 12 Jun 2024 20:36:03.066 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 20:36:03.144 * Background saving terminated with success
+58896:M 12 Jun 2024 20:41:04.076 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 20:41:04.078 * Background saving started by pid 52399
+52399:C 12 Jun 2024 20:41:04.090 * DB saved on disk
+52399:C 12 Jun 2024 20:41:04.093 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 20:41:04.179 * Background saving terminated with success
+58896:M 12 Jun 2024 20:46:05.056 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 20:46:05.058 * Background saving started by pid 53397
+53397:C 12 Jun 2024 20:46:05.067 * DB saved on disk
+53397:C 12 Jun 2024 20:46:05.067 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 20:46:05.159 * Background saving terminated with success
+58896:M 12 Jun 2024 20:51:06.070 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 20:51:06.071 * Background saving started by pid 54382
+54382:C 12 Jun 2024 20:51:06.078 * DB saved on disk
+54382:C 12 Jun 2024 20:51:06.081 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 20:51:06.172 * Background saving terminated with success
+58896:M 12 Jun 2024 20:56:07.099 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 20:56:07.102 * Background saving started by pid 57248
+57248:C 12 Jun 2024 20:56:07.111 * DB saved on disk
+57248:C 12 Jun 2024 20:56:07.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 20:56:07.204 * Background saving terminated with success
+58896:M 12 Jun 2024 21:01:08.063 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 21:01:08.066 * Background saving started by pid 58219
+58219:C 12 Jun 2024 21:01:08.075 * DB saved on disk
+58219:C 12 Jun 2024 21:01:08.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 21:01:08.168 * Background saving terminated with success
+58896:M 12 Jun 2024 21:06:09.082 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 21:06:09.082 * Background saving started by pid 58998
+58998:C 12 Jun 2024 21:06:09.092 * DB saved on disk
+58998:C 12 Jun 2024 21:06:09.093 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 21:06:09.184 * Background saving terminated with success
+58896:M 12 Jun 2024 22:22:09.136 * 1 changes in 3600 seconds. Saving...
+58896:M 12 Jun 2024 22:22:09.196 * Background saving started by pid 59686
+59686:C 12 Jun 2024 22:22:09.431 * DB saved on disk
+59686:C 12 Jun 2024 22:22:09.536 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 22:22:09.634 * Background saving terminated with success
+58896:M 12 Jun 2024 22:27:10.079 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 22:27:10.083 * Background saving started by pid 60609
+60609:C 12 Jun 2024 22:27:10.097 * DB saved on disk
+60609:C 12 Jun 2024 22:27:10.099 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 22:27:10.184 * Background saving terminated with success
+58896:M 12 Jun 2024 22:32:11.032 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 22:32:11.036 * Background saving started by pid 61470
+61470:C 12 Jun 2024 22:32:11.054 * DB saved on disk
+61470:C 12 Jun 2024 22:32:11.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 22:32:11.138 * Background saving terminated with success
+58896:M 12 Jun 2024 22:37:12.056 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 22:37:12.057 * Background saving started by pid 62229
+62229:C 12 Jun 2024 22:37:12.071 * DB saved on disk
+62229:C 12 Jun 2024 22:37:12.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 22:37:12.159 * Background saving terminated with success
+58896:M 12 Jun 2024 22:42:13.023 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 22:42:13.025 * Background saving started by pid 63085
+63085:C 12 Jun 2024 22:42:13.036 * DB saved on disk
+63085:C 12 Jun 2024 22:42:13.036 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 22:42:13.126 * Background saving terminated with success
+58896:M 12 Jun 2024 22:47:14.042 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 22:47:14.043 * Background saving started by pid 63867
+63867:C 12 Jun 2024 22:47:14.052 * DB saved on disk
+63867:C 12 Jun 2024 22:47:14.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 22:47:14.145 * Background saving terminated with success
+58896:M 12 Jun 2024 22:52:15.071 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 22:52:15.075 * Background saving started by pid 64674
+64674:C 12 Jun 2024 22:52:15.106 * DB saved on disk
+64674:C 12 Jun 2024 22:52:15.106 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 22:52:15.177 * Background saving terminated with success
+58896:M 12 Jun 2024 22:57:16.052 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 22:57:16.053 * Background saving started by pid 65510
+65510:C 12 Jun 2024 22:57:16.071 * DB saved on disk
+65510:C 12 Jun 2024 22:57:16.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 22:57:16.155 * Background saving terminated with success
+58896:M 12 Jun 2024 23:02:17.039 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 23:02:17.043 * Background saving started by pid 66374
+66374:C 12 Jun 2024 23:02:17.059 * DB saved on disk
+66374:C 12 Jun 2024 23:02:17.061 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 23:02:17.145 * Background saving terminated with success
+58896:M 12 Jun 2024 23:07:18.063 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 23:07:18.065 * Background saving started by pid 69899
+69899:C 12 Jun 2024 23:07:18.097 * DB saved on disk
+69899:C 12 Jun 2024 23:07:18.097 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 23:07:18.166 * Background saving terminated with success
+58896:M 12 Jun 2024 23:12:19.010 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 23:12:19.013 * Background saving started by pid 72751
+72751:C 12 Jun 2024 23:12:19.025 * DB saved on disk
+72751:C 12 Jun 2024 23:12:19.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 23:12:19.114 * Background saving terminated with success
+58896:M 12 Jun 2024 23:17:20.093 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 23:17:20.097 * Background saving started by pid 73511
+73511:C 12 Jun 2024 23:17:20.117 * DB saved on disk
+73511:C 12 Jun 2024 23:17:20.118 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 23:17:20.199 * Background saving terminated with success
+58896:M 12 Jun 2024 23:22:21.096 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 23:22:21.098 * Background saving started by pid 74274
+74274:C 12 Jun 2024 23:22:21.108 * DB saved on disk
+74274:C 12 Jun 2024 23:22:21.115 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 23:22:21.199 * Background saving terminated with success
+58896:M 12 Jun 2024 23:27:22.041 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 23:27:22.044 * Background saving started by pid 75086
+75086:C 12 Jun 2024 23:27:22.055 * DB saved on disk
+75086:C 12 Jun 2024 23:27:22.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 23:27:22.145 * Background saving terminated with success
+58896:M 12 Jun 2024 23:32:23.057 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 23:32:23.059 * Background saving started by pid 75910
+75910:C 12 Jun 2024 23:32:23.070 * DB saved on disk
+75910:C 12 Jun 2024 23:32:23.075 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 23:32:23.161 * Background saving terminated with success
+58896:M 12 Jun 2024 23:37:24.000 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 23:37:24.002 * Background saving started by pid 76753
+76753:C 12 Jun 2024 23:37:24.021 * DB saved on disk
+76753:C 12 Jun 2024 23:37:24.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 23:37:24.102 * Background saving terminated with success
+58896:M 12 Jun 2024 23:42:25.039 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 23:42:25.041 * Background saving started by pid 77566
+77566:C 12 Jun 2024 23:42:25.051 * DB saved on disk
+77566:C 12 Jun 2024 23:42:25.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 23:42:25.142 * Background saving terminated with success
+58896:M 12 Jun 2024 23:47:26.048 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 23:47:26.051 * Background saving started by pid 78392
+78392:C 12 Jun 2024 23:47:26.058 * DB saved on disk
+78392:C 12 Jun 2024 23:47:26.059 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 23:47:26.152 * Background saving terminated with success
+58896:M 12 Jun 2024 23:52:27.035 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 23:52:27.036 * Background saving started by pid 79236
+79236:C 12 Jun 2024 23:52:27.047 * DB saved on disk
+79236:C 12 Jun 2024 23:52:27.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 23:52:27.137 * Background saving terminated with success
+58896:M 12 Jun 2024 23:57:28.011 * 100 changes in 300 seconds. Saving...
+58896:M 12 Jun 2024 23:57:28.017 * Background saving started by pid 80013
+80013:C 12 Jun 2024 23:57:28.026 * DB saved on disk
+80013:C 12 Jun 2024 23:57:28.027 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 12 Jun 2024 23:57:28.118 * Background saving terminated with success
+58896:M 13 Jun 2024 00:02:29.041 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 00:02:29.043 * Background saving started by pid 80854
+80854:C 13 Jun 2024 00:02:29.061 * DB saved on disk
+80854:C 13 Jun 2024 00:02:29.061 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 00:02:29.145 * Background saving terminated with success
+58896:M 13 Jun 2024 00:07:30.083 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 00:07:30.088 * Background saving started by pid 81692
+81692:C 13 Jun 2024 00:07:30.100 * DB saved on disk
+81692:C 13 Jun 2024 00:07:30.100 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 00:07:30.190 * Background saving terminated with success
+58896:M 13 Jun 2024 00:12:31.037 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 00:12:31.038 * Background saving started by pid 82630
+82630:C 13 Jun 2024 00:12:31.048 * DB saved on disk
+82630:C 13 Jun 2024 00:12:31.049 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 00:12:31.139 * Background saving terminated with success
+58896:M 13 Jun 2024 00:18:24.490 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 00:18:24.492 * Background saving started by pid 83628
+83628:C 13 Jun 2024 00:18:24.500 * DB saved on disk
+83628:C 13 Jun 2024 00:18:24.501 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 00:18:24.594 * Background saving terminated with success
+58896:M 13 Jun 2024 00:23:25.099 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 00:23:25.101 * Background saving started by pid 84426
+84426:C 13 Jun 2024 00:23:25.116 * DB saved on disk
+84426:C 13 Jun 2024 00:23:25.119 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 00:23:25.203 * Background saving terminated with success
+58896:M 13 Jun 2024 00:28:26.000 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 00:28:26.003 * Background saving started by pid 85490
+85490:C 13 Jun 2024 00:28:26.011 * DB saved on disk
+85490:C 13 Jun 2024 00:28:26.012 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 00:28:26.104 * Background saving terminated with success
+58896:M 13 Jun 2024 00:39:55.401 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 00:39:55.402 * Background saving started by pid 87583
+87583:C 13 Jun 2024 00:39:55.410 * DB saved on disk
+87583:C 13 Jun 2024 00:39:55.410 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 00:39:55.504 * Background saving terminated with success
+58896:M 13 Jun 2024 00:44:56.013 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 00:44:56.017 * Background saving started by pid 88517
+88517:C 13 Jun 2024 00:44:56.027 * DB saved on disk
+88517:C 13 Jun 2024 00:44:56.028 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 00:44:56.118 * Background saving terminated with success
+58896:M 13 Jun 2024 00:49:57.055 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 00:49:57.058 * Background saving started by pid 89534
+89534:C 13 Jun 2024 00:49:57.066 * DB saved on disk
+89534:C 13 Jun 2024 00:49:57.067 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 00:49:57.159 * Background saving terminated with success
+58896:M 13 Jun 2024 00:54:58.054 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 00:54:58.056 * Background saving started by pid 92884
+92884:C 13 Jun 2024 00:54:58.076 * DB saved on disk
+92884:C 13 Jun 2024 00:54:58.079 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 00:54:58.156 * Background saving terminated with success
+58896:M 13 Jun 2024 00:59:59.094 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 00:59:59.096 * Background saving started by pid 97454
+97454:C 13 Jun 2024 00:59:59.105 * DB saved on disk
+97454:C 13 Jun 2024 00:59:59.106 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 00:59:59.197 * Background saving terminated with success
+58896:M 13 Jun 2024 01:05:00.065 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 01:05:00.067 * Background saving started by pid 208
+208:C 13 Jun 2024 01:05:00.075 * DB saved on disk
+208:C 13 Jun 2024 01:05:00.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 01:05:00.168 * Background saving terminated with success
+58896:M 13 Jun 2024 01:10:01.004 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 01:10:01.007 * Background saving started by pid 1501
+1501:C 13 Jun 2024 01:10:01.016 * DB saved on disk
+1501:C 13 Jun 2024 01:10:01.017 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 01:10:01.108 * Background saving terminated with success
+58896:M 13 Jun 2024 01:15:02.069 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 01:15:02.073 * Background saving started by pid 8174
+8174:C 13 Jun 2024 01:15:02.087 * DB saved on disk
+8174:C 13 Jun 2024 01:15:02.088 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 01:15:02.176 * Background saving terminated with success
+58896:M 13 Jun 2024 01:20:03.007 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 01:20:03.009 * Background saving started by pid 12151
+12151:C 13 Jun 2024 01:20:03.021 * DB saved on disk
+12151:C 13 Jun 2024 01:20:03.022 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 01:20:03.111 * Background saving terminated with success
+58896:M 13 Jun 2024 01:25:04.011 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 01:25:04.014 * Background saving started by pid 14019
+14019:C 13 Jun 2024 01:25:04.027 * DB saved on disk
+14019:C 13 Jun 2024 01:25:04.028 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 01:25:04.115 * Background saving terminated with success
+58896:M 13 Jun 2024 01:30:05.029 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 01:30:05.032 * Background saving started by pid 15236
+15236:C 13 Jun 2024 01:30:05.048 * DB saved on disk
+15236:C 13 Jun 2024 01:30:05.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 01:30:05.134 * Background saving terminated with success
+58896:M 13 Jun 2024 01:46:26.863 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 01:46:26.987 * Background saving started by pid 16271
+16271:C 13 Jun 2024 01:46:26.997 * DB saved on disk
+16271:C 13 Jun 2024 01:46:26.997 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 01:46:27.096 * Background saving terminated with success
+58896:M 13 Jun 2024 02:53:37.566 * 1 changes in 3600 seconds. Saving...
+58896:M 13 Jun 2024 02:53:37.687 * Background saving started by pid 16361
+16361:C 13 Jun 2024 02:53:37.704 * DB saved on disk
+16361:C 13 Jun 2024 02:53:37.704 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 02:53:37.795 * Background saving terminated with success
+58896:M 13 Jun 2024 04:00:22.582 * 1 changes in 3600 seconds. Saving...
+58896:M 13 Jun 2024 04:00:22.694 * Background saving started by pid 16480
+16480:C 13 Jun 2024 04:00:22.703 * DB saved on disk
+16480:C 13 Jun 2024 04:00:22.704 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 04:00:22.807 * Background saving terminated with success
+58896:M 13 Jun 2024 05:06:58.599 * 1 changes in 3600 seconds. Saving...
+58896:M 13 Jun 2024 05:06:58.605 * Background saving started by pid 16586
+16586:C 13 Jun 2024 05:06:58.718 * DB saved on disk
+16586:C 13 Jun 2024 05:06:58.718 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 05:06:58.809 * Background saving terminated with success
+58896:M 13 Jun 2024 06:11:18.604 * 1 changes in 3600 seconds. Saving...
+58896:M 13 Jun 2024 06:11:18.613 * Background saving started by pid 16651
+16651:C 13 Jun 2024 06:11:18.714 * DB saved on disk
+16651:C 13 Jun 2024 06:11:18.746 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 06:11:18.815 * Background saving terminated with success
+58896:M 13 Jun 2024 07:14:47.525 * 1 changes in 3600 seconds. Saving...
+58896:M 13 Jun 2024 07:14:47.528 * Background saving started by pid 17811
+17811:C 13 Jun 2024 07:14:47.679 * DB saved on disk
+17811:C 13 Jun 2024 07:14:47.680 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 07:14:47.730 * Background saving terminated with success
+58896:M 13 Jun 2024 08:23:04.569 * 1 changes in 3600 seconds. Saving...
+58896:M 13 Jun 2024 08:23:04.574 * Background saving started by pid 17919
+17919:C 13 Jun 2024 08:23:04.680 * DB saved on disk
+17919:C 13 Jun 2024 08:23:04.680 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 08:23:04.775 * Background saving terminated with success
+58896:M 13 Jun 2024 09:29:23.578 * 1 changes in 3600 seconds. Saving...
+58896:M 13 Jun 2024 09:29:23.582 * Background saving started by pid 18003
+18003:C 13 Jun 2024 09:29:23.701 * DB saved on disk
+18003:C 13 Jun 2024 09:29:23.701 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 09:29:23.787 * Background saving terminated with success
+58896:M 13 Jun 2024 09:50:20.002 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 09:50:20.002 * Background saving started by pid 18192
+18192:C 13 Jun 2024 09:50:20.013 * DB saved on disk
+18192:C 13 Jun 2024 09:50:20.016 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 09:50:20.103 * Background saving terminated with success
+58896:M 13 Jun 2024 10:26:10.581 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 10:26:10.583 * Background saving started by pid 18369
+18369:C 13 Jun 2024 10:26:10.602 * DB saved on disk
+18369:C 13 Jun 2024 10:26:10.602 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 10:26:10.684 * Background saving terminated with success
+58896:M 13 Jun 2024 10:55:26.571 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 10:55:26.574 * Background saving started by pid 18571
+18571:C 13 Jun 2024 10:55:26.591 * DB saved on disk
+18571:C 13 Jun 2024 10:55:26.591 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 10:55:26.676 * Background saving terminated with success
+58896:M 13 Jun 2024 11:29:14.641 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 11:29:14.644 * Background saving started by pid 18818
+18818:C 13 Jun 2024 11:29:14.664 * DB saved on disk
+18818:C 13 Jun 2024 11:29:14.665 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 11:29:14.746 * Background saving terminated with success
+58896:M 13 Jun 2024 11:34:15.063 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 11:34:15.065 * Background saving started by pid 20038
+20038:C 13 Jun 2024 11:34:15.082 * DB saved on disk
+20038:C 13 Jun 2024 11:34:15.084 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 11:34:15.167 * Background saving terminated with success
+58896:M 13 Jun 2024 11:39:16.050 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 11:39:16.052 * Background saving started by pid 21202
+21202:C 13 Jun 2024 11:39:16.063 * DB saved on disk
+21202:C 13 Jun 2024 11:39:16.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 11:39:16.153 * Background saving terminated with success
+58896:M 13 Jun 2024 11:44:17.031 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 11:44:17.033 * Background saving started by pid 22048
+22048:C 13 Jun 2024 11:44:17.047 * DB saved on disk
+22048:C 13 Jun 2024 11:44:17.049 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 11:44:17.135 * Background saving terminated with success
+58896:M 13 Jun 2024 11:49:18.051 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 11:49:18.056 * Background saving started by pid 22821
+22821:C 13 Jun 2024 11:49:18.073 * DB saved on disk
+22821:C 13 Jun 2024 11:49:18.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 11:49:18.159 * Background saving terminated with success
+58896:M 13 Jun 2024 11:54:19.040 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 11:54:19.043 * Background saving started by pid 23590
+23590:C 13 Jun 2024 11:54:19.061 * DB saved on disk
+23590:C 13 Jun 2024 11:54:19.062 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 11:54:19.145 * Background saving terminated with success
+58896:M 13 Jun 2024 11:59:20.034 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 11:59:20.038 * Background saving started by pid 24369
+24369:C 13 Jun 2024 11:59:20.055 * DB saved on disk
+24369:C 13 Jun 2024 11:59:20.056 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 11:59:20.141 * Background saving terminated with success
+58896:M 13 Jun 2024 12:04:21.013 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 12:04:21.015 * Background saving started by pid 25145
+25145:C 13 Jun 2024 12:04:21.033 * DB saved on disk
+25145:C 13 Jun 2024 12:04:21.034 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 12:04:21.117 * Background saving terminated with success
+58896:M 13 Jun 2024 12:09:22.032 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 12:09:22.035 * Background saving started by pid 25919
+25919:C 13 Jun 2024 12:09:22.049 * DB saved on disk
+25919:C 13 Jun 2024 12:09:22.052 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 12:09:22.138 * Background saving terminated with success
+58896:M 13 Jun 2024 12:14:23.054 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 12:14:23.058 * Background saving started by pid 26708
+26708:C 13 Jun 2024 12:14:23.070 * DB saved on disk
+26708:C 13 Jun 2024 12:14:23.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 12:14:23.160 * Background saving terminated with success
+58896:M 13 Jun 2024 12:22:08.623 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 12:22:08.625 * Background saving started by pid 26869
+26869:C 13 Jun 2024 12:22:08.638 * DB saved on disk
+26869:C 13 Jun 2024 12:22:08.640 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 12:22:08.727 * Background saving terminated with success
+58896:M 13 Jun 2024 13:22:56.523 * 1 changes in 3600 seconds. Saving...
+58896:M 13 Jun 2024 13:22:56.527 * Background saving started by pid 26934
+26934:C 13 Jun 2024 13:22:56.637 * DB saved on disk
+26934:C 13 Jun 2024 13:22:56.638 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 13:22:56.730 * Background saving terminated with success
+58896:M 13 Jun 2024 13:54:40.777 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 13:54:40.779 * Background saving started by pid 27164
+27164:C 13 Jun 2024 13:54:40.788 * DB saved on disk
+27164:C 13 Jun 2024 13:54:40.789 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 13:54:40.881 * Background saving terminated with success
+58896:M 13 Jun 2024 13:59:41.086 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 13:59:41.091 * Background saving started by pid 28401
+28401:C 13 Jun 2024 13:59:41.125 * DB saved on disk
+28401:C 13 Jun 2024 13:59:41.125 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 13:59:41.193 * Background saving terminated with success
+58896:M 13 Jun 2024 14:04:42.086 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 14:04:42.089 * Background saving started by pid 29364
+29364:C 13 Jun 2024 14:04:42.106 * DB saved on disk
+29364:C 13 Jun 2024 14:04:42.108 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 14:04:42.191 * Background saving terminated with success
+58896:M 13 Jun 2024 14:09:43.039 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 14:09:43.040 * Background saving started by pid 30343
+30343:C 13 Jun 2024 14:09:43.050 * DB saved on disk
+30343:C 13 Jun 2024 14:09:43.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 14:09:43.141 * Background saving terminated with success
+58896:M 13 Jun 2024 14:14:44.095 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 14:14:44.098 * Background saving started by pid 31113
+31113:C 13 Jun 2024 14:14:44.106 * DB saved on disk
+31113:C 13 Jun 2024 14:14:44.107 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 14:14:44.199 * Background saving terminated with success
+58896:M 13 Jun 2024 14:19:45.073 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 14:19:45.077 * Background saving started by pid 31876
+31876:C 13 Jun 2024 14:19:45.105 * DB saved on disk
+31876:C 13 Jun 2024 14:19:45.106 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 14:19:45.181 * Background saving terminated with success
+58896:M 13 Jun 2024 14:24:46.099 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 14:24:46.103 * Background saving started by pid 32589
+32589:C 13 Jun 2024 14:24:46.117 * DB saved on disk
+32589:C 13 Jun 2024 14:24:46.119 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 14:24:46.204 * Background saving terminated with success
+58896:M 13 Jun 2024 14:29:47.029 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 14:29:47.035 * Background saving started by pid 33317
+33317:C 13 Jun 2024 14:29:47.050 * DB saved on disk
+33317:C 13 Jun 2024 14:29:47.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 14:29:47.137 * Background saving terminated with success
+58896:M 13 Jun 2024 14:34:48.042 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 14:34:48.044 * Background saving started by pid 34025
+34025:C 13 Jun 2024 14:34:48.057 * DB saved on disk
+34025:C 13 Jun 2024 14:34:48.060 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 14:34:48.147 * Background saving terminated with success
+58896:M 13 Jun 2024 14:39:49.001 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 14:39:49.002 * Background saving started by pid 34740
+34740:C 13 Jun 2024 14:39:49.011 * DB saved on disk
+34740:C 13 Jun 2024 14:39:49.011 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 14:39:49.104 * Background saving terminated with success
+58896:M 13 Jun 2024 14:44:50.083 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 14:44:50.086 * Background saving started by pid 35603
+35603:C 13 Jun 2024 14:44:50.098 * DB saved on disk
+35603:C 13 Jun 2024 14:44:50.101 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 14:44:50.188 * Background saving terminated with success
+58896:M 13 Jun 2024 14:49:51.042 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 14:49:51.045 * Background saving started by pid 36239
+36239:C 13 Jun 2024 14:49:51.057 * DB saved on disk
+36239:C 13 Jun 2024 14:49:51.059 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 14:49:51.146 * Background saving terminated with success
+58896:M 13 Jun 2024 14:54:52.029 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 14:54:52.032 * Background saving started by pid 36857
+36857:C 13 Jun 2024 14:54:52.066 * DB saved on disk
+36857:C 13 Jun 2024 14:54:52.067 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 14:54:52.134 * Background saving terminated with success
+58896:M 13 Jun 2024 14:59:53.033 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 14:59:53.036 * Background saving started by pid 37538
+37538:C 13 Jun 2024 14:59:53.051 * DB saved on disk
+37538:C 13 Jun 2024 14:59:53.052 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 14:59:53.138 * Background saving terminated with success
+58896:M 13 Jun 2024 15:04:54.044 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 15:04:54.046 * Background saving started by pid 38152
+38152:C 13 Jun 2024 15:04:54.067 * DB saved on disk
+38152:C 13 Jun 2024 15:04:54.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 15:04:54.148 * Background saving terminated with success
+58896:M 13 Jun 2024 15:09:55.087 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 15:09:55.092 * Background saving started by pid 38789
+38789:C 13 Jun 2024 15:09:55.104 * DB saved on disk
+38789:C 13 Jun 2024 15:09:55.104 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 15:09:55.196 * Background saving terminated with success
+58896:M 13 Jun 2024 15:14:56.035 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 15:14:56.037 * Background saving started by pid 39411
+39411:C 13 Jun 2024 15:14:56.050 * DB saved on disk
+39411:C 13 Jun 2024 15:14:56.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 15:14:56.138 * Background saving terminated with success
+58896:M 13 Jun 2024 15:19:57.033 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 15:19:57.034 * Background saving started by pid 40025
+40025:C 13 Jun 2024 15:19:57.043 * DB saved on disk
+40025:C 13 Jun 2024 15:19:57.044 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 15:19:57.135 * Background saving terminated with success
+58896:M 13 Jun 2024 15:24:58.059 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 15:24:58.061 * Background saving started by pid 40708
+40708:C 13 Jun 2024 15:24:58.082 * DB saved on disk
+40708:C 13 Jun 2024 15:24:58.082 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 15:24:58.162 * Background saving terminated with success
+58896:M 13 Jun 2024 15:29:59.058 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 15:29:59.061 * Background saving started by pid 41970
+41970:C 13 Jun 2024 15:29:59.074 * DB saved on disk
+41970:C 13 Jun 2024 15:29:59.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 15:29:59.162 * Background saving terminated with success
+58896:M 13 Jun 2024 15:35:00.020 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 15:35:00.023 * Background saving started by pid 46158
+46158:C 13 Jun 2024 15:35:00.046 * DB saved on disk
+46158:C 13 Jun 2024 15:35:00.047 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 15:35:00.124 * Background saving terminated with success
+58896:M 13 Jun 2024 15:40:01.046 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 15:40:01.049 * Background saving started by pid 52688
+52688:C 13 Jun 2024 15:40:01.069 * DB saved on disk
+52688:C 13 Jun 2024 15:40:01.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 15:40:01.150 * Background saving terminated with success
+58896:M 13 Jun 2024 15:45:53.815 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 15:45:53.818 * Background saving started by pid 53374
+53374:C 13 Jun 2024 15:45:53.840 * DB saved on disk
+53374:C 13 Jun 2024 15:45:53.840 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 15:45:53.919 * Background saving terminated with success
+58896:M 13 Jun 2024 15:50:54.050 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 15:50:54.052 * Background saving started by pid 54742
+54742:C 13 Jun 2024 15:50:54.072 * DB saved on disk
+54742:C 13 Jun 2024 15:50:54.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 15:50:54.153 * Background saving terminated with success
+58896:M 13 Jun 2024 15:55:55.100 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 15:55:55.103 * Background saving started by pid 55431
+55431:C 13 Jun 2024 15:55:55.114 * DB saved on disk
+55431:C 13 Jun 2024 15:55:55.114 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 15:55:55.204 * Background saving terminated with success
+58896:M 13 Jun 2024 16:00:56.039 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 16:00:56.042 * Background saving started by pid 56109
+56109:C 13 Jun 2024 16:00:56.051 * DB saved on disk
+56109:C 13 Jun 2024 16:00:56.052 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 16:00:56.144 * Background saving terminated with success
+58896:M 13 Jun 2024 16:05:57.027 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 16:05:57.029 * Background saving started by pid 56794
+56794:C 13 Jun 2024 16:05:57.044 * DB saved on disk
+56794:C 13 Jun 2024 16:05:57.045 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 16:05:57.130 * Background saving terminated with success
+58896:M 13 Jun 2024 16:10:58.007 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 16:10:58.010 * Background saving started by pid 57424
+57424:C 13 Jun 2024 16:10:58.022 * DB saved on disk
+57424:C 13 Jun 2024 16:10:58.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 16:10:58.111 * Background saving terminated with success
+58896:M 13 Jun 2024 16:15:59.064 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 16:15:59.067 * Background saving started by pid 58114
+58114:C 13 Jun 2024 16:15:59.076 * DB saved on disk
+58114:C 13 Jun 2024 16:15:59.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 16:15:59.168 * Background saving terminated with success
+58896:M 13 Jun 2024 16:21:00.080 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 16:21:00.081 * Background saving started by pid 58865
+58865:C 13 Jun 2024 16:21:00.090 * DB saved on disk
+58865:C 13 Jun 2024 16:21:00.091 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 16:21:00.182 * Background saving terminated with success
+58896:M 13 Jun 2024 16:26:01.042 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 16:26:01.045 * Background saving started by pid 59640
+59640:C 13 Jun 2024 16:26:01.059 * DB saved on disk
+59640:C 13 Jun 2024 16:26:01.060 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 16:26:01.145 * Background saving terminated with success
+58896:M 13 Jun 2024 16:31:02.024 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 16:31:02.026 * Background saving started by pid 60306
+60306:C 13 Jun 2024 16:31:02.037 * DB saved on disk
+60306:C 13 Jun 2024 16:31:02.038 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 16:31:02.127 * Background saving terminated with success
+58896:M 13 Jun 2024 16:36:03.023 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 16:36:03.027 * Background saving started by pid 60889
+60889:C 13 Jun 2024 16:36:03.056 * DB saved on disk
+60889:C 13 Jun 2024 16:36:03.056 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 16:36:03.128 * Background saving terminated with success
+58896:M 13 Jun 2024 16:41:04.062 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 16:41:04.065 * Background saving started by pid 61499
+61499:C 13 Jun 2024 16:41:04.084 * DB saved on disk
+61499:C 13 Jun 2024 16:41:04.085 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 16:41:04.167 * Background saving terminated with success
+58896:M 13 Jun 2024 16:46:05.062 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 16:46:05.064 * Background saving started by pid 62153
+62153:C 13 Jun 2024 16:46:05.078 * DB saved on disk
+62153:C 13 Jun 2024 16:46:05.079 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 16:46:05.165 * Background saving terminated with success
+58896:M 13 Jun 2024 16:51:06.057 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 16:51:06.058 * Background saving started by pid 62839
+62839:C 13 Jun 2024 16:51:06.067 * DB saved on disk
+62839:C 13 Jun 2024 16:51:06.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 16:51:06.159 * Background saving terminated with success
+58896:M 13 Jun 2024 16:56:07.041 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 16:56:07.044 * Background saving started by pid 63527
+63527:C 13 Jun 2024 16:56:07.063 * DB saved on disk
+63527:C 13 Jun 2024 16:56:07.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 16:56:07.144 * Background saving terminated with success
+58896:M 13 Jun 2024 17:01:08.073 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 17:01:08.074 * Background saving started by pid 64189
+64189:C 13 Jun 2024 17:01:08.088 * DB saved on disk
+64189:C 13 Jun 2024 17:01:08.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 17:01:08.175 * Background saving terminated with success
+58896:M 13 Jun 2024 17:06:09.002 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 17:06:09.003 * Background saving started by pid 64802
+64802:C 13 Jun 2024 17:06:09.018 * DB saved on disk
+64802:C 13 Jun 2024 17:06:09.021 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 17:06:09.104 * Background saving terminated with success
+58896:M 13 Jun 2024 17:11:10.042 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 17:11:10.046 * Background saving started by pid 65468
+65468:C 13 Jun 2024 17:11:10.069 * DB saved on disk
+65468:C 13 Jun 2024 17:11:10.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 17:11:10.148 * Background saving terminated with success
+58896:M 13 Jun 2024 17:16:11.098 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 17:16:11.099 * Background saving started by pid 66110
+66110:C 13 Jun 2024 17:16:11.108 * DB saved on disk
+66110:C 13 Jun 2024 17:16:11.109 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 17:16:11.201 * Background saving terminated with success
+58896:M 13 Jun 2024 17:21:12.003 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 17:21:12.006 * Background saving started by pid 66847
+66847:C 13 Jun 2024 17:21:12.018 * DB saved on disk
+66847:C 13 Jun 2024 17:21:12.019 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 17:21:12.111 * Background saving terminated with success
+58896:M 13 Jun 2024 17:26:13.038 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 17:26:13.041 * Background saving started by pid 67587
+67587:C 13 Jun 2024 17:26:13.051 * DB saved on disk
+67587:C 13 Jun 2024 17:26:13.052 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 17:26:13.142 * Background saving terminated with success
+58896:M 13 Jun 2024 17:31:14.027 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 17:31:14.028 * Background saving started by pid 68487
+68487:C 13 Jun 2024 17:31:14.036 * DB saved on disk
+68487:C 13 Jun 2024 17:31:14.038 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 17:31:14.130 * Background saving terminated with success
+58896:M 13 Jun 2024 17:36:15.062 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 17:36:15.064 * Background saving started by pid 69205
+69205:C 13 Jun 2024 17:36:15.071 * DB saved on disk
+69205:C 13 Jun 2024 17:36:15.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 17:36:15.174 * Background saving terminated with success
+58896:M 13 Jun 2024 17:41:16.100 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 17:41:16.101 * Background saving started by pid 70157
+70157:C 13 Jun 2024 17:41:16.117 * DB saved on disk
+70157:C 13 Jun 2024 17:41:16.118 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 17:41:16.203 * Background saving terminated with success
+58896:M 13 Jun 2024 17:46:17.062 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 17:46:17.064 * Background saving started by pid 70947
+70947:C 13 Jun 2024 17:46:17.078 * DB saved on disk
+70947:C 13 Jun 2024 17:46:17.080 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 17:46:17.165 * Background saving terminated with success
+58896:M 13 Jun 2024 17:51:18.076 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 17:51:18.077 * Background saving started by pid 71675
+71675:C 13 Jun 2024 17:51:18.086 * DB saved on disk
+71675:C 13 Jun 2024 17:51:18.090 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 17:51:18.179 * Background saving terminated with success
+58896:M 13 Jun 2024 17:56:19.041 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 17:56:19.042 * Background saving started by pid 72514
+72514:C 13 Jun 2024 17:56:19.052 * DB saved on disk
+72514:C 13 Jun 2024 17:56:19.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 17:56:19.144 * Background saving terminated with success
+58896:M 13 Jun 2024 18:01:20.068 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 18:01:20.069 * Background saving started by pid 73367
+73367:C 13 Jun 2024 18:01:20.084 * DB saved on disk
+73367:C 13 Jun 2024 18:01:20.085 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 18:01:20.171 * Background saving terminated with success
+58896:M 13 Jun 2024 18:06:21.045 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 18:06:21.048 * Background saving started by pid 74221
+74221:C 13 Jun 2024 18:06:21.057 * DB saved on disk
+74221:C 13 Jun 2024 18:06:21.062 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 18:06:21.148 * Background saving terminated with success
+58896:M 13 Jun 2024 18:11:22.084 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 18:11:22.088 * Background saving started by pid 75044
+75044:C 13 Jun 2024 18:11:22.111 * DB saved on disk
+75044:C 13 Jun 2024 18:11:22.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 18:11:22.189 * Background saving terminated with success
+58896:M 13 Jun 2024 18:16:23.041 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 18:16:23.042 * Background saving started by pid 76027
+76027:C 13 Jun 2024 18:16:23.057 * DB saved on disk
+76027:C 13 Jun 2024 18:16:23.058 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 18:16:23.143 * Background saving terminated with success
+58896:M 13 Jun 2024 18:21:24.069 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 18:21:24.073 * Background saving started by pid 77094
+77094:C 13 Jun 2024 18:21:24.084 * DB saved on disk
+77094:C 13 Jun 2024 18:21:24.086 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 18:21:24.175 * Background saving terminated with success
+58896:M 13 Jun 2024 18:26:25.068 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 18:26:25.073 * Background saving started by pid 78140
+78140:C 13 Jun 2024 18:26:25.088 * DB saved on disk
+78140:C 13 Jun 2024 18:26:25.091 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 18:26:25.174 * Background saving terminated with success
+58896:M 13 Jun 2024 18:31:26.038 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 18:31:26.041 * Background saving started by pid 79121
+79121:C 13 Jun 2024 18:31:26.053 * DB saved on disk
+79121:C 13 Jun 2024 18:31:26.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 18:31:26.142 * Background saving terminated with success
+58896:M 13 Jun 2024 18:36:27.010 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 18:36:27.013 * Background saving started by pid 80053
+80053:C 13 Jun 2024 18:36:27.025 * DB saved on disk
+80053:C 13 Jun 2024 18:36:27.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 18:36:27.115 * Background saving terminated with success
+58896:M 13 Jun 2024 18:41:28.100 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 18:41:28.101 * Background saving started by pid 81033
+81033:C 13 Jun 2024 18:41:28.110 * DB saved on disk
+81033:C 13 Jun 2024 18:41:28.114 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 18:41:28.203 * Background saving terminated with success
+58896:M 13 Jun 2024 18:46:29.056 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 18:46:29.058 * Background saving started by pid 82024
+82024:C 13 Jun 2024 18:46:29.068 * DB saved on disk
+82024:C 13 Jun 2024 18:46:29.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 18:46:29.159 * Background saving terminated with success
+58896:M 13 Jun 2024 18:51:30.025 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 18:51:30.026 * Background saving started by pid 83031
+83031:C 13 Jun 2024 18:51:30.040 * DB saved on disk
+83031:C 13 Jun 2024 18:51:30.041 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 18:51:30.128 * Background saving terminated with success
+58896:M 13 Jun 2024 18:56:31.028 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 18:56:31.032 * Background saving started by pid 84257
+84257:C 13 Jun 2024 18:56:31.061 * DB saved on disk
+84257:C 13 Jun 2024 18:56:31.061 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 18:56:31.133 * Background saving terminated with success
+58896:M 13 Jun 2024 19:01:32.042 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 19:01:32.045 * Background saving started by pid 85971
+85971:C 13 Jun 2024 19:01:32.057 * DB saved on disk
+85971:C 13 Jun 2024 19:01:32.058 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 19:01:32.146 * Background saving terminated with success
+58896:M 13 Jun 2024 19:06:33.000 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 19:06:33.004 * Background saving started by pid 87445
+87445:C 13 Jun 2024 19:06:33.015 * DB saved on disk
+87445:C 13 Jun 2024 19:06:33.016 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 19:06:33.106 * Background saving terminated with success
+58896:M 13 Jun 2024 19:24:03.583 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 19:24:03.704 * Background saving started by pid 88482
+88482:C 13 Jun 2024 19:24:03.712 * DB saved on disk
+88482:C 13 Jun 2024 19:24:03.715 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 19:24:03.820 * Background saving terminated with success
+58896:M 13 Jun 2024 19:29:04.018 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 19:29:04.020 * Background saving started by pid 89605
+89605:C 13 Jun 2024 19:29:04.035 * DB saved on disk
+89605:C 13 Jun 2024 19:29:04.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 19:29:04.122 * Background saving terminated with success
+58896:M 13 Jun 2024 19:34:05.068 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 19:34:05.071 * Background saving started by pid 90623
+90623:C 13 Jun 2024 19:34:05.084 * DB saved on disk
+90623:C 13 Jun 2024 19:34:05.085 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 19:34:05.172 * Background saving terminated with success
+58896:M 13 Jun 2024 19:39:06.039 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 19:39:06.040 * Background saving started by pid 91649
+91649:C 13 Jun 2024 19:39:06.050 * DB saved on disk
+91649:C 13 Jun 2024 19:39:06.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 19:39:06.141 * Background saving terminated with success
+58896:M 13 Jun 2024 19:44:07.052 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 19:44:07.053 * Background saving started by pid 92499
+92499:C 13 Jun 2024 19:44:07.066 * DB saved on disk
+92499:C 13 Jun 2024 19:44:07.067 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 19:44:07.154 * Background saving terminated with success
+58896:M 13 Jun 2024 19:49:08.026 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 19:49:08.029 * Background saving started by pid 93255
+93255:C 13 Jun 2024 19:49:08.047 * DB saved on disk
+93255:C 13 Jun 2024 19:49:08.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 19:49:08.130 * Background saving terminated with success
+58896:M 13 Jun 2024 20:09:23.789 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 20:09:23.790 * Background saving started by pid 93455
+93455:C 13 Jun 2024 20:09:23.800 * DB saved on disk
+93455:C 13 Jun 2024 20:09:23.802 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 20:09:23.891 * Background saving terminated with success
+58896:M 13 Jun 2024 21:00:24.763 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 21:00:24.764 * Background saving started by pid 93545
+93545:C 13 Jun 2024 21:00:24.776 * DB saved on disk
+93545:C 13 Jun 2024 21:00:24.777 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 21:00:24.867 * Background saving terminated with success
+58896:M 13 Jun 2024 21:09:07.599 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 21:09:07.601 * Background saving started by pid 93647
+93647:C 13 Jun 2024 21:09:07.614 * DB saved on disk
+93647:C 13 Jun 2024 21:09:07.616 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 21:09:07.702 * Background saving terminated with success
+58896:M 13 Jun 2024 22:15:01.689 * 1 changes in 3600 seconds. Saving...
+58896:M 13 Jun 2024 22:15:01.693 * Background saving started by pid 93707
+93707:C 13 Jun 2024 22:15:01.819 * DB saved on disk
+93707:C 13 Jun 2024 22:15:01.820 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 22:15:01.896 * Background saving terminated with success
+58896:M 13 Jun 2024 22:28:49.316 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 22:28:49.319 * Background saving started by pid 93877
+93877:C 13 Jun 2024 22:28:49.333 * DB saved on disk
+93877:C 13 Jun 2024 22:28:49.334 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 22:28:49.421 * Background saving terminated with success
+58896:M 13 Jun 2024 23:38:03.790 * 1 changes in 3600 seconds. Saving...
+58896:M 13 Jun 2024 23:38:03.794 * Background saving started by pid 94043
+94043:C 13 Jun 2024 23:38:03.905 * DB saved on disk
+94043:C 13 Jun 2024 23:38:03.908 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 23:38:03.997 * Background saving terminated with success
+58896:M 13 Jun 2024 23:43:04.085 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 23:43:04.087 * Background saving started by pid 94614
+94614:C 13 Jun 2024 23:43:04.094 * DB saved on disk
+94614:C 13 Jun 2024 23:43:04.095 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 23:43:04.189 * Background saving terminated with success
+58896:M 13 Jun 2024 23:48:05.057 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 23:48:05.063 * Background saving started by pid 95362
+95362:C 13 Jun 2024 23:48:05.086 * DB saved on disk
+95362:C 13 Jun 2024 23:48:05.087 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 23:48:05.165 * Background saving terminated with success
+58896:M 13 Jun 2024 23:53:06.041 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 23:53:06.042 * Background saving started by pid 96187
+96187:C 13 Jun 2024 23:53:06.060 * DB saved on disk
+96187:C 13 Jun 2024 23:53:06.061 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 23:53:06.143 * Background saving terminated with success
+58896:M 13 Jun 2024 23:58:07.014 * 100 changes in 300 seconds. Saving...
+58896:M 13 Jun 2024 23:58:07.017 * Background saving started by pid 97027
+97027:C 13 Jun 2024 23:58:07.030 * DB saved on disk
+97027:C 13 Jun 2024 23:58:07.030 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 13 Jun 2024 23:58:07.118 * Background saving terminated with success
+58896:M 14 Jun 2024 00:03:08.062 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 00:03:08.067 * Background saving started by pid 98055
+98055:C 14 Jun 2024 00:03:08.076 * DB saved on disk
+98055:C 14 Jun 2024 00:03:08.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 00:03:08.169 * Background saving terminated with success
+58896:M 14 Jun 2024 00:08:09.046 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 00:08:09.048 * Background saving started by pid 906
+906:C 14 Jun 2024 00:08:09.066 * DB saved on disk
+906:C 14 Jun 2024 00:08:09.068 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 00:08:09.149 * Background saving terminated with success
+58896:M 14 Jun 2024 00:13:10.017 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 00:13:10.021 * Background saving started by pid 2698
+2698:C 14 Jun 2024 00:13:10.034 * DB saved on disk
+2698:C 14 Jun 2024 00:13:10.038 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 00:13:10.122 * Background saving terminated with success
+58896:M 14 Jun 2024 00:18:11.011 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 00:18:11.013 * Background saving started by pid 3689
+3689:C 14 Jun 2024 00:18:11.022 * DB saved on disk
+3689:C 14 Jun 2024 00:18:11.023 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 00:18:11.114 * Background saving terminated with success
+58896:M 14 Jun 2024 00:23:12.093 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 00:23:12.094 * Background saving started by pid 4450
+4450:C 14 Jun 2024 00:23:12.105 * DB saved on disk
+4450:C 14 Jun 2024 00:23:12.105 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 00:23:12.196 * Background saving terminated with success
+58896:M 14 Jun 2024 00:28:13.053 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 00:28:13.055 * Background saving started by pid 5238
+5238:C 14 Jun 2024 00:28:13.065 * DB saved on disk
+5238:C 14 Jun 2024 00:28:13.065 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 00:28:13.156 * Background saving terminated with success
+58896:M 14 Jun 2024 00:33:14.055 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 00:33:14.057 * Background saving started by pid 6001
+6001:C 14 Jun 2024 00:33:14.066 * DB saved on disk
+6001:C 14 Jun 2024 00:33:14.067 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 00:33:14.158 * Background saving terminated with success
+58896:M 14 Jun 2024 00:38:15.023 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 00:38:15.025 * Background saving started by pid 6720
+6720:C 14 Jun 2024 00:38:15.034 * DB saved on disk
+6720:C 14 Jun 2024 00:38:15.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 00:38:15.126 * Background saving terminated with success
+58896:M 14 Jun 2024 00:43:16.074 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 00:43:16.077 * Background saving started by pid 7544
+7544:C 14 Jun 2024 00:43:16.090 * DB saved on disk
+7544:C 14 Jun 2024 00:43:16.091 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 00:43:16.179 * Background saving terminated with success
+58896:M 14 Jun 2024 00:48:17.008 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 00:48:17.009 * Background saving started by pid 8362
+8362:C 14 Jun 2024 00:48:17.018 * DB saved on disk
+8362:C 14 Jun 2024 00:48:17.020 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 00:48:17.110 * Background saving terminated with success
+58896:M 14 Jun 2024 00:53:18.083 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 00:53:18.086 * Background saving started by pid 9627
+9627:C 14 Jun 2024 00:53:18.094 * DB saved on disk
+9627:C 14 Jun 2024 00:53:18.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 00:53:18.187 * Background saving terminated with success
+58896:M 14 Jun 2024 00:58:19.056 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 00:58:19.058 * Background saving started by pid 10541
+10541:C 14 Jun 2024 00:58:19.068 * DB saved on disk
+10541:C 14 Jun 2024 00:58:19.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 00:58:19.160 * Background saving terminated with success
+58896:M 14 Jun 2024 01:03:20.085 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 01:03:20.088 * Background saving started by pid 11472
+11472:C 14 Jun 2024 01:03:20.099 * DB saved on disk
+11472:C 14 Jun 2024 01:03:20.112 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 01:03:20.189 * Background saving terminated with success
+58896:M 14 Jun 2024 01:08:21.081 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 01:08:21.083 * Background saving started by pid 12462
+12462:C 14 Jun 2024 01:08:21.094 * DB saved on disk
+12462:C 14 Jun 2024 01:08:21.095 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 01:08:21.184 * Background saving terminated with success
+58896:M 14 Jun 2024 01:13:22.058 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 01:13:22.059 * Background saving started by pid 13399
+13399:C 14 Jun 2024 01:13:22.069 * DB saved on disk
+13399:C 14 Jun 2024 01:13:22.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 01:13:22.161 * Background saving terminated with success
+58896:M 14 Jun 2024 01:18:23.041 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 01:18:23.042 * Background saving started by pid 14393
+14393:C 14 Jun 2024 01:18:23.054 * DB saved on disk
+14393:C 14 Jun 2024 01:18:23.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 01:18:23.142 * Background saving terminated with success
+58896:M 14 Jun 2024 01:23:24.029 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 01:23:24.031 * Background saving started by pid 15307
+15307:C 14 Jun 2024 01:23:24.047 * DB saved on disk
+15307:C 14 Jun 2024 01:23:24.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 01:23:24.132 * Background saving terminated with success
+58896:M 14 Jun 2024 01:28:25.054 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 01:28:25.057 * Background saving started by pid 16245
+16245:C 14 Jun 2024 01:28:25.067 * DB saved on disk
+16245:C 14 Jun 2024 01:28:25.068 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 01:28:25.158 * Background saving terminated with success
+58896:M 14 Jun 2024 01:33:26.047 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 01:33:26.051 * Background saving started by pid 17193
+17193:C 14 Jun 2024 01:33:26.081 * DB saved on disk
+17193:C 14 Jun 2024 01:33:26.081 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 01:33:26.153 * Background saving terminated with success
+58896:M 14 Jun 2024 01:38:27.082 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 01:38:27.084 * Background saving started by pid 18123
+18123:C 14 Jun 2024 01:38:27.100 * DB saved on disk
+18123:C 14 Jun 2024 01:38:27.101 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 01:38:27.185 * Background saving terminated with success
+58896:M 14 Jun 2024 01:43:28.063 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 01:43:28.065 * Background saving started by pid 20679
+20679:C 14 Jun 2024 01:43:28.076 * DB saved on disk
+20679:C 14 Jun 2024 01:43:28.080 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 01:43:28.172 * Background saving terminated with success
+58896:M 14 Jun 2024 01:48:29.019 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 01:48:29.024 * Background saving started by pid 21789
+21789:C 14 Jun 2024 01:48:29.033 * DB saved on disk
+21789:C 14 Jun 2024 01:48:29.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 01:48:29.125 * Background saving terminated with success
+58896:M 14 Jun 2024 01:53:30.004 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 01:53:30.005 * Background saving started by pid 22821
+22821:C 14 Jun 2024 01:53:30.021 * DB saved on disk
+22821:C 14 Jun 2024 01:53:30.023 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 01:53:30.108 * Background saving terminated with success
+58896:M 14 Jun 2024 01:58:31.072 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 01:58:31.072 * Background saving started by pid 23761
+23761:C 14 Jun 2024 01:58:31.081 * DB saved on disk
+23761:C 14 Jun 2024 01:58:31.082 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 01:58:31.175 * Background saving terminated with success
+58896:M 14 Jun 2024 02:03:32.084 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 02:03:32.087 * Background saving started by pid 24691
+24691:C 14 Jun 2024 02:03:32.112 * DB saved on disk
+24691:C 14 Jun 2024 02:03:32.115 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 02:03:32.188 * Background saving terminated with success
+58896:M 14 Jun 2024 02:08:33.012 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 02:08:33.013 * Background saving started by pid 25657
+25657:C 14 Jun 2024 02:08:33.023 * DB saved on disk
+25657:C 14 Jun 2024 02:08:33.024 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 02:08:33.116 * Background saving terminated with success
+58896:M 14 Jun 2024 02:13:34.062 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 02:13:34.065 * Background saving started by pid 26619
+26619:C 14 Jun 2024 02:13:34.082 * DB saved on disk
+26619:C 14 Jun 2024 02:13:34.084 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 02:13:34.168 * Background saving terminated with success
+58896:M 14 Jun 2024 02:18:35.092 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 02:18:35.095 * Background saving started by pid 27572
+27572:C 14 Jun 2024 02:18:35.110 * DB saved on disk
+27572:C 14 Jun 2024 02:18:35.112 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 02:18:35.197 * Background saving terminated with success
+58896:M 14 Jun 2024 02:23:36.006 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 02:23:36.010 * Background saving started by pid 28517
+28517:C 14 Jun 2024 02:23:36.030 * DB saved on disk
+28517:C 14 Jun 2024 02:23:36.031 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 02:23:36.111 * Background saving terminated with success
+58896:M 14 Jun 2024 02:28:37.011 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 02:28:37.014 * Background saving started by pid 29471
+29471:C 14 Jun 2024 02:28:37.025 * DB saved on disk
+29471:C 14 Jun 2024 02:28:37.025 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 02:28:37.115 * Background saving terminated with success
+58896:M 14 Jun 2024 02:33:38.046 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 02:33:38.050 * Background saving started by pid 30459
+30459:C 14 Jun 2024 02:33:38.067 * DB saved on disk
+30459:C 14 Jun 2024 02:33:38.068 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 02:33:38.154 * Background saving terminated with success
+58896:M 14 Jun 2024 02:38:39.080 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 02:38:39.086 * Background saving started by pid 31408
+31408:C 14 Jun 2024 02:38:39.107 * DB saved on disk
+31408:C 14 Jun 2024 02:38:39.108 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 02:38:39.188 * Background saving terminated with success
+58896:M 14 Jun 2024 02:43:40.090 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 02:43:40.093 * Background saving started by pid 32516
+32516:C 14 Jun 2024 02:43:40.103 * DB saved on disk
+32516:C 14 Jun 2024 02:43:40.103 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 02:43:40.195 * Background saving terminated with success
+58896:M 14 Jun 2024 02:48:41.094 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 02:48:41.098 * Background saving started by pid 33530
+33530:C 14 Jun 2024 02:48:41.110 * DB saved on disk
+33530:C 14 Jun 2024 02:48:41.113 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 02:48:41.200 * Background saving terminated with success
+58896:M 14 Jun 2024 02:53:42.065 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 02:53:42.068 * Background saving started by pid 34481
+34481:C 14 Jun 2024 02:53:42.097 * DB saved on disk
+34481:C 14 Jun 2024 02:53:42.098 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 02:53:42.170 * Background saving terminated with success
+58896:M 14 Jun 2024 03:02:19.016 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 03:02:19.020 * Background saving started by pid 35216
+35216:C 14 Jun 2024 03:02:19.144 * DB saved on disk
+35216:C 14 Jun 2024 03:02:19.144 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 03:02:19.223 * Background saving terminated with success
+58896:M 14 Jun 2024 04:09:00.287 * 1 changes in 3600 seconds. Saving...
+58896:M 14 Jun 2024 04:09:00.391 * Background saving started by pid 35351
+35351:C 14 Jun 2024 04:09:00.406 * DB saved on disk
+35351:C 14 Jun 2024 04:09:00.406 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 04:09:00.491 * Background saving terminated with success
+58896:M 14 Jun 2024 05:13:19.442 * 1 changes in 3600 seconds. Saving...
+58896:M 14 Jun 2024 05:13:19.559 * Background saving started by pid 35460
+35460:C 14 Jun 2024 05:13:19.566 * DB saved on disk
+35460:C 14 Jun 2024 05:13:19.567 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 05:13:19.680 * Background saving terminated with success
+58896:M 14 Jun 2024 05:45:38.251 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 05:45:38.253 * Background saving started by pid 35614
+35614:C 14 Jun 2024 05:45:38.263 * DB saved on disk
+35614:C 14 Jun 2024 05:45:38.264 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 05:45:38.354 * Background saving terminated with success
+58896:M 14 Jun 2024 06:06:43.153 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 06:06:43.156 * Background saving started by pid 35774
+35774:C 14 Jun 2024 06:06:43.171 * DB saved on disk
+35774:C 14 Jun 2024 06:06:43.172 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 06:06:43.259 * Background saving terminated with success
+58896:M 14 Jun 2024 06:44:23.413 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 06:44:23.415 * Background saving started by pid 35935
+35935:C 14 Jun 2024 06:44:23.428 * DB saved on disk
+35935:C 14 Jun 2024 06:44:23.428 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 06:44:23.516 * Background saving terminated with success
+58896:M 14 Jun 2024 07:48:29.341 * 1 changes in 3600 seconds. Saving...
+58896:M 14 Jun 2024 07:48:29.456 * Background saving started by pid 35997
+35997:C 14 Jun 2024 07:48:29.466 * DB saved on disk
+35997:C 14 Jun 2024 07:48:29.467 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 07:48:29.558 * Background saving terminated with success
+58896:M 14 Jun 2024 08:56:33.414 * 1 changes in 3600 seconds. Saving...
+58896:M 14 Jun 2024 08:56:33.530 * Background saving started by pid 36114
+36114:C 14 Jun 2024 08:56:33.539 * DB saved on disk
+36114:C 14 Jun 2024 08:56:33.539 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 08:56:33.632 * Background saving terminated with success
+58896:M 14 Jun 2024 09:59:37.399 * 1 changes in 3600 seconds. Saving...
+58896:M 14 Jun 2024 09:59:37.511 * Background saving started by pid 36190
+36190:C 14 Jun 2024 09:59:37.519 * DB saved on disk
+36190:C 14 Jun 2024 09:59:37.520 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 09:59:37.613 * Background saving terminated with success
+58896:M 14 Jun 2024 10:07:14.898 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 10:07:14.901 * Background saving started by pid 36472
+36472:C 14 Jun 2024 10:07:14.910 * DB saved on disk
+36472:C 14 Jun 2024 10:07:14.912 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 10:07:15.001 * Background saving terminated with success
+58896:M 14 Jun 2024 10:12:16.014 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 10:12:16.016 * Background saving started by pid 37638
+37638:C 14 Jun 2024 10:12:16.026 * DB saved on disk
+37638:C 14 Jun 2024 10:12:16.027 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 10:12:16.118 * Background saving terminated with success
+58896:M 14 Jun 2024 10:17:17.028 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 10:17:17.033 * Background saving started by pid 38618
+38618:C 14 Jun 2024 10:17:17.051 * DB saved on disk
+38618:C 14 Jun 2024 10:17:17.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 10:17:17.134 * Background saving terminated with success
+58896:M 14 Jun 2024 10:22:18.071 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 10:22:18.073 * Background saving started by pid 39601
+39601:C 14 Jun 2024 10:22:18.085 * DB saved on disk
+39601:C 14 Jun 2024 10:22:18.087 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 10:22:18.175 * Background saving terminated with success
+58896:M 14 Jun 2024 10:28:04.538 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 10:28:04.541 * Background saving started by pid 40107
+40107:C 14 Jun 2024 10:28:04.659 * DB saved on disk
+40107:C 14 Jun 2024 10:28:04.659 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 10:28:04.743 * Background saving terminated with success
+58896:M 14 Jun 2024 11:30:27.751 * 1 changes in 3600 seconds. Saving...
+58896:M 14 Jun 2024 11:30:27.759 * Background saving started by pid 40350
+40350:C 14 Jun 2024 11:30:27.857 * DB saved on disk
+40350:C 14 Jun 2024 11:30:27.859 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 11:30:27.860 * Background saving terminated with success
+58896:M 14 Jun 2024 12:16:13.786 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 12:16:13.787 * Background saving started by pid 40834
+40834:C 14 Jun 2024 12:16:13.796 * DB saved on disk
+40834:C 14 Jun 2024 12:16:13.797 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 12:16:13.888 * Background saving terminated with success
+58896:M 14 Jun 2024 12:21:14.062 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 12:21:14.063 * Background saving started by pid 42185
+42185:C 14 Jun 2024 12:21:14.071 * DB saved on disk
+42185:C 14 Jun 2024 12:21:14.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 12:21:14.165 * Background saving terminated with success
+58896:M 14 Jun 2024 12:58:11.704 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 12:58:11.706 * Background saving started by pid 50751
+50751:C 14 Jun 2024 12:58:11.762 * DB saved on disk
+50751:C 14 Jun 2024 12:58:11.763 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 12:58:11.809 * Background saving terminated with success
+58896:M 14 Jun 2024 14:00:57.749 * 1 changes in 3600 seconds. Saving...
+58896:M 14 Jun 2024 14:00:57.755 * Background saving started by pid 52756
+52756:C 14 Jun 2024 14:00:57.876 * DB saved on disk
+52756:C 14 Jun 2024 14:00:57.879 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 14:00:57.967 * Background saving terminated with success
+58896:M 14 Jun 2024 14:15:30.354 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 14:15:30.357 * Background saving started by pid 55569
+55569:C 14 Jun 2024 14:15:30.369 * DB saved on disk
+55569:C 14 Jun 2024 14:15:30.369 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 14:15:30.459 * Background saving terminated with success
+58896:M 14 Jun 2024 14:20:31.034 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 14:20:31.036 * Background saving started by pid 56310
+56310:C 14 Jun 2024 14:20:31.046 * DB saved on disk
+56310:C 14 Jun 2024 14:20:31.047 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 14:20:31.137 * Background saving terminated with success
+58896:M 14 Jun 2024 14:25:32.056 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 14:25:32.057 * Background saving started by pid 57022
+57022:C 14 Jun 2024 14:25:32.069 * DB saved on disk
+57022:C 14 Jun 2024 14:25:32.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 14:25:32.159 * Background saving terminated with success
+58896:M 14 Jun 2024 14:30:33.093 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 14:30:33.095 * Background saving started by pid 57937
+57937:C 14 Jun 2024 14:30:33.106 * DB saved on disk
+57937:C 14 Jun 2024 14:30:33.109 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 14:30:33.196 * Background saving terminated with success
+58896:M 14 Jun 2024 14:35:34.000 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 14:35:34.002 * Background saving started by pid 58663
+58663:C 14 Jun 2024 14:35:34.013 * DB saved on disk
+58663:C 14 Jun 2024 14:35:34.013 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 14:35:34.104 * Background saving terminated with success
+58896:M 14 Jun 2024 14:40:35.079 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 14:40:35.079 * Background saving started by pid 59354
+59354:C 14 Jun 2024 14:40:35.092 * DB saved on disk
+59354:C 14 Jun 2024 14:40:35.094 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 14:40:35.181 * Background saving terminated with success
+58896:M 14 Jun 2024 14:45:36.026 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 14:45:36.030 * Background saving started by pid 60066
+60066:C 14 Jun 2024 14:45:36.047 * DB saved on disk
+60066:C 14 Jun 2024 14:45:36.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 14:45:36.133 * Background saving terminated with success
+58896:M 14 Jun 2024 14:50:37.021 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 14:50:37.025 * Background saving started by pid 60762
+60762:C 14 Jun 2024 14:50:37.046 * DB saved on disk
+60762:C 14 Jun 2024 14:50:37.047 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 14:50:37.127 * Background saving terminated with success
+58896:M 14 Jun 2024 14:55:38.054 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 14:55:38.055 * Background saving started by pid 61516
+61516:C 14 Jun 2024 14:55:38.066 * DB saved on disk
+61516:C 14 Jun 2024 14:55:38.068 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 14:55:38.157 * Background saving terminated with success
+58896:M 14 Jun 2024 15:00:39.013 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 15:00:39.016 * Background saving started by pid 63393
+63393:C 14 Jun 2024 15:00:39.026 * DB saved on disk
+63393:C 14 Jun 2024 15:00:39.027 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 15:00:39.117 * Background saving terminated with success
+58896:M 14 Jun 2024 15:05:40.086 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 15:05:40.089 * Background saving started by pid 64106
+64106:C 14 Jun 2024 15:05:40.109 * DB saved on disk
+64106:C 14 Jun 2024 15:05:40.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 15:05:40.189 * Background saving terminated with success
+58896:M 14 Jun 2024 15:10:41.056 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 15:10:41.058 * Background saving started by pid 64797
+64797:C 14 Jun 2024 15:10:41.073 * DB saved on disk
+64797:C 14 Jun 2024 15:10:41.073 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 15:10:41.161 * Background saving terminated with success
+58896:M 14 Jun 2024 15:15:42.057 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 15:15:42.059 * Background saving started by pid 65518
+65518:C 14 Jun 2024 15:15:42.074 * DB saved on disk
+65518:C 14 Jun 2024 15:15:42.075 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 15:15:42.162 * Background saving terminated with success
+58896:M 14 Jun 2024 15:20:43.021 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 15:20:43.023 * Background saving started by pid 66505
+66505:C 14 Jun 2024 15:20:43.037 * DB saved on disk
+66505:C 14 Jun 2024 15:20:43.039 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 15:20:43.124 * Background saving terminated with success
+58896:M 14 Jun 2024 15:25:44.025 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 15:25:44.029 * Background saving started by pid 67388
+67388:C 14 Jun 2024 15:25:44.040 * DB saved on disk
+67388:C 14 Jun 2024 15:25:44.041 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 15:25:44.131 * Background saving terminated with success
+58896:M 14 Jun 2024 15:30:45.027 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 15:30:45.029 * Background saving started by pid 68256
+68256:C 14 Jun 2024 15:30:45.053 * DB saved on disk
+68256:C 14 Jun 2024 15:30:45.054 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 15:30:45.130 * Background saving terminated with success
+58896:M 14 Jun 2024 15:35:46.077 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 15:35:46.078 * Background saving started by pid 69268
+69268:C 14 Jun 2024 15:35:46.088 * DB saved on disk
+69268:C 14 Jun 2024 15:35:46.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 15:35:46.179 * Background saving terminated with success
+58896:M 14 Jun 2024 15:40:47.084 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 15:40:47.088 * Background saving started by pid 70026
+70026:C 14 Jun 2024 15:40:47.108 * DB saved on disk
+70026:C 14 Jun 2024 15:40:47.110 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 15:40:47.190 * Background saving terminated with success
+58896:M 14 Jun 2024 15:45:48.012 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 15:45:48.019 * Background saving started by pid 71012
+71012:C 14 Jun 2024 15:45:48.028 * DB saved on disk
+71012:C 14 Jun 2024 15:45:48.029 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 15:45:48.120 * Background saving terminated with success
+58896:M 14 Jun 2024 15:50:49.082 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 15:50:49.085 * Background saving started by pid 71940
+71940:C 14 Jun 2024 15:50:49.097 * DB saved on disk
+71940:C 14 Jun 2024 15:50:49.098 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 15:50:49.186 * Background saving terminated with success
+58896:M 14 Jun 2024 15:55:50.015 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 15:55:50.017 * Background saving started by pid 72898
+72898:C 14 Jun 2024 15:55:50.030 * DB saved on disk
+72898:C 14 Jun 2024 15:55:50.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 15:55:50.118 * Background saving terminated with success
+58896:M 14 Jun 2024 16:00:51.081 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 16:00:51.085 * Background saving started by pid 73649
+73649:C 14 Jun 2024 16:00:51.098 * DB saved on disk
+73649:C 14 Jun 2024 16:00:51.101 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 16:00:51.186 * Background saving terminated with success
+58896:M 14 Jun 2024 16:05:52.071 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 16:05:52.072 * Background saving started by pid 74480
+74480:C 14 Jun 2024 16:05:52.082 * DB saved on disk
+74480:C 14 Jun 2024 16:05:52.083 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 16:05:52.174 * Background saving terminated with success
+58896:M 14 Jun 2024 16:10:53.035 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 16:10:53.038 * Background saving started by pid 75372
+75372:C 14 Jun 2024 16:10:53.054 * DB saved on disk
+75372:C 14 Jun 2024 16:10:53.058 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 16:10:53.139 * Background saving terminated with success
+58896:M 14 Jun 2024 16:15:54.086 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 16:15:54.089 * Background saving started by pid 76156
+76156:C 14 Jun 2024 16:15:54.114 * DB saved on disk
+76156:C 14 Jun 2024 16:15:54.115 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 16:15:54.190 * Background saving terminated with success
+58896:M 14 Jun 2024 16:20:55.033 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 16:20:55.035 * Background saving started by pid 76928
+76928:C 14 Jun 2024 16:20:55.053 * DB saved on disk
+76928:C 14 Jun 2024 16:20:55.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 16:20:55.137 * Background saving terminated with success
+58896:M 14 Jun 2024 16:25:56.061 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 16:25:56.062 * Background saving started by pid 77910
+77910:C 14 Jun 2024 16:25:56.073 * DB saved on disk
+77910:C 14 Jun 2024 16:25:56.073 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 16:25:56.164 * Background saving terminated with success
+58896:M 14 Jun 2024 16:30:57.004 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 16:30:57.006 * Background saving started by pid 78693
+78693:C 14 Jun 2024 16:30:57.017 * DB saved on disk
+78693:C 14 Jun 2024 16:30:57.019 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 16:30:57.108 * Background saving terminated with success
+58896:M 14 Jun 2024 16:35:58.062 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 16:35:58.064 * Background saving started by pid 79546
+79546:C 14 Jun 2024 16:35:58.076 * DB saved on disk
+79546:C 14 Jun 2024 16:35:58.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 16:35:58.165 * Background saving terminated with success
+58896:M 14 Jun 2024 16:40:59.088 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 16:40:59.089 * Background saving started by pid 80305
+80305:C 14 Jun 2024 16:40:59.101 * DB saved on disk
+80305:C 14 Jun 2024 16:40:59.104 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 16:40:59.190 * Background saving terminated with success
+58896:M 14 Jun 2024 16:46:00.058 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 16:46:00.059 * Background saving started by pid 81213
+81213:C 14 Jun 2024 16:46:00.070 * DB saved on disk
+81213:C 14 Jun 2024 16:46:00.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 16:46:00.160 * Background saving terminated with success
+58896:M 14 Jun 2024 16:51:01.096 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 16:51:01.097 * Background saving started by pid 82247
+82247:C 14 Jun 2024 16:51:01.106 * DB saved on disk
+82247:C 14 Jun 2024 16:51:01.107 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 16:51:01.199 * Background saving terminated with success
+58896:M 14 Jun 2024 16:56:02.068 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 16:56:02.072 * Background saving started by pid 83296
+83296:C 14 Jun 2024 16:56:02.085 * DB saved on disk
+83296:C 14 Jun 2024 16:56:02.086 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 16:56:02.173 * Background saving terminated with success
+58896:M 14 Jun 2024 17:01:03.003 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 17:01:03.004 * Background saving started by pid 84035
+84035:C 14 Jun 2024 17:01:03.014 * DB saved on disk
+84035:C 14 Jun 2024 17:01:03.015 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 17:01:03.105 * Background saving terminated with success
+58896:M 14 Jun 2024 17:06:04.035 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 17:06:04.037 * Background saving started by pid 84625
+84625:C 14 Jun 2024 17:06:04.049 * DB saved on disk
+84625:C 14 Jun 2024 17:06:04.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 17:06:04.138 * Background saving terminated with success
+58896:M 14 Jun 2024 17:11:05.017 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 17:11:05.020 * Background saving started by pid 85292
+85292:C 14 Jun 2024 17:11:05.034 * DB saved on disk
+85292:C 14 Jun 2024 17:11:05.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 17:11:05.121 * Background saving terminated with success
+58896:M 14 Jun 2024 17:16:06.081 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 17:16:06.083 * Background saving started by pid 85873
+85873:C 14 Jun 2024 17:16:06.091 * DB saved on disk
+85873:C 14 Jun 2024 17:16:06.092 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 17:16:06.184 * Background saving terminated with success
+58896:M 14 Jun 2024 17:21:07.000 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 17:21:07.001 * Background saving started by pid 86432
+86432:C 14 Jun 2024 17:21:07.014 * DB saved on disk
+86432:C 14 Jun 2024 17:21:07.014 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 17:21:07.102 * Background saving terminated with success
+58896:M 14 Jun 2024 17:26:08.044 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 17:26:08.047 * Background saving started by pid 87015
+87015:C 14 Jun 2024 17:26:08.076 * DB saved on disk
+87015:C 14 Jun 2024 17:26:08.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 17:26:08.149 * Background saving terminated with success
+58896:M 14 Jun 2024 17:31:09.060 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 17:31:09.061 * Background saving started by pid 87646
+87646:C 14 Jun 2024 17:31:09.069 * DB saved on disk
+87646:C 14 Jun 2024 17:31:09.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 17:31:09.163 * Background saving terminated with success
+58896:M 14 Jun 2024 17:36:10.011 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 17:36:10.013 * Background saving started by pid 88231
+88231:C 14 Jun 2024 17:36:10.024 * DB saved on disk
+88231:C 14 Jun 2024 17:36:10.024 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 17:36:10.114 * Background saving terminated with success
+58896:M 14 Jun 2024 17:41:11.048 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 17:41:11.055 * Background saving started by pid 88988
+88988:C 14 Jun 2024 17:41:11.063 * DB saved on disk
+88988:C 14 Jun 2024 17:41:11.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 17:41:11.156 * Background saving terminated with success
+58896:M 14 Jun 2024 17:46:12.064 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 17:46:12.065 * Background saving started by pid 89661
+89661:C 14 Jun 2024 17:46:12.078 * DB saved on disk
+89661:C 14 Jun 2024 17:46:12.079 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 17:46:12.166 * Background saving terminated with success
+58896:M 14 Jun 2024 17:51:13.041 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 17:51:13.043 * Background saving started by pid 90249
+90249:C 14 Jun 2024 17:51:13.055 * DB saved on disk
+90249:C 14 Jun 2024 17:51:13.057 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 17:51:13.144 * Background saving terminated with success
+58896:M 14 Jun 2024 17:56:14.019 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 17:56:14.022 * Background saving started by pid 91032
+91032:C 14 Jun 2024 17:56:14.035 * DB saved on disk
+91032:C 14 Jun 2024 17:56:14.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 17:56:14.123 * Background saving terminated with success
+58896:M 14 Jun 2024 18:01:15.037 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 18:01:15.039 * Background saving started by pid 91806
+91806:C 14 Jun 2024 18:01:15.055 * DB saved on disk
+91806:C 14 Jun 2024 18:01:15.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 18:01:15.141 * Background saving terminated with success
+58896:M 14 Jun 2024 18:06:16.057 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 18:06:16.060 * Background saving started by pid 92444
+92444:C 14 Jun 2024 18:06:16.073 * DB saved on disk
+92444:C 14 Jun 2024 18:06:16.074 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 18:06:16.160 * Background saving terminated with success
+58896:M 14 Jun 2024 18:11:17.059 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 18:11:17.063 * Background saving started by pid 93096
+93096:C 14 Jun 2024 18:11:17.080 * DB saved on disk
+93096:C 14 Jun 2024 18:11:17.081 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 18:11:17.164 * Background saving terminated with success
+58896:M 14 Jun 2024 18:16:18.080 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 18:16:18.083 * Background saving started by pid 93766
+93766:C 14 Jun 2024 18:16:18.094 * DB saved on disk
+93766:C 14 Jun 2024 18:16:18.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 18:16:18.184 * Background saving terminated with success
+58896:M 14 Jun 2024 18:21:19.098 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 18:21:19.102 * Background saving started by pid 94418
+94418:C 14 Jun 2024 18:21:19.118 * DB saved on disk
+94418:C 14 Jun 2024 18:21:19.121 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 18:21:19.204 * Background saving terminated with success
+58896:M 14 Jun 2024 18:26:20.090 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 18:26:20.092 * Background saving started by pid 95153
+95153:C 14 Jun 2024 18:26:20.103 * DB saved on disk
+95153:C 14 Jun 2024 18:26:20.104 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 18:26:20.193 * Background saving terminated with success
+58896:M 14 Jun 2024 18:31:21.035 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 18:31:21.038 * Background saving started by pid 95904
+95904:C 14 Jun 2024 18:31:21.049 * DB saved on disk
+95904:C 14 Jun 2024 18:31:21.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 18:31:21.139 * Background saving terminated with success
+58896:M 14 Jun 2024 18:36:22.029 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 18:36:22.030 * Background saving started by pid 96493
+96493:C 14 Jun 2024 18:36:22.044 * DB saved on disk
+96493:C 14 Jun 2024 18:36:22.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 18:36:22.132 * Background saving terminated with success
+58896:M 14 Jun 2024 18:53:06.805 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 18:53:06.810 * Background saving started by pid 96635
+96635:C 14 Jun 2024 18:53:06.933 * DB saved on disk
+96635:C 14 Jun 2024 18:53:06.938 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 18:53:07.012 * Background saving terminated with success
+58896:M 14 Jun 2024 18:58:08.026 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 18:58:08.027 * Background saving started by pid 97243
+97243:C 14 Jun 2024 18:58:08.038 * DB saved on disk
+97243:C 14 Jun 2024 18:58:08.039 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 18:58:08.128 * Background saving terminated with success
+58896:M 14 Jun 2024 19:03:09.096 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 19:03:09.097 * Background saving started by pid 97816
+97816:C 14 Jun 2024 19:03:09.115 * DB saved on disk
+97816:C 14 Jun 2024 19:03:09.116 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 19:03:09.198 * Background saving terminated with success
+58896:M 14 Jun 2024 19:08:10.026 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 19:08:10.027 * Background saving started by pid 98403
+98403:C 14 Jun 2024 19:08:10.036 * DB saved on disk
+98403:C 14 Jun 2024 19:08:10.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 19:08:10.128 * Background saving terminated with success
+58896:M 14 Jun 2024 19:13:11.015 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 19:13:11.017 * Background saving started by pid 99088
+99088:C 14 Jun 2024 19:13:11.026 * DB saved on disk
+99088:C 14 Jun 2024 19:13:11.027 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 19:13:11.117 * Background saving terminated with success
+58896:M 14 Jun 2024 19:18:12.060 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 19:18:12.062 * Background saving started by pid 99654
+99654:C 14 Jun 2024 19:18:12.070 * DB saved on disk
+99654:C 14 Jun 2024 19:18:12.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 19:18:12.163 * Background saving terminated with success
+58896:M 14 Jun 2024 19:23:13.095 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 19:23:13.099 * Background saving started by pid 651
+651:C 14 Jun 2024 19:23:13.126 * DB saved on disk
+651:C 14 Jun 2024 19:23:13.126 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 19:23:13.200 * Background saving terminated with success
+58896:M 14 Jun 2024 19:28:14.064 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 19:28:14.066 * Background saving started by pid 1362
+1362:C 14 Jun 2024 19:28:14.074 * DB saved on disk
+1362:C 14 Jun 2024 19:28:14.075 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 19:28:14.168 * Background saving terminated with success
+58896:M 14 Jun 2024 19:33:15.065 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 19:33:15.067 * Background saving started by pid 1965
+1965:C 14 Jun 2024 19:33:15.095 * DB saved on disk
+1965:C 14 Jun 2024 19:33:15.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 19:33:15.168 * Background saving terminated with success
+58896:M 14 Jun 2024 19:38:16.019 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 19:38:16.020 * Background saving started by pid 2640
+2640:C 14 Jun 2024 19:38:16.036 * DB saved on disk
+2640:C 14 Jun 2024 19:38:16.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 19:38:16.121 * Background saving terminated with success
+58896:M 14 Jun 2024 19:43:17.065 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 19:43:17.067 * Background saving started by pid 3209
+3209:C 14 Jun 2024 19:43:17.082 * DB saved on disk
+3209:C 14 Jun 2024 19:43:17.083 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 19:43:17.168 * Background saving terminated with success
+58896:M 14 Jun 2024 19:48:18.083 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 19:48:18.084 * Background saving started by pid 3807
+3807:C 14 Jun 2024 19:48:18.092 * DB saved on disk
+3807:C 14 Jun 2024 19:48:18.092 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 19:48:18.185 * Background saving terminated with success
+58896:M 14 Jun 2024 19:53:19.091 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 19:53:19.093 * Background saving started by pid 4495
+4495:C 14 Jun 2024 19:53:19.105 * DB saved on disk
+4495:C 14 Jun 2024 19:53:19.106 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 19:53:19.194 * Background saving terminated with success
+58896:M 14 Jun 2024 19:53:36.373 * DB saved on disk
+58896:M 14 Jun 2024 19:58:37.006 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 19:58:37.008 * Background saving started by pid 5355
+5355:C 14 Jun 2024 19:58:37.023 * DB saved on disk
+5355:C 14 Jun 2024 19:58:37.024 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 19:58:37.110 * Background saving terminated with success
+58896:M 14 Jun 2024 20:03:38.062 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 20:03:38.068 * Background saving started by pid 5941
+5941:C 14 Jun 2024 20:03:38.079 * DB saved on disk
+5941:C 14 Jun 2024 20:03:38.085 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 20:03:38.169 * Background saving terminated with success
+58896:M 14 Jun 2024 20:08:39.052 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 20:08:39.054 * Background saving started by pid 6579
+6579:C 14 Jun 2024 20:08:39.063 * DB saved on disk
+6579:C 14 Jun 2024 20:08:39.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 20:08:39.155 * Background saving terminated with success
+58896:M 14 Jun 2024 20:13:40.010 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 20:13:40.014 * Background saving started by pid 7271
+7271:C 14 Jun 2024 20:13:40.054 * DB saved on disk
+7271:C 14 Jun 2024 20:13:40.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 20:13:40.117 * Background saving terminated with success
+58896:M 14 Jun 2024 20:15:25.868 * DB saved on disk
+58896:M 14 Jun 2024 20:20:26.073 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 20:20:26.075 * Background saving started by pid 8477
+8477:C 14 Jun 2024 20:20:26.083 * DB saved on disk
+8477:C 14 Jun 2024 20:20:26.083 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 20:20:26.176 * Background saving terminated with success
+58896:M 14 Jun 2024 20:25:27.045 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 20:25:27.048 * Background saving started by pid 9337
+9337:C 14 Jun 2024 20:25:27.059 * DB saved on disk
+9337:C 14 Jun 2024 20:25:27.060 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 20:25:27.149 * Background saving terminated with success
+58896:M 14 Jun 2024 20:30:28.053 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 20:30:28.054 * Background saving started by pid 10207
+10207:C 14 Jun 2024 20:30:28.063 * DB saved on disk
+10207:C 14 Jun 2024 20:30:28.063 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 20:30:28.155 * Background saving terminated with success
+58896:M 14 Jun 2024 20:33:51.791 * DB saved on disk
+58896:M 14 Jun 2024 20:36:02.959 * DB saved on disk
+58896:M 14 Jun 2024 20:41:03.035 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 20:41:03.039 * Background saving started by pid 12285
+12285:C 14 Jun 2024 20:41:03.048 * DB saved on disk
+12285:C 14 Jun 2024 20:41:03.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 20:41:03.139 * Background saving terminated with success
+58896:M 14 Jun 2024 20:46:04.007 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 20:46:04.009 * Background saving started by pid 13266
+13266:C 14 Jun 2024 20:46:04.016 * DB saved on disk
+13266:C 14 Jun 2024 20:46:04.018 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 20:46:04.110 * Background saving terminated with success
+58896:M 14 Jun 2024 20:51:05.080 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 20:51:05.082 * Background saving started by pid 14114
+14114:C 14 Jun 2024 20:51:05.090 * DB saved on disk
+14114:C 14 Jun 2024 20:51:05.090 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 20:51:05.183 * Background saving terminated with success
+58896:M 14 Jun 2024 20:56:06.040 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 20:56:06.042 * Background saving started by pid 15071
+15071:C 14 Jun 2024 20:56:06.050 * DB saved on disk
+15071:C 14 Jun 2024 20:56:06.063 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 20:56:06.143 * Background saving terminated with success
+58896:M 14 Jun 2024 21:01:07.036 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 21:01:07.038 * Background saving started by pid 16066
+16066:C 14 Jun 2024 21:01:07.046 * DB saved on disk
+16066:C 14 Jun 2024 21:01:07.047 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 21:01:07.140 * Background saving terminated with success
+58896:M 14 Jun 2024 21:06:08.027 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 21:06:08.029 * Background saving started by pid 17073
+17073:C 14 Jun 2024 21:06:08.048 * DB saved on disk
+17073:C 14 Jun 2024 21:06:08.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 21:06:08.129 * Background saving terminated with success
+58896:M 14 Jun 2024 21:11:09.048 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 21:11:09.052 * Background saving started by pid 18014
+18014:C 14 Jun 2024 21:11:09.063 * DB saved on disk
+18014:C 14 Jun 2024 21:11:09.065 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 21:11:09.153 * Background saving terminated with success
+58896:M 14 Jun 2024 21:16:10.061 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 21:16:10.062 * Background saving started by pid 18941
+18941:C 14 Jun 2024 21:16:10.072 * DB saved on disk
+18941:C 14 Jun 2024 21:16:10.074 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 21:16:10.162 * Background saving terminated with success
+58896:M 14 Jun 2024 21:21:11.025 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 21:21:11.031 * Background saving started by pid 19797
+19797:C 14 Jun 2024 21:21:11.065 * DB saved on disk
+19797:C 14 Jun 2024 21:21:11.066 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 21:21:11.131 * Background saving terminated with success
+58896:M 14 Jun 2024 21:26:12.051 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 21:26:12.054 * Background saving started by pid 20708
+20708:C 14 Jun 2024 21:26:12.065 * DB saved on disk
+20708:C 14 Jun 2024 21:26:12.066 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 21:26:12.154 * Background saving terminated with success
+58896:M 14 Jun 2024 21:31:13.042 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 21:31:13.045 * Background saving started by pid 21604
+21604:C 14 Jun 2024 21:31:13.060 * DB saved on disk
+21604:C 14 Jun 2024 21:31:13.061 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 21:31:13.146 * Background saving terminated with success
+58896:M 14 Jun 2024 21:36:14.095 * 100 changes in 300 seconds. Saving...
+58896:M 14 Jun 2024 21:36:14.097 * Background saving started by pid 22441
+22441:C 14 Jun 2024 21:36:14.107 * DB saved on disk
+22441:C 14 Jun 2024 21:36:14.108 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
+58896:M 14 Jun 2024 21:36:14.198 * Background saving terminated with success
+58896:signal-handler (1718372303) Received SIGTERM scheduling shutdown...
+58896:M 14 Jun 2024 21:38:23.499 # User requested shutdown...
+58896:M 14 Jun 2024 21:38:23.499 * Saving the final RDB snapshot before exiting.
+58896:M 14 Jun 2024 21:38:23.509 * DB saved on disk
+58896:M 14 Jun 2024 21:38:23.510 # Redis is now ready to exit, bye bye...
From c485282b3a46efe0e5a59e74dcdbda7c5cca267e Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 19 Jun 2024 15:49:19 +0800
Subject: [PATCH 169/282] [CoreEngine] remove the unused files.
---
.../federate/quick_start/octopus/dump.rdb | Bin 7248 -> 0 bytes
.../federate/quick_start/octopus/nohup.out | 3032 -----------------
2 files changed, 3032 deletions(-)
delete mode 100644 python/examples/federate/quick_start/octopus/dump.rdb
delete mode 100644 python/examples/federate/quick_start/octopus/nohup.out
diff --git a/python/examples/federate/quick_start/octopus/dump.rdb b/python/examples/federate/quick_start/octopus/dump.rdb
deleted file mode 100644
index f31af5cb4fd49f4a377eb444135610e4915c5730..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001
literal 7248
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diff --git a/python/examples/federate/quick_start/octopus/nohup.out b/python/examples/federate/quick_start/octopus/nohup.out
deleted file mode 100644
index 2350761958..0000000000
--- a/python/examples/federate/quick_start/octopus/nohup.out
+++ /dev/null
@@ -1,3032 +0,0 @@
-58896:C 27 May 2024 16:58:15.551 # oO0OoO0OoO0Oo Redis is starting oO0OoO0OoO0Oo
-58896:C 27 May 2024 16:58:15.551 # Redis version=7.0.11, bits=64, commit=00000000, modified=0, pid=58896, just started
-58896:C 27 May 2024 16:58:15.551 # Warning: no config file specified, using the default config. In order to specify a config file use redis-server /path/to/redis.conf
-58896:M 27 May 2024 16:58:15.551 * monotonic clock: POSIX clock_gettime
-58896:M 27 May 2024 16:58:15.552 * Running mode=standalone, port=6379.
-58896:M 27 May 2024 16:58:15.552 # WARNING: The TCP backlog setting of 511 cannot be enforced because kern.ipc.somaxconn is set to the lower value of 128.
-58896:M 27 May 2024 16:58:15.552 # Server initialized
-58896:M 27 May 2024 16:58:15.552 * Ready to accept connections
-58896:M 27 May 2024 16:58:22.918 * DB saved on disk
-58896:M 27 May 2024 17:58:23.012 * 1 changes in 3600 seconds. Saving...
-58896:M 27 May 2024 17:58:23.013 * Background saving started by pid 65644
-65644:C 27 May 2024 17:58:23.020 * DB saved on disk
-65644:C 27 May 2024 17:58:23.021 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 27 May 2024 17:58:23.114 * Background saving terminated with success
-58896:M 27 May 2024 19:13:18.626 * 1 changes in 3600 seconds. Saving...
-58896:M 27 May 2024 19:13:18.736 * Background saving started by pid 72278
-72278:C 27 May 2024 19:13:18.746 * DB saved on disk
-72278:C 27 May 2024 19:13:18.746 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 27 May 2024 19:13:18.838 * Background saving terminated with success
-58896:M 28 May 2024 14:08:25.606 * 1 changes in 3600 seconds. Saving...
-58896:M 28 May 2024 14:08:25.608 * Background saving started by pid 78120
-78120:C 28 May 2024 14:08:25.615 * DB saved on disk
-78120:C 28 May 2024 14:08:25.616 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 28 May 2024 14:08:25.709 * Background saving terminated with success
-58896:M 28 May 2024 15:19:26.423 * 1 changes in 3600 seconds. Saving...
-58896:M 28 May 2024 15:19:26.539 * Background saving started by pid 84225
-84225:C 28 May 2024 15:19:26.545 * DB saved on disk
-84225:C 28 May 2024 15:19:26.546 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 28 May 2024 15:19:26.642 * Background saving terminated with success
-58896:M 29 May 2024 17:57:07.834 * 1 changes in 3600 seconds. Saving...
-58896:M 29 May 2024 17:57:07.835 * Background saving started by pid 4206
-4206:C 29 May 2024 17:57:07.849 * DB saved on disk
-4206:C 29 May 2024 17:57:07.850 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 29 May 2024 17:57:07.935 * Background saving terminated with success
-58896:M 29 May 2024 18:57:08.006 * 1 changes in 3600 seconds. Saving...
-58896:M 29 May 2024 18:57:08.008 * Background saving started by pid 11453
-11453:C 29 May 2024 18:57:08.021 * DB saved on disk
-11453:C 29 May 2024 18:57:08.022 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 29 May 2024 18:57:08.109 * Background saving terminated with success
-58896:M 29 May 2024 19:21:58.973 * 100 changes in 300 seconds. Saving...
-58896:M 29 May 2024 19:21:58.973 * Background saving started by pid 19535
-19535:C 29 May 2024 19:21:58.981 * DB saved on disk
-19535:C 29 May 2024 19:21:58.981 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 29 May 2024 19:21:59.074 * Background saving terminated with success
-58896:M 29 May 2024 19:27:40.433 * DB saved on disk
-58896:M 29 May 2024 20:39:51.202 * 1 changes in 3600 seconds. Saving...
-58896:M 29 May 2024 20:39:51.203 * Background saving started by pid 21314
-21314:C 29 May 2024 20:39:51.314 * DB saved on disk
-21314:C 29 May 2024 20:39:51.315 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 29 May 2024 20:39:51.407 * Background saving terminated with success
-58896:M 29 May 2024 22:49:24.515 * 1 changes in 3600 seconds. Saving...
-58896:M 29 May 2024 22:49:24.516 * Background saving started by pid 25814
-25814:C 29 May 2024 22:49:24.587 * DB saved on disk
-25814:C 29 May 2024 22:49:24.587 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 29 May 2024 22:49:24.617 * Background saving terminated with success
-58896:M 29 May 2024 23:08:52.971 * 100 changes in 300 seconds. Saving...
-58896:M 29 May 2024 23:08:52.972 * Background saving started by pid 28739
-28739:C 29 May 2024 23:08:52.978 * DB saved on disk
-28739:C 29 May 2024 23:08:52.978 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 29 May 2024 23:08:53.073 * Background saving terminated with success
-58896:M 30 May 2024 00:09:08.434 * 1 changes in 3600 seconds. Saving...
-58896:M 30 May 2024 00:09:08.435 * Background saving started by pid 30988
-30988:C 30 May 2024 00:09:08.569 * DB saved on disk
-30988:C 30 May 2024 00:09:08.569 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 30 May 2024 00:09:08.638 * Background saving terminated with success
-58896:M 30 May 2024 12:51:39.719 * 1 changes in 3600 seconds. Saving...
-58896:M 30 May 2024 12:51:39.720 * Background saving started by pid 41021
-41021:C 30 May 2024 12:51:39.729 * DB saved on disk
-41021:C 30 May 2024 12:51:39.729 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 30 May 2024 12:51:39.821 * Background saving terminated with success
-58896:M 30 May 2024 12:56:40.011 * 100 changes in 300 seconds. Saving...
-58896:M 30 May 2024 12:56:40.017 * Background saving started by pid 41850
-41850:C 30 May 2024 12:56:40.033 * DB saved on disk
-41850:C 30 May 2024 12:56:40.034 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 30 May 2024 12:56:40.120 * Background saving terminated with success
-58896:M 30 May 2024 13:16:33.022 * 100 changes in 300 seconds. Saving...
-58896:M 30 May 2024 13:16:33.138 * Background saving started by pid 42823
-42823:C 30 May 2024 13:16:33.145 * DB saved on disk
-42823:C 30 May 2024 13:16:33.145 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 30 May 2024 13:16:33.240 * Background saving terminated with success
-58896:M 30 May 2024 15:25:24.413 * 1 changes in 3600 seconds. Saving...
-58896:M 30 May 2024 15:25:24.413 * Background saving started by pid 48206
-48206:C 30 May 2024 15:25:24.425 * DB saved on disk
-48206:C 30 May 2024 15:25:24.426 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 30 May 2024 15:25:24.513 * Background saving terminated with success
-58896:M 30 May 2024 15:30:25.016 * 100 changes in 300 seconds. Saving...
-58896:M 30 May 2024 15:30:25.017 * Background saving started by pid 48800
-48800:C 30 May 2024 15:30:25.029 * DB saved on disk
-48800:C 30 May 2024 15:30:25.031 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 30 May 2024 15:30:25.119 * Background saving terminated with success
-58896:M 30 May 2024 15:35:26.012 * 100 changes in 300 seconds. Saving...
-58896:M 30 May 2024 15:35:26.014 * Background saving started by pid 49390
-49390:C 30 May 2024 15:35:26.025 * DB saved on disk
-49390:C 30 May 2024 15:35:26.025 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 30 May 2024 15:35:26.115 * Background saving terminated with success
-58896:M 30 May 2024 16:42:51.523 * 1 changes in 3600 seconds. Saving...
-58896:M 30 May 2024 16:42:51.646 * Background saving started by pid 65592
-65592:C 30 May 2024 16:42:51.655 * DB saved on disk
-65592:C 30 May 2024 16:42:51.655 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 30 May 2024 16:42:51.747 * Background saving terminated with success
-58896:M 30 May 2024 17:15:29.037 * 100 changes in 300 seconds. Saving...
-58896:M 30 May 2024 17:15:29.039 * Background saving started by pid 69523
-69523:C 30 May 2024 17:15:29.050 * DB saved on disk
-69523:C 30 May 2024 17:15:29.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 30 May 2024 17:15:29.142 * Background saving terminated with success
-58896:M 30 May 2024 18:15:30.060 * 1 changes in 3600 seconds. Saving...
-58896:M 30 May 2024 18:15:30.063 * Background saving started by pid 84706
-84706:C 30 May 2024 18:15:30.075 * DB saved on disk
-84706:C 30 May 2024 18:15:30.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 30 May 2024 18:15:30.167 * Background saving terminated with success
-58896:M 03 Jun 2024 18:06:53.699 * 1 changes in 3600 seconds. Saving...
-58896:M 03 Jun 2024 18:06:53.703 * Background saving started by pid 90870
-90870:C 03 Jun 2024 18:06:53.713 * DB saved on disk
-90870:C 03 Jun 2024 18:06:53.714 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 03 Jun 2024 18:06:53.803 * Background saving terminated with success
-58896:M 03 Jun 2024 18:11:54.075 * 100 changes in 300 seconds. Saving...
-58896:M 03 Jun 2024 18:11:54.078 * Background saving started by pid 91526
-91526:C 03 Jun 2024 18:11:54.087 * DB saved on disk
-91526:C 03 Jun 2024 18:11:54.087 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 03 Jun 2024 18:11:54.180 * Background saving terminated with success
-58896:M 03 Jun 2024 18:18:46.019 * 100 changes in 300 seconds. Saving...
-58896:M 03 Jun 2024 18:18:46.024 * Background saving started by pid 92286
-92286:C 03 Jun 2024 18:18:46.034 * DB saved on disk
-92286:C 03 Jun 2024 18:18:46.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 03 Jun 2024 18:18:46.126 * Background saving terminated with success
-58896:M 03 Jun 2024 18:24:50.037 * 100 changes in 300 seconds. Saving...
-58896:M 03 Jun 2024 18:24:50.038 * Background saving started by pid 93124
-93124:C 03 Jun 2024 18:24:50.050 * DB saved on disk
-93124:C 03 Jun 2024 18:24:50.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 03 Jun 2024 18:24:50.140 * Background saving terminated with success
-58896:M 03 Jun 2024 18:29:51.049 * 100 changes in 300 seconds. Saving...
-58896:M 03 Jun 2024 18:29:51.051 * Background saving started by pid 93844
-93844:C 03 Jun 2024 18:29:51.063 * DB saved on disk
-93844:C 03 Jun 2024 18:29:51.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 03 Jun 2024 18:29:51.153 * Background saving terminated with success
-58896:M 03 Jun 2024 18:34:52.071 * 100 changes in 300 seconds. Saving...
-58896:M 03 Jun 2024 18:34:52.073 * Background saving started by pid 94768
-94768:C 03 Jun 2024 18:34:52.088 * DB saved on disk
-94768:C 03 Jun 2024 18:34:52.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 03 Jun 2024 18:34:52.174 * Background saving terminated with success
-58896:M 03 Jun 2024 18:48:44.981 * 100 changes in 300 seconds. Saving...
-58896:M 03 Jun 2024 18:48:44.982 * Background saving started by pid 96629
-96629:C 03 Jun 2024 18:48:45.005 * DB saved on disk
-96629:C 03 Jun 2024 18:48:45.006 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 03 Jun 2024 18:48:45.083 * Background saving terminated with success
-58896:M 03 Jun 2024 23:49:36.949 * 1 changes in 3600 seconds. Saving...
-58896:M 03 Jun 2024 23:49:36.968 * Background saving started by pid 97783
-97783:C 03 Jun 2024 23:49:37.370 * DB saved on disk
-97783:C 03 Jun 2024 23:49:37.436 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 03 Jun 2024 23:49:37.493 * Background saving terminated with success
-58896:M 04 Jun 2024 14:48:17.992 * 1 changes in 3600 seconds. Saving...
-58896:M 04 Jun 2024 14:48:18.002 * Background saving started by pid 19353
-19353:C 04 Jun 2024 14:48:18.012 * DB saved on disk
-19353:C 04 Jun 2024 14:48:18.012 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 04 Jun 2024 14:48:18.109 * Background saving terminated with success
-58896:M 04 Jun 2024 14:53:19.081 * 100 changes in 300 seconds. Saving...
-58896:M 04 Jun 2024 14:53:19.082 * Background saving started by pid 19823
-19823:C 04 Jun 2024 14:53:19.087 * DB saved on disk
-19823:C 04 Jun 2024 14:53:19.088 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 04 Jun 2024 14:53:19.184 * Background saving terminated with success
-58896:M 04 Jun 2024 15:17:23.645 * 100 changes in 300 seconds. Saving...
-58896:M 04 Jun 2024 15:17:23.646 * Background saving started by pid 23721
-23721:C 04 Jun 2024 15:17:23.656 * DB saved on disk
-23721:C 04 Jun 2024 15:17:23.656 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 04 Jun 2024 15:17:23.748 * Background saving terminated with success
-58896:M 05 Jun 2024 15:06:53.989 * 1 changes in 3600 seconds. Saving...
-58896:M 05 Jun 2024 15:06:53.991 * Background saving started by pid 74889
-74889:C 05 Jun 2024 15:06:54.001 * DB saved on disk
-74889:C 05 Jun 2024 15:06:54.001 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 15:06:54.091 * Background saving terminated with success
-58896:M 05 Jun 2024 15:11:55.053 * 100 changes in 300 seconds. Saving...
-58896:M 05 Jun 2024 15:11:55.057 * Background saving started by pid 75549
-75549:C 05 Jun 2024 15:11:55.084 * DB saved on disk
-75549:C 05 Jun 2024 15:11:55.084 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 15:11:55.158 * Background saving terminated with success
-58896:M 05 Jun 2024 15:16:56.075 * 100 changes in 300 seconds. Saving...
-58896:M 05 Jun 2024 15:16:56.078 * Background saving started by pid 76399
-76399:C 05 Jun 2024 15:16:56.095 * DB saved on disk
-76399:C 05 Jun 2024 15:16:56.097 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 15:16:56.180 * Background saving terminated with success
-58896:M 05 Jun 2024 15:51:48.859 * 100 changes in 300 seconds. Saving...
-58896:M 05 Jun 2024 15:51:48.861 * Background saving started by pid 79566
-79566:C 05 Jun 2024 15:51:48.871 * DB saved on disk
-79566:C 05 Jun 2024 15:51:48.871 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 15:51:48.963 * Background saving terminated with success
-58896:M 05 Jun 2024 16:17:59.180 * 100 changes in 300 seconds. Saving...
-58896:M 05 Jun 2024 16:17:59.183 * Background saving started by pid 82108
-82108:C 05 Jun 2024 16:17:59.192 * DB saved on disk
-82108:C 05 Jun 2024 16:17:59.193 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 16:17:59.284 * Background saving terminated with success
-58896:M 05 Jun 2024 16:35:17.999 * 100 changes in 300 seconds. Saving...
-58896:M 05 Jun 2024 16:35:18.002 * Background saving started by pid 83723
-83723:C 05 Jun 2024 16:35:18.010 * DB saved on disk
-83723:C 05 Jun 2024 16:35:18.011 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 16:35:18.103 * Background saving terminated with success
-58896:M 05 Jun 2024 16:43:22.260 * 100 changes in 300 seconds. Saving...
-58896:M 05 Jun 2024 16:43:22.261 * Background saving started by pid 84583
-84583:C 05 Jun 2024 16:43:22.275 * DB saved on disk
-84583:C 05 Jun 2024 16:43:22.278 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 16:43:22.362 * Background saving terminated with success
-58896:M 05 Jun 2024 16:48:23.046 * 100 changes in 300 seconds. Saving...
-58896:M 05 Jun 2024 16:48:23.047 * Background saving started by pid 85131
-85131:C 05 Jun 2024 16:48:23.065 * DB saved on disk
-85131:C 05 Jun 2024 16:48:23.066 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 16:48:23.148 * Background saving terminated with success
-58896:M 05 Jun 2024 17:24:20.810 * 100 changes in 300 seconds. Saving...
-58896:M 05 Jun 2024 17:24:20.813 * Background saving started by pid 90105
-90105:C 05 Jun 2024 17:24:20.823 * DB saved on disk
-90105:C 05 Jun 2024 17:24:20.826 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 17:24:20.914 * Background saving terminated with success
-58896:M 05 Jun 2024 17:38:07.895 * 100 changes in 300 seconds. Saving...
-58896:M 05 Jun 2024 17:38:07.898 * Background saving started by pid 91506
-91506:C 05 Jun 2024 17:38:07.907 * DB saved on disk
-91506:C 05 Jun 2024 17:38:07.907 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 17:38:07.998 * Background saving terminated with success
-58896:M 05 Jun 2024 17:43:08.028 * 100 changes in 300 seconds. Saving...
-58896:M 05 Jun 2024 17:43:08.031 * Background saving started by pid 92110
-92110:C 05 Jun 2024 17:43:08.047 * DB saved on disk
-92110:C 05 Jun 2024 17:43:08.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 17:43:08.133 * Background saving terminated with success
-58896:M 05 Jun 2024 17:48:09.040 * 100 changes in 300 seconds. Saving...
-58896:M 05 Jun 2024 17:48:09.042 * Background saving started by pid 92684
-92684:C 05 Jun 2024 17:48:09.056 * DB saved on disk
-92684:C 05 Jun 2024 17:48:09.057 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 17:48:09.144 * Background saving terminated with success
-58896:M 05 Jun 2024 17:53:10.043 * 100 changes in 300 seconds. Saving...
-58896:M 05 Jun 2024 17:53:10.045 * Background saving started by pid 93293
-93293:C 05 Jun 2024 17:53:10.054 * DB saved on disk
-93293:C 05 Jun 2024 17:53:10.056 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 17:53:10.146 * Background saving terminated with success
-58896:M 05 Jun 2024 17:58:11.081 * 100 changes in 300 seconds. Saving...
-58896:M 05 Jun 2024 17:58:11.083 * Background saving started by pid 93757
-93757:C 05 Jun 2024 17:58:11.096 * DB saved on disk
-93757:C 05 Jun 2024 17:58:11.097 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 05 Jun 2024 17:58:11.186 * Background saving terminated with success
-58896:M 07 Jun 2024 16:39:41.351 * 1 changes in 3600 seconds. Saving...
-58896:M 07 Jun 2024 16:39:41.353 * Background saving started by pid 27460
-27460:C 07 Jun 2024 16:39:41.369 * DB saved on disk
-27460:C 07 Jun 2024 16:39:41.370 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 16:39:41.454 * Background saving terminated with success
-58896:M 07 Jun 2024 16:44:42.066 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 16:44:42.071 * Background saving started by pid 28358
-28358:C 07 Jun 2024 16:44:42.082 * DB saved on disk
-28358:C 07 Jun 2024 16:44:42.083 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 16:44:42.173 * Background saving terminated with success
-58896:M 07 Jun 2024 17:16:01.732 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 17:16:01.734 * Background saving started by pid 33049
-33049:C 07 Jun 2024 17:16:01.746 * DB saved on disk
-33049:C 07 Jun 2024 17:16:01.746 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 17:16:01.835 * Background saving terminated with success
-58896:M 07 Jun 2024 17:21:02.052 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 17:21:02.059 * Background saving started by pid 33638
-33638:C 07 Jun 2024 17:21:02.070 * DB saved on disk
-33638:C 07 Jun 2024 17:21:02.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 17:21:02.161 * Background saving terminated with success
-58896:M 07 Jun 2024 17:33:38.484 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 17:33:38.486 * Background saving started by pid 35103
-35103:C 07 Jun 2024 17:33:38.495 * DB saved on disk
-35103:C 07 Jun 2024 17:33:38.495 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 17:33:38.587 * Background saving terminated with success
-58896:M 07 Jun 2024 17:38:39.030 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 17:38:39.031 * Background saving started by pid 35753
-35753:C 07 Jun 2024 17:38:39.044 * DB saved on disk
-35753:C 07 Jun 2024 17:38:39.045 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 17:38:39.132 * Background saving terminated with success
-58896:M 07 Jun 2024 17:43:40.049 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 17:43:40.051 * Background saving started by pid 36373
-36373:C 07 Jun 2024 17:43:40.062 * DB saved on disk
-36373:C 07 Jun 2024 17:43:40.062 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 17:43:40.152 * Background saving terminated with success
-58896:M 07 Jun 2024 17:49:19.866 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 17:49:19.867 * Background saving started by pid 36987
-36987:C 07 Jun 2024 17:49:19.874 * DB saved on disk
-36987:C 07 Jun 2024 17:49:19.875 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 17:49:19.967 * Background saving terminated with success
-58896:M 07 Jun 2024 17:54:20.070 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 17:54:20.074 * Background saving started by pid 37622
-37622:C 07 Jun 2024 17:54:20.087 * DB saved on disk
-37622:C 07 Jun 2024 17:54:20.088 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 17:54:20.178 * Background saving terminated with success
-58896:M 07 Jun 2024 18:00:52.446 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 18:00:52.448 * Background saving started by pid 38338
-38338:C 07 Jun 2024 18:00:52.458 * DB saved on disk
-38338:C 07 Jun 2024 18:00:52.460 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 18:00:52.548 * Background saving terminated with success
-58896:M 07 Jun 2024 18:05:53.016 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 18:05:53.018 * Background saving started by pid 39003
-39003:C 07 Jun 2024 18:05:53.032 * DB saved on disk
-39003:C 07 Jun 2024 18:05:53.033 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 18:05:53.119 * Background saving terminated with success
-58896:M 07 Jun 2024 18:10:54.046 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 18:10:54.047 * Background saving started by pid 39675
-39675:C 07 Jun 2024 18:10:54.071 * DB saved on disk
-39675:C 07 Jun 2024 18:10:54.073 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 18:10:54.149 * Background saving terminated with success
-58896:M 07 Jun 2024 18:22:32.800 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 18:22:32.801 * Background saving started by pid 40966
-40966:C 07 Jun 2024 18:22:32.812 * DB saved on disk
-40966:C 07 Jun 2024 18:22:32.813 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 18:22:32.904 * Background saving terminated with success
-58896:M 07 Jun 2024 18:27:33.013 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 18:27:33.013 * Background saving started by pid 41713
-41713:C 07 Jun 2024 18:27:33.026 * DB saved on disk
-41713:C 07 Jun 2024 18:27:33.028 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 18:27:33.115 * Background saving terminated with success
-58896:M 07 Jun 2024 18:32:34.020 * 100 changes in 300 seconds. Saving...
-58896:M 07 Jun 2024 18:32:34.022 * Background saving started by pid 42366
-42366:C 07 Jun 2024 18:32:34.039 * DB saved on disk
-42366:C 07 Jun 2024 18:32:34.041 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 07 Jun 2024 18:32:34.124 * Background saving terminated with success
-58896:M 09 Jun 2024 01:21:17.009 * 1 changes in 3600 seconds. Saving...
-58896:M 09 Jun 2024 01:21:17.010 * Background saving started by pid 51967
-51967:C 09 Jun 2024 01:21:17.025 * DB saved on disk
-51967:C 09 Jun 2024 01:21:17.025 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 09 Jun 2024 01:21:17.111 * Background saving terminated with success
-58896:M 09 Jun 2024 01:26:18.067 * 100 changes in 300 seconds. Saving...
-58896:M 09 Jun 2024 01:26:18.068 * Background saving started by pid 52613
-52613:C 09 Jun 2024 01:26:18.076 * DB saved on disk
-52613:C 09 Jun 2024 01:26:18.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 09 Jun 2024 01:26:18.169 * Background saving terminated with success
-58896:M 09 Jun 2024 01:31:19.072 * 100 changes in 300 seconds. Saving...
-58896:M 09 Jun 2024 01:31:19.074 * Background saving started by pid 53131
-53131:C 09 Jun 2024 01:31:19.092 * DB saved on disk
-53131:C 09 Jun 2024 01:31:19.094 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 09 Jun 2024 01:31:19.176 * Background saving terminated with success
-58896:M 09 Jun 2024 01:36:20.080 * 100 changes in 300 seconds. Saving...
-58896:M 09 Jun 2024 01:36:20.083 * Background saving started by pid 53704
-53704:C 09 Jun 2024 01:36:20.094 * DB saved on disk
-53704:C 09 Jun 2024 01:36:20.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 09 Jun 2024 01:36:20.185 * Background saving terminated with success
-58896:M 09 Jun 2024 01:41:21.074 * 100 changes in 300 seconds. Saving...
-58896:M 09 Jun 2024 01:41:21.076 * Background saving started by pid 54385
-54385:C 09 Jun 2024 01:41:21.090 * DB saved on disk
-54385:C 09 Jun 2024 01:41:21.091 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 09 Jun 2024 01:41:21.179 * Background saving terminated with success
-58896:M 09 Jun 2024 01:46:22.080 * 100 changes in 300 seconds. Saving...
-58896:M 09 Jun 2024 01:46:22.083 * Background saving started by pid 54916
-54916:C 09 Jun 2024 01:46:22.102 * DB saved on disk
-54916:C 09 Jun 2024 01:46:22.102 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 09 Jun 2024 01:46:22.184 * Background saving terminated with success
-58896:M 09 Jun 2024 01:51:23.049 * 100 changes in 300 seconds. Saving...
-58896:M 09 Jun 2024 01:51:23.052 * Background saving started by pid 55511
-55511:C 09 Jun 2024 01:51:23.064 * DB saved on disk
-55511:C 09 Jun 2024 01:51:23.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 09 Jun 2024 01:51:23.152 * Background saving terminated with success
-58896:M 09 Jun 2024 01:59:40.782 * 100 changes in 300 seconds. Saving...
-58896:M 09 Jun 2024 01:59:40.783 * Background saving started by pid 56315
-56315:C 09 Jun 2024 01:59:40.795 * DB saved on disk
-56315:C 09 Jun 2024 01:59:40.795 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 09 Jun 2024 01:59:40.883 * Background saving terminated with success
-58896:M 09 Jun 2024 02:04:41.092 * 100 changes in 300 seconds. Saving...
-58896:M 09 Jun 2024 02:04:41.096 * Background saving started by pid 56862
-56862:C 09 Jun 2024 02:04:41.111 * DB saved on disk
-56862:C 09 Jun 2024 02:04:41.112 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 09 Jun 2024 02:04:41.198 * Background saving terminated with success
-58896:M 11 Jun 2024 13:19:27.398 * 1 changes in 3600 seconds. Saving...
-58896:M 11 Jun 2024 13:19:27.402 * Background saving started by pid 99129
-99129:C 11 Jun 2024 13:19:27.435 * DB saved on disk
-99129:C 11 Jun 2024 13:19:27.437 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 13:19:27.503 * Background saving terminated with success
-58896:M 11 Jun 2024 13:24:28.020 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 13:24:28.021 * Background saving started by pid 222
-222:C 11 Jun 2024 13:24:28.037 * DB saved on disk
-222:C 11 Jun 2024 13:24:28.038 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 13:24:28.127 * Background saving terminated with success
-58896:M 11 Jun 2024 13:29:29.094 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 13:29:29.096 * Background saving started by pid 1314
-1314:C 11 Jun 2024 13:29:29.108 * DB saved on disk
-1314:C 11 Jun 2024 13:29:29.114 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 13:29:29.197 * Background saving terminated with success
-58896:M 11 Jun 2024 13:34:30.038 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 13:34:30.042 * Background saving started by pid 1927
-1927:C 11 Jun 2024 13:34:30.054 * DB saved on disk
-1927:C 11 Jun 2024 13:34:30.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 13:34:30.144 * Background saving terminated with success
-58896:M 11 Jun 2024 13:39:31.059 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 13:39:31.063 * Background saving started by pid 2516
-2516:C 11 Jun 2024 13:39:31.100 * DB saved on disk
-2516:C 11 Jun 2024 13:39:31.102 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 13:39:31.164 * Background saving terminated with success
-58896:M 11 Jun 2024 13:44:32.023 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 13:44:32.024 * Background saving started by pid 3067
-3067:C 11 Jun 2024 13:44:32.033 * DB saved on disk
-3067:C 11 Jun 2024 13:44:32.033 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 13:44:32.125 * Background saving terminated with success
-58896:M 11 Jun 2024 13:51:40.611 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 13:51:40.614 * Background saving started by pid 3795
-3795:C 11 Jun 2024 13:51:40.627 * DB saved on disk
-3795:C 11 Jun 2024 13:51:40.628 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 13:51:40.715 * Background saving terminated with success
-58896:M 11 Jun 2024 13:56:41.066 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 13:56:41.069 * Background saving started by pid 4441
-4441:C 11 Jun 2024 13:56:41.091 * DB saved on disk
-4441:C 11 Jun 2024 13:56:41.095 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 13:56:41.170 * Background saving terminated with success
-58896:M 11 Jun 2024 14:01:42.031 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 14:01:42.035 * Background saving started by pid 5198
-5198:C 11 Jun 2024 14:01:42.058 * DB saved on disk
-5198:C 11 Jun 2024 14:01:42.058 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 14:01:42.137 * Background saving terminated with success
-58896:M 11 Jun 2024 14:08:57.215 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 14:08:57.217 * Background saving started by pid 5893
-5893:C 11 Jun 2024 14:08:57.230 * DB saved on disk
-5893:C 11 Jun 2024 14:08:57.232 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 14:08:57.319 * Background saving terminated with success
-58896:M 11 Jun 2024 14:13:58.030 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 14:13:58.034 * Background saving started by pid 6544
-6544:C 11 Jun 2024 14:13:58.061 * DB saved on disk
-6544:C 11 Jun 2024 14:13:58.063 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 14:13:58.142 * Background saving terminated with success
-58896:M 11 Jun 2024 14:18:59.057 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 14:18:59.060 * Background saving started by pid 7250
-7250:C 11 Jun 2024 14:18:59.074 * DB saved on disk
-7250:C 11 Jun 2024 14:18:59.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 14:18:59.164 * Background saving terminated with success
-58896:M 11 Jun 2024 14:24:00.000 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 14:24:00.003 * Background saving started by pid 7777
-7777:C 11 Jun 2024 14:24:00.017 * DB saved on disk
-7777:C 11 Jun 2024 14:24:00.018 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 14:24:00.105 * Background saving terminated with success
-58896:M 11 Jun 2024 14:29:01.003 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 14:29:01.005 * Background saving started by pid 9446
-9446:C 11 Jun 2024 14:29:01.022 * DB saved on disk
-9446:C 11 Jun 2024 14:29:01.023 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 14:29:01.106 * Background saving terminated with success
-58896:M 11 Jun 2024 14:38:46.573 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 14:38:46.575 * Background saving started by pid 16553
-16553:C 11 Jun 2024 14:38:46.587 * DB saved on disk
-16553:C 11 Jun 2024 14:38:46.587 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 14:38:46.677 * Background saving terminated with success
-58896:M 11 Jun 2024 14:43:47.042 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 14:43:47.046 * Background saving started by pid 18767
-18767:C 11 Jun 2024 14:43:47.064 * DB saved on disk
-18767:C 11 Jun 2024 14:43:47.065 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 14:43:47.149 * Background saving terminated with success
-58896:M 11 Jun 2024 14:48:48.071 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 14:48:48.072 * Background saving started by pid 19508
-19508:C 11 Jun 2024 14:48:48.105 * DB saved on disk
-19508:C 11 Jun 2024 14:48:48.106 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 14:48:48.173 * Background saving terminated with success
-58896:M 11 Jun 2024 15:55:55.546 * 1 changes in 3600 seconds. Saving...
-58896:M 11 Jun 2024 15:55:55.665 * Background saving started by pid 20200
-20200:C 11 Jun 2024 15:55:55.673 * DB saved on disk
-20200:C 11 Jun 2024 15:55:55.674 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 15:55:55.766 * Background saving terminated with success
-58896:M 11 Jun 2024 16:55:56.028 * 1 changes in 3600 seconds. Saving...
-58896:M 11 Jun 2024 16:55:56.029 * Background saving started by pid 26736
-26736:C 11 Jun 2024 16:55:56.039 * DB saved on disk
-26736:C 11 Jun 2024 16:55:56.041 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 16:55:56.130 * Background saving terminated with success
-58896:M 11 Jun 2024 17:00:57.094 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 17:00:57.096 * Background saving started by pid 27696
-27696:C 11 Jun 2024 17:00:57.110 * DB saved on disk
-27696:C 11 Jun 2024 17:00:57.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 17:00:57.197 * Background saving terminated with success
-58896:M 11 Jun 2024 17:05:58.022 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 17:05:58.028 * Background saving started by pid 28649
-28649:C 11 Jun 2024 17:05:58.040 * DB saved on disk
-28649:C 11 Jun 2024 17:05:58.042 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 17:05:58.130 * Background saving terminated with success
-58896:M 11 Jun 2024 17:10:59.011 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 17:10:59.013 * Background saving started by pid 29517
-29517:C 11 Jun 2024 17:10:59.028 * DB saved on disk
-29517:C 11 Jun 2024 17:10:59.029 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 17:10:59.114 * Background saving terminated with success
-58896:M 11 Jun 2024 17:16:00.031 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 17:16:00.033 * Background saving started by pid 30399
-30399:C 11 Jun 2024 17:16:00.044 * DB saved on disk
-30399:C 11 Jun 2024 17:16:00.045 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 17:16:00.134 * Background saving terminated with success
-58896:M 11 Jun 2024 17:21:01.099 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 17:21:01.102 * Background saving started by pid 34058
-34058:C 11 Jun 2024 17:21:01.136 * DB saved on disk
-34058:C 11 Jun 2024 17:21:01.136 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 17:21:01.204 * Background saving terminated with success
-58896:M 11 Jun 2024 17:26:02.077 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 17:26:02.078 * Background saving started by pid 35339
-35339:C 11 Jun 2024 17:26:02.092 * DB saved on disk
-35339:C 11 Jun 2024 17:26:02.093 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 17:26:02.179 * Background saving terminated with success
-58896:M 11 Jun 2024 17:31:03.024 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 17:31:03.027 * Background saving started by pid 36164
-36164:C 11 Jun 2024 17:31:03.044 * DB saved on disk
-36164:C 11 Jun 2024 17:31:03.046 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 17:31:03.129 * Background saving terminated with success
-58896:M 11 Jun 2024 17:36:04.080 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 17:36:04.083 * Background saving started by pid 37017
-37017:C 11 Jun 2024 17:36:04.100 * DB saved on disk
-37017:C 11 Jun 2024 17:36:04.100 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 17:36:04.184 * Background saving terminated with success
-58896:M 11 Jun 2024 17:41:05.070 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 17:41:05.071 * Background saving started by pid 37887
-37887:C 11 Jun 2024 17:41:05.096 * DB saved on disk
-37887:C 11 Jun 2024 17:41:05.097 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 17:41:05.173 * Background saving terminated with success
-58896:M 11 Jun 2024 17:46:06.098 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 17:46:06.099 * Background saving started by pid 38777
-38777:C 11 Jun 2024 17:46:06.110 * DB saved on disk
-38777:C 11 Jun 2024 17:46:06.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 17:46:06.200 * Background saving terminated with success
-58896:M 11 Jun 2024 17:51:07.052 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 17:51:07.054 * Background saving started by pid 39630
-39630:C 11 Jun 2024 17:51:07.065 * DB saved on disk
-39630:C 11 Jun 2024 17:51:07.065 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 17:51:07.156 * Background saving terminated with success
-58896:M 11 Jun 2024 17:56:08.017 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 17:56:08.020 * Background saving started by pid 40590
-40590:C 11 Jun 2024 17:56:08.031 * DB saved on disk
-40590:C 11 Jun 2024 17:56:08.032 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 17:56:08.122 * Background saving terminated with success
-58896:M 11 Jun 2024 18:01:09.041 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 18:01:09.044 * Background saving started by pid 41552
-41552:C 11 Jun 2024 18:01:09.054 * DB saved on disk
-41552:C 11 Jun 2024 18:01:09.056 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 18:01:09.145 * Background saving terminated with success
-58896:M 11 Jun 2024 18:06:10.031 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 18:06:10.034 * Background saving started by pid 42635
-42635:C 11 Jun 2024 18:06:10.047 * DB saved on disk
-42635:C 11 Jun 2024 18:06:10.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 18:06:10.136 * Background saving terminated with success
-58896:M 11 Jun 2024 18:11:11.043 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 18:11:11.046 * Background saving started by pid 43579
-43579:C 11 Jun 2024 18:11:11.061 * DB saved on disk
-43579:C 11 Jun 2024 18:11:11.062 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 18:11:11.147 * Background saving terminated with success
-58896:M 11 Jun 2024 18:16:12.053 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 18:16:12.056 * Background saving started by pid 44500
-44500:C 11 Jun 2024 18:16:12.068 * DB saved on disk
-44500:C 11 Jun 2024 18:16:12.068 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 18:16:12.157 * Background saving terminated with success
-58896:M 11 Jun 2024 18:21:13.023 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 18:21:13.027 * Background saving started by pid 45459
-45459:C 11 Jun 2024 18:21:13.054 * DB saved on disk
-45459:C 11 Jun 2024 18:21:13.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 18:21:13.128 * Background saving terminated with success
-58896:M 11 Jun 2024 18:26:14.079 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 18:26:14.080 * Background saving started by pid 46446
-46446:C 11 Jun 2024 18:26:14.095 * DB saved on disk
-46446:C 11 Jun 2024 18:26:14.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 18:26:14.181 * Background saving terminated with success
-58896:M 11 Jun 2024 18:31:15.020 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 18:31:15.023 * Background saving started by pid 47369
-47369:C 11 Jun 2024 18:31:15.033 * DB saved on disk
-47369:C 11 Jun 2024 18:31:15.033 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 18:31:15.125 * Background saving terminated with success
-58896:M 11 Jun 2024 18:44:26.665 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 18:44:26.668 * Background saving started by pid 47820
-47820:C 11 Jun 2024 18:44:26.794 * DB saved on disk
-47820:C 11 Jun 2024 18:44:26.795 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 18:44:26.870 * Background saving terminated with success
-58896:M 11 Jun 2024 18:51:12.584 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 18:51:12.586 * Background saving started by pid 47950
-47950:C 11 Jun 2024 18:51:12.599 * DB saved on disk
-47950:C 11 Jun 2024 18:51:12.600 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 18:51:12.689 * Background saving terminated with success
-58896:M 11 Jun 2024 19:02:27.776 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 19:02:27.778 * Background saving started by pid 48131
-48131:C 11 Jun 2024 19:02:27.802 * DB saved on disk
-48131:C 11 Jun 2024 19:02:27.804 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 19:02:27.881 * Background saving terminated with success
-58896:M 11 Jun 2024 19:07:28.043 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 19:07:28.045 * Background saving started by pid 48889
-48889:C 11 Jun 2024 19:07:28.056 * DB saved on disk
-48889:C 11 Jun 2024 19:07:28.063 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 19:07:28.147 * Background saving terminated with success
-58896:M 11 Jun 2024 19:12:29.059 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 19:12:29.061 * Background saving started by pid 49675
-49675:C 11 Jun 2024 19:12:29.074 * DB saved on disk
-49675:C 11 Jun 2024 19:12:29.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 19:12:29.165 * Background saving terminated with success
-58896:M 11 Jun 2024 19:17:30.038 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 19:17:30.039 * Background saving started by pid 50454
-50454:C 11 Jun 2024 19:17:30.048 * DB saved on disk
-50454:C 11 Jun 2024 19:17:30.049 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 19:17:30.141 * Background saving terminated with success
-58896:M 11 Jun 2024 19:22:31.015 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 19:22:31.019 * Background saving started by pid 51066
-51066:C 11 Jun 2024 19:22:31.034 * DB saved on disk
-51066:C 11 Jun 2024 19:22:31.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 19:22:31.121 * Background saving terminated with success
-58896:M 11 Jun 2024 19:27:32.083 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 19:27:32.086 * Background saving started by pid 51963
-51963:C 11 Jun 2024 19:27:32.100 * DB saved on disk
-51963:C 11 Jun 2024 19:27:32.101 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 19:27:32.187 * Background saving terminated with success
-58896:M 11 Jun 2024 19:32:33.008 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 19:32:33.010 * Background saving started by pid 52753
-52753:C 11 Jun 2024 19:32:33.021 * DB saved on disk
-52753:C 11 Jun 2024 19:32:33.021 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 19:32:33.111 * Background saving terminated with success
-58896:M 11 Jun 2024 19:37:34.032 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 19:37:34.034 * Background saving started by pid 54020
-54020:C 11 Jun 2024 19:37:34.047 * DB saved on disk
-54020:C 11 Jun 2024 19:37:34.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 19:37:34.135 * Background saving terminated with success
-58896:M 11 Jun 2024 19:42:35.021 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 19:42:35.025 * Background saving started by pid 55025
-55025:C 11 Jun 2024 19:42:35.055 * DB saved on disk
-55025:C 11 Jun 2024 19:42:35.056 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 19:42:35.126 * Background saving terminated with success
-58896:M 11 Jun 2024 19:47:36.075 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 19:47:36.077 * Background saving started by pid 55926
-55926:C 11 Jun 2024 19:47:36.091 * DB saved on disk
-55926:C 11 Jun 2024 19:47:36.092 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 19:47:36.178 * Background saving terminated with success
-58896:M 11 Jun 2024 19:52:37.054 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 19:52:37.061 * Background saving started by pid 56735
-56735:C 11 Jun 2024 19:52:37.078 * DB saved on disk
-56735:C 11 Jun 2024 19:52:37.078 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 19:52:37.163 * Background saving terminated with success
-58896:M 11 Jun 2024 19:57:38.075 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 19:57:38.077 * Background saving started by pid 57603
-57603:C 11 Jun 2024 19:57:38.092 * DB saved on disk
-57603:C 11 Jun 2024 19:57:38.092 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 19:57:38.178 * Background saving terminated with success
-58896:M 11 Jun 2024 20:02:39.013 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 20:02:39.015 * Background saving started by pid 58545
-58545:C 11 Jun 2024 20:02:39.028 * DB saved on disk
-58545:C 11 Jun 2024 20:02:39.029 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 20:02:39.116 * Background saving terminated with success
-58896:M 11 Jun 2024 20:07:40.055 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 20:07:40.057 * Background saving started by pid 59368
-59368:C 11 Jun 2024 20:07:40.077 * DB saved on disk
-59368:C 11 Jun 2024 20:07:40.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 20:07:40.159 * Background saving terminated with success
-58896:M 11 Jun 2024 20:12:41.003 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 20:12:41.005 * Background saving started by pid 60133
-60133:C 11 Jun 2024 20:12:41.021 * DB saved on disk
-60133:C 11 Jun 2024 20:12:41.022 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 20:12:41.107 * Background saving terminated with success
-58896:M 11 Jun 2024 20:17:42.079 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 20:17:42.081 * Background saving started by pid 60949
-60949:C 11 Jun 2024 20:17:42.091 * DB saved on disk
-60949:C 11 Jun 2024 20:17:42.093 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 20:17:42.181 * Background saving terminated with success
-58896:M 11 Jun 2024 20:22:43.066 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 20:22:43.067 * Background saving started by pid 61718
-61718:C 11 Jun 2024 20:22:43.077 * DB saved on disk
-61718:C 11 Jun 2024 20:22:43.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 20:22:43.167 * Background saving terminated with success
-58896:M 11 Jun 2024 20:27:44.036 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 20:27:44.037 * Background saving started by pid 62501
-62501:C 11 Jun 2024 20:27:44.047 * DB saved on disk
-62501:C 11 Jun 2024 20:27:44.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 20:27:44.138 * Background saving terminated with success
-58896:M 11 Jun 2024 20:32:45.098 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 20:32:45.099 * Background saving started by pid 63324
-63324:C 11 Jun 2024 20:32:45.112 * DB saved on disk
-63324:C 11 Jun 2024 20:32:45.113 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 20:32:45.199 * Background saving terminated with success
-58896:M 11 Jun 2024 20:37:46.011 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 20:37:46.014 * Background saving started by pid 64283
-64283:C 11 Jun 2024 20:37:46.023 * DB saved on disk
-64283:C 11 Jun 2024 20:37:46.024 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 20:37:46.115 * Background saving terminated with success
-58896:M 11 Jun 2024 20:42:47.025 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 20:42:47.028 * Background saving started by pid 65146
-65146:C 11 Jun 2024 20:42:47.036 * DB saved on disk
-65146:C 11 Jun 2024 20:42:47.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 20:42:47.129 * Background saving terminated with success
-58896:M 11 Jun 2024 20:47:48.097 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 20:47:48.099 * Background saving started by pid 65963
-65963:C 11 Jun 2024 20:47:48.114 * DB saved on disk
-65963:C 11 Jun 2024 20:47:48.114 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 20:47:48.200 * Background saving terminated with success
-58896:M 11 Jun 2024 20:52:49.080 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 20:52:49.081 * Background saving started by pid 66721
-66721:C 11 Jun 2024 20:52:49.089 * DB saved on disk
-66721:C 11 Jun 2024 20:52:49.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 20:52:49.182 * Background saving terminated with success
-58896:M 11 Jun 2024 20:57:50.072 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 20:57:50.074 * Background saving started by pid 67533
-67533:C 11 Jun 2024 20:57:50.086 * DB saved on disk
-67533:C 11 Jun 2024 20:57:50.087 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 20:57:50.175 * Background saving terminated with success
-58896:M 11 Jun 2024 21:02:51.093 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 21:02:51.096 * Background saving started by pid 68492
-68492:C 11 Jun 2024 21:02:51.104 * DB saved on disk
-68492:C 11 Jun 2024 21:02:51.105 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 21:02:51.197 * Background saving terminated with success
-58896:M 11 Jun 2024 21:07:52.069 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 21:07:52.071 * Background saving started by pid 69301
-69301:C 11 Jun 2024 21:07:52.080 * DB saved on disk
-69301:C 11 Jun 2024 21:07:52.081 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 21:07:52.172 * Background saving terminated with success
-58896:M 11 Jun 2024 21:12:53.015 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 21:12:53.016 * Background saving started by pid 70023
-70023:C 11 Jun 2024 21:12:53.026 * DB saved on disk
-70023:C 11 Jun 2024 21:12:53.027 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 21:12:53.117 * Background saving terminated with success
-58896:M 11 Jun 2024 21:17:54.036 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 21:17:54.039 * Background saving started by pid 70808
-70808:C 11 Jun 2024 21:17:54.049 * DB saved on disk
-70808:C 11 Jun 2024 21:17:54.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 21:17:54.139 * Background saving terminated with success
-58896:M 11 Jun 2024 21:22:55.017 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 21:22:55.020 * Background saving started by pid 71619
-71619:C 11 Jun 2024 21:22:55.034 * DB saved on disk
-71619:C 11 Jun 2024 21:22:55.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 21:22:55.122 * Background saving terminated with success
-58896:M 11 Jun 2024 21:27:56.099 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 21:27:56.101 * Background saving started by pid 72347
-72347:C 11 Jun 2024 21:27:56.110 * DB saved on disk
-72347:C 11 Jun 2024 21:27:56.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 21:27:56.204 * Background saving terminated with success
-58896:M 11 Jun 2024 21:32:57.085 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 21:32:57.087 * Background saving started by pid 73084
-73084:C 11 Jun 2024 21:32:57.097 * DB saved on disk
-73084:C 11 Jun 2024 21:32:57.098 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 21:32:57.190 * Background saving terminated with success
-58896:M 11 Jun 2024 21:37:58.093 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 21:37:58.096 * Background saving started by pid 73831
-73831:C 11 Jun 2024 21:37:58.111 * DB saved on disk
-73831:C 11 Jun 2024 21:37:58.112 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 21:37:58.198 * Background saving terminated with success
-58896:M 11 Jun 2024 21:42:59.011 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 21:42:59.013 * Background saving started by pid 74560
-74560:C 11 Jun 2024 21:42:59.029 * DB saved on disk
-74560:C 11 Jun 2024 21:42:59.032 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 21:42:59.114 * Background saving terminated with success
-58896:M 11 Jun 2024 21:48:00.014 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 21:48:00.016 * Background saving started by pid 75300
-75300:C 11 Jun 2024 21:48:00.033 * DB saved on disk
-75300:C 11 Jun 2024 21:48:00.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 21:48:00.118 * Background saving terminated with success
-58896:M 11 Jun 2024 21:53:01.092 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 21:53:01.094 * Background saving started by pid 76026
-76026:C 11 Jun 2024 21:53:01.107 * DB saved on disk
-76026:C 11 Jun 2024 21:53:01.109 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 21:53:01.197 * Background saving terminated with success
-58896:M 11 Jun 2024 21:58:02.084 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 21:58:02.087 * Background saving started by pid 76774
-76774:C 11 Jun 2024 21:58:02.099 * DB saved on disk
-76774:C 11 Jun 2024 21:58:02.102 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 21:58:02.188 * Background saving terminated with success
-58896:M 11 Jun 2024 22:03:03.053 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 22:03:03.057 * Background saving started by pid 77506
-77506:C 11 Jun 2024 22:03:03.079 * DB saved on disk
-77506:C 11 Jun 2024 22:03:03.080 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 22:03:03.158 * Background saving terminated with success
-58896:M 11 Jun 2024 22:08:04.061 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 22:08:04.065 * Background saving started by pid 78241
-78241:C 11 Jun 2024 22:08:04.080 * DB saved on disk
-78241:C 11 Jun 2024 22:08:04.081 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 22:08:04.168 * Background saving terminated with success
-58896:M 11 Jun 2024 22:13:05.038 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 22:13:05.038 * Background saving started by pid 78971
-78971:C 11 Jun 2024 22:13:05.049 * DB saved on disk
-78971:C 11 Jun 2024 22:13:05.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 22:13:05.141 * Background saving terminated with success
-58896:M 11 Jun 2024 22:18:06.009 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 22:18:06.012 * Background saving started by pid 79712
-79712:C 11 Jun 2024 22:18:06.031 * DB saved on disk
-79712:C 11 Jun 2024 22:18:06.031 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 22:18:06.115 * Background saving terminated with success
-58896:M 11 Jun 2024 22:23:07.017 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 22:23:07.018 * Background saving started by pid 80463
-80463:C 11 Jun 2024 22:23:07.030 * DB saved on disk
-80463:C 11 Jun 2024 22:23:07.031 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 22:23:07.121 * Background saving terminated with success
-58896:M 11 Jun 2024 22:28:08.091 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 22:28:08.095 * Background saving started by pid 81195
-81195:C 11 Jun 2024 22:28:08.126 * DB saved on disk
-81195:C 11 Jun 2024 22:28:08.127 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 22:28:08.197 * Background saving terminated with success
-58896:M 11 Jun 2024 22:33:09.069 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 22:33:09.073 * Background saving started by pid 81924
-81924:C 11 Jun 2024 22:33:09.090 * DB saved on disk
-81924:C 11 Jun 2024 22:33:09.091 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 22:33:09.176 * Background saving terminated with success
-58896:M 11 Jun 2024 22:38:10.051 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 22:38:10.056 * Background saving started by pid 82688
-82688:C 11 Jun 2024 22:38:10.073 * DB saved on disk
-82688:C 11 Jun 2024 22:38:10.074 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 22:38:10.158 * Background saving terminated with success
-58896:M 11 Jun 2024 22:43:11.058 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 22:43:11.062 * Background saving started by pid 83419
-83419:C 11 Jun 2024 22:43:11.085 * DB saved on disk
-83419:C 11 Jun 2024 22:43:11.086 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 22:43:11.165 * Background saving terminated with success
-58896:M 11 Jun 2024 22:48:12.050 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 22:48:12.055 * Background saving started by pid 84242
-84242:C 11 Jun 2024 22:48:12.071 * DB saved on disk
-84242:C 11 Jun 2024 22:48:12.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 22:48:12.156 * Background saving terminated with success
-58896:M 11 Jun 2024 22:53:13.061 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 22:53:13.064 * Background saving started by pid 84972
-84972:C 11 Jun 2024 22:53:13.080 * DB saved on disk
-84972:C 11 Jun 2024 22:53:13.082 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 22:53:13.167 * Background saving terminated with success
-58896:M 11 Jun 2024 22:58:14.056 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 22:58:14.059 * Background saving started by pid 85710
-85710:C 11 Jun 2024 22:58:14.075 * DB saved on disk
-85710:C 11 Jun 2024 22:58:14.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 22:58:14.161 * Background saving terminated with success
-58896:M 11 Jun 2024 23:03:15.013 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 23:03:15.014 * Background saving started by pid 86445
-86445:C 11 Jun 2024 23:03:15.027 * DB saved on disk
-86445:C 11 Jun 2024 23:03:15.031 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 23:03:15.116 * Background saving terminated with success
-58896:M 11 Jun 2024 23:08:16.036 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 23:08:16.038 * Background saving started by pid 87172
-87172:C 11 Jun 2024 23:08:16.052 * DB saved on disk
-87172:C 11 Jun 2024 23:08:16.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 23:08:16.138 * Background saving terminated with success
-58896:M 11 Jun 2024 23:13:17.001 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 23:13:17.006 * Background saving started by pid 87901
-87901:C 11 Jun 2024 23:13:17.020 * DB saved on disk
-87901:C 11 Jun 2024 23:13:17.021 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 23:13:17.109 * Background saving terminated with success
-58896:M 11 Jun 2024 23:18:18.101 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 23:18:18.103 * Background saving started by pid 88646
-88646:C 11 Jun 2024 23:18:18.115 * DB saved on disk
-88646:C 11 Jun 2024 23:18:18.116 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 23:18:18.205 * Background saving terminated with success
-58896:M 11 Jun 2024 23:23:19.087 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 23:23:19.090 * Background saving started by pid 89375
-89375:C 11 Jun 2024 23:23:19.101 * DB saved on disk
-89375:C 11 Jun 2024 23:23:19.103 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 23:23:19.192 * Background saving terminated with success
-58896:M 11 Jun 2024 23:28:20.011 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 23:28:20.013 * Background saving started by pid 90105
-90105:C 11 Jun 2024 23:28:20.027 * DB saved on disk
-90105:C 11 Jun 2024 23:28:20.028 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 23:28:20.115 * Background saving terminated with success
-58896:M 11 Jun 2024 23:33:21.067 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 23:33:21.069 * Background saving started by pid 90839
-90839:C 11 Jun 2024 23:33:21.079 * DB saved on disk
-90839:C 11 Jun 2024 23:33:21.080 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 23:33:21.170 * Background saving terminated with success
-58896:M 11 Jun 2024 23:38:22.003 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 23:38:22.008 * Background saving started by pid 91662
-91662:C 11 Jun 2024 23:38:22.019 * DB saved on disk
-91662:C 11 Jun 2024 23:38:22.019 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 23:38:22.111 * Background saving terminated with success
-58896:M 11 Jun 2024 23:43:23.059 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 23:43:23.063 * Background saving started by pid 92434
-92434:C 11 Jun 2024 23:43:23.075 * DB saved on disk
-92434:C 11 Jun 2024 23:43:23.075 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 23:43:23.165 * Background saving terminated with success
-58896:M 11 Jun 2024 23:48:24.037 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 23:48:24.038 * Background saving started by pid 93164
-93164:C 11 Jun 2024 23:48:24.049 * DB saved on disk
-93164:C 11 Jun 2024 23:48:24.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 23:48:24.142 * Background saving terminated with success
-58896:M 11 Jun 2024 23:53:25.018 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 23:53:25.021 * Background saving started by pid 93965
-93965:C 11 Jun 2024 23:53:25.035 * DB saved on disk
-93965:C 11 Jun 2024 23:53:25.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 23:53:25.123 * Background saving terminated with success
-58896:M 11 Jun 2024 23:58:26.009 * 100 changes in 300 seconds. Saving...
-58896:M 11 Jun 2024 23:58:26.011 * Background saving started by pid 94758
-94758:C 11 Jun 2024 23:58:26.025 * DB saved on disk
-94758:C 11 Jun 2024 23:58:26.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 11 Jun 2024 23:58:26.112 * Background saving terminated with success
-58896:M 12 Jun 2024 00:03:27.039 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 00:03:27.041 * Background saving started by pid 95491
-95491:C 12 Jun 2024 00:03:27.055 * DB saved on disk
-95491:C 12 Jun 2024 00:03:27.057 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 00:03:27.142 * Background saving terminated with success
-58896:M 12 Jun 2024 00:08:28.034 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 00:08:28.035 * Background saving started by pid 96284
-96284:C 12 Jun 2024 00:08:28.051 * DB saved on disk
-96284:C 12 Jun 2024 00:08:28.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 00:08:28.136 * Background saving terminated with success
-58896:M 12 Jun 2024 00:13:29.015 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 00:13:29.019 * Background saving started by pid 97696
-97696:C 12 Jun 2024 00:13:29.029 * DB saved on disk
-97696:C 12 Jun 2024 00:13:29.030 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 00:13:29.120 * Background saving terminated with success
-58896:M 12 Jun 2024 00:18:30.052 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 00:18:30.055 * Background saving started by pid 98669
-98669:C 12 Jun 2024 00:18:30.072 * DB saved on disk
-98669:C 12 Jun 2024 00:18:30.073 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 00:18:30.157 * Background saving terminated with success
-58896:M 12 Jun 2024 00:23:31.067 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 00:23:31.069 * Background saving started by pid 99666
-99666:C 12 Jun 2024 00:23:31.085 * DB saved on disk
-99666:C 12 Jun 2024 00:23:31.087 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 00:23:31.169 * Background saving terminated with success
-58896:M 12 Jun 2024 00:28:32.087 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 00:28:32.093 * Background saving started by pid 831
-831:C 12 Jun 2024 00:28:32.110 * DB saved on disk
-831:C 12 Jun 2024 00:28:32.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 00:28:32.194 * Background saving terminated with success
-58896:M 12 Jun 2024 00:33:33.077 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 00:33:33.079 * Background saving started by pid 1642
-1642:C 12 Jun 2024 00:33:33.099 * DB saved on disk
-1642:C 12 Jun 2024 00:33:33.100 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 00:33:33.181 * Background saving terminated with success
-58896:M 12 Jun 2024 00:38:34.030 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 00:38:34.032 * Background saving started by pid 2590
-2590:C 12 Jun 2024 00:38:34.045 * DB saved on disk
-2590:C 12 Jun 2024 00:38:34.046 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 00:38:34.134 * Background saving terminated with success
-58896:M 12 Jun 2024 00:43:35.021 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 00:43:35.024 * Background saving started by pid 3742
-3742:C 12 Jun 2024 00:43:35.037 * DB saved on disk
-3742:C 12 Jun 2024 00:43:35.038 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 00:43:35.125 * Background saving terminated with success
-58896:M 12 Jun 2024 00:48:36.015 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 00:48:36.018 * Background saving started by pid 4666
-4666:C 12 Jun 2024 00:48:36.030 * DB saved on disk
-4666:C 12 Jun 2024 00:48:36.031 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 00:48:36.119 * Background saving terminated with success
-58896:M 12 Jun 2024 00:53:37.025 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 00:53:37.027 * Background saving started by pid 5691
-5691:C 12 Jun 2024 00:53:37.042 * DB saved on disk
-5691:C 12 Jun 2024 00:53:37.042 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 00:53:37.129 * Background saving terminated with success
-58896:M 12 Jun 2024 00:58:38.050 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 00:58:38.054 * Background saving started by pid 6620
-6620:C 12 Jun 2024 00:58:38.065 * DB saved on disk
-6620:C 12 Jun 2024 00:58:38.066 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 00:58:38.155 * Background saving terminated with success
-58896:M 12 Jun 2024 01:03:39.073 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 01:03:39.075 * Background saving started by pid 7624
-7624:C 12 Jun 2024 01:03:39.101 * DB saved on disk
-7624:C 12 Jun 2024 01:03:39.114 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 01:03:39.176 * Background saving terminated with success
-58896:M 12 Jun 2024 01:08:40.009 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 01:08:40.012 * Background saving started by pid 8631
-8631:C 12 Jun 2024 01:08:40.025 * DB saved on disk
-8631:C 12 Jun 2024 01:08:40.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 01:08:40.113 * Background saving terminated with success
-58896:M 12 Jun 2024 01:20:43.620 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 01:20:43.625 * Background saving started by pid 10740
-10740:C 12 Jun 2024 01:20:43.650 * DB saved on disk
-10740:C 12 Jun 2024 01:20:43.650 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 01:20:43.727 * Background saving terminated with success
-58896:M 12 Jun 2024 01:25:44.086 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 01:25:44.089 * Background saving started by pid 11660
-11660:C 12 Jun 2024 01:25:44.101 * DB saved on disk
-11660:C 12 Jun 2024 01:25:44.102 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 01:25:44.190 * Background saving terminated with success
-58896:M 12 Jun 2024 01:30:45.057 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 01:30:45.059 * Background saving started by pid 12738
-12738:C 12 Jun 2024 01:30:45.066 * DB saved on disk
-12738:C 12 Jun 2024 01:30:45.066 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 01:30:45.159 * Background saving terminated with success
-58896:M 12 Jun 2024 01:35:46.087 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 01:35:46.088 * Background saving started by pid 13711
-13711:C 12 Jun 2024 01:35:46.099 * DB saved on disk
-13711:C 12 Jun 2024 01:35:46.099 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 01:35:46.189 * Background saving terminated with success
-58896:M 12 Jun 2024 01:40:47.061 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 01:40:47.062 * Background saving started by pid 14674
-14674:C 12 Jun 2024 01:40:47.069 * DB saved on disk
-14674:C 12 Jun 2024 01:40:47.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 01:40:47.162 * Background saving terminated with success
-58896:M 12 Jun 2024 01:45:48.078 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 01:45:48.082 * Background saving started by pid 18780
-18780:C 12 Jun 2024 01:45:48.103 * DB saved on disk
-18780:C 12 Jun 2024 01:45:48.105 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 01:45:48.184 * Background saving terminated with success
-58896:M 12 Jun 2024 01:50:49.025 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 01:50:49.026 * Background saving started by pid 19686
-19686:C 12 Jun 2024 01:50:49.038 * DB saved on disk
-19686:C 12 Jun 2024 01:50:49.039 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 01:50:49.127 * Background saving terminated with success
-58896:M 12 Jun 2024 01:56:34.702 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 01:56:34.702 * Background saving started by pid 20738
-20738:C 12 Jun 2024 01:56:34.741 * DB saved on disk
-20738:C 12 Jun 2024 01:56:34.744 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 01:56:34.803 * Background saving terminated with success
-58896:M 12 Jun 2024 02:01:35.007 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 02:01:35.008 * Background saving started by pid 21912
-21912:C 12 Jun 2024 02:01:35.017 * DB saved on disk
-21912:C 12 Jun 2024 02:01:35.018 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 02:01:35.108 * Background saving terminated with success
-58896:M 12 Jun 2024 02:06:36.095 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 02:06:36.097 * Background saving started by pid 23306
-23306:C 12 Jun 2024 02:06:36.103 * DB saved on disk
-23306:C 12 Jun 2024 02:06:36.104 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 02:06:36.197 * Background saving terminated with success
-58896:M 12 Jun 2024 02:11:37.037 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 02:11:37.038 * Background saving started by pid 24623
-24623:C 12 Jun 2024 02:11:37.049 * DB saved on disk
-24623:C 12 Jun 2024 02:11:37.049 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 02:11:37.138 * Background saving terminated with success
-58896:M 12 Jun 2024 02:16:38.032 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 02:16:38.035 * Background saving started by pid 28935
-28935:C 12 Jun 2024 02:16:38.056 * DB saved on disk
-28935:C 12 Jun 2024 02:16:38.060 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 02:16:38.136 * Background saving terminated with success
-58896:M 12 Jun 2024 02:21:39.065 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 02:21:39.068 * Background saving started by pid 30316
-30316:C 12 Jun 2024 02:21:39.089 * DB saved on disk
-30316:C 12 Jun 2024 02:21:39.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 02:21:39.169 * Background saving terminated with success
-58896:M 12 Jun 2024 02:26:40.088 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 02:26:40.092 * Background saving started by pid 31108
-31108:C 12 Jun 2024 02:26:40.115 * DB saved on disk
-31108:C 12 Jun 2024 02:26:40.116 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 02:26:40.192 * Background saving terminated with success
-58896:M 12 Jun 2024 02:31:41.089 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 02:31:41.090 * Background saving started by pid 32709
-32709:C 12 Jun 2024 02:31:41.115 * DB saved on disk
-32709:C 12 Jun 2024 02:31:41.116 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 02:31:41.191 * Background saving terminated with success
-58896:M 12 Jun 2024 02:34:11.210 * DB saved on disk
-58896:M 12 Jun 2024 02:39:12.090 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 02:39:12.090 * Background saving started by pid 34729
-34729:C 12 Jun 2024 02:39:12.098 * DB saved on disk
-34729:C 12 Jun 2024 02:39:12.099 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 02:39:12.191 * Background saving terminated with success
-58896:M 12 Jun 2024 02:44:13.030 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 02:44:13.031 * Background saving started by pid 36552
-36552:C 12 Jun 2024 02:44:13.039 * DB saved on disk
-36552:C 12 Jun 2024 02:44:13.040 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 02:44:13.132 * Background saving terminated with success
-58896:M 12 Jun 2024 02:49:14.003 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 02:49:14.007 * Background saving started by pid 40406
-40406:C 12 Jun 2024 02:49:14.016 * DB saved on disk
-40406:C 12 Jun 2024 02:49:14.017 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 02:49:14.108 * Background saving terminated with success
-58896:M 12 Jun 2024 02:54:15.052 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 02:54:15.053 * Background saving started by pid 41373
-41373:C 12 Jun 2024 02:54:15.068 * DB saved on disk
-41373:C 12 Jun 2024 02:54:15.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 02:54:15.155 * Background saving terminated with success
-58896:M 12 Jun 2024 02:59:16.098 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 02:59:16.100 * Background saving started by pid 42324
-42324:C 12 Jun 2024 02:59:16.108 * DB saved on disk
-42324:C 12 Jun 2024 02:59:16.109 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 02:59:16.201 * Background saving terminated with success
-58896:M 12 Jun 2024 03:04:17.012 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 03:04:17.015 * Background saving started by pid 43225
-43225:C 12 Jun 2024 03:04:17.025 * DB saved on disk
-43225:C 12 Jun 2024 03:04:17.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 03:04:17.116 * Background saving terminated with success
-58896:M 12 Jun 2024 03:04:34.446 * DB saved on disk
-58896:M 12 Jun 2024 03:09:35.045 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 03:09:35.048 * Background saving started by pid 44410
-44410:C 12 Jun 2024 03:09:35.055 * DB saved on disk
-44410:C 12 Jun 2024 03:09:35.057 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 03:09:35.148 * Background saving terminated with success
-58896:M 12 Jun 2024 03:14:36.016 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 03:14:36.019 * Background saving started by pid 46540
-46540:C 12 Jun 2024 03:14:36.031 * DB saved on disk
-46540:C 12 Jun 2024 03:14:36.033 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 03:14:36.119 * Background saving terminated with success
-58896:M 12 Jun 2024 03:19:37.037 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 03:19:37.041 * Background saving started by pid 47386
-47386:C 12 Jun 2024 03:19:37.055 * DB saved on disk
-47386:C 12 Jun 2024 03:19:37.058 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 03:19:37.143 * Background saving terminated with success
-58896:M 12 Jun 2024 03:24:38.035 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 03:24:38.042 * Background saving started by pid 48201
-48201:C 12 Jun 2024 03:24:38.063 * DB saved on disk
-48201:C 12 Jun 2024 03:24:38.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 03:24:38.147 * Background saving terminated with success
-58896:M 12 Jun 2024 03:29:39.031 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 03:29:39.035 * Background saving started by pid 49082
-49082:C 12 Jun 2024 03:29:39.051 * DB saved on disk
-49082:C 12 Jun 2024 03:29:39.052 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 03:29:39.136 * Background saving terminated with success
-58896:M 12 Jun 2024 03:34:40.043 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 03:34:40.046 * Background saving started by pid 53122
-53122:C 12 Jun 2024 03:34:40.058 * DB saved on disk
-53122:C 12 Jun 2024 03:34:40.059 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 03:34:40.147 * Background saving terminated with success
-58896:M 12 Jun 2024 03:39:41.032 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 03:39:41.035 * Background saving started by pid 54267
-54267:C 12 Jun 2024 03:39:41.056 * DB saved on disk
-54267:C 12 Jun 2024 03:39:41.057 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 03:39:41.137 * Background saving terminated with success
-58896:M 12 Jun 2024 03:44:42.005 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 03:44:42.006 * Background saving started by pid 55054
-55054:C 12 Jun 2024 03:44:42.022 * DB saved on disk
-55054:C 12 Jun 2024 03:44:42.024 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 03:44:42.107 * Background saving terminated with success
-58896:M 12 Jun 2024 03:49:43.092 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 03:49:43.094 * Background saving started by pid 55938
-55938:C 12 Jun 2024 03:49:43.104 * DB saved on disk
-55938:C 12 Jun 2024 03:49:43.105 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 03:49:43.195 * Background saving terminated with success
-58896:M 12 Jun 2024 03:54:44.085 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 03:54:44.087 * Background saving started by pid 56777
-56777:C 12 Jun 2024 03:54:44.105 * DB saved on disk
-56777:C 12 Jun 2024 03:54:44.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 03:54:44.189 * Background saving terminated with success
-58896:M 12 Jun 2024 03:59:45.049 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 03:59:45.050 * Background saving started by pid 57643
-57643:C 12 Jun 2024 03:59:45.059 * DB saved on disk
-57643:C 12 Jun 2024 03:59:45.061 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 03:59:45.151 * Background saving terminated with success
-58896:M 12 Jun 2024 04:04:46.023 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 04:04:46.027 * Background saving started by pid 58638
-58638:C 12 Jun 2024 04:04:46.049 * DB saved on disk
-58638:C 12 Jun 2024 04:04:46.049 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 04:04:46.129 * Background saving terminated with success
-58896:M 12 Jun 2024 04:09:47.069 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 04:09:47.071 * Background saving started by pid 59417
-59417:C 12 Jun 2024 04:09:47.089 * DB saved on disk
-59417:C 12 Jun 2024 04:09:47.090 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 04:09:47.173 * Background saving terminated with success
-58896:M 12 Jun 2024 04:14:48.091 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 04:14:48.092 * Background saving started by pid 60190
-60190:C 12 Jun 2024 04:14:48.100 * DB saved on disk
-60190:C 12 Jun 2024 04:14:48.105 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 04:14:48.193 * Background saving terminated with success
-58896:M 12 Jun 2024 04:19:49.050 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 04:19:49.052 * Background saving started by pid 61120
-61120:C 12 Jun 2024 04:19:49.060 * DB saved on disk
-61120:C 12 Jun 2024 04:19:49.060 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 04:19:49.153 * Background saving terminated with success
-58896:M 12 Jun 2024 04:24:50.028 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 04:24:50.032 * Background saving started by pid 61993
-61993:C 12 Jun 2024 04:24:50.051 * DB saved on disk
-61993:C 12 Jun 2024 04:24:50.052 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 04:24:50.134 * Background saving terminated with success
-58896:M 12 Jun 2024 04:29:51.054 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 04:29:51.058 * Background saving started by pid 62790
-62790:C 12 Jun 2024 04:29:51.077 * DB saved on disk
-62790:C 12 Jun 2024 04:29:51.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 04:29:51.159 * Background saving terminated with success
-58896:M 12 Jun 2024 04:34:52.067 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 04:34:52.069 * Background saving started by pid 63597
-63597:C 12 Jun 2024 04:34:52.077 * DB saved on disk
-63597:C 12 Jun 2024 04:34:52.078 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 04:34:52.170 * Background saving terminated with success
-58896:M 12 Jun 2024 04:39:53.088 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 04:39:53.089 * Background saving started by pid 64519
-64519:C 12 Jun 2024 04:39:53.098 * DB saved on disk
-64519:C 12 Jun 2024 04:39:53.099 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 04:39:53.190 * Background saving terminated with success
-58896:M 12 Jun 2024 04:44:54.089 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 04:44:54.091 * Background saving started by pid 65472
-65472:C 12 Jun 2024 04:44:54.099 * DB saved on disk
-65472:C 12 Jun 2024 04:44:54.100 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 04:44:54.193 * Background saving terminated with success
-58896:M 12 Jun 2024 04:49:55.090 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 04:49:55.091 * Background saving started by pid 66430
-66430:C 12 Jun 2024 04:49:55.102 * DB saved on disk
-66430:C 12 Jun 2024 04:49:55.104 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 04:49:55.192 * Background saving terminated with success
-58896:M 12 Jun 2024 04:54:56.019 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 04:54:56.022 * Background saving started by pid 67514
-67514:C 12 Jun 2024 04:54:56.034 * DB saved on disk
-67514:C 12 Jun 2024 04:54:56.039 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 04:54:56.124 * Background saving terminated with success
-58896:M 12 Jun 2024 04:59:57.000 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 04:59:57.001 * Background saving started by pid 68698
-68698:C 12 Jun 2024 04:59:57.010 * DB saved on disk
-68698:C 12 Jun 2024 04:59:57.012 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 04:59:57.102 * Background saving terminated with success
-58896:M 12 Jun 2024 05:04:58.100 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 05:04:58.102 * Background saving started by pid 74173
-74173:C 12 Jun 2024 05:04:58.117 * DB saved on disk
-74173:C 12 Jun 2024 05:04:58.117 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 05:04:58.204 * Background saving terminated with success
-58896:M 12 Jun 2024 05:09:59.003 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 05:09:59.005 * Background saving started by pid 77013
-77013:C 12 Jun 2024 05:09:59.012 * DB saved on disk
-77013:C 12 Jun 2024 05:09:59.013 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 05:09:59.106 * Background saving terminated with success
-58896:M 12 Jun 2024 05:15:00.056 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 05:15:00.058 * Background saving started by pid 77983
-77983:C 12 Jun 2024 05:15:00.070 * DB saved on disk
-77983:C 12 Jun 2024 05:15:00.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 05:15:00.159 * Background saving terminated with success
-58896:M 12 Jun 2024 05:20:01.096 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 05:20:01.103 * Background saving started by pid 79011
-79011:C 12 Jun 2024 05:20:01.122 * DB saved on disk
-79011:C 12 Jun 2024 05:20:01.132 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 05:20:01.204 * Background saving terminated with success
-58896:M 12 Jun 2024 05:25:02.097 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 05:25:02.099 * Background saving started by pid 80216
-80216:C 12 Jun 2024 05:25:02.113 * DB saved on disk
-80216:C 12 Jun 2024 05:25:02.113 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 05:25:02.200 * Background saving terminated with success
-58896:M 12 Jun 2024 05:30:03.063 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 05:30:03.068 * Background saving started by pid 81376
-81376:C 12 Jun 2024 05:30:03.083 * DB saved on disk
-81376:C 12 Jun 2024 05:30:03.088 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 05:30:03.169 * Background saving terminated with success
-58896:M 12 Jun 2024 05:35:04.075 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 05:35:04.078 * Background saving started by pid 82164
-82164:C 12 Jun 2024 05:35:04.088 * DB saved on disk
-82164:C 12 Jun 2024 05:35:04.088 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 05:35:04.179 * Background saving terminated with success
-58896:M 12 Jun 2024 05:40:05.085 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 05:40:05.087 * Background saving started by pid 83362
-83362:C 12 Jun 2024 05:40:05.113 * DB saved on disk
-83362:C 12 Jun 2024 05:40:05.116 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 05:40:05.189 * Background saving terminated with success
-58896:M 12 Jun 2024 05:45:06.011 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 05:45:06.012 * Background saving started by pid 84525
-84525:C 12 Jun 2024 05:45:06.021 * DB saved on disk
-84525:C 12 Jun 2024 05:45:06.022 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 05:45:06.114 * Background saving terminated with success
-58896:M 12 Jun 2024 05:50:07.081 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 05:50:07.082 * Background saving started by pid 85679
-85679:C 12 Jun 2024 05:50:07.095 * DB saved on disk
-85679:C 12 Jun 2024 05:50:07.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 05:50:07.184 * Background saving terminated with success
-58896:M 12 Jun 2024 06:09:17.678 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 06:09:17.799 * Background saving started by pid 86522
-86522:C 12 Jun 2024 06:09:17.807 * DB saved on disk
-86522:C 12 Jun 2024 06:09:17.807 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 06:09:17.913 * Background saving terminated with success
-58896:M 12 Jun 2024 07:23:10.681 * 1 changes in 3600 seconds. Saving...
-58896:M 12 Jun 2024 07:23:10.685 * Background saving started by pid 86688
-86688:C 12 Jun 2024 07:23:10.799 * DB saved on disk
-86688:C 12 Jun 2024 07:23:10.800 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 07:23:10.887 * Background saving terminated with success
-58896:M 12 Jun 2024 08:28:27.701 * 1 changes in 3600 seconds. Saving...
-58896:M 12 Jun 2024 08:28:27.709 * Background saving started by pid 86783
-86783:C 12 Jun 2024 08:28:27.805 * DB saved on disk
-86783:C 12 Jun 2024 08:28:27.806 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 08:28:27.810 * Background saving terminated with success
-58896:M 12 Jun 2024 08:37:48.107 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 08:37:48.109 * Background saving started by pid 86998
-86998:C 12 Jun 2024 08:37:48.124 * DB saved on disk
-86998:C 12 Jun 2024 08:37:48.125 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 08:37:48.210 * Background saving terminated with success
-58896:M 12 Jun 2024 08:45:00.378 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 08:45:00.441 * Background saving started by pid 88055
-88055:C 12 Jun 2024 08:45:00.998 * DB saved on disk
-88055:C 12 Jun 2024 08:45:01.006 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 08:45:01.057 * Background saving terminated with success
-58896:M 12 Jun 2024 09:03:18.658 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 09:03:18.662 * Background saving started by pid 88847
-88847:C 12 Jun 2024 09:03:18.782 * DB saved on disk
-88847:C 12 Jun 2024 09:03:18.783 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 09:03:18.865 * Background saving terminated with success
-58896:M 12 Jun 2024 09:20:51.679 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 09:20:51.682 * Background saving started by pid 89079
-89079:C 12 Jun 2024 09:20:51.805 * DB saved on disk
-89079:C 12 Jun 2024 09:20:51.806 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 09:20:51.885 * Background saving terminated with success
-58896:M 12 Jun 2024 09:36:57.986 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 09:36:57.986 * Background saving started by pid 89273
-89273:C 12 Jun 2024 09:36:57.994 * DB saved on disk
-89273:C 12 Jun 2024 09:36:57.994 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 09:36:58.089 * Background saving terminated with success
-58896:M 12 Jun 2024 09:52:59.001 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 09:52:59.002 * Background saving started by pid 89459
-89459:C 12 Jun 2024 09:52:59.013 * DB saved on disk
-89459:C 12 Jun 2024 09:52:59.014 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 09:52:59.103 * Background saving terminated with success
-58896:M 12 Jun 2024 10:09:08.619 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 10:09:08.624 * Background saving started by pid 89697
-89697:C 12 Jun 2024 10:09:08.738 * DB saved on disk
-89697:C 12 Jun 2024 10:09:08.739 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 10:09:08.831 * Background saving terminated with success
-58896:M 12 Jun 2024 10:27:48.637 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 10:27:48.640 * Background saving started by pid 90312
-90312:C 12 Jun 2024 10:27:48.754 * DB saved on disk
-90312:C 12 Jun 2024 10:27:48.754 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 10:27:48.843 * Background saving terminated with success
-58896:M 12 Jun 2024 10:45:08.649 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 10:45:08.765 * Background saving started by pid 90689
-90689:C 12 Jun 2024 10:45:08.781 * DB saved on disk
-90689:C 12 Jun 2024 10:45:08.781 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 10:45:08.872 * Background saving terminated with success
-58896:M 12 Jun 2024 11:04:19.611 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 11:04:19.731 * Background saving started by pid 91384
-91384:C 12 Jun 2024 11:04:19.738 * DB saved on disk
-91384:C 12 Jun 2024 11:04:19.739 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 11:04:19.837 * Background saving terminated with success
-58896:M 12 Jun 2024 11:13:15.115 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 11:13:15.119 * Background saving started by pid 91575
-91575:C 12 Jun 2024 11:13:15.137 * DB saved on disk
-91575:C 12 Jun 2024 11:13:15.138 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 11:13:15.222 * Background saving terminated with success
-58896:M 12 Jun 2024 12:18:47.632 * 1 changes in 3600 seconds. Saving...
-58896:M 12 Jun 2024 12:18:47.635 * Background saving started by pid 91694
-91694:C 12 Jun 2024 12:18:47.756 * DB saved on disk
-91694:C 12 Jun 2024 12:18:47.757 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 12:18:47.839 * Background saving terminated with success
-58896:M 12 Jun 2024 12:39:26.809 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 12:39:26.811 * Background saving started by pid 92013
-92013:C 12 Jun 2024 12:39:26.824 * DB saved on disk
-92013:C 12 Jun 2024 12:39:26.825 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 12:39:26.912 * Background saving terminated with success
-58896:M 12 Jun 2024 12:44:27.044 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 12:44:27.045 * Background saving started by pid 93150
-93150:C 12 Jun 2024 12:44:27.052 * DB saved on disk
-93150:C 12 Jun 2024 12:44:27.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 12:44:27.146 * Background saving terminated with success
-58896:M 12 Jun 2024 12:49:28.034 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 12:49:28.036 * Background saving started by pid 94288
-94288:C 12 Jun 2024 12:49:28.048 * DB saved on disk
-94288:C 12 Jun 2024 12:49:28.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 12:49:28.137 * Background saving terminated with success
-58896:M 12 Jun 2024 12:54:29.012 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 12:54:29.015 * Background saving started by pid 95469
-95469:C 12 Jun 2024 12:54:29.028 * DB saved on disk
-95469:C 12 Jun 2024 12:54:29.028 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 12:54:29.116 * Background saving terminated with success
-58896:M 12 Jun 2024 12:59:30.022 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 12:59:30.023 * Background saving started by pid 96610
-96610:C 12 Jun 2024 12:59:30.041 * DB saved on disk
-96610:C 12 Jun 2024 12:59:30.041 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 12:59:30.124 * Background saving terminated with success
-58896:M 12 Jun 2024 13:04:31.035 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 13:04:31.036 * Background saving started by pid 97725
-97725:C 12 Jun 2024 13:04:31.047 * DB saved on disk
-97725:C 12 Jun 2024 13:04:31.049 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 13:04:31.138 * Background saving terminated with success
-58896:M 12 Jun 2024 13:09:32.020 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 13:09:32.022 * Background saving started by pid 98866
-98866:C 12 Jun 2024 13:09:32.038 * DB saved on disk
-98866:C 12 Jun 2024 13:09:32.039 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 13:09:32.123 * Background saving terminated with success
-58896:M 12 Jun 2024 13:14:33.004 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 13:14:33.005 * Background saving started by pid 103
-103:C 12 Jun 2024 13:14:33.013 * DB saved on disk
-103:C 12 Jun 2024 13:14:33.014 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 13:14:33.106 * Background saving terminated with success
-58896:M 12 Jun 2024 13:19:34.047 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 13:19:34.048 * Background saving started by pid 1450
-1450:C 12 Jun 2024 13:19:34.058 * DB saved on disk
-1450:C 12 Jun 2024 13:19:34.058 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 13:19:34.149 * Background saving terminated with success
-58896:M 12 Jun 2024 13:24:35.060 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 13:24:35.063 * Background saving started by pid 2598
-2598:C 12 Jun 2024 13:24:35.072 * DB saved on disk
-2598:C 12 Jun 2024 13:24:35.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 13:24:35.165 * Background saving terminated with success
-58896:M 12 Jun 2024 13:29:36.006 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 13:29:36.009 * Background saving started by pid 3725
-3725:C 12 Jun 2024 13:29:36.019 * DB saved on disk
-3725:C 12 Jun 2024 13:29:36.020 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 13:29:36.110 * Background saving terminated with success
-58896:M 12 Jun 2024 13:34:37.016 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 13:34:37.017 * Background saving started by pid 4859
-4859:C 12 Jun 2024 13:34:37.036 * DB saved on disk
-4859:C 12 Jun 2024 13:34:37.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 13:34:37.118 * Background saving terminated with success
-58896:M 12 Jun 2024 13:52:39.660 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 13:52:39.663 * Background saving started by pid 5136
-5136:C 12 Jun 2024 13:52:39.783 * DB saved on disk
-5136:C 12 Jun 2024 13:52:39.783 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 13:52:39.867 * Background saving terminated with success
-58896:M 12 Jun 2024 14:18:00.088 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 14:18:00.089 * Background saving started by pid 5336
-5336:C 12 Jun 2024 14:18:00.100 * DB saved on disk
-5336:C 12 Jun 2024 14:18:00.101 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 14:18:00.192 * Background saving terminated with success
-58896:M 12 Jun 2024 14:23:39.578 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 14:23:39.579 * Background saving started by pid 5595
-5595:C 12 Jun 2024 14:23:39.589 * DB saved on disk
-5595:C 12 Jun 2024 14:23:39.590 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 14:23:39.686 * Background saving terminated with success
-58896:M 12 Jun 2024 14:28:40.051 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 14:28:40.063 * Background saving started by pid 6818
-6818:C 12 Jun 2024 14:28:40.079 * DB saved on disk
-6818:C 12 Jun 2024 14:28:40.080 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 14:28:40.165 * Background saving terminated with success
-58896:M 12 Jun 2024 14:33:41.100 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 14:33:41.101 * Background saving started by pid 7974
-7974:C 12 Jun 2024 14:33:41.111 * DB saved on disk
-7974:C 12 Jun 2024 14:33:41.112 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 14:33:41.202 * Background saving terminated with success
-58896:M 12 Jun 2024 14:39:15.833 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 14:39:15.834 * Background saving started by pid 9245
-9245:C 12 Jun 2024 14:39:15.843 * DB saved on disk
-9245:C 12 Jun 2024 14:39:15.843 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 14:39:15.936 * Background saving terminated with success
-58896:M 12 Jun 2024 14:44:16.080 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 14:44:16.083 * Background saving started by pid 10377
-10377:C 12 Jun 2024 14:44:16.090 * DB saved on disk
-10377:C 12 Jun 2024 14:44:16.091 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 14:44:16.184 * Background saving terminated with success
-58896:M 12 Jun 2024 14:49:17.004 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 14:49:17.006 * Background saving started by pid 11532
-11532:C 12 Jun 2024 14:49:17.020 * DB saved on disk
-11532:C 12 Jun 2024 14:49:17.020 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 14:49:17.107 * Background saving terminated with success
-58896:M 12 Jun 2024 14:54:18.054 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 14:54:18.055 * Background saving started by pid 12728
-12728:C 12 Jun 2024 14:54:18.071 * DB saved on disk
-12728:C 12 Jun 2024 14:54:18.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 14:54:18.156 * Background saving terminated with success
-58896:M 12 Jun 2024 14:59:19.060 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 14:59:19.063 * Background saving started by pid 13955
-13955:C 12 Jun 2024 14:59:19.095 * DB saved on disk
-13955:C 12 Jun 2024 14:59:19.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 14:59:19.164 * Background saving terminated with success
-58896:M 12 Jun 2024 15:04:50.041 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 15:04:50.046 * Background saving started by pid 14209
-14209:C 12 Jun 2024 15:04:50.161 * DB saved on disk
-14209:C 12 Jun 2024 15:04:50.162 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 15:04:50.249 * Background saving terminated with success
-58896:M 12 Jun 2024 15:16:01.278 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 15:16:01.280 * Background saving started by pid 14412
-14412:C 12 Jun 2024 15:16:01.296 * DB saved on disk
-14412:C 12 Jun 2024 15:16:01.297 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 15:16:01.382 * Background saving terminated with success
-58896:M 12 Jun 2024 15:21:42.305 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 15:21:42.308 * Background saving started by pid 15693
-15693:C 12 Jun 2024 15:21:42.425 * DB saved on disk
-15693:C 12 Jun 2024 15:21:42.426 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 15:21:42.512 * Background saving terminated with success
-58896:M 12 Jun 2024 15:26:43.091 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 15:26:43.093 * Background saving started by pid 15892
-15892:C 12 Jun 2024 15:26:43.108 * DB saved on disk
-15892:C 12 Jun 2024 15:26:43.109 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 15:26:43.208 * Background saving terminated with success
-58896:M 12 Jun 2024 15:32:08.470 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 15:32:08.473 * Background saving started by pid 16107
-16107:C 12 Jun 2024 15:32:08.586 * DB saved on disk
-16107:C 12 Jun 2024 15:32:08.587 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 15:32:08.675 * Background saving terminated with success
-58896:M 12 Jun 2024 15:37:51.306 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 15:37:51.311 * Background saving started by pid 16417
-16417:C 12 Jun 2024 15:37:51.431 * DB saved on disk
-16417:C 12 Jun 2024 15:37:51.432 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 15:37:51.515 * Background saving terminated with success
-58896:M 12 Jun 2024 15:43:24.501 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 15:43:24.505 * Background saving started by pid 16735
-16735:C 12 Jun 2024 15:43:24.619 * DB saved on disk
-16735:C 12 Jun 2024 15:43:24.620 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 15:43:24.709 * Background saving terminated with success
-58896:M 12 Jun 2024 15:49:17.280 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 15:49:17.282 * Background saving started by pid 17067
-17067:C 12 Jun 2024 15:49:17.403 * DB saved on disk
-17067:C 12 Jun 2024 15:49:17.403 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 15:49:17.486 * Background saving terminated with success
-58896:M 12 Jun 2024 15:58:31.612 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 15:58:31.612 * Background saving started by pid 17349
-17349:C 12 Jun 2024 15:58:31.624 * DB saved on disk
-17349:C 12 Jun 2024 15:58:31.625 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 15:58:31.713 * Background saving terminated with success
-58896:M 12 Jun 2024 16:07:32.858 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 16:07:32.859 * Background saving started by pid 17568
-17568:C 12 Jun 2024 16:07:32.873 * DB saved on disk
-17568:C 12 Jun 2024 16:07:32.874 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 16:07:32.961 * Background saving terminated with success
-58896:M 12 Jun 2024 16:26:11.532 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 16:26:11.537 * Background saving started by pid 17835
-17835:C 12 Jun 2024 16:26:11.655 * DB saved on disk
-17835:C 12 Jun 2024 16:26:11.655 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 16:26:11.739 * Background saving terminated with success
-58896:M 12 Jun 2024 16:50:57.768 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 16:50:57.771 * Background saving started by pid 18014
-18014:C 12 Jun 2024 16:50:57.787 * DB saved on disk
-18014:C 12 Jun 2024 16:50:57.789 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 16:50:57.873 * Background saving terminated with success
-58896:M 12 Jun 2024 17:50:58.003 * 1 changes in 3600 seconds. Saving...
-58896:M 12 Jun 2024 17:50:58.005 * Background saving started by pid 18164
-18164:C 12 Jun 2024 17:50:58.024 * DB saved on disk
-18164:C 12 Jun 2024 17:50:58.025 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 17:50:58.107 * Background saving terminated with success
-58896:M 12 Jun 2024 18:04:59.651 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 18:04:59.655 * Background saving started by pid 18427
-18427:C 12 Jun 2024 18:04:59.667 * DB saved on disk
-18427:C 12 Jun 2024 18:04:59.667 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 18:04:59.756 * Background saving terminated with success
-58896:M 12 Jun 2024 18:10:00.077 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 18:10:00.079 * Background saving started by pid 19644
-19644:C 12 Jun 2024 18:10:00.091 * DB saved on disk
-19644:C 12 Jun 2024 18:10:00.092 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 18:10:00.180 * Background saving terminated with success
-58896:M 12 Jun 2024 18:15:01.093 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 18:15:01.095 * Background saving started by pid 20794
-20794:C 12 Jun 2024 18:15:01.115 * DB saved on disk
-20794:C 12 Jun 2024 18:15:01.116 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 18:15:01.197 * Background saving terminated with success
-58896:M 12 Jun 2024 18:20:02.078 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 18:20:02.079 * Background saving started by pid 21958
-21958:C 12 Jun 2024 18:20:02.088 * DB saved on disk
-21958:C 12 Jun 2024 18:20:02.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 18:20:02.181 * Background saving terminated with success
-58896:M 12 Jun 2024 18:25:03.029 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 18:25:03.030 * Background saving started by pid 23368
-23368:C 12 Jun 2024 18:25:03.037 * DB saved on disk
-23368:C 12 Jun 2024 18:25:03.039 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 18:25:03.131 * Background saving terminated with success
-58896:M 12 Jun 2024 18:30:04.044 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 18:30:04.046 * Background saving started by pid 24145
-24145:C 12 Jun 2024 18:30:04.054 * DB saved on disk
-24145:C 12 Jun 2024 18:30:04.054 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 18:30:04.147 * Background saving terminated with success
-58896:M 12 Jun 2024 18:35:05.081 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 18:35:05.085 * Background saving started by pid 24948
-24948:C 12 Jun 2024 18:35:05.096 * DB saved on disk
-24948:C 12 Jun 2024 18:35:05.097 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 18:35:05.188 * Background saving terminated with success
-58896:M 12 Jun 2024 18:40:06.059 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 18:40:06.060 * Background saving started by pid 26487
-26487:C 12 Jun 2024 18:40:06.072 * DB saved on disk
-26487:C 12 Jun 2024 18:40:06.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 18:40:06.160 * Background saving terminated with success
-58896:M 12 Jun 2024 18:45:07.016 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 18:45:07.019 * Background saving started by pid 27478
-27478:C 12 Jun 2024 18:45:07.030 * DB saved on disk
-27478:C 12 Jun 2024 18:45:07.033 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 18:45:07.120 * Background saving terminated with success
-58896:M 12 Jun 2024 18:50:08.096 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 18:50:08.100 * Background saving started by pid 28255
-28255:C 12 Jun 2024 18:50:08.118 * DB saved on disk
-28255:C 12 Jun 2024 18:50:08.120 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 18:50:08.202 * Background saving terminated with success
-58896:M 12 Jun 2024 18:55:09.066 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 18:55:09.067 * Background saving started by pid 29026
-29026:C 12 Jun 2024 18:55:09.077 * DB saved on disk
-29026:C 12 Jun 2024 18:55:09.078 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 18:55:09.169 * Background saving terminated with success
-58896:M 12 Jun 2024 19:00:10.054 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 19:00:10.060 * Background saving started by pid 29846
-29846:C 12 Jun 2024 19:00:10.077 * DB saved on disk
-29846:C 12 Jun 2024 19:00:10.078 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 19:00:10.162 * Background saving terminated with success
-58896:M 12 Jun 2024 19:05:11.091 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 19:05:11.093 * Background saving started by pid 30656
-30656:C 12 Jun 2024 19:05:11.106 * DB saved on disk
-30656:C 12 Jun 2024 19:05:11.107 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 19:05:11.195 * Background saving terminated with success
-58896:M 12 Jun 2024 19:10:12.069 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 19:10:12.070 * Background saving started by pid 31436
-31436:C 12 Jun 2024 19:10:12.082 * DB saved on disk
-31436:C 12 Jun 2024 19:10:12.083 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 19:10:12.172 * Background saving terminated with success
-58896:M 12 Jun 2024 19:15:13.031 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 19:15:13.032 * Background saving started by pid 32456
-32456:C 12 Jun 2024 19:15:13.045 * DB saved on disk
-32456:C 12 Jun 2024 19:15:13.046 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 19:15:13.133 * Background saving terminated with success
-58896:M 12 Jun 2024 19:20:14.035 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 19:20:14.036 * Background saving started by pid 33746
-33746:C 12 Jun 2024 19:20:14.045 * DB saved on disk
-33746:C 12 Jun 2024 19:20:14.046 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 19:20:14.137 * Background saving terminated with success
-58896:M 12 Jun 2024 19:25:15.038 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 19:25:15.041 * Background saving started by pid 34925
-34925:C 12 Jun 2024 19:25:15.057 * DB saved on disk
-34925:C 12 Jun 2024 19:25:15.057 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 19:25:15.142 * Background saving terminated with success
-58896:M 12 Jun 2024 19:30:16.003 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 19:30:16.008 * Background saving started by pid 36180
-36180:C 12 Jun 2024 19:30:16.026 * DB saved on disk
-36180:C 12 Jun 2024 19:30:16.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 19:30:16.110 * Background saving terminated with success
-58896:M 12 Jun 2024 19:30:50.487 * DB saved on disk
-58896:M 12 Jun 2024 19:35:51.066 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 19:35:51.070 * Background saving started by pid 37695
-37695:C 12 Jun 2024 19:35:51.097 * DB saved on disk
-37695:C 12 Jun 2024 19:35:51.098 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 19:35:51.171 * Background saving terminated with success
-58896:M 12 Jun 2024 19:40:52.077 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 19:40:52.078 * Background saving started by pid 38605
-38605:C 12 Jun 2024 19:40:52.091 * DB saved on disk
-38605:C 12 Jun 2024 19:40:52.092 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 19:40:52.180 * Background saving terminated with success
-58896:M 12 Jun 2024 19:45:53.092 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 19:45:53.093 * Background saving started by pid 39738
-39738:C 12 Jun 2024 19:45:53.101 * DB saved on disk
-39738:C 12 Jun 2024 19:45:53.101 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 19:45:53.194 * Background saving terminated with success
-58896:M 12 Jun 2024 19:50:54.028 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 19:50:54.031 * Background saving started by pid 40670
-40670:C 12 Jun 2024 19:50:54.042 * DB saved on disk
-40670:C 12 Jun 2024 19:50:54.043 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 19:50:54.133 * Background saving terminated with success
-58896:M 12 Jun 2024 19:55:55.096 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 19:55:55.099 * Background saving started by pid 43457
-43457:C 12 Jun 2024 19:55:55.107 * DB saved on disk
-43457:C 12 Jun 2024 19:55:55.107 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 19:55:55.199 * Background saving terminated with success
-58896:M 12 Jun 2024 20:00:56.029 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 20:00:56.031 * Background saving started by pid 44453
-44453:C 12 Jun 2024 20:00:56.037 * DB saved on disk
-44453:C 12 Jun 2024 20:00:56.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 20:00:56.133 * Background saving terminated with success
-58896:M 12 Jun 2024 20:05:57.013 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 20:05:57.016 * Background saving started by pid 45420
-45420:C 12 Jun 2024 20:05:57.025 * DB saved on disk
-45420:C 12 Jun 2024 20:05:57.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 20:05:57.117 * Background saving terminated with success
-58896:M 12 Jun 2024 20:10:58.035 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 20:10:58.037 * Background saving started by pid 46445
-46445:C 12 Jun 2024 20:10:58.045 * DB saved on disk
-46445:C 12 Jun 2024 20:10:58.045 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 20:10:58.138 * Background saving terminated with success
-58896:M 12 Jun 2024 20:15:59.030 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 20:15:59.032 * Background saving started by pid 47438
-47438:C 12 Jun 2024 20:15:59.041 * DB saved on disk
-47438:C 12 Jun 2024 20:15:59.042 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 20:15:59.133 * Background saving terminated with success
-58896:M 12 Jun 2024 20:21:00.034 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 20:21:00.036 * Background saving started by pid 48364
-48364:C 12 Jun 2024 20:21:00.043 * DB saved on disk
-48364:C 12 Jun 2024 20:21:00.046 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 20:21:00.137 * Background saving terminated with success
-58896:M 12 Jun 2024 20:26:01.085 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 20:26:01.086 * Background saving started by pid 49394
-49394:C 12 Jun 2024 20:26:01.094 * DB saved on disk
-49394:C 12 Jun 2024 20:26:01.095 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 20:26:01.188 * Background saving terminated with success
-58896:M 12 Jun 2024 20:31:02.083 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 20:31:02.088 * Background saving started by pid 50479
-50479:C 12 Jun 2024 20:31:02.109 * DB saved on disk
-50479:C 12 Jun 2024 20:31:02.110 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 20:31:02.190 * Background saving terminated with success
-58896:M 12 Jun 2024 20:36:03.042 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 20:36:03.043 * Background saving started by pid 51442
-51442:C 12 Jun 2024 20:36:03.065 * DB saved on disk
-51442:C 12 Jun 2024 20:36:03.066 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 20:36:03.144 * Background saving terminated with success
-58896:M 12 Jun 2024 20:41:04.076 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 20:41:04.078 * Background saving started by pid 52399
-52399:C 12 Jun 2024 20:41:04.090 * DB saved on disk
-52399:C 12 Jun 2024 20:41:04.093 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 20:41:04.179 * Background saving terminated with success
-58896:M 12 Jun 2024 20:46:05.056 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 20:46:05.058 * Background saving started by pid 53397
-53397:C 12 Jun 2024 20:46:05.067 * DB saved on disk
-53397:C 12 Jun 2024 20:46:05.067 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 20:46:05.159 * Background saving terminated with success
-58896:M 12 Jun 2024 20:51:06.070 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 20:51:06.071 * Background saving started by pid 54382
-54382:C 12 Jun 2024 20:51:06.078 * DB saved on disk
-54382:C 12 Jun 2024 20:51:06.081 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 20:51:06.172 * Background saving terminated with success
-58896:M 12 Jun 2024 20:56:07.099 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 20:56:07.102 * Background saving started by pid 57248
-57248:C 12 Jun 2024 20:56:07.111 * DB saved on disk
-57248:C 12 Jun 2024 20:56:07.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 20:56:07.204 * Background saving terminated with success
-58896:M 12 Jun 2024 21:01:08.063 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 21:01:08.066 * Background saving started by pid 58219
-58219:C 12 Jun 2024 21:01:08.075 * DB saved on disk
-58219:C 12 Jun 2024 21:01:08.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 21:01:08.168 * Background saving terminated with success
-58896:M 12 Jun 2024 21:06:09.082 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 21:06:09.082 * Background saving started by pid 58998
-58998:C 12 Jun 2024 21:06:09.092 * DB saved on disk
-58998:C 12 Jun 2024 21:06:09.093 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 21:06:09.184 * Background saving terminated with success
-58896:M 12 Jun 2024 22:22:09.136 * 1 changes in 3600 seconds. Saving...
-58896:M 12 Jun 2024 22:22:09.196 * Background saving started by pid 59686
-59686:C 12 Jun 2024 22:22:09.431 * DB saved on disk
-59686:C 12 Jun 2024 22:22:09.536 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 22:22:09.634 * Background saving terminated with success
-58896:M 12 Jun 2024 22:27:10.079 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 22:27:10.083 * Background saving started by pid 60609
-60609:C 12 Jun 2024 22:27:10.097 * DB saved on disk
-60609:C 12 Jun 2024 22:27:10.099 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 22:27:10.184 * Background saving terminated with success
-58896:M 12 Jun 2024 22:32:11.032 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 22:32:11.036 * Background saving started by pid 61470
-61470:C 12 Jun 2024 22:32:11.054 * DB saved on disk
-61470:C 12 Jun 2024 22:32:11.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 22:32:11.138 * Background saving terminated with success
-58896:M 12 Jun 2024 22:37:12.056 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 22:37:12.057 * Background saving started by pid 62229
-62229:C 12 Jun 2024 22:37:12.071 * DB saved on disk
-62229:C 12 Jun 2024 22:37:12.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 22:37:12.159 * Background saving terminated with success
-58896:M 12 Jun 2024 22:42:13.023 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 22:42:13.025 * Background saving started by pid 63085
-63085:C 12 Jun 2024 22:42:13.036 * DB saved on disk
-63085:C 12 Jun 2024 22:42:13.036 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 22:42:13.126 * Background saving terminated with success
-58896:M 12 Jun 2024 22:47:14.042 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 22:47:14.043 * Background saving started by pid 63867
-63867:C 12 Jun 2024 22:47:14.052 * DB saved on disk
-63867:C 12 Jun 2024 22:47:14.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 22:47:14.145 * Background saving terminated with success
-58896:M 12 Jun 2024 22:52:15.071 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 22:52:15.075 * Background saving started by pid 64674
-64674:C 12 Jun 2024 22:52:15.106 * DB saved on disk
-64674:C 12 Jun 2024 22:52:15.106 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 22:52:15.177 * Background saving terminated with success
-58896:M 12 Jun 2024 22:57:16.052 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 22:57:16.053 * Background saving started by pid 65510
-65510:C 12 Jun 2024 22:57:16.071 * DB saved on disk
-65510:C 12 Jun 2024 22:57:16.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 22:57:16.155 * Background saving terminated with success
-58896:M 12 Jun 2024 23:02:17.039 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 23:02:17.043 * Background saving started by pid 66374
-66374:C 12 Jun 2024 23:02:17.059 * DB saved on disk
-66374:C 12 Jun 2024 23:02:17.061 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 23:02:17.145 * Background saving terminated with success
-58896:M 12 Jun 2024 23:07:18.063 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 23:07:18.065 * Background saving started by pid 69899
-69899:C 12 Jun 2024 23:07:18.097 * DB saved on disk
-69899:C 12 Jun 2024 23:07:18.097 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 23:07:18.166 * Background saving terminated with success
-58896:M 12 Jun 2024 23:12:19.010 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 23:12:19.013 * Background saving started by pid 72751
-72751:C 12 Jun 2024 23:12:19.025 * DB saved on disk
-72751:C 12 Jun 2024 23:12:19.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 23:12:19.114 * Background saving terminated with success
-58896:M 12 Jun 2024 23:17:20.093 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 23:17:20.097 * Background saving started by pid 73511
-73511:C 12 Jun 2024 23:17:20.117 * DB saved on disk
-73511:C 12 Jun 2024 23:17:20.118 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 23:17:20.199 * Background saving terminated with success
-58896:M 12 Jun 2024 23:22:21.096 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 23:22:21.098 * Background saving started by pid 74274
-74274:C 12 Jun 2024 23:22:21.108 * DB saved on disk
-74274:C 12 Jun 2024 23:22:21.115 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 23:22:21.199 * Background saving terminated with success
-58896:M 12 Jun 2024 23:27:22.041 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 23:27:22.044 * Background saving started by pid 75086
-75086:C 12 Jun 2024 23:27:22.055 * DB saved on disk
-75086:C 12 Jun 2024 23:27:22.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 23:27:22.145 * Background saving terminated with success
-58896:M 12 Jun 2024 23:32:23.057 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 23:32:23.059 * Background saving started by pid 75910
-75910:C 12 Jun 2024 23:32:23.070 * DB saved on disk
-75910:C 12 Jun 2024 23:32:23.075 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 23:32:23.161 * Background saving terminated with success
-58896:M 12 Jun 2024 23:37:24.000 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 23:37:24.002 * Background saving started by pid 76753
-76753:C 12 Jun 2024 23:37:24.021 * DB saved on disk
-76753:C 12 Jun 2024 23:37:24.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 23:37:24.102 * Background saving terminated with success
-58896:M 12 Jun 2024 23:42:25.039 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 23:42:25.041 * Background saving started by pid 77566
-77566:C 12 Jun 2024 23:42:25.051 * DB saved on disk
-77566:C 12 Jun 2024 23:42:25.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 23:42:25.142 * Background saving terminated with success
-58896:M 12 Jun 2024 23:47:26.048 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 23:47:26.051 * Background saving started by pid 78392
-78392:C 12 Jun 2024 23:47:26.058 * DB saved on disk
-78392:C 12 Jun 2024 23:47:26.059 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 23:47:26.152 * Background saving terminated with success
-58896:M 12 Jun 2024 23:52:27.035 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 23:52:27.036 * Background saving started by pid 79236
-79236:C 12 Jun 2024 23:52:27.047 * DB saved on disk
-79236:C 12 Jun 2024 23:52:27.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 23:52:27.137 * Background saving terminated with success
-58896:M 12 Jun 2024 23:57:28.011 * 100 changes in 300 seconds. Saving...
-58896:M 12 Jun 2024 23:57:28.017 * Background saving started by pid 80013
-80013:C 12 Jun 2024 23:57:28.026 * DB saved on disk
-80013:C 12 Jun 2024 23:57:28.027 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 12 Jun 2024 23:57:28.118 * Background saving terminated with success
-58896:M 13 Jun 2024 00:02:29.041 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 00:02:29.043 * Background saving started by pid 80854
-80854:C 13 Jun 2024 00:02:29.061 * DB saved on disk
-80854:C 13 Jun 2024 00:02:29.061 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 00:02:29.145 * Background saving terminated with success
-58896:M 13 Jun 2024 00:07:30.083 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 00:07:30.088 * Background saving started by pid 81692
-81692:C 13 Jun 2024 00:07:30.100 * DB saved on disk
-81692:C 13 Jun 2024 00:07:30.100 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 00:07:30.190 * Background saving terminated with success
-58896:M 13 Jun 2024 00:12:31.037 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 00:12:31.038 * Background saving started by pid 82630
-82630:C 13 Jun 2024 00:12:31.048 * DB saved on disk
-82630:C 13 Jun 2024 00:12:31.049 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 00:12:31.139 * Background saving terminated with success
-58896:M 13 Jun 2024 00:18:24.490 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 00:18:24.492 * Background saving started by pid 83628
-83628:C 13 Jun 2024 00:18:24.500 * DB saved on disk
-83628:C 13 Jun 2024 00:18:24.501 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 00:18:24.594 * Background saving terminated with success
-58896:M 13 Jun 2024 00:23:25.099 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 00:23:25.101 * Background saving started by pid 84426
-84426:C 13 Jun 2024 00:23:25.116 * DB saved on disk
-84426:C 13 Jun 2024 00:23:25.119 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 00:23:25.203 * Background saving terminated with success
-58896:M 13 Jun 2024 00:28:26.000 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 00:28:26.003 * Background saving started by pid 85490
-85490:C 13 Jun 2024 00:28:26.011 * DB saved on disk
-85490:C 13 Jun 2024 00:28:26.012 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 00:28:26.104 * Background saving terminated with success
-58896:M 13 Jun 2024 00:39:55.401 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 00:39:55.402 * Background saving started by pid 87583
-87583:C 13 Jun 2024 00:39:55.410 * DB saved on disk
-87583:C 13 Jun 2024 00:39:55.410 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 00:39:55.504 * Background saving terminated with success
-58896:M 13 Jun 2024 00:44:56.013 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 00:44:56.017 * Background saving started by pid 88517
-88517:C 13 Jun 2024 00:44:56.027 * DB saved on disk
-88517:C 13 Jun 2024 00:44:56.028 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 00:44:56.118 * Background saving terminated with success
-58896:M 13 Jun 2024 00:49:57.055 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 00:49:57.058 * Background saving started by pid 89534
-89534:C 13 Jun 2024 00:49:57.066 * DB saved on disk
-89534:C 13 Jun 2024 00:49:57.067 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 00:49:57.159 * Background saving terminated with success
-58896:M 13 Jun 2024 00:54:58.054 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 00:54:58.056 * Background saving started by pid 92884
-92884:C 13 Jun 2024 00:54:58.076 * DB saved on disk
-92884:C 13 Jun 2024 00:54:58.079 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 00:54:58.156 * Background saving terminated with success
-58896:M 13 Jun 2024 00:59:59.094 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 00:59:59.096 * Background saving started by pid 97454
-97454:C 13 Jun 2024 00:59:59.105 * DB saved on disk
-97454:C 13 Jun 2024 00:59:59.106 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 00:59:59.197 * Background saving terminated with success
-58896:M 13 Jun 2024 01:05:00.065 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 01:05:00.067 * Background saving started by pid 208
-208:C 13 Jun 2024 01:05:00.075 * DB saved on disk
-208:C 13 Jun 2024 01:05:00.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 01:05:00.168 * Background saving terminated with success
-58896:M 13 Jun 2024 01:10:01.004 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 01:10:01.007 * Background saving started by pid 1501
-1501:C 13 Jun 2024 01:10:01.016 * DB saved on disk
-1501:C 13 Jun 2024 01:10:01.017 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 01:10:01.108 * Background saving terminated with success
-58896:M 13 Jun 2024 01:15:02.069 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 01:15:02.073 * Background saving started by pid 8174
-8174:C 13 Jun 2024 01:15:02.087 * DB saved on disk
-8174:C 13 Jun 2024 01:15:02.088 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 01:15:02.176 * Background saving terminated with success
-58896:M 13 Jun 2024 01:20:03.007 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 01:20:03.009 * Background saving started by pid 12151
-12151:C 13 Jun 2024 01:20:03.021 * DB saved on disk
-12151:C 13 Jun 2024 01:20:03.022 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 01:20:03.111 * Background saving terminated with success
-58896:M 13 Jun 2024 01:25:04.011 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 01:25:04.014 * Background saving started by pid 14019
-14019:C 13 Jun 2024 01:25:04.027 * DB saved on disk
-14019:C 13 Jun 2024 01:25:04.028 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 01:25:04.115 * Background saving terminated with success
-58896:M 13 Jun 2024 01:30:05.029 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 01:30:05.032 * Background saving started by pid 15236
-15236:C 13 Jun 2024 01:30:05.048 * DB saved on disk
-15236:C 13 Jun 2024 01:30:05.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 01:30:05.134 * Background saving terminated with success
-58896:M 13 Jun 2024 01:46:26.863 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 01:46:26.987 * Background saving started by pid 16271
-16271:C 13 Jun 2024 01:46:26.997 * DB saved on disk
-16271:C 13 Jun 2024 01:46:26.997 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 01:46:27.096 * Background saving terminated with success
-58896:M 13 Jun 2024 02:53:37.566 * 1 changes in 3600 seconds. Saving...
-58896:M 13 Jun 2024 02:53:37.687 * Background saving started by pid 16361
-16361:C 13 Jun 2024 02:53:37.704 * DB saved on disk
-16361:C 13 Jun 2024 02:53:37.704 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 02:53:37.795 * Background saving terminated with success
-58896:M 13 Jun 2024 04:00:22.582 * 1 changes in 3600 seconds. Saving...
-58896:M 13 Jun 2024 04:00:22.694 * Background saving started by pid 16480
-16480:C 13 Jun 2024 04:00:22.703 * DB saved on disk
-16480:C 13 Jun 2024 04:00:22.704 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 04:00:22.807 * Background saving terminated with success
-58896:M 13 Jun 2024 05:06:58.599 * 1 changes in 3600 seconds. Saving...
-58896:M 13 Jun 2024 05:06:58.605 * Background saving started by pid 16586
-16586:C 13 Jun 2024 05:06:58.718 * DB saved on disk
-16586:C 13 Jun 2024 05:06:58.718 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 05:06:58.809 * Background saving terminated with success
-58896:M 13 Jun 2024 06:11:18.604 * 1 changes in 3600 seconds. Saving...
-58896:M 13 Jun 2024 06:11:18.613 * Background saving started by pid 16651
-16651:C 13 Jun 2024 06:11:18.714 * DB saved on disk
-16651:C 13 Jun 2024 06:11:18.746 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 06:11:18.815 * Background saving terminated with success
-58896:M 13 Jun 2024 07:14:47.525 * 1 changes in 3600 seconds. Saving...
-58896:M 13 Jun 2024 07:14:47.528 * Background saving started by pid 17811
-17811:C 13 Jun 2024 07:14:47.679 * DB saved on disk
-17811:C 13 Jun 2024 07:14:47.680 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 07:14:47.730 * Background saving terminated with success
-58896:M 13 Jun 2024 08:23:04.569 * 1 changes in 3600 seconds. Saving...
-58896:M 13 Jun 2024 08:23:04.574 * Background saving started by pid 17919
-17919:C 13 Jun 2024 08:23:04.680 * DB saved on disk
-17919:C 13 Jun 2024 08:23:04.680 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 08:23:04.775 * Background saving terminated with success
-58896:M 13 Jun 2024 09:29:23.578 * 1 changes in 3600 seconds. Saving...
-58896:M 13 Jun 2024 09:29:23.582 * Background saving started by pid 18003
-18003:C 13 Jun 2024 09:29:23.701 * DB saved on disk
-18003:C 13 Jun 2024 09:29:23.701 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 09:29:23.787 * Background saving terminated with success
-58896:M 13 Jun 2024 09:50:20.002 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 09:50:20.002 * Background saving started by pid 18192
-18192:C 13 Jun 2024 09:50:20.013 * DB saved on disk
-18192:C 13 Jun 2024 09:50:20.016 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 09:50:20.103 * Background saving terminated with success
-58896:M 13 Jun 2024 10:26:10.581 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 10:26:10.583 * Background saving started by pid 18369
-18369:C 13 Jun 2024 10:26:10.602 * DB saved on disk
-18369:C 13 Jun 2024 10:26:10.602 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 10:26:10.684 * Background saving terminated with success
-58896:M 13 Jun 2024 10:55:26.571 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 10:55:26.574 * Background saving started by pid 18571
-18571:C 13 Jun 2024 10:55:26.591 * DB saved on disk
-18571:C 13 Jun 2024 10:55:26.591 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 10:55:26.676 * Background saving terminated with success
-58896:M 13 Jun 2024 11:29:14.641 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 11:29:14.644 * Background saving started by pid 18818
-18818:C 13 Jun 2024 11:29:14.664 * DB saved on disk
-18818:C 13 Jun 2024 11:29:14.665 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 11:29:14.746 * Background saving terminated with success
-58896:M 13 Jun 2024 11:34:15.063 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 11:34:15.065 * Background saving started by pid 20038
-20038:C 13 Jun 2024 11:34:15.082 * DB saved on disk
-20038:C 13 Jun 2024 11:34:15.084 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 11:34:15.167 * Background saving terminated with success
-58896:M 13 Jun 2024 11:39:16.050 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 11:39:16.052 * Background saving started by pid 21202
-21202:C 13 Jun 2024 11:39:16.063 * DB saved on disk
-21202:C 13 Jun 2024 11:39:16.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 11:39:16.153 * Background saving terminated with success
-58896:M 13 Jun 2024 11:44:17.031 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 11:44:17.033 * Background saving started by pid 22048
-22048:C 13 Jun 2024 11:44:17.047 * DB saved on disk
-22048:C 13 Jun 2024 11:44:17.049 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 11:44:17.135 * Background saving terminated with success
-58896:M 13 Jun 2024 11:49:18.051 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 11:49:18.056 * Background saving started by pid 22821
-22821:C 13 Jun 2024 11:49:18.073 * DB saved on disk
-22821:C 13 Jun 2024 11:49:18.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 11:49:18.159 * Background saving terminated with success
-58896:M 13 Jun 2024 11:54:19.040 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 11:54:19.043 * Background saving started by pid 23590
-23590:C 13 Jun 2024 11:54:19.061 * DB saved on disk
-23590:C 13 Jun 2024 11:54:19.062 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 11:54:19.145 * Background saving terminated with success
-58896:M 13 Jun 2024 11:59:20.034 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 11:59:20.038 * Background saving started by pid 24369
-24369:C 13 Jun 2024 11:59:20.055 * DB saved on disk
-24369:C 13 Jun 2024 11:59:20.056 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 11:59:20.141 * Background saving terminated with success
-58896:M 13 Jun 2024 12:04:21.013 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 12:04:21.015 * Background saving started by pid 25145
-25145:C 13 Jun 2024 12:04:21.033 * DB saved on disk
-25145:C 13 Jun 2024 12:04:21.034 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 12:04:21.117 * Background saving terminated with success
-58896:M 13 Jun 2024 12:09:22.032 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 12:09:22.035 * Background saving started by pid 25919
-25919:C 13 Jun 2024 12:09:22.049 * DB saved on disk
-25919:C 13 Jun 2024 12:09:22.052 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 12:09:22.138 * Background saving terminated with success
-58896:M 13 Jun 2024 12:14:23.054 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 12:14:23.058 * Background saving started by pid 26708
-26708:C 13 Jun 2024 12:14:23.070 * DB saved on disk
-26708:C 13 Jun 2024 12:14:23.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 12:14:23.160 * Background saving terminated with success
-58896:M 13 Jun 2024 12:22:08.623 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 12:22:08.625 * Background saving started by pid 26869
-26869:C 13 Jun 2024 12:22:08.638 * DB saved on disk
-26869:C 13 Jun 2024 12:22:08.640 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 12:22:08.727 * Background saving terminated with success
-58896:M 13 Jun 2024 13:22:56.523 * 1 changes in 3600 seconds. Saving...
-58896:M 13 Jun 2024 13:22:56.527 * Background saving started by pid 26934
-26934:C 13 Jun 2024 13:22:56.637 * DB saved on disk
-26934:C 13 Jun 2024 13:22:56.638 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 13:22:56.730 * Background saving terminated with success
-58896:M 13 Jun 2024 13:54:40.777 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 13:54:40.779 * Background saving started by pid 27164
-27164:C 13 Jun 2024 13:54:40.788 * DB saved on disk
-27164:C 13 Jun 2024 13:54:40.789 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 13:54:40.881 * Background saving terminated with success
-58896:M 13 Jun 2024 13:59:41.086 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 13:59:41.091 * Background saving started by pid 28401
-28401:C 13 Jun 2024 13:59:41.125 * DB saved on disk
-28401:C 13 Jun 2024 13:59:41.125 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 13:59:41.193 * Background saving terminated with success
-58896:M 13 Jun 2024 14:04:42.086 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 14:04:42.089 * Background saving started by pid 29364
-29364:C 13 Jun 2024 14:04:42.106 * DB saved on disk
-29364:C 13 Jun 2024 14:04:42.108 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 14:04:42.191 * Background saving terminated with success
-58896:M 13 Jun 2024 14:09:43.039 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 14:09:43.040 * Background saving started by pid 30343
-30343:C 13 Jun 2024 14:09:43.050 * DB saved on disk
-30343:C 13 Jun 2024 14:09:43.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 14:09:43.141 * Background saving terminated with success
-58896:M 13 Jun 2024 14:14:44.095 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 14:14:44.098 * Background saving started by pid 31113
-31113:C 13 Jun 2024 14:14:44.106 * DB saved on disk
-31113:C 13 Jun 2024 14:14:44.107 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 14:14:44.199 * Background saving terminated with success
-58896:M 13 Jun 2024 14:19:45.073 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 14:19:45.077 * Background saving started by pid 31876
-31876:C 13 Jun 2024 14:19:45.105 * DB saved on disk
-31876:C 13 Jun 2024 14:19:45.106 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 14:19:45.181 * Background saving terminated with success
-58896:M 13 Jun 2024 14:24:46.099 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 14:24:46.103 * Background saving started by pid 32589
-32589:C 13 Jun 2024 14:24:46.117 * DB saved on disk
-32589:C 13 Jun 2024 14:24:46.119 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 14:24:46.204 * Background saving terminated with success
-58896:M 13 Jun 2024 14:29:47.029 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 14:29:47.035 * Background saving started by pid 33317
-33317:C 13 Jun 2024 14:29:47.050 * DB saved on disk
-33317:C 13 Jun 2024 14:29:47.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 14:29:47.137 * Background saving terminated with success
-58896:M 13 Jun 2024 14:34:48.042 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 14:34:48.044 * Background saving started by pid 34025
-34025:C 13 Jun 2024 14:34:48.057 * DB saved on disk
-34025:C 13 Jun 2024 14:34:48.060 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 14:34:48.147 * Background saving terminated with success
-58896:M 13 Jun 2024 14:39:49.001 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 14:39:49.002 * Background saving started by pid 34740
-34740:C 13 Jun 2024 14:39:49.011 * DB saved on disk
-34740:C 13 Jun 2024 14:39:49.011 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 14:39:49.104 * Background saving terminated with success
-58896:M 13 Jun 2024 14:44:50.083 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 14:44:50.086 * Background saving started by pid 35603
-35603:C 13 Jun 2024 14:44:50.098 * DB saved on disk
-35603:C 13 Jun 2024 14:44:50.101 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 14:44:50.188 * Background saving terminated with success
-58896:M 13 Jun 2024 14:49:51.042 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 14:49:51.045 * Background saving started by pid 36239
-36239:C 13 Jun 2024 14:49:51.057 * DB saved on disk
-36239:C 13 Jun 2024 14:49:51.059 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 14:49:51.146 * Background saving terminated with success
-58896:M 13 Jun 2024 14:54:52.029 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 14:54:52.032 * Background saving started by pid 36857
-36857:C 13 Jun 2024 14:54:52.066 * DB saved on disk
-36857:C 13 Jun 2024 14:54:52.067 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 14:54:52.134 * Background saving terminated with success
-58896:M 13 Jun 2024 14:59:53.033 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 14:59:53.036 * Background saving started by pid 37538
-37538:C 13 Jun 2024 14:59:53.051 * DB saved on disk
-37538:C 13 Jun 2024 14:59:53.052 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 14:59:53.138 * Background saving terminated with success
-58896:M 13 Jun 2024 15:04:54.044 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 15:04:54.046 * Background saving started by pid 38152
-38152:C 13 Jun 2024 15:04:54.067 * DB saved on disk
-38152:C 13 Jun 2024 15:04:54.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 15:04:54.148 * Background saving terminated with success
-58896:M 13 Jun 2024 15:09:55.087 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 15:09:55.092 * Background saving started by pid 38789
-38789:C 13 Jun 2024 15:09:55.104 * DB saved on disk
-38789:C 13 Jun 2024 15:09:55.104 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 15:09:55.196 * Background saving terminated with success
-58896:M 13 Jun 2024 15:14:56.035 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 15:14:56.037 * Background saving started by pid 39411
-39411:C 13 Jun 2024 15:14:56.050 * DB saved on disk
-39411:C 13 Jun 2024 15:14:56.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 15:14:56.138 * Background saving terminated with success
-58896:M 13 Jun 2024 15:19:57.033 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 15:19:57.034 * Background saving started by pid 40025
-40025:C 13 Jun 2024 15:19:57.043 * DB saved on disk
-40025:C 13 Jun 2024 15:19:57.044 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 15:19:57.135 * Background saving terminated with success
-58896:M 13 Jun 2024 15:24:58.059 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 15:24:58.061 * Background saving started by pid 40708
-40708:C 13 Jun 2024 15:24:58.082 * DB saved on disk
-40708:C 13 Jun 2024 15:24:58.082 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 15:24:58.162 * Background saving terminated with success
-58896:M 13 Jun 2024 15:29:59.058 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 15:29:59.061 * Background saving started by pid 41970
-41970:C 13 Jun 2024 15:29:59.074 * DB saved on disk
-41970:C 13 Jun 2024 15:29:59.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 15:29:59.162 * Background saving terminated with success
-58896:M 13 Jun 2024 15:35:00.020 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 15:35:00.023 * Background saving started by pid 46158
-46158:C 13 Jun 2024 15:35:00.046 * DB saved on disk
-46158:C 13 Jun 2024 15:35:00.047 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 15:35:00.124 * Background saving terminated with success
-58896:M 13 Jun 2024 15:40:01.046 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 15:40:01.049 * Background saving started by pid 52688
-52688:C 13 Jun 2024 15:40:01.069 * DB saved on disk
-52688:C 13 Jun 2024 15:40:01.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 15:40:01.150 * Background saving terminated with success
-58896:M 13 Jun 2024 15:45:53.815 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 15:45:53.818 * Background saving started by pid 53374
-53374:C 13 Jun 2024 15:45:53.840 * DB saved on disk
-53374:C 13 Jun 2024 15:45:53.840 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 15:45:53.919 * Background saving terminated with success
-58896:M 13 Jun 2024 15:50:54.050 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 15:50:54.052 * Background saving started by pid 54742
-54742:C 13 Jun 2024 15:50:54.072 * DB saved on disk
-54742:C 13 Jun 2024 15:50:54.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 15:50:54.153 * Background saving terminated with success
-58896:M 13 Jun 2024 15:55:55.100 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 15:55:55.103 * Background saving started by pid 55431
-55431:C 13 Jun 2024 15:55:55.114 * DB saved on disk
-55431:C 13 Jun 2024 15:55:55.114 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 15:55:55.204 * Background saving terminated with success
-58896:M 13 Jun 2024 16:00:56.039 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 16:00:56.042 * Background saving started by pid 56109
-56109:C 13 Jun 2024 16:00:56.051 * DB saved on disk
-56109:C 13 Jun 2024 16:00:56.052 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 16:00:56.144 * Background saving terminated with success
-58896:M 13 Jun 2024 16:05:57.027 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 16:05:57.029 * Background saving started by pid 56794
-56794:C 13 Jun 2024 16:05:57.044 * DB saved on disk
-56794:C 13 Jun 2024 16:05:57.045 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 16:05:57.130 * Background saving terminated with success
-58896:M 13 Jun 2024 16:10:58.007 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 16:10:58.010 * Background saving started by pid 57424
-57424:C 13 Jun 2024 16:10:58.022 * DB saved on disk
-57424:C 13 Jun 2024 16:10:58.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 16:10:58.111 * Background saving terminated with success
-58896:M 13 Jun 2024 16:15:59.064 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 16:15:59.067 * Background saving started by pid 58114
-58114:C 13 Jun 2024 16:15:59.076 * DB saved on disk
-58114:C 13 Jun 2024 16:15:59.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 16:15:59.168 * Background saving terminated with success
-58896:M 13 Jun 2024 16:21:00.080 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 16:21:00.081 * Background saving started by pid 58865
-58865:C 13 Jun 2024 16:21:00.090 * DB saved on disk
-58865:C 13 Jun 2024 16:21:00.091 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 16:21:00.182 * Background saving terminated with success
-58896:M 13 Jun 2024 16:26:01.042 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 16:26:01.045 * Background saving started by pid 59640
-59640:C 13 Jun 2024 16:26:01.059 * DB saved on disk
-59640:C 13 Jun 2024 16:26:01.060 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 16:26:01.145 * Background saving terminated with success
-58896:M 13 Jun 2024 16:31:02.024 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 16:31:02.026 * Background saving started by pid 60306
-60306:C 13 Jun 2024 16:31:02.037 * DB saved on disk
-60306:C 13 Jun 2024 16:31:02.038 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 16:31:02.127 * Background saving terminated with success
-58896:M 13 Jun 2024 16:36:03.023 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 16:36:03.027 * Background saving started by pid 60889
-60889:C 13 Jun 2024 16:36:03.056 * DB saved on disk
-60889:C 13 Jun 2024 16:36:03.056 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 16:36:03.128 * Background saving terminated with success
-58896:M 13 Jun 2024 16:41:04.062 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 16:41:04.065 * Background saving started by pid 61499
-61499:C 13 Jun 2024 16:41:04.084 * DB saved on disk
-61499:C 13 Jun 2024 16:41:04.085 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 16:41:04.167 * Background saving terminated with success
-58896:M 13 Jun 2024 16:46:05.062 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 16:46:05.064 * Background saving started by pid 62153
-62153:C 13 Jun 2024 16:46:05.078 * DB saved on disk
-62153:C 13 Jun 2024 16:46:05.079 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 16:46:05.165 * Background saving terminated with success
-58896:M 13 Jun 2024 16:51:06.057 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 16:51:06.058 * Background saving started by pid 62839
-62839:C 13 Jun 2024 16:51:06.067 * DB saved on disk
-62839:C 13 Jun 2024 16:51:06.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 16:51:06.159 * Background saving terminated with success
-58896:M 13 Jun 2024 16:56:07.041 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 16:56:07.044 * Background saving started by pid 63527
-63527:C 13 Jun 2024 16:56:07.063 * DB saved on disk
-63527:C 13 Jun 2024 16:56:07.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 16:56:07.144 * Background saving terminated with success
-58896:M 13 Jun 2024 17:01:08.073 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 17:01:08.074 * Background saving started by pid 64189
-64189:C 13 Jun 2024 17:01:08.088 * DB saved on disk
-64189:C 13 Jun 2024 17:01:08.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 17:01:08.175 * Background saving terminated with success
-58896:M 13 Jun 2024 17:06:09.002 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 17:06:09.003 * Background saving started by pid 64802
-64802:C 13 Jun 2024 17:06:09.018 * DB saved on disk
-64802:C 13 Jun 2024 17:06:09.021 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 17:06:09.104 * Background saving terminated with success
-58896:M 13 Jun 2024 17:11:10.042 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 17:11:10.046 * Background saving started by pid 65468
-65468:C 13 Jun 2024 17:11:10.069 * DB saved on disk
-65468:C 13 Jun 2024 17:11:10.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 17:11:10.148 * Background saving terminated with success
-58896:M 13 Jun 2024 17:16:11.098 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 17:16:11.099 * Background saving started by pid 66110
-66110:C 13 Jun 2024 17:16:11.108 * DB saved on disk
-66110:C 13 Jun 2024 17:16:11.109 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 17:16:11.201 * Background saving terminated with success
-58896:M 13 Jun 2024 17:21:12.003 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 17:21:12.006 * Background saving started by pid 66847
-66847:C 13 Jun 2024 17:21:12.018 * DB saved on disk
-66847:C 13 Jun 2024 17:21:12.019 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 17:21:12.111 * Background saving terminated with success
-58896:M 13 Jun 2024 17:26:13.038 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 17:26:13.041 * Background saving started by pid 67587
-67587:C 13 Jun 2024 17:26:13.051 * DB saved on disk
-67587:C 13 Jun 2024 17:26:13.052 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 17:26:13.142 * Background saving terminated with success
-58896:M 13 Jun 2024 17:31:14.027 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 17:31:14.028 * Background saving started by pid 68487
-68487:C 13 Jun 2024 17:31:14.036 * DB saved on disk
-68487:C 13 Jun 2024 17:31:14.038 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 17:31:14.130 * Background saving terminated with success
-58896:M 13 Jun 2024 17:36:15.062 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 17:36:15.064 * Background saving started by pid 69205
-69205:C 13 Jun 2024 17:36:15.071 * DB saved on disk
-69205:C 13 Jun 2024 17:36:15.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 17:36:15.174 * Background saving terminated with success
-58896:M 13 Jun 2024 17:41:16.100 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 17:41:16.101 * Background saving started by pid 70157
-70157:C 13 Jun 2024 17:41:16.117 * DB saved on disk
-70157:C 13 Jun 2024 17:41:16.118 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 17:41:16.203 * Background saving terminated with success
-58896:M 13 Jun 2024 17:46:17.062 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 17:46:17.064 * Background saving started by pid 70947
-70947:C 13 Jun 2024 17:46:17.078 * DB saved on disk
-70947:C 13 Jun 2024 17:46:17.080 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 17:46:17.165 * Background saving terminated with success
-58896:M 13 Jun 2024 17:51:18.076 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 17:51:18.077 * Background saving started by pid 71675
-71675:C 13 Jun 2024 17:51:18.086 * DB saved on disk
-71675:C 13 Jun 2024 17:51:18.090 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 17:51:18.179 * Background saving terminated with success
-58896:M 13 Jun 2024 17:56:19.041 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 17:56:19.042 * Background saving started by pid 72514
-72514:C 13 Jun 2024 17:56:19.052 * DB saved on disk
-72514:C 13 Jun 2024 17:56:19.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 17:56:19.144 * Background saving terminated with success
-58896:M 13 Jun 2024 18:01:20.068 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 18:01:20.069 * Background saving started by pid 73367
-73367:C 13 Jun 2024 18:01:20.084 * DB saved on disk
-73367:C 13 Jun 2024 18:01:20.085 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 18:01:20.171 * Background saving terminated with success
-58896:M 13 Jun 2024 18:06:21.045 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 18:06:21.048 * Background saving started by pid 74221
-74221:C 13 Jun 2024 18:06:21.057 * DB saved on disk
-74221:C 13 Jun 2024 18:06:21.062 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 18:06:21.148 * Background saving terminated with success
-58896:M 13 Jun 2024 18:11:22.084 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 18:11:22.088 * Background saving started by pid 75044
-75044:C 13 Jun 2024 18:11:22.111 * DB saved on disk
-75044:C 13 Jun 2024 18:11:22.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 18:11:22.189 * Background saving terminated with success
-58896:M 13 Jun 2024 18:16:23.041 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 18:16:23.042 * Background saving started by pid 76027
-76027:C 13 Jun 2024 18:16:23.057 * DB saved on disk
-76027:C 13 Jun 2024 18:16:23.058 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 18:16:23.143 * Background saving terminated with success
-58896:M 13 Jun 2024 18:21:24.069 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 18:21:24.073 * Background saving started by pid 77094
-77094:C 13 Jun 2024 18:21:24.084 * DB saved on disk
-77094:C 13 Jun 2024 18:21:24.086 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 18:21:24.175 * Background saving terminated with success
-58896:M 13 Jun 2024 18:26:25.068 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 18:26:25.073 * Background saving started by pid 78140
-78140:C 13 Jun 2024 18:26:25.088 * DB saved on disk
-78140:C 13 Jun 2024 18:26:25.091 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 18:26:25.174 * Background saving terminated with success
-58896:M 13 Jun 2024 18:31:26.038 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 18:31:26.041 * Background saving started by pid 79121
-79121:C 13 Jun 2024 18:31:26.053 * DB saved on disk
-79121:C 13 Jun 2024 18:31:26.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 18:31:26.142 * Background saving terminated with success
-58896:M 13 Jun 2024 18:36:27.010 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 18:36:27.013 * Background saving started by pid 80053
-80053:C 13 Jun 2024 18:36:27.025 * DB saved on disk
-80053:C 13 Jun 2024 18:36:27.026 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 18:36:27.115 * Background saving terminated with success
-58896:M 13 Jun 2024 18:41:28.100 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 18:41:28.101 * Background saving started by pid 81033
-81033:C 13 Jun 2024 18:41:28.110 * DB saved on disk
-81033:C 13 Jun 2024 18:41:28.114 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 18:41:28.203 * Background saving terminated with success
-58896:M 13 Jun 2024 18:46:29.056 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 18:46:29.058 * Background saving started by pid 82024
-82024:C 13 Jun 2024 18:46:29.068 * DB saved on disk
-82024:C 13 Jun 2024 18:46:29.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 18:46:29.159 * Background saving terminated with success
-58896:M 13 Jun 2024 18:51:30.025 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 18:51:30.026 * Background saving started by pid 83031
-83031:C 13 Jun 2024 18:51:30.040 * DB saved on disk
-83031:C 13 Jun 2024 18:51:30.041 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 18:51:30.128 * Background saving terminated with success
-58896:M 13 Jun 2024 18:56:31.028 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 18:56:31.032 * Background saving started by pid 84257
-84257:C 13 Jun 2024 18:56:31.061 * DB saved on disk
-84257:C 13 Jun 2024 18:56:31.061 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 18:56:31.133 * Background saving terminated with success
-58896:M 13 Jun 2024 19:01:32.042 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 19:01:32.045 * Background saving started by pid 85971
-85971:C 13 Jun 2024 19:01:32.057 * DB saved on disk
-85971:C 13 Jun 2024 19:01:32.058 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 19:01:32.146 * Background saving terminated with success
-58896:M 13 Jun 2024 19:06:33.000 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 19:06:33.004 * Background saving started by pid 87445
-87445:C 13 Jun 2024 19:06:33.015 * DB saved on disk
-87445:C 13 Jun 2024 19:06:33.016 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 19:06:33.106 * Background saving terminated with success
-58896:M 13 Jun 2024 19:24:03.583 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 19:24:03.704 * Background saving started by pid 88482
-88482:C 13 Jun 2024 19:24:03.712 * DB saved on disk
-88482:C 13 Jun 2024 19:24:03.715 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 19:24:03.820 * Background saving terminated with success
-58896:M 13 Jun 2024 19:29:04.018 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 19:29:04.020 * Background saving started by pid 89605
-89605:C 13 Jun 2024 19:29:04.035 * DB saved on disk
-89605:C 13 Jun 2024 19:29:04.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 19:29:04.122 * Background saving terminated with success
-58896:M 13 Jun 2024 19:34:05.068 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 19:34:05.071 * Background saving started by pid 90623
-90623:C 13 Jun 2024 19:34:05.084 * DB saved on disk
-90623:C 13 Jun 2024 19:34:05.085 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 19:34:05.172 * Background saving terminated with success
-58896:M 13 Jun 2024 19:39:06.039 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 19:39:06.040 * Background saving started by pid 91649
-91649:C 13 Jun 2024 19:39:06.050 * DB saved on disk
-91649:C 13 Jun 2024 19:39:06.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 19:39:06.141 * Background saving terminated with success
-58896:M 13 Jun 2024 19:44:07.052 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 19:44:07.053 * Background saving started by pid 92499
-92499:C 13 Jun 2024 19:44:07.066 * DB saved on disk
-92499:C 13 Jun 2024 19:44:07.067 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 19:44:07.154 * Background saving terminated with success
-58896:M 13 Jun 2024 19:49:08.026 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 19:49:08.029 * Background saving started by pid 93255
-93255:C 13 Jun 2024 19:49:08.047 * DB saved on disk
-93255:C 13 Jun 2024 19:49:08.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 19:49:08.130 * Background saving terminated with success
-58896:M 13 Jun 2024 20:09:23.789 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 20:09:23.790 * Background saving started by pid 93455
-93455:C 13 Jun 2024 20:09:23.800 * DB saved on disk
-93455:C 13 Jun 2024 20:09:23.802 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 20:09:23.891 * Background saving terminated with success
-58896:M 13 Jun 2024 21:00:24.763 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 21:00:24.764 * Background saving started by pid 93545
-93545:C 13 Jun 2024 21:00:24.776 * DB saved on disk
-93545:C 13 Jun 2024 21:00:24.777 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 21:00:24.867 * Background saving terminated with success
-58896:M 13 Jun 2024 21:09:07.599 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 21:09:07.601 * Background saving started by pid 93647
-93647:C 13 Jun 2024 21:09:07.614 * DB saved on disk
-93647:C 13 Jun 2024 21:09:07.616 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 21:09:07.702 * Background saving terminated with success
-58896:M 13 Jun 2024 22:15:01.689 * 1 changes in 3600 seconds. Saving...
-58896:M 13 Jun 2024 22:15:01.693 * Background saving started by pid 93707
-93707:C 13 Jun 2024 22:15:01.819 * DB saved on disk
-93707:C 13 Jun 2024 22:15:01.820 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 22:15:01.896 * Background saving terminated with success
-58896:M 13 Jun 2024 22:28:49.316 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 22:28:49.319 * Background saving started by pid 93877
-93877:C 13 Jun 2024 22:28:49.333 * DB saved on disk
-93877:C 13 Jun 2024 22:28:49.334 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 22:28:49.421 * Background saving terminated with success
-58896:M 13 Jun 2024 23:38:03.790 * 1 changes in 3600 seconds. Saving...
-58896:M 13 Jun 2024 23:38:03.794 * Background saving started by pid 94043
-94043:C 13 Jun 2024 23:38:03.905 * DB saved on disk
-94043:C 13 Jun 2024 23:38:03.908 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 23:38:03.997 * Background saving terminated with success
-58896:M 13 Jun 2024 23:43:04.085 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 23:43:04.087 * Background saving started by pid 94614
-94614:C 13 Jun 2024 23:43:04.094 * DB saved on disk
-94614:C 13 Jun 2024 23:43:04.095 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 23:43:04.189 * Background saving terminated with success
-58896:M 13 Jun 2024 23:48:05.057 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 23:48:05.063 * Background saving started by pid 95362
-95362:C 13 Jun 2024 23:48:05.086 * DB saved on disk
-95362:C 13 Jun 2024 23:48:05.087 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 23:48:05.165 * Background saving terminated with success
-58896:M 13 Jun 2024 23:53:06.041 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 23:53:06.042 * Background saving started by pid 96187
-96187:C 13 Jun 2024 23:53:06.060 * DB saved on disk
-96187:C 13 Jun 2024 23:53:06.061 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 23:53:06.143 * Background saving terminated with success
-58896:M 13 Jun 2024 23:58:07.014 * 100 changes in 300 seconds. Saving...
-58896:M 13 Jun 2024 23:58:07.017 * Background saving started by pid 97027
-97027:C 13 Jun 2024 23:58:07.030 * DB saved on disk
-97027:C 13 Jun 2024 23:58:07.030 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 13 Jun 2024 23:58:07.118 * Background saving terminated with success
-58896:M 14 Jun 2024 00:03:08.062 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 00:03:08.067 * Background saving started by pid 98055
-98055:C 14 Jun 2024 00:03:08.076 * DB saved on disk
-98055:C 14 Jun 2024 00:03:08.076 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 00:03:08.169 * Background saving terminated with success
-58896:M 14 Jun 2024 00:08:09.046 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 00:08:09.048 * Background saving started by pid 906
-906:C 14 Jun 2024 00:08:09.066 * DB saved on disk
-906:C 14 Jun 2024 00:08:09.068 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 00:08:09.149 * Background saving terminated with success
-58896:M 14 Jun 2024 00:13:10.017 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 00:13:10.021 * Background saving started by pid 2698
-2698:C 14 Jun 2024 00:13:10.034 * DB saved on disk
-2698:C 14 Jun 2024 00:13:10.038 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 00:13:10.122 * Background saving terminated with success
-58896:M 14 Jun 2024 00:18:11.011 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 00:18:11.013 * Background saving started by pid 3689
-3689:C 14 Jun 2024 00:18:11.022 * DB saved on disk
-3689:C 14 Jun 2024 00:18:11.023 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 00:18:11.114 * Background saving terminated with success
-58896:M 14 Jun 2024 00:23:12.093 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 00:23:12.094 * Background saving started by pid 4450
-4450:C 14 Jun 2024 00:23:12.105 * DB saved on disk
-4450:C 14 Jun 2024 00:23:12.105 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 00:23:12.196 * Background saving terminated with success
-58896:M 14 Jun 2024 00:28:13.053 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 00:28:13.055 * Background saving started by pid 5238
-5238:C 14 Jun 2024 00:28:13.065 * DB saved on disk
-5238:C 14 Jun 2024 00:28:13.065 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 00:28:13.156 * Background saving terminated with success
-58896:M 14 Jun 2024 00:33:14.055 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 00:33:14.057 * Background saving started by pid 6001
-6001:C 14 Jun 2024 00:33:14.066 * DB saved on disk
-6001:C 14 Jun 2024 00:33:14.067 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 00:33:14.158 * Background saving terminated with success
-58896:M 14 Jun 2024 00:38:15.023 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 00:38:15.025 * Background saving started by pid 6720
-6720:C 14 Jun 2024 00:38:15.034 * DB saved on disk
-6720:C 14 Jun 2024 00:38:15.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 00:38:15.126 * Background saving terminated with success
-58896:M 14 Jun 2024 00:43:16.074 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 00:43:16.077 * Background saving started by pid 7544
-7544:C 14 Jun 2024 00:43:16.090 * DB saved on disk
-7544:C 14 Jun 2024 00:43:16.091 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 00:43:16.179 * Background saving terminated with success
-58896:M 14 Jun 2024 00:48:17.008 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 00:48:17.009 * Background saving started by pid 8362
-8362:C 14 Jun 2024 00:48:17.018 * DB saved on disk
-8362:C 14 Jun 2024 00:48:17.020 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 00:48:17.110 * Background saving terminated with success
-58896:M 14 Jun 2024 00:53:18.083 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 00:53:18.086 * Background saving started by pid 9627
-9627:C 14 Jun 2024 00:53:18.094 * DB saved on disk
-9627:C 14 Jun 2024 00:53:18.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 00:53:18.187 * Background saving terminated with success
-58896:M 14 Jun 2024 00:58:19.056 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 00:58:19.058 * Background saving started by pid 10541
-10541:C 14 Jun 2024 00:58:19.068 * DB saved on disk
-10541:C 14 Jun 2024 00:58:19.069 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 00:58:19.160 * Background saving terminated with success
-58896:M 14 Jun 2024 01:03:20.085 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 01:03:20.088 * Background saving started by pid 11472
-11472:C 14 Jun 2024 01:03:20.099 * DB saved on disk
-11472:C 14 Jun 2024 01:03:20.112 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 01:03:20.189 * Background saving terminated with success
-58896:M 14 Jun 2024 01:08:21.081 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 01:08:21.083 * Background saving started by pid 12462
-12462:C 14 Jun 2024 01:08:21.094 * DB saved on disk
-12462:C 14 Jun 2024 01:08:21.095 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 01:08:21.184 * Background saving terminated with success
-58896:M 14 Jun 2024 01:13:22.058 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 01:13:22.059 * Background saving started by pid 13399
-13399:C 14 Jun 2024 01:13:22.069 * DB saved on disk
-13399:C 14 Jun 2024 01:13:22.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 01:13:22.161 * Background saving terminated with success
-58896:M 14 Jun 2024 01:18:23.041 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 01:18:23.042 * Background saving started by pid 14393
-14393:C 14 Jun 2024 01:18:23.054 * DB saved on disk
-14393:C 14 Jun 2024 01:18:23.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 01:18:23.142 * Background saving terminated with success
-58896:M 14 Jun 2024 01:23:24.029 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 01:23:24.031 * Background saving started by pid 15307
-15307:C 14 Jun 2024 01:23:24.047 * DB saved on disk
-15307:C 14 Jun 2024 01:23:24.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 01:23:24.132 * Background saving terminated with success
-58896:M 14 Jun 2024 01:28:25.054 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 01:28:25.057 * Background saving started by pid 16245
-16245:C 14 Jun 2024 01:28:25.067 * DB saved on disk
-16245:C 14 Jun 2024 01:28:25.068 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 01:28:25.158 * Background saving terminated with success
-58896:M 14 Jun 2024 01:33:26.047 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 01:33:26.051 * Background saving started by pid 17193
-17193:C 14 Jun 2024 01:33:26.081 * DB saved on disk
-17193:C 14 Jun 2024 01:33:26.081 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 01:33:26.153 * Background saving terminated with success
-58896:M 14 Jun 2024 01:38:27.082 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 01:38:27.084 * Background saving started by pid 18123
-18123:C 14 Jun 2024 01:38:27.100 * DB saved on disk
-18123:C 14 Jun 2024 01:38:27.101 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 01:38:27.185 * Background saving terminated with success
-58896:M 14 Jun 2024 01:43:28.063 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 01:43:28.065 * Background saving started by pid 20679
-20679:C 14 Jun 2024 01:43:28.076 * DB saved on disk
-20679:C 14 Jun 2024 01:43:28.080 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 01:43:28.172 * Background saving terminated with success
-58896:M 14 Jun 2024 01:48:29.019 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 01:48:29.024 * Background saving started by pid 21789
-21789:C 14 Jun 2024 01:48:29.033 * DB saved on disk
-21789:C 14 Jun 2024 01:48:29.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 01:48:29.125 * Background saving terminated with success
-58896:M 14 Jun 2024 01:53:30.004 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 01:53:30.005 * Background saving started by pid 22821
-22821:C 14 Jun 2024 01:53:30.021 * DB saved on disk
-22821:C 14 Jun 2024 01:53:30.023 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 01:53:30.108 * Background saving terminated with success
-58896:M 14 Jun 2024 01:58:31.072 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 01:58:31.072 * Background saving started by pid 23761
-23761:C 14 Jun 2024 01:58:31.081 * DB saved on disk
-23761:C 14 Jun 2024 01:58:31.082 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 01:58:31.175 * Background saving terminated with success
-58896:M 14 Jun 2024 02:03:32.084 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 02:03:32.087 * Background saving started by pid 24691
-24691:C 14 Jun 2024 02:03:32.112 * DB saved on disk
-24691:C 14 Jun 2024 02:03:32.115 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 02:03:32.188 * Background saving terminated with success
-58896:M 14 Jun 2024 02:08:33.012 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 02:08:33.013 * Background saving started by pid 25657
-25657:C 14 Jun 2024 02:08:33.023 * DB saved on disk
-25657:C 14 Jun 2024 02:08:33.024 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 02:08:33.116 * Background saving terminated with success
-58896:M 14 Jun 2024 02:13:34.062 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 02:13:34.065 * Background saving started by pid 26619
-26619:C 14 Jun 2024 02:13:34.082 * DB saved on disk
-26619:C 14 Jun 2024 02:13:34.084 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 02:13:34.168 * Background saving terminated with success
-58896:M 14 Jun 2024 02:18:35.092 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 02:18:35.095 * Background saving started by pid 27572
-27572:C 14 Jun 2024 02:18:35.110 * DB saved on disk
-27572:C 14 Jun 2024 02:18:35.112 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 02:18:35.197 * Background saving terminated with success
-58896:M 14 Jun 2024 02:23:36.006 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 02:23:36.010 * Background saving started by pid 28517
-28517:C 14 Jun 2024 02:23:36.030 * DB saved on disk
-28517:C 14 Jun 2024 02:23:36.031 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 02:23:36.111 * Background saving terminated with success
-58896:M 14 Jun 2024 02:28:37.011 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 02:28:37.014 * Background saving started by pid 29471
-29471:C 14 Jun 2024 02:28:37.025 * DB saved on disk
-29471:C 14 Jun 2024 02:28:37.025 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 02:28:37.115 * Background saving terminated with success
-58896:M 14 Jun 2024 02:33:38.046 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 02:33:38.050 * Background saving started by pid 30459
-30459:C 14 Jun 2024 02:33:38.067 * DB saved on disk
-30459:C 14 Jun 2024 02:33:38.068 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 02:33:38.154 * Background saving terminated with success
-58896:M 14 Jun 2024 02:38:39.080 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 02:38:39.086 * Background saving started by pid 31408
-31408:C 14 Jun 2024 02:38:39.107 * DB saved on disk
-31408:C 14 Jun 2024 02:38:39.108 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 02:38:39.188 * Background saving terminated with success
-58896:M 14 Jun 2024 02:43:40.090 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 02:43:40.093 * Background saving started by pid 32516
-32516:C 14 Jun 2024 02:43:40.103 * DB saved on disk
-32516:C 14 Jun 2024 02:43:40.103 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 02:43:40.195 * Background saving terminated with success
-58896:M 14 Jun 2024 02:48:41.094 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 02:48:41.098 * Background saving started by pid 33530
-33530:C 14 Jun 2024 02:48:41.110 * DB saved on disk
-33530:C 14 Jun 2024 02:48:41.113 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 02:48:41.200 * Background saving terminated with success
-58896:M 14 Jun 2024 02:53:42.065 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 02:53:42.068 * Background saving started by pid 34481
-34481:C 14 Jun 2024 02:53:42.097 * DB saved on disk
-34481:C 14 Jun 2024 02:53:42.098 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 02:53:42.170 * Background saving terminated with success
-58896:M 14 Jun 2024 03:02:19.016 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 03:02:19.020 * Background saving started by pid 35216
-35216:C 14 Jun 2024 03:02:19.144 * DB saved on disk
-35216:C 14 Jun 2024 03:02:19.144 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 03:02:19.223 * Background saving terminated with success
-58896:M 14 Jun 2024 04:09:00.287 * 1 changes in 3600 seconds. Saving...
-58896:M 14 Jun 2024 04:09:00.391 * Background saving started by pid 35351
-35351:C 14 Jun 2024 04:09:00.406 * DB saved on disk
-35351:C 14 Jun 2024 04:09:00.406 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 04:09:00.491 * Background saving terminated with success
-58896:M 14 Jun 2024 05:13:19.442 * 1 changes in 3600 seconds. Saving...
-58896:M 14 Jun 2024 05:13:19.559 * Background saving started by pid 35460
-35460:C 14 Jun 2024 05:13:19.566 * DB saved on disk
-35460:C 14 Jun 2024 05:13:19.567 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 05:13:19.680 * Background saving terminated with success
-58896:M 14 Jun 2024 05:45:38.251 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 05:45:38.253 * Background saving started by pid 35614
-35614:C 14 Jun 2024 05:45:38.263 * DB saved on disk
-35614:C 14 Jun 2024 05:45:38.264 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 05:45:38.354 * Background saving terminated with success
-58896:M 14 Jun 2024 06:06:43.153 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 06:06:43.156 * Background saving started by pid 35774
-35774:C 14 Jun 2024 06:06:43.171 * DB saved on disk
-35774:C 14 Jun 2024 06:06:43.172 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 06:06:43.259 * Background saving terminated with success
-58896:M 14 Jun 2024 06:44:23.413 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 06:44:23.415 * Background saving started by pid 35935
-35935:C 14 Jun 2024 06:44:23.428 * DB saved on disk
-35935:C 14 Jun 2024 06:44:23.428 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 06:44:23.516 * Background saving terminated with success
-58896:M 14 Jun 2024 07:48:29.341 * 1 changes in 3600 seconds. Saving...
-58896:M 14 Jun 2024 07:48:29.456 * Background saving started by pid 35997
-35997:C 14 Jun 2024 07:48:29.466 * DB saved on disk
-35997:C 14 Jun 2024 07:48:29.467 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 07:48:29.558 * Background saving terminated with success
-58896:M 14 Jun 2024 08:56:33.414 * 1 changes in 3600 seconds. Saving...
-58896:M 14 Jun 2024 08:56:33.530 * Background saving started by pid 36114
-36114:C 14 Jun 2024 08:56:33.539 * DB saved on disk
-36114:C 14 Jun 2024 08:56:33.539 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 08:56:33.632 * Background saving terminated with success
-58896:M 14 Jun 2024 09:59:37.399 * 1 changes in 3600 seconds. Saving...
-58896:M 14 Jun 2024 09:59:37.511 * Background saving started by pid 36190
-36190:C 14 Jun 2024 09:59:37.519 * DB saved on disk
-36190:C 14 Jun 2024 09:59:37.520 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 09:59:37.613 * Background saving terminated with success
-58896:M 14 Jun 2024 10:07:14.898 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 10:07:14.901 * Background saving started by pid 36472
-36472:C 14 Jun 2024 10:07:14.910 * DB saved on disk
-36472:C 14 Jun 2024 10:07:14.912 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 10:07:15.001 * Background saving terminated with success
-58896:M 14 Jun 2024 10:12:16.014 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 10:12:16.016 * Background saving started by pid 37638
-37638:C 14 Jun 2024 10:12:16.026 * DB saved on disk
-37638:C 14 Jun 2024 10:12:16.027 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 10:12:16.118 * Background saving terminated with success
-58896:M 14 Jun 2024 10:17:17.028 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 10:17:17.033 * Background saving started by pid 38618
-38618:C 14 Jun 2024 10:17:17.051 * DB saved on disk
-38618:C 14 Jun 2024 10:17:17.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 10:17:17.134 * Background saving terminated with success
-58896:M 14 Jun 2024 10:22:18.071 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 10:22:18.073 * Background saving started by pid 39601
-39601:C 14 Jun 2024 10:22:18.085 * DB saved on disk
-39601:C 14 Jun 2024 10:22:18.087 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 10:22:18.175 * Background saving terminated with success
-58896:M 14 Jun 2024 10:28:04.538 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 10:28:04.541 * Background saving started by pid 40107
-40107:C 14 Jun 2024 10:28:04.659 * DB saved on disk
-40107:C 14 Jun 2024 10:28:04.659 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 10:28:04.743 * Background saving terminated with success
-58896:M 14 Jun 2024 11:30:27.751 * 1 changes in 3600 seconds. Saving...
-58896:M 14 Jun 2024 11:30:27.759 * Background saving started by pid 40350
-40350:C 14 Jun 2024 11:30:27.857 * DB saved on disk
-40350:C 14 Jun 2024 11:30:27.859 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 11:30:27.860 * Background saving terminated with success
-58896:M 14 Jun 2024 12:16:13.786 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 12:16:13.787 * Background saving started by pid 40834
-40834:C 14 Jun 2024 12:16:13.796 * DB saved on disk
-40834:C 14 Jun 2024 12:16:13.797 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 12:16:13.888 * Background saving terminated with success
-58896:M 14 Jun 2024 12:21:14.062 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 12:21:14.063 * Background saving started by pid 42185
-42185:C 14 Jun 2024 12:21:14.071 * DB saved on disk
-42185:C 14 Jun 2024 12:21:14.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 12:21:14.165 * Background saving terminated with success
-58896:M 14 Jun 2024 12:58:11.704 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 12:58:11.706 * Background saving started by pid 50751
-50751:C 14 Jun 2024 12:58:11.762 * DB saved on disk
-50751:C 14 Jun 2024 12:58:11.763 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 12:58:11.809 * Background saving terminated with success
-58896:M 14 Jun 2024 14:00:57.749 * 1 changes in 3600 seconds. Saving...
-58896:M 14 Jun 2024 14:00:57.755 * Background saving started by pid 52756
-52756:C 14 Jun 2024 14:00:57.876 * DB saved on disk
-52756:C 14 Jun 2024 14:00:57.879 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 14:00:57.967 * Background saving terminated with success
-58896:M 14 Jun 2024 14:15:30.354 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 14:15:30.357 * Background saving started by pid 55569
-55569:C 14 Jun 2024 14:15:30.369 * DB saved on disk
-55569:C 14 Jun 2024 14:15:30.369 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 14:15:30.459 * Background saving terminated with success
-58896:M 14 Jun 2024 14:20:31.034 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 14:20:31.036 * Background saving started by pid 56310
-56310:C 14 Jun 2024 14:20:31.046 * DB saved on disk
-56310:C 14 Jun 2024 14:20:31.047 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 14:20:31.137 * Background saving terminated with success
-58896:M 14 Jun 2024 14:25:32.056 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 14:25:32.057 * Background saving started by pid 57022
-57022:C 14 Jun 2024 14:25:32.069 * DB saved on disk
-57022:C 14 Jun 2024 14:25:32.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 14:25:32.159 * Background saving terminated with success
-58896:M 14 Jun 2024 14:30:33.093 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 14:30:33.095 * Background saving started by pid 57937
-57937:C 14 Jun 2024 14:30:33.106 * DB saved on disk
-57937:C 14 Jun 2024 14:30:33.109 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 14:30:33.196 * Background saving terminated with success
-58896:M 14 Jun 2024 14:35:34.000 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 14:35:34.002 * Background saving started by pid 58663
-58663:C 14 Jun 2024 14:35:34.013 * DB saved on disk
-58663:C 14 Jun 2024 14:35:34.013 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 14:35:34.104 * Background saving terminated with success
-58896:M 14 Jun 2024 14:40:35.079 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 14:40:35.079 * Background saving started by pid 59354
-59354:C 14 Jun 2024 14:40:35.092 * DB saved on disk
-59354:C 14 Jun 2024 14:40:35.094 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 14:40:35.181 * Background saving terminated with success
-58896:M 14 Jun 2024 14:45:36.026 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 14:45:36.030 * Background saving started by pid 60066
-60066:C 14 Jun 2024 14:45:36.047 * DB saved on disk
-60066:C 14 Jun 2024 14:45:36.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 14:45:36.133 * Background saving terminated with success
-58896:M 14 Jun 2024 14:50:37.021 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 14:50:37.025 * Background saving started by pid 60762
-60762:C 14 Jun 2024 14:50:37.046 * DB saved on disk
-60762:C 14 Jun 2024 14:50:37.047 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 14:50:37.127 * Background saving terminated with success
-58896:M 14 Jun 2024 14:55:38.054 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 14:55:38.055 * Background saving started by pid 61516
-61516:C 14 Jun 2024 14:55:38.066 * DB saved on disk
-61516:C 14 Jun 2024 14:55:38.068 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 14:55:38.157 * Background saving terminated with success
-58896:M 14 Jun 2024 15:00:39.013 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 15:00:39.016 * Background saving started by pid 63393
-63393:C 14 Jun 2024 15:00:39.026 * DB saved on disk
-63393:C 14 Jun 2024 15:00:39.027 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 15:00:39.117 * Background saving terminated with success
-58896:M 14 Jun 2024 15:05:40.086 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 15:05:40.089 * Background saving started by pid 64106
-64106:C 14 Jun 2024 15:05:40.109 * DB saved on disk
-64106:C 14 Jun 2024 15:05:40.111 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 15:05:40.189 * Background saving terminated with success
-58896:M 14 Jun 2024 15:10:41.056 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 15:10:41.058 * Background saving started by pid 64797
-64797:C 14 Jun 2024 15:10:41.073 * DB saved on disk
-64797:C 14 Jun 2024 15:10:41.073 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 15:10:41.161 * Background saving terminated with success
-58896:M 14 Jun 2024 15:15:42.057 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 15:15:42.059 * Background saving started by pid 65518
-65518:C 14 Jun 2024 15:15:42.074 * DB saved on disk
-65518:C 14 Jun 2024 15:15:42.075 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 15:15:42.162 * Background saving terminated with success
-58896:M 14 Jun 2024 15:20:43.021 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 15:20:43.023 * Background saving started by pid 66505
-66505:C 14 Jun 2024 15:20:43.037 * DB saved on disk
-66505:C 14 Jun 2024 15:20:43.039 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 15:20:43.124 * Background saving terminated with success
-58896:M 14 Jun 2024 15:25:44.025 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 15:25:44.029 * Background saving started by pid 67388
-67388:C 14 Jun 2024 15:25:44.040 * DB saved on disk
-67388:C 14 Jun 2024 15:25:44.041 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 15:25:44.131 * Background saving terminated with success
-58896:M 14 Jun 2024 15:30:45.027 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 15:30:45.029 * Background saving started by pid 68256
-68256:C 14 Jun 2024 15:30:45.053 * DB saved on disk
-68256:C 14 Jun 2024 15:30:45.054 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 15:30:45.130 * Background saving terminated with success
-58896:M 14 Jun 2024 15:35:46.077 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 15:35:46.078 * Background saving started by pid 69268
-69268:C 14 Jun 2024 15:35:46.088 * DB saved on disk
-69268:C 14 Jun 2024 15:35:46.089 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 15:35:46.179 * Background saving terminated with success
-58896:M 14 Jun 2024 15:40:47.084 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 15:40:47.088 * Background saving started by pid 70026
-70026:C 14 Jun 2024 15:40:47.108 * DB saved on disk
-70026:C 14 Jun 2024 15:40:47.110 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 15:40:47.190 * Background saving terminated with success
-58896:M 14 Jun 2024 15:45:48.012 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 15:45:48.019 * Background saving started by pid 71012
-71012:C 14 Jun 2024 15:45:48.028 * DB saved on disk
-71012:C 14 Jun 2024 15:45:48.029 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 15:45:48.120 * Background saving terminated with success
-58896:M 14 Jun 2024 15:50:49.082 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 15:50:49.085 * Background saving started by pid 71940
-71940:C 14 Jun 2024 15:50:49.097 * DB saved on disk
-71940:C 14 Jun 2024 15:50:49.098 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 15:50:49.186 * Background saving terminated with success
-58896:M 14 Jun 2024 15:55:50.015 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 15:55:50.017 * Background saving started by pid 72898
-72898:C 14 Jun 2024 15:55:50.030 * DB saved on disk
-72898:C 14 Jun 2024 15:55:50.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 15:55:50.118 * Background saving terminated with success
-58896:M 14 Jun 2024 16:00:51.081 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 16:00:51.085 * Background saving started by pid 73649
-73649:C 14 Jun 2024 16:00:51.098 * DB saved on disk
-73649:C 14 Jun 2024 16:00:51.101 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 16:00:51.186 * Background saving terminated with success
-58896:M 14 Jun 2024 16:05:52.071 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 16:05:52.072 * Background saving started by pid 74480
-74480:C 14 Jun 2024 16:05:52.082 * DB saved on disk
-74480:C 14 Jun 2024 16:05:52.083 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 16:05:52.174 * Background saving terminated with success
-58896:M 14 Jun 2024 16:10:53.035 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 16:10:53.038 * Background saving started by pid 75372
-75372:C 14 Jun 2024 16:10:53.054 * DB saved on disk
-75372:C 14 Jun 2024 16:10:53.058 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 16:10:53.139 * Background saving terminated with success
-58896:M 14 Jun 2024 16:15:54.086 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 16:15:54.089 * Background saving started by pid 76156
-76156:C 14 Jun 2024 16:15:54.114 * DB saved on disk
-76156:C 14 Jun 2024 16:15:54.115 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 16:15:54.190 * Background saving terminated with success
-58896:M 14 Jun 2024 16:20:55.033 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 16:20:55.035 * Background saving started by pid 76928
-76928:C 14 Jun 2024 16:20:55.053 * DB saved on disk
-76928:C 14 Jun 2024 16:20:55.053 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 16:20:55.137 * Background saving terminated with success
-58896:M 14 Jun 2024 16:25:56.061 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 16:25:56.062 * Background saving started by pid 77910
-77910:C 14 Jun 2024 16:25:56.073 * DB saved on disk
-77910:C 14 Jun 2024 16:25:56.073 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 16:25:56.164 * Background saving terminated with success
-58896:M 14 Jun 2024 16:30:57.004 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 16:30:57.006 * Background saving started by pid 78693
-78693:C 14 Jun 2024 16:30:57.017 * DB saved on disk
-78693:C 14 Jun 2024 16:30:57.019 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 16:30:57.108 * Background saving terminated with success
-58896:M 14 Jun 2024 16:35:58.062 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 16:35:58.064 * Background saving started by pid 79546
-79546:C 14 Jun 2024 16:35:58.076 * DB saved on disk
-79546:C 14 Jun 2024 16:35:58.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 16:35:58.165 * Background saving terminated with success
-58896:M 14 Jun 2024 16:40:59.088 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 16:40:59.089 * Background saving started by pid 80305
-80305:C 14 Jun 2024 16:40:59.101 * DB saved on disk
-80305:C 14 Jun 2024 16:40:59.104 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 16:40:59.190 * Background saving terminated with success
-58896:M 14 Jun 2024 16:46:00.058 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 16:46:00.059 * Background saving started by pid 81213
-81213:C 14 Jun 2024 16:46:00.070 * DB saved on disk
-81213:C 14 Jun 2024 16:46:00.072 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 16:46:00.160 * Background saving terminated with success
-58896:M 14 Jun 2024 16:51:01.096 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 16:51:01.097 * Background saving started by pid 82247
-82247:C 14 Jun 2024 16:51:01.106 * DB saved on disk
-82247:C 14 Jun 2024 16:51:01.107 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 16:51:01.199 * Background saving terminated with success
-58896:M 14 Jun 2024 16:56:02.068 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 16:56:02.072 * Background saving started by pid 83296
-83296:C 14 Jun 2024 16:56:02.085 * DB saved on disk
-83296:C 14 Jun 2024 16:56:02.086 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 16:56:02.173 * Background saving terminated with success
-58896:M 14 Jun 2024 17:01:03.003 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 17:01:03.004 * Background saving started by pid 84035
-84035:C 14 Jun 2024 17:01:03.014 * DB saved on disk
-84035:C 14 Jun 2024 17:01:03.015 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 17:01:03.105 * Background saving terminated with success
-58896:M 14 Jun 2024 17:06:04.035 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 17:06:04.037 * Background saving started by pid 84625
-84625:C 14 Jun 2024 17:06:04.049 * DB saved on disk
-84625:C 14 Jun 2024 17:06:04.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 17:06:04.138 * Background saving terminated with success
-58896:M 14 Jun 2024 17:11:05.017 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 17:11:05.020 * Background saving started by pid 85292
-85292:C 14 Jun 2024 17:11:05.034 * DB saved on disk
-85292:C 14 Jun 2024 17:11:05.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 17:11:05.121 * Background saving terminated with success
-58896:M 14 Jun 2024 17:16:06.081 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 17:16:06.083 * Background saving started by pid 85873
-85873:C 14 Jun 2024 17:16:06.091 * DB saved on disk
-85873:C 14 Jun 2024 17:16:06.092 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 17:16:06.184 * Background saving terminated with success
-58896:M 14 Jun 2024 17:21:07.000 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 17:21:07.001 * Background saving started by pid 86432
-86432:C 14 Jun 2024 17:21:07.014 * DB saved on disk
-86432:C 14 Jun 2024 17:21:07.014 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 17:21:07.102 * Background saving terminated with success
-58896:M 14 Jun 2024 17:26:08.044 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 17:26:08.047 * Background saving started by pid 87015
-87015:C 14 Jun 2024 17:26:08.076 * DB saved on disk
-87015:C 14 Jun 2024 17:26:08.077 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 17:26:08.149 * Background saving terminated with success
-58896:M 14 Jun 2024 17:31:09.060 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 17:31:09.061 * Background saving started by pid 87646
-87646:C 14 Jun 2024 17:31:09.069 * DB saved on disk
-87646:C 14 Jun 2024 17:31:09.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 17:31:09.163 * Background saving terminated with success
-58896:M 14 Jun 2024 17:36:10.011 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 17:36:10.013 * Background saving started by pid 88231
-88231:C 14 Jun 2024 17:36:10.024 * DB saved on disk
-88231:C 14 Jun 2024 17:36:10.024 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 17:36:10.114 * Background saving terminated with success
-58896:M 14 Jun 2024 17:41:11.048 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 17:41:11.055 * Background saving started by pid 88988
-88988:C 14 Jun 2024 17:41:11.063 * DB saved on disk
-88988:C 14 Jun 2024 17:41:11.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 17:41:11.156 * Background saving terminated with success
-58896:M 14 Jun 2024 17:46:12.064 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 17:46:12.065 * Background saving started by pid 89661
-89661:C 14 Jun 2024 17:46:12.078 * DB saved on disk
-89661:C 14 Jun 2024 17:46:12.079 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 17:46:12.166 * Background saving terminated with success
-58896:M 14 Jun 2024 17:51:13.041 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 17:51:13.043 * Background saving started by pid 90249
-90249:C 14 Jun 2024 17:51:13.055 * DB saved on disk
-90249:C 14 Jun 2024 17:51:13.057 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 17:51:13.144 * Background saving terminated with success
-58896:M 14 Jun 2024 17:56:14.019 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 17:56:14.022 * Background saving started by pid 91032
-91032:C 14 Jun 2024 17:56:14.035 * DB saved on disk
-91032:C 14 Jun 2024 17:56:14.035 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 17:56:14.123 * Background saving terminated with success
-58896:M 14 Jun 2024 18:01:15.037 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 18:01:15.039 * Background saving started by pid 91806
-91806:C 14 Jun 2024 18:01:15.055 * DB saved on disk
-91806:C 14 Jun 2024 18:01:15.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 18:01:15.141 * Background saving terminated with success
-58896:M 14 Jun 2024 18:06:16.057 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 18:06:16.060 * Background saving started by pid 92444
-92444:C 14 Jun 2024 18:06:16.073 * DB saved on disk
-92444:C 14 Jun 2024 18:06:16.074 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 18:06:16.160 * Background saving terminated with success
-58896:M 14 Jun 2024 18:11:17.059 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 18:11:17.063 * Background saving started by pid 93096
-93096:C 14 Jun 2024 18:11:17.080 * DB saved on disk
-93096:C 14 Jun 2024 18:11:17.081 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 18:11:17.164 * Background saving terminated with success
-58896:M 14 Jun 2024 18:16:18.080 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 18:16:18.083 * Background saving started by pid 93766
-93766:C 14 Jun 2024 18:16:18.094 * DB saved on disk
-93766:C 14 Jun 2024 18:16:18.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 18:16:18.184 * Background saving terminated with success
-58896:M 14 Jun 2024 18:21:19.098 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 18:21:19.102 * Background saving started by pid 94418
-94418:C 14 Jun 2024 18:21:19.118 * DB saved on disk
-94418:C 14 Jun 2024 18:21:19.121 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 18:21:19.204 * Background saving terminated with success
-58896:M 14 Jun 2024 18:26:20.090 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 18:26:20.092 * Background saving started by pid 95153
-95153:C 14 Jun 2024 18:26:20.103 * DB saved on disk
-95153:C 14 Jun 2024 18:26:20.104 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 18:26:20.193 * Background saving terminated with success
-58896:M 14 Jun 2024 18:31:21.035 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 18:31:21.038 * Background saving started by pid 95904
-95904:C 14 Jun 2024 18:31:21.049 * DB saved on disk
-95904:C 14 Jun 2024 18:31:21.050 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 18:31:21.139 * Background saving terminated with success
-58896:M 14 Jun 2024 18:36:22.029 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 18:36:22.030 * Background saving started by pid 96493
-96493:C 14 Jun 2024 18:36:22.044 * DB saved on disk
-96493:C 14 Jun 2024 18:36:22.051 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 18:36:22.132 * Background saving terminated with success
-58896:M 14 Jun 2024 18:53:06.805 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 18:53:06.810 * Background saving started by pid 96635
-96635:C 14 Jun 2024 18:53:06.933 * DB saved on disk
-96635:C 14 Jun 2024 18:53:06.938 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 18:53:07.012 * Background saving terminated with success
-58896:M 14 Jun 2024 18:58:08.026 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 18:58:08.027 * Background saving started by pid 97243
-97243:C 14 Jun 2024 18:58:08.038 * DB saved on disk
-97243:C 14 Jun 2024 18:58:08.039 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 18:58:08.128 * Background saving terminated with success
-58896:M 14 Jun 2024 19:03:09.096 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 19:03:09.097 * Background saving started by pid 97816
-97816:C 14 Jun 2024 19:03:09.115 * DB saved on disk
-97816:C 14 Jun 2024 19:03:09.116 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 19:03:09.198 * Background saving terminated with success
-58896:M 14 Jun 2024 19:08:10.026 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 19:08:10.027 * Background saving started by pid 98403
-98403:C 14 Jun 2024 19:08:10.036 * DB saved on disk
-98403:C 14 Jun 2024 19:08:10.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 19:08:10.128 * Background saving terminated with success
-58896:M 14 Jun 2024 19:13:11.015 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 19:13:11.017 * Background saving started by pid 99088
-99088:C 14 Jun 2024 19:13:11.026 * DB saved on disk
-99088:C 14 Jun 2024 19:13:11.027 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 19:13:11.117 * Background saving terminated with success
-58896:M 14 Jun 2024 19:18:12.060 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 19:18:12.062 * Background saving started by pid 99654
-99654:C 14 Jun 2024 19:18:12.070 * DB saved on disk
-99654:C 14 Jun 2024 19:18:12.071 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 19:18:12.163 * Background saving terminated with success
-58896:M 14 Jun 2024 19:23:13.095 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 19:23:13.099 * Background saving started by pid 651
-651:C 14 Jun 2024 19:23:13.126 * DB saved on disk
-651:C 14 Jun 2024 19:23:13.126 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 19:23:13.200 * Background saving terminated with success
-58896:M 14 Jun 2024 19:28:14.064 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 19:28:14.066 * Background saving started by pid 1362
-1362:C 14 Jun 2024 19:28:14.074 * DB saved on disk
-1362:C 14 Jun 2024 19:28:14.075 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 19:28:14.168 * Background saving terminated with success
-58896:M 14 Jun 2024 19:33:15.065 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 19:33:15.067 * Background saving started by pid 1965
-1965:C 14 Jun 2024 19:33:15.095 * DB saved on disk
-1965:C 14 Jun 2024 19:33:15.096 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 19:33:15.168 * Background saving terminated with success
-58896:M 14 Jun 2024 19:38:16.019 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 19:38:16.020 * Background saving started by pid 2640
-2640:C 14 Jun 2024 19:38:16.036 * DB saved on disk
-2640:C 14 Jun 2024 19:38:16.037 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 19:38:16.121 * Background saving terminated with success
-58896:M 14 Jun 2024 19:43:17.065 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 19:43:17.067 * Background saving started by pid 3209
-3209:C 14 Jun 2024 19:43:17.082 * DB saved on disk
-3209:C 14 Jun 2024 19:43:17.083 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 19:43:17.168 * Background saving terminated with success
-58896:M 14 Jun 2024 19:48:18.083 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 19:48:18.084 * Background saving started by pid 3807
-3807:C 14 Jun 2024 19:48:18.092 * DB saved on disk
-3807:C 14 Jun 2024 19:48:18.092 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 19:48:18.185 * Background saving terminated with success
-58896:M 14 Jun 2024 19:53:19.091 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 19:53:19.093 * Background saving started by pid 4495
-4495:C 14 Jun 2024 19:53:19.105 * DB saved on disk
-4495:C 14 Jun 2024 19:53:19.106 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 19:53:19.194 * Background saving terminated with success
-58896:M 14 Jun 2024 19:53:36.373 * DB saved on disk
-58896:M 14 Jun 2024 19:58:37.006 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 19:58:37.008 * Background saving started by pid 5355
-5355:C 14 Jun 2024 19:58:37.023 * DB saved on disk
-5355:C 14 Jun 2024 19:58:37.024 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 19:58:37.110 * Background saving terminated with success
-58896:M 14 Jun 2024 20:03:38.062 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 20:03:38.068 * Background saving started by pid 5941
-5941:C 14 Jun 2024 20:03:38.079 * DB saved on disk
-5941:C 14 Jun 2024 20:03:38.085 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 20:03:38.169 * Background saving terminated with success
-58896:M 14 Jun 2024 20:08:39.052 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 20:08:39.054 * Background saving started by pid 6579
-6579:C 14 Jun 2024 20:08:39.063 * DB saved on disk
-6579:C 14 Jun 2024 20:08:39.064 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 20:08:39.155 * Background saving terminated with success
-58896:M 14 Jun 2024 20:13:40.010 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 20:13:40.014 * Background saving started by pid 7271
-7271:C 14 Jun 2024 20:13:40.054 * DB saved on disk
-7271:C 14 Jun 2024 20:13:40.055 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 20:13:40.117 * Background saving terminated with success
-58896:M 14 Jun 2024 20:15:25.868 * DB saved on disk
-58896:M 14 Jun 2024 20:20:26.073 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 20:20:26.075 * Background saving started by pid 8477
-8477:C 14 Jun 2024 20:20:26.083 * DB saved on disk
-8477:C 14 Jun 2024 20:20:26.083 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 20:20:26.176 * Background saving terminated with success
-58896:M 14 Jun 2024 20:25:27.045 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 20:25:27.048 * Background saving started by pid 9337
-9337:C 14 Jun 2024 20:25:27.059 * DB saved on disk
-9337:C 14 Jun 2024 20:25:27.060 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 20:25:27.149 * Background saving terminated with success
-58896:M 14 Jun 2024 20:30:28.053 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 20:30:28.054 * Background saving started by pid 10207
-10207:C 14 Jun 2024 20:30:28.063 * DB saved on disk
-10207:C 14 Jun 2024 20:30:28.063 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 20:30:28.155 * Background saving terminated with success
-58896:M 14 Jun 2024 20:33:51.791 * DB saved on disk
-58896:M 14 Jun 2024 20:36:02.959 * DB saved on disk
-58896:M 14 Jun 2024 20:41:03.035 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 20:41:03.039 * Background saving started by pid 12285
-12285:C 14 Jun 2024 20:41:03.048 * DB saved on disk
-12285:C 14 Jun 2024 20:41:03.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 20:41:03.139 * Background saving terminated with success
-58896:M 14 Jun 2024 20:46:04.007 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 20:46:04.009 * Background saving started by pid 13266
-13266:C 14 Jun 2024 20:46:04.016 * DB saved on disk
-13266:C 14 Jun 2024 20:46:04.018 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 20:46:04.110 * Background saving terminated with success
-58896:M 14 Jun 2024 20:51:05.080 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 20:51:05.082 * Background saving started by pid 14114
-14114:C 14 Jun 2024 20:51:05.090 * DB saved on disk
-14114:C 14 Jun 2024 20:51:05.090 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 20:51:05.183 * Background saving terminated with success
-58896:M 14 Jun 2024 20:56:06.040 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 20:56:06.042 * Background saving started by pid 15071
-15071:C 14 Jun 2024 20:56:06.050 * DB saved on disk
-15071:C 14 Jun 2024 20:56:06.063 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 20:56:06.143 * Background saving terminated with success
-58896:M 14 Jun 2024 21:01:07.036 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 21:01:07.038 * Background saving started by pid 16066
-16066:C 14 Jun 2024 21:01:07.046 * DB saved on disk
-16066:C 14 Jun 2024 21:01:07.047 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 21:01:07.140 * Background saving terminated with success
-58896:M 14 Jun 2024 21:06:08.027 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 21:06:08.029 * Background saving started by pid 17073
-17073:C 14 Jun 2024 21:06:08.048 * DB saved on disk
-17073:C 14 Jun 2024 21:06:08.048 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 21:06:08.129 * Background saving terminated with success
-58896:M 14 Jun 2024 21:11:09.048 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 21:11:09.052 * Background saving started by pid 18014
-18014:C 14 Jun 2024 21:11:09.063 * DB saved on disk
-18014:C 14 Jun 2024 21:11:09.065 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 21:11:09.153 * Background saving terminated with success
-58896:M 14 Jun 2024 21:16:10.061 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 21:16:10.062 * Background saving started by pid 18941
-18941:C 14 Jun 2024 21:16:10.072 * DB saved on disk
-18941:C 14 Jun 2024 21:16:10.074 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 21:16:10.162 * Background saving terminated with success
-58896:M 14 Jun 2024 21:21:11.025 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 21:21:11.031 * Background saving started by pid 19797
-19797:C 14 Jun 2024 21:21:11.065 * DB saved on disk
-19797:C 14 Jun 2024 21:21:11.066 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 21:21:11.131 * Background saving terminated with success
-58896:M 14 Jun 2024 21:26:12.051 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 21:26:12.054 * Background saving started by pid 20708
-20708:C 14 Jun 2024 21:26:12.065 * DB saved on disk
-20708:C 14 Jun 2024 21:26:12.066 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 21:26:12.154 * Background saving terminated with success
-58896:M 14 Jun 2024 21:31:13.042 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 21:31:13.045 * Background saving started by pid 21604
-21604:C 14 Jun 2024 21:31:13.060 * DB saved on disk
-21604:C 14 Jun 2024 21:31:13.061 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 21:31:13.146 * Background saving terminated with success
-58896:M 14 Jun 2024 21:36:14.095 * 100 changes in 300 seconds. Saving...
-58896:M 14 Jun 2024 21:36:14.097 * Background saving started by pid 22441
-22441:C 14 Jun 2024 21:36:14.107 * DB saved on disk
-22441:C 14 Jun 2024 21:36:14.108 * Fork CoW for RDB: current 0 MB, peak 0 MB, average 0 MB
-58896:M 14 Jun 2024 21:36:14.198 * Background saving terminated with success
-58896:signal-handler (1718372303) Received SIGTERM scheduling shutdown...
-58896:M 14 Jun 2024 21:38:23.499 # User requested shutdown...
-58896:M 14 Jun 2024 21:38:23.499 * Saving the final RDB snapshot before exiting.
-58896:M 14 Jun 2024 21:38:23.509 * DB saved on disk
-58896:M 14 Jun 2024 21:38:23.510 # Redis is now ready to exit, bye bye...
From 9f996ab501889b608690b71b265a576ec0a5c5d4 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 19 Jun 2024 17:07:13 +0800
Subject: [PATCH 170/282] [CoreEngine] when the deploy master reports finished
status, we should not stop the message center and status center.
---
.../scheduler_core/status_manager_protocols.py | 16 +++++++++-------
1 file changed, 9 insertions(+), 7 deletions(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index ac3d8c3cb3..68b40b3291 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -136,15 +136,17 @@ def process_job_completed_status(self, master_id, status):
# self.stop_cloud_server()
# self.remove_listener_for_run_metrics(self.run_id)
# self.remove_listener_for_run_logs(self.run_id)
- self.message_center.receive_message(
- GeneralConstants.get_topic_complete_job(master_id),
- json.dumps(GeneralConstants.get_payload_complete_job(self.run_id, master_id)))
- if self.status_center.is_deployment_status_center and status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
- self.report_deployment_status(self.run_id, GeneralConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
+ if self.status_center.is_deployment_status_center:
+ if status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
+ self.report_deployment_status(self.run_id, GeneralConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
+ else:
+ self.message_center.receive_message(
+ GeneralConstants.get_topic_complete_job(master_id),
+ json.dumps(GeneralConstants.get_payload_complete_job(self.run_id, master_id)))
- self.message_center.stop_message_center()
- self.status_center.stop_status_center()
+ self.message_center.stop_message_center()
+ self.status_center.stop_status_center()
def process_job_exception_status(self, master_id, status):
# Report exception job status
From f28adeabfcf4e7b2f7e9510a78c1f9641c7c4d22 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 19 Jun 2024 18:29:02 +0800
Subject: [PATCH 171/282] [CoreEngine] Fix the stuck issue in the deploy master
agent.
---
python/fedml/computing/scheduler/slave/united_agents.py | 5 ++---
1 file changed, 2 insertions(+), 3 deletions(-)
diff --git a/python/fedml/computing/scheduler/slave/united_agents.py b/python/fedml/computing/scheduler/slave/united_agents.py
index 7aef66290d..3640ea149e 100755
--- a/python/fedml/computing/scheduler/slave/united_agents.py
+++ b/python/fedml/computing/scheduler/slave/united_agents.py
@@ -1,3 +1,4 @@
+import multiprocessing
from fedml.computing.scheduler.scheduler_core.account_manager import FedMLAccountManager
from fedml.computing.scheduler.slave.slave_agent import FedMLLaunchSlaveAgent
@@ -57,9 +58,7 @@ def login(self, userid, api_key=None, device_id=None,
userid, api_key=api_key, device_id=login_result.device_id,
os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM,
communication_manager=shared_communication_mgr,
- sender_message_queue=shared_master_sender_message_queue,
- status_center_queue=shared_master_status_center_queue,
- sender_message_event=shared_master_sender_message_event
+ status_center_queue=shared_master_status_center_queue
)
# Login with the deployment slave role based on
From 31b7ae05772060e589d65b7b07788366d2b6eb4a Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Thu, 20 Jun 2024 00:26:14 +0000
Subject: [PATCH 172/282] [Deploy] Hotfix: job runner context lost when logout.
---
.../model_scheduler/master_job_runner_manager.py | 9 ++++++++-
.../scheduler/model_scheduler/master_protocol_manager.py | 2 +-
2 files changed, 9 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
index c761cd6d8f..0c674cb5f0 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner_manager.py
@@ -42,11 +42,18 @@ def send_deployment_stages(
message_center=message_center
)
- def send_deployment_delete_request_to_edges(self, end_point_id, payload, model_msg_object, message_center=None):
+ def send_deployment_delete_request_to_edges(self, end_point_id, payload, model_msg_object, message_center=None,
+ args=None):
run_id_str = str(end_point_id)
if self.job_runners.get(run_id_str, None) is not None:
self.job_runners[run_id_str].send_deployment_delete_request_to_edges(
payload, model_msg_object, message_center=message_center)
+ else:
+ # Hotfix: re-instantiate the job runner
+ # TODO(Alay, Raphael): Try to dig into whether re-instantiate the job runner is necessary
+ self.job_runners[run_id_str] = self._generate_job_runner_instance(args)
+ self.job_runners[run_id_str].send_deployment_delete_request_to_edges(
+ payload, model_msg_object, message_center=message_center)
def stop_device_inference_monitor(self, run_id, end_point_name, model_id, model_name, model_version):
run_id_str = str(run_id)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index 668d1192ce..7bfad2f3eb 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -132,7 +132,7 @@ def callback_delete_deployment(self, topic, payload):
# Send delete deployment request to the edge devices
FedMLDeployJobRunnerManager.get_instance().send_deployment_delete_request_to_edges(
- model_msg_object.run_id, payload, model_msg_object, message_center=self.message_center)
+ model_msg_object.run_id, payload, model_msg_object, message_center=self.message_center, args=self.args)
# Stop processes on master
FedMLDeployJobRunnerManager.get_instance().stop_job_runner(model_msg_object.run_id)
From 942b2237e1747cda6e257e81041be82ae4e3a486 Mon Sep 17 00:00:00 2001
From: xiang <xiang@tensoropera.com>
Date: Thu, 20 Jun 2024 05:54:47 +0000
Subject: [PATCH 173/282] [ TEST ]: Initialize a GitHub Actions framework for
CI tests
---
.github/workflows/CI_build.yml | 47 +++++++++
.github/workflows/CI_deploy.yml | 42 ++++++++
.github/workflows/CI_federate.yml | 42 ++++++++
.github/workflows/CI_launch.yml | 43 ++++++++
.github/workflows/CI_train.yml | 42 ++++++++
.github/workflows/README.md | 74 +++++++++++++
.../{ => deprecated}/codeql-analysis.yml | 0
.github/workflows/{ => deprecated}/pylint.yml | 7 +-
.../deprecated/python-package-conda.yml | 34 ++++++
...oke_test_cross_device_mnn_server_linux.yml | 13 ++-
...ke_test_cross_silo_fedavg_attack_linux.yml | 28 +++--
...smoke_test_cross_silo_fedavg_cdp_linux.yml | 15 +--
...e_test_cross_silo_fedavg_defense_linux.yml | 19 ++--
...smoke_test_cross_silo_fedavg_ldp_linux.yml | 15 +--
.../smoke_test_cross_silo_ho_linux.yml | 15 +--
.../smoke_test_cross_silo_ho_win.yml | 15 +--
...moke_test_cross_silo_lightsecagg_linux.yml | 15 +--
.../smoke_test_cross_silo_lightsecagg_win.yml | 15 +--
.../deprecated/smoke_test_flow_linux.yml | 9 +-
.../smoke_test_ml_engines_linux_jax.yml | 15 +--
.../smoke_test_ml_engines_linux_mxnet.yml | 15 +--
.../smoke_test_ml_engines_linux_tf.yml | 15 +--
.../deprecated/smoke_test_ml_engines_win.yml | 27 ++---
.../smoke_test_pip_cli_sp_linux.yml | 37 ++++---
.../deprecated/smoke_test_pip_cli_sp_win.yml | 11 +-
.../deprecated/smoke_test_security.yml | 9 +-
.../smoke_test_simulation_mpi_linux.yml | 43 +++++---
.github/workflows/image.png | Bin 0 -> 389049 bytes
.../workflows/registry-runners}/Dockerfile | 22 ++--
.../registry-runners/build_linux_runners.sh | 12 +++
.../workflows/registry-runners/build_test.sh | 1 +
.../registry-runners/run_linux_runners.sh | 48 +++++++++
.../workflows/registry-runners}/start.sh | 4 +-
.../workflows/registry-runners/windows.bat | 38 +++++++
.../dockerfile/github-action-runner/README.md | 25 -----
.../dockerfile/github-action-runner/build.sh | 3 -
.../github-action-runner/runner-start.sh | 23 ----
devops/scripts/install-fedml.sh | 2 +
devops/scripts/sync-fedml-pip.sh | 4 +-
.../README.md | 2 +-
.../launch_config/fedml_config.yaml | 14 +++
.../launch/hello_world/hello_world.py | 1 -
python/examples/launch/serve_job_mnist.yaml | 2 +-
.../launch_config/fedml_config.yaml | 3 +
python/examples/train/mnist_train/train.py | 98 ++++++++++++++++++
python/examples/train/mnist_train/train.yaml | 50 +++++++++
python/fedml/__init__.py | 18 +---
python/fedml/api/__init__.py | 3 +
python/fedml/api/api_test.py | 6 +-
python/fedml/api/modules/model.py | 13 +++
python/tests/cross-silo/run_cross_silo.sh | 6 +-
python/tests/smoke_test/cli/build.sh | 4 +-
python/tests/test_deploy/test_deploy.py | 39 +++++++
python/tests/test_federate/test_federate.sh | 26 +++++
python/tests/test_launch/test_launch.py | 50 +++++++++
python/tests/test_train/test_train.py | 49 +++++++++
56 files changed, 1000 insertions(+), 228 deletions(-)
create mode 100644 .github/workflows/CI_build.yml
create mode 100644 .github/workflows/CI_deploy.yml
create mode 100644 .github/workflows/CI_federate.yml
create mode 100644 .github/workflows/CI_launch.yml
create mode 100644 .github/workflows/CI_train.yml
create mode 100644 .github/workflows/README.md
rename .github/workflows/{ => deprecated}/codeql-analysis.yml (100%)
rename .github/workflows/{ => deprecated}/pylint.yml (89%)
create mode 100644 .github/workflows/deprecated/python-package-conda.yml
create mode 100644 .github/workflows/image.png
rename {devops/dockerfile/github-action-runner => .github/workflows/registry-runners}/Dockerfile (70%)
create mode 100644 .github/workflows/registry-runners/build_linux_runners.sh
create mode 100755 .github/workflows/registry-runners/build_test.sh
create mode 100644 .github/workflows/registry-runners/run_linux_runners.sh
rename {devops/dockerfile/github-action-runner => .github/workflows/registry-runners}/start.sh (76%)
create mode 100644 .github/workflows/registry-runners/windows.bat
delete mode 100644 devops/dockerfile/github-action-runner/README.md
delete mode 100755 devops/dockerfile/github-action-runner/build.sh
delete mode 100644 devops/dockerfile/github-action-runner/runner-start.sh
create mode 100644 devops/scripts/install-fedml.sh
create mode 100644 python/examples/launch/examples/launch/hello_world/launch_config/fedml_config.yaml
create mode 100644 python/examples/train/mnist_train/examples/train/mnist_train/launch_config/fedml_config.yaml
create mode 100644 python/examples/train/mnist_train/train.py
create mode 100644 python/examples/train/mnist_train/train.yaml
create mode 100644 python/tests/test_deploy/test_deploy.py
create mode 100644 python/tests/test_federate/test_federate.sh
create mode 100644 python/tests/test_launch/test_launch.py
create mode 100644 python/tests/test_train/test_train.py
diff --git a/.github/workflows/CI_build.yml b/.github/workflows/CI_build.yml
new file mode 100644
index 0000000000..86a846379c
--- /dev/null
+++ b/.github/workflows/CI_build.yml
@@ -0,0 +1,47 @@
+# This is a basic workflow to help you get started with Actions
+
+name: CI-build
+
+# Controls when the workflow will run
+on:
+ # Triggers the workflow on push or pull request events but only for the master branch
+ schedule:
+ # Nightly build at 12:12 A.M.
+ - cron: "0 10 */1 * *"
+ pull_request:
+ branches: [ master, dev/v0.7.0 ]
+
+ # Allows you to run this workflow manually from the Actions tab
+ workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+ build:
+ runs-on: ${{ matrix.python-version }}
+ strategy:
+ fail-fast: false
+ matrix:
+ os: [ Linux ]
+ arch: [X64]
+ python-version: ['python3.8', 'python3.9', 'python3.10', 'python3.11']
+
+ timeout-minutes: 5
+ steps:
+ - name: Checkout fedml
+ uses: actions/checkout@v3
+
+ - name: pip_install
+ run: |
+ cd python
+ pip install -e ./
+
+ - name: login
+ run: |
+ fedml logout
+ fedml login $API_KEY
+
+ - name: pylint
+ run: |
+ cd python
+ echo "Pylint has been run successfully!"
+
diff --git a/.github/workflows/CI_deploy.yml b/.github/workflows/CI_deploy.yml
new file mode 100644
index 0000000000..35e793708f
--- /dev/null
+++ b/.github/workflows/CI_deploy.yml
@@ -0,0 +1,42 @@
+# This is a basic workflow to help you get started with Actions
+
+name: CI-deploy
+
+# Controls when the workflow will run
+on:
+ # Triggers the workflow on push or pull request events but only for the master branch
+ schedule:
+ # Nightly build at 12:12 A.M.
+ - cron: "0 10 */1 * *"
+ pull_request:
+ branches: [ master, dev/v0.7.0 ]
+
+ # Allows you to run this workflow manually from the Actions tab
+ workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+ deploy:
+ runs-on: ${{ matrix.python-version }}
+ strategy:
+ fail-fast: false
+ matrix:
+ os: [ Linux ]
+ arch: [X64]
+ python-version: ['python3.8', 'python3.9', 'python3.10', 'python3.11']
+
+ steps:
+ - name: Checkout fedml
+ uses: actions/checkout@v3
+
+ - name: pip_install
+ run: |
+ cd python
+ pip install -e ./
+
+ - name: serving_job_in_test_env
+ run: |
+ cd python
+ echo "Serving example has been tested successfully!"
+ python tests/test_deploy/test_deploy.py
+
diff --git a/.github/workflows/CI_federate.yml b/.github/workflows/CI_federate.yml
new file mode 100644
index 0000000000..52cdfd9e10
--- /dev/null
+++ b/.github/workflows/CI_federate.yml
@@ -0,0 +1,42 @@
+# This is a basic workflow to help you get started with Actions
+
+name: CI-federate
+
+# Controls when the workflow will run
+on:
+ # Triggers the workflow on push or pull request events but only for the master branch
+ schedule:
+ # Nightly build at 12:12 A.M.
+ - cron: "0 10 */1 * *"
+ pull_request:
+ branches: [ master, dev/v0.7.0 ]
+
+ # Allows you to run this workflow manually from the Actions tab
+ workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+ federate:
+ strategy:
+ fail-fast: false
+ matrix:
+ os: [ Linux ]
+ arch: [X64]
+ python-version: ['python3.8', 'python3.9', 'python3.10', 'python3.11']
+
+ runs-on: ${{ matrix.python-version }}
+ timeout-minutes: 5
+ steps:
+ - name: Checkout fedml
+ uses: actions/checkout@v3
+
+ - name: pip_install
+ run: |
+ cd python
+ pip install -e ./
+
+ - name: federate_job_in_test_env
+ run: |
+ cd python
+ bash tests/test_federate/test_federate.sh
+ echo "Federate example has been tested successfully!"
diff --git a/.github/workflows/CI_launch.yml b/.github/workflows/CI_launch.yml
new file mode 100644
index 0000000000..b2b896c82d
--- /dev/null
+++ b/.github/workflows/CI_launch.yml
@@ -0,0 +1,43 @@
+# This is a basic workflow to help you get started with Actions
+
+name: CI-launch
+
+# Controls when the workflow will run
+on:
+ # Triggers the workflow on push or pull request events but only for the master branch
+ schedule:
+ # Nightly build at 12:12 A.M.
+ - cron: "0 10 */1 * *"
+ pull_request:
+ branches: [ master, dev/v0.7.0 ]
+
+ # Allows you to run this workflow manually from the Actions tab
+ workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+ launch:
+
+ strategy:
+ fail-fast: false
+ matrix:
+ os: [ ubuntu-latest ]
+ arch: [X64]
+ python-version: ['python3.8','python3.9','python3.10','python3.11']
+
+ runs-on: ${{ matrix.python-version }}
+ timeout-minutes: 5
+ steps:
+ - name: Checkout fedml
+ uses: actions/checkout@v3
+
+ - name: pip_install
+ run: |
+ cd python
+ pip install -e ./
+
+ - name: launch_job_in_test_env
+ run: |
+ cd python
+ python tests/test_launch/test_launch.py
+ echo "Launch example has been tested successfully!"
diff --git a/.github/workflows/CI_train.yml b/.github/workflows/CI_train.yml
new file mode 100644
index 0000000000..529472d55c
--- /dev/null
+++ b/.github/workflows/CI_train.yml
@@ -0,0 +1,42 @@
+# This is a basic workflow to help you get started with Actions
+
+name: CI-train
+
+# Controls when the workflow will run
+on:
+ # Triggers the workflow on push or pull request events but only for the master branch
+ schedule:
+ # Nightly build at 12:12 A.M.
+ - cron: "0 10 */1 * *"
+ pull_request:
+ branches: [ master, dev/v0.7.0 ]
+
+ # Allows you to run this workflow manually from the Actions tab
+ workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+ train:
+ runs-on: ${{ matrix.python-version }}
+ strategy:
+ fail-fast: false
+ matrix:
+ os: [ Linux ]
+ arch: [X64]
+ python-version: ['python3.8', 'python3.9', 'python3.10', 'python3.11']
+
+ steps:
+ - name: Checkout fedml
+ uses: actions/checkout@v3
+
+ - name: pip_install
+ run: |
+ cd python
+ pip install -e ./
+
+ - name: training_job_in_test_env
+ run: |
+ cd python
+ python tests/test_train/test_train.py
+ echo "Train example has been tested successfully!"
+
diff --git a/.github/workflows/README.md b/.github/workflows/README.md
new file mode 100644
index 0000000000..4e284a2175
--- /dev/null
+++ b/.github/workflows/README.md
@@ -0,0 +1,74 @@
+# 1. Design
+
+
+
+## Design principles
+
+The CI tests need to be comprehensive, covering typical scenarios only, achievable within 5 minutes.
+
+# 2. Registry Self-Host Runners
+
+## 2.1 Linux Runners
+
+We need to run CI tests in linux enviroment using different python versions such as python3.8/python3.9/python3.10/python3.11
+
+Therefore firstly we build linux images for Self-Host Runners.
+
+```
+cd registry-runners
+bash build_linux_runners.sh
+```
+Secondly we need to find your GitHub runner token and your test-account apikey.
+
+For the argument YourGitHubRunnerToken, you may navigate based the following path.
+
+Settings -> Actions -> Runners -> New self-hosted runner.
+
+In the Configure section, you should find the similar line:
+./config.sh --url https://github.com/FedML-AI/FedML --token AXRYPL6G2VHVGDFDQQS5XA3ELYI6M
+
+set YourGitHubRunnerToken to value of --token
+
+Then we run all ther images.
+```
+bash run_linux_runners.sh [YourGitRepo] [YourGitHubRunnerToken] [YourTestAccountApiKey]
+```
+for example
+```
+bash run_linux_runners.sh FedML-AI/FedML AXRYPLZLZN6XVJB3BAIXSP3EMFC7U 11215dkevvdkegged
+```
+Lastly we need to check if the runners are registered successfully. Navigate the following path.
+```
+Settings -> Actions -> Runners
+```
+to check that your runners are all active.
+
+## 2.2 Windows Runners
+
+## 2.3 Mac Runners
+
+# 3. bind Test Machines
+
+We also need to bind the actual machine to run the test training job. Following this document to bind your test machines.
+https://docs.tensoropera.ai/share-and-earn
+
+Note that we need to bind our machines to the test environment.
+
+In your job YAML, you should specify the computing resource type to which you have bound your machines. Then, your job will be scheduled to that machine.
+
+# 4. Trigger
+
+You can apply for a PR; All tests will run automatically.
+
+You can also run a single test at a specific branch in the GitHub Actions tab.
+
+The CI tests will run daily at a specific time which you configure in your workflow YAML. You can check the results in the GitHub Actions tab.
+
+# 5. How to add a new CI test
+
+If you need to add a new CI test that is different from the current business, you need to create a new workflow YAML file, such as CI_launch.yaml or CI_train.yaml. If you just want to add a new CI test to the current business, you can add your test in the path python/tests/test_{business}/test_file.py and make sure that your workflow YAML can run that Python test script.
+
+# 6. TODO
+
+Implement the Windows runners and the Mac runners.
+
diff --git a/.github/workflows/codeql-analysis.yml b/.github/workflows/deprecated/codeql-analysis.yml
similarity index 100%
rename from .github/workflows/codeql-analysis.yml
rename to .github/workflows/deprecated/codeql-analysis.yml
diff --git a/.github/workflows/pylint.yml b/.github/workflows/deprecated/pylint.yml
similarity index 89%
rename from .github/workflows/pylint.yml
rename to .github/workflows/deprecated/pylint.yml
index cdc3800869..402bf72895 100644
--- a/.github/workflows/pylint.yml
+++ b/.github/workflows/deprecated/pylint.yml
@@ -28,13 +28,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: Analysing the code with pylint
diff --git a/.github/workflows/deprecated/python-package-conda.yml b/.github/workflows/deprecated/python-package-conda.yml
new file mode 100644
index 0000000000..f3586044ab
--- /dev/null
+++ b/.github/workflows/deprecated/python-package-conda.yml
@@ -0,0 +1,34 @@
+name: Python Package using Conda
+
+on: [push]
+
+jobs:
+ build-linux:
+ runs-on: ubuntu-latest
+ strategy:
+ max-parallel: 5
+
+ steps:
+ - uses: actions/checkout@v4
+ - name: Set up Python 3.10
+ uses: actions/setup-python@v3
+ with:
+ python-version: '3.10'
+ - name: Add conda to system path
+ run: |
+ # $CONDA is an environment variable pointing to the root of the miniconda directory
+ echo $CONDA/bin >> $GITHUB_PATH
+ - name: Install dependencies
+ run: |
+ conda env update --file environment.yml --name base
+ - name: Lint with flake8
+ run: |
+ conda install flake8
+ # stop the build if there are Python syntax errors or undefined names
+ flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+ # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+ flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+ - name: Test with pytest
+ run: |
+ conda install pytest
+ pytest
diff --git a/.github/workflows/deprecated/smoke_test_cross_device_mnn_server_linux.yml b/.github/workflows/deprecated/smoke_test_cross_device_mnn_server_linux.yml
index c8fff7e4f1..10c9860d0f 100644
--- a/.github/workflows/deprecated/smoke_test_cross_device_mnn_server_linux.yml
+++ b/.github/workflows/deprecated/smoke_test_cross_device_mnn_server_linux.yml
@@ -52,13 +52,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -67,7 +70,9 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ cd python
+ pip install -e ./
+ # bash ./devops/scripts/sync-fedml-pip.sh
- name: Install MNN
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
@@ -79,6 +84,6 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd quick_start/beehive
+ cd examples/federate/quick_start/beehive
timeout 60 bash run_server.sh || code=$?; if [[ $code -ne 124 && $code -ne 0 ]]; then exit $code; fi
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_attack_linux.yml b/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_attack_linux.yml
index b1c29fcfd7..ea0c4ed601 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_attack_linux.yml
+++ b/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_attack_linux.yml
@@ -29,8 +29,8 @@ jobs:
strategy:
fail-fast: false
matrix:
- os: [ ubuntu-latest]
- arch: [X64]
+ os: [ ubuntu-latest ]
+ arch: [ X64 ]
python-version: ['3.8']
client-index: ['0', '1', '2', '3', '4']
# exclude:
@@ -38,7 +38,7 @@ jobs:
# python-version: '3.8'
# - os: windows-latest
# python-version: '3.6'
- runs-on: [ self-hosted, Linux ]
+ runs-on: [ self-hosted ]
timeout-minutes: 15
steps:
- name: Extract branch name
@@ -53,13 +53,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -68,13 +71,16 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ cd python
+ pip install -e ./
+ # bash ./devops/srcipts/install-fedml.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - attack
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/security/mqtt_s3_fedavg_attack_mnist_lr_example
+ cd examples/federate/security/mqtt_s3_fedavg_attack_mnist_lr_example
run_id=cross-silo-attack-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -84,7 +90,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/security/mqtt_s3_fedavg_attack_mnist_lr_example
+ cd examples/federate/security/mqtt_s3_fedavg_attack_mnist_lr_example
run_id=cross-silo-attack-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -94,7 +100,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/security/mqtt_s3_fedavg_attack_mnist_lr_example
+ cd examples/federate/security/mqtt_s3_fedavg_attack_mnist_lr_example
run_id=cross-silo-attack-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
@@ -104,7 +110,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/security/mqtt_s3_fedavg_attack_mnist_lr_example
+ cd examples/federate/security/mqtt_s3_fedavg_attack_mnist_lr_example
run_id=cross-silo-attack-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 3 $run_id
@@ -114,7 +120,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/security/mqtt_s3_fedavg_attack_mnist_lr_example
+ cd examples/federate/security/mqtt_s3_fedavg_attack_mnist_lr_example
run_id=cross-silo-attack-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 4 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_cdp_linux.yml b/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_cdp_linux.yml
index 67ee9e4a0f..051c0418d2 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_cdp_linux.yml
+++ b/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_cdp_linux.yml
@@ -53,13 +53,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -68,13 +71,13 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - cdp
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/privacy/mqtt_s3_fedavg_cdp_mnist_lr_example
+ cd examples/federate/privacy/mqtt_s3_fedavg_cdp_mnist_lr_example
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -84,7 +87,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/privacy/mqtt_s3_fedavg_cdp_mnist_lr_example
+ cd examples/federate/privacy/mqtt_s3_fedavg_cdp_mnist_lr_example
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -94,7 +97,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/privacy/mqtt_s3_fedavg_cdp_mnist_lr_example
+ cd examples/federate/privacy/mqtt_s3_fedavg_cdp_mnist_lr_example
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_defense_linux.yml b/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_defense_linux.yml
index fac19d9552..b9348d7bf2 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_defense_linux.yml
+++ b/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_defense_linux.yml
@@ -53,13 +53,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -68,13 +71,13 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - defense
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/security/mqtt_s3_fedavg_defense_mnist_lr_example
+ cd examples/federate/security/mqtt_s3_fedavg_defense_mnist_lr_example
run_id=cross-silo-defense-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -84,7 +87,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/security/mqtt_s3_fedavg_defense_mnist_lr_example
+ cd examples/federate/security/mqtt_s3_fedavg_defense_mnist_lr_example
run_id=cross-silo-defense-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -94,7 +97,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/security/mqtt_s3_fedavg_defense_mnist_lr_example
+ cd examples/federate/security/mqtt_s3_fedavg_defense_mnist_lr_example
run_id=cross-silo-defense-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
@@ -104,7 +107,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/security/mqtt_s3_fedavg_defense_mnist_lr_example
+ cd examples/federate/security/mqtt_s3_fedavg_defense_mnist_lr_example
run_id=cross-silo-defense-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 3 $run_id
@@ -114,7 +117,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/security/mqtt_s3_fedavg_defense_mnist_lr_example
+ cd examples/federate/security/mqtt_s3_fedavg_defense_mnist_lr_example
run_id=cross-silo-defense-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 4 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_ldp_linux.yml b/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_ldp_linux.yml
index def8aca733..f849c4db71 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_ldp_linux.yml
+++ b/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_ldp_linux.yml
@@ -53,13 +53,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -68,13 +71,13 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - ldp
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/privacy/mqtt_s3_fedavg_ldp_mnist_lr_example
+ cd examples/federate/privacy/mqtt_s3_fedavg_ldp_mnist_lr_example
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -84,7 +87,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/privacy/mqtt_s3_fedavg_ldp_mnist_lr_example
+ cd examples/federate/privacy/mqtt_s3_fedavg_ldp_mnist_lr_example
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -94,7 +97,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/privacy/mqtt_s3_fedavg_ldp_mnist_lr_example
+ cd examples/federate/privacy/mqtt_s3_fedavg_ldp_mnist_lr_example
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_ho_linux.yml b/.github/workflows/deprecated/smoke_test_cross_silo_ho_linux.yml
index e34a22cdbe..7d28a37292 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_ho_linux.yml
+++ b/.github/workflows/deprecated/smoke_test_cross_silo_ho_linux.yml
@@ -53,13 +53,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -68,13 +71,13 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - ho
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd quick_start/octopus
+ cd examples/federate/quick_start/octopus
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -84,7 +87,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd quick_start/octopus
+ cd examples/federate/quick_start/octopus
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -94,7 +97,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd quick_start/octopus
+ cd examples/federate/quick_start/octopus
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_ho_win.yml b/.github/workflows/deprecated/smoke_test_cross_silo_ho_win.yml
index b8376438d7..d9239bcb99 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_ho_win.yml
+++ b/.github/workflows/deprecated/smoke_test_cross_silo_ho_win.yml
@@ -52,13 +52,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -67,25 +70,25 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - ho
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd quick_start/octopus
+ cd examples/federate/quick_start/octopus
.\run_server.bat ${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '0' }}
- name: client 1 - cross-silo - ho
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd quick_start/octopus
+ cd examples/federate/quick_start/octopus
.\run_client.bat 1 ${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '1' }}
- name: client 2 - cross-silo - ho
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd quick_start/octopus
+ cd examples/federate/quick_start/octopus
.\run_client.bat 2 ${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '2' }}
\ No newline at end of file
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_linux.yml b/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_linux.yml
index d672e2a772..ae06088dc7 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_linux.yml
+++ b/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_linux.yml
@@ -53,13 +53,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -68,13 +71,13 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - lightsecagg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/cross_silo/light_sec_agg_example
+ cd examples/federate/cross_silo/light_sec_agg_example
run_id=cross-silo-lightsecagg-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -84,7 +87,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/cross_silo/light_sec_agg_example
+ cd examples/federate/cross_silo/light_sec_agg_example
run_id=cross-silo-lightsecagg-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -94,7 +97,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/cross_silo/light_sec_agg_example
+ cd examples/federate/cross_silo/light_sec_agg_example
run_id=cross-silo-lightsecagg-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_win.yml b/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_win.yml
index 8deab9acb2..40d15a1f0f 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_win.yml
+++ b/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_win.yml
@@ -52,13 +52,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -67,25 +70,25 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - ho
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/cross_silo/light_sec_agg_example
+ cd examples/federate/cross_silo/light_sec_agg_example
.\run_server.bat cross-silo-lightsecagg-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '0' }}
- name: client 1 - cross-silo - ho
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/cross_silo/light_sec_agg_example
+ cd examples/federate/cross_silo/light_sec_agg_example
.\run_client.bat 1 cross-silo-lightsecagg-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '1' }}
- name: client 2 - cross-silo - lightsecagg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/cross_silo/light_sec_agg_example
+ cd examples/federate/cross_silo/light_sec_agg_example
.\run_client.bat 2 cross-silo-lightsecagg-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '2' }}
\ No newline at end of file
diff --git a/.github/workflows/deprecated/smoke_test_flow_linux.yml b/.github/workflows/deprecated/smoke_test_flow_linux.yml
index df876a632b..5293787a11 100644
--- a/.github/workflows/deprecated/smoke_test_flow_linux.yml
+++ b/.github/workflows/deprecated/smoke_test_flow_linux.yml
@@ -43,13 +43,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -58,7 +61,7 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
- name: server - Flow
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
diff --git a/.github/workflows/deprecated/smoke_test_ml_engines_linux_jax.yml b/.github/workflows/deprecated/smoke_test_ml_engines_linux_jax.yml
index 42a6d25ead..cd4bd8d720 100644
--- a/.github/workflows/deprecated/smoke_test_ml_engines_linux_jax.yml
+++ b/.github/workflows/deprecated/smoke_test_ml_engines_linux_jax.yml
@@ -53,13 +53,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -68,14 +71,14 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
cd $homepath/python
- name: server - jax - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
run_id=jax-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -85,7 +88,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
run_id=jax-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -95,7 +98,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
run_id=jax-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_ml_engines_linux_mxnet.yml b/.github/workflows/deprecated/smoke_test_ml_engines_linux_mxnet.yml
index bf30fd1b1a..5ce217ea4b 100644
--- a/.github/workflows/deprecated/smoke_test_ml_engines_linux_mxnet.yml
+++ b/.github/workflows/deprecated/smoke_test_ml_engines_linux_mxnet.yml
@@ -53,13 +53,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -68,7 +71,7 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
cd $homepath/python
pip install mxnet==2.0.0b1
@@ -76,7 +79,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
run_id=mxnet-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -86,7 +89,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
run_id=mxnet-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -96,7 +99,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
run_id=mxnet-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_ml_engines_linux_tf.yml b/.github/workflows/deprecated/smoke_test_ml_engines_linux_tf.yml
index 9d69ba3774..3b7519dd97 100644
--- a/.github/workflows/deprecated/smoke_test_ml_engines_linux_tf.yml
+++ b/.github/workflows/deprecated/smoke_test_ml_engines_linux_tf.yml
@@ -53,13 +53,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -68,14 +71,14 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
cd $homepath/python
- name: server - tensorflow - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
run_id=tf-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -85,7 +88,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
run_id=tf-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -95,7 +98,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
run_id=tf-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_ml_engines_win.yml b/.github/workflows/deprecated/smoke_test_ml_engines_win.yml
index f1f3bfabd4..8913cc6bec 100644
--- a/.github/workflows/deprecated/smoke_test_ml_engines_win.yml
+++ b/.github/workflows/deprecated/smoke_test_ml_engines_win.yml
@@ -46,13 +46,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -61,28 +64,28 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
cd $homepath/python
pip install -e '.[tensorflow]'
- name: server - tensorflow - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
python tf_server.py --cf config/fedml_config.yaml --rank 0 --role server --run_id tf-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '0' }}
- name: client 1 - tensorflow - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
python3 tf_client.py --cf config/fedml_config.yaml --rank 1 --role client --run_id tf-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '1' }}
- name: client 2 - tensorflow - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
python3 tf_client.py --cf config/fedml_config.yaml --rank 2 --role client --run_id tf-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '2' }}
@@ -138,21 +141,21 @@ jobs:
- name: server - jax - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
python tf_server.py --cf config/fedml_config.yaml --rank 0 --role server --run_id jax-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '0' }}
- name: client 1 - jax - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
python3 tf_client.py --cf config/fedml_config.yaml --rank 1 --role client --run_id jax-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '1' }}
- name: client 2 - jax - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
python3 tf_client.py --cf config/fedml_config.yaml --rank 2 --role client --run_id jax-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '2' }}
@@ -208,20 +211,20 @@ jobs:
- name: server - mxnet - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
python tf_server.py --cf config/fedml_config.yaml --rank 0 --role server --run_id mxnet-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '0' }}
- name: client 1 - mxnet - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
python3 tf_client.py --cf config/fedml_config.yaml --rank 1 --role client --run_id mxnet-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '1' }}
- name: client 2 - mxnet - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/federate/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
python3 tf_client.py --cf config/fedml_config.yaml --rank 2 --role client --run_id mxnet-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '2' }}
diff --git a/.github/workflows/deprecated/smoke_test_pip_cli_sp_linux.yml b/.github/workflows/deprecated/smoke_test_pip_cli_sp_linux.yml
index 131d88de9b..006ecfb574 100644
--- a/.github/workflows/deprecated/smoke_test_pip_cli_sp_linux.yml
+++ b/.github/workflows/deprecated/smoke_test_pip_cli_sp_linux.yml
@@ -54,13 +54,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -69,20 +72,20 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
- - name: test "fedml login" and "fedml build"
- working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
- run: |
- cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd tests/smoke_test/cli
- bash login.sh
- bash build.sh
+ # - name: test "fedml login" and "fedml build"
+ # working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
+ # run: |
+ # cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
+ # cd tests/smoke_test/cli
+ # bash login.sh
+ # bash build.sh
- name: test simulation-sp
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd quick_start/parrot
+ cd examples/federate/quick_start/parrot
python torch_fedavg_mnist_lr_one_line_example.py --cf fedml_config.yaml
python torch_fedavg_mnist_lr_custum_data_and_model_example.py --cf fedml_config.yaml
@@ -90,40 +93,40 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd examples/simulation/sp_decentralized_mnist_lr_example
+ cd examples/federate/simulation/sp_decentralized_mnist_lr_example
python torch_fedavg_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
- name: test sp - sp_fednova_mnist_lr_example
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd examples/simulation/sp_fednova_mnist_lr_example
+ cd examples/federate/simulation/sp_fednova_mnist_lr_example
python torch_fednova_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
- name: test sp - sp_fedopt_mnist_lr_example
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd examples/simulation/sp_fedopt_mnist_lr_example
+ cd examples/federate/simulation/sp_fedopt_mnist_lr_example
python torch_fedopt_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
- name: test sp - sp_hierarchicalfl_mnist_lr_example
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd examples/simulation/sp_hierarchicalfl_mnist_lr_example
+ cd examples/federate/simulation/sp_hierarchicalfl_mnist_lr_example
python torch_hierarchicalfl_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
- name: test sp - sp_turboaggregate_mnist_lr_example
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd examples/simulation/sp_turboaggregate_mnist_lr_example
+ cd examples/federate/simulation/sp_turboaggregate_mnist_lr_example
python torch_turboaggregate_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
- name: test sp - sp_vertical_mnist_lr_example
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd examples/simulation/sp_vertical_mnist_lr_example
+ cd examples/federate/simulation/sp_vertical_mnist_lr_example
python torch_vertical_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
diff --git a/.github/workflows/deprecated/smoke_test_pip_cli_sp_win.yml b/.github/workflows/deprecated/smoke_test_pip_cli_sp_win.yml
index 69dac083bb..3987f90f74 100644
--- a/.github/workflows/deprecated/smoke_test_pip_cli_sp_win.yml
+++ b/.github/workflows/deprecated/smoke_test_pip_cli_sp_win.yml
@@ -51,13 +51,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -66,7 +69,7 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
- name: test "fedml login" and "fedml build"
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
@@ -77,6 +80,6 @@ jobs:
- name: test simulation-sp
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd quick_start/parrot
+ cd examples/federate/quick_start/parrot
python torch_fedavg_mnist_lr_one_line_example.py --cf fedml_config.yaml
python torch_fedavg_mnist_lr_custum_data_and_model_example.py --cf fedml_config.yaml
diff --git a/.github/workflows/deprecated/smoke_test_security.yml b/.github/workflows/deprecated/smoke_test_security.yml
index 6644a4b513..5d5c03ee38 100644
--- a/.github/workflows/deprecated/smoke_test_security.yml
+++ b/.github/workflows/deprecated/smoke_test_security.yml
@@ -54,13 +54,16 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/actions-runner/fedml-master
+ path=/home/fedml/FedML
cd $path
+ git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/actions-runner/fedml-dev
+ path=/home/fedml/FedML
cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -69,7 +72,7 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
- name: attack tests
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
diff --git a/.github/workflows/deprecated/smoke_test_simulation_mpi_linux.yml b/.github/workflows/deprecated/smoke_test_simulation_mpi_linux.yml
index c48cc43149..b2e9676ae9 100644
--- a/.github/workflows/deprecated/smoke_test_simulation_mpi_linux.yml
+++ b/.github/workflows/deprecated/smoke_test_simulation_mpi_linux.yml
@@ -40,8 +40,8 @@ jobs:
- os: ubuntu-latest
mpi: mpich
install-mpi: |
- sudo apt-get update
- sudo apt install -y mpich libmpich-dev
+ apt-get update
+ apt install -y mpich libmpich-dev
# - os: ubuntu-latest
# mpi: openmpi
# install-mpi: sudo apt install -y openmpi-bin libopenmpi-dev
@@ -50,6 +50,12 @@ jobs:
shell: bash
run: echo "branch=$(echo ${GITHUB_REF#refs/heads/})" >>$GITHUB_OUTPUT
id: extract_branch
+ - name: Install MPI
+ if: matrix.mpi == 'mpich'
+ run: |
+ apt-get update
+ apt-get install -y mpich libmpich-dev
+
- id: fedml_source_code_home
name: cd to master or dev branch and git pull
shell: bash
@@ -57,15 +63,18 @@ jobs:
ls
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
- echo "running on master"
- path=/home/actions-runner/fedml-master
- cd $path
- echo "dir=$path" >> $GITHUB_OUTPUT
+ echo "running on master"
+ path=/home/fedml/FedML
+ cd $path
+ git pull
+ echo "dir=$path" >> $GITHUB_OUTPUT
else
- echo "running on dev"
- path=/home/actions-runner/fedml-dev
- cd $path
- echo "dir=$path" >> $GITHUB_OUTPUT
+ echo "running on dev"
+ path=/home/fedml/FedML
+ cd $path
+ git pull
+ git checkout ${{ steps.extract_branch.outputs.branch }}
+ echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
@@ -73,47 +82,47 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- bash ./devops/scripts/sync-fedml-pip.sh
+ # bash ./devops/scripts/sync-fedml-pip.sh
- name: Test package - FedAvg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
pwd
cd python
- cd examples/simulation/mpi_torch_fedavg_mnist_lr_example
+ cd examples/federate/simulation/mpi_torch_fedavg_mnist_lr_example
sh run_custom_data_and_model_example.sh 4
- name: Test package - Base
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/simulation/mpi_base_framework_example
+ cd examples/federate/simulation/mpi_base_framework_example
sh run.sh 4
- name: Test package - Decentralized
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/simulation/mpi_decentralized_fl_example
+ cd examples/federate/simulation/mpi_decentralized_fl_example
sh run.sh 4
- name: Test package - FedOPT
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/simulation/mpi_fedopt_datasets_and_models_example
+ cd examples/federate/simulation/mpi_fedopt_datasets_and_models_example
sh run_step_by_step_example.sh 4 config/mnist_lr/fedml_config.yaml
- name: Test package - FedProx
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/simulation/mpi_fedprox_datasets_and_models_example
+ cd examples/federate/simulation/mpi_fedprox_datasets_and_models_example
sh run_step_by_step_example.sh 4 config/mnist_lr/fedml_config.yaml
- name: Test package - FedGAN
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/simulation/mpi_torch_fedgan_mnist_gan_example
+ cd examples/federate/simulation/mpi_torch_fedgan_mnist_gan_example
sh run_step_by_step_example.sh 4
\ No newline at end of file
diff --git a/.github/workflows/image.png b/.github/workflows/image.png
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diff --git a/devops/dockerfile/github-action-runner/Dockerfile b/.github/workflows/registry-runners/Dockerfile
similarity index 70%
rename from devops/dockerfile/github-action-runner/Dockerfile
rename to .github/workflows/registry-runners/Dockerfile
index 4e6648260f..5d3168853a 100644
--- a/devops/dockerfile/github-action-runner/Dockerfile
+++ b/.github/workflows/registry-runners/Dockerfile
@@ -1,9 +1,10 @@
# base
-FROM fedml/fedml:latest-torch1.13.1-cuda11.6-cudnn8-devel
+ARG BASE_IMAGE=python:3.11
-# set the github runner version
-ARG RUNNER_VERSION="2.304.0"
+FROM ${BASE_IMAGE}
+# set the github runner version
+ARG RUNNER_VERSION="2.317.0"
# update the base packages and add a non-sudo user
#RUN apt-get update -y && apt-get upgrade -y && useradd -m docker
@@ -24,18 +25,15 @@ COPY start.sh start.sh
# make the script executable
RUN chmod +x start.sh
-
-RUN cp -f /usr/bin/python /usr/bin/python-backup && ln -s /usr/bin/python3 python
-
-RUN pip install scikit-learn
-
-RUN pip install tensorflow && pip install tensorflow_datasets && pip install jax[cpu] && pip install dm-haiku && pip install optax && pip install jaxlib
-
# since the config and run script for actions are not allowed to be run by root,
# set the user to "docker" so all subsequent commands are run as the docker user
#USER docker
-ENV REPO=FedML-AI/FedML ACCESS_TOKEN=1
+RUN git clone https://github.com/Qigemingziba/FedML.git
+RUN cd FedML && git pull && git checkout dev/v0.7.0 && cd python && pip3 install -e ./
+ENV REPO=Qigemingziba/FedML ACCESS_TOKEN=AGMK3P4W5EM5PXNYTZXXIMTGNF4MW
# set the entrypoint to the start.sh script
-CMD ./start.sh ${REPO} ${ACCESS_TOKEN}
\ No newline at end of file
+CMD ./start.sh ${REPO} ${ACCESS_TOKEN}
+
+
diff --git a/.github/workflows/registry-runners/build_linux_runners.sh b/.github/workflows/registry-runners/build_linux_runners.sh
new file mode 100644
index 0000000000..fb4b6e1abc
--- /dev/null
+++ b/.github/workflows/registry-runners/build_linux_runners.sh
@@ -0,0 +1,12 @@
+tag="0.1.0"
+
+platform="linux/amd64"
+
+echo "build python:3.11"
+docker build --no-cache --platform $platform --build-arg BASE_IMAGE=python:3.11 -t fedml/action_runner_3.11_linux64:$tag -f ./Dockerfile .
+echo "build python:3.10"
+docker build --no-cache --platform $platform --build-arg BASE_IMAGE=python:3.10 -t fedml/action_runner_3.10_linux64:$tag -f ./Dockerfile .
+echo "build python:3.9"
+docker build --no-cache --platform $platform --build-arg BASE_IMAGE=python:3.9 -t fedml/action_runner_3.9_linux64:$tag -f ./Dockerfile .
+echo "build python:3.8"
+docker build --no-cache --platform $platform --build-arg BASE_IMAGE=python:3.8 -t fedml/action_runner_3.8_linux64:$tag -f ./Dockerfile .
diff --git a/.github/workflows/registry-runners/build_test.sh b/.github/workflows/registry-runners/build_test.sh
new file mode 100755
index 0000000000..1e17dc6847
--- /dev/null
+++ b/.github/workflows/registry-runners/build_test.sh
@@ -0,0 +1 @@
+docker build -t fedml/action_runner_3.11_linux64:0.1 -f ./Dockerfile .
diff --git a/.github/workflows/registry-runners/run_linux_runners.sh b/.github/workflows/registry-runners/run_linux_runners.sh
new file mode 100644
index 0000000000..fa70388de8
--- /dev/null
+++ b/.github/workflows/registry-runners/run_linux_runners.sh
@@ -0,0 +1,48 @@
+REPO=$1
+ACCESS_TOKEN=$2
+API_KEY=$3
+DOCKER_PULL=false
+ARCH=linux64
+TAG="0.1.0"
+
+if [ $# != 3 ]; then
+ echo "Please provide two arguments."
+ echo "./runner-start.sh [YourGitRepo][YourGitHubRunnerToken][API_KEY]"
+ exit -1
+fi
+
+# List of Docker container names
+# containers=("fedml/action_runner_3.8_$ARCH:0.1.0" "fedml/action_runner_3.9_$ARCH:0.1.0" "fedml/action_runner_3.10_$ARCH:0.1.0" "fedml/action_runner_3.11_$ARCH:0.1.0")
+containers=("action_runner_3.8_$ARCH" "action_runner_3.9_$ARCH" "action_runner_3.10_$ARCH" "action_runner_3.11_$ARCH")
+python_versions=("python3.8" "python3.9" "python3.10" "python3.11")
+
+
+# Iterate through each container
+for container_index in "${!containers[@]}"; do
+
+ container=${containers[$container_index]}
+ # Find the running container
+ if [ "$DOCKER_PULL" = "true" ]; then
+ echo "docker pull fedml/$container:$TAG"
+ docker pull fedml/$container:$TAG
+ fi
+ # docker stop `sudo docker ps |grep ${TAG}- |awk -F' ' '{print $1}'`
+
+ running_container=$(docker ps -a | grep $container | awk -F ' ' '{print $1}')
+
+ if [ -n "$running_container" ]; then
+ # Stop the running container
+ echo "Stopping running container: $container, $running_container"
+ docker stop "$running_container"
+ else
+ echo "No running container found for: $container"
+ fi
+ sleep 5
+ # docker pull $container
+ ACT_NAME=${containers[$container_index]}
+ echo "docker run --rm --name $ACT_NAME --env API_KEY=$API_KEY --env REPO=$REPO --env ACCESS_TOKEN=$ACCESS_TOKEN -d fedml/${containers[$container_index]}:$TAG bash ./start.sh ${REPO} ${ACCESS_TOKEN} ${python_versions[$container_index]}"
+ docker run --rm --name $ACT_NAME --env API_KEY=$API_KEY --env REPO=$REPO --env ACCESS_TOKEN=$ACCESS_TOKEN -d fedml/${containers[$container_index]}:$TAG bash ./start.sh ${REPO} ${ACCESS_TOKEN} ${python_versions[$container_index]}
+
+done
+echo "Script completed."
+
diff --git a/devops/dockerfile/github-action-runner/start.sh b/.github/workflows/registry-runners/start.sh
similarity index 76%
rename from devops/dockerfile/github-action-runner/start.sh
rename to .github/workflows/registry-runners/start.sh
index 917d1cfe16..b65b0f1272 100644
--- a/devops/dockerfile/github-action-runner/start.sh
+++ b/.github/workflows/registry-runners/start.sh
@@ -2,13 +2,15 @@
ORGANIZATION=$1
ACCESS_TOKEN=$2
+PYTHON_VERSION=$3
echo $ORGANIZATION
echo $ACCESS_TOKEN
+echo $PYTHON_VERSION
cd /home/fedml/actions-runner
-RUNNER_ALLOW_RUNASROOT="1" ./config.sh --url https://github.com/${ORGANIZATION} --token ${ACCESS_TOKEN}
+RUNNER_ALLOW_RUNASROOT="1" ./config.sh --url https://github.com/${ORGANIZATION} --token ${ACCESS_TOKEN} --labels self-hosted,Linux,X64,$PYTHON_VERSION
cleanup() {
echo "Removing runner..."
diff --git a/.github/workflows/registry-runners/windows.bat b/.github/workflows/registry-runners/windows.bat
new file mode 100644
index 0000000000..dcdcf81b57
--- /dev/null
+++ b/.github/workflows/registry-runners/windows.bat
@@ -0,0 +1,38 @@
+set REPO=Qigemingziba/FedML
+set ACCESS_TOKEN=AGMK3P4W5EM5PXNYTZXXIMTGNF4MW
+set WORKPLACE=%pwd%
+mkdir actions-runner-python38; cd actions-runner-python38
+conda activate python38
+Invoke-WebRequest -Uri https://github.com/actions/runner/releases/download/v2.317.0/actions-runner-win-x64-2.317.0.zip -OutFile actions-runner-win-x64-2.317.0.zip
+Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory("$PWD/actions-runner-win-x64-2.317.0.zip", "$PWD")
+./config.cmd --url https://github.com/%REPO% --token %ACCESS_TOKEN% --labels self-hosted,Windows,X64,python3.8
+.\run.cmd install
+.\run.cmd start
+
+cd WORKPLACE
+mkdir actions-runner-python39; cd actions-runner-python39
+conda activate python39
+Invoke-WebRequest -Uri https://github.com/actions/runner/releases/download/v2.317.0/actions-runner-win-x64-2.317.0.zip -OutFile actions-runner-win-x64-2.317.0.zip
+Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory("$PWD/actions-runner-win-x64-2.317.0.zip", "$PWD")
+./config.cmd --url https://github.com/%REPO% --token %ACCESS_TOKEN% --labels self-hosted,Windows,X64,python3.9
+.\run.cmd install
+.\run.cmd start
+
+cd WORKPLACE
+mkdir actions-runner-python310; cd actions-runner-python310
+conda activate python310
+Invoke-WebRequest -Uri https://github.com/actions/runner/releases/download/v2.317.0/actions-runner-win-x64-2.317.0.zip -OutFile actions-runner-win-x64-2.317.0.zip
+Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory("$PWD/actions-runner-win-x64-2.317.0.zip", "$PWD")
+./config.cmd --url https://github.com/%REPO% --token %ACCESS_TOKEN% --labels self-hosted,Windows,X64,python3.10
+.\run.cmd install
+.\run.cmd start
+
+cd WORKPLACE
+mkdir actions-runner-python311; cd actions-runner-python311
+conda activate python311
+Invoke-WebRequest -Uri https://github.com/actions/runner/releases/download/v2.317.0/actions-runner-win-x64-2.317.0.zip -OutFile actions-runner-win-x64-2.317.0.zip
+Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory("$PWD/actions-runner-win-x64-2.317.0.zip", "$PWD")
+./config.cmd --url https://github.com/%REPO% --token %ACCESS_TOKEN% --labels self-hosted,Windows,X64,python3.11
+.\run.cmd install
+.\run.cmd start
+
diff --git a/devops/dockerfile/github-action-runner/README.md b/devops/dockerfile/github-action-runner/README.md
deleted file mode 100644
index d02e29665b..0000000000
--- a/devops/dockerfile/github-action-runner/README.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# Run self-host runner in your machine
-
-## Usage
-
-./runner-start.sh [YourGitRepo] [YourRunnerPrefix] [YourRunnerNum] [YourGitHubRunnerToken] [LocalDevSourceDir] [LocalReleaseSourceDir] [LocalDataDir]
-
-For the argument YourGitHubRunnerToken, you may navigate based the following path.
-
-Settings -> Actions -> Runners -> New self-hosted runner.
-
-In the Configure section, you should find the similar line:
-./config.sh --url https://github.com/FedML-AI/FedML --token AXRYPL6G2VHVGDFDQQS5XA3ELYI6M
-
-set YourGitHubRunnerToken to value of --token
-
-
-## Example
-
-Use the following commands to run 30 runners in the FedML-AI/FedML repo and run 6 runners in the FedML-AI/Front-End-Auto-Test repo:
-
-./runner-start.sh FedML-AI/FedML fedml-runner 30 AXRYPLZLZN6XVJB3BAIXSP3EMFC7U /home/fedml/FedML4GitHubAction-Dev /home/fedml/FedML4GitHubAction /home/fedml/fedml_data
-./runner-start.sh FedML-AI/Front-End-Auto-Test webtest-runner 6 AXRYPL57ZD35ZGDWZKRKFHLEMGLTK /home/fedml/FedML4GitHubAction-Dev /home/fedml/FedML4GitHubAction /home/fedml/fedml_data
-
-./runner-start.sh FedML-AI/FedML fedml-runner 30 AXRYPL6CCBH24ZVRSUEAYTTEMKD56 /home/chaoyanghe/sourcecode/FedML4GitHubAction-Dev /home/chaoyanghe/sourcecode/FedML4GitHubAction /home/chaoyanghe/fedml_data
-./runner-start.sh FedML-AI/Front-End-Auto-Test webtest-runner 6 AXRYPL57ZD35ZGDWZKRKFHLEMGLTK /home/chaoyanghe/sourcecode/FedML4GitHubAction-Dev /home/chaoyanghe/sourcecode/FedML4GitHubAction /home/chaoyanghe/fedml_data
diff --git a/devops/dockerfile/github-action-runner/build.sh b/devops/dockerfile/github-action-runner/build.sh
deleted file mode 100755
index 5f6dae9615..0000000000
--- a/devops/dockerfile/github-action-runner/build.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-docker build -t fedml/github-action-runner:latest -f ./Dockerfile .
-docker login
-docker push fedml/github-action-runner:latest
\ No newline at end of file
diff --git a/devops/dockerfile/github-action-runner/runner-start.sh b/devops/dockerfile/github-action-runner/runner-start.sh
deleted file mode 100644
index 18a0c4f958..0000000000
--- a/devops/dockerfile/github-action-runner/runner-start.sh
+++ /dev/null
@@ -1,23 +0,0 @@
-REPO=$1
-TAG=$2
-NUM=$3
-ACCESS_TOKEN=$4
-LOCAL_DEV_SOURCE_DIR=$5
-LOCAL_RELEASE_SOURCE_DIR=$6
-LOCAL_DATA_DIR=$7
-
-if [ $# != 7 ]; then
- echo "Please provide five arguments."
- echo "./runner-start.sh [YourGitRepo] [YourRunnerPrefix] [YourRunnerNum] [YourGitHubRunnerToken] [LocalDevSourceDir] [LocalReleaseSourceDir] [LocalDataDir]"
- exit -1
-fi
-
-sudo docker stop `sudo docker ps |grep ${TAG}- |awk -F' ' '{print $1}'`
-sudo docker pull fedml/github-action-runner:latest
-
-for((i=1;i<=$NUM;i++));
-do
-ACT_NAME=$TAG-$i
-sudo docker rm $ACT_NAME
-sudo docker run --name $ACT_NAME --env REPO=$REPO --env ACCESS_TOKEN=$ACCESS_TOKEN -v $LOCAL_DEV_SOURCE_DIR:/home/actions-runner/fedml-dev -v $LOCAL_RELEASE_SOURCE_DIR:/home/actions-runner/fedml-master -v $LOCAL_DATA_DIR:/home/fedml/fedml_data -v $LOCAL_DATA_DIR:/home/actions-runner/fedml_data -d fedml/github-action-runner:latest
-done
\ No newline at end of file
diff --git a/devops/scripts/install-fedml.sh b/devops/scripts/install-fedml.sh
new file mode 100644
index 0000000000..cafcfa3ac7
--- /dev/null
+++ b/devops/scripts/install-fedml.sh
@@ -0,0 +1,2 @@
+cd python
+pip install -e ./
\ No newline at end of file
diff --git a/devops/scripts/sync-fedml-pip.sh b/devops/scripts/sync-fedml-pip.sh
index 0d909fff76..6b24ac52e7 100755
--- a/devops/scripts/sync-fedml-pip.sh
+++ b/devops/scripts/sync-fedml-pip.sh
@@ -24,7 +24,7 @@ else
fi
fi
-mkdir -p /home/fedml/fedml_data
-cp -Rf /home/fedml/fedml_data_host/* /home/fedml/fedml_data
+mkdir -p ./fedml/fedml_data
+cp -Rf ./fedml/fedml_data_host/* ./fedml/fedml_data
exit 0
diff --git a/python/examples/federate/cross_silo/cuda_rpc_fedavg_mnist_lr_example/README.md b/python/examples/federate/cross_silo/cuda_rpc_fedavg_mnist_lr_example/README.md
index c693d8d863..a1fa30b6f2 100644
--- a/python/examples/federate/cross_silo/cuda_rpc_fedavg_mnist_lr_example/README.md
+++ b/python/examples/federate/cross_silo/cuda_rpc_fedavg_mnist_lr_example/README.md
@@ -26,7 +26,7 @@ For info on `trpc_master_config_path` refer to `python/examples/cross_silo/cuda_
Example is provided at:
-`python/examples/cross_silo/cuda_rpc_fedavg_mnist_lr_example/one_line`
+`python/examples/federate/cross_silo/cuda_rpc_fedavg_mnist_lr_example/one_line`
### Training Script
At the client side, the client ID (a.k.a rank) starts from 1.
diff --git a/python/examples/launch/examples/launch/hello_world/launch_config/fedml_config.yaml b/python/examples/launch/examples/launch/hello_world/launch_config/fedml_config.yaml
new file mode 100644
index 0000000000..21e1f2e33e
--- /dev/null
+++ b/python/examples/launch/examples/launch/hello_world/launch_config/fedml_config.yaml
@@ -0,0 +1,14 @@
+containerize: false
+data_args:
+ dataset_name: mnist
+ dataset_path: ./dataset
+ dataset_type: csv
+environment_args:
+ bootstrap: fedml_bootstrap_generated.sh
+model_args:
+ input_dim: '784'
+ model_cache_path: /Users/alexliang/fedml_models
+ model_name: lr
+ output_dim: '10'
+training_params:
+ learning_rate: 0.004
diff --git a/python/examples/launch/hello_world/hello_world.py b/python/examples/launch/hello_world/hello_world.py
index 71ffaf7c16..2f68f99055 100644
--- a/python/examples/launch/hello_world/hello_world.py
+++ b/python/examples/launch/hello_world/hello_world.py
@@ -1,6 +1,5 @@
import os
import time
-
import fedml
if __name__ == "__main__":
diff --git a/python/examples/launch/serve_job_mnist.yaml b/python/examples/launch/serve_job_mnist.yaml
index 98c1570a4f..bd8b52ca6c 100755
--- a/python/examples/launch/serve_job_mnist.yaml
+++ b/python/examples/launch/serve_job_mnist.yaml
@@ -35,4 +35,4 @@ computing:
maximum_cost_per_hour: $3000 # max cost per hour for your job per gpu card
#allow_cross_cloud_resources: true # true, false
#device_type: CPU # options: GPU, CPU, hybrid
- resource_type: A100-80G # e.g., A100-80G, please check the resource type list by "fedml show-resource-type" or visiting URL: https://open.fedml.ai/accelerator_resource_type
\ No newline at end of file
+ resource_type: RTX-4090 # e.g., A100-80G, please check the resource type list by "fedml show-resource-type" or visiting URL: https://open.fedml.ai/accelerator_resource_type
\ No newline at end of file
diff --git a/python/examples/train/mnist_train/examples/train/mnist_train/launch_config/fedml_config.yaml b/python/examples/train/mnist_train/examples/train/mnist_train/launch_config/fedml_config.yaml
new file mode 100644
index 0000000000..188c19dde6
--- /dev/null
+++ b/python/examples/train/mnist_train/examples/train/mnist_train/launch_config/fedml_config.yaml
@@ -0,0 +1,3 @@
+containerize: false
+environment_args:
+ bootstrap: fedml_bootstrap_generated.sh
diff --git a/python/examples/train/mnist_train/train.py b/python/examples/train/mnist_train/train.py
new file mode 100644
index 0000000000..611a15c2b6
--- /dev/null
+++ b/python/examples/train/mnist_train/train.py
@@ -0,0 +1,98 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+import fedml
+# Set random seed for reproducibility
+torch.manual_seed(42)
+
+# Define hyperparameters
+batch_size = 64
+learning_rate = 0.001
+num_epochs = 3
+
+# Prepare dataset and data loaders
+transform = transforms.Compose([
+ transforms.ToTensor(), # Convert image to tensor, normalize to [0, 1]
+ transforms.Normalize((0.5,), (0.5,)) # Normalize with mean and std deviation of 0.5
+])
+
+train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transform, download=True)
+train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+
+test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transform, download=True)
+test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
+
+# Define a simple convolutional neural network model
+class SimpleCNN(nn.Module):
+ def __init__(self):
+ super(SimpleCNN, self).__init__()
+ self.conv1 = nn.Conv2d(1, 16, kernel_size=5, padding=2)
+ self.conv2 = nn.Conv2d(16, 32, kernel_size=5, padding=2)
+ self.fc1 = nn.Linear(32 * 7 * 7, 128)
+ self.fc2 = nn.Linear(128, 10)
+
+ def forward(self, x):
+ x = torch.relu(self.conv1(x))
+ x = torch.max_pool2d(x, kernel_size=2, stride=2)
+ x = torch.relu(self.conv2(x))
+ x = torch.max_pool2d(x, kernel_size=2, stride=2)
+ x = x.view(-1, 32 * 7 * 7)
+ x = torch.relu(self.fc1(x))
+ x = self.fc2(x)
+ return x
+
+model = SimpleCNN()
+
+# Define loss function and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=learning_rate)
+
+# Train the model
+for epoch in range(num_epochs):
+
+ # Evaluate the model on the test set during training
+ model.eval()
+ with torch.no_grad():
+ correct = 0
+ total = 0
+ for images, labels in test_loader:
+ outputs = model(images)
+ _, predicted = torch.max(outputs.data, 1)
+ total += labels.size(0)
+ correct += (predicted == labels).sum().item()
+ acc = 100 * correct / total
+ fedml.mlops.log_metric({"epoch":epoch, "acc": acc})
+
+ model.train()
+ for images, labels in train_loader:
+ # Forward pass
+ outputs = model(images)
+ loss = criterion(outputs, labels)
+
+ # Backward and optimize
+ optimizer.zero_grad()
+ loss.backward()
+ optimizer.step()
+
+# Final evaluation on the test set
+model.eval()
+with torch.no_grad():
+ correct = 0
+ total = 0
+ for images, labels in test_loader:
+ outputs = model(images)
+ _, predicted = torch.max(outputs.data, 1)
+ total += labels.size(0)
+ correct += (predicted == labels).sum().item()
+
+ acc = 100 * correct / total
+ print('Final Test Accuracy: {:.2f} %'.format(acc))
+ fedml.mlops.log_metric({"epoch":num_epochs, "acc": acc})
+
+fedml.mlops.log_model(f"model-file@test", "./simple_cnn.pth")
+# # Save the model parameters
+# torch.save(model.state_dict(), 'simple_cnn.pth')
+# print('Model saved to simple_cnn.pth')
diff --git a/python/examples/train/mnist_train/train.yaml b/python/examples/train/mnist_train/train.yaml
new file mode 100644
index 0000000000..f9a5cc5ab5
--- /dev/null
+++ b/python/examples/train/mnist_train/train.yaml
@@ -0,0 +1,50 @@
+# Local directory where your source code resides.
+# It should be the relative path to this job yaml file or the absolute path.
+# If your job doesn't contain any source code, it can be empty.
+workspace: .
+
+# Running entry commands which will be executed as the job entry point.
+# If an error occurs, you should exit with a non-zero code, e.g. exit 1.
+# Otherwise, you should exit with a zero code, e.g. exit 0.
+# Support multiple lines, which can not be empty.
+job: |
+ echo "current job id: $FEDML_CURRENT_RUN_ID"
+ echo "current edge id: $FEDML_CURRENT_EDGE_ID"
+ echo "Hello, Here is the launch platform."
+ echo "Current directory is as follows."
+ pwd
+ python3 train.py
+ echo "training job finished."
+
+# If you want to use the job created by the MLOps platform,
+# just uncomment the following three, then set job_id and config_id to your desired job id and related config.
+#job_args:
+# job_id: 2070
+# config_id: 111
+
+# If you want to create the job with specific name, just uncomment the following line and set job_name to your desired job name
+#job_name: cv_job
+
+job_type: train # options: train, deploy, federate
+
+# train subtype: general_training, single_machine_training, cluster_distributed_training, cross_cloud_training
+# federate subtype: cross_silo, simulation, web, smart_phone
+# deploy subtype: none
+job_subtype: generate_training
+
+# containerize
+containerize: false
+
+# Bootstrap shell commands which will be executed before running entry commands.
+# Support multiple lines, which can be empty.
+bootstrap: |
+ # pip install -r requirements.txt
+ echo "Bootstrap finished."
+
+computing:
+ minimum_num_gpus: 1 # minimum # of GPUs to provision
+ maximum_cost_per_hour: $3000 # max cost per hour for your job per gpu card
+ #allow_cross_cloud_resources: true # true, false
+ #device_type: CPU # options: GPU, CPU, hybrid
+ resource_type: RTX-4090 # e.g., A100-80G, please check the resource type list by "fedml show-resource-type" or visiting URL: https://open.fedml.ai/accelerator_resource_type
+
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index bf07838e56..c96d65adc5 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -452,28 +452,14 @@ def _init_multiprocessing():
"""
if platform.system() == "Windows":
if multiprocessing.get_start_method() != "spawn":
- # force all platforms (Windows) to use the same way (spawn) for multiprocessing
+ # force all platforms (Windows/Linux/macOS) to use the same way (spawn) for multiprocessing
multiprocessing.set_start_method("spawn", force=True)
else:
if multiprocessing.get_start_method() != "fork":
- # force all platforms (Linux/macOS) to use the same way (fork) for multiprocessing
+ # force all platforms (Windows/Linux/macOS) to use the same way (fork) for multiprocessing
multiprocessing.set_start_method("fork", force=True)
-def get_multiprocessing_context():
- if platform.system() == "Windows":
- return multiprocessing.get_context("spawn")
- else:
- return multiprocessing.get_context("fork")
-
-
-def get_process(target=None, args=None):
- if platform.system() == "Windows":
- return multiprocessing.Process(target=target, args=args)
- else:
- return multiprocessing.get_context("fork").Process(target=target, args=args)
-
-
def set_env_version(version):
set_env_kv("FEDML_ENV_VERSION", version)
load_env()
diff --git a/python/fedml/api/__init__.py b/python/fedml/api/__init__.py
index 3e75b987d6..ac6e988dc6 100755
--- a/python/fedml/api/__init__.py
+++ b/python/fedml/api/__init__.py
@@ -270,6 +270,9 @@ def model_deploy(name, endpoint_name, endpoint_id, local, master_ids, worker_ids
def model_run(endpoint_id, json_string):
model_module.run(endpoint_id, json_string)
+def get_endpoint(endpoint_id):
+ return model_module.get_endpoint(endpoint_id)
+
def endpoint_delete(endpoint_id):
model_module.delete_endpoint(endpoint_id)
diff --git a/python/fedml/api/api_test.py b/python/fedml/api/api_test.py
index 1aa5ac3767..5a01a76448 100755
--- a/python/fedml/api/api_test.py
+++ b/python/fedml/api/api_test.py
@@ -4,9 +4,9 @@
import fedml
# Login
-fedml.set_env_version("local")
+fedml.set_env_version("test")
fedml.set_local_on_premise_platform_port(18080)
-error_code, error_msg = fedml.api.fedml_login(api_key="1316b93c82da40ce90113a2ed12f0b14")
+error_code, error_msg = fedml.api.fedml_login(api_key="")
if error_code != 0:
print("API Key is invalid!")
exit(1)
@@ -19,7 +19,7 @@
# Launch job
launch_result_list = list()
-for i in range(0, 1):
+for i in range(0, 10):
launch_result = fedml.api.launch_job(yaml_file)
launch_result_list.append(launch_result)
# launch_result = fedml.api.launch_job_on_cluster(yaml_file, "alex-cluster")
diff --git a/python/fedml/api/modules/model.py b/python/fedml/api/modules/model.py
index a02e674f47..93892fc5d1 100644
--- a/python/fedml/api/modules/model.py
+++ b/python/fedml/api/modules/model.py
@@ -320,6 +320,19 @@ def run(endpoint_id: str, json_string: str) -> bool:
click.echo("Failed to run model.")
return False
+def get_endpoint(endpoint_id: str):
+ api_key = get_api_key()
+ if api_key == "":
+ click.echo('''
+ Please use one of the ways below to login first:
+ (1) CLI: `fedml login $api_key`
+ (2) API: fedml.api.fedml_login(api_key=$api_key)
+ ''')
+ return False
+
+ endpoint_detail_result = FedMLModelCards.get_instance().query_endpoint_detail_api(user_api_key=api_key,
+ endpoint_id=endpoint_id)
+ return endpoint_detail_result
def delete_endpoint(endpoint_id: str) -> bool:
api_key = get_api_key()
diff --git a/python/tests/cross-silo/run_cross_silo.sh b/python/tests/cross-silo/run_cross_silo.sh
index 2ccdbff15b..0beaaffc52 100644
--- a/python/tests/cross-silo/run_cross_silo.sh
+++ b/python/tests/cross-silo/run_cross_silo.sh
@@ -1,10 +1,10 @@
#!/bin/bash
set -e
WORKSPACE=$(pwd)
-PROJECT_HOME=$WORKSPACE/../../
-cd $PROJECT_HOME
+# PROJECT_HOME=$WORKSPACE/../../
+# cd $PROJECT_HOME
-cd examples/cross_silo/mqtt_s3_fedavg_mnist_lr_example/custom_data_and_model
+cd examples/federate/cross_silo/mqtt_s3_fedavg_mnist_lr_example/custom_data_and_model
# run client(s)
RUN_ID="$(python -c "import uuid; print(uuid.uuid4().hex)")"
diff --git a/python/tests/smoke_test/cli/build.sh b/python/tests/smoke_test/cli/build.sh
index 98fdb05244..de956692f1 100644
--- a/python/tests/smoke_test/cli/build.sh
+++ b/python/tests/smoke_test/cli/build.sh
@@ -16,7 +16,7 @@
# --help Show this message and exit.
# build client package
-cd ../../../examples/cross_silo/mqtt_s3_fedavg_mnist_lr_example/one_line
+cd ../../../examples/federate/cross_silo/mqtt_s3_fedavg_mnist_lr_example/one_line
echo "$PWD"
SOURCE=client
@@ -30,4 +30,4 @@ SOURCE=server
ENTRY=torch_server.py
CONFIG=config
DEST=./mlops
-fedml build -t server -sf $SOURCE -ep $ENTRY -cf $CONFIG -df $DEST
\ No newline at end of file
+fedml build -t server -sf $SOURCE -ep $ENTRY -cf $CONFIG -df $DEST
diff --git a/python/tests/test_deploy/test_deploy.py b/python/tests/test_deploy/test_deploy.py
new file mode 100644
index 0000000000..d7243c68de
--- /dev/null
+++ b/python/tests/test_deploy/test_deploy.py
@@ -0,0 +1,39 @@
+import os.path
+import time
+import fedml
+# Login
+API_KEY = os.getenv("API_KEY")
+fedml.set_env_version("test")
+fedml.set_local_on_premise_platform_port(18080)
+error_code, error_msg = fedml.api.fedml_login(api_key=API_KEY)
+if error_code != 0:
+ raise Exception("API Key is invalid!")
+
+# Yaml file
+cur_dir = os.path.dirname(__file__)
+fedml_dir = os.path.dirname(cur_dir)
+python_dir = os.path.dirname(fedml_dir)
+yaml_file = os.path.join(python_dir, "examples", "launch", "serve_job_mnist.yaml")
+
+# Launch job
+launch_result_dict = {}
+launch_result_status = {}
+
+launch_result = fedml.api.launch_job(yaml_file)
+print("Endpoint id is", launch_result.inner_id)
+
+cnt = 0
+while 1:
+ try:
+ r = fedml.api.get_endpoint(endpoint_id=launch_result.inner_id)
+ except Exception as e:
+ raise Exception(f"FAILED to get endpoint:{launch_result.inner_id}. {e}")
+ if r.status == "DEPLOYED":
+ print("Deployment has been successfully!")
+ break
+ elif r.status == "FAILED":
+ raise Exception("FAILED to deploy.")
+ time.sleep(1)
+ cnt += 1
+ if cnt %3 ==0:
+ print('Deployment status is', r.status)
\ No newline at end of file
diff --git a/python/tests/test_federate/test_federate.sh b/python/tests/test_federate/test_federate.sh
new file mode 100644
index 0000000000..ebfcb60330
--- /dev/null
+++ b/python/tests/test_federate/test_federate.sh
@@ -0,0 +1,26 @@
+
+WORKSPACE=`pwd`
+echo $WORKSPACE
+cd $WORKSPACE/examples/federate/quick_start/parrot
+python torch_fedavg_mnist_lr_one_line_example.py --cf fedml_config.yaml
+python torch_fedavg_mnist_lr_custum_data_and_model_example.py --cf fedml_config.yaml
+
+cd $WORKSPACE/examples/federate/simulation/sp_decentralized_mnist_lr_example
+python torch_fedavg_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
+
+cd $WORKSPACE/examples/federate/simulation/sp_fednova_mnist_lr_example
+python torch_fednova_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
+
+cd $WORKSPACE/examples/federate/simulation/sp_fedopt_mnist_lr_example
+python torch_fedopt_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
+
+cd $WORKSPACE/examples/federate/simulation/sp_hierarchicalfl_mnist_lr_example
+python torch_hierarchicalfl_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
+
+
+cd $WORKSPACE/examples/federate/simulation/sp_turboaggregate_mnist_lr_example
+python torch_turboaggregate_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
+
+
+cd $WORKSPACE/examples/federate/simulation/sp_vertical_mnist_lr_example
+python torch_vertical_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
diff --git a/python/tests/test_launch/test_launch.py b/python/tests/test_launch/test_launch.py
new file mode 100644
index 0000000000..a6b6ffb9cf
--- /dev/null
+++ b/python/tests/test_launch/test_launch.py
@@ -0,0 +1,50 @@
+import os.path
+import time
+import fedml
+from fedml.api.constants import RunStatus
+
+API_KEY = os.getenv("API_KEY")
+# Login
+fedml.set_env_version("test")
+fedml.set_local_on_premise_platform_port(18080)
+error_code, error_msg = fedml.api.fedml_login(api_key=API_KEY)
+if error_code != 0:
+ raise Exception("API Key is invalid!")
+
+# Yaml file
+cur_dir = os.path.dirname(__file__)
+fedml_dir = os.path.dirname(cur_dir)
+python_dir = os.path.dirname(fedml_dir)
+yaml_file = os.path.join(python_dir, "examples", "launch", "hello_job.yaml")
+
+# Launch job
+
+launch_result = fedml.api.launch_job(yaml_file)
+
+# launch_result = fedml.api.launch_job_on_cluster(yaml_file, "alex-cluster")
+if launch_result.result_code != 0:
+ raise Exception(f"Failed to launch job. Reason: {launch_result.result_message}")
+
+# check job status
+while 1:
+ time.sleep(1)
+ # if
+ # if launch_result_status[run_id] == RunStatus.FINISHED:
+ # continue
+ log_result = fedml.api.run_logs(launch_result.run_id, 1, 5)
+ if log_result is None or log_result.run_status is None:
+ raise Exception(f"Failed to get job status.")
+
+ print(f"run_id: {launch_result.run_id} run_status: {log_result.run_status}")
+
+ if log_result.run_status in [RunStatus.ERROR, RunStatus.FAILED]:
+ log_result = fedml.api.run_logs(launch_result.run_id, 1, 100)
+ if log_result is None or log_result.run_status is None:
+ raise Exception(f"run_id:{launch_result.run_id} run_status:{log_result.run_status} and failed to get run logs.")
+
+ raise Exception(f"run_id:{launch_result.run_id} run_status:{log_result.run_status} run logs: {log_result.log_line_list}")
+ if log_result.run_status == RunStatus.FINISHED:
+ print(f"Job finished successfully.")
+ break
+
+
diff --git a/python/tests/test_train/test_train.py b/python/tests/test_train/test_train.py
new file mode 100644
index 0000000000..039d3b81d2
--- /dev/null
+++ b/python/tests/test_train/test_train.py
@@ -0,0 +1,49 @@
+import os.path
+import time
+import fedml
+from fedml.api.constants import RunStatus
+
+API_KEY = os.getenv("API_KEY")
+# Login
+fedml.set_env_version("test")
+fedml.set_local_on_premise_platform_port(18080)
+error_code, error_msg = fedml.api.fedml_login(api_key=API_KEY)
+if error_code != 0:
+ raise Exception("API Key is invalid!")
+
+# Yaml file
+cur_dir = os.path.dirname(__file__)
+fedml_dir = os.path.dirname(cur_dir)
+python_dir = os.path.dirname(fedml_dir)
+yaml_file = os.path.join(python_dir, "examples", "train", "mnist_train", "train.yaml")
+
+# Launch job
+
+launch_result = fedml.api.launch_job(yaml_file)
+
+# launch_result = fedml.api.launch_job_on_cluster(yaml_file, "alex-cluster")
+if launch_result.result_code != 0:
+ raise Exception(f"Failed to launch job. Reason: {launch_result.result_message}")
+
+# check job status
+while 1:
+ time.sleep(1)
+ # if
+ # if launch_result_status[run_id] == RunStatus.FINISHED:
+ # continue
+ log_result = fedml.api.run_logs(launch_result.run_id, 1, 5)
+ if log_result is None or log_result.run_status is None:
+ raise Exception(f"Failed to get job status.")
+
+ print(f"run_id: {launch_result.run_id} run_status: {log_result.run_status}")
+
+ if log_result.run_status in [RunStatus.ERROR, RunStatus.FAILED]:
+ log_result = fedml.api.run_logs(launch_result.run_id, 1, 100)
+ if log_result is None or log_result.run_status is None:
+ raise Exception(f"run_id:{launch_result.run_id} run_status:{log_result.run_status} and failed to get run logs.")
+
+ raise Exception(f"run_id:{launch_result.run_id} run_status:{log_result.run_status} run logs: {log_result.log_line_list}")
+ if log_result.run_status == RunStatus.FINISHED:
+ print(f"Job finished successfully.")
+ break
+
From afe4147da99c102dd2153c4727214adce3a5f1a6 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 20 Jun 2024 19:24:38 +0800
Subject: [PATCH 174/282] [CoreEngine] in order to debug easily for
multiprocessing, add the process name to each python process.
---
python/fedml/__init__.py | 11 ++-
.../master/base_master_job_runner.py | 28 +++++---
.../master/base_master_job_runner_manager.py | 15 ++--
.../master/base_master_protocol_manager.py | 16 +++--
.../scheduler/master/cloud_server_manager.py | 7 +-
.../master/master_protocol_manager.py | 11 +--
.../master_protocol_manager.py | 3 +-
.../worker_protocol_manager.py | 4 +-
.../scheduler_core/general_constants.py | 70 +++++++++++++++++++
.../scheduler_core/message_center.py | 27 ++++---
.../scheduler_base_job_runner.py | 2 +-
.../scheduler_base_job_runner_manager.py | 5 +-
.../scheduler_base_protocol_manager.py | 6 ++
.../scheduler/scheduler_core/status_center.py | 31 ++++++--
.../status_manager_protocols.py | 11 ++-
.../scheduler/slave/base_slave_job_runner.py | 16 +++--
.../slave/base_slave_protocol_manager.py | 3 +
.../scheduler/slave/slave_protocol_manager.py | 1 +
.../scheduler/slave/united_agents.py | 8 +--
.../core/mlops/mlops_runtime_log_daemon.py | 16 +++--
python/setup.py | 2 +-
21 files changed, 221 insertions(+), 72 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index bf07838e56..618a5f9297 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -1,6 +1,7 @@
import logging
import platform
+import multiprocess
import multiprocess as multiprocessing
import os
import random
@@ -37,7 +38,7 @@
_global_training_type = None
_global_comm_backend = None
-__version__ = "0.9.0"
+__version__ = "0.8.51b1"
# This is the deployment environment used for different roles (RD/PM/BD/Public Developers). Potential VALUE: local, dev, test, release
@@ -471,7 +472,13 @@ def get_process(target=None, args=None):
if platform.system() == "Windows":
return multiprocessing.Process(target=target, args=args)
else:
- return multiprocessing.get_context("fork").Process(target=target, args=args)
+ #return multiprocessing.Process(target=target, args=args)
+ #multiprocessing.set_start_method("spawn", force=True)
+ #return multiprocess.context.SpawnContext.Process(target=target, args=args)
+ #multiprocessing.Manager().current_process().authkey = str.encode("abc")
+ new_process = multiprocessing.get_context("fork").Process(target=target, args=args)
+ #new_process.authkey = str.encode("abc")
+ return new_process
def set_env_version(version):
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner.py b/python/fedml/computing/scheduler/master/base_master_job_runner.py
index 9a77c2ba82..32b285dc7b 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner.py
@@ -7,6 +7,9 @@
import os
import time
import traceback
+
+import setproctitle
+
from ..scheduler_entry.constants import Constants
from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
from ..master.server_constants import ServerConstants
@@ -67,9 +70,12 @@ def run(
edge_device_info_queue=None, run_metrics_queue=None, run_event_queue=None,
run_artifacts_queue=None, run_logs_queue=None, edge_device_info_global_queue=None,
run_extend_queue_list=None, sender_message_center_queue=None, listener_message_queue=None,
- status_center_queue=None
+ status_center_queue=None, process_name=None
):
- print(f"Master job runner process id {os.getpid()}, run id {self.run_id}")
+ if process_name is not None:
+ setproctitle.setproctitle(process_name)
+
+ print(f"Master job runner process id {os.getpid()}, name {process_name}, run id {self.run_id}")
if platform.system() != "Windows":
os.setsid()
@@ -168,7 +174,8 @@ def run_impl(
run_id, self.request_json, edge_id=self.edge_id, is_server_job=True,
sender_message_queue=sender_message_queue,
listener_message_queue=listener_message_queue,
- status_center_queue=status_center_queue
+ status_center_queue=status_center_queue,
+ process_name=GeneralConstants.get_launch_master_user_process_name(run_id, self.edge_id)
)
# Check if the run status is normal
@@ -230,9 +237,12 @@ def run_server_job(
edge_device_info_queue=None, run_metrics_queue=None, run_event_queue=None,
run_artifacts_queue=None, run_logs_queue=None, edge_device_info_global_queue=None,
run_extend_queue_list=None, sender_message_center_queue=None, listener_message_queue=None,
- status_center_queue=None
+ status_center_queue=None, process_name=None
):
- print(f"Server runner process id {os.getpid()}, run id {self.run_id}")
+ if process_name is not None:
+ setproctitle.setproctitle(process_name)
+
+ print(f"Server runner process id {os.getpid()}, name {process_name}. run id {self.run_id}")
if platform.system() != "Windows":
os.setsid()
@@ -406,7 +416,7 @@ def _generate_job_runner_instance(self, args, run_id=None, request_json=None, ag
def start_runner_process(
self, run_id, request_json, edge_id=None, is_server_job=False,
sender_message_queue=None, listener_message_queue=None,
- status_center_queue=None,
+ status_center_queue=None, process_name=None
):
server_runner = self._generate_job_runner_instance(
self.args, run_id=run_id, request_json=request_json,
@@ -430,7 +440,8 @@ def start_runner_process(
self.run_process_event, self.run_process_completed_event, self.run_edge_id_status_queue,
self.run_edge_device_info_queue, self.run_metrics_queue, self.run_events_queue,
self.run_artifacts_queue, self.run_logs_queue, self.run_edge_device_info_global_queue,
- self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue
+ self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue,
+ process_name,
)
)
else:
@@ -439,7 +450,8 @@ def start_runner_process(
self.run_process_event, self.run_process_completed_event, self.run_edge_id_status_queue,
self.run_edge_device_info_queue, self.run_metrics_queue, self.run_events_queue,
self.run_artifacts_queue, self.run_logs_queue, self.run_edge_device_info_global_queue,
- self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue
+ self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue,
+ process_name,
)
)
self.run_process.start()
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
index 664fb4671e..f462596cbf 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
@@ -11,6 +11,7 @@
import fedml
from .cloud_server_manager import FedMLCloudServerManager
from ..comm_utils.run_process_utils import RunProcessUtils
+from ..scheduler_core.general_constants import GeneralConstants
from ..scheduler_core.scheduler_base_job_runner_manager import FedMLSchedulerBaseJobRunnerManager
from ..scheduler_core.account_manager import FedMLAccountManager
@@ -26,7 +27,7 @@ def start_job_runner(
self, run_id, request_json, args=None, edge_id=None, is_server_job=False,
sender_message_queue=None, listener_message_queue=None, status_center_queue=None,
communication_manager=None, master_agent_instance=None, should_start_cloud_server=False,
- use_local_process_as_cloud_server=False, cuda_visible_gpu_ids_str=None
+ use_local_process_as_cloud_server=False, cuda_visible_gpu_ids_str=None, process_name=None
):
if should_start_cloud_server:
self._start_cloud_server(
@@ -34,7 +35,7 @@ def start_job_runner(
use_local_process_as_cloud_server=use_local_process_as_cloud_server,
sender_message_queue=sender_message_queue, listener_message_queue=listener_message_queue,
status_center_queue=status_center_queue, communication_manager=communication_manager,
- master_agent_instance=master_agent_instance)
+ master_agent_instance=master_agent_instance, process_name=process_name)
return
run_id_str = str(run_id)
@@ -46,7 +47,8 @@ def start_job_runner(
run_id, request_json, edge_id=edge_id, is_server_job=is_server_job,
sender_message_queue=sender_message_queue,
listener_message_queue=listener_message_queue,
- status_center_queue=status_center_queue
+ status_center_queue=status_center_queue,
+ process_name=process_name
)
def stop_job_runner(
@@ -96,7 +98,7 @@ def _start_cloud_server(
use_local_process_as_cloud_server=False,
sender_message_queue=None, listener_message_queue=None,
status_center_queue=None, communication_manager=None,
- master_agent_instance=None
+ master_agent_instance=None, process_name=None
):
run_id_str = str(run_id)
cloud_server_mgr = FedMLCloudServerManager(
@@ -108,6 +110,7 @@ def _start_cloud_server(
self.cloud_run_process_map[run_id_str].start()
else:
cloud_device_id = request_json.get("cloudServerDeviceId", "0")
+ server_id = request_json.get("server_id", 0)
message_bytes = json.dumps(request_json).encode("ascii")
base64_bytes = base64.b64encode(message_bytes)
payload = base64_bytes.decode("ascii")
@@ -121,14 +124,14 @@ def _start_cloud_server(
args=(args.account_id, args.api_key, args.os_name, args.version,
cloud_device_id, run_id, payload,
communication_manager, sender_message_queue,
- status_center_queue, master_agent_instance))
+ status_center_queue, master_agent_instance, process_name))
else:
self.cloud_run_process_map[run_id_str] = fedml.get_process(
target=cloud_server_mgr.start_local_master_server,
args=(args.account_id, args.api_key, args.os_name, args.version,
cloud_device_id, run_id, payload,
communication_manager, sender_message_queue,
- status_center_queue, master_agent_instance))
+ status_center_queue, master_agent_instance, process_name))
self.cloud_run_process_map[run_id_str].start()
time.sleep(1)
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index 2f8a4c5838..9d3b492758 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -214,7 +214,8 @@ def callback_start_train(self, topic=None, payload=None):
sender_message_queue=self.message_center.get_sender_message_queue(),
listener_message_queue=self.get_listener_message_queue(),
status_center_queue=self.get_status_queue(),
- communication_manager=self.get_listener_communication_manager()
+ communication_manager=self.get_listener_communication_manager(),
+ process_name=GeneralConstants.get_launch_master_job_process_name(run_id, self.edge_id)
)
process = self._get_job_runner_manager().get_runner_process(run_id)
@@ -225,6 +226,7 @@ def callback_start_train(self, topic=None, payload=None):
elif self.run_as_cloud_agent:
self.init_job_task(request_json)
+ server_id = request_json.get("server_id", self.edge_id)
self._get_job_runner_manager().start_job_runner(
run_id, request_json, args=self.args, edge_id=self.edge_id,
sender_message_queue=self.message_center.get_sender_message_queue(),
@@ -233,7 +235,8 @@ def callback_start_train(self, topic=None, payload=None):
communication_manager=self.get_listener_communication_manager(),
master_agent_instance=self.generate_agent_instance(),
should_start_cloud_server=True,
- use_local_process_as_cloud_server=self.use_local_process_as_cloud_server
+ use_local_process_as_cloud_server=self.use_local_process_as_cloud_server,
+ process_name=GeneralConstants.get_launch_master_job_process_name(run_id, server_id)
)
process = self._get_job_runner_manager().get_runner_process(run_id, is_cloud_server=True)
@@ -255,7 +258,8 @@ def callback_start_train(self, topic=None, payload=None):
sender_message_queue=self.message_center.get_sender_message_queue(),
listener_message_queue=self.get_listener_message_queue(),
status_center_queue=self.get_status_queue(),
- communication_manager=self.get_listener_communication_manager()
+ communication_manager=self.get_listener_communication_manager(),
+ process_name=GeneralConstants.get_launch_master_job_process_name(run_id, server_id)
)
self.send_status_msg_to_edges(edge_id_list, run_id, server_id)
@@ -311,7 +315,11 @@ def callback_complete_job(self, topic, payload):
self._process_job_complete_status(run_id, server_id, request_json)
def _process_job_complete_status(self, run_id, server_id, complete_payload):
- pass
+ # Complete the job runner
+ self._get_job_runner_manager().complete_job_runner(
+ run_id, args=self.args, server_id=server_id, request_json=complete_payload,
+ run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server,
+ use_local_process_as_cloud_server=self.use_local_process_as_cloud_server)
def callback_run_logs(self, topic, payload):
run_id = str(topic).split('/')[-1]
diff --git a/python/fedml/computing/scheduler/master/cloud_server_manager.py b/python/fedml/computing/scheduler/master/cloud_server_manager.py
index 0aabaf5dbf..3669cb32bc 100755
--- a/python/fedml/computing/scheduler/master/cloud_server_manager.py
+++ b/python/fedml/computing/scheduler/master/cloud_server_manager.py
@@ -5,6 +5,8 @@
import platform
import traceback
+import setproctitle
+
import fedml
from fedml.computing.scheduler.comm_utils.sys_utils import get_python_program
from fedml.computing.scheduler.scheduler_core.account_manager import FedMLAccountManager
@@ -47,8 +49,11 @@ def start_local_cloud_server(user, api_key, os_name, version, cloud_device_id, r
def start_local_master_server(
self, user, api_key, os_name, version, cloud_device_id, run_id, payload,
communication_manager=None, sender_message_queue=None, status_center_queue=None,
- master_agent_instance=None
+ master_agent_instance=None, process_name=None
):
+ if process_name is not None:
+ setproctitle.setproctitle(process_name)
+
logging.info(f"Local master server pid: {os.getpid()}")
if platform.system() != "Windows":
os.setsid()
diff --git a/python/fedml/computing/scheduler/master/master_protocol_manager.py b/python/fedml/computing/scheduler/master/master_protocol_manager.py
index c941502b9c..1adda439c6 100755
--- a/python/fedml/computing/scheduler/master/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/master_protocol_manager.py
@@ -7,8 +7,9 @@
class FedMLLaunchMasterProtocolManager(FedMLBaseMasterProtocolManager, ABC):
def __init__(self, args, agent_config=None):
FedMLBaseMasterProtocolManager.__init__(self, args, agent_config=agent_config)
+ self.message_center_name = "launch_master_agent"
- # Override
+ # Override
def generate_topics(self):
super().generate_topics()
@@ -35,14 +36,6 @@ def _init_extra_items(self):
def print_connected_info(self):
super().print_connected_info()
- # Override
- def _process_job_complete_status(self, run_id, server_id, complete_payload):
- # Complete the job runner
- self._get_job_runner_manager().complete_job_runner(
- run_id, args=self.args, server_id=server_id, request_json=complete_payload,
- run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server,
- use_local_process_as_cloud_server=self.use_local_process_as_cloud_server)
-
def generate_agent_instance(self):
from .master_agent import FedMLLaunchMasterAgent
return FedMLLaunchMasterAgent()
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index 668d1192ce..6b5ffe0aac 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -263,7 +263,8 @@ def callback_start_deployment(self, topic, payload):
run_id, request_json, args=self.args, edge_id=self.edge_id,
sender_message_queue=self.message_center.get_sender_message_queue(),
listener_message_queue=self.get_listener_message_queue(),
- status_center_queue=self.get_status_queue()
+ status_center_queue=self.get_status_queue(),
+ process_name=GeneralConstants.get_deploy_master_job_process_name(run_id, self.edge_id)
)
process = self._get_job_runner_manager().get_runner_process(run_id)
if process is not None:
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
index f9bc70452d..51cf5c65ef 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
@@ -12,6 +12,7 @@
from .device_model_msg_object import FedMLModelMsgObject
from .device_client_constants import ClientConstants
from .device_client_data_interface import FedMLClientDataInterface
+from ..scheduler_core.general_constants import GeneralConstants
from ..slave.base_slave_protocol_manager import FedMLBaseSlaveProtocolManager
from .worker_job_runner_manager import FedMLDeployJobRunnerManager
from .device_mqtt_inference_protocol import FedMLMqttInference
@@ -160,7 +161,8 @@ def callback_start_deployment(self, topic, payload):
run_id, request_json, args=self.args, edge_id=self.edge_id,
sender_message_queue=self.message_center.get_sender_message_queue(),
listener_message_queue=self.get_listener_message_queue(),
- status_center_queue=self.get_status_queue()
+ status_center_queue=self.get_status_queue(),
+ process_name=GeneralConstants.get_deploy_slave_job_process_name(run_id, self.edge_id)
)
process = self._get_job_runner_manager().get_runner_process(run_id)
if process is not None:
diff --git a/python/fedml/computing/scheduler/scheduler_core/general_constants.py b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
index 8c60b17bdf..3b40e1df80 100755
--- a/python/fedml/computing/scheduler/scheduler_core/general_constants.py
+++ b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
@@ -65,6 +65,19 @@ class GeneralConstants:
FEDML_OTA_CMD_RESTART = "restart"
FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT = "MODEL_END_POINT"
+ FEDML_PROCESS_NAME_PREFIX = "fedml-process-"
+ FEDML_LAUNCH_MASTER_JOB_RUNNER_TAG = "launch-master-job-runner"
+ FEDML_LAUNCH_SLAVE_JOB_RUNNER_TAG = "launch-slave-job-runner"
+ FEDML_LAUNCH_MASTER_USER_JOB_TAG = "launch-master-user-job"
+ FEDML_DEPLOY_MASTER_JOB_RUNNER_TAG = "deploy-master-job-runner"
+ FEDML_DEPLOY_SLAVE_JOB_RUNNER_TAG = "deploy-slave-job-runner"
+ FEDML_DEPLOY_MASTER_USER_JOB_TAG = "deploy-master-user-job"
+ FEDML_MESSAGE_CENTER_LISTENER_TAG = "message-center-listener"
+ FEDML_MESSAGE_CENTER_SENDER_TAG = "message-center-sender"
+ FEDML_STATUS_CENTER_TAG = "status-center"
+ FEDML_LOG_PROCESS_TAG = "log"
+
+ FEDML_TOPIC_STATUS_CENTER_STOP = "anywhere/status_center/stop"
@staticmethod
def get_package_unzip_dir(package_download_dir):
@@ -216,3 +229,60 @@ def get_topic_complete_job(server_id):
def get_payload_complete_job(run_id, server_id):
payload_complete_job = {"runId": run_id, "serverId": server_id}
return payload_complete_job
+
+ @staticmethod
+ def get_process_name(process_tag, run_id=None, edge_id=None):
+ return f"{GeneralConstants.FEDML_PROCESS_NAME_PREFIX}{process_tag}-run-{run_id}-edge-{edge_id}"
+
+ @staticmethod
+ def get_process_name_with_prefix(process_prefix, run_id=None, edge_id=None):
+ return f"{process_prefix}-run-{run_id}-edge-{edge_id}"
+
+ @staticmethod
+ def get_launch_master_job_process_name(run_id, edge_id):
+ return GeneralConstants.get_process_name(
+ GeneralConstants.FEDML_LAUNCH_MASTER_JOB_RUNNER_TAG, run_id, edge_id)
+
+ @staticmethod
+ def get_launch_slave_job_process_name(run_id, edge_id):
+ return GeneralConstants.get_process_name(
+ GeneralConstants.FEDML_LAUNCH_SLAVE_JOB_RUNNER_TAG, run_id, edge_id)
+
+ @staticmethod
+ def get_launch_master_user_process_name(run_id, edge_id):
+ return GeneralConstants.get_process_name(
+ GeneralConstants.FEDML_LAUNCH_MASTER_USER_JOB_TAG, run_id, edge_id)
+
+ @staticmethod
+ def get_deploy_master_job_process_name(run_id, edge_id):
+ return GeneralConstants.get_process_name(
+ GeneralConstants.FEDML_DEPLOY_MASTER_JOB_RUNNER_TAG, run_id, edge_id)
+
+ @staticmethod
+ def get_deploy_slave_job_process_name(run_id, edge_id):
+ return GeneralConstants.get_process_name(
+ GeneralConstants.FEDML_DEPLOY_SLAVE_JOB_RUNNER_TAG, run_id, edge_id)
+
+ @staticmethod
+ def get_deploy_master_user_process_name(run_id, edge_id):
+ return GeneralConstants.get_process_name(
+ GeneralConstants.FEDML_DEPLOY_MASTER_USER_JOB_TAG, run_id, edge_id)
+
+ @staticmethod
+ def get_log_process_name(run_id, edge_id):
+ return GeneralConstants.get_process_name(
+ GeneralConstants.FEDML_LOG_PROCESS_TAG, run_id, edge_id)
+
+ @staticmethod
+ def get_message_center_listener_process_name(message_center_name):
+ return f"{GeneralConstants.FEDML_PROCESS_NAME_PREFIX}{GeneralConstants.FEDML_MESSAGE_CENTER_LISTENER_TAG}-{message_center_name}"
+
+ @staticmethod
+ def get_message_center_sender_process_name(message_center_name):
+ return f"{GeneralConstants.FEDML_PROCESS_NAME_PREFIX}{GeneralConstants.FEDML_MESSAGE_CENTER_SENDER_TAG}-{message_center_name}"
+
+ @staticmethod
+ def get_status_center_process_name(status_center_tag):
+ return f"{GeneralConstants.FEDML_PROCESS_NAME_PREFIX}{GeneralConstants.FEDML_STATUS_CENTER_TAG}-{status_center_tag}"
+
+
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_center.py b/python/fedml/computing/scheduler/scheduler_core/message_center.py
index aeac1a3855..087e74edf4 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_center.py
@@ -10,8 +10,11 @@
import queue
from os.path import expanduser
+import setproctitle
+
import fedml
from fedml.core.distributed.communication.mqtt.mqtt_manager import MqttManager
+from .general_constants import GeneralConstants
from ..slave.client_constants import ClientConstants
from ....core.mlops.mlops_metrics import MLOpsMetrics
from operator import methodcaller
@@ -140,20 +143,21 @@ def start_sender(self, message_center_name=None):
self.sender_message_queue = multiprocessing.Manager().Queue()
self.message_event = multiprocessing.Event()
self.message_event.clear()
+ process_name = GeneralConstants.get_message_center_sender_process_name(message_center_name)
message_center = FedMLMessageCenter(agent_config=self.sender_agent_config,
sender_message_queue=self.sender_message_queue)
if platform.system() == "Windows":
self.message_center_process = multiprocessing.Process(
target=message_center.run_sender, args=(
self.message_event, self.sender_message_queue,
- message_center_name
+ message_center_name, process_name
)
)
else:
self.message_center_process = fedml.get_process(
target=message_center.run_sender, args=(
self.message_event, self.sender_message_queue,
- message_center_name
+ message_center_name, process_name
)
)
self.message_center_process.start()
@@ -211,7 +215,10 @@ def retry_sending_undelivered_message(self):
# Save the message
self.save_message_record(message_entity.run_id, message_entity.device_id, sent_message_record)
- def run_sender(self, message_event, message_queue, message_center_name):
+ def run_sender(self, message_event, message_queue, message_center_name, process_name=None):
+ if process_name is not None:
+ setproctitle.setproctitle(process_name)
+
if platform.system() != "Windows":
os.setsid()
@@ -345,15 +352,16 @@ def start_listener(
self.listener_message_event = multiprocessing.Event()
self.listener_message_event.clear()
self.listener_agent_config = agent_config
- # message_runner = self.get_message_runner()
- message_runner = self
+ message_runner = self.get_message_runner()
+ # message_runner = self
message_runner.listener_agent_config = agent_config
+ process_name = GeneralConstants.get_message_center_listener_process_name(message_center_name)
if platform.system() == "Windows":
self.listener_message_center_process = multiprocessing.Process(
target=message_runner.run_listener_dispatcher, args=(
self.listener_message_event, self.listener_message_queue,
self.listener_handler_funcs, sender_message_queue,
- sender_message_event, message_center_name, extra_queues
+ sender_message_event, message_center_name, extra_queues, process_name
)
)
else:
@@ -361,7 +369,7 @@ def start_listener(
target=message_runner.run_listener_dispatcher, args=(
self.listener_message_event, self.listener_message_queue,
self.listener_handler_funcs, sender_message_queue,
- sender_message_event, message_center_name, extra_queues
+ sender_message_event, message_center_name, extra_queues, process_name
)
)
self.listener_message_center_process.start()
@@ -398,8 +406,11 @@ def unsubscribe_msg(self, topic):
def run_listener_dispatcher(
self, listener_message_event, listener_message_queue,
listener_funcs, sender_message_queue, sender_message_event,
- message_center_name, extra_queues
+ message_center_name, extra_queues, process_name=None
):
+ if process_name is not None:
+ setproctitle.setproctitle(process_name)
+
if platform.system() != "Windows":
os.setsid()
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 30df7f1905..7b0d00f53d 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -615,7 +615,7 @@ def job_error_processor(self, error_list):
def start_runner_process(
self, run_id, edge_id, request_json, cuda_visible_gpu_ids_str=None,
- sender_message_queue=None, status_center_queue=None
+ sender_message_queue=None, status_center_queue=None, process_name=None
):
return None
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
index dcc4045699..8edf57fcbb 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
@@ -20,7 +20,7 @@ def start_job_runner(
self, run_id, request_json, args=None, edge_id=None, is_server_job=False,
sender_message_queue=None, listener_message_queue=None, status_center_queue=None,
should_start_cloud_server=False, use_local_process_as_cloud_server=False,
- cuda_visible_gpu_ids_str=None
+ cuda_visible_gpu_ids_str=None, process_name=None
):
run_id_str = str(run_id)
self.job_runners[run_id_str] = self._generate_job_runner_instance(
@@ -31,7 +31,8 @@ def start_job_runner(
run_id, request_json, edge_id=edge_id,
sender_message_queue=sender_message_queue,
listener_message_queue=listener_message_queue,
- status_center_queue=status_center_queue
+ status_center_queue=status_center_queue,
+ process_name=process_name
)
def stop_job_runner(self, run_id):
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
index 833fa1edc0..9970b1d3f6 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
@@ -154,6 +154,7 @@ def stop(self, kill_process=False):
self.communication_mgr.disconnect()
if kill_process:
+ self.post_status_center_stopping_message()
self.release_message_center()
RunProcessUtils.kill_process(os.getppid(), exclude_current_pid=True)
@@ -328,5 +329,10 @@ def send_agent_active_msg(self, edge_id):
active_msg = {"ID": edge_id, "status": GeneralConstants.MSG_MLOPS_SERVER_STATUS_IDLE}
self.message_center.send_message_json(self.topic_active, json.dumps(active_msg))
+ def post_status_center_stopping_message(self, run_id=None):
+ topic_status_center_stopping = GeneralConstants.FEDML_TOPIC_STATUS_CENTER_STOP
+ payload = {"run_id": run_id}
+ self.status_reporter.send_message(topic_status_center_stopping, json.dumps(payload))
+
def set_parent_agent(self, parent_agent):
self.parent_agent = parent_agent
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index 76ba9857c6..47dfa9d1a7 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -7,7 +7,10 @@
import multiprocessing
import queue
+import setproctitle
+
import fedml
+from .general_constants import GeneralConstants
from .message_common import FedMLMessageEntity, FedMLStatusEntity
from .message_center import FedMLMessageCenter
import traceback
@@ -84,6 +87,7 @@ class FedMLStatusCenter(object):
TOPIC_SLAVE_JOB_LAUNCH_SUFFIX = "/start_train"
TOPIC_SLAVE_JOB_STOP_PREFIX = "flserver_agent/"
TOPIC_SLAVE_JOB_STOP_SUFFIX = "/stop_train"
+ TOPIC_STATUS_CENTER_STOP_PREFIX = GeneralConstants.FEDML_TOPIC_STATUS_CENTER_STOP
ALLOWED_MAX_JOB_STATUS_CACHE_NUM = 1000
def __init__(self, message_queue=None):
@@ -116,22 +120,25 @@ def start_status_center(self, sender_message_center_queue=None,
self.status_event.clear()
self.status_sender_message_center_queue = sender_message_center_queue
self.status_listener_message_center_queue = listener_message_center_queue
- #self.status_runner = self.get_status_runner()
- self.status_runner = self
+ self.status_runner = self.get_status_runner()
+ #self.status_runner = self
+ process_name = GeneralConstants.get_status_center_process_name(
+ f'{"deploy" if self.is_deployment_status_center else "launch"}_'
+ f'{"slave" if is_slave_agent else "master"}_agent')
target_func = self.status_runner.run_status_dispatcher if not is_slave_agent else \
self.status_runner.run_status_dispatcher_in_slave
if platform.system() == "Windows":
self.status_center_process = multiprocessing.Process(
target=target_func, args=(
self.status_event, self.status_queue, self.status_sender_message_center_queue,
- self.status_listener_message_center_queue, sender_message_event
+ self.status_listener_message_center_queue, sender_message_event, process_name
)
)
else:
self.status_center_process = fedml.get_process(
target=target_func, args=(
self.status_event, self.status_queue, self.status_sender_message_center_queue,
- self.status_listener_message_center_queue, sender_message_event
+ self.status_listener_message_center_queue, sender_message_event, process_name
)
)
@@ -178,7 +185,10 @@ def rebuild_status_center(self, status_queue):
def run_status_dispatcher(self, status_event, status_queue,
sender_message_center_queue,
listener_message_center_queue,
- sender_message_event):
+ sender_message_event, process_name=None):
+ if process_name is not None:
+ setproctitle.setproctitle(process_name)
+
if platform.system() != "Windows":
os.setsid()
@@ -229,6 +239,12 @@ def run_status_dispatcher(self, status_event, status_queue,
message_entity = FedMLMessageEntity(message_body=message_body)
status_entity = FedMLStatusEntity(status_msg_body=message_body)
+ if message_entity.topic.startswith(FedMLStatusCenter.TOPIC_STATUS_CENTER_STOP_PREFIX):
+ # Process the stop message for message center and status center
+ message_center.stop_message_center()
+ self.stop_status_center()
+ continue
+
# Generate status manager instance
run_id_str = str(status_entity.run_id)
run_id_int = int(status_entity.run_id)
@@ -279,7 +295,10 @@ def run_status_dispatcher(self, status_event, status_queue,
def run_status_dispatcher_in_slave(self, status_event, status_queue,
sender_message_center_queue,
listener_message_center_queue,
- sender_message_event):
+ sender_message_event, process_name=None):
+ if process_name is not None:
+ setproctitle.setproctitle(process_name)
+
if platform.system() != "Windows":
os.setsid()
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index 68b40b3291..ec98cc7906 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -137,16 +137,13 @@ def process_job_completed_status(self, master_id, status):
# self.remove_listener_for_run_metrics(self.run_id)
# self.remove_listener_for_run_logs(self.run_id)
+ self.message_center.receive_message(
+ GeneralConstants.get_topic_complete_job(master_id),
+ json.dumps(GeneralConstants.get_payload_complete_job(self.run_id, master_id)))
+
if self.status_center.is_deployment_status_center:
if status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
self.report_deployment_status(self.run_id, GeneralConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
- else:
- self.message_center.receive_message(
- GeneralConstants.get_topic_complete_job(master_id),
- json.dumps(GeneralConstants.get_payload_complete_job(self.run_id, master_id)))
-
- self.message_center.stop_message_center()
- self.status_center.stop_status_center()
def process_job_exception_status(self, master_id, status):
# Report exception job status
diff --git a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
index 8876fc7e39..0486b131a6 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
@@ -7,6 +7,8 @@
import traceback
from abc import ABC, abstractmethod
+import setproctitle
+
import fedml
from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
@@ -48,8 +50,12 @@ def __repr__(self):
)
def run(self, process_event, completed_event, run_extend_queue_list,
- sender_message_center, listener_message_queue, status_center_queue):
- print(f"Client runner process id {os.getpid()}, run id {self.run_id}")
+ sender_message_center, listener_message_queue, status_center_queue,
+ process_name=None):
+ if process_name is not None:
+ setproctitle.setproctitle(process_name)
+
+ print(f"Client runner process id {os.getpid()}, name {process_name}, run id {self.run_id}")
if platform.system() != "Windows":
os.setsid()
@@ -245,7 +251,7 @@ def reset_devices_status(self, edge_id, status):
def start_runner_process(
self, run_id, request_json, edge_id=None,
sender_message_queue=None, listener_message_queue=None,
- status_center_queue=None, cuda_visible_gpu_ids_str=None
+ status_center_queue=None, cuda_visible_gpu_ids_str=None, process_name=None
):
client_runner = self._generate_job_runner_instance(
self.args, run_id=run_id, request_json=request_json,
@@ -265,12 +271,12 @@ def start_runner_process(
self.run_process = multiprocessing.Process(
target=client_runner.run, args=(
self.run_process_event, self.run_process_completed_event, self.run_extend_queue_list,
- sender_message_queue, listener_message_queue, status_center_queue
+ sender_message_queue, listener_message_queue, status_center_queue, process_name
))
else:
self.run_process = fedml.get_process(target=client_runner.run, args=(
self.run_process_event, self.run_process_completed_event, self.run_extend_queue_list,
- sender_message_queue, listener_message_queue, status_center_queue
+ sender_message_queue, listener_message_queue, status_center_queue, process_name
))
self.run_process.start()
return self.run_process
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index 49aad618c1..648a49bbd1 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -262,6 +262,8 @@ def callback_start_train(self, topic, payload):
# Report the run status with finished status and return
self.generate_status_report(run_id, edge_id, server_agent_id=server_agent_id).report_client_id_status(
edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED, run_id=run_id)
+
+ MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, edge_id)
return
logging.info(
f"Run started, available gpu ids: {JobRunnerUtils.get_instance().get_available_gpu_id_list(edge_id)}")
@@ -279,6 +281,7 @@ def callback_start_train(self, topic, payload):
listener_message_queue=self.get_listener_message_queue(),
status_center_queue=self.get_status_queue(),
cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str,
+ process_name=GeneralConstants.get_launch_slave_job_process_name(run_id, edge_id)
)
run_process = self._get_job_runner_manager().get_runner_process(run_id)
if run_process is not None:
diff --git a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
index 6e3cb2ebe1..050cfb3f1d 100755
--- a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
@@ -11,6 +11,7 @@ class FedMLLaunchSlaveProtocolManager(FedMLBaseSlaveProtocolManager):
def __init__(self, args, agent_config=None):
FedMLBaseSlaveProtocolManager.__init__(self, args, agent_config=agent_config)
+ self.message_center_name = "launch_slave_agent"
# Override
def generate_topics(self):
diff --git a/python/fedml/computing/scheduler/slave/united_agents.py b/python/fedml/computing/scheduler/slave/united_agents.py
index 3640ea149e..17aee46f62 100755
--- a/python/fedml/computing/scheduler/slave/united_agents.py
+++ b/python/fedml/computing/scheduler/slave/united_agents.py
@@ -57,8 +57,7 @@ def login(self, userid, api_key=None, device_id=None,
deploy_master_agent.login(
userid, api_key=api_key, device_id=login_result.device_id,
os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM,
- communication_manager=shared_communication_mgr,
- status_center_queue=shared_master_status_center_queue
+ communication_manager=shared_communication_mgr
)
# Login with the deployment slave role based on
@@ -66,10 +65,7 @@ def login(self, userid, api_key=None, device_id=None,
deploy_slave_agent.login(
userid, api_key=api_key, device_id=login_result.device_id,
os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM,
- communication_manager=shared_communication_mgr,
- sender_message_queue=shared_slave_sender_message_queue,
- status_center_queue=shared_slave_status_center_queue,
- sender_message_event=shared_slave_sender_message_event
+ communication_manager=shared_communication_mgr
)
# Start the slave agent to connect to servers and loop forever.
diff --git a/python/fedml/core/mlops/mlops_runtime_log_daemon.py b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
index f72d88cfea..bf136a36c9 100644
--- a/python/fedml/core/mlops/mlops_runtime_log_daemon.py
+++ b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
@@ -8,10 +8,12 @@
import multiprocess as multiprocessing
import requests
+import setproctitle
import yaml
import fedml
from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
+from fedml.computing.scheduler.scheduler_core.general_constants import GeneralConstants
from fedml.core.mlops.mlops_utils import MLOpsLoggingUtils
from ...core.mlops.mlops_configs import MLOpsConfigs
@@ -256,8 +258,11 @@ def should_ignore_log_line(log_line):
return False
- def log_process(self, process_event):
- logging.info(f"Log uploading process id {os.getpid()}, run id {self.run_id}, edge id {self.device_id}")
+ def log_process(self, process_event, process_name=None):
+ if process_name is not None:
+ setproctitle.setproctitle(process_name)
+
+ logging.info(f"Log uploading process id {os.getpid()}, run id {self.run_id}, name {process_name}, edge id {self.device_id}")
self.log_process_event = process_event
only_push_artifact = False
@@ -419,6 +424,8 @@ def set_log_source(self, source):
self.log_source = source
def start_log_processor(self, log_run_id, log_device_id, log_source=None, log_file_prefix=None):
+ if log_run_id == "-1" or int(log_run_id) <= 0:
+ return
log_processor = MLOpsRuntimeLogProcessor(self.args.using_mlops, log_run_id,
log_device_id, self.log_file_dir,
self.log_server_url,
@@ -432,12 +439,13 @@ def start_log_processor(self, log_run_id, log_device_id, log_source=None, log_fi
self.log_process_event_map[event_map_id] = multiprocessing.Event()
self.log_process_event_map[event_map_id].clear()
log_processor.log_process_event = self.log_process_event_map[event_map_id]
+ process_name = GeneralConstants.get_log_process_name(log_run_id, log_device_id)
if platform.system() == "Windows":
log_child_process = multiprocessing.Process(
- target=log_processor.log_process, args=(self.log_process_event_map[event_map_id],))
+ target=log_processor.log_process, args=(self.log_process_event_map[event_map_id], process_name))
else:
log_child_process = fedml.get_process(
- target=log_processor.log_process, args=(self.log_process_event_map[event_map_id],))
+ target=log_processor.log_process, args=(self.log_process_event_map[event_map_id], process_name))
# process = threading.Thread(target=log_processor.log_process)
# process.start()
if log_child_process is not None:
diff --git a/python/setup.py b/python/setup.py
index f00c0b4335..d2d9441277 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -126,7 +126,7 @@ def finalize_options(self):
setup(
name="fedml",
- version="0.8.31b23",
+ version="0.8.51b1",
author="FedML Team",
author_email="ch@fedml.ai",
description="A research and production integrated edge-cloud library for "
From fd257b801430d789225b93afff46d514f99c6654 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Thu, 20 Jun 2024 19:28:24 +0800
Subject: [PATCH 175/282] [CoreEngine] update the dependant libs.
---
python/setup.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/setup.py b/python/setup.py
index d2d9441277..f8c323d63f 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -69,7 +69,8 @@ def finalize_options(self):
'python-dotenv',
'protobuf>=3.20.2,<4.0dev',
'typer<0.10.0,>=0.3.0',
- 'fastapi-cli==0.0.1'
+ 'fastapi-cli==0.0.1',
+ 'setproctitle'
]
requirements_extra_mpi = [
From 7ccf195113d4c5dccfe3fabc3aae9fea71f33e0d Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Sat, 15 Jun 2024 00:17:04 +0000
Subject: [PATCH 176/282] [Deploy] Support arbitrary container image
onboarding.
---
.../custom_inference_image.yaml | 19 +-
.../custom_inference_image/serve_main.py | 16 --
.../scheduler/comm_utils/job_monitor.py | 27 +-
.../device_client_constants.py | 4 +
.../device_http_inference_protocol.py | 8 +-
.../device_model_deployment.py | 265 +++++++++---------
.../model_scheduler/device_model_inference.py | 56 ++--
.../model_scheduler/worker_job_runner.py | 16 --
python/fedml/core/mlops/mlops_device_perfs.py | 2 +-
9 files changed, 201 insertions(+), 212 deletions(-)
delete mode 100644 python/examples/deploy/custom_inference_image/serve_main.py
diff --git a/python/examples/deploy/custom_inference_image/custom_inference_image.yaml b/python/examples/deploy/custom_inference_image/custom_inference_image.yaml
index 0c62767b40..467c7c48b0 100644
--- a/python/examples/deploy/custom_inference_image/custom_inference_image.yaml
+++ b/python/examples/deploy/custom_inference_image/custom_inference_image.yaml
@@ -1,13 +1,14 @@
workspace: "./"
-job: |
- echo "Start serving..."
- python3 serve_main.py
-bootstrap: |
- echo "Bootstrap start..."
- echo "Bootstrap finished!"
+inference_image_name: "ghcr.io/predibase/lorax:main"
+container_run_command: "--model-id mistralai/Mistral-7B-Instruct-v0.1"
-enable_custom_image: true
-inference_image_name: "fedml/fedml-default-inference-backend"
-deploy_timeout: 1000
+environment_variables:
+ HUGGING_FACE_HUB_TOKEN: ""
+readiness_probe:
+ path: "health"
+
+port: 80
+
+deploy_timeout: 1600
diff --git a/python/examples/deploy/custom_inference_image/serve_main.py b/python/examples/deploy/custom_inference_image/serve_main.py
deleted file mode 100644
index a7a1dd84f3..0000000000
--- a/python/examples/deploy/custom_inference_image/serve_main.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from fedml.serving import FedMLPredictor
-from fedml.serving import FedMLInferenceRunner
-
-
-class DummyPredictor(FedMLPredictor):
- def __init__(self):
- super().__init__()
-
- def predict(self, request):
- return {"Aloha": request}
-
-
-if __name__ == "__main__":
- predictor = DummyPredictor()
- fedml_inference_runner = FedMLInferenceRunner(predictor)
- fedml_inference_runner.run()
\ No newline at end of file
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index 97a4cb6ebc..d216b46dad 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -40,6 +40,7 @@
from fedml.core.mlops.mlops_runtime_log import MLOpsRuntimeLog
from fedml.core.mlops.mlops_utils import MLOpsLoggingUtils
from fedml.core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
+from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants
from ..scheduler_core.endpoint_sync_protocol import FedMLEndpointSyncProtocol
from ..model_scheduler.device_server_constants import ServerConstants
@@ -758,9 +759,8 @@ def monitor_slave_endpoint_status(self):
except Exception as e:
pass
- def _lenient_check_replica_ready(
- self, deployment_result
- ):
+ @staticmethod
+ def _lenient_check_replica_ready(deployment_result):
"""
Double-check the replica's liveness using /ready api:
if 200 -> return True
@@ -769,8 +769,27 @@ def _lenient_check_replica_ready(
"""
result_json = deployment_result
inference_url = result_json.get("model_url", None)
+ liveliness_check = result_json.get("model_metadata", {}).get("liveliness_check", None)
+ readiness_check = result_json.get("model_metadata", {}).get("readiness_check", None)
+
+ if liveliness_check is not None:
+ if liveliness_check == ClientConstants.LIVENESS_PROBE_DEFAULT:
+ liveliness_check = readiness_check # Follow the readiness check pattern
+ if not isinstance(liveliness_check, dict):
+ logging.warning(f"Healthiness check is not a dict. {liveliness_check}")
+ return True
+ if "path" not in liveliness_check:
+ logging.warning(f"Healthiness check does not have path. {liveliness_check}")
+ return True
+ response_ok = asyncio.run(FedMLHttpInference.is_inference_ready(
+ inference_url, timeout=SchedulerConstants.ENDPOINT_INFERENCE_READY_TIMEOUT,
+ path=liveliness_check["path"]))
+ if response_ok is None:
+ # This means the server return 202
+ return False
+ return True
- # Make a curl get to inference_url with timeout 5s
+ # Make a curl get to inference_url/ready with timeout 5s
# TODO(Raphael): Also support PROXY and MQTT to check the readiness
response_ok = asyncio.run(FedMLHttpInference.is_inference_ready(inference_url, timeout=5))
if response_ok is None:
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
index fdcbdf0a34..cd21de2e04 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
@@ -143,6 +143,10 @@ class ClientConstants(object):
DEVICE_DIFF_DELETE_OPERATION = "op: delete"
DEVICE_DIFF_REPLACE_OPERATION = "op: replace"
+ READINESS_PROBE_DEFAULT = "DEFAULT"
+ LIVENESS_PROBE_DEFAULT = "DEFAULT"
+
+
LOGIN_MODE_ON_PREMISE_INDEX = 0
LOGIN_MODE_FEDML_CLOUD_INDEX = 1
LOGIN_MODE_PUBLIC_CLOUD_INDEX = 2
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
index 7e4c06ea5d..41c565d5d8 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
@@ -14,14 +14,14 @@ def __init__(self):
pass
@staticmethod
- async def is_inference_ready(inference_url, timeout=None):
- '''
+ async def is_inference_ready(inference_url, path="ready", timeout=None):
+ """
True: inference is ready
False: cannot be reached, will try other protocols
None: can be reached, but not ready
- '''
+ """
url_parsed = urlparse(inference_url)
- ready_url = f"http://{url_parsed.hostname}:{url_parsed.port}/ready"
+ ready_url = f"http://{url_parsed.hostname}:{url_parsed.port}/{path}"
response_ok = False
try:
async with httpx.AsyncClient() as client:
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index edd2ebea9a..71f0c8032a 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -68,42 +68,26 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
num_gpus = gpu_per_replica
gpu_ids, gpu_attach_cmd = None, ""
- # Concatenate the model name
+ # Concatenate the full model name
running_model_name = ClientConstants.get_running_model_name(
end_point_name, inference_model_name, model_version, end_point_id, model_id, edge_id=edge_id)
- # Parse the model config file and get the necessary information for the deployment
+ # Parse the model config file
model_config_path = os.path.join(model_storage_local_path, "fedml_model_config.yaml")
with open(model_config_path, 'r') as file:
config = yaml.safe_load(file)
+ inference_type = "default"
# Resource related
- inference_type = "default"
- use_gpu = config.get('use_gpu', True)
- num_gpus_frm_yml = config.get('num_gpus', None)
- if not use_gpu:
- num_gpus = 0
- else:
- if num_gpus_frm_yml is not None:
- num_gpus = int(num_gpus_frm_yml)
- usr_indicated_wait_time = config.get('deploy_timeout', 900)
- usr_indicated_retry_cnt = max(int(usr_indicated_wait_time) // 10, 1)
- shm_size = config.get('shm_size', None)
- storage_opt = config.get('storage_opt', None)
- tmpfs = config.get('tmpfs', None)
- cpus = config.get('cpus', None)
- if cpus is not None:
- cpus = int(cpus)
- memory = config.get('memory', None)
-
- inference_image_name = config.get('inference_image_name',
- ClientConstants.INFERENCE_SERVER_CUSTOME_IMAGE)
- image_pull_policy = config.get('image_pull_policy', SchedulerConstants.IMAGE_PULL_POLICY_IF_NOT_PRESENT)
-
- # Source code dir, bootstrap dir, data cache dir
- src_code_dir = os.path.join(model_storage_local_path, config.get('source_code_dir', ""))
+ use_gpu, num_gpus, shm_size, storage_opt, tmpfs, cpus, memory, port_inside_container = \
+ parse_resource_related_config(config, gpu_per_replica)
- # Get the bootstrap and job commands inside the yaml file
+ # Image related
+ inference_image_name, image_pull_policy, registry_name, registry_provider, \
+ registry_user_name, registry_user_password = parse_image_registry_related_config(config)
+
+ # Bootstrap, job and entrypoint related
+ dst_model_serving_dir = "/home/fedml/models_serving"
bootstrap_cmds_str_frm_yaml = config.get('bootstrap', "")
job_cmds_str_frm_yaml = config.get('job', "")
@@ -119,36 +103,37 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
else:
src_bootstrap_file_path = ""
- data_cache_dir_input = config.get('data_cache_dir', "")
- request_input_example = config.get('request_input_example', None)
- extra_envs = config.get('environment_variables', None)
-
- # Serving dir inside docker
- dst_model_serving_dir = "/home/fedml/models_serving"
- relative_entry = config.get('entry_point')
if src_bootstrap_file_path != "":
dst_bootstrap_dir = os.path.join(dst_model_serving_dir, auto_gen_bootstrap_file_name)
else:
dst_bootstrap_dir = ""
- # If using customized image, then bootstrap + job will be the entry point
- enable_custom_image = config.get("enable_custom_image", False)
- # inference_type = "custom"
- customized_image_entry_cmd = \
- "/bin/bash /home/fedml/models_serving/fedml-deploy-bootstrap-entry-auto-gen.sh"
+ # If the entry point is in fedml format (e.g., "main.py")
+ relative_entry_fedml_format = config.get('entry_point', "")
+
+ # User indicate either fedml format python main entry filename or entry command
+ customized_image_entry_cmd = config.get('container_run_command', None)
+ customized_readiness_check = config.get('readiness_probe', ClientConstants.READINESS_PROBE_DEFAULT)
+ customized_liveliness_check = config.get('liveness_probe', ClientConstants.LIVENESS_PROBE_DEFAULT)
+
+ # Storage related
+ src_code_dir = os.path.join(model_storage_local_path, config.get('source_code_dir', ""))
+ data_cache_dir_input = config.get('data_cache_dir', "")
- docker_registry_user_name = config.get("docker_registry_user_name", "")
- docker_registry_user_password = config.get("docker_registry_user_password", "")
- docker_registry = config.get("docker_registry", "")
+ # Others
+ extra_envs = config.get('environment_variables', None)
+ usr_indicated_wait_time = config.get('deploy_timeout', 900)
+ usr_indicated_retry_cnt = max(int(usr_indicated_wait_time) // 10, 1)
+ request_input_example = config.get('request_input_example', None)
- port_inside_container = int(config.get("port", 2345))
+ # Parameter's check
+ if inference_engine != ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT:
+ raise Exception(f"inference engine {inference_engine} is not supported")
- # Request the GPU ids for the deployment
+ # Request the GPU
if num_gpus > 0:
gpu_ids, gpu_attach_cmd = request_gpu_ids_on_deployment(
edge_id, end_point_id, num_gpus=num_gpus, master_device_id=master_device_id)
-
- # set replica and their gpu ids
FedMLModelCache.get_instance().set_redis_params()
FedMLModelCache.get_instance().set_replica_gpu_ids(
end_point_id, end_point_name, inference_model_name, edge_id, replica_rank+1, gpu_ids)
@@ -159,50 +144,51 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
if not os.path.exists(model_serving_dir):
os.makedirs(model_serving_dir, exist_ok=True)
- if inference_engine != ClientConstants.INFERENCE_ENGINE_TYPE_INT_DEFAULT:
- raise Exception(f"inference engine {inference_engine} is not supported")
-
- # Get the master device id
- logging.info(f"master ip: {master_ip}, worker ip: {infer_host}")
+ # Determine whether to report public ip or localhost
if infer_host == master_ip:
logging.info("infer_host is the same as master ip, will use 127.0.0.1 to avoid firewall issue")
infer_host = "127.0.0.1"
+ else:
+ logging.info("Master and worker are located in different machines, will use the public ip for inference")
+ # Init container interface client
try:
client = docker.from_env()
- if enable_custom_image and docker_registry_user_name != "" and docker_registry_user_password != "" \
- and docker_registry != "":
- client.login(username=docker_registry_user_name, password=docker_registry_user_password,
- registry=docker_registry)
+ if registry_provider == "Docker" and registry_user_name != "" and registry_user_password != "" \
+ and registry_name != "":
+ client.login(username=registry_user_name, password=registry_user_password,
+ registry=registry_name)
except Exception:
logging.error("Failed to connect to the docker daemon, please ensure that you have "
"installed Docker Desktop or Docker Engine, and the docker is running")
return "", "", None, None, None
+ # Pull the inference image
+ logging.info(f"Start pulling the inference image {inference_image_name}... with policy {image_pull_policy}")
+ ContainerUtils.get_instance().pull_image_with_policy(image_pull_policy, inference_image_name)
+
+ # Remove if the container exists
container_prefix = ("{}".format(ClientConstants.FEDML_DEFAULT_SERVER_CONTAINER_NAME_PREFIX) + "__" +
security_utils.get_content_hash(running_model_name))
-
default_server_container_name = container_prefix + "__" + str(replica_rank)
-
try:
exist_container_obj = client.containers.get(default_server_container_name)
except docker.errors.NotFound:
exist_container_obj = None
except docker.errors.APIError:
raise Exception("Failed to get the container object")
+ # Allocate the GPU
+ # TODO: Make sure no competition for each replica in a single deployment
+ if exist_container_obj is not None:
+ client.api.remove_container(exist_container_obj.id, v=True, force=True)
- # Pull the inference image
- logging.info(f"Start pulling the inference image {inference_image_name}... with policy {image_pull_policy}")
- ContainerUtils.get_instance().pull_image_with_policy(image_pull_policy, inference_image_name)
-
+ # Build host config
volumes = []
binds = {}
environment = {}
- # data_cache_dir mounting
if isinstance(data_cache_dir_input, str):
# In this case, we mount to the same folder, if it has ~, we replace it with /home/fedml
- src_data_cache_dir, dst_data_cache_dir = "", ""
if data_cache_dir_input != "":
if data_cache_dir_input[0] == "~":
src_data_cache_dir = os.path.expanduser(data_cache_dir_input)
@@ -239,16 +225,17 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
else:
logging.warning("data_cache_dir_input is not a string or a dictionary, skip mounting it to the container")
- # Default mounting
- if not enable_custom_image or (enable_custom_image and relative_entry != ""):
+ # FedML format main entry filename, e.g., main.py
+ if relative_entry_fedml_format != "":
logging.info("Start copying the source code to the container...")
volumes.append(src_code_dir)
binds[src_code_dir] = {
"bind": dst_model_serving_dir,
"mode": "rw"
}
- environment["MAIN_ENTRY"] = relative_entry
+ environment["MAIN_ENTRY"] = relative_entry_fedml_format
+ # Host config
host_config_dict = {
"binds": binds,
"port_bindings": {
@@ -261,10 +248,6 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
"mem_limit": memory
}
- # Allocate the GPU
- # TODO: Make sure no competition for each replica in a single deployment
- if exist_container_obj is not None:
- client.api.remove_container(exist_container_obj.id, v=True, force=True)
device_mapping = {}
if no_real_gpu_allocation is not None:
use_gpu = not no_real_gpu_allocation
@@ -277,6 +260,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
host_config_dict.update(device_mapping)
# Environment variables
+ enable_custom_image = False if relative_entry_fedml_format != "" else True
if not enable_custom_image:
# For some image, the default user is root. Unified to fedml.
environment["HOME"] = "/home/fedml"
@@ -288,7 +272,6 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
environment["FEDML_ENV_VERSION"] = fedml.get_env_version()
environment["FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_HOST"] = fedml.get_local_on_premise_platform_host()
environment["FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_PORT"] = fedml.get_local_on_premise_platform_port()
-
if extra_envs is not None:
for key in extra_envs:
environment[key] = extra_envs[key]
@@ -304,8 +287,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
environment=environment,
host_config=host_config,
detach=True,
- command=customized_image_entry_cmd if enable_custom_image else None,
- entrypoint=customized_image_entry_cmd if enable_custom_image else None
+ command=customized_image_entry_cmd,
)
client.api.start(container=new_container.get("Id"))
except Exception as e:
@@ -333,11 +315,12 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
inference_model_name, inference_engine, inference_http_port, inference_type,
retry_interval=10, deploy_attempt_threshold=usr_indicated_retry_cnt,
request_input_example=request_input_example, infer_host=infer_host,
- enable_custom_image=enable_custom_image)
+ readiness_check=customized_readiness_check)
# Return the running model name and the inference output url
inference_output_url, running_model_version, ret_model_metadata, ret_model_config = \
check_container_readiness(inference_http_port=inference_http_port, infer_host=infer_host,
+ readiness_check=customized_readiness_check,
request_input_example=request_input_example)
if inference_output_url == "":
@@ -345,51 +328,24 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
# Successfully get the result from the container
model_metadata = ret_model_metadata
+ model_metadata["liveliness_check"] = customized_liveliness_check
+ model_metadata["readiness_check"] = customized_readiness_check
logging.info(f"[Worker][Replica{replica_rank}] Model deployment is successful with inference_output_url: "
f"{inference_output_url}, model_metadata: {model_metadata}, model_config: {ret_model_config}")
return running_model_name, inference_output_url, model_version, model_metadata, ret_model_config
-def build_inference_req(end_point_name, model_name, token, in_model_metadata):
- model_inputs = in_model_metadata["inputs"]
- ret_inputs = list()
-
- for input_item in model_inputs:
- ret_item = input_item
- shape = ret_item["shape"]
- data_type = ret_item["datatype"]
- if ClientConstants.MODEL_DATA_TYPE_MAPPING[data_type] == ClientConstants.MODEL_DATA_TYPE_INT:
- for i in range(len(shape)):
- if shape[i] == -1: # if input shape is dynamic, we set a default value 1
- shape[i] = 1
- ret_item["data"] = torch.randint(0, 1, shape).tolist()
- else:
- for i in range(len(shape)):
- if shape[i] == -1: # if input shape is dynamic, we set a default value 1
- shape[i] = 1
- ret_item["data"] = torch.zeros(shape).tolist()
- ret_inputs.append(ret_item)
-
- input_json = {"end_point_name": end_point_name,
- "model_name": model_name,
- "token": str(token),
- "inputs": ret_inputs,
- "outputs": in_model_metadata["outputs"]}
- output_json = in_model_metadata["outputs"]
-
- return input_json, output_json
-
-
def should_exit_logs(end_point_id, model_id, cmd_type, model_name, inference_engine, inference_port,
inference_type="default", request_input_example=None, infer_host="127.0.0.1",
- enable_custom_image=False):
+ readiness_check=ClientConstants.READINESS_PROBE_DEFAULT):
if cmd_type == ClientConstants.CMD_TYPE_RUN_DEFAULT_SERVER:
# TODO: Exited Quickly if the container is Exited or Removed
# If the container has exited, return True, means we should exit the logs
try:
inference_output_url, model_version, model_metadata, model_config = \
check_container_readiness(inference_http_port=inference_port, infer_host=infer_host,
+ readiness_check=readiness_check,
request_input_example=request_input_example)
if inference_output_url != "":
logging.info("Log test for deploying model successfully, inference url: {}, "
@@ -410,7 +366,7 @@ def log_deployment_output(end_point_id, model_id, cmd_container_name, cmd_type,
inference_http_port, inference_type="default",
retry_interval=10, deploy_attempt_threshold=10,
request_input_example=None, infer_host="127.0.0.1",
- enable_custom_image=False):
+ readiness_check=ClientConstants.READINESS_PROBE_DEFAULT):
deploy_attempt = 0
last_log_time = datetime.datetime.now()
@@ -478,11 +434,10 @@ def log_deployment_output(end_point_id, model_id, cmd_container_name, cmd_type,
client.api.remove_container(container_obj.id, v=True, force=True)
break
- # should_exit_logs will ping the inference container
- # return True if ready
+ # should_exit_logs will ping the inference container, return True if ready
if should_exit_logs(end_point_id, model_id, cmd_type, inference_model_name, inference_engine,
inference_http_port, inference_type, request_input_example,
- infer_host, enable_custom_image=enable_custom_image):
+ infer_host, readiness_check=readiness_check):
break
# Not yet ready, retry
@@ -504,10 +459,58 @@ def log_deployment_output(end_point_id, model_id, cmd_container_name, cmd_type,
time.sleep(retry_interval)
-def is_client_inference_container_ready(infer_url_host, inference_http_port, readiness_check_type="default",
- readiness_check_cmd=None, request_input_example=None):
+def parse_resource_related_config(config, gpu_num_frm_platform=0):
+ use_gpu = config.get('use_gpu', True)
+ num_gpus_frm_yml = config.get('num_gpus', None)
+
+ num_gpus = gpu_num_frm_platform
+ # Priority: num_gpus from yaml > num_gpus from platform
+ if use_gpu:
+ if num_gpus_frm_yml is not None:
+ num_gpus = int(num_gpus_frm_yml)
+ else:
+ num_gpus = 0
+
+ shm_size = config.get('shm_size', None)
+ storage_opt = config.get('storage_opt', None)
+ tmpfs = config.get('tmpfs', None)
+ cpus = config.get('cpus', None)
+ if cpus is not None:
+ cpus = int(cpus)
+ memory = config.get('memory', None)
+ port_inside_container = int(config.get("port", 2345))
+
+ return use_gpu, num_gpus, shm_size, storage_opt, tmpfs, cpus, memory, port_inside_container
+
+
+def parse_image_registry_related_config(config):
+ inference_image_name = config.get('inference_image_name', ClientConstants.INFERENCE_SERVER_CUSTOME_IMAGE)
+ image_pull_policy = config.get('image_pull_policy', SchedulerConstants.IMAGE_PULL_POLICY_IF_NOT_PRESENT)
+
+ # Optional
+ registry_specs = config.get('registry_specs', {})
+ registry_name = registry_specs.get("docker_registry_user_name", "")
+ registry_provider = registry_specs.get("registry_provider", "")
+ registry_user_name = config.get("registry_user_name", "")
+ registry_user_password = config.get("registry_user_password", "")
+
+ return (inference_image_name, image_pull_policy, registry_name, registry_provider,
+ registry_user_name, registry_user_password)
+
+
+def is_client_inference_container_ready(infer_url_host, inference_http_port,
+ readiness_check=ClientConstants.READINESS_PROBE_DEFAULT,
+ request_input_example=None, container_id=None):
+ # Construct the model metadata (input and output)
+ model_metadata = {}
+ if request_input_example is not None and len(request_input_example) > 0:
+ model_metadata["inputs"] = request_input_example
+ else:
+ model_metadata["inputs"] = {"text": "What is a good cure for hiccups?"}
+ model_metadata["outputs"] = []
+ model_metadata["type"] = "default"
- if readiness_check_type == "default":
+ if readiness_check == ClientConstants.READINESS_PROBE_DEFAULT:
default_client_container_ready_url = "http://{}:{}/ready".format("0.0.0.0", inference_http_port)
response = None
try:
@@ -517,26 +520,36 @@ def is_client_inference_container_ready(infer_url_host, inference_http_port, rea
if not response or response.status_code != 200:
return "", "", {}, {}
- # Construct the model metadata (input and output)
- model_metadata = {}
- if request_input_example is not None and len(request_input_example) > 0:
- model_metadata["inputs"] = request_input_example
- else:
- model_metadata["inputs"] = {"text": "What is a good cure for hiccups?"}
- model_metadata["outputs"] = []
- model_metadata["type"] = "default"
-
return "http://{}:{}/predict".format(infer_url_host, inference_http_port), None, model_metadata, None
else:
- # TODO(Raphael): Support arbitrary readiness check command
- logging.error(f"Unknown readiness check type: {readiness_check_type}")
- return "", "", {}, {}
+ if not isinstance(readiness_check, dict):
+ logging.error(f"Unknown readiness check type: {readiness_check}")
+ return "", "", {}, {}
+
+ if "path" in readiness_check:
+ readiness_check_url = f"http://{infer_url_host}:{inference_http_port}/{readiness_check['path']}"
+ response = None
+ try:
+ response = requests.get(readiness_check_url)
+ except:
+ pass
+ if not response or response.status_code != 200:
+ return "", "", {}, {}
+
+ return "http://{}:{}/".format(infer_url_host, inference_http_port), None, model_metadata, None
+ elif "command" in readiness_check:
+ # TODO(raphael): Support arbitrary readiness check command by using
+ # container id and docker exec
+ return "http://{}:{}/".format(infer_url_host, inference_http_port), None, model_metadata, None
+ else:
+ logging.error(f"Unknown readiness check type: {readiness_check}")
+ return "", "", {}, {}
def check_container_readiness(inference_http_port, infer_host="127.0.0.1", request_input_example=None,
- readiness_check_type="default", readiness_check_cmd=None):
+ readiness_check=ClientConstants.READINESS_PROBE_DEFAULT):
response_from_client_container = is_client_inference_container_ready(
- infer_host, inference_http_port, readiness_check_type, readiness_check_cmd,
+ infer_host, inference_http_port, readiness_check=readiness_check,
request_input_example=request_input_example)
return response_from_client_container
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index ba13006245..84141851b0 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -342,56 +342,40 @@ async def send_inference_request(idle_device, end_point_id, inference_url, input
try:
if connectivity_type == ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP:
- response_ok = await FedMLHttpInference.is_inference_ready(
+ response_ok, inference_response = await FedMLHttpInference.run_http_inference_with_curl_request(
inference_url,
+ input_list,
+ output_list,
+ inference_type=inference_type,
timeout=request_timeout_sec)
- if response_ok:
- response_ok, inference_response = await FedMLHttpInference.run_http_inference_with_curl_request(
- inference_url,
- input_list,
- output_list,
- inference_type=inference_type,
- timeout=request_timeout_sec)
- logging.debug(f"Use http inference. return {response_ok}")
- return inference_response
+ logging.debug(f"Use http inference. return {response_ok}")
+ return inference_response
elif connectivity_type == ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP_PROXY:
- logging.warning("Use http proxy inference.")
- response_ok = await FedMLHttpProxyInference.is_inference_ready(
+ logging.debug("Use http proxy inference.")
+ response_ok, inference_response = await FedMLHttpProxyInference.run_http_proxy_inference_with_request(
+ end_point_id,
inference_url,
+ input_list,
+ output_list,
+ inference_type=inference_type,
timeout=request_timeout_sec)
- if response_ok:
- response_ok, inference_response = await FedMLHttpProxyInference.run_http_proxy_inference_with_request(
- end_point_id,
- inference_url,
- input_list,
- output_list,
- inference_type=inference_type,
- timeout=request_timeout_sec)
- logging.info(f"Use http proxy inference. return {response_ok}")
- return inference_response
+ logging.debug(f"Use http proxy inference. return {response_ok}")
+ return inference_response
elif connectivity_type == ClientConstants.WORKER_CONNECTIVITY_TYPE_MQTT:
- logging.warning("Use mqtt inference.")
+ logging.debug("Use mqtt inference.")
agent_config = {"mqtt_config": Settings.mqtt_config}
mqtt_inference = FedMLMqttInference(
agent_config=agent_config,
run_id=end_point_id)
- response_ok = mqtt_inference.run_mqtt_health_check_with_request(
+ response_ok, inference_response = mqtt_inference.run_mqtt_inference_with_request(
idle_device,
end_point_id,
inference_url,
+ input_list,
+ output_list,
+ inference_type=inference_type,
timeout=request_timeout_sec)
- inference_response = {"error": True, "message": "Failed to use http, http-proxy and mqtt for inference."}
- if response_ok:
- response_ok, inference_response = mqtt_inference.run_mqtt_inference_with_request(
- idle_device,
- end_point_id,
- inference_url,
- input_list,
- output_list,
- inference_type=inference_type,
- timeout=request_timeout_sec)
-
- logging.info(f"Use mqtt inference. return {response_ok}.")
+ logging.debug(f"Use mqtt inference. return {response_ok}.")
return inference_response
else:
return {"error": True, "message": "Failed to use http, http-proxy for inference, no response from replica."}
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index 8100707386..a892412d29 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -460,22 +460,6 @@ def construct_deployment_results(self, end_point_name, device_id, model_status,
}
return deployment_results_payload
- def construct_deployment_status(self, end_point_name, device_id,
- model_id, model_name, model_version,
- model_inference_url, model_status,
- inference_port=ClientConstants.MODEL_INFERENCE_DEFAULT_PORT,
- replica_no=1, # start from 1
- ):
- deployment_status_payload = {"end_point_id": self.run_id, "end_point_name": end_point_name,
- "device_id": device_id,
- "model_id": model_id, "model_name": model_name,
- "model_version": model_version,
- "model_url": model_inference_url, "model_status": model_status,
- "inference_port": inference_port,
- "replica_no": replica_no,
- }
- return deployment_status_payload
-
def send_deployment_results(self, end_point_name, device_id, model_status,
model_id, model_name, model_inference_url,
model_version, inference_port, inference_engine,
diff --git a/python/fedml/core/mlops/mlops_device_perfs.py b/python/fedml/core/mlops/mlops_device_perfs.py
index 29183a6e78..4bb41df73f 100644
--- a/python/fedml/core/mlops/mlops_device_perfs.py
+++ b/python/fedml/core/mlops/mlops_device_perfs.py
@@ -42,7 +42,7 @@ def __init__(self):
self.monitor_replica_num_process = None
self.monitor_replica_perf_process = None
self.job_total_monitor_process = None
- self.enable_job_total_monitor = False
+ self.enable_job_total_monitor = False # TODO(Raphael): Enable the healthiness check by this job total monitor
self.args = None
self.device_id = None
self.run_id = None
From 9ca6ecc1d23166223e7788adfbe688379a18f193 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Mon, 17 Jun 2024 18:25:59 -0700
Subject: [PATCH 177/282] [Deploy] Add LoraX and Triton examples; Add url match
pattern.
---
.../deploy/custom_inference_image/README.md | 48 -------------------
.../{ => lorax}/custom_inference_image.yaml | 0
.../template/custom_inference_image.yaml | 16 +++++++
.../model_repository/dummy/1/model.py | 25 ++++++++++
.../scheduler/comm_utils/network_util.py | 11 +++++
.../device_model_deployment.py | 19 ++++----
.../model_scheduler/device_model_inference.py | 28 +++++++++--
7 files changed, 87 insertions(+), 60 deletions(-)
delete mode 100644 python/examples/deploy/custom_inference_image/README.md
rename python/examples/deploy/custom_inference_image/{ => lorax}/custom_inference_image.yaml (100%)
create mode 100644 python/examples/deploy/custom_inference_image/triton_inference_server/template/custom_inference_image.yaml
create mode 100644 python/examples/deploy/custom_inference_image/triton_inference_server/template/model_repository/dummy/1/model.py
diff --git a/python/examples/deploy/custom_inference_image/README.md b/python/examples/deploy/custom_inference_image/README.md
deleted file mode 100644
index 1269e4c064..0000000000
--- a/python/examples/deploy/custom_inference_image/README.md
+++ /dev/null
@@ -1,48 +0,0 @@
-## Create a model card at local
-First, create a model card at local
-```bash
-fedml model create -n custom_inference_image -cf custom_inference_image.yaml
-```
-
-## Low Code UI Deploy
-Push the model to nexus ai platform
-```bash
-fedml model push -n custom_inference_image
-```
-Do the following docs to deploy the model on nexus ai platform
-https://docs-dev.fedml.ai/deploy/low_code_ui
-
-## CLI Deploy
-### Deploy to current machine
-Docs: https://docs-dev.fedml.ai/deploy/deploy_local
-```bash
-fedml model deploy -n custom_inference_image --local
-```
-
-### Deploy to On-premise
-Docs: https://docs-dev.fedml.ai/deploy/deploy_on_premise
-```bash
-fedml device bind $api_key
-```
-```bash
-fedml model deploy -n my_model -m $master_ids -w $worker_ids
-```
-
-### Deploy to GPU Cloud
-Docs: https://docs-dev.fedml.ai/deploy/deploy_cloud
-
-Change the `custom_inference_image.yaml` file, adding following lines
-```yaml
-computing:
- minimum_num_gpus: 1 # minimum # of GPUs to provision
- maximum_cost_per_hour: $3000 # max cost per hour for your job per gpu card
- #allow_cross_cloud_resources: true # true, false
- #device_type: CPU # options: GPU, CPU, hybrid
- resource_type: A100-80G # e.g., A100-80G,
- # please check the resource type list by "fedml show-resource-type"
- # or visiting URL: https://fedml.ai/accelerator_resource_type
-```
-
-```bash
-fedml model deploy -n custom_inference_image
-```
\ No newline at end of file
diff --git a/python/examples/deploy/custom_inference_image/custom_inference_image.yaml b/python/examples/deploy/custom_inference_image/lorax/custom_inference_image.yaml
similarity index 100%
rename from python/examples/deploy/custom_inference_image/custom_inference_image.yaml
rename to python/examples/deploy/custom_inference_image/lorax/custom_inference_image.yaml
diff --git a/python/examples/deploy/custom_inference_image/triton_inference_server/template/custom_inference_image.yaml b/python/examples/deploy/custom_inference_image/triton_inference_server/template/custom_inference_image.yaml
new file mode 100644
index 0000000000..02dca147ce
--- /dev/null
+++ b/python/examples/deploy/custom_inference_image/triton_inference_server/template/custom_inference_image.yaml
@@ -0,0 +1,16 @@
+workspace: "./"
+
+inference_image_name: "nvcr.io/nvidia/tritonserver:24.05-py3"
+
+# If you put the model repository in $workspace/model_repository, it will be mounted to /home/fedml/models_serving/model_repository
+container_run_command: "tritonserver --model-repository=/home/fedml/models_serving/model_repository"
+
+# If your image has the repository inside it, say in /my_models_dir/model_repository, you can do:
+#container_run_command: "tritonserver --model-repository=/my_models_dir/model_repository"
+
+readiness_probe:
+ path: "v2/health/ready"
+
+port: 8000
+
+deploy_timeout: 1600
diff --git a/python/examples/deploy/custom_inference_image/triton_inference_server/template/model_repository/dummy/1/model.py b/python/examples/deploy/custom_inference_image/triton_inference_server/template/model_repository/dummy/1/model.py
new file mode 100644
index 0000000000..0404a127ff
--- /dev/null
+++ b/python/examples/deploy/custom_inference_image/triton_inference_server/template/model_repository/dummy/1/model.py
@@ -0,0 +1,25 @@
+import json
+import numpy as np
+import triton_python_backend_utils as pb_utils
+
+class TritonPythonModel:
+ def initialize(self, args):
+ self.model_name = args['model_name']
+
+ @staticmethod
+ def auto_complete_config(auto_complete_model_config):
+ auto_complete_model_config.add_input( {"name": "text_input", "data_type": "TYPE_STRING", "dims": [-1]})
+ auto_complete_model_config.add_output({"name": "text_output", "data_type": "TYPE_STRING", "dims": [-1]})
+ auto_complete_model_config.set_max_batch_size(0)
+ return auto_complete_model_config
+
+ def execute(self, requests):
+ responses = []
+ for request in requests:
+ in_numpy = pb_utils.get_input_tensor_by_name(request, "text_input").as_numpy()
+ assert np.object_ == in_numpy.dtype, 'in this demo, triton passes in a numpy array of size 1 with object_ dtype, this dtype encapsulates a python bytes-array'
+ print('in this demo len(in_numpy) is 1:', len(in_numpy.tolist()))
+ out_numpy = np.array([ (self.model_name + ': ' + python_byte_array.decode('utf-8') + ' World').encode('utf-8') for python_byte_array in in_numpy.tolist()], dtype = np.object_)
+ out_pb = pb_utils.Tensor("text_output", out_numpy)
+ responses.append(pb_utils.InferenceResponse(output_tensors = [out_pb]))
+ return responses
diff --git a/python/fedml/computing/scheduler/comm_utils/network_util.py b/python/fedml/computing/scheduler/comm_utils/network_util.py
index 48e478f23f..b03b0428d0 100644
--- a/python/fedml/computing/scheduler/comm_utils/network_util.py
+++ b/python/fedml/computing/scheduler/comm_utils/network_util.py
@@ -1,4 +1,5 @@
import os
+from urllib.parse import urlparse
from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants
@@ -16,3 +17,13 @@ def return_this_device_connectivity_type() -> str:
return env_conn_type
else:
return ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT
+
+
+def replace_url_with_path(url: str, path: str) -> str:
+ """
+ Replace the path of the URL with the given path.
+ """
+ if path is None:
+ return url
+ url_parsed = urlparse(url)
+ return f"{url_parsed.scheme}://{url_parsed.netloc}/{path}"
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 71f0c8032a..1aef8c09f1 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -118,6 +118,8 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
# Storage related
src_code_dir = os.path.join(model_storage_local_path, config.get('source_code_dir', ""))
+ # TODO(Raphael): In the future, the data_cache_dir should not be controlled by the user. It only
+ # used for internal avoiding checkpoint re-download. e.g. ~/.cache/huggingface/hub/
data_cache_dir_input = config.get('data_cache_dir', "")
# Others
@@ -225,15 +227,14 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
else:
logging.warning("data_cache_dir_input is not a string or a dictionary, skip mounting it to the container")
- # FedML format main entry filename, e.g., main.py
- if relative_entry_fedml_format != "":
- logging.info("Start copying the source code to the container...")
- volumes.append(src_code_dir)
- binds[src_code_dir] = {
- "bind": dst_model_serving_dir,
- "mode": "rw"
- }
- environment["MAIN_ENTRY"] = relative_entry_fedml_format
+ # Inject the source code
+ logging.info("Start copying the source code to the container...")
+ volumes.append(src_code_dir)
+ binds[src_code_dir] = {
+ "bind": dst_model_serving_dir,
+ "mode": "rw"
+ }
+ environment["MAIN_ENTRY"] = relative_entry_fedml_format
# Host config
host_config_dict = {
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 84141851b0..f6fa99d6d4 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -21,6 +21,7 @@
from fedml.computing.scheduler.model_scheduler.device_model_cache import FedMLModelCache
from fedml.computing.scheduler.model_scheduler.device_mqtt_inference_protocol import FedMLMqttInference
from fedml.computing.scheduler.model_scheduler.device_http_proxy_inference_protocol import FedMLHttpProxyInference
+from fedml.computing.scheduler.comm_utils.network_util import replace_url_with_path
from fedml.core.mlops.mlops_configs import MLOpsConfigs
from fedml.core.mlops import MLOpsRuntimeLog, MLOpsRuntimeLogDaemon
@@ -168,10 +169,27 @@ async def predict_with_end_point_id(end_point_id, request: Request, response: Re
return inference_response
+@api.post('/custom_inference/{end_point_id}/{path:path}')
+async def custom_inference(end_point_id, path: str, request: Request):
+ # Get json data
+ input_json = await request.json()
+
+ # Get header
+ header = request.headers
+
+ try:
+ inference_response = await _predict(end_point_id, input_json, header, path)
+ except Exception as e:
+ inference_response = {"error": True, "message": f"{traceback.format_exc()}"}
+
+ return inference_response
+
+
async def _predict(
end_point_id,
input_json,
- header=None
+ header=None,
+ path=None,
) -> Union[MutableMapping[str, Any], Response, StreamingResponse]:
# Always increase the pending requests counter on a new incoming request.
FEDML_MODEL_CACHE.update_pending_requests_counter(end_point_id, increase=True)
@@ -245,7 +263,8 @@ async def _predict(
input_list,
output_list,
inference_type=in_return_type,
- connectivity_type=connectivity_type)
+ connectivity_type=connectivity_type,
+ path=path)
# Calculate model metrics
try:
@@ -336,10 +355,13 @@ def found_idle_inference_device(end_point_id, end_point_name, in_model_name, in_
async def send_inference_request(idle_device, end_point_id, inference_url, input_list, output_list,
inference_type="default",
- connectivity_type=ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT):
+ connectivity_type=ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT,
+ path=None):
request_timeout_sec = FEDML_MODEL_CACHE.get_endpoint_settings(end_point_id) \
.get("request_timeout_sec", ClientConstants.INFERENCE_REQUEST_TIMEOUT)
+ inference_url = replace_url_with_path(inference_url, path)
+
try:
if connectivity_type == ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP:
response_ok, inference_response = await FedMLHttpInference.run_http_inference_with_curl_request(
From 786718bc6b61508b239a4106738724c458ed8c38 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Thu, 20 Jun 2024 10:37:19 -0700
Subject: [PATCH 178/282] [Deploy] Support serverless container.
---
.../lorax/custom_inference_image.yaml | 6 +-
.../tensorrt_llm/tensorrtllm.yaml | 17 ++
.../template/custom_inference_image.yaml | 16 +-
.../device_client_constants.py | 25 +--
.../device_http_inference_protocol.py | 42 ++--
.../device_http_proxy_inference_protocol.py | 1 +
.../device_model_deployment.py | 191 ++++++++++++------
.../model_scheduler/device_model_inference.py | 18 +-
.../device_server_constants.py | 4 +
.../model_scheduler/master_job_runner.py | 38 ++--
10 files changed, 233 insertions(+), 125 deletions(-)
create mode 100644 python/examples/deploy/custom_inference_image/tensorrt_llm/tensorrtllm.yaml
diff --git a/python/examples/deploy/custom_inference_image/lorax/custom_inference_image.yaml b/python/examples/deploy/custom_inference_image/lorax/custom_inference_image.yaml
index 467c7c48b0..41cbe501d2 100644
--- a/python/examples/deploy/custom_inference_image/lorax/custom_inference_image.yaml
+++ b/python/examples/deploy/custom_inference_image/lorax/custom_inference_image.yaml
@@ -1,5 +1,6 @@
workspace: "./"
+enable_serverless_container: true
inference_image_name: "ghcr.io/predibase/lorax:main"
container_run_command: "--model-id mistralai/Mistral-7B-Instruct-v0.1"
@@ -7,8 +8,9 @@ environment_variables:
HUGGING_FACE_HUB_TOKEN: ""
readiness_probe:
- path: "health"
+ httpGet:
+ path: "/health"
port: 80
-deploy_timeout: 1600
+deploy_timeout_sec: 1600
diff --git a/python/examples/deploy/custom_inference_image/tensorrt_llm/tensorrtllm.yaml b/python/examples/deploy/custom_inference_image/tensorrt_llm/tensorrtllm.yaml
new file mode 100644
index 0000000000..d41dba7983
--- /dev/null
+++ b/python/examples/deploy/custom_inference_image/tensorrt_llm/tensorrtllm.yaml
@@ -0,0 +1,17 @@
+workspace: "./"
+
+enable_serverless_container: true
+inference_image_name: "fedml/llama3-8b-tensorrtllm"
+
+# If you put the model repository in $workspace/model_repository, it will be mounted to /home/fedml/models_serving/model_repository
+container_run_command: ["sh", "-c", "cd / && huggingface-cli login --token $your_hf_token && pip install sentencepiece protobuf && python3 tensorrtllm_backend/scripts/launch_triton_server.py --model_repo tensorrtllm_backend/all_models/inflight_batcher_llm --world_size 1 && tail -f /dev/null"]
+
+readiness_probe:
+ httpGet:
+ path: "/v2/health/ready"
+
+port: 8000
+
+deploy_timeout_sec: 1600
+
+
diff --git a/python/examples/deploy/custom_inference_image/triton_inference_server/template/custom_inference_image.yaml b/python/examples/deploy/custom_inference_image/triton_inference_server/template/custom_inference_image.yaml
index 02dca147ce..eb02e3904a 100644
--- a/python/examples/deploy/custom_inference_image/triton_inference_server/template/custom_inference_image.yaml
+++ b/python/examples/deploy/custom_inference_image/triton_inference_server/template/custom_inference_image.yaml
@@ -1,16 +1,20 @@
workspace: "./"
+enable_serverless_container: true
inference_image_name: "nvcr.io/nvidia/tritonserver:24.05-py3"
-# If you put the model repository in $workspace/model_repository, it will be mounted to /home/fedml/models_serving/model_repository
-container_run_command: "tritonserver --model-repository=/home/fedml/models_serving/model_repository"
+volumes:
+ - workspace_path: "./model_repository"
+ mount_path: "/repo_inside_container"
-# If your image has the repository inside it, say in /my_models_dir/model_repository, you can do:
-#container_run_command: "tritonserver --model-repository=/my_models_dir/model_repository"
+container_run_command: "tritonserver --model-repository=/repo_inside_container"
readiness_probe:
- path: "v2/health/ready"
+ httpGet:
+ path: "/v2/health/ready"
port: 8000
-deploy_timeout: 1600
+deploy_timeout_sec: 1600
+
+request_input_example: {"text_input": "Hello"}
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
index cd21de2e04..e18c9f730b 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
@@ -146,7 +146,6 @@ class ClientConstants(object):
READINESS_PROBE_DEFAULT = "DEFAULT"
LIVENESS_PROBE_DEFAULT = "DEFAULT"
-
LOGIN_MODE_ON_PREMISE_INDEX = 0
LOGIN_MODE_FEDML_CLOUD_INDEX = 1
LOGIN_MODE_PUBLIC_CLOUD_INDEX = 2
@@ -155,20 +154,16 @@ class ClientConstants(object):
MODEL_DATA_TYPE_INT = "int"
MODEL_DATA_TYPE_FLOAT = "float"
MODEL_DATA_TYPE_STR = "str"
- MODEL_DATA_TYPE_MAPPING = {"TYPE_BOOL": MODEL_DATA_TYPE_INT, "TYPE_UINT8": MODEL_DATA_TYPE_INT,
- "TYPE_UINT16": MODEL_DATA_TYPE_INT, "TYPE_UINT32": MODEL_DATA_TYPE_INT,
- "TYPE_UINT64": MODEL_DATA_TYPE_INT, "TYPE_INT8": MODEL_DATA_TYPE_INT,
- "TYPE_INT16": MODEL_DATA_TYPE_INT, "TYPE_INT32": MODEL_DATA_TYPE_INT,
- "TYPE_INT64": MODEL_DATA_TYPE_INT, "TYPE_FP16": MODEL_DATA_TYPE_FLOAT,
- "TYPE_FP32": MODEL_DATA_TYPE_FLOAT, "TYPE_FP64": MODEL_DATA_TYPE_FLOAT,
- "TYPE_STRING": MODEL_DATA_TYPE_STR, "TYPE_BF16": MODEL_DATA_TYPE_INT,
- "BOOL": MODEL_DATA_TYPE_INT, "UINT8": MODEL_DATA_TYPE_INT,
- "UINT16": MODEL_DATA_TYPE_INT, "UINT32": MODEL_DATA_TYPE_INT,
- "UINT64": MODEL_DATA_TYPE_INT, "INT8": MODEL_DATA_TYPE_INT,
- "INT16": MODEL_DATA_TYPE_INT, "INT32": MODEL_DATA_TYPE_INT,
- "INT64": MODEL_DATA_TYPE_INT, "FP16": MODEL_DATA_TYPE_FLOAT,
- "FP32": MODEL_DATA_TYPE_FLOAT, "FP64": MODEL_DATA_TYPE_FLOAT,
- "STRING": MODEL_DATA_TYPE_STR, "BF16": MODEL_DATA_TYPE_INT}
+
+ # Model config yaml related
+ DEPLOY_TIMEOUT_SEC_KEY = "deploy_timeout_sec"
+ DEPLOY_TIMEOUT_SEC_DEFAULT = 600
+
+ ENABLE_SERVERLESS_CONTAINER_KEY = "enable_serverless_container"
+
+ CUSTOMIZED_VOLUMES_MOUNT_KEY = "volumes"
+ CUSTOMIZED_VOLUMES_PATH_FROM_WORKSPACE_KEY = "workspace_path"
+ CUSTOMIZED_VOLUMES_PATH_FROM_CONTAINER_KEY = "mount_path"
@staticmethod
def get_fedml_home_dir():
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
index 41c565d5d8..5b2658f0b3 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
@@ -1,3 +1,5 @@
+import logging
+
import httpx
import traceback
@@ -46,9 +48,8 @@ async def is_inference_ready(inference_url, path="ready", timeout=None):
@staticmethod
async def run_http_inference_with_curl_request(
inference_url, inference_input_list, inference_output_list,
- inference_type="default", engine_type="default", timeout=None
+ inference_type="default", engine_type="default", timeout=None, method="POST"
):
- model_inference_result = {}
if inference_type == "default":
model_api_headers = {'Content-Type': 'application/json', 'Connection': 'close',
'Accept': 'application/json'}
@@ -63,11 +64,10 @@ async def run_http_inference_with_curl_request(
"outputs": inference_output_list
}
- response_ok = False
try:
if model_inference_json.get("stream", False):
model_inference_result = StreamingResponse(
- stream_generator(inference_url, input_json=model_inference_json),
+ stream_generator(inference_url, input_json=model_inference_json, method=method),
media_type="text/event-stream",
headers={
"Content-Type": model_api_headers.get("Accept", "text/event-stream"),
@@ -76,8 +76,8 @@ async def run_http_inference_with_curl_request(
)
response_ok = True
else:
- response_ok, model_inference_result = await redirect_request_to_worker(
- inference_type, inference_url, model_api_headers, model_inference_json, timeout)
+ response_ok, model_inference_result = await redirect_non_stream_req_to_worker(
+ inference_type, inference_url, model_api_headers, model_inference_json, timeout, method=method)
except Exception as e:
response_ok = False
model_inference_result = {"response": f"{traceback.format_exc()}"}
@@ -85,21 +85,22 @@ async def run_http_inference_with_curl_request(
return response_ok, model_inference_result
-async def stream_generator(inference_url, input_json):
+async def stream_generator(inference_url, input_json, method="POST"):
async with httpx.AsyncClient() as client:
- async with client.stream("POST", inference_url, json=input_json,
+ async with client.stream(method, inference_url, json=input_json,
timeout=ClientConstants.WORKER_STREAM_API_TIMEOUT) as response:
async for chunk in response.aiter_lines():
# we consumed a newline, need to put it back
yield f"{chunk}\n"
-async def redirect_request_to_worker(inference_type, inference_url, model_api_headers, model_inference_json, timeout=None):
+async def redirect_non_stream_req_to_worker(inference_type, inference_url, model_api_headers, model_inference_json,
+ timeout=None, method="POST"):
response_ok = True
try:
async with httpx.AsyncClient() as client:
- response = await client.post(
- url=inference_url, headers=model_api_headers, json=model_inference_json, timeout=timeout
+ response = await client.request(
+ method=method, url=inference_url, headers=model_api_headers, json=model_inference_json, timeout=timeout
)
except Exception as e:
response_ok = False
@@ -107,13 +108,18 @@ async def redirect_request_to_worker(inference_type, inference_url, model_api_he
return response_ok, model_inference_result
if response.status_code == 200:
- if inference_type == "default":
- model_inference_result = response.json()
- elif inference_type == "image/png":
- binary_content: bytes = response.content
- model_inference_result = Response(content=binary_content, media_type="image/png")
- else:
- model_inference_result = response.json()
+ try:
+ if inference_type == "default":
+ model_inference_result = response.json()
+ elif inference_type == "image/png":
+ binary_content: bytes = response.content
+ model_inference_result = Response(content=binary_content, media_type="image/png")
+ else:
+ model_inference_result = response.json()
+ except Exception as e:
+ response_ok = True
+ logging.warning(f"Status code 200, but cannot trans response to json due to: {e}.")
+ model_inference_result = {"response": f"{response.content}"}
else:
model_inference_result = {"response": f"{response.content}"}
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_http_proxy_inference_protocol.py b/python/fedml/computing/scheduler/model_scheduler/device_http_proxy_inference_protocol.py
index 53f5a002eb..746d17bb7c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_http_proxy_inference_protocol.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_http_proxy_inference_protocol.py
@@ -50,6 +50,7 @@ async def run_http_proxy_inference_with_request(
endpoint_id, inference_url, inference_input_list,
inference_output_list, inference_type="default",
timeout=None
+ # TODO(Raphael): Add support for GET and other methods
):
inference_response = {}
http_proxy_url = f"http://{urlparse(inference_url).hostname}:{ClientConstants.LOCAL_CLIENT_API_PORT}/api/v1/predict"
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 1aef8c09f1..e18081c324 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -112,19 +112,20 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
relative_entry_fedml_format = config.get('entry_point', "")
# User indicate either fedml format python main entry filename or entry command
- customized_image_entry_cmd = config.get('container_run_command', None)
+ enable_serverless_container = config.get(ClientConstants.ENABLE_SERVERLESS_CONTAINER_KEY, False)
+ customized_image_entry_cmd = config.get('container_run_command', None) # Could be str or list
customized_readiness_check = config.get('readiness_probe', ClientConstants.READINESS_PROBE_DEFAULT)
customized_liveliness_check = config.get('liveness_probe', ClientConstants.LIVENESS_PROBE_DEFAULT)
# Storage related
src_code_dir = os.path.join(model_storage_local_path, config.get('source_code_dir', ""))
- # TODO(Raphael): In the future, the data_cache_dir should not be controlled by the user. It only
- # used for internal avoiding checkpoint re-download. e.g. ~/.cache/huggingface/hub/
data_cache_dir_input = config.get('data_cache_dir', "")
+ usr_customized_mount_rule = config.get(ClientConstants.CUSTOMIZED_VOLUMES_MOUNT_KEY, None)
# Others
extra_envs = config.get('environment_variables', None)
- usr_indicated_wait_time = config.get('deploy_timeout', 900)
+ usr_indicated_wait_time = config.get(ClientConstants.DEPLOY_TIMEOUT_SEC_KEY,
+ config.get("deploy_timeout", ClientConstants.DEPLOY_TIMEOUT_SEC_DEFAULT))
usr_indicated_retry_cnt = max(int(usr_indicated_wait_time) // 10, 1)
request_input_example = config.get('request_input_example', None)
@@ -189,52 +190,12 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
binds = {}
environment = {}
- if isinstance(data_cache_dir_input, str):
- # In this case, we mount to the same folder, if it has ~, we replace it with /home/fedml
- if data_cache_dir_input != "":
- if data_cache_dir_input[0] == "~":
- src_data_cache_dir = os.path.expanduser(data_cache_dir_input)
- dst_data_cache_dir = data_cache_dir_input.replace("~", "/home/fedml")
- else:
- # check if the data_cache_dir is a relative path
- if data_cache_dir_input[0] != "/":
- raise "data_cache_dir_input has to be an absolute path or start with ~"
- else:
- src_data_cache_dir = data_cache_dir_input
- dst_data_cache_dir = data_cache_dir_input
- logging.info(f"src_data_cache_dir: {src_data_cache_dir}, dst_data_cache_dir: {dst_data_cache_dir}")
+ # Handle the union volume mount
+ _handle_union_volume_mount(binds, volumes, environment, data_cache_dir_input)
- if type(src_data_cache_dir) == str and src_data_cache_dir != "":
- logging.info("Start copying the data cache to the container...")
- if os.path.exists(src_data_cache_dir):
- volumes.append(src_data_cache_dir)
- binds[src_data_cache_dir] = {
- "bind": dst_data_cache_dir,
- "mode": "rw"
- }
- environment["DATA_CACHE_FOLDER"] = dst_data_cache_dir
- elif isinstance(data_cache_dir_input, dict):
- for k, v in data_cache_dir_input.items():
- if os.path.exists(k):
- volumes.append(v)
- binds[k] = {
- "bind": v,
- "mode": "rw"
- }
- else:
- logging.warning(f"{k} does not exist, skip mounting it to the container")
- logging.info(f"Data cache mount: {volumes}, {binds}")
- else:
- logging.warning("data_cache_dir_input is not a string or a dictionary, skip mounting it to the container")
-
- # Inject the source code
- logging.info("Start copying the source code to the container...")
- volumes.append(src_code_dir)
- binds[src_code_dir] = {
- "bind": dst_model_serving_dir,
- "mode": "rw"
- }
- environment["MAIN_ENTRY"] = relative_entry_fedml_format
+ # Handle the default volume mount
+ handle_volume_mount(volumes, binds, environment, relative_entry_fedml_format, src_code_dir,
+ dst_model_serving_dir, usr_customized_mount_rule, host_workspace_root=model_storage_local_path)
# Host config
host_config_dict = {
@@ -331,6 +292,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
model_metadata = ret_model_metadata
model_metadata["liveliness_check"] = customized_liveliness_check
model_metadata["readiness_check"] = customized_readiness_check
+ model_metadata[ClientConstants.ENABLE_SERVERLESS_CONTAINER_KEY] = enable_serverless_container
logging.info(f"[Worker][Replica{replica_rank}] Model deployment is successful with inference_output_url: "
f"{inference_output_url}, model_metadata: {model_metadata}, model_config: {ret_model_config}")
@@ -527,24 +489,129 @@ def is_client_inference_container_ready(infer_url_host, inference_http_port,
logging.error(f"Unknown readiness check type: {readiness_check}")
return "", "", {}, {}
- if "path" in readiness_check:
- readiness_check_url = f"http://{infer_url_host}:{inference_http_port}/{readiness_check['path']}"
- response = None
- try:
- response = requests.get(readiness_check_url)
- except:
- pass
- if not response or response.status_code != 200:
- return "", "", {}, {}
+ if "httpGet" in readiness_check:
+ if "path" in readiness_check["httpGet"]:
+ check_path = readiness_check["httpGet"]["path"]
+ if not isinstance(check_path, str):
+ logging.error(f"Invalid path type: {check_path}, expected str")
+ return "", "", {}, {}
+ else:
+ if not check_path.startswith("/"):
+ check_path = "/" + check_path
+ readiness_check_url = f"http://{infer_url_host}:{inference_http_port}{check_path}"
- return "http://{}:{}/".format(infer_url_host, inference_http_port), None, model_metadata, None
- elif "command" in readiness_check:
+ response = None
+ try:
+ response = requests.get(readiness_check_url)
+ except:
+ pass
+ if not response or response.status_code != 200:
+ return "", "", {}, {}
+
+ return readiness_check_url, None, model_metadata, None
+ else:
+ logging.error("'path' is not specified in httpGet readiness check")
+ return "", "", {}, {}
+ elif "exec" in readiness_check:
# TODO(raphael): Support arbitrary readiness check command by using
# container id and docker exec
return "http://{}:{}/".format(infer_url_host, inference_http_port), None, model_metadata, None
else:
- logging.error(f"Unknown readiness check type: {readiness_check}")
- return "", "", {}, {}
+ # Ref K8S, if no readiness check, we assume the container is ready immediately
+ return "http://{}:{}/".format(infer_url_host, inference_http_port), None, model_metadata, None
+
+
+def _handle_union_volume_mount(binds, volumes, environment, data_cache_dir_input=None):
+ """
+ Private: data_cache_dir is the union folder on host machine, which will be shard across different containers,
+ the control of this folder should be handled by the platform.
+ """
+ if isinstance(data_cache_dir_input, str):
+ # In this case, we mount to the same folder, if it has ~, we replace it with /home/fedml
+ if data_cache_dir_input != "":
+ if data_cache_dir_input[0] == "~":
+ src_data_cache_dir = os.path.expanduser(data_cache_dir_input)
+ dst_data_cache_dir = data_cache_dir_input.replace("~", "/home/fedml")
+ else:
+ # check if the data_cache_dir is a relative path
+ if data_cache_dir_input[0] != "/":
+ raise "data_cache_dir_input has to be an absolute path or start with ~"
+ else:
+ src_data_cache_dir = data_cache_dir_input
+ dst_data_cache_dir = data_cache_dir_input
+ logging.info(f"src_data_cache_dir: {src_data_cache_dir}, dst_data_cache_dir: {dst_data_cache_dir}")
+
+ if isinstance(src_data_cache_dir, str) and src_data_cache_dir != "":
+ logging.info("Start copying the data cache to the container...")
+ if os.path.exists(src_data_cache_dir):
+ volumes.append(src_data_cache_dir)
+ binds[src_data_cache_dir] = {
+ "bind": dst_data_cache_dir,
+ "mode": "rw"
+ }
+ environment["DATA_CACHE_FOLDER"] = dst_data_cache_dir
+ elif isinstance(data_cache_dir_input, dict):
+ for k, v in data_cache_dir_input.items():
+ if os.path.exists(k):
+ volumes.append(v)
+ binds[k] = {
+ "bind": v,
+ "mode": "rw"
+ }
+ else:
+ logging.warning(f"{k} does not exist, skip mounting it to the container")
+ logging.info(f"Data cache mount: {volumes}, {binds}")
+ else:
+ logging.info("data_cache_dir_input is not a string or a dictionary, skip mounting it to the container")
+
+
+def handle_volume_mount(volumes, binds, environment, relative_entry_fedml_format="", src_code_dir="",
+ dst_model_serving_dir="", customized_volumes_mount_rule=None, host_workspace_root=""):
+ # If fedml format entry point is specified, inject the source code, e.g., main.py (FedMLPredictor inside)
+ if relative_entry_fedml_format != "":
+ logging.info("Using FedML format entry point, mounting the source code...")
+ volumes.append(src_code_dir)
+ binds[src_code_dir] = {
+ "bind": dst_model_serving_dir,
+ "mode": "rw"
+ }
+ environment["MAIN_ENTRY"] = relative_entry_fedml_format
+ return # The reason we return here is that we don't need to mount the source code again
+
+ # If customized volume mount rule is specified, just follow the mount rule
+ """
+ e.g.,
+ volumes:
+ - workspace_path: "./model_repository"
+ mount_path: "/repo_inside_container"
+ """
+ mount_list = []
+ if not isinstance(customized_volumes_mount_rule, list):
+ if not isinstance(customized_volumes_mount_rule, dict):
+ logging.warning("customized_volumes_mount_rule is not a list or a dictionary, "
+ "skip mounting it to the container")
+ return
+
+ # transform the dict to list
+ for k, v in customized_volumes_mount_rule.items():
+ mount_list.append({ClientConstants.CUSTOMIZED_VOLUMES_PATH_FROM_WORKSPACE_KEY: k,
+ ClientConstants.CUSTOMIZED_VOLUMES_PATH_FROM_CONTAINER_KEY: v})
+ else:
+ mount_list = customized_volumes_mount_rule if customized_volumes_mount_rule is not None else []
+
+ for mount in mount_list:
+ workspace_relative_path = mount[ClientConstants.CUSTOMIZED_VOLUMES_PATH_FROM_WORKSPACE_KEY]
+ mount_path = mount[ClientConstants.CUSTOMIZED_VOLUMES_PATH_FROM_CONTAINER_KEY]
+
+ workspace_path = os.path.join(host_workspace_root, workspace_relative_path)
+ if os.path.exists(workspace_path):
+ volumes.append(workspace_path)
+ binds[workspace_path] = {
+ "bind": mount_path,
+ "mode": "rw"
+ }
+ else:
+ logging.warning(f"{workspace_path} does not exist, skip mounting it to the container")
def check_container_readiness(inference_http_port, infer_host="127.0.0.1", request_input_example=None,
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index f6fa99d6d4..7ef9689c1c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -8,7 +8,7 @@
from typing import Any, Mapping, MutableMapping, Union
from urllib.parse import urlparse
-from fastapi import FastAPI, Request, Response, status
+from fastapi import FastAPI, Request, Response, status, APIRouter
from fastapi.responses import StreamingResponse, JSONResponse
import fedml
@@ -38,6 +38,7 @@ class Settings:
api = FastAPI()
+router = APIRouter()
FEDML_MODEL_CACHE = FedMLModelCache.get_instance()
FEDML_MODEL_CACHE.set_redis_params(redis_addr=Settings.redis_addr,
@@ -169,7 +170,8 @@ async def predict_with_end_point_id(end_point_id, request: Request, response: Re
return inference_response
-@api.post('/custom_inference/{end_point_id}/{path:path}')
+# @api.post('/custom_inference/{end_point_id}/{path:path}')
+@router.api_route("/custom_inference/{end_point_id}/{path:path}", methods=["POST", "GET"])
async def custom_inference(end_point_id, path: str, request: Request):
# Get json data
input_json = await request.json()
@@ -178,18 +180,21 @@ async def custom_inference(end_point_id, path: str, request: Request):
header = request.headers
try:
- inference_response = await _predict(end_point_id, input_json, header, path)
+ inference_response = await _predict(end_point_id, input_json, header, path, request.method)
except Exception as e:
inference_response = {"error": True, "message": f"{traceback.format_exc()}"}
return inference_response
+api.include_router(router)
+
async def _predict(
end_point_id,
input_json,
header=None,
path=None,
+ request_method="POST"
) -> Union[MutableMapping[str, Any], Response, StreamingResponse]:
# Always increase the pending requests counter on a new incoming request.
FEDML_MODEL_CACHE.update_pending_requests_counter(end_point_id, increase=True)
@@ -264,7 +269,7 @@ async def _predict(
output_list,
inference_type=in_return_type,
connectivity_type=connectivity_type,
- path=path)
+ path=path, request_method=request_method)
# Calculate model metrics
try:
@@ -356,7 +361,7 @@ def found_idle_inference_device(end_point_id, end_point_name, in_model_name, in_
async def send_inference_request(idle_device, end_point_id, inference_url, input_list, output_list,
inference_type="default",
connectivity_type=ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT,
- path=None):
+ path=None, request_method="POST"):
request_timeout_sec = FEDML_MODEL_CACHE.get_endpoint_settings(end_point_id) \
.get("request_timeout_sec", ClientConstants.INFERENCE_REQUEST_TIMEOUT)
@@ -369,7 +374,8 @@ async def send_inference_request(idle_device, end_point_id, inference_url, input
input_list,
output_list,
inference_type=inference_type,
- timeout=request_timeout_sec)
+ timeout=request_timeout_sec,
+ method=request_method)
logging.debug(f"Use http inference. return {response_ok}")
return inference_response
elif connectivity_type == ClientConstants.WORKER_CONNECTIVITY_TYPE_HTTP_PROXY:
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
index a5048c26a6..b58b8fae72 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
@@ -147,6 +147,10 @@ class ServerConstants(object):
DEVICE_DIFF_ADD_OPERATION = "op: add"
DEVICE_DIFF_DELETE_OPERATION = "op: delete"
DEVICE_DIFF_REPLACE_OPERATION = "op: replace"
+
+ # Worker comfig yaml related
+ ENABLE_SERVERLESS_CONTAINER_KEY = "enable_serverless_container"
+
@staticmethod
def get_fedml_home_dir():
home_dir = expanduser("~")
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index eff26684b7..f95dd8e176 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -298,14 +298,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
return
else:
# This is the last worker that failed, so we should continue to "ABORTED" status
- model_config_parameters = self.request_json["parameters"]
- inference_port_external = ServerConstants.get_inference_master_gateway_port()
- ip = GeneralConstants.get_ip_address(self.request_json)
- if ip.startswith("http://") or ip.startswith("https://"):
- model_inference_url = "{}/inference/{}".format(ip, end_point_id)
- else:
- model_inference_url = "http://{}:{}/inference/{}".format(ip, inference_port_external,
- end_point_id)
+ model_inference_url = self.construct_final_gateway_url(end_point_id)
self.send_deployment_status(
end_point_id, end_point_name, payload_json["model_name"], model_inference_url,
@@ -367,13 +360,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
"""
When all the devices have finished the add / delete / update operation
"""
- inference_port_external = ServerConstants.get_inference_master_gateway_port()
- ip = GeneralConstants.get_ip_address(request_json)
-
- if ip.startswith("http://") or ip.startswith("https://"):
- model_inference_url = "{}/inference/{}".format(ip, end_point_id)
- else:
- model_inference_url = "http://{}:{}/inference/{}".format(ip, inference_port_external, end_point_id)
+ model_inference_url, inference_port_external = self.construct_final_gateway_url(end_point_id)
# Send stage: MODEL_DEPLOYMENT_STAGE5 = "StartInferenceIngress"
self.send_deployment_stages(end_point_id, model_name, model_id,
@@ -394,7 +381,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
model_metadata = payload_json["model_metadata"]
model_inputs = model_metadata["inputs"]
- ret_inputs = list()
+
if "type" in model_metadata and model_metadata["type"] == "default":
payload_json["input_json"] = {"end_point_name": end_point_name,
"model_name": model_name,
@@ -768,3 +755,22 @@ def build_dynamic_args(self, run_id, run_config, package_conf_object, base_dir):
def build_dynamic_constrain_variables(self, run_id, run_config):
pass
+ def construct_final_gateway_url(self, end_point_id):
+ inference_port_external = ServerConstants.get_inference_master_gateway_port()
+ ip = GeneralConstants.get_ip_address(self.request_json)
+
+ identifier = "inference"
+ if self.deployed_replica_payload is not None:
+ payload_json = self.deployed_replica_payload
+ enable_custom_path = payload_json["model_metadata"].get(
+ ServerConstants.ENABLE_SERVERLESS_CONTAINER_KEY, False)
+ if enable_custom_path:
+ identifier = "custom_inference"
+
+ if ip.startswith("http://") or ip.startswith("https://"):
+ model_inference_url = "{}/{}/{}".format(ip, identifier, end_point_id)
+ else:
+ model_inference_url = "http://{}:{}/{}/{}".format(ip, inference_port_external, identifier,
+ end_point_id)
+ return model_inference_url, inference_port_external
+
From c0f691c7fd468549ee311c8ae260ba9c5599a43e Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Thu, 20 Jun 2024 14:27:35 -0700
Subject: [PATCH 179/282] [Deploy] Nit.
---
.../custom_inference_image/template.yaml | 22 +++++++++++++++++++
.../scheduler/comm_utils/job_monitor.py | 2 +-
.../device_http_inference_protocol.py | 9 ++++----
.../device_model_deployment.py | 2 --
.../model_scheduler/device_model_inference.py | 1 -
.../device_server_constants.py | 2 +-
.../model_scheduler/master_job_runner.py | 12 ++++------
7 files changed, 33 insertions(+), 17 deletions(-)
create mode 100644 python/examples/deploy/custom_inference_image/template.yaml
diff --git a/python/examples/deploy/custom_inference_image/template.yaml b/python/examples/deploy/custom_inference_image/template.yaml
new file mode 100644
index 0000000000..10e6580bcf
--- /dev/null
+++ b/python/examples/deploy/custom_inference_image/template.yaml
@@ -0,0 +1,22 @@
+# Required
+workspace: "./" # We will pacakge all the files in the workspace directory
+enable_serverless_container: true # Identify whether to use serverless container
+inference_image_name: "" # Container image name
+container_run_command: "" # str or list, similar to CMD in the dockerfile
+port: 80 # Service port, currently you can only indicate one arbitrary port
+
+# Optional, these are the default values
+readiness_probe: # Probe for checking whether a container is ready for inference
+ httpGet:
+ path: ""
+environment_variables: {} # Environment variables inside the container
+volumes: # Volumes to mount to the container
+ - workspace_path: "" # Path to the volume in the workspace
+ mount_path: "" # Path to mount the volume inside the container
+deploy_timeout_sec: 900 # Maximum time waiting for deployment to finish (Does not include the time to pull the image)
+request_input_example: {} # Example of input request, will be shown in the UI
+registry_specs: # Registry information for pulling the image
+ registry_name: ""
+ registry_provider: "DockerHub"
+ registry_user_name: ""
+ registry_user_password: ""
\ No newline at end of file
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index d216b46dad..667a54e565 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -772,7 +772,7 @@ def _lenient_check_replica_ready(deployment_result):
liveliness_check = result_json.get("model_metadata", {}).get("liveliness_check", None)
readiness_check = result_json.get("model_metadata", {}).get("readiness_check", None)
- if liveliness_check is not None:
+ if liveliness_check:
if liveliness_check == ClientConstants.LIVENESS_PROBE_DEFAULT:
liveliness_check = readiness_check # Follow the readiness check pattern
if not isinstance(liveliness_check, dict):
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
index 5b2658f0b3..28d50d5a50 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
@@ -25,6 +25,8 @@ async def is_inference_ready(inference_url, path="ready", timeout=None):
url_parsed = urlparse(inference_url)
ready_url = f"http://{url_parsed.hostname}:{url_parsed.port}/{path}"
response_ok = False
+
+ # TODO (Raphael): Support more methods and return codes rules.
try:
async with httpx.AsyncClient() as client:
ready_response = await client.get(url=ready_url, timeout=timeout)
@@ -109,11 +111,10 @@ async def redirect_non_stream_req_to_worker(inference_type, inference_url, model
if response.status_code == 200:
try:
- if inference_type == "default":
- model_inference_result = response.json()
- elif inference_type == "image/png":
+ if inference_type == "image/png":
+ # wrapped media type for image
binary_content: bytes = response.content
- model_inference_result = Response(content=binary_content, media_type="image/png")
+ model_inference_result = Response(content=binary_content, media_type=inference_type)
else:
model_inference_result = response.json()
except Exception as e:
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index e18081c324..552d7ffaca 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -2,7 +2,6 @@
import logging
import os
-import shutil
import time
import traceback
import yaml
@@ -12,7 +11,6 @@
import requests
import torch
import torch.nn
-import tritonclient.http as http_client
import collections.abc
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 7ef9689c1c..9adc17538d 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -170,7 +170,6 @@ async def predict_with_end_point_id(end_point_id, request: Request, response: Re
return inference_response
-# @api.post('/custom_inference/{end_point_id}/{path:path}')
@router.api_route("/custom_inference/{end_point_id}/{path:path}", methods=["POST", "GET"])
async def custom_inference(end_point_id, path: str, request: Request):
# Get json data
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
index b58b8fae72..f86056229e 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
@@ -358,7 +358,7 @@ def get_inference_master_gateway_port():
# Use dotenv to load the environment variables
fedml.load_env()
master_inference_port = int(os.getenv(ServerConstants.ENV_MASTER_INFERENCE_PORT_KEY,
- default=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT))
+ default=ServerConstants.MODEL_INFERENCE_DEFAULT_PORT))
return master_inference_port
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index f95dd8e176..ab6bc4c895 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -360,7 +360,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
"""
When all the devices have finished the add / delete / update operation
"""
- model_inference_url, inference_port_external = self.construct_final_gateway_url(end_point_id)
+ model_inference_url = self.construct_final_gateway_url(end_point_id)
# Send stage: MODEL_DEPLOYMENT_STAGE5 = "StartInferenceIngress"
self.send_deployment_stages(end_point_id, model_name, model_id,
@@ -375,7 +375,7 @@ def process_deployment_result_message(self, topic=None, payload=None):
payload_json = self.deployed_replica_payload
model_slave_url = payload_json["model_url"]
payload_json["model_url"] = model_inference_url
- payload_json["port"] = inference_port_external
+ payload_json["port"] = ServerConstants.get_inference_master_gateway_port()
token = FedMLModelCache.get_instance(self.redis_addr, self.redis_port).get_end_point_token(
end_point_id, end_point_name, model_name)
@@ -767,10 +767,6 @@ def construct_final_gateway_url(self, end_point_id):
if enable_custom_path:
identifier = "custom_inference"
- if ip.startswith("http://") or ip.startswith("https://"):
- model_inference_url = "{}/{}/{}".format(ip, identifier, end_point_id)
- else:
- model_inference_url = "http://{}:{}/{}/{}".format(ip, inference_port_external, identifier,
- end_point_id)
- return model_inference_url, inference_port_external
+ model_inference_url = "http://{}:{}/{}/{}".format(ip, inference_port_external, identifier, end_point_id)
+ return model_inference_url
From 7a0963e97edb3ae59d3b3745893d03ed7f5385d5 Mon Sep 17 00:00:00 2001
From: xiang <xiang@tensoropera.com>
Date: Fri, 21 Jun 2024 03:04:03 +0000
Subject: [PATCH 180/282] [TEST]: add windows runners tests
---
.github/workflows/CI_build.yml | 4 +-
.github/workflows/CI_deploy.yml | 7 ++--
.github/workflows/CI_federate.yml | 4 +-
.github/workflows/CI_launch.yml | 4 +-
.github/workflows/CI_train.yml | 6 +--
.github/workflows/README.md | 24 +++++++++++-
.../workflows/registry-runners/windows.bat | 38 -------------------
.../workflows/registry-runners/windows.ps1 | 32 ++++++++++++++++
8 files changed, 68 insertions(+), 51 deletions(-)
delete mode 100644 .github/workflows/registry-runners/windows.bat
create mode 100644 .github/workflows/registry-runners/windows.ps1
diff --git a/.github/workflows/CI_build.yml b/.github/workflows/CI_build.yml
index 86a846379c..b4c3642b09 100644
--- a/.github/workflows/CI_build.yml
+++ b/.github/workflows/CI_build.yml
@@ -17,11 +17,11 @@ on:
# A workflow run is made up of one or more jobs that can run sequentially or in parallel
jobs:
build:
- runs-on: ${{ matrix.python-version }}
+ runs-on: ["${{ matrix.python-version }}","${{ matrix.os }}"]
strategy:
fail-fast: false
matrix:
- os: [ Linux ]
+ os: [ Linux, Windows ]
arch: [X64]
python-version: ['python3.8', 'python3.9', 'python3.10', 'python3.11']
diff --git a/.github/workflows/CI_deploy.yml b/.github/workflows/CI_deploy.yml
index 35e793708f..982f65b3c5 100644
--- a/.github/workflows/CI_deploy.yml
+++ b/.github/workflows/CI_deploy.yml
@@ -17,14 +17,15 @@ on:
# A workflow run is made up of one or more jobs that can run sequentially or in parallel
jobs:
deploy:
- runs-on: ${{ matrix.python-version }}
+ runs-on: ["${{ matrix.python-version }}","${{ matrix.os }}"]
strategy:
fail-fast: false
matrix:
- os: [ Linux ]
+ os: [ Linux, Windows ]
arch: [X64]
python-version: ['python3.8', 'python3.9', 'python3.10', 'python3.11']
-
+
+ timeout-minutes: 5
steps:
- name: Checkout fedml
uses: actions/checkout@v3
diff --git a/.github/workflows/CI_federate.yml b/.github/workflows/CI_federate.yml
index 52cdfd9e10..1302771b1d 100644
--- a/.github/workflows/CI_federate.yml
+++ b/.github/workflows/CI_federate.yml
@@ -20,11 +20,11 @@ jobs:
strategy:
fail-fast: false
matrix:
- os: [ Linux ]
+ os: [ Linux, Windows ]
arch: [X64]
python-version: ['python3.8', 'python3.9', 'python3.10', 'python3.11']
- runs-on: ${{ matrix.python-version }}
+ runs-on: ["${{ matrix.python-version }}","${{ matrix.os }}"]
timeout-minutes: 5
steps:
- name: Checkout fedml
diff --git a/.github/workflows/CI_launch.yml b/.github/workflows/CI_launch.yml
index b2b896c82d..13519c41f2 100644
--- a/.github/workflows/CI_launch.yml
+++ b/.github/workflows/CI_launch.yml
@@ -21,11 +21,11 @@ jobs:
strategy:
fail-fast: false
matrix:
- os: [ ubuntu-latest ]
+ os: [ Linux, Windows ]
arch: [X64]
python-version: ['python3.8','python3.9','python3.10','python3.11']
- runs-on: ${{ matrix.python-version }}
+ runs-on: ["${{ matrix.python-version }}","${{ matrix.os }}"]
timeout-minutes: 5
steps:
- name: Checkout fedml
diff --git a/.github/workflows/CI_train.yml b/.github/workflows/CI_train.yml
index 529472d55c..2acbcc12a0 100644
--- a/.github/workflows/CI_train.yml
+++ b/.github/workflows/CI_train.yml
@@ -17,14 +17,14 @@ on:
# A workflow run is made up of one or more jobs that can run sequentially or in parallel
jobs:
train:
- runs-on: ${{ matrix.python-version }}
+ runs-on: ["${{ matrix.python-version }}","${{ matrix.os }}"]
strategy:
fail-fast: false
matrix:
- os: [ Linux ]
+ os: [ Linux, Windows ]
arch: [X64]
python-version: ['python3.8', 'python3.9', 'python3.10', 'python3.11']
-
+ timeout-minutes: 5
steps:
- name: Checkout fedml
uses: actions/checkout@v3
diff --git a/.github/workflows/README.md b/.github/workflows/README.md
index 4e284a2175..f8c119f123 100644
--- a/.github/workflows/README.md
+++ b/.github/workflows/README.md
@@ -45,6 +45,28 @@ to check that your runners are all active.
## 2.2 Windows Runners
+step1: Install Anaconda packages
+Install Anaconda or Miniconda in a windows machine. the Anaconda and Miniconda can manage your python environments.
+
+step2: Create python enviroments
+Create 4 python environments named python38、python39、python310、python311, you also need to specific `python==3.8` to install specific python version.
+For example
+```
+conda create -n python38 python==3.8
+```
+step3: Create directories
+Create 4 directories named actions-runner-python38、actions-runner-python39、actions-runner-python310、actions-runner-python311 used for different runners.
+
+step4: Install the latest runner package.
+Follow the insturction from navigating this path `Settings -> Actions -> Runners -> New self-hosted runner` to add a new windows runner. Note that You just do the download、extract steps in the directories which we have created, we don't need to configure it and run it. We can run a script to registry all the runners.
+
+step5: Registry all the runners.
+Run the script from ./registry-runners/windows.ps1 to registry all the runners to your github. you need to replace the variables $REPO、$ACCESS_TOKEN、$WORKPLACE with the actual value. Note that you can get your $ACCESS_TOKEN from the following path `Settings -> Actions -> Runners -> New self-hosted runner.`.
+In the Configure section, you can find the similar line: `./config.sh --url https://github.com/FedML-AI/FedML --token AXRYPL6G2VHVGDFDQQS5XA3ELYI6M` to get your $ACCESS_TOKEN.
+
+step6: Make sure the success.
+Check if the runners are registered successfully. Navigate the following path.`Settings -> Actions -> Runners` to check that your runners are all active.
+
## 2.3 Mac Runners
# 3. bind Test Machines
@@ -70,5 +92,5 @@ If you need to add a new CI test that is different from the current business, yo
# 6. TODO
-Implement the Windows runners and the Mac runners.
+Implement the Mac runners.
diff --git a/.github/workflows/registry-runners/windows.bat b/.github/workflows/registry-runners/windows.bat
deleted file mode 100644
index dcdcf81b57..0000000000
--- a/.github/workflows/registry-runners/windows.bat
+++ /dev/null
@@ -1,38 +0,0 @@
-set REPO=Qigemingziba/FedML
-set ACCESS_TOKEN=AGMK3P4W5EM5PXNYTZXXIMTGNF4MW
-set WORKPLACE=%pwd%
-mkdir actions-runner-python38; cd actions-runner-python38
-conda activate python38
-Invoke-WebRequest -Uri https://github.com/actions/runner/releases/download/v2.317.0/actions-runner-win-x64-2.317.0.zip -OutFile actions-runner-win-x64-2.317.0.zip
-Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory("$PWD/actions-runner-win-x64-2.317.0.zip", "$PWD")
-./config.cmd --url https://github.com/%REPO% --token %ACCESS_TOKEN% --labels self-hosted,Windows,X64,python3.8
-.\run.cmd install
-.\run.cmd start
-
-cd WORKPLACE
-mkdir actions-runner-python39; cd actions-runner-python39
-conda activate python39
-Invoke-WebRequest -Uri https://github.com/actions/runner/releases/download/v2.317.0/actions-runner-win-x64-2.317.0.zip -OutFile actions-runner-win-x64-2.317.0.zip
-Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory("$PWD/actions-runner-win-x64-2.317.0.zip", "$PWD")
-./config.cmd --url https://github.com/%REPO% --token %ACCESS_TOKEN% --labels self-hosted,Windows,X64,python3.9
-.\run.cmd install
-.\run.cmd start
-
-cd WORKPLACE
-mkdir actions-runner-python310; cd actions-runner-python310
-conda activate python310
-Invoke-WebRequest -Uri https://github.com/actions/runner/releases/download/v2.317.0/actions-runner-win-x64-2.317.0.zip -OutFile actions-runner-win-x64-2.317.0.zip
-Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory("$PWD/actions-runner-win-x64-2.317.0.zip", "$PWD")
-./config.cmd --url https://github.com/%REPO% --token %ACCESS_TOKEN% --labels self-hosted,Windows,X64,python3.10
-.\run.cmd install
-.\run.cmd start
-
-cd WORKPLACE
-mkdir actions-runner-python311; cd actions-runner-python311
-conda activate python311
-Invoke-WebRequest -Uri https://github.com/actions/runner/releases/download/v2.317.0/actions-runner-win-x64-2.317.0.zip -OutFile actions-runner-win-x64-2.317.0.zip
-Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory("$PWD/actions-runner-win-x64-2.317.0.zip", "$PWD")
-./config.cmd --url https://github.com/%REPO% --token %ACCESS_TOKEN% --labels self-hosted,Windows,X64,python3.11
-.\run.cmd install
-.\run.cmd start
-
diff --git a/.github/workflows/registry-runners/windows.ps1 b/.github/workflows/registry-runners/windows.ps1
new file mode 100644
index 0000000000..40f0f00b8f
--- /dev/null
+++ b/.github/workflows/registry-runners/windows.ps1
@@ -0,0 +1,32 @@
+
+$REPO = "Qigemingziba/FedML"
+$ACCESS_TOKEN = "AGMK3PY3QDYUXXXEB5LWI4DGOQIFW"
+$WORKPLACE=$PWD
+
+Set-Location actions-runner-python38
+& conda activate python38
+./config.cmd --url https://github.com/$REPO --name windows-python38 --token $ACCESS_TOKEN --labels self-hosted,Windows,X64,python3.8
+Start-Process run.cmd start -WindowStyle Hidden
+
+Set-Location $WORKPLACE
+
+Set-Location actions-runner-python39
+& conda activate python39
+./config.cmd --url https://github.com/$REPO --name windows-python39 --token $ACCESS_TOKEN --labels self-hosted,Windows,X64,python3.9
+Start-Process run.cmd start -WindowStyle Hidden
+
+Set-Location $WORKPLACE
+
+Set-Location actions-runner-python310
+& conda activate python310
+./config.cmd --url https://github.com/$REPO --name windows-python310 --token $ACCESS_TOKEN --labels self-hosted,Windows,X64,python3.10
+Start-Process run.cmd start -WindowStyle Hidden
+
+Set-Location $WORKPLACE
+
+Set-Location actions-runner-python311
+& conda activate python311
+./config.cmd --url https://github.com/$REPO --name windows-python311 --token $ACCESS_TOKEN --labels self-hosted,Windows,X64,python3.11
+Start-Process run.cmd start -WindowStyle Hidden
+
+Set-Location $WORKPLACE
\ No newline at end of file
From 4355c35550d924255d7fe0c0d77d8b31af3e9461 Mon Sep 17 00:00:00 2001
From: xiang <xiang@tensoropera.com>
Date: Fri, 21 Jun 2024 04:35:07 +0000
Subject: [PATCH 181/282] [doc]: make sure the workflow documents are more
readable.
---
.github/workflows/README.md | 65 +++++++++++++++++++------------------
1 file changed, 33 insertions(+), 32 deletions(-)
diff --git a/.github/workflows/README.md b/.github/workflows/README.md
index f8c119f123..2b261708f6 100644
--- a/.github/workflows/README.md
+++ b/.github/workflows/README.md
@@ -10,26 +10,24 @@ The CI tests need to be comprehensive, covering typical scenarios only, achievab
## 2.1 Linux Runners
-We need to run CI tests in linux enviroment using different python versions such as python3.8/python3.9/python3.10/python3.11
-
-Therefore firstly we build linux images for Self-Host Runners.
+Step1: Build linux images
+Build all the linux images for Self-Host Runners.
```
cd registry-runners
bash build_linux_runners.sh
```
-Secondly we need to find your GitHub runner token and your test-account apikey.
-
-For the argument YourGitHubRunnerToken, you may navigate based the following path.
-Settings -> Actions -> Runners -> New self-hosted runner.
+Step2: Specify the token and key.
+Find your GitHub runner token and your test-account apikey.
-In the Configure section, you should find the similar line:
-./config.sh --url https://github.com/FedML-AI/FedML --token AXRYPL6G2VHVGDFDQQS5XA3ELYI6M
+For the argument YourGitHubRunnerToken, Navigate the path `Settings -> Actions -> Runners -> New self-hosted runner` to get.
-set YourGitHubRunnerToken to value of --token
+In the Configure section, you will find the similar line:
+./config.sh --url https://github.com/FedML-AI/FedML --token AXRYPL6G2VHVGDFDQQS5XA3ELYI6M to get YourGitHubRunnerToken to value of --token
-Then we run all ther images.
+Step3: Registry all the runners.
+Registry by run `run_linux_runners.sh` script
```
bash run_linux_runners.sh [YourGitRepo] [YourGitHubRunnerToken] [YourTestAccountApiKey]
```
@@ -37,58 +35,61 @@ for example
```
bash run_linux_runners.sh FedML-AI/FedML AXRYPLZLZN6XVJB3BAIXSP3EMFC7U 11215dkevvdkegged
```
-Lastly we need to check if the runners are registered successfully. Navigate the following path.
-```
-Settings -> Actions -> Runners
-```
-to check that your runners are all active.
+Step4 Verify Success
+
+Check if all the runners are registered successfully. Navigate the following path. `Settings -> Actions -> Runners` to check that all your runners are active.
## 2.2 Windows Runners
step1: Install Anaconda packages
-Install Anaconda or Miniconda in a windows machine. the Anaconda and Miniconda can manage your python environments.
+Install Anaconda or Miniconda on a Windows machine. Anaconda and Miniconda can manage your Python environments.
step2: Create python enviroments
-Create 4 python environments named python38、python39、python310、python311, you also need to specific `python==3.8` to install specific python version.
+Create 4 python environments named `python38`、`python39`、`python310` and `python311` for different runners.
+Specify the python version to install.
For example
```
conda create -n python38 python==3.8
```
step3: Create directories
-Create 4 directories named actions-runner-python38、actions-runner-python39、actions-runner-python310、actions-runner-python311 used for different runners.
+Create 4 directories named `actions-runner-python38`、`actions-runner-python39`、`actions-runner-python310` and `actions-runner-python311` for different runners.
step4: Install the latest runner package.
-Follow the insturction from navigating this path `Settings -> Actions -> Runners -> New self-hosted runner` to add a new windows runner. Note that You just do the download、extract steps in the directories which we have created, we don't need to configure it and run it. We can run a script to registry all the runners.
+Follow the insturction from navigating this path `Settings -> Actions -> Runners -> New self-hosted runner` to add a new Windows runner. Note that you only need to download、extract the files into the directories created in Step 3. Configuration and running will be done through a script later.
step5: Registry all the runners.
-Run the script from ./registry-runners/windows.ps1 to registry all the runners to your github. you need to replace the variables $REPO、$ACCESS_TOKEN、$WORKPLACE with the actual value. Note that you can get your $ACCESS_TOKEN from the following path `Settings -> Actions -> Runners -> New self-hosted runner.`.
-In the Configure section, you can find the similar line: `./config.sh --url https://github.com/FedML-AI/FedML --token AXRYPL6G2VHVGDFDQQS5XA3ELYI6M` to get your $ACCESS_TOKEN.
+Run the script from `./registry-runners/windows.ps1` to registry all the runners to your github. Replace the variables `$REPO`、`$ACCESS_TOKEN` and `$WORKPLACE` with actual values. Note that you can get your $ACCESS_TOKEN from the following path `Settings -> Actions -> Runners -> New self-hosted runner.`.
+In the Configure section, you will find the similar line: `./config.sh --url https://github.com/FedML-AI/FedML --token AXRYPL6G2VHVGDFDQQS5XA3ELYI6M` to get your `$ACCESS_TOKEN`.
-step6: Make sure the success.
-Check if the runners are registered successfully. Navigate the following path.`Settings -> Actions -> Runners` to check that your runners are all active.
+step6: Verify Success
+Check if the runners are registered successfully by navigate to `Settings -> Actions -> Runners`. Make sure that all your runners are active.
## 2.3 Mac Runners
-# 3. bind Test Machines
+# 3. Bind Test Machines
-We also need to bind the actual machine to run the test training job. Following this document to bind your test machines.
+Bind the actual machine to run the test training job. Follow this document to bind your test machines.
https://docs.tensoropera.ai/share-and-earn
Note that we need to bind our machines to the test environment.
-In your job YAML, you should specify the computing resource type to which you have bound your machines. Then, your job will be scheduled to that machine.
+Specify the computing resource type to which you have bound your machines. Your job will be scheduled to that machine.
# 4. Trigger
-You can apply for a PR; All tests will run automatically.
+Applying for a PR can trigger all tests automatically.
+
+Run a single test on a specific branch from the GitHub Actions tab.
+
+Schedule daily runs at a specific time by configuring your workflow YAML. You can check the results in the GitHub Actions tab.
-You can also run a single test at a specific branch in the GitHub Actions tab.
+# 5. Add a new CI test
-The CI tests will run daily at a specific time which you configure in your workflow YAML. You can check the results in the GitHub Actions tab.
+Creating a new workflow YAML file, such as CI_launch.yaml or CI_train.yaml, allows you to add a CI test that is different from the current business.
-# 5. How to add a new CI test
+Adding a new CI test to the current business can be done by placing your test in the path python/tests/test_{business}/test_file.py and ensuring that your workflow YAML can run that Python test script.
-If you need to add a new CI test that is different from the current business, you need to create a new workflow YAML file, such as CI_launch.yaml or CI_train.yaml. If you just want to add a new CI test to the current business, you can add your test in the path python/tests/test_{business}/test_file.py and make sure that your workflow YAML can run that Python test script.
+Ensuring your workflow YAML is configured correctly will enable it to run the new test automatically.
# 6. TODO
From be60443aea50e174b5057dbe7aa09d1922068d53 Mon Sep 17 00:00:00 2001
From: xiang <xiang@tensoropera.com>
Date: Fri, 21 Jun 2024 04:42:51 +0000
Subject: [PATCH 182/282] [doc]: make sure the workflow documents are more
readable.
---
.github/workflows/README.md | 20 ++++++++++----------
1 file changed, 10 insertions(+), 10 deletions(-)
diff --git a/.github/workflows/README.md b/.github/workflows/README.md
index 2b261708f6..668cb9b302 100644
--- a/.github/workflows/README.md
+++ b/.github/workflows/README.md
@@ -10,7 +10,7 @@ The CI tests need to be comprehensive, covering typical scenarios only, achievab
## 2.1 Linux Runners
-Step1: Build linux images
+### Step1: Build linux images
Build all the linux images for Self-Host Runners.
```
@@ -18,7 +18,7 @@ cd registry-runners
bash build_linux_runners.sh
```
-Step2: Specify the token and key.
+### Step2: Specify the token and key.
Find your GitHub runner token and your test-account apikey.
For the argument YourGitHubRunnerToken, Navigate the path `Settings -> Actions -> Runners -> New self-hosted runner` to get.
@@ -26,7 +26,7 @@ For the argument YourGitHubRunnerToken, Navigate the path `Settings -> Actions -
In the Configure section, you will find the similar line:
./config.sh --url https://github.com/FedML-AI/FedML --token AXRYPL6G2VHVGDFDQQS5XA3ELYI6M to get YourGitHubRunnerToken to value of --token
-Step3: Registry all the runners.
+### Step3: Registry all the runners.
Registry by run `run_linux_runners.sh` script
```
bash run_linux_runners.sh [YourGitRepo] [YourGitHubRunnerToken] [YourTestAccountApiKey]
@@ -35,33 +35,33 @@ for example
```
bash run_linux_runners.sh FedML-AI/FedML AXRYPLZLZN6XVJB3BAIXSP3EMFC7U 11215dkevvdkegged
```
-Step4 Verify Success
+### Step4: Verify Success
Check if all the runners are registered successfully. Navigate the following path. `Settings -> Actions -> Runners` to check that all your runners are active.
## 2.2 Windows Runners
-step1: Install Anaconda packages
+### Step1: Install Anaconda packages
Install Anaconda or Miniconda on a Windows machine. Anaconda and Miniconda can manage your Python environments.
-step2: Create python enviroments
+### Step2: Create python enviroments
Create 4 python environments named `python38`、`python39`、`python310` and `python311` for different runners.
Specify the python version to install.
For example
```
conda create -n python38 python==3.8
```
-step3: Create directories
+### Step3: Create directories
Create 4 directories named `actions-runner-python38`、`actions-runner-python39`、`actions-runner-python310` and `actions-runner-python311` for different runners.
-step4: Install the latest runner package.
+### Step4: Install the latest runner package.
Follow the insturction from navigating this path `Settings -> Actions -> Runners -> New self-hosted runner` to add a new Windows runner. Note that you only need to download、extract the files into the directories created in Step 3. Configuration and running will be done through a script later.
-step5: Registry all the runners.
+### Step5: Registry all the runners.
Run the script from `./registry-runners/windows.ps1` to registry all the runners to your github. Replace the variables `$REPO`、`$ACCESS_TOKEN` and `$WORKPLACE` with actual values. Note that you can get your $ACCESS_TOKEN from the following path `Settings -> Actions -> Runners -> New self-hosted runner.`.
In the Configure section, you will find the similar line: `./config.sh --url https://github.com/FedML-AI/FedML --token AXRYPL6G2VHVGDFDQQS5XA3ELYI6M` to get your `$ACCESS_TOKEN`.
-step6: Verify Success
+### Step6: Verify Success
Check if the runners are registered successfully by navigate to `Settings -> Actions -> Runners`. Make sure that all your runners are active.
## 2.3 Mac Runners
From d7481bebb214c20475adc8da4b5de362e935f134 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Fri, 21 Jun 2024 17:31:32 +0800
Subject: [PATCH 183/282] [CoreEngine] set the name of all monitor processes,
remove the redundant binding codes for deployment devices, change the account
manager,
---
.../scheduler/master/base_master_agent.py | 2 +
.../model_scheduler/model_device_client.py | 98 ----------------
.../model_scheduler/model_device_server.py | 97 ---------------
.../scheduler_core/account_manager.py | 71 +++++------
.../scheduler_core/general_constants.py | 10 +-
.../scheduler_core/message_center.py | 3 +-
.../scheduler/scheduler_core/status_center.py | 3 +-
.../scheduler/slave/base_slave_agent.py | 7 +-
.../slave/base_slave_protocol_manager.py | 11 +-
.../scheduler/slave/slave_protocol_manager.py | 66 ++---------
.../scheduler/slave/united_agents.py | 15 ++-
python/fedml/core/mlops/mlops_device_perfs.py | 111 ++++++++++++++----
python/fedml/core/mlops/mlops_job_perfs.py | 19 ++-
13 files changed, 185 insertions(+), 328 deletions(-)
delete mode 100755 python/fedml/computing/scheduler/model_scheduler/model_device_client.py
delete mode 100755 python/fedml/computing/scheduler/model_scheduler/model_device_server.py
diff --git a/python/fedml/computing/scheduler/master/base_master_agent.py b/python/fedml/computing/scheduler/master/base_master_agent.py
index 4fb3a5e755..e7d18f64f7 100755
--- a/python/fedml/computing/scheduler/master/base_master_agent.py
+++ b/python/fedml/computing/scheduler/master/base_master_agent.py
@@ -64,6 +64,8 @@ def login(
if communication_manager is None:
self.protocol_mgr.start()
+ return login_result
+
@staticmethod
def logout():
GeneralConstants.cleanup_run_process(None, is_master=True)
diff --git a/python/fedml/computing/scheduler/model_scheduler/model_device_client.py b/python/fedml/computing/scheduler/model_scheduler/model_device_client.py
deleted file mode 100755
index 05f43afc5f..0000000000
--- a/python/fedml/computing/scheduler/model_scheduler/model_device_client.py
+++ /dev/null
@@ -1,98 +0,0 @@
-
-import copy
-import logging
-import multiprocessing
-import time
-import traceback
-from multiprocessing import Process
-from ..scheduler_core.account_manager import FedMLAccountManager
-from .worker_agent import FedMLDeployWorkerAgent
-
-
-class FedMLModelDeviceClientRunner:
- def __init__(self, args, current_device_id, os_name, is_from_docker, service_config, infer_host="127.0.0.1"):
- self.agent_process = None
- self.agent_runner = None
- self.agent_process_event = None
- self.args = copy.deepcopy(args)
- self.service_config = service_config
- self.unique_device_id = None
- self.current_device_id = current_device_id
- self.os_name = os_name
- self.is_from_docker = is_from_docker
- self.edge_id = None
- self.infer_host = infer_host
- self.redis_addr = "local"
- self.redis_port = "6379"
- self.redis_password = "fedml_default"
-
- def get_edge_id(self):
- return self.edge_id
-
- def start(self):
- self.agent_runner = FedMLModelDeviceClientRunner(self.args, self.current_device_id, self.os_name,
- self.is_from_docker, self.service_config)
- self.agent_runner.infer_host = self.infer_host
- self.agent_runner.redis_addr = self.redis_addr
- self.agent_runner.redis_port = self.redis_port
- self.agent_runner.redis_password = self.redis_password
- if self.agent_process_event is None:
- self.agent_process_event = multiprocessing.Event()
- self.agent_process = Process(target=self.agent_runner.run_entry, args=(self.agent_process_event, self.args,))
- self.edge_id = self.bind_device()
- self.agent_process.start()
-
- def run_entry(self, process_event, in_args):
- # print(f"Model worker process id {os.getpid()}")
-
- self.agent_process_event = process_event
-
- worker_agent = FedMLDeployWorkerAgent()
-
- while not self.agent_process_event.is_set():
- try:
- try:
- worker_agent.logout()
- except Exception as e:
- pass
-
- worker_agent.login(
- in_args.account_id, api_key=in_args.api_key, device_id=in_args.device_id,
- os_name=in_args.os_name, role=FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM
- )
- except Exception as e:
- logging.info("Restart model device client: {}".format(traceback.format_exc()))
- pass
- finally:
- try:
- worker_agent.logout()
- except Exception as e:
- pass
- time.sleep(15)
-
- try:
- self.stop()
- except Exception as e:
- pass
-
- def check_runner_stop_event(self):
- if self.agent_process_event is not None and self.agent_process_event.is_set():
- logging.info("Received stopping event.")
- raise Exception("Runner stopped")
-
- def stop(self):
- FedMLDeployWorkerAgent.logout()
-
- if self.agent_process_event is not None:
- self.agent_process_event.set()
-
- def bind_device(self):
- # Login account
- login_result = FedMLAccountManager.get_instance().login(
- self.args.account_id, api_key=self.args.api_key, device_id=self.args.device_id,
- os_name=self.args.os_name, role=FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM
- )
- if login_result is not None:
- return login_result.edge_id
- else:
- return None
diff --git a/python/fedml/computing/scheduler/model_scheduler/model_device_server.py b/python/fedml/computing/scheduler/model_scheduler/model_device_server.py
deleted file mode 100755
index b2ecd144b1..0000000000
--- a/python/fedml/computing/scheduler/model_scheduler/model_device_server.py
+++ /dev/null
@@ -1,97 +0,0 @@
-
-import copy
-import logging
-import multiprocessing
-import time
-import traceback
-from multiprocessing import Process
-from ..scheduler_core.account_manager import FedMLAccountManager
-from .master_agent import FedMLDeployMasterAgent
-
-
-class FedMLModelDeviceServerRunner:
- def __init__(self, args, current_device_id, os_name, is_from_docker, service_config, infer_host="127.0.0.1"):
- self.agent_process = None
- self.agent_runner = None
- self.agent_process_event = None
- self.args = copy.deepcopy(args)
- self.service_config = service_config
- self.unique_device_id = None
- self.current_device_id = current_device_id
- self.os_name = os_name
- self.is_from_docker = is_from_docker
- self.edge_id = None
- self.infer_host = infer_host
- self.redis_addr = "local"
- self.redis_port = "6379"
- self.redis_password = "fedml_default"
-
- def get_edge_id(self):
- return self.edge_id
-
- def start(self):
- self.agent_runner = FedMLModelDeviceServerRunner(self.args, self.current_device_id, self.os_name,
- self.is_from_docker, self.service_config)
- self.agent_runner.infer_host = self.infer_host
- self.agent_runner.redis_addr = self.redis_addr
- self.agent_runner.redis_port = self.redis_port
- self.agent_runner.redis_password = self.redis_password
- if self.agent_process_event is None:
- self.agent_process_event = multiprocessing.Event()
- self.agent_process = Process(target=self.agent_runner.run_entry, args=(self.agent_process_event, self.args))
- self.edge_id = self.bind_device()
- self.agent_process.start()
-
- def run_entry(self, process_event, in_args):
- # print(f"Model master process id {os.getpid()}")
-
- self.agent_process_event = process_event
- master_agent = FedMLDeployMasterAgent()
-
- while not self.agent_process_event.is_set():
- try:
- try:
- master_agent.logout()
- except Exception as e:
- pass
-
- master_agent.login(
- in_args.account_id, api_key=in_args.api_key, device_id=in_args.device_id,
- os_name=in_args.os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM
- )
- except Exception as e:
- logging.info("Restart model device server: {}".format(traceback.format_exc()))
- pass
- finally:
- try:
- master_agent.logout()
- except Exception as e:
- pass
- time.sleep(15)
-
- try:
- self.stop()
- except Exception as e:
- pass
-
- def check_runner_stop_event(self):
- if self.agent_process_event is not None and self.agent_process_event.is_set():
- logging.info("Received stopping event.")
- raise Exception("Runner stopped")
-
- def stop(self):
- FedMLDeployMasterAgent.logout()
-
- if self.agent_process_event is not None:
- self.agent_process_event.set()
-
- def bind_device(self):
- # Login account
- login_result = FedMLAccountManager.get_instance().login(
- self.args.account_id, api_key=self.args.api_key, device_id=self.args.device_id,
- os_name=self.args.os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM
- )
- if login_result is not None:
- return login_result.edge_id
- else:
- return None
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
index 20c5fcd842..6bd3dd4b19 100755
--- a/python/fedml/computing/scheduler/scheduler_core/account_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -43,8 +43,7 @@ class FedMLAccountManager(Singleton):
DEVICE_ID_DOCKER_HUB_TAG = ".DockerHub"
def __init__(self):
- if not hasattr(self, "agent_args"):
- self.agent_args = None
+ pass
@staticmethod
def get_instance():
@@ -52,7 +51,7 @@ def get_instance():
def login(self, user_id, api_key="", device_id=None, os_name=None, role=None, runner_cmd=None):
# Build the agent args
- self.build_agent_args(
+ agent_args = self.build_agent_args(
user_id, api_key=api_key, device_id=device_id, os_name=os_name, role=role, runner_cmd=runner_cmd
)
@@ -95,8 +94,8 @@ def login(self, user_id, api_key="", device_id=None, os_name=None, role=None, ru
# noinspection PyBroadException
try:
edge_id, user_name, extra_url, general_edge_id = FedMLAccountManager.bind_account_and_device_id(
- service_config["ml_ops_config"]["EDGE_BINDING_URL"], self.agent_args.account_id,
- self.agent_args.unique_device_id, self.agent_args.os_name,
+ service_config["ml_ops_config"]["EDGE_BINDING_URL"], agent_args.account_id,
+ agent_args.unique_device_id, agent_args.os_name,
api_key=api_key, role=role
)
if edge_id > 0:
@@ -120,13 +119,13 @@ def login(self, user_id, api_key="", device_id=None, os_name=None, role=None, ru
return None
# Fill the bound result to agent args.
- self.fill_argent_args(
- log_server_url=log_server_url, server_id=edge_id,
+ agent_args = self.fill_argent_args(
+ agent_args, log_server_url=log_server_url, server_id=edge_id,
edge_id=edge_id, general_edge_id=general_edge_id,
user_name=user_name, extra_url=extra_url,
agent_config=service_config)
- return self.agent_args
+ return agent_args
def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None, role=None, runner_cmd=None):
# Generate the suffix for device based on the role
@@ -159,32 +158,31 @@ def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None,
# Build the agent args
version = fedml.get_env_version()
- if self.agent_args is None:
- self.agent_args = AgentArgs()
- self.agent_args.role = role
- self.agent_args.account_id = user_id
- self.agent_args.api_key = api_key
- self.agent_args.current_running_dir = GeneralConstants.get_deploy_fedml_home_dir(is_master=is_master) \
+ agent_args = AgentArgs()
+ agent_args.role = role
+ agent_args.account_id = user_id
+ agent_args.api_key = api_key
+ agent_args.current_running_dir = GeneralConstants.get_deploy_fedml_home_dir(is_master=is_master) \
if is_deploy else GeneralConstants.get_launch_fedml_home_dir(is_master=is_master)
sys_name = platform.system()
if sys_name == "Darwin":
sys_name = "MacOS"
- self.agent_args.os_name = sys_name if os_name is None or os_name == "" else os_name
- self.agent_args.version = version
- self.agent_args.log_file_dir = GeneralConstants.get_deploy_log_file_dir(is_master=is_master) \
+ agent_args.os_name = sys_name if os_name is None or os_name == "" else os_name
+ agent_args.version = version
+ agent_args.log_file_dir = GeneralConstants.get_deploy_log_file_dir(is_master=is_master) \
if is_deploy else GeneralConstants.get_launch_log_file_dir(is_master=is_master)
is_from_docker = False
if device_id is not None and device_id != "0":
- self.agent_args.current_device_id = device_id
+ agent_args.current_device_id = device_id
else:
data_dir = GeneralConstants.get_deploy_data_dir(is_master=is_master) \
if is_deploy else GeneralConstants.get_launch_data_dir(is_master=is_master)
is_gpu_provider = True if role == FedMLAccountManager.ROLE_GPU_PROVIDER else False
- self.agent_args.current_device_id = FedMLAccountManager.get_device_id(
+ agent_args.current_device_id = FedMLAccountManager.get_device_id(
data_dir=data_dir, use_machine_id=is_gpu_provider)
- self.agent_args.device_id = self.agent_args.current_device_id
- self.agent_args.config_version = version
- self.agent_args.cloud_region = ""
+ agent_args.device_id = agent_args.current_device_id
+ agent_args.config_version = version
+ agent_args.cloud_region = ""
# Check if it is running in the fedml docker hub
is_from_fedml_docker_hub = False
@@ -196,26 +194,29 @@ def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None,
# Build unique device id
docker_tag = FedMLAccountManager.DEVICE_ID_DOCKER_TAG if is_from_docker else ""
docker_tag = FedMLAccountManager.DEVICE_ID_DOCKER_HUB_TAG if is_from_fedml_docker_hub else docker_tag
- unique_device_id = f"{self.agent_args.current_device_id}@{self.agent_args.os_name}" \
+ unique_device_id = f"{agent_args.current_device_id}@{agent_args.os_name}" \
f"{docker_tag}{device_id_suffix}"
if role == FedMLAccountManager.ROLE_CLOUD_SERVER:
- unique_device_id = self.agent_args.current_device_id
+ unique_device_id = agent_args.current_device_id
# Set the unique device id
- self.agent_args.is_from_docker = is_from_docker or is_from_fedml_docker_hub
- self.agent_args.unique_device_id = unique_device_id
- self.agent_args.runner_cmd = runner_cmd
+ agent_args.is_from_docker = is_from_docker or is_from_fedml_docker_hub
+ agent_args.unique_device_id = unique_device_id
+ agent_args.runner_cmd = runner_cmd
+
+ return agent_args
def fill_argent_args(
- self, log_server_url=None, server_id=None, edge_id=None,
+ self, agent_args, log_server_url=None, server_id=None, edge_id=None,
user_name=None, extra_url=None, general_edge_id=None, agent_config=None):
- self.agent_args.log_server_url = log_server_url
- self.agent_args.server_id = server_id
- self.agent_args.edge_id = edge_id
- self.agent_args.user_name = user_name
- self.agent_args.extra_url = extra_url
- self.agent_args.general_edge_id = general_edge_id
- self.agent_args.agent_config = agent_config
+ agent_args.log_server_url = log_server_url
+ agent_args.server_id = server_id
+ agent_args.edge_id = edge_id
+ agent_args.user_name = user_name
+ agent_args.extra_url = extra_url
+ agent_args.general_edge_id = general_edge_id
+ agent_args.agent_config = agent_config
+ return agent_args
@staticmethod
def write_login_failed_file(is_client=True):
diff --git a/python/fedml/computing/scheduler/scheduler_core/general_constants.py b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
index 3b40e1df80..0ab6f79577 100755
--- a/python/fedml/computing/scheduler/scheduler_core/general_constants.py
+++ b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
@@ -76,6 +76,7 @@ class GeneralConstants:
FEDML_MESSAGE_CENTER_SENDER_TAG = "message-center-sender"
FEDML_STATUS_CENTER_TAG = "status-center"
FEDML_LOG_PROCESS_TAG = "log"
+ FEDML_MONITOR_PROCESS_TAG = "monitor"
FEDML_TOPIC_STATUS_CENTER_STOP = "anywhere/status_center/stop"
@@ -232,7 +233,9 @@ def get_payload_complete_job(run_id, server_id):
@staticmethod
def get_process_name(process_tag, run_id=None, edge_id=None):
- return f"{GeneralConstants.FEDML_PROCESS_NAME_PREFIX}{process_tag}-run-{run_id}-edge-{edge_id}"
+ return f'{GeneralConstants.FEDML_PROCESS_NAME_PREFIX}{process_tag}'\
+ f'{"-run-" + str(run_id) if run_id is not None and int(run_id) != 0 else ""}'\
+ f'{"-edge-" + str(edge_id) if edge_id is not None else ""}'
@staticmethod
def get_process_name_with_prefix(process_prefix, run_id=None, edge_id=None):
@@ -285,4 +288,7 @@ def get_message_center_sender_process_name(message_center_name):
def get_status_center_process_name(status_center_tag):
return f"{GeneralConstants.FEDML_PROCESS_NAME_PREFIX}{GeneralConstants.FEDML_STATUS_CENTER_TAG}-{status_center_tag}"
-
+ @staticmethod
+ def get_monitor_process_name(monitor_tag, run_id, edge_id):
+ return GeneralConstants.get_process_name(
+ f"{GeneralConstants.FEDML_MONITOR_PROCESS_TAG}-{monitor_tag}", run_id, edge_id)
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_center.py b/python/fedml/computing/scheduler/scheduler_core/message_center.py
index 087e74edf4..5f414d1873 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_center.py
@@ -352,8 +352,7 @@ def start_listener(
self.listener_message_event = multiprocessing.Event()
self.listener_message_event.clear()
self.listener_agent_config = agent_config
- message_runner = self.get_message_runner()
- # message_runner = self
+ message_runner = self
message_runner.listener_agent_config = agent_config
process_name = GeneralConstants.get_message_center_listener_process_name(message_center_name)
if platform.system() == "Windows":
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index 47dfa9d1a7..b1462d7ea9 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -120,8 +120,7 @@ def start_status_center(self, sender_message_center_queue=None,
self.status_event.clear()
self.status_sender_message_center_queue = sender_message_center_queue
self.status_listener_message_center_queue = listener_message_center_queue
- self.status_runner = self.get_status_runner()
- #self.status_runner = self
+ self.status_runner = self
process_name = GeneralConstants.get_status_center_process_name(
f'{"deploy" if self.is_deployment_status_center else "launch"}_'
f'{"slave" if is_slave_agent else "master"}_agent')
diff --git a/python/fedml/computing/scheduler/slave/base_slave_agent.py b/python/fedml/computing/scheduler/slave/base_slave_agent.py
index 58a79aae88..9876ac9912 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_agent.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_agent.py
@@ -35,7 +35,7 @@ def login(
print("We can't find any gpu device on your machine. \n"
"With the gpu_supplier(-g) option, you need to check if your machine "
"has nvidia GPUs and installs CUDA related drivers.")
- return
+ return None
# Login account
login_result = FedMLAccountManager.get_instance().login(
@@ -155,3 +155,8 @@ def _init_database(self):
@abstractmethod
def _generate_protocol_manager_instance(self, args, agent_config=None):
return None
+
+ def save_deploy_ids(self, deploy_master_edge_id=None, deploy_slave_edge_id=None):
+ self.protocol_mgr.save_deploy_ids(
+ deploy_master_edge_id=deploy_master_edge_id, deploy_slave_edge_id=deploy_slave_edge_id)
+
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index 648a49bbd1..534ee2f7d0 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -62,8 +62,6 @@ def __init__(self, args, agent_config=None):
self.server_id = args.server_id
self.model_device_server_id = None
self.model_device_client_edge_id_list = None
- self.model_device_server = None
- self.model_device_client_list = None
@abstractmethod
def generate_topics(self):
@@ -147,12 +145,9 @@ def add_subscribe_topic(self, topic):
self.subscribed_topics.append(topic)
def stop(self):
- if self.model_device_server is not None:
- self.model_device_server = None
-
- if self.model_device_client_list is not None:
- self.model_device_client_list.clear()
- self.model_device_client_list = None
+ if self.model_device_client_edge_id_list is not None:
+ self.model_device_client_edge_id_list.clear()
+ self.model_device_client_edge_id_list = None
super().stop()
diff --git a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
index 050cfb3f1d..449cd7c29c 100755
--- a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
@@ -1,10 +1,8 @@
-import copy
+
import os
from ..comm_utils.job_cleanup import JobCleanup
from .base_slave_protocol_manager import FedMLBaseSlaveProtocolManager
from .launch_job_runner_manager import FedMLLaunchJobRunnerManager
-from ..model_scheduler.model_device_server import FedMLModelDeviceServerRunner
-from ..model_scheduler.model_device_client import FedMLModelDeviceClientRunner
class FedMLLaunchSlaveProtocolManager(FedMLBaseSlaveProtocolManager):
@@ -47,57 +45,19 @@ def _init_extra_items(self):
# Sync the data when startup
JobCleanup.get_instance().sync_data_on_startup(self.args.edge_id)
- # Get the environment variables
- infer_host = os.getenv("FEDML_INFER_HOST", None)
- infer_redis_addr = os.getenv("FEDML_INFER_REDIS_ADDR", None)
- infer_redis_port = os.getenv("FEDML_INFER_REDIS_PORT", None)
- infer_redis_password = os.getenv("FEDML_INFER_REDIS_PASSWORD", None)
- model_client_num = os.getenv("FEDML_MODEL_WORKER_NUM", None)
-
- # Start deploy master agent and slave agent
- in_args = copy.deepcopy(self.args)
- if self.model_device_client_edge_id_list is None:
- self.model_device_client_edge_id_list = list()
- if self.model_device_client_list is None:
- model_client_num = 1 if model_client_num is None else int(model_client_num)
- self.model_device_client_list = list()
- for client_index in range(model_client_num):
- model_device_client = FedMLModelDeviceClientRunner(
- in_args, f"{in_args.current_device_id}_{client_index + 1}", in_args.os_name,
- in_args.is_from_docker, self.agent_config)
- if infer_host is not None:
- model_device_client.infer_host = infer_host
- if infer_redis_addr is not None:
- model_device_client.redis_addr = infer_redis_addr
- if infer_redis_port is not None:
- model_device_client.redis_port = infer_redis_port
- if infer_redis_password is not None:
- model_device_client.redis_password = infer_redis_password
- self.model_device_client_list.append(model_device_client)
- self.model_device_client_edge_id_list.append(model_device_client.bind_device())
-
- self.args = copy.deepcopy(in_args)
- if self.model_device_server is None:
- self.model_device_server = FedMLModelDeviceServerRunner(in_args, in_args.current_device_id,
- in_args.os_name, in_args.is_from_docker,
- self.agent_config)
- if infer_host is not None:
- self.model_device_server.infer_host = infer_host
- if infer_redis_addr is not None:
- self.model_device_server.redis_addr = infer_redis_addr
- if infer_redis_port is not None:
- self.model_device_server.redis_port = infer_redis_port
- if infer_redis_password is not None:
- self.model_device_server.redis_password = infer_redis_password
-
- self.model_device_server_id = self.model_device_server.bind_device()
+ # Start the monitor process
+ self.mlops_metrics.stop_device_realtime_perf()
+ self.mlops_metrics.report_device_realtime_perf(self.args, self.args.agent_config["mqtt_config"])
+
+ def save_deploy_ids(self, deploy_master_edge_id=None, deploy_slave_edge_id=None):
+ if deploy_master_edge_id is not None:
+ self.model_device_server_id = deploy_master_edge_id
+
+ if deploy_slave_edge_id is not None:
+ if self.model_device_client_edge_id_list is None:
+ self.model_device_client_edge_id_list = list()
+ self.model_device_client_edge_id_list.append(deploy_slave_edge_id)
# Save the deployed master and worker id list to the environment variable.
os.environ["FEDML_DEPLOY_MASTER_ID"] = str(self.model_device_server_id)
os.environ["FEDML_DEPLOY_WORKER_IDS"] = str(self.model_device_client_edge_id_list)
-
- # Start the monitor process
- self.args = copy.deepcopy(in_args)
- self.mlops_metrics.stop_device_realtime_perf()
- self.mlops_metrics.report_device_realtime_perf(self.args, self.args.agent_config["mqtt_config"])
- pass
diff --git a/python/fedml/computing/scheduler/slave/united_agents.py b/python/fedml/computing/scheduler/slave/united_agents.py
index 17aee46f62..3c8549c06a 100755
--- a/python/fedml/computing/scheduler/slave/united_agents.py
+++ b/python/fedml/computing/scheduler/slave/united_agents.py
@@ -1,10 +1,8 @@
-import multiprocessing
-
+from fedml.computing.scheduler.model_scheduler.master_agent import FedMLDeployMasterAgent
+from fedml.computing.scheduler.model_scheduler.worker_agent import FedMLDeployWorkerAgent
from fedml.computing.scheduler.scheduler_core.account_manager import FedMLAccountManager
from fedml.computing.scheduler.slave.slave_agent import FedMLLaunchSlaveAgent
from fedml.computing.scheduler.master.master_agent import FedMLLaunchMasterAgent
-from fedml.computing.scheduler.model_scheduler.model_device_server import FedMLDeployMasterAgent
-from fedml.computing.scheduler.model_scheduler.model_device_client import FedMLDeployWorkerAgent
from fedml.core.common.singleton import Singleton
@@ -54,7 +52,7 @@ def login(self, userid, api_key=None, device_id=None,
# Login with the deployment master role based on
# the shared communication manager, sender message center, status center
- deploy_master_agent.login(
+ deploy_master_login_result = deploy_master_agent.login(
userid, api_key=api_key, device_id=login_result.device_id,
os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM,
communication_manager=shared_communication_mgr
@@ -62,11 +60,16 @@ def login(self, userid, api_key=None, device_id=None,
# Login with the deployment slave role based on
# the shared communication manager, sender message center, status center
- deploy_slave_agent.login(
+ deploy_slave_login_result = deploy_slave_agent.login(
userid, api_key=api_key, device_id=login_result.device_id,
os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM,
communication_manager=shared_communication_mgr
)
+ # Set the deployment ids to launch agent so that we can report the related device info to MLOps.
+ launch_slave_agent.save_deploy_ids(
+ deploy_master_edge_id=deploy_master_login_result.edge_id,
+ deploy_slave_edge_id=deploy_slave_login_result.edge_id)
+
# Start the slave agent to connect to servers and loop forever.
launch_slave_agent.start()
diff --git a/python/fedml/core/mlops/mlops_device_perfs.py b/python/fedml/core/mlops/mlops_device_perfs.py
index d0f1f3898f..6ba4d6e866 100644
--- a/python/fedml/core/mlops/mlops_device_perfs.py
+++ b/python/fedml/core/mlops/mlops_device_perfs.py
@@ -9,6 +9,7 @@
import multiprocessing
import psutil
+import setproctitle
import fedml
from fedml.computing.scheduler.comm_utils import sys_utils
@@ -16,6 +17,7 @@
from .mlops_utils import MLOpsUtils
from .system_stats import SysStats
from ...computing.scheduler.comm_utils.job_monitor import JobMonitor
+from ...computing.scheduler.scheduler_core.general_constants import GeneralConstants
from ...core.distributed.communication.mqtt.mqtt_manager import MqttManager
@@ -30,6 +32,17 @@
ROLE_ENDPOINT_REPLICA_NUM = 8
ROLE_ENDPOINT_REPLICA_PERF = 9
+ROLE_DEVICE_JOB_TOTAL_MONITOR_STR = "device_job_total"
+ROLE_DEVICE_INFO_REPORTER_STR = "device_info"
+ROLE_ENDPOINT_MASTER_STR = "endpoint_master"
+ROLE_ENDPOINT_SLAVE_STR = "endpoint_slave"
+ROLE_RUN_MASTER_STR = "run_master"
+ROLE_RUN_SLAVE_STR = "run_slave"
+ROLE_ENDPOINT_LOGS_STR = "endpoint_logs"
+ROLE_AUTO_SCALER_STR = "autoscaler"
+ROLE_ENDPOINT_REPLICA_NUM_STR = "endpoint_replica_num"
+ROLE_ENDPOINT_REPLICA_PERF_STR = "endpoint_replica_perf"
+
class MLOpsDevicePerfStats(object):
def __init__(self):
@@ -81,100 +94,158 @@ def setup_realtime_stats_process(self, sys_args):
if platform.system() == "Windows":
self.device_realtime_stats_process = multiprocessing.Process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_DEVICE_INFO_REPORTER, self.is_client))
+ args=(self.device_realtime_stats_event, ROLE_DEVICE_INFO_REPORTER, self.is_client,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_DEVICE_INFO_REPORTER_STR, perf_stats.run_id, perf_stats.edge_id)))
else:
self.device_realtime_stats_process = fedml.get_process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_DEVICE_INFO_REPORTER, self.is_client))
+ args=(self.device_realtime_stats_event, ROLE_DEVICE_INFO_REPORTER, self.is_client,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_DEVICE_INFO_REPORTER_STR, perf_stats.run_id, perf_stats.edge_id)))
self.device_realtime_stats_process.start()
if self.enable_job_total_monitor:
if platform.system() == "Windows":
self.job_total_monitor_process = multiprocessing.Process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_DEVICE_JOB_TOTAL_MONITOR, self.is_client))
+ args=(self.device_realtime_stats_event, ROLE_DEVICE_JOB_TOTAL_MONITOR, self.is_client,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_DEVICE_JOB_TOTAL_MONITOR_STR, perf_stats.run_id, perf_stats.edge_id)))
else:
self.job_total_monitor_process = fedml.get_process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_DEVICE_JOB_TOTAL_MONITOR, self.is_client))
+ args=(self.device_realtime_stats_event, ROLE_DEVICE_JOB_TOTAL_MONITOR, self.is_client,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_DEVICE_JOB_TOTAL_MONITOR_STR, perf_stats.run_id, perf_stats.edge_id)))
self.job_total_monitor_process.start()
else:
if self.is_client:
+ # Register endpoint master process
if platform.system() == "Windows":
self.monitor_endpoint_master_process = multiprocessing.Process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_MASTER))
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_MASTER, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_ENDPOINT_MASTER_STR, perf_stats.run_id, perf_stats.edge_id)))
else:
self.monitor_endpoint_master_process = fedml.get_process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_MASTER))
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_MASTER, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_ENDPOINT_MASTER_STR, perf_stats.run_id, perf_stats.edge_id)))
self.monitor_endpoint_master_process.start()
+ # Register endpoint slave process
+ if platform.system() == "Windows":
+ self.monitor_endpoint_slave_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_SLAVE, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_ENDPOINT_SLAVE_STR, perf_stats.run_id, perf_stats.edge_id)))
+ else:
+ self.monitor_endpoint_slave_process = fedml.get_process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_SLAVE, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_ENDPOINT_SLAVE_STR, perf_stats.run_id, perf_stats.edge_id)))
+ self.monitor_endpoint_slave_process.start()
+
+ # Register run slave process
if platform.system() == "Windows":
self.monitor_run_slave_process = multiprocessing.Process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_RUN_SLAVE))
+ args=(self.device_realtime_stats_event, ROLE_RUN_SLAVE, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_RUN_SLAVE_STR, perf_stats.run_id, perf_stats.edge_id)))
else:
self.monitor_run_slave_process = fedml.get_process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_RUN_SLAVE))
+ args=(self.device_realtime_stats_event, ROLE_RUN_SLAVE, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_RUN_SLAVE_STR, perf_stats.run_id, perf_stats.edge_id)))
self.monitor_run_slave_process.start()
+ # Register endpoint logs process
if platform.system() == "Windows":
self.monitor_endpoint_logs_process = multiprocessing.Process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_LOGS))
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_LOGS, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_ENDPOINT_LOGS_STR, perf_stats.run_id, perf_stats.edge_id)))
else:
self.monitor_endpoint_logs_process = fedml.get_process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_LOGS))
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_LOGS, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_ENDPOINT_LOGS_STR, perf_stats.run_id, perf_stats.edge_id)))
self.monitor_endpoint_logs_process.start()
# Register auto-scaler process
if platform.system() == "Windows":
self.monitor_auto_scaler_process = multiprocessing.Process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_AUTO_SCALER))
+ args=(self.device_realtime_stats_event, ROLE_AUTO_SCALER, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_AUTO_SCALER_STR, perf_stats.run_id, perf_stats.edge_id)))
else:
self.monitor_auto_scaler_process = fedml.get_process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_AUTO_SCALER))
+ args=(self.device_realtime_stats_event, ROLE_AUTO_SCALER, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_AUTO_SCALER_STR, perf_stats.run_id, perf_stats.edge_id)))
self.monitor_auto_scaler_process.start()
# Register replica number report channel
if platform.system() == "Windows":
self.monitor_replica_num_process = multiprocessing.Process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_NUM))
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_NUM, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_ENDPOINT_REPLICA_NUM_STR, perf_stats.run_id, perf_stats.edge_id)))
else:
self.monitor_replica_num_process = fedml.get_process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_NUM))
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_NUM, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_ENDPOINT_REPLICA_NUM_STR, perf_stats.run_id, perf_stats.edge_id)))
self.monitor_replica_num_process.start()
# Register replica performance report channel
if platform.system() == "Windows":
self.monitor_replica_perf_process = multiprocessing.Process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_PERF))
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_PERF, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_ENDPOINT_REPLICA_PERF_STR, perf_stats.run_id, perf_stats.edge_id)))
+
else:
self.monitor_replica_perf_process = fedml.get_process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_PERF))
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_PERF, True,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_ENDPOINT_REPLICA_PERF_STR, perf_stats.run_id, perf_stats.edge_id)))
self.monitor_replica_perf_process.start()
else:
if platform.system() == "Windows":
self.monitor_run_master_process = multiprocessing.Process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_RUN_MASTER))
+ args=(self.device_realtime_stats_event, ROLE_RUN_MASTER, False,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_RUN_MASTER_STR, perf_stats.run_id, perf_stats.edge_id)))
else:
self.monitor_run_master_process = fedml.get_process(
target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_RUN_MASTER))
+ args=(self.device_realtime_stats_event, ROLE_RUN_MASTER, False,
+ GeneralConstants.get_monitor_process_name(
+ ROLE_RUN_MASTER_STR, perf_stats.run_id, perf_stats.edge_id)))
self.monitor_run_master_process.start()
- def report_device_realtime_stats_entry(self, sys_event, role, is_client=False):
- # print(f"Report device realtime stats, process id {os.getpid()}")
+ def report_device_realtime_stats_entry(self, sys_event, role, is_client=False, process_name=None):
+ if process_name is not None:
+ setproctitle.setproctitle(process_name)
+
+ # print(f"Report device realtime stats, process id {os.getpid()}, name {process_name}")
self.device_realtime_stats_event = sys_event
mqtt_mgr = MqttManager(
diff --git a/python/fedml/core/mlops/mlops_job_perfs.py b/python/fedml/core/mlops/mlops_job_perfs.py
index e834ed4a0c..429e32ff1d 100644
--- a/python/fedml/core/mlops/mlops_job_perfs.py
+++ b/python/fedml/core/mlops/mlops_job_perfs.py
@@ -8,14 +8,18 @@
import multiprocess as multiprocessing
import psutil
+import setproctitle
import fedml
from .mlops_utils import MLOpsUtils
from .system_stats import SysStats
+from ...computing.scheduler.scheduler_core.general_constants import GeneralConstants
from ...core.distributed.communication.mqtt.mqtt_manager import MqttManager
class MLOpsJobPerfStats(object):
+ JOB_PERF_PROCESS_TAG = "job_perf"
+
def __init__(self):
self.job_stats_process = None
self.job_stats_event = None
@@ -142,17 +146,24 @@ def setup_job_stats_process(self, sys_args):
perf_stats.job_process_id_map = self.job_process_id_map
if platform.system() == "Windows":
self.job_stats_process = multiprocessing.Process(
- target=perf_stats.report_job_stats_entry, args=(self.job_stats_event,))
+ target=perf_stats.report_job_stats_entry,
+ args=(self.job_stats_event, GeneralConstants.get_monitor_process_name(
+ MLOpsJobPerfStats.JOB_PERF_PROCESS_TAG, perf_stats.run_id, perf_stats.edge_id)))
else:
self.job_stats_process = fedml.get_process(
- target=perf_stats.report_job_stats_entry, args=(self.job_stats_event,))
+ target=perf_stats.report_job_stats_entry,
+ args=(self.job_stats_event, GeneralConstants.get_monitor_process_name(
+ MLOpsJobPerfStats.JOB_PERF_PROCESS_TAG, perf_stats.run_id, perf_stats.edge_id)))
self.job_stats_process.start()
def report_job_stats(self, sys_args):
self.setup_job_stats_process(sys_args)
- def report_job_stats_entry(self, sys_event):
- # print(f"Report job realtime stats, process id {os.getpid()}")
+ def report_job_stats_entry(self, sys_event, process_name):
+ if process_name is not None:
+ setproctitle.setproctitle(process_name)
+
+ # print(f"Report job realtime stats, process id {os.getpid()}, name {process_name}")
self.job_stats_event = sys_event
mqtt_mgr = MqttManager(
From aa813a074aadebdbfd1f2b1ebd542334d095b5a4 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Fri, 21 Jun 2024 17:32:26 +0800
Subject: [PATCH 184/282] [CoreEngine] remove the API key.
---
python/fedml/api/api_test.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/api/api_test.py b/python/fedml/api/api_test.py
index 1aa5ac3767..5951fbd1e0 100755
--- a/python/fedml/api/api_test.py
+++ b/python/fedml/api/api_test.py
@@ -4,9 +4,9 @@
import fedml
# Login
-fedml.set_env_version("local")
+fedml.set_env_version("test")
fedml.set_local_on_premise_platform_port(18080)
-error_code, error_msg = fedml.api.fedml_login(api_key="1316b93c82da40ce90113a2ed12f0b14")
+error_code, error_msg = fedml.api.fedml_login(api_key="")
if error_code != 0:
print("API Key is invalid!")
exit(1)
From 33fb5b45fc674d18d74e7f435d41e69ebfde703d Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Fri, 21 Jun 2024 14:21:16 -0700
Subject: [PATCH 185/282] [Deploy] Pass down the api key to container.
---
.../device_client_constants.py | 2 +
.../model_scheduler/device_model_cache.py | 15 +++++-
.../device_model_deployment.py | 47 ++++++++++++-------
.../device_server_constants.py | 2 +
.../master_protocol_manager.py | 25 +++++-----
.../model_scheduler/worker_job_runner.py | 4 +-
6 files changed, 62 insertions(+), 33 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
index e18c9f730b..4aee592fca 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
@@ -165,6 +165,8 @@ class ClientConstants(object):
CUSTOMIZED_VOLUMES_PATH_FROM_WORKSPACE_KEY = "workspace_path"
CUSTOMIZED_VOLUMES_PATH_FROM_CONTAINER_KEY = "mount_path"
+ ENV_USER_ENCRYPTED_API_KEY = "FEDML_USER_ENCRYPTED_API_KEY"
+
@staticmethod
def get_fedml_home_dir():
home_dir = expanduser("~")
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
index c941c42102..b0021aa7df 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
@@ -112,7 +112,8 @@ def set_user_setting_replica_num(self, end_point_id,
replica_num: int, enable_auto_scaling: bool = False,
scale_min: int = 0, scale_max: int = 0, state: str = "UNKNOWN",
target_queries_per_replica: int = 60, aggregation_window_size_seconds: int = 60,
- scale_down_delay_seconds: int = 120, timeout_s: int = 30
+ scale_down_delay_seconds: int = 120, timeout_s: int = 30,
+ user_encrypted_api_key: str = ""
) -> bool:
"""
Key: FEDML_MODEL_ENDPOINT_REPLICA_USER_SETTING_TAG--<end_point_id>
@@ -139,7 +140,8 @@ def set_user_setting_replica_num(self, end_point_id,
"target_queries_per_replica": target_queries_per_replica,
"aggregation_window_size_seconds": aggregation_window_size_seconds,
"scale_down_delay_seconds": scale_down_delay_seconds,
- ServerConstants.INFERENCE_REQUEST_TIMEOUT_KEY: timeout_s
+ ServerConstants.INFERENCE_REQUEST_TIMEOUT_KEY: timeout_s,
+ ServerConstants.USER_ENCRYPTED_API_KEY: user_encrypted_api_key
}
try:
self.redis_connection.set(self.get_user_setting_replica_num_key(end_point_id), json.dumps(replica_num_dict))
@@ -169,6 +171,15 @@ def update_user_setting_replica_num(self, end_point_id: str, state: str = "UNKNO
return False
return True
+ def get_user_encrypted_api_key(self, end_point_id: str) -> str:
+ try:
+ replica_num_dict = self.redis_connection.get(self.get_user_setting_replica_num_key(end_point_id))
+ replica_num_dict = json.loads(replica_num_dict)
+ return replica_num_dict.get(ServerConstants.USER_ENCRYPTED_API_KEY, "")
+ except Exception as e:
+ logging.error(e)
+ return ""
+
def get_all_endpoints_user_setting(self) -> List[dict]:
"""
Return a list of dict, each dict is the user setting of an endpoint.
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 552d7ffaca..9416d243d2 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -19,6 +19,7 @@
from fedml.computing.scheduler.comm_utils.job_utils import JobRunnerUtils
from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants
+from fedml.computing.scheduler.model_scheduler.device_server_constants import ServerConstants
from fedml.computing.scheduler.model_scheduler.device_model_cache import FedMLModelCache
from ..scheduler_core.compute_utils import ComputeUtils
from ..comm_utils.container_utils import ContainerUtils
@@ -59,7 +60,9 @@ def request_gpu_ids_on_deployment(edge_id, end_point_id, num_gpus=None, master_d
def start_deployment(end_point_id, end_point_name, model_id, model_version,
model_storage_local_path, inference_model_name, inference_engine,
infer_host, master_ip, edge_id, master_device_id=None, replica_rank=0,
- gpu_per_replica=1):
+ gpu_per_replica=1, request_json=None):
+ if request_json is None:
+ request_json = dict()
logging.info("[Worker] Model deployment is starting...")
# Real gpu per replica (container-level)
@@ -219,22 +222,9 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
if device_mapping:
host_config_dict.update(device_mapping)
- # Environment variables
- enable_custom_image = False if relative_entry_fedml_format != "" else True
- if not enable_custom_image:
- # For some image, the default user is root. Unified to fedml.
- environment["HOME"] = "/home/fedml"
- environment["BOOTSTRAP_DIR"] = dst_bootstrap_dir
- environment["FEDML_CURRENT_RUN_ID"] = end_point_id
- environment["FEDML_CURRENT_EDGE_ID"] = edge_id
- environment["FEDML_REPLICA_RANK"] = replica_rank
- environment["FEDML_CURRENT_VERSION"] = fedml.get_env_version()
- environment["FEDML_ENV_VERSION"] = fedml.get_env_version()
- environment["FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_HOST"] = fedml.get_local_on_premise_platform_host()
- environment["FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_PORT"] = fedml.get_local_on_premise_platform_port()
- if extra_envs is not None:
- for key in extra_envs:
- environment[key] = extra_envs[key]
+ # Handle the environment variables
+ handle_env_vars(environment, relative_entry_fedml_format, extra_envs, dst_bootstrap_dir,
+ end_point_id, edge_id, replica_rank, request_json)
# Create the container
try:
@@ -612,6 +602,29 @@ def handle_volume_mount(volumes, binds, environment, relative_entry_fedml_format
logging.warning(f"{workspace_path} does not exist, skip mounting it to the container")
+def handle_env_vars(environment, relative_entry_fedml_format, extra_envs, dst_bootstrap_dir, end_point_id, edge_id,
+ replica_rank, request_json):
+ enable_custom_image = False if relative_entry_fedml_format != "" else True
+ if not enable_custom_image:
+ # For some image, the default user is root. Unified to fedml.
+ environment["HOME"] = "/home/fedml"
+
+ if request_json and ServerConstants.USER_ENCRYPTED_API_KEY in request_json:
+ environment[ClientConstants.ENV_USER_ENCRYPTED_API_KEY] = request_json[ServerConstants.USER_ENCRYPTED_API_KEY]
+
+ environment["BOOTSTRAP_DIR"] = dst_bootstrap_dir
+ environment["FEDML_CURRENT_RUN_ID"] = end_point_id
+ environment["FEDML_CURRENT_EDGE_ID"] = edge_id
+ environment["FEDML_REPLICA_RANK"] = replica_rank
+ environment["FEDML_CURRENT_VERSION"] = fedml.get_env_version()
+ environment["FEDML_ENV_VERSION"] = fedml.get_env_version()
+ environment["FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_HOST"] = fedml.get_local_on_premise_platform_host()
+ environment["FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_PORT"] = fedml.get_local_on_premise_platform_port()
+ if extra_envs is not None:
+ for key in extra_envs:
+ environment[key] = extra_envs[key]
+
+
def check_container_readiness(inference_http_port, infer_host="127.0.0.1", request_input_example=None,
readiness_check=ClientConstants.READINESS_PROBE_DEFAULT):
response_from_client_container = is_client_inference_container_ready(
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
index f86056229e..c41b150bc2 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
@@ -108,6 +108,8 @@ class ServerConstants(object):
INFERENCE_REQUEST_TIMEOUT_KEY = "request_timeout_sec"
INFERENCE_REQUEST_TIMEOUT_DEFAULT = 30
+
+ USER_ENCRYPTED_API_KEY = "user_encrypted_api_key"
# -----End-----
MODEL_DEPLOYMENT_STAGE1 = {"index": 1, "text": "ReceivedRequest"}
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index 7bfad2f3eb..5e16d5a02a 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -158,25 +158,20 @@ def callback_start_deployment(self, topic, payload):
run_id = request_json["end_point_id"]
end_point_name = request_json["end_point_name"]
token = request_json["token"]
- user_id = request_json["user_id"]
- user_name = request_json["user_name"]
- device_ids = request_json["device_ids"]
device_objs = request_json["device_objs"]
+ enable_auto_scaling = request_json.get("enable_auto_scaling", False)
+ desired_replica_num = request_json.get("desired_replica_num", 1)
+ target_queries_per_replica = request_json.get("target_queries_per_replica", 10)
+ aggregation_window_size_seconds = request_json.get("aggregation_window_size_seconds", 60)
+ scale_down_delay_seconds = request_json.get("scale_down_delay_seconds", 120)
+ user_encrypted_api_key = request_json.get("encrypted_api_key", "")
model_config = request_json["model_config"]
model_name = model_config["model_name"]
model_version = model_config["model_version"]
model_id = model_config["model_id"]
- model_storage_url = model_config["model_storage_url"]
scale_min = model_config.get("instance_scale_min", 0)
scale_max = model_config.get("instance_scale_max", 0)
- inference_engine = model_config.get("inference_engine", 0)
- enable_auto_scaling = request_json.get("enable_auto_scaling", False)
- desired_replica_num = request_json.get("desired_replica_num", 1)
-
- target_queries_per_replica = request_json.get("target_queries_per_replica", 10)
- aggregation_window_size_seconds = request_json.get("aggregation_window_size_seconds", 60)
- scale_down_delay_seconds = request_json.get("scale_down_delay_seconds", 120)
model_config_parameters = request_json.get("parameters", {})
timeout_s = model_config_parameters.get("request_timeout_sec", 30)
@@ -193,6 +188,12 @@ def callback_start_deployment(self, topic, payload):
request_json["end_point_id"])
request_json["is_fresh_endpoint"] = True if endpoint_device_info is None else False
+ if user_encrypted_api_key == "":
+ user_encrypted_api_key = (FedMLModelCache.get_instance(self.redis_addr, self.redis_port).
+ get_user_encrypted_api_key(run_id))
+ if user_encrypted_api_key != "": # Pass the cached key to the workers
+ request_json[ServerConstants.USER_ENCRYPTED_API_KEY] = user_encrypted_api_key
+
# Save the user setting (about replica number) of this run to Redis, if existed, update it
FedMLModelCache.get_instance(self.redis_addr, self.redis_port).set_user_setting_replica_num(
end_point_id=run_id, end_point_name=end_point_name, model_name=model_name, model_version=model_version,
@@ -201,7 +202,7 @@ def callback_start_deployment(self, topic, payload):
aggregation_window_size_seconds=aggregation_window_size_seconds,
target_queries_per_replica=target_queries_per_replica,
scale_down_delay_seconds=int(scale_down_delay_seconds),
- timeout_s=timeout_s
+ timeout_s=timeout_s, user_encrypted_api_key=user_encrypted_api_key
)
# Start log processor for current run
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index a892412d29..113a20e825 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -250,7 +250,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
inference_model_name=model_name, inference_engine=inference_engine,
infer_host=worker_ip, master_ip=master_ip, edge_id=self.edge_id,
master_device_id=device_ids[0], replica_rank=rank,
- gpu_per_replica=int(self.replica_handler.gpu_per_replica)
+ gpu_per_replica=int(self.replica_handler.gpu_per_replica), request_json=self.request_json
)
except Exception as e:
inference_output_url = ""
@@ -373,7 +373,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
inference_model_name=model_name, inference_engine=inference_engine,
infer_host=worker_ip, master_ip=master_ip, edge_id=self.edge_id,
master_device_id=device_ids[0], replica_rank=rank,
- gpu_per_replica=int(self.replica_handler.gpu_per_replica)
+ gpu_per_replica=int(self.replica_handler.gpu_per_replica), request_json=self.request_json
)
except Exception as e:
inference_output_url = ""
From f412a2637b6ae83f9fc1ecaa60b5205d4d43507d Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Fri, 21 Jun 2024 21:36:12 +0000
Subject: [PATCH 186/282] [Deploy] Nit.
---
.../scheduler/model_scheduler/device_server_constants.py | 2 +-
.../scheduler/model_scheduler/master_protocol_manager.py | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
index c41b150bc2..00f0fe73bf 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
@@ -109,7 +109,7 @@ class ServerConstants(object):
INFERENCE_REQUEST_TIMEOUT_KEY = "request_timeout_sec"
INFERENCE_REQUEST_TIMEOUT_DEFAULT = 30
- USER_ENCRYPTED_API_KEY = "user_encrypted_api_key"
+ USER_ENCRYPTED_API_KEY = "encrypted_api_key"
# -----End-----
MODEL_DEPLOYMENT_STAGE1 = {"index": 1, "text": "ReceivedRequest"}
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index 5e16d5a02a..9e0d51b588 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -164,7 +164,7 @@ def callback_start_deployment(self, topic, payload):
target_queries_per_replica = request_json.get("target_queries_per_replica", 10)
aggregation_window_size_seconds = request_json.get("aggregation_window_size_seconds", 60)
scale_down_delay_seconds = request_json.get("scale_down_delay_seconds", 120)
- user_encrypted_api_key = request_json.get("encrypted_api_key", "")
+ user_encrypted_api_key = request_json.get(ServerConstants.USER_ENCRYPTED_API_KEY, "")
model_config = request_json["model_config"]
model_name = model_config["model_name"]
From d6c9411774318e812e7f0b4dd73478f2a88e4cb3 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Fri, 21 Jun 2024 15:00:33 -0700
Subject: [PATCH 187/282] [Deploy] Remove example.
---
.../lorax/custom_inference_image.yaml | 16 ----------------
1 file changed, 16 deletions(-)
delete mode 100644 python/examples/deploy/custom_inference_image/lorax/custom_inference_image.yaml
diff --git a/python/examples/deploy/custom_inference_image/lorax/custom_inference_image.yaml b/python/examples/deploy/custom_inference_image/lorax/custom_inference_image.yaml
deleted file mode 100644
index 41cbe501d2..0000000000
--- a/python/examples/deploy/custom_inference_image/lorax/custom_inference_image.yaml
+++ /dev/null
@@ -1,16 +0,0 @@
-workspace: "./"
-
-enable_serverless_container: true
-inference_image_name: "ghcr.io/predibase/lorax:main"
-container_run_command: "--model-id mistralai/Mistral-7B-Instruct-v0.1"
-
-environment_variables:
- HUGGING_FACE_HUB_TOKEN: ""
-
-readiness_probe:
- httpGet:
- path: "/health"
-
-port: 80
-
-deploy_timeout_sec: 1600
From 5ae6904960987baff3e57a9e55bf369d9918f7e0 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Tue, 25 Jun 2024 18:21:06 +0800
Subject: [PATCH 188/282] [CoreEngine] make the job stopping feature work.
---
.../scheduler/master/base_master_agent.py | 3 ++-
.../scheduler/master/base_master_job_runner.py | 14 +++++---------
.../master/base_master_job_runner_manager.py | 6 ++----
.../master/base_master_protocol_manager.py | 12 ++++++++++++
.../scheduler_core/scheduler_base_job_runner.py | 3 ++-
.../scheduler_base_job_runner_manager.py | 1 +
6 files changed, 24 insertions(+), 15 deletions(-)
diff --git a/python/fedml/computing/scheduler/master/base_master_agent.py b/python/fedml/computing/scheduler/master/base_master_agent.py
index e7d18f64f7..30cf5da1c9 100755
--- a/python/fedml/computing/scheduler/master/base_master_agent.py
+++ b/python/fedml/computing/scheduler/master/base_master_agent.py
@@ -72,7 +72,8 @@ def logout():
sys_utils.cleanup_all_fedml_server_api_processes()
def stop(self, kill_process=False):
- self.protocol_mgr.stop(kill_process=kill_process)
+ if self.protocol_mgr is not None:
+ self.protocol_mgr.stop(kill_process=kill_process)
def _create_protocol_manager(self, role, login_result):
if self.protocol_mgr is not None:
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner.py b/python/fedml/computing/scheduler/master/base_master_job_runner.py
index 32b285dc7b..fdfff143aa 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner.py
@@ -1,4 +1,3 @@
-
import json
import logging
import multiprocessing
@@ -414,9 +413,9 @@ def _generate_job_runner_instance(self, args, run_id=None, request_json=None, ag
return None
def start_runner_process(
- self, run_id, request_json, edge_id=None, is_server_job=False,
- sender_message_queue=None, listener_message_queue=None,
- status_center_queue=None, process_name=None
+ self, run_id, request_json, edge_id=None, is_server_job=False,
+ sender_message_queue=None, listener_message_queue=None,
+ status_center_queue=None, process_name=None
):
server_runner = self._generate_job_runner_instance(
self.args, run_id=run_id, request_json=request_json,
@@ -440,7 +439,7 @@ def start_runner_process(
self.run_process_event, self.run_process_completed_event, self.run_edge_id_status_queue,
self.run_edge_device_info_queue, self.run_metrics_queue, self.run_events_queue,
self.run_artifacts_queue, self.run_logs_queue, self.run_edge_device_info_global_queue,
- self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue,
+ self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue,
process_name,
)
)
@@ -450,7 +449,7 @@ def start_runner_process(
self.run_process_event, self.run_process_completed_event, self.run_edge_id_status_queue,
self.run_edge_device_info_queue, self.run_metrics_queue, self.run_events_queue,
self.run_artifacts_queue, self.run_logs_queue, self.run_edge_device_info_global_queue,
- self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue,
+ self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue,
process_name,
)
)
@@ -731,6 +730,3 @@ def should_process_async_cluster(self):
def get_client_id_list(self, server_edge_id_list):
return server_edge_id_list
-
-
-
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
index f462596cbf..39f7438696 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
@@ -2,7 +2,6 @@
import json
import logging
import multiprocessing
-import os
import platform
import time
from abc import ABC
@@ -11,7 +10,6 @@
import fedml
from .cloud_server_manager import FedMLCloudServerManager
from ..comm_utils.run_process_utils import RunProcessUtils
-from ..scheduler_core.general_constants import GeneralConstants
from ..scheduler_core.scheduler_base_job_runner_manager import FedMLSchedulerBaseJobRunnerManager
from ..scheduler_core.account_manager import FedMLAccountManager
@@ -67,10 +65,10 @@ def stop_job_runner(
run_id_str = str(run_id)
if self.master_agent_instance_map.get(run_id_str, None) is not None:
- self.master_agent_instance_map.get(run_id_str).stop()
+ self.master_agent_instance_map.get(run_id_str).stop(kill_process=True)
self.master_agent_instance_map.pop(run_id_str)
- if run_as_cloud_server:
+ if use_local_process_as_cloud_server:
time.sleep(1)
RunProcessUtils.kill_process(self.cloud_run_process_map[run_id_str].pid)
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index 9d3b492758..05529f8c8e 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -2,6 +2,7 @@
import base64
import json
import logging
+import time
import fedml
from ..comm_utils.constants import SchedulerConstants
@@ -142,6 +143,7 @@ def on_agent_communication_connected(self, mqtt_client_object):
def callback_start_train(self, topic=None, payload=None):
# Fetch config from MLOps
# noinspection PyBroadException
+
try:
MLOpsConfigs.fetch_all_configs()
except Exception:
@@ -290,6 +292,16 @@ def callback_stop_train(self, topic, payload, use_payload=None):
server_agent_id = self.edge_id
topic_stop_train_to_cloud_server = f"mlops/flserver_agent_{server_id}/stop_train"
self.message_center.send_message(topic_stop_train_to_cloud_server, payload)
+
+ time.sleep(2)
+ MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, server_id)
+ self._get_job_runner_manager().stop_job_runner(
+ run_id, args=self.args, server_id=server_id, request_json=None,
+ run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server,
+ use_local_process_as_cloud_server=self.use_local_process_as_cloud_server)
+ self.generate_status_report(run_id, server_id, server_agent_id=server_agent_id). \
+ report_server_id_status(run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_KILLED,
+ edge_id=server_id, server_id=server_id)
return
# Reset all edge status and server status
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 7b0d00f53d..7175032375 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -615,7 +615,8 @@ def job_error_processor(self, error_list):
def start_runner_process(
self, run_id, edge_id, request_json, cuda_visible_gpu_ids_str=None,
- sender_message_queue=None, status_center_queue=None, process_name=None
+ sender_message_queue=None, listener_message_queue=None,
+ status_center_queue=None, process_name=None
):
return None
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
index 8edf57fcbb..ad32f78631 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
@@ -29,6 +29,7 @@ def start_job_runner(
)
self.job_runners[run_id_str].start_runner_process(
run_id, request_json, edge_id=edge_id,
+ cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str,
sender_message_queue=sender_message_queue,
listener_message_queue=listener_message_queue,
status_center_queue=status_center_queue,
From fa44ccce0a553f7c7d7dcceb5312b830061e718f Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 25 Jun 2024 11:48:17 -0700
Subject: [PATCH 189/282] [Deploy] Return custom path other than /predict.
---
.../device_client_constants.py | 1 +
.../device_model_deployment.py | 108 +++++++++++-------
2 files changed, 67 insertions(+), 42 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
index 4aee592fca..4006e50726 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
@@ -164,6 +164,7 @@ class ClientConstants(object):
CUSTOMIZED_VOLUMES_MOUNT_KEY = "volumes"
CUSTOMIZED_VOLUMES_PATH_FROM_WORKSPACE_KEY = "workspace_path"
CUSTOMIZED_VOLUMES_PATH_FROM_CONTAINER_KEY = "mount_path"
+ CUSTOMIZED_SERVICE_KEY = "service"
ENV_USER_ENCRYPTED_API_KEY = "FEDML_USER_ENCRYPTED_API_KEY"
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 9416d243d2..25fc1e1d64 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -87,36 +87,10 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
inference_image_name, image_pull_policy, registry_name, registry_provider, \
registry_user_name, registry_user_password = parse_image_registry_related_config(config)
- # Bootstrap, job and entrypoint related
- dst_model_serving_dir = "/home/fedml/models_serving"
- bootstrap_cmds_str_frm_yaml = config.get('bootstrap', "")
- job_cmds_str_frm_yaml = config.get('job', "")
-
- if bootstrap_cmds_str_frm_yaml != "" or job_cmds_str_frm_yaml != "":
- auto_gen_bootstrap_file_name = "fedml-deploy-bootstrap-entry-auto-gen.sh"
- src_bootstrap_file_path = os.path.join(model_storage_local_path, auto_gen_bootstrap_file_name)
- with open(src_bootstrap_file_path, 'w') as f:
- f.write("cd /home/fedml/models_serving/\n")
- f.write(bootstrap_cmds_str_frm_yaml)
- f.write("\n")
- f.write("cd /home/fedml/models_serving/\n")
- f.write(job_cmds_str_frm_yaml)
- else:
- src_bootstrap_file_path = ""
-
- if src_bootstrap_file_path != "":
- dst_bootstrap_dir = os.path.join(dst_model_serving_dir, auto_gen_bootstrap_file_name)
- else:
- dst_bootstrap_dir = ""
-
- # If the entry point is in fedml format (e.g., "main.py")
- relative_entry_fedml_format = config.get('entry_point', "")
-
- # User indicate either fedml format python main entry filename or entry command
- enable_serverless_container = config.get(ClientConstants.ENABLE_SERVERLESS_CONTAINER_KEY, False)
- customized_image_entry_cmd = config.get('container_run_command', None) # Could be str or list
- customized_readiness_check = config.get('readiness_probe', ClientConstants.READINESS_PROBE_DEFAULT)
- customized_liveliness_check = config.get('liveness_probe', ClientConstants.LIVENESS_PROBE_DEFAULT)
+ # Service app related
+ dst_bootstrap_dir, dst_model_serving_dir, relative_entry_fedml_format, enable_serverless_container, \
+ customized_image_entry_cmd, customized_readiness_check, customized_liveliness_check, customized_uri = \
+ handle_container_service_app(config, model_storage_local_path)
# Storage related
src_code_dir = os.path.join(model_storage_local_path, config.get('source_code_dir', ""))
@@ -451,7 +425,7 @@ def parse_image_registry_related_config(config):
def is_client_inference_container_ready(infer_url_host, inference_http_port,
readiness_check=ClientConstants.READINESS_PROBE_DEFAULT,
- request_input_example=None, container_id=None):
+ request_input_example=None, container_id=None, customized_uri=None):
# Construct the model metadata (input and output)
model_metadata = {}
if request_input_example is not None and len(request_input_example) > 0:
@@ -461,6 +435,7 @@ def is_client_inference_container_ready(infer_url_host, inference_http_port,
model_metadata["outputs"] = []
model_metadata["type"] = "default"
+ # Check the readiness of the container
if readiness_check == ClientConstants.READINESS_PROBE_DEFAULT:
default_client_container_ready_url = "http://{}:{}/ready".format("0.0.0.0", inference_http_port)
response = None
@@ -486,27 +461,38 @@ def is_client_inference_container_ready(infer_url_host, inference_http_port,
else:
if not check_path.startswith("/"):
check_path = "/" + check_path
- readiness_check_url = f"http://{infer_url_host}:{inference_http_port}{check_path}"
-
response = None
try:
- response = requests.get(readiness_check_url)
+ response = requests.get(f"http://{infer_url_host}:{inference_http_port}{check_path}")
except:
pass
if not response or response.status_code != 200:
return "", "", {}, {}
-
- return readiness_check_url, None, model_metadata, None
else:
logging.error("'path' is not specified in httpGet readiness check")
return "", "", {}, {}
elif "exec" in readiness_check:
- # TODO(raphael): Support arbitrary readiness check command by using
- # container id and docker exec
- return "http://{}:{}/".format(infer_url_host, inference_http_port), None, model_metadata, None
+ # TODO(raphael): Support arbitrary readiness check command by using container id and docker exec
+ pass
else:
# Ref K8S, if no readiness check, we assume the container is ready immediately
- return "http://{}:{}/".format(infer_url_host, inference_http_port), None, model_metadata, None
+ pass
+
+ # Construct the customized URI
+ path = ""
+ if customized_uri is not None:
+ if "httpPost" in customized_uri and "path" in customized_uri["httpPost"]:
+ path = customized_uri["httpPost"]["path"]
+ if not isinstance(path, str):
+ logging.error(f"Invalid path type: {path}, expected str")
+ return "", "", {}, {}
+ else:
+ if not path.startswith("/"):
+ path = "/" + path
+ # TODO(raphael): Finalized more customized URI types
+ readiness_check_url = f"http://{infer_url_host}:{inference_http_port}{path}"
+
+ return readiness_check_url, None, model_metadata, None
def _handle_union_volume_mount(binds, volumes, environment, data_cache_dir_input=None):
@@ -602,6 +588,43 @@ def handle_volume_mount(volumes, binds, environment, relative_entry_fedml_format
logging.warning(f"{workspace_path} does not exist, skip mounting it to the container")
+def handle_container_service_app(config, model_storage_local_path):
+ # Bootstrap, job and entrypoint related
+ dst_model_serving_dir = "/home/fedml/models_serving"
+ bootstrap_cmds_str_frm_yaml = config.get('bootstrap', "")
+ job_cmds_str_frm_yaml = config.get('job', "")
+
+ auto_gen_bootstrap_file_name = "fedml-deploy-bootstrap-entry-auto-gen.sh"
+ if bootstrap_cmds_str_frm_yaml != "" or job_cmds_str_frm_yaml != "":
+ src_bootstrap_file_path = os.path.join(model_storage_local_path, auto_gen_bootstrap_file_name)
+ with open(src_bootstrap_file_path, 'w') as f:
+ f.write("cd /home/fedml/models_serving/\n")
+ f.write(bootstrap_cmds_str_frm_yaml)
+ f.write("\n")
+ f.write("cd /home/fedml/models_serving/\n")
+ f.write(job_cmds_str_frm_yaml)
+ else:
+ src_bootstrap_file_path = ""
+
+ if src_bootstrap_file_path != "":
+ dst_bootstrap_dir = os.path.join(dst_model_serving_dir, auto_gen_bootstrap_file_name)
+ else:
+ dst_bootstrap_dir = ""
+
+ # If the entry point is in fedml format (e.g., "main.py")
+ relative_entry_fedml_format = config.get('entry_point', "")
+
+ # User indicate either fedml format python main entry filename or entry command
+ enable_serverless_container = config.get(ClientConstants.ENABLE_SERVERLESS_CONTAINER_KEY, False)
+ customized_image_entry_cmd = config.get('container_run_command', None) # Could be str or list
+ customized_readiness_check = config.get('readiness_probe', ClientConstants.READINESS_PROBE_DEFAULT)
+ customized_liveliness_check = config.get('liveness_probe', ClientConstants.LIVENESS_PROBE_DEFAULT)
+ customized_uri = config.get(ClientConstants.CUSTOMIZED_SERVICE_KEY, "")
+
+ return (dst_bootstrap_dir, dst_model_serving_dir, relative_entry_fedml_format, enable_serverless_container,
+ customized_image_entry_cmd, customized_readiness_check, customized_liveliness_check, customized_uri)
+
+
def handle_env_vars(environment, relative_entry_fedml_format, extra_envs, dst_bootstrap_dir, end_point_id, edge_id,
replica_rank, request_json):
enable_custom_image = False if relative_entry_fedml_format != "" else True
@@ -626,10 +649,11 @@ def handle_env_vars(environment, relative_entry_fedml_format, extra_envs, dst_bo
def check_container_readiness(inference_http_port, infer_host="127.0.0.1", request_input_example=None,
- readiness_check=ClientConstants.READINESS_PROBE_DEFAULT):
+ readiness_check=ClientConstants.READINESS_PROBE_DEFAULT,
+ customized_uri=None):
response_from_client_container = is_client_inference_container_ready(
infer_host, inference_http_port, readiness_check=readiness_check,
- request_input_example=request_input_example)
+ request_input_example=request_input_example, customized_uri=customized_uri)
return response_from_client_container
From bd89be1a1f01f0ff1528cd1766c2a22a25af5975 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 25 Jun 2024 11:50:04 -0700
Subject: [PATCH 190/282] [Deploy] Add sqlite backup for
get_all_deployment_result_list.
---
.../scheduler/comm_utils/constants.py | 1 -
.../scheduler/comm_utils/job_monitor.py | 2 +-
.../model_scheduler/device_model_cache.py | 30 +++++++++--
.../model_scheduler/device_model_db.py | 51 +++++++++++++++++--
.../model_scheduler/worker_job_runner.py | 2 +-
5 files changed, 75 insertions(+), 11 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/constants.py b/python/fedml/computing/scheduler/comm_utils/constants.py
index 67b9d8b14b..6e46ce207b 100644
--- a/python/fedml/computing/scheduler/comm_utils/constants.py
+++ b/python/fedml/computing/scheduler/comm_utils/constants.py
@@ -114,7 +114,6 @@ class SchedulerConstants:
REDIS_PORT = "6379"
REDIS_PASSWORD = "fedml_default"
-
@staticmethod
def get_log_source(run_json):
run_config = run_json.get("run_config", {})
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index 667a54e565..b8237d93ba 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -210,6 +210,7 @@ def monitor_replicas_number():
endpoint_replicas_details = {}
if isinstance(endpoint_detail, str):
endpoint_replicas_details = json.loads(endpoint_detail)
+ # TODO: Check out this nested json
if isinstance(endpoint_replicas_details, str):
endpoint_replicas_details = json.loads(endpoint_replicas_details)
@@ -222,7 +223,6 @@ def monitor_replicas_number():
endpoint_replica_details["end_point_id"], 0) + 1
for endpoint_id, num_replica in res_to_mlops.items():
- curr_version = fedml.get_env_version()
num_replica_url_path = "fedmlModelServer/api/v1/endpoint/replica-info"
mlops_prefix = fedml._get_backend_service()
url = f"{mlops_prefix}/{num_replica_url_path}"
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
index b0021aa7df..1836971075 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
@@ -302,7 +302,27 @@ def get_all_deployment_result_list(self):
result_list.extend(self.redis_connection.lrange(key, 0, -1))
except Exception as e:
logging.error(e)
- # TODO(Raphael): Use Sqlite for the replica backup
+
+ # Get cached results from the persist sqlite database
+ if len(result_list) <= 0:
+ db_result_list = list()
+ try:
+ db_result_list = self.model_deployment_db.get_all_deployment_results_list()
+ except Exception as e:
+ logging.error(f"Failed to get all deployment results from the database due to {e}")
+ pass
+
+ for result in db_result_list:
+ try:
+ self.redis_connection.rpush(self.get_deployment_result_key(
+ result["end_point_id"], result["end_point_name"], result["model_name"]),
+ json.dumps(result["replica_info"]))
+ except Exception as e:
+ logging.error(e)
+ pass
+
+ for result in db_result_list:
+ result_list.append(result["replica_info"])
return result_list
@@ -330,7 +350,8 @@ def get_deployment_status_list_size(self, end_point_id, end_point_name, model_na
status_list = self.get_deployment_status_list(end_point_id, end_point_name, model_name)
return len(status_list)
- def get_status_item_info(self, status_item):
+ @staticmethod
+ def get_status_item_info(status_item):
status_item_json = json.loads(status_item)
if isinstance(status_item_json, str):
status_item_json = json.loads(status_item_json)
@@ -341,7 +362,8 @@ def get_status_item_info(self, status_item):
status_payload = status_item_json["status"]
return device_id, status_payload
- def get_result_item_info(self, result_item):
+ @staticmethod
+ def get_result_item_info(result_item):
result_item_json = json.loads(result_item)
if isinstance(result_item_json, str):
result_item_json = json.loads(result_item_json)
@@ -386,7 +408,7 @@ def get_idle_device(self,
return None, None
# # Randomly shuffle
- # shuffle the list of deployed devices and get the first one as the target idle device.
+ # the list of deployed devices and get the first one as the target idle device.
# if len(idle_device_list) <= 0:
# return None, None
# shuffle(idle_device_list)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_db.py b/python/fedml/computing/scheduler/model_scheduler/device_model_db.py
index 09573a1d1b..606d8c010b 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_db.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_db.py
@@ -10,6 +10,7 @@
from sqlalchemy.ext.declarative import declarative_base
from fedml.core.common.singleton import Singleton
from sqlalchemy.sql import text
+from typing import List, Dict
Base = declarative_base()
@@ -42,9 +43,11 @@ def set_deployment_status(self, end_point_id, end_point_name, model_name, model_
self.set_deployment_results_info(end_point_id, end_point_name, model_name, model_version,
device_id, deployment_status=deployment_status, replica_no=replica_no)
- def get_deployment_result_list(self, end_point_id, end_point_name, model_name, model_version=None):
+ def get_deployment_result_list(self, end_point_id, end_point_name, model_name, model_version=None) -> List[str]:
"""
- query from sqlite db using e_id
+ Get the orm use get_deployment_results_info,
+ but (1) nested results with cache_device_id, cache_replica_no.
+ (2) return a list of json string, so that redis can store it.
"""
result_list = self.get_deployment_results_info(end_point_id, end_point_name, model_name, model_version)
ret_result_list = list()
@@ -55,6 +58,39 @@ def get_deployment_result_list(self, end_point_id, end_point_name, model_name, m
ret_result_list.append(json.dumps(result_dict))
return ret_result_list
+ def get_all_deployment_results_list(self) -> List[Dict]:
+ """
+ Similar to _get_all_deployment_results_info,
+ but return a list of json string, so that redis can store it.
+
+ return a list of dict, for each item:
+ [
+ {
+ "end_point_id": "",
+ "end_point_name": "",
+ "model_name":"",
+ "replica_res": "" # Json string
+ },
+ ]
+ value in the dict is a string that contains the deployment result.
+ """
+ flat_ep_list = self._get_all_deployment_results_info()
+ ret_result_list = list()
+ for result in flat_ep_list:
+ result_dict = {
+ "end_point_id": result.end_point_id,
+ "end_point_name": result.end_point_name,
+ "model_name": result.model_name,
+ "replica_info": json.dumps(
+ {
+ "cache_device_id": result.device_id,
+ "cache_replica_no": int(result.replica_no),
+ "result": result.deployment_result
+ }
+ )
+ }
+ ret_result_list.append(result_dict)
+ return ret_result_list
def get_deployment_status_list(self, end_point_id, end_point_name, model_name, model_version=None):
result_list = self.get_deployment_results_info(end_point_id, end_point_name, model_name, model_version)
@@ -156,7 +192,8 @@ def delete_deployment_run_info(self, end_point_id):
end_point_id=f'{end_point_id}').delete()
self.db_connection.commit()
- def get_result_item_info(self, result_item):
+ @staticmethod
+ def get_result_item_info(result_item):
result_item_json = json.loads(result_item)
if isinstance(result_item_json, dict):
result_item_json = json.loads(result_item)
@@ -169,7 +206,8 @@ def get_result_item_info(self, result_item):
result_payload = result_item_json["result"]
return device_id, replica_no, result_payload
- def get_status_item_info(self, status_item):
+ @staticmethod
+ def get_status_item_info(status_item):
status_item_json = json.loads(status_item)
if isinstance(status_item_json, dict):
status_item_json = json.loads(status_item)
@@ -320,6 +358,11 @@ def get_deployment_results_info(self, end_point_id, end_point_name, model_name,
FedMLDeploymentResultInfoModel.model_version == f'{model_version}')).all()
return result_info
+ def _get_all_deployment_results_info(self):
+ self.open_job_db()
+ result_info = self.db_connection.query(FedMLDeploymentResultInfoModel).all()
+ return result_info
+
def set_deployment_results_info(self, end_point_id, end_point_name,
model_name, model_version, device_id,
deployment_result=None, deployment_status=None, replica_no=None):
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index 113a20e825..c73630fb65 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -260,7 +260,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
logging.error("[Worker] Failed to deploy the model.")
# Send failed result back to master
- result_payload = self.send_deployment_results(
+ _ = self.send_deployment_results(
end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
model_id, model_name, inference_output_url, inference_model_version, inference_port,
inference_engine, model_metadata, model_config)
From 43f99cf0acf9df685272fc02a5890981ac3d0ee2 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 25 Jun 2024 11:55:14 -0700
Subject: [PATCH 191/282] [Deploy] Nit.
---
.../scheduler/model_scheduler/device_model_deployment.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 25fc1e1d64..a47f9dbc20 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -245,7 +245,8 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
inference_output_url, running_model_version, ret_model_metadata, ret_model_config = \
check_container_readiness(inference_http_port=inference_http_port, infer_host=infer_host,
readiness_check=customized_readiness_check,
- request_input_example=request_input_example)
+ request_input_example=request_input_example,
+ customized_uri=customized_uri)
if inference_output_url == "":
return running_model_name, "", None, None, None
From 766c52aaf7a5b1dd567e2b91730780a05c594d36 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 25 Jun 2024 11:59:14 -0700
Subject: [PATCH 192/282] [Deploy] Nit.
---
.../trt-llm-openai/config.yaml | 22 +++++++++++++++++++
.../device_model_deployment.py | 2 +-
2 files changed, 23 insertions(+), 1 deletion(-)
create mode 100644 python/examples/deploy/custom_inference_image/trt-llm-openai/config.yaml
diff --git a/python/examples/deploy/custom_inference_image/trt-llm-openai/config.yaml b/python/examples/deploy/custom_inference_image/trt-llm-openai/config.yaml
new file mode 100644
index 0000000000..1bdcf32f75
--- /dev/null
+++ b/python/examples/deploy/custom_inference_image/trt-llm-openai/config.yaml
@@ -0,0 +1,22 @@
+workspace: "./"
+
+inference_image_name: "fedml/trt-llm-openai"
+
+# The image has its self-contained cmd, no need for rewriting the command
+container_run_command: null
+
+port: 3000
+
+readiness_probe:
+ httpGet:
+ path: "/health_check"
+
+# If you do not use serverless container mode, and you want to indicate another resource path,
+# e.g. localhost:3000/v1/chat/completions, you can set the following uri:
+service:
+ httpPost:
+ path: "/v1/chat/completions"
+
+deploy_timeout_sec: 1600
+
+endpoint_api_type: "text2text_llm_openai_chat_completions"
\ No newline at end of file
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index a47f9dbc20..665bb4082e 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -233,7 +233,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
raise Exception("Failed to get the port allocation")
time.sleep(3)
- # Logging the info from the container when starting
+ # Logging the info from the container when initializing
log_deployment_output(end_point_id, model_id, default_server_container_name,
ClientConstants.CMD_TYPE_RUN_DEFAULT_SERVER,
inference_model_name, inference_engine, inference_http_port, inference_type,
From 0c29c4990d9f8f06940d3f3a658f9ffd1f0ddc86 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 25 Jun 2024 17:22:02 -0700
Subject: [PATCH 193/282] [Deploy] Hot fix hash exist.
---
.../computing/scheduler/model_scheduler/device_model_cache.py | 2 ++
1 file changed, 2 insertions(+)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
index b0021aa7df..0d92466169 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
@@ -990,6 +990,8 @@ def delete_endpoint_scaling_down_decision_time(self, end_point_id) -> bool:
end_point_id))
def get_pending_requests_counter(self, end_point_id) -> int:
+ if not end_point_id:
+ return 0
# If the endpoint does not exist inside the Hash collection, set its counter to 0.
if self.redis_connection.hexists(self.FEDML_PENDING_REQUESTS_COUNTER, end_point_id):
return int(self.redis_connection.hget(self.FEDML_PENDING_REQUESTS_COUNTER, end_point_id))
From 36378f876018163508f03592fca556afa3a9ec8f Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 25 Jun 2024 17:52:33 -0700
Subject: [PATCH 194/282] [Deploy] Indicate worker connection type through cli
and api.
---
python/fedml/api/__init__.py | 12 ++++++++----
python/fedml/api/modules/device.py | 8 +++++---
python/fedml/cli/modules/login.py | 12 ++++++++++--
3 files changed, 23 insertions(+), 9 deletions(-)
diff --git a/python/fedml/api/__init__.py b/python/fedml/api/__init__.py
index f753e4255b..b03c72b675 100755
--- a/python/fedml/api/__init__.py
+++ b/python/fedml/api/__init__.py
@@ -213,16 +213,20 @@ def fedml_build(platform, type, source_folder, entry_point, config_folder, dest_
def login(api_key, computing, server, supplier,
master_inference_gateway_port: int = ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
- worker_inference_proxy_port: int = ClientConstants.LOCAL_CLIENT_API_PORT):
- device_bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port)
+ worker_inference_proxy_port: int = ClientConstants.LOCAL_CLIENT_API_PORT,
+ worker_connection_type: str = ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT):
+ device_bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port,
+ worker_connection_type)
def logout(computing, server):
device_unbind(computing, server)
-def device_bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port):
- device.bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port)
+def device_bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port,
+ worker_connection_type):
+ device.bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port,
+ worker_connection_type)
def device_unbind(computing, server):
diff --git a/python/fedml/api/modules/device.py b/python/fedml/api/modules/device.py
index 14591147a6..7c4e52c8b5 100644
--- a/python/fedml/api/modules/device.py
+++ b/python/fedml/api/modules/device.py
@@ -21,7 +21,8 @@
def bind(
api_key, computing, server, supplier,
master_inference_gateway_port=DeviceServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
- worker_inference_proxy_port=DeviceClientConstants.LOCAL_CLIENT_API_PORT
+ worker_inference_proxy_port=DeviceClientConstants.LOCAL_CLIENT_API_PORT,
+ worker_connection_type=DeviceClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT
):
userid = api_key
runner_cmd = "{}"
@@ -47,13 +48,13 @@ def bind(
_bind(
userid, computing, server,
api_key, role, runner_cmd, device_id, os_name,
- docker, master_inference_gateway_port, worker_inference_proxy_port)
+ docker, master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type)
def _bind(
userid, computing, server,
api_key, role, runner_cmd, device_id, os_name,
- docker, master_inference_gateway_port, worker_inference_proxy_port):
+ docker, master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type):
fedml.load_env()
if os.getenv(ModuleConstants.ENV_FEDML_INFER_HOST) is None:
fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_HOST, SchedulerConstants.REDIS_INFER_HOST)
@@ -66,6 +67,7 @@ def _bind(
fedml.set_env_kv(DeviceServerConstants.ENV_MASTER_INFERENCE_PORT_KEY, str(master_inference_gateway_port))
fedml.set_env_kv(DeviceClientConstants.ENV_CLIENT_PROXY_PORT_KEY, str(worker_inference_proxy_port))
+ fedml.set_env_kv(DeviceClientConstants.ENV_CONNECTION_TYPE_KEY, worker_connection_type)
url = fedml._get_backend_service()
platform_name = platform.system()
diff --git a/python/fedml/cli/modules/login.py b/python/fedml/cli/modules/login.py
index f3c982f456..7ec4191a3e 100644
--- a/python/fedml/cli/modules/login.py
+++ b/python/fedml/cli/modules/login.py
@@ -67,10 +67,17 @@
default=ClientConstants.LOCAL_CLIENT_API_PORT,
help="The port for worker inference proxy.",
)
+@click.option(
+ "--worker_connection_type",
+ "-wct",
+ type=str,
+ default=ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT,
+ help="The connection type for worker inference proxy.",
+)
def fedml_login(
api_key, version, compute_node, server, provider, deploy_worker_num,
local_on_premise_platform, local_on_premise_platform_port,
- master_inference_gateway_port, worker_inference_proxy_port
+ master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type
):
fedml.set_env_version(version)
fedml.set_local_on_premise_platform_host(local_on_premise_platform)
@@ -84,4 +91,5 @@ def fedml_login(
print(f"Maybe you are using account id to login, we will try to login with account {api_key}.")
pass
os.environ["FEDML_MODEL_WORKER_NUM"] = str(deploy_worker_num)
- fedml.api.login(api_key, compute_node, server, provider, master_inference_gateway_port, worker_inference_proxy_port)
+ fedml.api.login(api_key, compute_node, server, provider, master_inference_gateway_port,
+ worker_inference_proxy_port, worker_connection_type)
From 5097ff29bf48b7f6d8c097721d96e44f421a4192 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Tue, 25 Jun 2024 18:01:29 -0700
Subject: [PATCH 195/282] [Deploy] Nit.
---
.../scheduler/model_scheduler/device_model_cache.py | 6 ++----
1 file changed, 2 insertions(+), 4 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
index 0d92466169..7e79126fa6 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
@@ -989,11 +989,9 @@ def delete_endpoint_scaling_down_decision_time(self, end_point_id) -> bool:
self.FEDML_MODEL_ENDPOINT_SCALING_DOWN_DECISION_TIME_TAG,
end_point_id))
- def get_pending_requests_counter(self, end_point_id) -> int:
- if not end_point_id:
- return 0
+ def get_pending_requests_counter(self, end_point_id=None) -> int:
# If the endpoint does not exist inside the Hash collection, set its counter to 0.
- if self.redis_connection.hexists(self.FEDML_PENDING_REQUESTS_COUNTER, end_point_id):
+ if end_point_id and self.redis_connection.hexists(self.FEDML_PENDING_REQUESTS_COUNTER, end_point_id):
return int(self.redis_connection.hget(self.FEDML_PENDING_REQUESTS_COUNTER, end_point_id))
return 0
From 084781f3982c8aae98fe8d3564badc90f7824451 Mon Sep 17 00:00:00 2001
From: alaydshah <alay11shah@gmail.com>
Date: Tue, 2 Jul 2024 19:19:51 +0000
Subject: [PATCH 196/282] Add logs in occupy_gpu_ids, and funcs in
hardware_utils for debugging
---
.../scheduler/comm_utils/hardware_utils.py | 25 +++++++++++++++++++
.../scheduler/comm_utils/job_utils.py | 7 ++++++
2 files changed, 32 insertions(+)
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index c876948145..56a75fe3e1 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -11,6 +11,29 @@
GPU_CARD_UTILS = [NvidiaGPUtil, QualcommNPUtil]
+# This function is just for debugging, can be removed at later point
+def get_gpu_list_and_realtime_gpu_available_ids() -> (List[dict], List[int]):
+ gpu_list = HardwareUtil.get_gpus()
+ gpu_count = len(gpu_list)
+ realtime_available_gpu_ids = HardwareUtil.get_available_gpu_ids(order='memory', limit=gpu_count, max_load=0.01,
+ max_memory=0.01)
+ return gpu_list, realtime_available_gpu_ids
+
+# This function is just for debugging, can be removed at later point
+def trim_unavailable_gpu_ids(gpu_ids) -> List[int]:
+ # Trim the gpu ids based on the realtime available gpu id list.
+ available_gpu_ids = [int(gpu_id) for gpu_id in gpu_ids]
+ gpu_list, realtime_available_gpu_ids = get_gpu_list_and_realtime_gpu_available_ids()
+ unavailable_gpu_ids = list()
+
+ for gpu_id in available_gpu_ids:
+ if gpu_id not in realtime_available_gpu_ids:
+ unavailable_gpu_ids.append(gpu_id)
+
+ trimmed_gpu_ids = list(set(available_gpu_ids) - set(unavailable_gpu_ids))
+ return trimmed_gpu_ids.copy()
+
+
class HardwareUtil(metaclass=Singleton):
__gpu_util: Optional[GPUCardUtil] = None
@@ -60,6 +83,8 @@ def get_docker_gpu_ids_by_container_name(container_name: str, docker_client: Doc
if __name__ == "__main__":
gpus = HardwareUtil.get_gpus()
get_available_gpu_cards = HardwareUtil.get_available_gpu_ids(limit=len(gpus))
+ trimmed_gpu_ids = trim_unavailable_gpu_ids(get_available_gpu_cards)
+ print(trimmed_gpu_ids)
device_mapping = HardwareUtil.get_docker_gpu_device_mapping(get_available_gpu_cards, len(get_available_gpu_cards))
print(gpus)
print(get_available_gpu_cards)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_utils.py b/python/fedml/computing/scheduler/comm_utils/job_utils.py
index 5b9a2c812a..8a917e539d 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_utils.py
@@ -86,6 +86,8 @@ def occupy_gpu_ids(self, run_id, request_gpu_num, device_id, inner_id=None,
# Get the available GPU list, FEDML_GLOBAL_DEVICE_AVAILABLE_GPU_IDS_TAG-${device_id}
available_gpu_ids = ComputeCacheManager.get_instance().get_gpu_cache().get_device_available_gpu_ids(
device_id)
+ logging.info(
+ f"Available GPU Ids fetched from cache: {available_gpu_ids}")
logging.info(f"Check worker({device_id})'s realtime gpu availability in DB"
f" for run {run_id}: {available_gpu_ids}")
@@ -94,8 +96,11 @@ def occupy_gpu_ids(self, run_id, request_gpu_num, device_id, inner_id=None,
if available_gpu_ids is None:
# Get realtime GPU availability list from the system
available_gpu_ids = JobRunnerUtils.get_realtime_gpu_available_ids().copy()
+ logging.info(f"Cache not set yet, fetching realtime available GPU Ids: {available_gpu_ids}")
else:
available_gpu_ids = JobRunnerUtils.trim_unavailable_gpu_ids(available_gpu_ids)
+ logging.info(
+ f"Trimmed available GPU Ids: {available_gpu_ids}")
# Get the matched gpu ids string by the request gpu num
cuda_visible_gpu_ids_str, matched_gpu_num = JobRunnerUtils.request_gpu_ids(request_gpu_num,
@@ -119,6 +124,8 @@ def occupy_gpu_ids(self, run_id, request_gpu_num, device_id, inner_id=None,
ComputeCacheManager.get_instance().get_gpu_cache().set_device_available_gpu_ids(
device_id, available_gpu_ids)
+
+ logging.info(f"Updated cache with following available gpu ids: {available_gpu_ids}")
# For a single run, could be scale up. So if existed such a key, should extend, not replace
existed_gpu_nums = ComputeCacheManager.get_instance().get_gpu_cache().get_device_run_num_gpus(
From 37be694a17b6bfdd205ec6e5b526b21856b1af87 Mon Sep 17 00:00:00 2001
From: Alay Dilipbhai Shah <alay@fedml.ai>
Date: Tue, 2 Jul 2024 15:37:48 -0700
Subject: [PATCH 197/282] Revert "Adjust the design of FedML Python Agent to a
decentralized architecture that supports Launch Master, Launch Slave, Deploy
Master, and Deploy Slave at the same time."
---
.github/workflows/CI_build.yml | 47 -----
.github/workflows/CI_deploy.yml | 43 -----
.github/workflows/CI_federate.yml | 42 -----
.github/workflows/CI_launch.yml | 43 -----
.github/workflows/CI_train.yml | 42 -----
.github/workflows/README.md | 97 ----------
.../build_wheels_and_releases.yml-backup | 0
.../{deprecated => }/codeql-analysis.yml | 0
.../deprecated/python-package-conda.yml | 34 ----
.../{deprecated => }/full_e2e_test.yml-bakcup | 0
.github/workflows/image.png | Bin 389049 -> 0 bytes
.github/workflows/{deprecated => }/pylint.yml | 7 +-
.../registry-runners/build_linux_runners.sh | 12 --
.../workflows/registry-runners/build_test.sh | 1 -
.../registry-runners/run_linux_runners.sh | 48 -----
.../workflows/registry-runners/windows.ps1 | 32 ----
.github/workflows/{deprecated => }/runner.md | 0
...oke_test_cross_device_mnn_server_linux.yml | 13 +-
...ke_test_cross_silo_fedavg_attack_linux.yml | 28 ++-
...smoke_test_cross_silo_fedavg_cdp_linux.yml | 15 +-
...e_test_cross_silo_fedavg_defense_linux.yml | 19 +-
...smoke_test_cross_silo_fedavg_ldp_linux.yml | 15 +-
.../smoke_test_cross_silo_ho_linux.yml | 15 +-
.../smoke_test_cross_silo_ho_win.yml | 15 +-
...moke_test_cross_silo_lightsecagg_linux.yml | 15 +-
.../smoke_test_cross_silo_lightsecagg_win.yml | 15 +-
.../smoke_test_flow_linux.yml | 9 +-
.../smoke_test_ml_engines_linux_jax.yml | 15 +-
.../smoke_test_ml_engines_linux_mxnet.yml | 15 +-
.../smoke_test_ml_engines_linux_tf.yml | 15 +-
.../smoke_test_ml_engines_win.yml | 27 ++-
.../smoke_test_pip_cli_sp_linux.yml | 37 ++--
.../smoke_test_pip_cli_sp_win.yml | 11 +-
.../{deprecated => }/smoke_test_security.yml | 9 +-
.../smoke_test_simulation_mpi_linux.yml | 43 ++---
.../github-action-runner}/Dockerfile | 22 ++-
.../dockerfile/github-action-runner/README.md | 25 +++
.../dockerfile/github-action-runner/build.sh | 3 +
.../github-action-runner/runner-start.sh | 23 +++
.../dockerfile/github-action-runner}/start.sh | 4 +-
devops/scripts/install-fedml.sh | 2 -
devops/scripts/sync-fedml-pip.sh | 4 +-
.../README.md | 2 +-
.../launch_config/fedml_config.yaml | 14 --
.../launch/hello_world/hello_world.py | 1 +
python/examples/launch/serve_job_mnist.yaml | 2 +-
.../launch_config/fedml_config.yaml | 3 -
python/examples/train/mnist_train/train.py | 98 ----------
python/examples/train/mnist_train/train.yaml | 50 -----
python/fedml/__init__.py | 23 +--
python/fedml/api/__init__.py | 3 -
python/fedml/api/modules/model.py | 13 --
.../scheduler/comm_utils/job_monitor.py | 12 +-
.../scheduler/comm_utils/run_process_utils.py | 4 +-
.../scheduler/comm_utils/sys_utils.py | 21 ---
.../scheduler/master/base_master_agent.py | 54 +-----
.../master/base_master_job_runner.py | 77 +++-----
.../master/base_master_job_runner_manager.py | 115 ++----------
.../master/base_master_protocol_manager.py | 60 +-----
.../scheduler/master/cloud_server_manager.py | 31 +--
.../master/master_protocol_manager.py | 12 +-
.../scheduler/master/server_login.py | 1 -
.../model_scheduler/master_job_runner.py | 4 +-
.../master_protocol_manager.py | 3 +-
.../model_scheduler/model_device_client.py | 98 ++++++++++
.../model_scheduler/model_device_server.py | 97 ++++++++++
.../worker_protocol_manager.py | 4 +-
.../scheduler_core/account_manager.py | 76 ++++----
.../scheduler_core/general_constants.py | 76 --------
.../scheduler_core/message_center.py | 123 +++---------
.../scheduler_base_job_runner.py | 22 +--
.../scheduler_base_job_runner_manager.py | 6 +-
.../scheduler_base_protocol_manager.py | 107 ++---------
.../scheduler/scheduler_core/status_center.py | 85 ++-------
.../status_manager_protocols.py | 27 ++-
.../scheduler/slave/base_slave_agent.py | 37 +---
.../scheduler/slave/base_slave_job_runner.py | 29 +--
.../slave/base_slave_protocol_manager.py | 17 +-
.../scheduler/slave/client_data_interface.py | 9 -
.../computing/scheduler/slave/client_login.py | 14 +-
.../scheduler/slave/slave_protocol_manager.py | 74 ++++++--
.../scheduler/slave/united_agents.py | 75 --------
python/fedml/core/mlops/__init__.py | 16 +-
python/fedml/core/mlops/mlops_device_perfs.py | 176 +++---------------
python/fedml/core/mlops/mlops_job_perfs.py | 26 +--
.../core/mlops/mlops_runtime_log_daemon.py | 21 +--
python/setup.py | 7 +-
python/tests/cross-silo/run_cross_silo.sh | 6 +-
python/tests/smoke_test/cli/build.sh | 4 +-
python/tests/test_deploy/test_deploy.py | 39 ----
python/tests/test_federate/test_federate.sh | 26 ---
python/tests/test_launch/test_launch.py | 50 -----
python/tests/test_train/test_train.py | 49 -----
93 files changed, 741 insertions(+), 2120 deletions(-)
delete mode 100644 .github/workflows/CI_build.yml
delete mode 100644 .github/workflows/CI_deploy.yml
delete mode 100644 .github/workflows/CI_federate.yml
delete mode 100644 .github/workflows/CI_launch.yml
delete mode 100644 .github/workflows/CI_train.yml
delete mode 100644 .github/workflows/README.md
rename .github/workflows/{deprecated => }/build_wheels_and_releases.yml-backup (100%)
rename .github/workflows/{deprecated => }/codeql-analysis.yml (100%)
delete mode 100644 .github/workflows/deprecated/python-package-conda.yml
rename .github/workflows/{deprecated => }/full_e2e_test.yml-bakcup (100%)
delete mode 100644 .github/workflows/image.png
rename .github/workflows/{deprecated => }/pylint.yml (89%)
delete mode 100644 .github/workflows/registry-runners/build_linux_runners.sh
delete mode 100755 .github/workflows/registry-runners/build_test.sh
delete mode 100644 .github/workflows/registry-runners/run_linux_runners.sh
delete mode 100644 .github/workflows/registry-runners/windows.ps1
rename .github/workflows/{deprecated => }/runner.md (100%)
rename .github/workflows/{deprecated => }/smoke_test_cross_device_mnn_server_linux.yml (88%)
rename .github/workflows/{deprecated => }/smoke_test_cross_silo_fedavg_attack_linux.yml (83%)
rename .github/workflows/{deprecated => }/smoke_test_cross_silo_fedavg_cdp_linux.yml (87%)
rename .github/workflows/{deprecated => }/smoke_test_cross_silo_fedavg_defense_linux.yml (86%)
rename .github/workflows/{deprecated => }/smoke_test_cross_silo_fedavg_ldp_linux.yml (87%)
rename .github/workflows/{deprecated => }/smoke_test_cross_silo_ho_linux.yml (89%)
rename .github/workflows/{deprecated => }/smoke_test_cross_silo_ho_win.yml (88%)
rename .github/workflows/{deprecated => }/smoke_test_cross_silo_lightsecagg_linux.yml (88%)
rename .github/workflows/{deprecated => }/smoke_test_cross_silo_lightsecagg_win.yml (88%)
rename .github/workflows/{deprecated => }/smoke_test_flow_linux.yml (92%)
rename .github/workflows/{deprecated => }/smoke_test_ml_engines_linux_jax.yml (87%)
rename .github/workflows/{deprecated => }/smoke_test_ml_engines_linux_mxnet.yml (87%)
rename .github/workflows/{deprecated => }/smoke_test_ml_engines_linux_tf.yml (87%)
rename .github/workflows/{deprecated => }/smoke_test_ml_engines_win.yml (90%)
rename .github/workflows/{deprecated => }/smoke_test_pip_cli_sp_linux.yml (80%)
rename .github/workflows/{deprecated => }/smoke_test_pip_cli_sp_win.yml (90%)
rename .github/workflows/{deprecated => }/smoke_test_security.yml (91%)
rename .github/workflows/{deprecated => }/smoke_test_simulation_mpi_linux.yml (73%)
rename {.github/workflows/registry-runners => devops/dockerfile/github-action-runner}/Dockerfile (70%)
create mode 100644 devops/dockerfile/github-action-runner/README.md
create mode 100755 devops/dockerfile/github-action-runner/build.sh
create mode 100644 devops/dockerfile/github-action-runner/runner-start.sh
rename {.github/workflows/registry-runners => devops/dockerfile/github-action-runner}/start.sh (76%)
delete mode 100644 devops/scripts/install-fedml.sh
delete mode 100644 python/examples/launch/examples/launch/hello_world/launch_config/fedml_config.yaml
delete mode 100644 python/examples/train/mnist_train/examples/train/mnist_train/launch_config/fedml_config.yaml
delete mode 100644 python/examples/train/mnist_train/train.py
delete mode 100644 python/examples/train/mnist_train/train.yaml
create mode 100755 python/fedml/computing/scheduler/model_scheduler/model_device_client.py
create mode 100755 python/fedml/computing/scheduler/model_scheduler/model_device_server.py
delete mode 100755 python/fedml/computing/scheduler/slave/united_agents.py
delete mode 100644 python/tests/test_deploy/test_deploy.py
delete mode 100644 python/tests/test_federate/test_federate.sh
delete mode 100644 python/tests/test_launch/test_launch.py
delete mode 100644 python/tests/test_train/test_train.py
diff --git a/.github/workflows/CI_build.yml b/.github/workflows/CI_build.yml
deleted file mode 100644
index b4c3642b09..0000000000
--- a/.github/workflows/CI_build.yml
+++ /dev/null
@@ -1,47 +0,0 @@
-# This is a basic workflow to help you get started with Actions
-
-name: CI-build
-
-# Controls when the workflow will run
-on:
- # Triggers the workflow on push or pull request events but only for the master branch
- schedule:
- # Nightly build at 12:12 A.M.
- - cron: "0 10 */1 * *"
- pull_request:
- branches: [ master, dev/v0.7.0 ]
-
- # Allows you to run this workflow manually from the Actions tab
- workflow_dispatch:
-
-# A workflow run is made up of one or more jobs that can run sequentially or in parallel
-jobs:
- build:
- runs-on: ["${{ matrix.python-version }}","${{ matrix.os }}"]
- strategy:
- fail-fast: false
- matrix:
- os: [ Linux, Windows ]
- arch: [X64]
- python-version: ['python3.8', 'python3.9', 'python3.10', 'python3.11']
-
- timeout-minutes: 5
- steps:
- - name: Checkout fedml
- uses: actions/checkout@v3
-
- - name: pip_install
- run: |
- cd python
- pip install -e ./
-
- - name: login
- run: |
- fedml logout
- fedml login $API_KEY
-
- - name: pylint
- run: |
- cd python
- echo "Pylint has been run successfully!"
-
diff --git a/.github/workflows/CI_deploy.yml b/.github/workflows/CI_deploy.yml
deleted file mode 100644
index 982f65b3c5..0000000000
--- a/.github/workflows/CI_deploy.yml
+++ /dev/null
@@ -1,43 +0,0 @@
-# This is a basic workflow to help you get started with Actions
-
-name: CI-deploy
-
-# Controls when the workflow will run
-on:
- # Triggers the workflow on push or pull request events but only for the master branch
- schedule:
- # Nightly build at 12:12 A.M.
- - cron: "0 10 */1 * *"
- pull_request:
- branches: [ master, dev/v0.7.0 ]
-
- # Allows you to run this workflow manually from the Actions tab
- workflow_dispatch:
-
-# A workflow run is made up of one or more jobs that can run sequentially or in parallel
-jobs:
- deploy:
- runs-on: ["${{ matrix.python-version }}","${{ matrix.os }}"]
- strategy:
- fail-fast: false
- matrix:
- os: [ Linux, Windows ]
- arch: [X64]
- python-version: ['python3.8', 'python3.9', 'python3.10', 'python3.11']
-
- timeout-minutes: 5
- steps:
- - name: Checkout fedml
- uses: actions/checkout@v3
-
- - name: pip_install
- run: |
- cd python
- pip install -e ./
-
- - name: serving_job_in_test_env
- run: |
- cd python
- echo "Serving example has been tested successfully!"
- python tests/test_deploy/test_deploy.py
-
diff --git a/.github/workflows/CI_federate.yml b/.github/workflows/CI_federate.yml
deleted file mode 100644
index 1302771b1d..0000000000
--- a/.github/workflows/CI_federate.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-# This is a basic workflow to help you get started with Actions
-
-name: CI-federate
-
-# Controls when the workflow will run
-on:
- # Triggers the workflow on push or pull request events but only for the master branch
- schedule:
- # Nightly build at 12:12 A.M.
- - cron: "0 10 */1 * *"
- pull_request:
- branches: [ master, dev/v0.7.0 ]
-
- # Allows you to run this workflow manually from the Actions tab
- workflow_dispatch:
-
-# A workflow run is made up of one or more jobs that can run sequentially or in parallel
-jobs:
- federate:
- strategy:
- fail-fast: false
- matrix:
- os: [ Linux, Windows ]
- arch: [X64]
- python-version: ['python3.8', 'python3.9', 'python3.10', 'python3.11']
-
- runs-on: ["${{ matrix.python-version }}","${{ matrix.os }}"]
- timeout-minutes: 5
- steps:
- - name: Checkout fedml
- uses: actions/checkout@v3
-
- - name: pip_install
- run: |
- cd python
- pip install -e ./
-
- - name: federate_job_in_test_env
- run: |
- cd python
- bash tests/test_federate/test_federate.sh
- echo "Federate example has been tested successfully!"
diff --git a/.github/workflows/CI_launch.yml b/.github/workflows/CI_launch.yml
deleted file mode 100644
index 13519c41f2..0000000000
--- a/.github/workflows/CI_launch.yml
+++ /dev/null
@@ -1,43 +0,0 @@
-# This is a basic workflow to help you get started with Actions
-
-name: CI-launch
-
-# Controls when the workflow will run
-on:
- # Triggers the workflow on push or pull request events but only for the master branch
- schedule:
- # Nightly build at 12:12 A.M.
- - cron: "0 10 */1 * *"
- pull_request:
- branches: [ master, dev/v0.7.0 ]
-
- # Allows you to run this workflow manually from the Actions tab
- workflow_dispatch:
-
-# A workflow run is made up of one or more jobs that can run sequentially or in parallel
-jobs:
- launch:
-
- strategy:
- fail-fast: false
- matrix:
- os: [ Linux, Windows ]
- arch: [X64]
- python-version: ['python3.8','python3.9','python3.10','python3.11']
-
- runs-on: ["${{ matrix.python-version }}","${{ matrix.os }}"]
- timeout-minutes: 5
- steps:
- - name: Checkout fedml
- uses: actions/checkout@v3
-
- - name: pip_install
- run: |
- cd python
- pip install -e ./
-
- - name: launch_job_in_test_env
- run: |
- cd python
- python tests/test_launch/test_launch.py
- echo "Launch example has been tested successfully!"
diff --git a/.github/workflows/CI_train.yml b/.github/workflows/CI_train.yml
deleted file mode 100644
index 2acbcc12a0..0000000000
--- a/.github/workflows/CI_train.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-# This is a basic workflow to help you get started with Actions
-
-name: CI-train
-
-# Controls when the workflow will run
-on:
- # Triggers the workflow on push or pull request events but only for the master branch
- schedule:
- # Nightly build at 12:12 A.M.
- - cron: "0 10 */1 * *"
- pull_request:
- branches: [ master, dev/v0.7.0 ]
-
- # Allows you to run this workflow manually from the Actions tab
- workflow_dispatch:
-
-# A workflow run is made up of one or more jobs that can run sequentially or in parallel
-jobs:
- train:
- runs-on: ["${{ matrix.python-version }}","${{ matrix.os }}"]
- strategy:
- fail-fast: false
- matrix:
- os: [ Linux, Windows ]
- arch: [X64]
- python-version: ['python3.8', 'python3.9', 'python3.10', 'python3.11']
- timeout-minutes: 5
- steps:
- - name: Checkout fedml
- uses: actions/checkout@v3
-
- - name: pip_install
- run: |
- cd python
- pip install -e ./
-
- - name: training_job_in_test_env
- run: |
- cd python
- python tests/test_train/test_train.py
- echo "Train example has been tested successfully!"
-
diff --git a/.github/workflows/README.md b/.github/workflows/README.md
deleted file mode 100644
index 668cb9b302..0000000000
--- a/.github/workflows/README.md
+++ /dev/null
@@ -1,97 +0,0 @@
-# 1. Design
-
-
-
-## Design principles
-
-The CI tests need to be comprehensive, covering typical scenarios only, achievable within 5 minutes.
-
-# 2. Registry Self-Host Runners
-
-## 2.1 Linux Runners
-
-### Step1: Build linux images
-
-Build all the linux images for Self-Host Runners.
-```
-cd registry-runners
-bash build_linux_runners.sh
-```
-
-### Step2: Specify the token and key.
-Find your GitHub runner token and your test-account apikey.
-
-For the argument YourGitHubRunnerToken, Navigate the path `Settings -> Actions -> Runners -> New self-hosted runner` to get.
-
-In the Configure section, you will find the similar line:
-./config.sh --url https://github.com/FedML-AI/FedML --token AXRYPL6G2VHVGDFDQQS5XA3ELYI6M to get YourGitHubRunnerToken to value of --token
-
-### Step3: Registry all the runners.
-Registry by run `run_linux_runners.sh` script
-```
-bash run_linux_runners.sh [YourGitRepo] [YourGitHubRunnerToken] [YourTestAccountApiKey]
-```
-for example
-```
-bash run_linux_runners.sh FedML-AI/FedML AXRYPLZLZN6XVJB3BAIXSP3EMFC7U 11215dkevvdkegged
-```
-### Step4: Verify Success
-
-Check if all the runners are registered successfully. Navigate the following path. `Settings -> Actions -> Runners` to check that all your runners are active.
-
-## 2.2 Windows Runners
-
-### Step1: Install Anaconda packages
-Install Anaconda or Miniconda on a Windows machine. Anaconda and Miniconda can manage your Python environments.
-
-### Step2: Create python enviroments
-Create 4 python environments named `python38`、`python39`、`python310` and `python311` for different runners.
-Specify the python version to install.
-For example
-```
-conda create -n python38 python==3.8
-```
-### Step3: Create directories
-Create 4 directories named `actions-runner-python38`、`actions-runner-python39`、`actions-runner-python310` and `actions-runner-python311` for different runners.
-
-### Step4: Install the latest runner package.
-Follow the insturction from navigating this path `Settings -> Actions -> Runners -> New self-hosted runner` to add a new Windows runner. Note that you only need to download、extract the files into the directories created in Step 3. Configuration and running will be done through a script later.
-
-### Step5: Registry all the runners.
-Run the script from `./registry-runners/windows.ps1` to registry all the runners to your github. Replace the variables `$REPO`、`$ACCESS_TOKEN` and `$WORKPLACE` with actual values. Note that you can get your $ACCESS_TOKEN from the following path `Settings -> Actions -> Runners -> New self-hosted runner.`.
-In the Configure section, you will find the similar line: `./config.sh --url https://github.com/FedML-AI/FedML --token AXRYPL6G2VHVGDFDQQS5XA3ELYI6M` to get your `$ACCESS_TOKEN`.
-
-### Step6: Verify Success
-Check if the runners are registered successfully by navigate to `Settings -> Actions -> Runners`. Make sure that all your runners are active.
-
-## 2.3 Mac Runners
-
-# 3. Bind Test Machines
-
-Bind the actual machine to run the test training job. Follow this document to bind your test machines.
-https://docs.tensoropera.ai/share-and-earn
-
-Note that we need to bind our machines to the test environment.
-
-Specify the computing resource type to which you have bound your machines. Your job will be scheduled to that machine.
-
-# 4. Trigger
-
-Applying for a PR can trigger all tests automatically.
-
-Run a single test on a specific branch from the GitHub Actions tab.
-
-Schedule daily runs at a specific time by configuring your workflow YAML. You can check the results in the GitHub Actions tab.
-
-# 5. Add a new CI test
-
-Creating a new workflow YAML file, such as CI_launch.yaml or CI_train.yaml, allows you to add a CI test that is different from the current business.
-
-Adding a new CI test to the current business can be done by placing your test in the path python/tests/test_{business}/test_file.py and ensuring that your workflow YAML can run that Python test script.
-
-Ensuring your workflow YAML is configured correctly will enable it to run the new test automatically.
-
-# 6. TODO
-
-Implement the Mac runners.
-
diff --git a/.github/workflows/deprecated/build_wheels_and_releases.yml-backup b/.github/workflows/build_wheels_and_releases.yml-backup
similarity index 100%
rename from .github/workflows/deprecated/build_wheels_and_releases.yml-backup
rename to .github/workflows/build_wheels_and_releases.yml-backup
diff --git a/.github/workflows/deprecated/codeql-analysis.yml b/.github/workflows/codeql-analysis.yml
similarity index 100%
rename from .github/workflows/deprecated/codeql-analysis.yml
rename to .github/workflows/codeql-analysis.yml
diff --git a/.github/workflows/deprecated/python-package-conda.yml b/.github/workflows/deprecated/python-package-conda.yml
deleted file mode 100644
index f3586044ab..0000000000
--- a/.github/workflows/deprecated/python-package-conda.yml
+++ /dev/null
@@ -1,34 +0,0 @@
-name: Python Package using Conda
-
-on: [push]
-
-jobs:
- build-linux:
- runs-on: ubuntu-latest
- strategy:
- max-parallel: 5
-
- steps:
- - uses: actions/checkout@v4
- - name: Set up Python 3.10
- uses: actions/setup-python@v3
- with:
- python-version: '3.10'
- - name: Add conda to system path
- run: |
- # $CONDA is an environment variable pointing to the root of the miniconda directory
- echo $CONDA/bin >> $GITHUB_PATH
- - name: Install dependencies
- run: |
- conda env update --file environment.yml --name base
- - name: Lint with flake8
- run: |
- conda install flake8
- # stop the build if there are Python syntax errors or undefined names
- flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
- # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
- flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
- - name: Test with pytest
- run: |
- conda install pytest
- pytest
diff --git a/.github/workflows/deprecated/full_e2e_test.yml-bakcup b/.github/workflows/full_e2e_test.yml-bakcup
similarity index 100%
rename from .github/workflows/deprecated/full_e2e_test.yml-bakcup
rename to .github/workflows/full_e2e_test.yml-bakcup
diff --git a/.github/workflows/image.png b/.github/workflows/image.png
deleted file mode 100644
index 330e630c0a3f784f45b7349741a82e03855babc1..0000000000000000000000000000000000000000
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diff --git a/.github/workflows/deprecated/pylint.yml b/.github/workflows/pylint.yml
similarity index 89%
rename from .github/workflows/deprecated/pylint.yml
rename to .github/workflows/pylint.yml
index 402bf72895..cdc3800869 100644
--- a/.github/workflows/deprecated/pylint.yml
+++ b/.github/workflows/pylint.yml
@@ -28,16 +28,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: Analysing the code with pylint
diff --git a/.github/workflows/registry-runners/build_linux_runners.sh b/.github/workflows/registry-runners/build_linux_runners.sh
deleted file mode 100644
index fb4b6e1abc..0000000000
--- a/.github/workflows/registry-runners/build_linux_runners.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-tag="0.1.0"
-
-platform="linux/amd64"
-
-echo "build python:3.11"
-docker build --no-cache --platform $platform --build-arg BASE_IMAGE=python:3.11 -t fedml/action_runner_3.11_linux64:$tag -f ./Dockerfile .
-echo "build python:3.10"
-docker build --no-cache --platform $platform --build-arg BASE_IMAGE=python:3.10 -t fedml/action_runner_3.10_linux64:$tag -f ./Dockerfile .
-echo "build python:3.9"
-docker build --no-cache --platform $platform --build-arg BASE_IMAGE=python:3.9 -t fedml/action_runner_3.9_linux64:$tag -f ./Dockerfile .
-echo "build python:3.8"
-docker build --no-cache --platform $platform --build-arg BASE_IMAGE=python:3.8 -t fedml/action_runner_3.8_linux64:$tag -f ./Dockerfile .
diff --git a/.github/workflows/registry-runners/build_test.sh b/.github/workflows/registry-runners/build_test.sh
deleted file mode 100755
index 1e17dc6847..0000000000
--- a/.github/workflows/registry-runners/build_test.sh
+++ /dev/null
@@ -1 +0,0 @@
-docker build -t fedml/action_runner_3.11_linux64:0.1 -f ./Dockerfile .
diff --git a/.github/workflows/registry-runners/run_linux_runners.sh b/.github/workflows/registry-runners/run_linux_runners.sh
deleted file mode 100644
index fa70388de8..0000000000
--- a/.github/workflows/registry-runners/run_linux_runners.sh
+++ /dev/null
@@ -1,48 +0,0 @@
-REPO=$1
-ACCESS_TOKEN=$2
-API_KEY=$3
-DOCKER_PULL=false
-ARCH=linux64
-TAG="0.1.0"
-
-if [ $# != 3 ]; then
- echo "Please provide two arguments."
- echo "./runner-start.sh [YourGitRepo][YourGitHubRunnerToken][API_KEY]"
- exit -1
-fi
-
-# List of Docker container names
-# containers=("fedml/action_runner_3.8_$ARCH:0.1.0" "fedml/action_runner_3.9_$ARCH:0.1.0" "fedml/action_runner_3.10_$ARCH:0.1.0" "fedml/action_runner_3.11_$ARCH:0.1.0")
-containers=("action_runner_3.8_$ARCH" "action_runner_3.9_$ARCH" "action_runner_3.10_$ARCH" "action_runner_3.11_$ARCH")
-python_versions=("python3.8" "python3.9" "python3.10" "python3.11")
-
-
-# Iterate through each container
-for container_index in "${!containers[@]}"; do
-
- container=${containers[$container_index]}
- # Find the running container
- if [ "$DOCKER_PULL" = "true" ]; then
- echo "docker pull fedml/$container:$TAG"
- docker pull fedml/$container:$TAG
- fi
- # docker stop `sudo docker ps |grep ${TAG}- |awk -F' ' '{print $1}'`
-
- running_container=$(docker ps -a | grep $container | awk -F ' ' '{print $1}')
-
- if [ -n "$running_container" ]; then
- # Stop the running container
- echo "Stopping running container: $container, $running_container"
- docker stop "$running_container"
- else
- echo "No running container found for: $container"
- fi
- sleep 5
- # docker pull $container
- ACT_NAME=${containers[$container_index]}
- echo "docker run --rm --name $ACT_NAME --env API_KEY=$API_KEY --env REPO=$REPO --env ACCESS_TOKEN=$ACCESS_TOKEN -d fedml/${containers[$container_index]}:$TAG bash ./start.sh ${REPO} ${ACCESS_TOKEN} ${python_versions[$container_index]}"
- docker run --rm --name $ACT_NAME --env API_KEY=$API_KEY --env REPO=$REPO --env ACCESS_TOKEN=$ACCESS_TOKEN -d fedml/${containers[$container_index]}:$TAG bash ./start.sh ${REPO} ${ACCESS_TOKEN} ${python_versions[$container_index]}
-
-done
-echo "Script completed."
-
diff --git a/.github/workflows/registry-runners/windows.ps1 b/.github/workflows/registry-runners/windows.ps1
deleted file mode 100644
index 40f0f00b8f..0000000000
--- a/.github/workflows/registry-runners/windows.ps1
+++ /dev/null
@@ -1,32 +0,0 @@
-
-$REPO = "Qigemingziba/FedML"
-$ACCESS_TOKEN = "AGMK3PY3QDYUXXXEB5LWI4DGOQIFW"
-$WORKPLACE=$PWD
-
-Set-Location actions-runner-python38
-& conda activate python38
-./config.cmd --url https://github.com/$REPO --name windows-python38 --token $ACCESS_TOKEN --labels self-hosted,Windows,X64,python3.8
-Start-Process run.cmd start -WindowStyle Hidden
-
-Set-Location $WORKPLACE
-
-Set-Location actions-runner-python39
-& conda activate python39
-./config.cmd --url https://github.com/$REPO --name windows-python39 --token $ACCESS_TOKEN --labels self-hosted,Windows,X64,python3.9
-Start-Process run.cmd start -WindowStyle Hidden
-
-Set-Location $WORKPLACE
-
-Set-Location actions-runner-python310
-& conda activate python310
-./config.cmd --url https://github.com/$REPO --name windows-python310 --token $ACCESS_TOKEN --labels self-hosted,Windows,X64,python3.10
-Start-Process run.cmd start -WindowStyle Hidden
-
-Set-Location $WORKPLACE
-
-Set-Location actions-runner-python311
-& conda activate python311
-./config.cmd --url https://github.com/$REPO --name windows-python311 --token $ACCESS_TOKEN --labels self-hosted,Windows,X64,python3.11
-Start-Process run.cmd start -WindowStyle Hidden
-
-Set-Location $WORKPLACE
\ No newline at end of file
diff --git a/.github/workflows/deprecated/runner.md b/.github/workflows/runner.md
similarity index 100%
rename from .github/workflows/deprecated/runner.md
rename to .github/workflows/runner.md
diff --git a/.github/workflows/deprecated/smoke_test_cross_device_mnn_server_linux.yml b/.github/workflows/smoke_test_cross_device_mnn_server_linux.yml
similarity index 88%
rename from .github/workflows/deprecated/smoke_test_cross_device_mnn_server_linux.yml
rename to .github/workflows/smoke_test_cross_device_mnn_server_linux.yml
index 10c9860d0f..c8fff7e4f1 100644
--- a/.github/workflows/deprecated/smoke_test_cross_device_mnn_server_linux.yml
+++ b/.github/workflows/smoke_test_cross_device_mnn_server_linux.yml
@@ -52,16 +52,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -70,9 +67,7 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- cd python
- pip install -e ./
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- name: Install MNN
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
@@ -84,6 +79,6 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/quick_start/beehive
+ cd quick_start/beehive
timeout 60 bash run_server.sh || code=$?; if [[ $code -ne 124 && $code -ne 0 ]]; then exit $code; fi
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_attack_linux.yml b/.github/workflows/smoke_test_cross_silo_fedavg_attack_linux.yml
similarity index 83%
rename from .github/workflows/deprecated/smoke_test_cross_silo_fedavg_attack_linux.yml
rename to .github/workflows/smoke_test_cross_silo_fedavg_attack_linux.yml
index ea0c4ed601..b1c29fcfd7 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_attack_linux.yml
+++ b/.github/workflows/smoke_test_cross_silo_fedavg_attack_linux.yml
@@ -29,8 +29,8 @@ jobs:
strategy:
fail-fast: false
matrix:
- os: [ ubuntu-latest ]
- arch: [ X64 ]
+ os: [ ubuntu-latest]
+ arch: [X64]
python-version: ['3.8']
client-index: ['0', '1', '2', '3', '4']
# exclude:
@@ -38,7 +38,7 @@ jobs:
# python-version: '3.8'
# - os: windows-latest
# python-version: '3.6'
- runs-on: [ self-hosted ]
+ runs-on: [ self-hosted, Linux ]
timeout-minutes: 15
steps:
- name: Extract branch name
@@ -53,16 +53,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -71,16 +68,13 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- cd python
- pip install -e ./
- # bash ./devops/srcipts/install-fedml.sh
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - attack
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/security/mqtt_s3_fedavg_attack_mnist_lr_example
+ cd examples/security/mqtt_s3_fedavg_attack_mnist_lr_example
run_id=cross-silo-attack-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -90,7 +84,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/security/mqtt_s3_fedavg_attack_mnist_lr_example
+ cd examples/security/mqtt_s3_fedavg_attack_mnist_lr_example
run_id=cross-silo-attack-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -100,7 +94,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/security/mqtt_s3_fedavg_attack_mnist_lr_example
+ cd examples/security/mqtt_s3_fedavg_attack_mnist_lr_example
run_id=cross-silo-attack-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
@@ -110,7 +104,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/security/mqtt_s3_fedavg_attack_mnist_lr_example
+ cd examples/security/mqtt_s3_fedavg_attack_mnist_lr_example
run_id=cross-silo-attack-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 3 $run_id
@@ -120,7 +114,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/security/mqtt_s3_fedavg_attack_mnist_lr_example
+ cd examples/security/mqtt_s3_fedavg_attack_mnist_lr_example
run_id=cross-silo-attack-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 4 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_cdp_linux.yml b/.github/workflows/smoke_test_cross_silo_fedavg_cdp_linux.yml
similarity index 87%
rename from .github/workflows/deprecated/smoke_test_cross_silo_fedavg_cdp_linux.yml
rename to .github/workflows/smoke_test_cross_silo_fedavg_cdp_linux.yml
index 051c0418d2..67ee9e4a0f 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_cdp_linux.yml
+++ b/.github/workflows/smoke_test_cross_silo_fedavg_cdp_linux.yml
@@ -53,16 +53,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -71,13 +68,13 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - cdp
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/privacy/mqtt_s3_fedavg_cdp_mnist_lr_example
+ cd examples/privacy/mqtt_s3_fedavg_cdp_mnist_lr_example
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -87,7 +84,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/privacy/mqtt_s3_fedavg_cdp_mnist_lr_example
+ cd examples/privacy/mqtt_s3_fedavg_cdp_mnist_lr_example
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -97,7 +94,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/privacy/mqtt_s3_fedavg_cdp_mnist_lr_example
+ cd examples/privacy/mqtt_s3_fedavg_cdp_mnist_lr_example
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_defense_linux.yml b/.github/workflows/smoke_test_cross_silo_fedavg_defense_linux.yml
similarity index 86%
rename from .github/workflows/deprecated/smoke_test_cross_silo_fedavg_defense_linux.yml
rename to .github/workflows/smoke_test_cross_silo_fedavg_defense_linux.yml
index b9348d7bf2..fac19d9552 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_defense_linux.yml
+++ b/.github/workflows/smoke_test_cross_silo_fedavg_defense_linux.yml
@@ -53,16 +53,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -71,13 +68,13 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - defense
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/security/mqtt_s3_fedavg_defense_mnist_lr_example
+ cd examples/security/mqtt_s3_fedavg_defense_mnist_lr_example
run_id=cross-silo-defense-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -87,7 +84,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/security/mqtt_s3_fedavg_defense_mnist_lr_example
+ cd examples/security/mqtt_s3_fedavg_defense_mnist_lr_example
run_id=cross-silo-defense-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -97,7 +94,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/security/mqtt_s3_fedavg_defense_mnist_lr_example
+ cd examples/security/mqtt_s3_fedavg_defense_mnist_lr_example
run_id=cross-silo-defense-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
@@ -107,7 +104,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/security/mqtt_s3_fedavg_defense_mnist_lr_example
+ cd examples/security/mqtt_s3_fedavg_defense_mnist_lr_example
run_id=cross-silo-defense-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 3 $run_id
@@ -117,7 +114,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/security/mqtt_s3_fedavg_defense_mnist_lr_example
+ cd examples/security/mqtt_s3_fedavg_defense_mnist_lr_example
run_id=cross-silo-defense-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 4 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_ldp_linux.yml b/.github/workflows/smoke_test_cross_silo_fedavg_ldp_linux.yml
similarity index 87%
rename from .github/workflows/deprecated/smoke_test_cross_silo_fedavg_ldp_linux.yml
rename to .github/workflows/smoke_test_cross_silo_fedavg_ldp_linux.yml
index f849c4db71..def8aca733 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_fedavg_ldp_linux.yml
+++ b/.github/workflows/smoke_test_cross_silo_fedavg_ldp_linux.yml
@@ -53,16 +53,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -71,13 +68,13 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - ldp
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/privacy/mqtt_s3_fedavg_ldp_mnist_lr_example
+ cd examples/privacy/mqtt_s3_fedavg_ldp_mnist_lr_example
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -87,7 +84,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/privacy/mqtt_s3_fedavg_ldp_mnist_lr_example
+ cd examples/privacy/mqtt_s3_fedavg_ldp_mnist_lr_example
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -97,7 +94,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/privacy/mqtt_s3_fedavg_ldp_mnist_lr_example
+ cd examples/privacy/mqtt_s3_fedavg_ldp_mnist_lr_example
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_ho_linux.yml b/.github/workflows/smoke_test_cross_silo_ho_linux.yml
similarity index 89%
rename from .github/workflows/deprecated/smoke_test_cross_silo_ho_linux.yml
rename to .github/workflows/smoke_test_cross_silo_ho_linux.yml
index 7d28a37292..e34a22cdbe 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_ho_linux.yml
+++ b/.github/workflows/smoke_test_cross_silo_ho_linux.yml
@@ -53,16 +53,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -71,13 +68,13 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - ho
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/quick_start/octopus
+ cd quick_start/octopus
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -87,7 +84,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/quick_start/octopus
+ cd quick_start/octopus
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -97,7 +94,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/quick_start/octopus
+ cd quick_start/octopus
run_id=cross-silo-ho-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_ho_win.yml b/.github/workflows/smoke_test_cross_silo_ho_win.yml
similarity index 88%
rename from .github/workflows/deprecated/smoke_test_cross_silo_ho_win.yml
rename to .github/workflows/smoke_test_cross_silo_ho_win.yml
index d9239bcb99..b8376438d7 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_ho_win.yml
+++ b/.github/workflows/smoke_test_cross_silo_ho_win.yml
@@ -52,16 +52,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -70,25 +67,25 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - ho
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/quick_start/octopus
+ cd quick_start/octopus
.\run_server.bat ${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '0' }}
- name: client 1 - cross-silo - ho
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/quick_start/octopus
+ cd quick_start/octopus
.\run_client.bat 1 ${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '1' }}
- name: client 2 - cross-silo - ho
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/quick_start/octopus
+ cd quick_start/octopus
.\run_client.bat 2 ${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '2' }}
\ No newline at end of file
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_linux.yml b/.github/workflows/smoke_test_cross_silo_lightsecagg_linux.yml
similarity index 88%
rename from .github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_linux.yml
rename to .github/workflows/smoke_test_cross_silo_lightsecagg_linux.yml
index ae06088dc7..d672e2a772 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_linux.yml
+++ b/.github/workflows/smoke_test_cross_silo_lightsecagg_linux.yml
@@ -53,16 +53,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -71,13 +68,13 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - lightsecagg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/cross_silo/light_sec_agg_example
+ cd examples/cross_silo/light_sec_agg_example
run_id=cross-silo-lightsecagg-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -87,7 +84,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/cross_silo/light_sec_agg_example
+ cd examples/cross_silo/light_sec_agg_example
run_id=cross-silo-lightsecagg-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -97,7 +94,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/cross_silo/light_sec_agg_example
+ cd examples/cross_silo/light_sec_agg_example
run_id=cross-silo-lightsecagg-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_win.yml b/.github/workflows/smoke_test_cross_silo_lightsecagg_win.yml
similarity index 88%
rename from .github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_win.yml
rename to .github/workflows/smoke_test_cross_silo_lightsecagg_win.yml
index 40d15a1f0f..8deab9acb2 100644
--- a/.github/workflows/deprecated/smoke_test_cross_silo_lightsecagg_win.yml
+++ b/.github/workflows/smoke_test_cross_silo_lightsecagg_win.yml
@@ -52,16 +52,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -70,25 +67,25 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- name: server - cross-silo - ho
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/cross_silo/light_sec_agg_example
+ cd examples/cross_silo/light_sec_agg_example
.\run_server.bat cross-silo-lightsecagg-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '0' }}
- name: client 1 - cross-silo - ho
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/cross_silo/light_sec_agg_example
+ cd examples/cross_silo/light_sec_agg_example
.\run_client.bat 1 cross-silo-lightsecagg-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '1' }}
- name: client 2 - cross-silo - lightsecagg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/cross_silo/light_sec_agg_example
+ cd examples/cross_silo/light_sec_agg_example
.\run_client.bat 2 cross-silo-lightsecagg-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '2' }}
\ No newline at end of file
diff --git a/.github/workflows/deprecated/smoke_test_flow_linux.yml b/.github/workflows/smoke_test_flow_linux.yml
similarity index 92%
rename from .github/workflows/deprecated/smoke_test_flow_linux.yml
rename to .github/workflows/smoke_test_flow_linux.yml
index 5293787a11..df876a632b 100644
--- a/.github/workflows/deprecated/smoke_test_flow_linux.yml
+++ b/.github/workflows/smoke_test_flow_linux.yml
@@ -43,16 +43,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -61,7 +58,7 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- name: server - Flow
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
diff --git a/.github/workflows/deprecated/smoke_test_ml_engines_linux_jax.yml b/.github/workflows/smoke_test_ml_engines_linux_jax.yml
similarity index 87%
rename from .github/workflows/deprecated/smoke_test_ml_engines_linux_jax.yml
rename to .github/workflows/smoke_test_ml_engines_linux_jax.yml
index cd4bd8d720..42a6d25ead 100644
--- a/.github/workflows/deprecated/smoke_test_ml_engines_linux_jax.yml
+++ b/.github/workflows/smoke_test_ml_engines_linux_jax.yml
@@ -53,16 +53,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -71,14 +68,14 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
cd $homepath/python
- name: server - jax - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
run_id=jax-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -88,7 +85,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
run_id=jax-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -98,7 +95,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
run_id=jax-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_ml_engines_linux_mxnet.yml b/.github/workflows/smoke_test_ml_engines_linux_mxnet.yml
similarity index 87%
rename from .github/workflows/deprecated/smoke_test_ml_engines_linux_mxnet.yml
rename to .github/workflows/smoke_test_ml_engines_linux_mxnet.yml
index 5ce217ea4b..bf30fd1b1a 100644
--- a/.github/workflows/deprecated/smoke_test_ml_engines_linux_mxnet.yml
+++ b/.github/workflows/smoke_test_ml_engines_linux_mxnet.yml
@@ -53,16 +53,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -71,7 +68,7 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
cd $homepath/python
pip install mxnet==2.0.0b1
@@ -79,7 +76,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
run_id=mxnet-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -89,7 +86,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
run_id=mxnet-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -99,7 +96,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
run_id=mxnet-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_ml_engines_linux_tf.yml b/.github/workflows/smoke_test_ml_engines_linux_tf.yml
similarity index 87%
rename from .github/workflows/deprecated/smoke_test_ml_engines_linux_tf.yml
rename to .github/workflows/smoke_test_ml_engines_linux_tf.yml
index 3b7519dd97..9d69ba3774 100644
--- a/.github/workflows/deprecated/smoke_test_ml_engines_linux_tf.yml
+++ b/.github/workflows/smoke_test_ml_engines_linux_tf.yml
@@ -53,16 +53,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -71,14 +68,14 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
cd $homepath/python
- name: server - tensorflow - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
run_id=tf-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_server.sh $run_id
@@ -88,7 +85,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
run_id=tf-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 1 $run_id
@@ -98,7 +95,7 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
run_id=tf-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
echo ${run_id}
bash run_client.sh 2 $run_id
diff --git a/.github/workflows/deprecated/smoke_test_ml_engines_win.yml b/.github/workflows/smoke_test_ml_engines_win.yml
similarity index 90%
rename from .github/workflows/deprecated/smoke_test_ml_engines_win.yml
rename to .github/workflows/smoke_test_ml_engines_win.yml
index 8913cc6bec..f1f3bfabd4 100644
--- a/.github/workflows/deprecated/smoke_test_ml_engines_win.yml
+++ b/.github/workflows/smoke_test_ml_engines_win.yml
@@ -46,16 +46,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -64,28 +61,28 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
cd $homepath/python
pip install -e '.[tensorflow]'
- name: server - tensorflow - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
python tf_server.py --cf config/fedml_config.yaml --rank 0 --role server --run_id tf-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '0' }}
- name: client 1 - tensorflow - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
python3 tf_client.py --cf config/fedml_config.yaml --rank 1 --role client --run_id tf-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '1' }}
- name: client 2 - tensorflow - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/tf_mqtt_s3_fedavg_mnist_lr_example
python3 tf_client.py --cf config/fedml_config.yaml --rank 2 --role client --run_id tf-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '2' }}
@@ -141,21 +138,21 @@ jobs:
- name: server - jax - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
python tf_server.py --cf config/fedml_config.yaml --rank 0 --role server --run_id jax-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '0' }}
- name: client 1 - jax - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
python3 tf_client.py --cf config/fedml_config.yaml --rank 1 --role client --run_id jax-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '1' }}
- name: client 2 - jax - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/jax_haiku_mqtt_s3_fedavg_mnist_lr_example
python3 tf_client.py --cf config/fedml_config.yaml --rank 2 --role client --run_id jax-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '2' }}
@@ -211,20 +208,20 @@ jobs:
- name: server - mxnet - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
python tf_server.py --cf config/fedml_config.yaml --rank 0 --role server --run_id mxnet-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '0' }}
- name: client 1 - mxnet - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
python3 tf_client.py --cf config/fedml_config.yaml --rank 1 --role client --run_id mxnet-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '1' }}
- name: client 2 - mxnet - fedavg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
+ cd examples/cross_silo/mxnet_mqtt_s3_fedavg_mnist_lr_example
python3 tf_client.py --cf config/fedml_config.yaml --rank 2 --role client --run_id mxnet-ml-engine-${{ format('{0}{1}{2}{3}', github.run_id, matrix.os, matrix.arch, matrix.python-version) }}
if: ${{ matrix.client-index == '2' }}
diff --git a/.github/workflows/deprecated/smoke_test_pip_cli_sp_linux.yml b/.github/workflows/smoke_test_pip_cli_sp_linux.yml
similarity index 80%
rename from .github/workflows/deprecated/smoke_test_pip_cli_sp_linux.yml
rename to .github/workflows/smoke_test_pip_cli_sp_linux.yml
index 006ecfb574..131d88de9b 100644
--- a/.github/workflows/deprecated/smoke_test_pip_cli_sp_linux.yml
+++ b/.github/workflows/smoke_test_pip_cli_sp_linux.yml
@@ -54,16 +54,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -72,20 +69,20 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- # - name: test "fedml login" and "fedml build"
- # working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
- # run: |
- # cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- # cd tests/smoke_test/cli
- # bash login.sh
- # bash build.sh
+ - name: test "fedml login" and "fedml build"
+ working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
+ run: |
+ cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
+ cd tests/smoke_test/cli
+ bash login.sh
+ bash build.sh
- name: test simulation-sp
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd examples/federate/quick_start/parrot
+ cd quick_start/parrot
python torch_fedavg_mnist_lr_one_line_example.py --cf fedml_config.yaml
python torch_fedavg_mnist_lr_custum_data_and_model_example.py --cf fedml_config.yaml
@@ -93,40 +90,40 @@ jobs:
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd examples/federate/simulation/sp_decentralized_mnist_lr_example
+ cd examples/simulation/sp_decentralized_mnist_lr_example
python torch_fedavg_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
- name: test sp - sp_fednova_mnist_lr_example
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd examples/federate/simulation/sp_fednova_mnist_lr_example
+ cd examples/simulation/sp_fednova_mnist_lr_example
python torch_fednova_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
- name: test sp - sp_fedopt_mnist_lr_example
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd examples/federate/simulation/sp_fedopt_mnist_lr_example
+ cd examples/simulation/sp_fedopt_mnist_lr_example
python torch_fedopt_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
- name: test sp - sp_hierarchicalfl_mnist_lr_example
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd examples/federate/simulation/sp_hierarchicalfl_mnist_lr_example
+ cd examples/simulation/sp_hierarchicalfl_mnist_lr_example
python torch_hierarchicalfl_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
- name: test sp - sp_turboaggregate_mnist_lr_example
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd examples/federate/simulation/sp_turboaggregate_mnist_lr_example
+ cd examples/simulation/sp_turboaggregate_mnist_lr_example
python torch_turboaggregate_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
- name: test sp - sp_vertical_mnist_lr_example
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd ${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}/python
- cd examples/federate/simulation/sp_vertical_mnist_lr_example
+ cd examples/simulation/sp_vertical_mnist_lr_example
python torch_vertical_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
diff --git a/.github/workflows/deprecated/smoke_test_pip_cli_sp_win.yml b/.github/workflows/smoke_test_pip_cli_sp_win.yml
similarity index 90%
rename from .github/workflows/deprecated/smoke_test_pip_cli_sp_win.yml
rename to .github/workflows/smoke_test_pip_cli_sp_win.yml
index 3987f90f74..69dac083bb 100644
--- a/.github/workflows/deprecated/smoke_test_pip_cli_sp_win.yml
+++ b/.github/workflows/smoke_test_pip_cli_sp_win.yml
@@ -51,16 +51,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -69,7 +66,7 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- name: test "fedml login" and "fedml build"
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
@@ -80,6 +77,6 @@ jobs:
- name: test simulation-sp
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
- cd examples/federate/quick_start/parrot
+ cd quick_start/parrot
python torch_fedavg_mnist_lr_one_line_example.py --cf fedml_config.yaml
python torch_fedavg_mnist_lr_custum_data_and_model_example.py --cf fedml_config.yaml
diff --git a/.github/workflows/deprecated/smoke_test_security.yml b/.github/workflows/smoke_test_security.yml
similarity index 91%
rename from .github/workflows/deprecated/smoke_test_security.yml
rename to .github/workflows/smoke_test_security.yml
index 5d5c03ee38..6644a4b513 100644
--- a/.github/workflows/deprecated/smoke_test_security.yml
+++ b/.github/workflows/smoke_test_security.yml
@@ -54,16 +54,13 @@ jobs:
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
echo "running on master"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-master
cd $path
- git pull
echo "dir=$path" >> $GITHUB_OUTPUT
else
echo "running on dev"
- path=/home/fedml/FedML
+ path=/home/actions-runner/fedml-dev
cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
@@ -72,7 +69,7 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- name: attack tests
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
diff --git a/.github/workflows/deprecated/smoke_test_simulation_mpi_linux.yml b/.github/workflows/smoke_test_simulation_mpi_linux.yml
similarity index 73%
rename from .github/workflows/deprecated/smoke_test_simulation_mpi_linux.yml
rename to .github/workflows/smoke_test_simulation_mpi_linux.yml
index b2e9676ae9..c48cc43149 100644
--- a/.github/workflows/deprecated/smoke_test_simulation_mpi_linux.yml
+++ b/.github/workflows/smoke_test_simulation_mpi_linux.yml
@@ -40,8 +40,8 @@ jobs:
- os: ubuntu-latest
mpi: mpich
install-mpi: |
- apt-get update
- apt install -y mpich libmpich-dev
+ sudo apt-get update
+ sudo apt install -y mpich libmpich-dev
# - os: ubuntu-latest
# mpi: openmpi
# install-mpi: sudo apt install -y openmpi-bin libopenmpi-dev
@@ -50,12 +50,6 @@ jobs:
shell: bash
run: echo "branch=$(echo ${GITHUB_REF#refs/heads/})" >>$GITHUB_OUTPUT
id: extract_branch
- - name: Install MPI
- if: matrix.mpi == 'mpich'
- run: |
- apt-get update
- apt-get install -y mpich libmpich-dev
-
- id: fedml_source_code_home
name: cd to master or dev branch and git pull
shell: bash
@@ -63,18 +57,15 @@ jobs:
ls
echo ${{ steps.extract_branch.outputs.branch }}
if [[ ${{ steps.extract_branch.outputs.branch }} == "master" ]]; then
- echo "running on master"
- path=/home/fedml/FedML
- cd $path
- git pull
- echo "dir=$path" >> $GITHUB_OUTPUT
+ echo "running on master"
+ path=/home/actions-runner/fedml-master
+ cd $path
+ echo "dir=$path" >> $GITHUB_OUTPUT
else
- echo "running on dev"
- path=/home/fedml/FedML
- cd $path
- git pull
- git checkout ${{ steps.extract_branch.outputs.branch }}
- echo "dir=$path" >> $GITHUB_OUTPUT
+ echo "running on dev"
+ path=/home/actions-runner/fedml-dev
+ cd $path
+ echo "dir=$path" >> $GITHUB_OUTPUT
fi
- name: sync git repo to local pip
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
@@ -82,47 +73,47 @@ jobs:
homepath=${{ format('{0}', steps.fedml_source_code_home.outputs.dir) }}
echo $Homepath
cd $homepath
- # bash ./devops/scripts/sync-fedml-pip.sh
+ bash ./devops/scripts/sync-fedml-pip.sh
- name: Test package - FedAvg
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
pwd
cd python
- cd examples/federate/simulation/mpi_torch_fedavg_mnist_lr_example
+ cd examples/simulation/mpi_torch_fedavg_mnist_lr_example
sh run_custom_data_and_model_example.sh 4
- name: Test package - Base
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/simulation/mpi_base_framework_example
+ cd examples/simulation/mpi_base_framework_example
sh run.sh 4
- name: Test package - Decentralized
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/simulation/mpi_decentralized_fl_example
+ cd examples/simulation/mpi_decentralized_fl_example
sh run.sh 4
- name: Test package - FedOPT
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/simulation/mpi_fedopt_datasets_and_models_example
+ cd examples/simulation/mpi_fedopt_datasets_and_models_example
sh run_step_by_step_example.sh 4 config/mnist_lr/fedml_config.yaml
- name: Test package - FedProx
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/simulation/mpi_fedprox_datasets_and_models_example
+ cd examples/simulation/mpi_fedprox_datasets_and_models_example
sh run_step_by_step_example.sh 4 config/mnist_lr/fedml_config.yaml
- name: Test package - FedGAN
working-directory: ${{ steps.fedml_source_code_home.outputs.dir }}
run: |
cd python
- cd examples/federate/simulation/mpi_torch_fedgan_mnist_gan_example
+ cd examples/simulation/mpi_torch_fedgan_mnist_gan_example
sh run_step_by_step_example.sh 4
\ No newline at end of file
diff --git a/.github/workflows/registry-runners/Dockerfile b/devops/dockerfile/github-action-runner/Dockerfile
similarity index 70%
rename from .github/workflows/registry-runners/Dockerfile
rename to devops/dockerfile/github-action-runner/Dockerfile
index 5d3168853a..4e6648260f 100644
--- a/.github/workflows/registry-runners/Dockerfile
+++ b/devops/dockerfile/github-action-runner/Dockerfile
@@ -1,10 +1,9 @@
# base
-ARG BASE_IMAGE=python:3.11
-
-FROM ${BASE_IMAGE}
+FROM fedml/fedml:latest-torch1.13.1-cuda11.6-cudnn8-devel
# set the github runner version
-ARG RUNNER_VERSION="2.317.0"
+ARG RUNNER_VERSION="2.304.0"
+
# update the base packages and add a non-sudo user
#RUN apt-get update -y && apt-get upgrade -y && useradd -m docker
@@ -25,15 +24,18 @@ COPY start.sh start.sh
# make the script executable
RUN chmod +x start.sh
+
+RUN cp -f /usr/bin/python /usr/bin/python-backup && ln -s /usr/bin/python3 python
+
+RUN pip install scikit-learn
+
+RUN pip install tensorflow && pip install tensorflow_datasets && pip install jax[cpu] && pip install dm-haiku && pip install optax && pip install jaxlib
+
# since the config and run script for actions are not allowed to be run by root,
# set the user to "docker" so all subsequent commands are run as the docker user
#USER docker
-RUN git clone https://github.com/Qigemingziba/FedML.git
-RUN cd FedML && git pull && git checkout dev/v0.7.0 && cd python && pip3 install -e ./
-ENV REPO=Qigemingziba/FedML ACCESS_TOKEN=AGMK3P4W5EM5PXNYTZXXIMTGNF4MW
+ENV REPO=FedML-AI/FedML ACCESS_TOKEN=1
# set the entrypoint to the start.sh script
-CMD ./start.sh ${REPO} ${ACCESS_TOKEN}
-
-
+CMD ./start.sh ${REPO} ${ACCESS_TOKEN}
\ No newline at end of file
diff --git a/devops/dockerfile/github-action-runner/README.md b/devops/dockerfile/github-action-runner/README.md
new file mode 100644
index 0000000000..d02e29665b
--- /dev/null
+++ b/devops/dockerfile/github-action-runner/README.md
@@ -0,0 +1,25 @@
+# Run self-host runner in your machine
+
+## Usage
+
+./runner-start.sh [YourGitRepo] [YourRunnerPrefix] [YourRunnerNum] [YourGitHubRunnerToken] [LocalDevSourceDir] [LocalReleaseSourceDir] [LocalDataDir]
+
+For the argument YourGitHubRunnerToken, you may navigate based the following path.
+
+Settings -> Actions -> Runners -> New self-hosted runner.
+
+In the Configure section, you should find the similar line:
+./config.sh --url https://github.com/FedML-AI/FedML --token AXRYPL6G2VHVGDFDQQS5XA3ELYI6M
+
+set YourGitHubRunnerToken to value of --token
+
+
+## Example
+
+Use the following commands to run 30 runners in the FedML-AI/FedML repo and run 6 runners in the FedML-AI/Front-End-Auto-Test repo:
+
+./runner-start.sh FedML-AI/FedML fedml-runner 30 AXRYPLZLZN6XVJB3BAIXSP3EMFC7U /home/fedml/FedML4GitHubAction-Dev /home/fedml/FedML4GitHubAction /home/fedml/fedml_data
+./runner-start.sh FedML-AI/Front-End-Auto-Test webtest-runner 6 AXRYPL57ZD35ZGDWZKRKFHLEMGLTK /home/fedml/FedML4GitHubAction-Dev /home/fedml/FedML4GitHubAction /home/fedml/fedml_data
+
+./runner-start.sh FedML-AI/FedML fedml-runner 30 AXRYPL6CCBH24ZVRSUEAYTTEMKD56 /home/chaoyanghe/sourcecode/FedML4GitHubAction-Dev /home/chaoyanghe/sourcecode/FedML4GitHubAction /home/chaoyanghe/fedml_data
+./runner-start.sh FedML-AI/Front-End-Auto-Test webtest-runner 6 AXRYPL57ZD35ZGDWZKRKFHLEMGLTK /home/chaoyanghe/sourcecode/FedML4GitHubAction-Dev /home/chaoyanghe/sourcecode/FedML4GitHubAction /home/chaoyanghe/fedml_data
diff --git a/devops/dockerfile/github-action-runner/build.sh b/devops/dockerfile/github-action-runner/build.sh
new file mode 100755
index 0000000000..5f6dae9615
--- /dev/null
+++ b/devops/dockerfile/github-action-runner/build.sh
@@ -0,0 +1,3 @@
+docker build -t fedml/github-action-runner:latest -f ./Dockerfile .
+docker login
+docker push fedml/github-action-runner:latest
\ No newline at end of file
diff --git a/devops/dockerfile/github-action-runner/runner-start.sh b/devops/dockerfile/github-action-runner/runner-start.sh
new file mode 100644
index 0000000000..18a0c4f958
--- /dev/null
+++ b/devops/dockerfile/github-action-runner/runner-start.sh
@@ -0,0 +1,23 @@
+REPO=$1
+TAG=$2
+NUM=$3
+ACCESS_TOKEN=$4
+LOCAL_DEV_SOURCE_DIR=$5
+LOCAL_RELEASE_SOURCE_DIR=$6
+LOCAL_DATA_DIR=$7
+
+if [ $# != 7 ]; then
+ echo "Please provide five arguments."
+ echo "./runner-start.sh [YourGitRepo] [YourRunnerPrefix] [YourRunnerNum] [YourGitHubRunnerToken] [LocalDevSourceDir] [LocalReleaseSourceDir] [LocalDataDir]"
+ exit -1
+fi
+
+sudo docker stop `sudo docker ps |grep ${TAG}- |awk -F' ' '{print $1}'`
+sudo docker pull fedml/github-action-runner:latest
+
+for((i=1;i<=$NUM;i++));
+do
+ACT_NAME=$TAG-$i
+sudo docker rm $ACT_NAME
+sudo docker run --name $ACT_NAME --env REPO=$REPO --env ACCESS_TOKEN=$ACCESS_TOKEN -v $LOCAL_DEV_SOURCE_DIR:/home/actions-runner/fedml-dev -v $LOCAL_RELEASE_SOURCE_DIR:/home/actions-runner/fedml-master -v $LOCAL_DATA_DIR:/home/fedml/fedml_data -v $LOCAL_DATA_DIR:/home/actions-runner/fedml_data -d fedml/github-action-runner:latest
+done
\ No newline at end of file
diff --git a/.github/workflows/registry-runners/start.sh b/devops/dockerfile/github-action-runner/start.sh
similarity index 76%
rename from .github/workflows/registry-runners/start.sh
rename to devops/dockerfile/github-action-runner/start.sh
index b65b0f1272..917d1cfe16 100644
--- a/.github/workflows/registry-runners/start.sh
+++ b/devops/dockerfile/github-action-runner/start.sh
@@ -2,15 +2,13 @@
ORGANIZATION=$1
ACCESS_TOKEN=$2
-PYTHON_VERSION=$3
echo $ORGANIZATION
echo $ACCESS_TOKEN
-echo $PYTHON_VERSION
cd /home/fedml/actions-runner
-RUNNER_ALLOW_RUNASROOT="1" ./config.sh --url https://github.com/${ORGANIZATION} --token ${ACCESS_TOKEN} --labels self-hosted,Linux,X64,$PYTHON_VERSION
+RUNNER_ALLOW_RUNASROOT="1" ./config.sh --url https://github.com/${ORGANIZATION} --token ${ACCESS_TOKEN}
cleanup() {
echo "Removing runner..."
diff --git a/devops/scripts/install-fedml.sh b/devops/scripts/install-fedml.sh
deleted file mode 100644
index cafcfa3ac7..0000000000
--- a/devops/scripts/install-fedml.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-cd python
-pip install -e ./
\ No newline at end of file
diff --git a/devops/scripts/sync-fedml-pip.sh b/devops/scripts/sync-fedml-pip.sh
index 6b24ac52e7..0d909fff76 100755
--- a/devops/scripts/sync-fedml-pip.sh
+++ b/devops/scripts/sync-fedml-pip.sh
@@ -24,7 +24,7 @@ else
fi
fi
-mkdir -p ./fedml/fedml_data
-cp -Rf ./fedml/fedml_data_host/* ./fedml/fedml_data
+mkdir -p /home/fedml/fedml_data
+cp -Rf /home/fedml/fedml_data_host/* /home/fedml/fedml_data
exit 0
diff --git a/python/examples/federate/cross_silo/cuda_rpc_fedavg_mnist_lr_example/README.md b/python/examples/federate/cross_silo/cuda_rpc_fedavg_mnist_lr_example/README.md
index a1fa30b6f2..c693d8d863 100644
--- a/python/examples/federate/cross_silo/cuda_rpc_fedavg_mnist_lr_example/README.md
+++ b/python/examples/federate/cross_silo/cuda_rpc_fedavg_mnist_lr_example/README.md
@@ -26,7 +26,7 @@ For info on `trpc_master_config_path` refer to `python/examples/cross_silo/cuda_
Example is provided at:
-`python/examples/federate/cross_silo/cuda_rpc_fedavg_mnist_lr_example/one_line`
+`python/examples/cross_silo/cuda_rpc_fedavg_mnist_lr_example/one_line`
### Training Script
At the client side, the client ID (a.k.a rank) starts from 1.
diff --git a/python/examples/launch/examples/launch/hello_world/launch_config/fedml_config.yaml b/python/examples/launch/examples/launch/hello_world/launch_config/fedml_config.yaml
deleted file mode 100644
index 21e1f2e33e..0000000000
--- a/python/examples/launch/examples/launch/hello_world/launch_config/fedml_config.yaml
+++ /dev/null
@@ -1,14 +0,0 @@
-containerize: false
-data_args:
- dataset_name: mnist
- dataset_path: ./dataset
- dataset_type: csv
-environment_args:
- bootstrap: fedml_bootstrap_generated.sh
-model_args:
- input_dim: '784'
- model_cache_path: /Users/alexliang/fedml_models
- model_name: lr
- output_dim: '10'
-training_params:
- learning_rate: 0.004
diff --git a/python/examples/launch/hello_world/hello_world.py b/python/examples/launch/hello_world/hello_world.py
index 2f68f99055..71ffaf7c16 100644
--- a/python/examples/launch/hello_world/hello_world.py
+++ b/python/examples/launch/hello_world/hello_world.py
@@ -1,5 +1,6 @@
import os
import time
+
import fedml
if __name__ == "__main__":
diff --git a/python/examples/launch/serve_job_mnist.yaml b/python/examples/launch/serve_job_mnist.yaml
index bd8b52ca6c..98c1570a4f 100755
--- a/python/examples/launch/serve_job_mnist.yaml
+++ b/python/examples/launch/serve_job_mnist.yaml
@@ -35,4 +35,4 @@ computing:
maximum_cost_per_hour: $3000 # max cost per hour for your job per gpu card
#allow_cross_cloud_resources: true # true, false
#device_type: CPU # options: GPU, CPU, hybrid
- resource_type: RTX-4090 # e.g., A100-80G, please check the resource type list by "fedml show-resource-type" or visiting URL: https://open.fedml.ai/accelerator_resource_type
\ No newline at end of file
+ resource_type: A100-80G # e.g., A100-80G, please check the resource type list by "fedml show-resource-type" or visiting URL: https://open.fedml.ai/accelerator_resource_type
\ No newline at end of file
diff --git a/python/examples/train/mnist_train/examples/train/mnist_train/launch_config/fedml_config.yaml b/python/examples/train/mnist_train/examples/train/mnist_train/launch_config/fedml_config.yaml
deleted file mode 100644
index 188c19dde6..0000000000
--- a/python/examples/train/mnist_train/examples/train/mnist_train/launch_config/fedml_config.yaml
+++ /dev/null
@@ -1,3 +0,0 @@
-containerize: false
-environment_args:
- bootstrap: fedml_bootstrap_generated.sh
diff --git a/python/examples/train/mnist_train/train.py b/python/examples/train/mnist_train/train.py
deleted file mode 100644
index 611a15c2b6..0000000000
--- a/python/examples/train/mnist_train/train.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import torchvision
-import torchvision.transforms as transforms
-from torch.utils.data import DataLoader
-import fedml
-# Set random seed for reproducibility
-torch.manual_seed(42)
-
-# Define hyperparameters
-batch_size = 64
-learning_rate = 0.001
-num_epochs = 3
-
-# Prepare dataset and data loaders
-transform = transforms.Compose([
- transforms.ToTensor(), # Convert image to tensor, normalize to [0, 1]
- transforms.Normalize((0.5,), (0.5,)) # Normalize with mean and std deviation of 0.5
-])
-
-train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transform, download=True)
-train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
-
-test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transform, download=True)
-test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
-
-# Define a simple convolutional neural network model
-class SimpleCNN(nn.Module):
- def __init__(self):
- super(SimpleCNN, self).__init__()
- self.conv1 = nn.Conv2d(1, 16, kernel_size=5, padding=2)
- self.conv2 = nn.Conv2d(16, 32, kernel_size=5, padding=2)
- self.fc1 = nn.Linear(32 * 7 * 7, 128)
- self.fc2 = nn.Linear(128, 10)
-
- def forward(self, x):
- x = torch.relu(self.conv1(x))
- x = torch.max_pool2d(x, kernel_size=2, stride=2)
- x = torch.relu(self.conv2(x))
- x = torch.max_pool2d(x, kernel_size=2, stride=2)
- x = x.view(-1, 32 * 7 * 7)
- x = torch.relu(self.fc1(x))
- x = self.fc2(x)
- return x
-
-model = SimpleCNN()
-
-# Define loss function and optimizer
-criterion = nn.CrossEntropyLoss()
-optimizer = optim.Adam(model.parameters(), lr=learning_rate)
-
-# Train the model
-for epoch in range(num_epochs):
-
- # Evaluate the model on the test set during training
- model.eval()
- with torch.no_grad():
- correct = 0
- total = 0
- for images, labels in test_loader:
- outputs = model(images)
- _, predicted = torch.max(outputs.data, 1)
- total += labels.size(0)
- correct += (predicted == labels).sum().item()
- acc = 100 * correct / total
- fedml.mlops.log_metric({"epoch":epoch, "acc": acc})
-
- model.train()
- for images, labels in train_loader:
- # Forward pass
- outputs = model(images)
- loss = criterion(outputs, labels)
-
- # Backward and optimize
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
-
-# Final evaluation on the test set
-model.eval()
-with torch.no_grad():
- correct = 0
- total = 0
- for images, labels in test_loader:
- outputs = model(images)
- _, predicted = torch.max(outputs.data, 1)
- total += labels.size(0)
- correct += (predicted == labels).sum().item()
-
- acc = 100 * correct / total
- print('Final Test Accuracy: {:.2f} %'.format(acc))
- fedml.mlops.log_metric({"epoch":num_epochs, "acc": acc})
-
-fedml.mlops.log_model(f"model-file@test", "./simple_cnn.pth")
-# # Save the model parameters
-# torch.save(model.state_dict(), 'simple_cnn.pth')
-# print('Model saved to simple_cnn.pth')
diff --git a/python/examples/train/mnist_train/train.yaml b/python/examples/train/mnist_train/train.yaml
deleted file mode 100644
index f9a5cc5ab5..0000000000
--- a/python/examples/train/mnist_train/train.yaml
+++ /dev/null
@@ -1,50 +0,0 @@
-# Local directory where your source code resides.
-# It should be the relative path to this job yaml file or the absolute path.
-# If your job doesn't contain any source code, it can be empty.
-workspace: .
-
-# Running entry commands which will be executed as the job entry point.
-# If an error occurs, you should exit with a non-zero code, e.g. exit 1.
-# Otherwise, you should exit with a zero code, e.g. exit 0.
-# Support multiple lines, which can not be empty.
-job: |
- echo "current job id: $FEDML_CURRENT_RUN_ID"
- echo "current edge id: $FEDML_CURRENT_EDGE_ID"
- echo "Hello, Here is the launch platform."
- echo "Current directory is as follows."
- pwd
- python3 train.py
- echo "training job finished."
-
-# If you want to use the job created by the MLOps platform,
-# just uncomment the following three, then set job_id and config_id to your desired job id and related config.
-#job_args:
-# job_id: 2070
-# config_id: 111
-
-# If you want to create the job with specific name, just uncomment the following line and set job_name to your desired job name
-#job_name: cv_job
-
-job_type: train # options: train, deploy, federate
-
-# train subtype: general_training, single_machine_training, cluster_distributed_training, cross_cloud_training
-# federate subtype: cross_silo, simulation, web, smart_phone
-# deploy subtype: none
-job_subtype: generate_training
-
-# containerize
-containerize: false
-
-# Bootstrap shell commands which will be executed before running entry commands.
-# Support multiple lines, which can be empty.
-bootstrap: |
- # pip install -r requirements.txt
- echo "Bootstrap finished."
-
-computing:
- minimum_num_gpus: 1 # minimum # of GPUs to provision
- maximum_cost_per_hour: $3000 # max cost per hour for your job per gpu card
- #allow_cross_cloud_resources: true # true, false
- #device_type: CPU # options: GPU, CPU, hybrid
- resource_type: RTX-4090 # e.g., A100-80G, please check the resource type list by "fedml show-resource-type" or visiting URL: https://open.fedml.ai/accelerator_resource_type
-
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index c2fc2e3a0f..c96d65adc5 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -1,7 +1,6 @@
import logging
import platform
-import multiprocess
import multiprocess as multiprocessing
import os
import random
@@ -38,7 +37,7 @@
_global_training_type = None
_global_comm_backend = None
-__version__ = "0.8.51b1"
+__version__ = "0.9.0"
# This is the deployment environment used for different roles (RD/PM/BD/Public Developers). Potential VALUE: local, dev, test, release
@@ -461,26 +460,6 @@ def _init_multiprocessing():
multiprocessing.set_start_method("fork", force=True)
-def get_multiprocessing_context():
- if platform.system() == "Windows":
- return multiprocessing.get_context("spawn")
- else:
- return multiprocessing.get_context("fork")
-
-
-def get_process(target=None, args=None):
- if platform.system() == "Windows":
- return multiprocessing.Process(target=target, args=args)
- else:
- #return multiprocessing.Process(target=target, args=args)
- #multiprocessing.set_start_method("spawn", force=True)
- #return multiprocess.context.SpawnContext.Process(target=target, args=args)
- #multiprocessing.Manager().current_process().authkey = str.encode("abc")
- new_process = multiprocessing.get_context("fork").Process(target=target, args=args)
- #new_process.authkey = str.encode("abc")
- return new_process
-
-
def set_env_version(version):
set_env_kv("FEDML_ENV_VERSION", version)
load_env()
diff --git a/python/fedml/api/__init__.py b/python/fedml/api/__init__.py
index ff2b0c7307..b03c72b675 100755
--- a/python/fedml/api/__init__.py
+++ b/python/fedml/api/__init__.py
@@ -278,9 +278,6 @@ def model_deploy(name, endpoint_name, endpoint_id, local, master_ids, worker_ids
def model_run(endpoint_id, json_string):
model_module.run(endpoint_id, json_string)
-def get_endpoint(endpoint_id):
- return model_module.get_endpoint(endpoint_id)
-
def endpoint_delete(endpoint_id):
model_module.delete_endpoint(endpoint_id)
diff --git a/python/fedml/api/modules/model.py b/python/fedml/api/modules/model.py
index 93892fc5d1..a02e674f47 100644
--- a/python/fedml/api/modules/model.py
+++ b/python/fedml/api/modules/model.py
@@ -320,19 +320,6 @@ def run(endpoint_id: str, json_string: str) -> bool:
click.echo("Failed to run model.")
return False
-def get_endpoint(endpoint_id: str):
- api_key = get_api_key()
- if api_key == "":
- click.echo('''
- Please use one of the ways below to login first:
- (1) CLI: `fedml login $api_key`
- (2) API: fedml.api.fedml_login(api_key=$api_key)
- ''')
- return False
-
- endpoint_detail_result = FedMLModelCards.get_instance().query_endpoint_detail_api(user_api_key=api_key,
- endpoint_id=endpoint_id)
- return endpoint_detail_result
def delete_endpoint(endpoint_id: str) -> bool:
api_key = get_api_key()
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index 50ca315a10..b8237d93ba 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -167,7 +167,7 @@ def autoscaler_reconcile_after_interval(self):
# Get cached token for authorization of autoscale request
cached_token = fedml_model_cache.get_end_point_token(e_id, e_name, model_name)
if cached_token is None:
- # logging.error(f"Failed to get the cached token for endpoint {e_id}.")
+ logging.error(f"Failed to get the cached token for endpoint {e_id}.")
continue
req_header = {
@@ -229,7 +229,7 @@ def monitor_replicas_number():
cached_token = FedMLModelCache.get_instance().get_end_point_token_with_eid(endpoint_id)
if cached_token is None:
- # logging.error(f"Failed to get the cached token for endpoint {endpoint_id}.")
+ logging.error(f"Failed to get the cached token for endpoint {endpoint_id}.")
return
req_header = {
@@ -339,10 +339,6 @@ def monitor_replicas_perf(edge_id, mqtt_mgr=None):
def monitor_slave_run_process_status(self):
try:
count = 0
- try:
- client_data_interface.FedMLClientDataInterface.get_instance().create_job_table()
- except Exception as e:
- pass
job_list = client_data_interface.FedMLClientDataInterface.get_instance().get_jobs_from_db()
for job in job_list.job_list:
count += 1
@@ -452,10 +448,6 @@ def monitor_master_run_process_status(self, server_id, device_info_reporter=None
try:
ComputeCacheManager.get_instance().set_redis_params()
count = 0
- try:
- server_data_interface.FedMLServerDataInterface.get_instance().create_job_table()
- except Exception as e:
- pass
job_list = server_data_interface.FedMLServerDataInterface.get_instance().get_jobs_from_db()
for job in job_list.job_list:
count += 1
diff --git a/python/fedml/computing/scheduler/comm_utils/run_process_utils.py b/python/fedml/computing/scheduler/comm_utils/run_process_utils.py
index 6dd575f307..05cc342e36 100644
--- a/python/fedml/computing/scheduler/comm_utils/run_process_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/run_process_utils.py
@@ -135,15 +135,13 @@ def save_run_process(run_id, process_id, data_dir, info_dir,
pass
@staticmethod
- def kill_process(process_id, exclude_current_pid=False):
+ def kill_process(process_id):
try:
process = psutil.Process(process_id)
if process is None:
return
child_processes = process.children(recursive=True)
for sub_process in child_processes:
- if exclude_current_pid and sub_process.pid == os.getpid():
- continue
if platform.system() == 'Windows':
os.system("taskkill /PID {} /T /F".format(sub_process.pid))
else:
diff --git a/python/fedml/computing/scheduler/comm_utils/sys_utils.py b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
index 065482c23b..aaa37bc4db 100644
--- a/python/fedml/computing/scheduler/comm_utils/sys_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
@@ -114,8 +114,6 @@ def get_sys_runner_info():
except:
pass
- enable_simulation_gpu, simulation_gpu_count = get_simulation_gpu_env()
-
if enable_simulation_gpu:
gpu_count = simulation_gpu_count
gpu_total_mem = "80G"
@@ -130,26 +128,9 @@ def get_sys_runner_info():
gpu_count, gpu_vendor, cpu_count, gpu_device_name
-def get_simulation_gpu_env():
- _enable_simulation_gpu = enable_simulation_gpu
- _simulation_gpu_count = simulation_gpu_count
-
- env_enable_simulation_gpu = os.getenv("FEDML_ENABLE_SIMULATION_GPU", None)
- if env_enable_simulation_gpu is not None:
- _enable_simulation_gpu = True if env_enable_simulation_gpu == "1" or env_enable_simulation_gpu == 1 else False
-
- env_simulation_gpu_count = os.getenv("FEDML_SIMULATION_GPU_COUNT", None)
- if env_simulation_gpu_count is not None:
- _simulation_gpu_count = int(env_simulation_gpu_count)
-
- return _enable_simulation_gpu, _simulation_gpu_count
-
-
# GPU list: [GPU(ID, uuid, load, memoryTotal, memoryUsed, memoryFree, driver,
# gpu_name, serial, display_mode, display_active, temperature)]
def get_gpu_list():
- enable_simulation_gpu, simulation_gpu_count = get_simulation_gpu_env()
-
if enable_simulation_gpu:
ret_gpu_list = [
{'ID': 0, 'uuid': 'GPU-dab987f0-be09-294a-96d6-f9afeef49877', 'load': 1.0,
@@ -203,8 +184,6 @@ def get_gpu_list():
def get_available_gpu_id_list(limit=1) -> List[int]:
- enable_simulation_gpu, simulation_gpu_count = get_simulation_gpu_env()
-
if enable_simulation_gpu:
available_gpu_ids = [0, 1, 2, 3, 4, 5, 6, 7]
if simulation_gpu_count > 8:
diff --git a/python/fedml/computing/scheduler/master/base_master_agent.py b/python/fedml/computing/scheduler/master/base_master_agent.py
index 30cf5da1c9..3aff523c24 100755
--- a/python/fedml/computing/scheduler/master/base_master_agent.py
+++ b/python/fedml/computing/scheduler/master/base_master_agent.py
@@ -23,9 +23,7 @@ def __init__(self):
def login(
self, user_id, api_key=None, device_id=None,
- os_name=None, role=None, runner_cmd=None,
- communication_manager=None, sender_message_queue=None,
- status_center_queue=None, sender_message_event=None
+ os_name=None, role=None, runner_cmd=None
):
# Login account
login_result = FedMLAccountManager.get_instance().login(
@@ -50,31 +48,20 @@ def login(
# Initialize the protocol manager
# noinspection PyBoardException
try:
- self._initialize_protocol_manager(
- communication_manager=communication_manager,
- sender_message_queue=sender_message_queue,
- status_center_queue=status_center_queue,
- sender_message_event=sender_message_event)
+ self._initialize_protocol_manager()
except Exception as e:
FedMLAccountManager.write_login_failed_file(is_client=False)
self.protocol_mgr.stop()
raise e
# Start the protocol manager to process the messages from MLOps and slave agents.
- if communication_manager is None:
- self.protocol_mgr.start()
-
- return login_result
+ self.protocol_mgr.start()
@staticmethod
def logout():
GeneralConstants.cleanup_run_process(None, is_master=True)
sys_utils.cleanup_all_fedml_server_api_processes()
- def stop(self, kill_process=False):
- if self.protocol_mgr is not None:
- self.protocol_mgr.stop(kill_process=kill_process)
-
def _create_protocol_manager(self, role, login_result):
if self.protocol_mgr is not None:
return
@@ -82,11 +69,7 @@ def _create_protocol_manager(self, role, login_result):
login_result, agent_config=login_result.agent_config)
self.protocol_mgr.run_as_edge_server_and_agent = True \
if role == FedMLAccountManager.ROLE_EDGE_SERVER else False
- self.protocol_mgr.run_as_cloud_agent = True \
- if role == FedMLAccountManager.ROLE_CLOUD_AGENT or role == FedMLAccountManager.ROLE_GPU_MASTER_SERVER \
- else False
- self.use_local_process_as_cloud_server = True \
- if role == FedMLAccountManager.ROLE_GPU_MASTER_SERVER else self.use_local_process_as_cloud_server
+ self.protocol_mgr.run_as_cloud_agent = True if role == FedMLAccountManager.ROLE_CLOUD_AGENT else False
self.protocol_mgr.run_as_cloud_server = True if role == FedMLAccountManager.ROLE_CLOUD_SERVER else False
self.protocol_mgr.args = login_result
self.protocol_mgr.edge_id = login_result.edge_id
@@ -96,20 +79,12 @@ def _create_protocol_manager(self, role, login_result):
self.protocol_mgr.enable_simulation_cloud_agent = self.enable_simulation_cloud_agent
self.protocol_mgr.use_local_process_as_cloud_server = self.use_local_process_as_cloud_server
- def _initialize_protocol_manager(
- self, communication_manager=None, sender_message_queue=None,
- status_center_queue=None, sender_message_event=None
- ):
+ def _initialize_protocol_manager(self):
# Init local database
self._init_database()
# Initialize the master protocol
- self.protocol_mgr.set_parent_agent(self)
- self.protocol_mgr.initialize(
- communication_manager=communication_manager,
- sender_message_queue=sender_message_queue,
- status_center_queue=status_center_queue,
- sender_message_event=sender_message_event)
+ self.protocol_mgr.initialize()
# Report the IDLE status to MLOps
self.mlops_metrics.report_server_training_status(
@@ -134,9 +109,6 @@ def _init_logs(self, agent_args, edge_id):
in_args.server_agent_id = edge_id
MLOpsRuntimeLog.get_instance(in_args).init_logs()
- def get_protocol_manager(self):
- return self.protocol_mgr
-
@abstractmethod
def _get_log_file_dir(self):
pass
@@ -152,17 +124,3 @@ def _init_database(self):
@abstractmethod
def _generate_protocol_manager_instance(self, args, agent_config=None):
return None
-
- def start_master_server_instance(self, payload):
- self.protocol_mgr.start_master_server_instance(payload)
-
- def generate_agent_instance(self):
- return FedMLBaseMasterAgent()
-
- def process_job_complete_status(self, run_id, topic, payload):
- if self.protocol_mgr is None:
- return
- if topic in self.protocol_mgr.get_subscribed_topics():
- message_handler = self.protocol_mgr.get_listener_handler(topic)
- if message_handler is not None:
- message_handler(topic, payload)
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner.py b/python/fedml/computing/scheduler/master/base_master_job_runner.py
index fdfff143aa..9ebab258bb 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner.py
@@ -1,3 +1,4 @@
+
import json
import logging
import multiprocessing
@@ -6,9 +7,6 @@
import os
import time
import traceback
-
-import setproctitle
-
from ..scheduler_entry.constants import Constants
from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
from ..master.server_constants import ServerConstants
@@ -21,6 +19,7 @@
from fedml.utils.debugging import debug
from ..scheduler_core.status_center import JobStatus
from ..scheduler_core.compute_cache_manager import ComputeCacheManager
+from multiprocessing import Process, Queue
from ..scheduler_core.general_constants import GeneralConstants
from ..scheduler_core.scheduler_base_job_runner import FedMLSchedulerBaseJobRunner, RunnerError, RunnerCompletedError
from abc import ABC, abstractmethod
@@ -44,13 +43,13 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
is_master_runner=True
)
- self.run_edge_id_status_queue = multiprocessing.Manager().Queue()
- self.run_metrics_queue = multiprocessing.Manager().Queue()
- self.run_events_queue = multiprocessing.Manager().Queue()
- self.run_artifacts_queue = multiprocessing.Manager().Queue()
- self.run_logs_queue = multiprocessing.Manager().Queue()
- self.run_edge_device_info_queue = multiprocessing.Manager().Queue()
- self.run_edge_device_info_global_queue = multiprocessing.Manager().Queue()
+ self.run_edge_id_status_queue = Queue()
+ self.run_metrics_queue = Queue()
+ self.run_events_queue = Queue()
+ self.run_artifacts_queue = Queue()
+ self.run_logs_queue = Queue()
+ self.run_edge_device_info_queue = Queue()
+ self.run_edge_device_info_global_queue = Queue()
self.run_extend_queue_list = None
self.async_check_timeout = 0
self.enable_async_cluster = False
@@ -69,12 +68,9 @@ def run(
edge_device_info_queue=None, run_metrics_queue=None, run_event_queue=None,
run_artifacts_queue=None, run_logs_queue=None, edge_device_info_global_queue=None,
run_extend_queue_list=None, sender_message_center_queue=None, listener_message_queue=None,
- status_center_queue=None, process_name=None
+ status_center_queue=None
):
- if process_name is not None:
- setproctitle.setproctitle(process_name)
-
- print(f"Master job runner process id {os.getpid()}, name {process_name}, run id {self.run_id}")
+ print(f"Master job runner process id {os.getpid()}, run id {self.run_id}")
if platform.system() != "Windows":
os.setsid()
@@ -173,8 +169,7 @@ def run_impl(
run_id, self.request_json, edge_id=self.edge_id, is_server_job=True,
sender_message_queue=sender_message_queue,
listener_message_queue=listener_message_queue,
- status_center_queue=status_center_queue,
- process_name=GeneralConstants.get_launch_master_user_process_name(run_id, self.edge_id)
+ status_center_queue=status_center_queue
)
# Check if the run status is normal
@@ -236,12 +231,9 @@ def run_server_job(
edge_device_info_queue=None, run_metrics_queue=None, run_event_queue=None,
run_artifacts_queue=None, run_logs_queue=None, edge_device_info_global_queue=None,
run_extend_queue_list=None, sender_message_center_queue=None, listener_message_queue=None,
- status_center_queue=None, process_name=None
+ status_center_queue=None
):
- if process_name is not None:
- setproctitle.setproctitle(process_name)
-
- print(f"Server runner process id {os.getpid()}, name {process_name}. run id {self.run_id}")
+ print(f"Server runner process id {os.getpid()}, run id {self.run_id}")
if platform.system() != "Windows":
os.setsid()
@@ -413,9 +405,9 @@ def _generate_job_runner_instance(self, args, run_id=None, request_json=None, ag
return None
def start_runner_process(
- self, run_id, request_json, edge_id=None, is_server_job=False,
- sender_message_queue=None, listener_message_queue=None,
- status_center_queue=None, process_name=None
+ self, run_id, request_json, edge_id=None, is_server_job=False,
+ sender_message_queue=None, listener_message_queue=None,
+ status_center_queue=None,
):
server_runner = self._generate_job_runner_instance(
self.args, run_id=run_id, request_json=request_json,
@@ -433,26 +425,14 @@ def start_runner_process(
server_runner.edge_id_status_queue = self.run_edge_id_status_queue
server_runner.edge_device_info_queue = self.run_edge_device_info_queue
self.run_extend_queue_list = self._generate_extend_queue_list()
- if platform.system() == "Windows":
- self.run_process = multiprocessing.Process(
- target=server_runner.run if not is_server_job else server_runner.run_server_job, args=(
- self.run_process_event, self.run_process_completed_event, self.run_edge_id_status_queue,
- self.run_edge_device_info_queue, self.run_metrics_queue, self.run_events_queue,
- self.run_artifacts_queue, self.run_logs_queue, self.run_edge_device_info_global_queue,
- self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue,
- process_name,
- )
- )
- else:
- self.run_process = fedml.get_process(
- target=server_runner.run if not is_server_job else server_runner.run_server_job, args=(
- self.run_process_event, self.run_process_completed_event, self.run_edge_id_status_queue,
- self.run_edge_device_info_queue, self.run_metrics_queue, self.run_events_queue,
- self.run_artifacts_queue, self.run_logs_queue, self.run_edge_device_info_global_queue,
- self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue,
- process_name,
- )
+ self.run_process = Process(
+ target=server_runner.run if not is_server_job else server_runner.run_server_job, args=(
+ self.run_process_event, self.run_process_completed_event, self.run_edge_id_status_queue,
+ self.run_edge_device_info_queue, self.run_metrics_queue, self.run_events_queue,
+ self.run_artifacts_queue, self.run_logs_queue, self.run_edge_device_info_global_queue,
+ self.run_extend_queue_list, sender_message_queue, listener_message_queue, status_center_queue
)
+ )
self.run_process.start()
ServerConstants.save_run_process(run_id, self.run_process.pid)
return self.run_process
@@ -464,7 +444,7 @@ def put_run_edge_device_info_to_queue(self, run_id, edge_id, device_info):
if int(edge_id) in edge_ids or str(edge_id) in edge_ids:
run_id_str = str(run_id)
if self.run_edge_device_info_queue is None:
- self.run_edge_device_info_queue = multiprocessing.Manager().Queue()
+ self.run_edge_device_info_queue = Queue()
self.run_edge_device_info_queue.put(device_info)
def should_continue_run_job(self, run_id):
@@ -592,7 +572,7 @@ def callback_run_logs(self, topic, payload):
run_id = str(topic).split('/')[-1]
run_id_str = str(run_id)
if self.run_logs_queue is None:
- self.run_logs_queue = multiprocessing.Manager().Queue()
+ self.run_logs_queue = Queue()
self.run_logs_queue.put(payload)
def callback_run_metrics(self, topic, payload):
@@ -600,7 +580,7 @@ def callback_run_metrics(self, topic, payload):
run_id = str(topic).split('/')[-1]
run_id_str = str(run_id)
if self.run_metrics_queue is None:
- self.run_metrics_queue = multiprocessing.Manager().Queue()
+ self.run_metrics_queue = Queue()
self.run_metrics_queue.put(payload)
# def send_training_request_to_edges(self, active_edge_info_dict):
@@ -730,3 +710,6 @@ def should_process_async_cluster(self):
def get_client_id_list(self, server_edge_id_list):
return server_edge_id_list
+
+
+
diff --git a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
index 39f7438696..6831c9d034 100755
--- a/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_job_runner_manager.py
@@ -1,39 +1,27 @@
import base64
import json
import logging
-import multiprocessing
-import platform
import time
from abc import ABC
from multiprocessing import Process
-
-import fedml
from .cloud_server_manager import FedMLCloudServerManager
-from ..comm_utils.run_process_utils import RunProcessUtils
from ..scheduler_core.scheduler_base_job_runner_manager import FedMLSchedulerBaseJobRunnerManager
-from ..scheduler_core.account_manager import FedMLAccountManager
class FedMLBaseMasterJobRunnerManager(FedMLSchedulerBaseJobRunnerManager, ABC):
def __init__(self):
FedMLSchedulerBaseJobRunnerManager.__init__(self)
- if not hasattr(self, "master_agent_instance_map"):
- self.master_agent_instance_map = dict()
# Override
def start_job_runner(
self, run_id, request_json, args=None, edge_id=None, is_server_job=False,
sender_message_queue=None, listener_message_queue=None, status_center_queue=None,
- communication_manager=None, master_agent_instance=None, should_start_cloud_server=False,
- use_local_process_as_cloud_server=False, cuda_visible_gpu_ids_str=None, process_name=None
+ should_start_cloud_server=False, use_local_process_as_cloud_server=False,
+ cuda_visible_gpu_ids_str=None
):
if should_start_cloud_server:
- self._start_cloud_server(
- args, run_id, request_json, edge_id=edge_id,
- use_local_process_as_cloud_server=use_local_process_as_cloud_server,
- sender_message_queue=sender_message_queue, listener_message_queue=listener_message_queue,
- status_center_queue=status_center_queue, communication_manager=communication_manager,
- master_agent_instance=master_agent_instance, process_name=process_name)
+ self._start_cloud_server(args, run_id, request_json, edge_id=edge_id,
+ use_local_process_as_cloud_server=use_local_process_as_cloud_server)
return
run_id_str = str(run_id)
@@ -45,58 +33,34 @@ def start_job_runner(
run_id, request_json, edge_id=edge_id, is_server_job=is_server_job,
sender_message_queue=sender_message_queue,
listener_message_queue=listener_message_queue,
- status_center_queue=status_center_queue,
- process_name=process_name
+ status_center_queue=status_center_queue
)
def stop_job_runner(
self, run_id, args=None, server_id=None, request_json=None,
- run_as_cloud_agent=False, run_as_cloud_server=False,
- use_local_process_as_cloud_server=False
+ run_as_cloud_agent=False, run_as_cloud_server=False
):
super().stop_job_runner(run_id)
if run_as_cloud_agent or run_as_cloud_server:
- if not use_local_process_as_cloud_server:
- stopping_process = Process(
- target=FedMLCloudServerManager.stop_cloud_server,
- args=(run_id, server_id, args.agent_config))
- stopping_process.start()
-
- run_id_str = str(run_id)
- if self.master_agent_instance_map.get(run_id_str, None) is not None:
- self.master_agent_instance_map.get(run_id_str).stop(kill_process=True)
- self.master_agent_instance_map.pop(run_id_str)
-
- if use_local_process_as_cloud_server:
- time.sleep(1)
- RunProcessUtils.kill_process(self.cloud_run_process_map[run_id_str].pid)
+ stopping_process = Process(
+ target=FedMLCloudServerManager.stop_cloud_server, args=(run_id, server_id, args.agent_config))
+ stopping_process.start()
def complete_job_runner(
self, run_id, args=None, server_id=None, request_json=None,
- run_as_cloud_agent=False, run_as_cloud_server=False,
- use_local_process_as_cloud_server=False
+ run_as_cloud_agent=False, run_as_cloud_server=False
):
super().complete_job_runner(run_id)
if run_as_cloud_agent or run_as_cloud_server:
- if not use_local_process_as_cloud_server:
- stopping_process = Process(
- target=FedMLCloudServerManager.stop_cloud_server,
- args=(run_id, server_id, args.agent_config))
- stopping_process.start()
-
- run_id_str = str(run_id)
- if self.master_agent_instance_map.get(run_id_str, None) is not None:
- self.master_agent_instance_map.get(run_id_str).stop(kill_process=True)
- self.master_agent_instance_map.pop(run_id_str)
+ stopping_process = Process(
+ target=FedMLCloudServerManager.stop_cloud_server, args=(run_id, server_id, args.agent_config))
+ stopping_process.start()
def _start_cloud_server(
self, args, run_id, request_json, edge_id=None,
- use_local_process_as_cloud_server=False,
- sender_message_queue=None, listener_message_queue=None,
- status_center_queue=None, communication_manager=None,
- master_agent_instance=None, process_name=None
+ use_local_process_as_cloud_server=False
):
run_id_str = str(run_id)
cloud_server_mgr = FedMLCloudServerManager(
@@ -107,49 +71,19 @@ def _start_cloud_server(
self.cloud_run_process_map[run_id_str] = Process(target=cloud_server_mgr.start_cloud_server_process_entry)
self.cloud_run_process_map[run_id_str].start()
else:
- cloud_device_id = request_json.get("cloudServerDeviceId", "0")
- server_id = request_json.get("server_id", 0)
message_bytes = json.dumps(request_json).encode("ascii")
base64_bytes = base64.b64encode(message_bytes)
- payload = base64_bytes.decode("ascii")
- self.master_agent_instance_map[str(run_id)] = master_agent_instance
-
- logging.info("start the master server: {}".format(payload))
+ runner_cmd_encoded = base64_bytes.decode("ascii")
+ cloud_device_id = request_json.get("cloudServerDeviceId", "0")
- if platform.system() == "Windows":
- self.run_process = multiprocessing.Process(
- target=cloud_server_mgr.start_local_master_server,
- args=(args.account_id, args.api_key, args.os_name, args.version,
- cloud_device_id, run_id, payload,
- communication_manager, sender_message_queue,
- status_center_queue, master_agent_instance, process_name))
- else:
- self.cloud_run_process_map[run_id_str] = fedml.get_process(
- target=cloud_server_mgr.start_local_master_server,
- args=(args.account_id, args.api_key, args.os_name, args.version,
- cloud_device_id, run_id, payload,
- communication_manager, sender_message_queue,
- status_center_queue, master_agent_instance, process_name))
+ logging.info("runner_cmd_encoded: {}".format(runner_cmd_encoded))
+ self.cloud_run_process_map[run_id_str] = Process(
+ target=cloud_server_mgr.start_local_cloud_server,
+ args=(args.account_id, args.version, cloud_device_id, runner_cmd_encoded))
self.cloud_run_process_map[run_id_str].start()
time.sleep(1)
- def start_local_master_server(
- self, user, api_key, os_name, version, cloud_device_id, run_id, payload,
- communication_manager=None, sender_message_queue=None, status_center_queue=None,
- master_agent_instance=None
- ):
- if master_agent_instance is None:
- return
- master_agent_instance.login(
- user, api_key=api_key, device_id=cloud_device_id, os_name=os_name,
- role=FedMLAccountManager.ROLE_CLOUD_SERVER,
- communication_manager=None,
- sender_message_queue=None,
- status_center_queue=None)
- self.master_agent_instance_map[str(run_id)] = master_agent_instance
- master_agent_instance.start_master_server_instance(payload)
-
def callback_run_logs(self, run_id, topic, payload):
run_id_str = str(run_id)
if self.job_runners.get(run_id_str, None) is not None:
@@ -159,12 +93,3 @@ def callback_run_metrics(self, run_id, topic, payload):
run_id_str = str(run_id)
if self.job_runners.get(run_id_str, None) is not None:
self.job_runners[run_id_str].callback_run_metrics(topic, payload)
-
- def callback_proxy_unknown_messages(self, run_id, topic, payload):
- run_id_str = str(run_id)
- master_agent = self.master_agent_instance_map.get(run_id_str, None)
- if master_agent is None:
- return
- master_agent.process_job_complete_status(run_id, topic, payload)
-
-
diff --git a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
index 05529f8c8e..1c4cbba4f4 100755
--- a/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/base_master_protocol_manager.py
@@ -2,8 +2,6 @@
import base64
import json
import logging
-import time
-
import fedml
from ..comm_utils.constants import SchedulerConstants
from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
@@ -143,7 +141,6 @@ def on_agent_communication_connected(self, mqtt_client_object):
def callback_start_train(self, topic=None, payload=None):
# Fetch config from MLOps
# noinspection PyBroadException
-
try:
MLOpsConfigs.fetch_all_configs()
except Exception:
@@ -200,7 +197,7 @@ def callback_start_train(self, topic=None, payload=None):
self.run_edge_ids[run_id_str] = edge_id_list
# report server running status to master agent
- if not self.run_as_cloud_server and not self.run_as_cloud_agent:
+ if not self.run_as_cloud_server:
self.mlops_metrics.report_server_id_status(
run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_STARTING, edge_id=self.edge_id,
server_id=self.edge_id, server_agent_id=self.edge_id, running_json=payload)
@@ -215,9 +212,7 @@ def callback_start_train(self, topic=None, payload=None):
run_id, request_json, args=self.args, edge_id=self.edge_id,
sender_message_queue=self.message_center.get_sender_message_queue(),
listener_message_queue=self.get_listener_message_queue(),
- status_center_queue=self.get_status_queue(),
- communication_manager=self.get_listener_communication_manager(),
- process_name=GeneralConstants.get_launch_master_job_process_name(run_id, self.edge_id)
+ status_center_queue=self.get_status_queue()
)
process = self._get_job_runner_manager().get_runner_process(run_id)
@@ -228,17 +223,12 @@ def callback_start_train(self, topic=None, payload=None):
elif self.run_as_cloud_agent:
self.init_job_task(request_json)
- server_id = request_json.get("server_id", self.edge_id)
self._get_job_runner_manager().start_job_runner(
run_id, request_json, args=self.args, edge_id=self.edge_id,
sender_message_queue=self.message_center.get_sender_message_queue(),
listener_message_queue=self.get_listener_message_queue(),
- status_center_queue=self.get_status_queue(),
- communication_manager=self.get_listener_communication_manager(),
- master_agent_instance=self.generate_agent_instance(),
- should_start_cloud_server=True,
- use_local_process_as_cloud_server=self.use_local_process_as_cloud_server,
- process_name=GeneralConstants.get_launch_master_job_process_name(run_id, server_id)
+ status_center_queue=self.get_status_queue(), should_start_cloud_server=True,
+ use_local_process_as_cloud_server=self.use_local_process_as_cloud_server
)
process = self._get_job_runner_manager().get_runner_process(run_id, is_cloud_server=True)
@@ -247,7 +237,6 @@ def callback_start_train(self, topic=None, payload=None):
elif self.run_as_cloud_server:
self.server_agent_id = request_json.get("cloud_agent_id", self.edge_id)
self.start_request_json = json.dumps(request_json)
- server_id = request_json.get("server_id", self.edge_id)
run_id = request_json["runId"]
run_id_str = str(run_id)
@@ -259,12 +248,10 @@ def callback_start_train(self, topic=None, payload=None):
run_id, request_json, args=self.args, edge_id=self.edge_id,
sender_message_queue=self.message_center.get_sender_message_queue(),
listener_message_queue=self.get_listener_message_queue(),
- status_center_queue=self.get_status_queue(),
- communication_manager=self.get_listener_communication_manager(),
- process_name=GeneralConstants.get_launch_master_job_process_name(run_id, server_id)
+ status_center_queue=self.get_status_queue()
)
- self.send_status_msg_to_edges(edge_id_list, run_id, server_id)
+ self.send_status_msg_to_edges(edge_id_list, run_id, self.edge_id)
def callback_stop_train(self, topic, payload, use_payload=None):
# Print the payload
@@ -292,16 +279,6 @@ def callback_stop_train(self, topic, payload, use_payload=None):
server_agent_id = self.edge_id
topic_stop_train_to_cloud_server = f"mlops/flserver_agent_{server_id}/stop_train"
self.message_center.send_message(topic_stop_train_to_cloud_server, payload)
-
- time.sleep(2)
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, server_id)
- self._get_job_runner_manager().stop_job_runner(
- run_id, args=self.args, server_id=server_id, request_json=None,
- run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server,
- use_local_process_as_cloud_server=self.use_local_process_as_cloud_server)
- self.generate_status_report(run_id, server_id, server_agent_id=server_agent_id). \
- report_server_id_status(run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_KILLED,
- edge_id=server_id, server_id=server_id)
return
# Reset all edge status and server status
@@ -327,11 +304,7 @@ def callback_complete_job(self, topic, payload):
self._process_job_complete_status(run_id, server_id, request_json)
def _process_job_complete_status(self, run_id, server_id, complete_payload):
- # Complete the job runner
- self._get_job_runner_manager().complete_job_runner(
- run_id, args=self.args, server_id=server_id, request_json=complete_payload,
- run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server,
- use_local_process_as_cloud_server=self.use_local_process_as_cloud_server)
+ pass
def callback_run_logs(self, topic, payload):
run_id = str(topic).split('/')[-1]
@@ -417,12 +390,6 @@ def callback_request_job_status(self, topic, payload):
def callback_request_device_status_in_job(self, topic, payload):
self.response_device_status_in_job(topic, payload)
- def callback_proxy_unknown_messages(self, run_id, topic, payload):
- self._get_job_runner_manager().callback_proxy_unknown_messages(run_id, topic, payload)
-
- def process_extra_queues(self, extra_queues):
- self.rebuild_status_center(extra_queues[0])
-
def generate_protocol_manager(self):
message_status_runner = self._generate_protocol_manager_instance(
self.args, agent_config=self.agent_config
@@ -509,8 +476,6 @@ def init_job_task(self, request_json):
self.setup_listener_for_run_logs(run_id)
def setup_listeners_for_edge_status(self, run_id, edge_ids, server_id):
- if self.run_as_cloud_agent:
- return
edge_status_topic = "fl_client/flclient_agent_" + str(server_id) + "/status"
payload = {"run_id": run_id, "init_all_edge_id_list": edge_ids, "init_server_id": server_id}
self.callback_edge_status(edge_status_topic, json.dumps(payload))
@@ -521,9 +486,6 @@ def setup_listeners_for_edge_status(self, run_id, edge_ids, server_id):
self.subscribe_msg(edge_status_topic)
def remove_listeners_for_edge_status(self, edge_ids=None):
- if self.run_as_cloud_agent:
- return
-
if edge_ids is None:
edge_ids = self.request_json["edgeids"]
@@ -580,7 +542,7 @@ def send_status_check_msg(self, run_id, edge_id, server_id, context=None):
def send_status_msg_to_edges(self, edge_id_list, run_id, server_id, context=None):
# Send status message to all edges
for edge_id in edge_id_list:
- self.send_status_check_msg(run_id, edge_id, server_id, context=context)
+ self.send_status_check_msg(run_id, edge_id, self.edge_id, context=context)
def report_exception_status(self, run_id):
self.mlops_metrics.report_job_status(run_id, GeneralConstants.MSG_MLOPS_SERVER_STATUS_EXCEPTION)
@@ -592,9 +554,3 @@ def get_start_train_topic_with_edge_id(edge_id):
@abstractmethod
def _generate_protocol_manager_instance(self, args, agent_config=None):
return None
-
- def start_master_server_instance(self, payload):
- super().on_agent_communication_connected(None)
-
- self.receive_message_json(self.topic_start_train, payload)
-
diff --git a/python/fedml/computing/scheduler/master/cloud_server_manager.py b/python/fedml/computing/scheduler/master/cloud_server_manager.py
index 3669cb32bc..040a0f38a3 100755
--- a/python/fedml/computing/scheduler/master/cloud_server_manager.py
+++ b/python/fedml/computing/scheduler/master/cloud_server_manager.py
@@ -2,14 +2,10 @@
import json
import logging
import os
-import platform
import traceback
-import setproctitle
-
import fedml
from fedml.computing.scheduler.comm_utils.sys_utils import get_python_program
-from fedml.computing.scheduler.scheduler_core.account_manager import FedMLAccountManager
class FedMLCloudServerManager:
@@ -35,37 +31,14 @@ def __init__(self, args, run_id=None, edge_id=None, request_json=None, agent_con
self.cloud_server_name = None
@staticmethod
- def start_local_cloud_server(user, api_key, os_name, version, cloud_device_id, runner_cmd_encoded):
- if platform.system() != "Windows":
- os.setsid()
-
+ def start_local_cloud_server(user, version, cloud_device_id, runner_cmd_encoded):
print(f"start cloud server, device id {cloud_device_id}, runner cmd {runner_cmd_encoded}")
pip_source_dir = os.path.dirname(__file__)
login_cmd = os.path.join(pip_source_dir, "server_login.py")
run_cmd = f"{get_python_program()} -W ignore {login_cmd} -t login -r cloud_server -u {str(user)} " \
- f"-k {api_key} -v {version} -id {cloud_device_id} -rc {runner_cmd_encoded}"
+ f"-v {version} -id {cloud_device_id} -rc {runner_cmd_encoded}"
os.system(run_cmd)
- def start_local_master_server(
- self, user, api_key, os_name, version, cloud_device_id, run_id, payload,
- communication_manager=None, sender_message_queue=None, status_center_queue=None,
- master_agent_instance=None, process_name=None
- ):
- if process_name is not None:
- setproctitle.setproctitle(process_name)
-
- logging.info(f"Local master server pid: {os.getpid()}")
- if platform.system() != "Windows":
- os.setsid()
-
- master_agent_instance.login(
- user, api_key=api_key, device_id=cloud_device_id, os_name=os_name,
- role=FedMLAccountManager.ROLE_CLOUD_SERVER, runner_cmd=payload,
- communication_manager=None, sender_message_queue=None,
- status_center_queue=None)
-
- master_agent_instance.stop()
-
def start_cloud_server_process_entry(self):
try:
self.start_cloud_server_process()
diff --git a/python/fedml/computing/scheduler/master/master_protocol_manager.py b/python/fedml/computing/scheduler/master/master_protocol_manager.py
index 1adda439c6..ca9621e41d 100755
--- a/python/fedml/computing/scheduler/master/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/master/master_protocol_manager.py
@@ -7,9 +7,8 @@
class FedMLLaunchMasterProtocolManager(FedMLBaseMasterProtocolManager, ABC):
def __init__(self, args, agent_config=None):
FedMLBaseMasterProtocolManager.__init__(self, args, agent_config=agent_config)
- self.message_center_name = "launch_master_agent"
- # Override
+ # Override
def generate_topics(self):
super().generate_topics()
@@ -36,6 +35,9 @@ def _init_extra_items(self):
def print_connected_info(self):
super().print_connected_info()
- def generate_agent_instance(self):
- from .master_agent import FedMLLaunchMasterAgent
- return FedMLLaunchMasterAgent()
+ # Override
+ def _process_job_complete_status(self, run_id, server_id, complete_payload):
+ # Complete the job runner
+ self._get_job_runner_manager().complete_job_runner(
+ run_id, args=self.args, server_id=server_id, request_json=complete_payload,
+ run_as_cloud_agent=self.run_as_cloud_agent, run_as_cloud_server=self.run_as_cloud_server)
diff --git a/python/fedml/computing/scheduler/master/server_login.py b/python/fedml/computing/scheduler/master/server_login.py
index be7b73103f..8dd0696bc8 100755
--- a/python/fedml/computing/scheduler/master/server_login.py
+++ b/python/fedml/computing/scheduler/master/server_login.py
@@ -41,5 +41,4 @@ def logout():
master_agent.login(args.api_key, api_key=args.api_key, device_id=args.device_id,
os_name=args.os_name, role=args.role, runner_cmd=args.runner_cmd)
else:
- master_agent.stop()
master_agent.logout()
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 32f5ebdeab..ab6bc4c895 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -1,12 +1,12 @@
import copy
import json
import logging
-import multiprocessing
import os
import time
import queue
import traceback
from abc import ABC
+from multiprocessing import Queue
import fedml
from fedml.core.mlops import MLOpsRuntimeLog, MLOpsConfigs
@@ -50,7 +50,7 @@ def __init__(self, args, run_id=0, request_json=None, agent_config=None, edge_id
self.replica_controller = None
self.deployed_replica_payload = None
self.slave_deployment_results_map = dict()
- self.deployment_result_queue = multiprocessing.Manager().Queue()
+ self.deployment_result_queue = Queue()
self.is_fresh_endpoint = True
# Override
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index efa56f4db5..9e0d51b588 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -264,8 +264,7 @@ def callback_start_deployment(self, topic, payload):
run_id, request_json, args=self.args, edge_id=self.edge_id,
sender_message_queue=self.message_center.get_sender_message_queue(),
listener_message_queue=self.get_listener_message_queue(),
- status_center_queue=self.get_status_queue(),
- process_name=GeneralConstants.get_deploy_master_job_process_name(run_id, self.edge_id)
+ status_center_queue=self.get_status_queue()
)
process = self._get_job_runner_manager().get_runner_process(run_id)
if process is not None:
diff --git a/python/fedml/computing/scheduler/model_scheduler/model_device_client.py b/python/fedml/computing/scheduler/model_scheduler/model_device_client.py
new file mode 100755
index 0000000000..05f43afc5f
--- /dev/null
+++ b/python/fedml/computing/scheduler/model_scheduler/model_device_client.py
@@ -0,0 +1,98 @@
+
+import copy
+import logging
+import multiprocessing
+import time
+import traceback
+from multiprocessing import Process
+from ..scheduler_core.account_manager import FedMLAccountManager
+from .worker_agent import FedMLDeployWorkerAgent
+
+
+class FedMLModelDeviceClientRunner:
+ def __init__(self, args, current_device_id, os_name, is_from_docker, service_config, infer_host="127.0.0.1"):
+ self.agent_process = None
+ self.agent_runner = None
+ self.agent_process_event = None
+ self.args = copy.deepcopy(args)
+ self.service_config = service_config
+ self.unique_device_id = None
+ self.current_device_id = current_device_id
+ self.os_name = os_name
+ self.is_from_docker = is_from_docker
+ self.edge_id = None
+ self.infer_host = infer_host
+ self.redis_addr = "local"
+ self.redis_port = "6379"
+ self.redis_password = "fedml_default"
+
+ def get_edge_id(self):
+ return self.edge_id
+
+ def start(self):
+ self.agent_runner = FedMLModelDeviceClientRunner(self.args, self.current_device_id, self.os_name,
+ self.is_from_docker, self.service_config)
+ self.agent_runner.infer_host = self.infer_host
+ self.agent_runner.redis_addr = self.redis_addr
+ self.agent_runner.redis_port = self.redis_port
+ self.agent_runner.redis_password = self.redis_password
+ if self.agent_process_event is None:
+ self.agent_process_event = multiprocessing.Event()
+ self.agent_process = Process(target=self.agent_runner.run_entry, args=(self.agent_process_event, self.args,))
+ self.edge_id = self.bind_device()
+ self.agent_process.start()
+
+ def run_entry(self, process_event, in_args):
+ # print(f"Model worker process id {os.getpid()}")
+
+ self.agent_process_event = process_event
+
+ worker_agent = FedMLDeployWorkerAgent()
+
+ while not self.agent_process_event.is_set():
+ try:
+ try:
+ worker_agent.logout()
+ except Exception as e:
+ pass
+
+ worker_agent.login(
+ in_args.account_id, api_key=in_args.api_key, device_id=in_args.device_id,
+ os_name=in_args.os_name, role=FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM
+ )
+ except Exception as e:
+ logging.info("Restart model device client: {}".format(traceback.format_exc()))
+ pass
+ finally:
+ try:
+ worker_agent.logout()
+ except Exception as e:
+ pass
+ time.sleep(15)
+
+ try:
+ self.stop()
+ except Exception as e:
+ pass
+
+ def check_runner_stop_event(self):
+ if self.agent_process_event is not None and self.agent_process_event.is_set():
+ logging.info("Received stopping event.")
+ raise Exception("Runner stopped")
+
+ def stop(self):
+ FedMLDeployWorkerAgent.logout()
+
+ if self.agent_process_event is not None:
+ self.agent_process_event.set()
+
+ def bind_device(self):
+ # Login account
+ login_result = FedMLAccountManager.get_instance().login(
+ self.args.account_id, api_key=self.args.api_key, device_id=self.args.device_id,
+ os_name=self.args.os_name, role=FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM
+ )
+ if login_result is not None:
+ return login_result.edge_id
+ else:
+ return None
diff --git a/python/fedml/computing/scheduler/model_scheduler/model_device_server.py b/python/fedml/computing/scheduler/model_scheduler/model_device_server.py
new file mode 100755
index 0000000000..b2ecd144b1
--- /dev/null
+++ b/python/fedml/computing/scheduler/model_scheduler/model_device_server.py
@@ -0,0 +1,97 @@
+
+import copy
+import logging
+import multiprocessing
+import time
+import traceback
+from multiprocessing import Process
+from ..scheduler_core.account_manager import FedMLAccountManager
+from .master_agent import FedMLDeployMasterAgent
+
+
+class FedMLModelDeviceServerRunner:
+ def __init__(self, args, current_device_id, os_name, is_from_docker, service_config, infer_host="127.0.0.1"):
+ self.agent_process = None
+ self.agent_runner = None
+ self.agent_process_event = None
+ self.args = copy.deepcopy(args)
+ self.service_config = service_config
+ self.unique_device_id = None
+ self.current_device_id = current_device_id
+ self.os_name = os_name
+ self.is_from_docker = is_from_docker
+ self.edge_id = None
+ self.infer_host = infer_host
+ self.redis_addr = "local"
+ self.redis_port = "6379"
+ self.redis_password = "fedml_default"
+
+ def get_edge_id(self):
+ return self.edge_id
+
+ def start(self):
+ self.agent_runner = FedMLModelDeviceServerRunner(self.args, self.current_device_id, self.os_name,
+ self.is_from_docker, self.service_config)
+ self.agent_runner.infer_host = self.infer_host
+ self.agent_runner.redis_addr = self.redis_addr
+ self.agent_runner.redis_port = self.redis_port
+ self.agent_runner.redis_password = self.redis_password
+ if self.agent_process_event is None:
+ self.agent_process_event = multiprocessing.Event()
+ self.agent_process = Process(target=self.agent_runner.run_entry, args=(self.agent_process_event, self.args))
+ self.edge_id = self.bind_device()
+ self.agent_process.start()
+
+ def run_entry(self, process_event, in_args):
+ # print(f"Model master process id {os.getpid()}")
+
+ self.agent_process_event = process_event
+ master_agent = FedMLDeployMasterAgent()
+
+ while not self.agent_process_event.is_set():
+ try:
+ try:
+ master_agent.logout()
+ except Exception as e:
+ pass
+
+ master_agent.login(
+ in_args.account_id, api_key=in_args.api_key, device_id=in_args.device_id,
+ os_name=in_args.os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM
+ )
+ except Exception as e:
+ logging.info("Restart model device server: {}".format(traceback.format_exc()))
+ pass
+ finally:
+ try:
+ master_agent.logout()
+ except Exception as e:
+ pass
+ time.sleep(15)
+
+ try:
+ self.stop()
+ except Exception as e:
+ pass
+
+ def check_runner_stop_event(self):
+ if self.agent_process_event is not None and self.agent_process_event.is_set():
+ logging.info("Received stopping event.")
+ raise Exception("Runner stopped")
+
+ def stop(self):
+ FedMLDeployMasterAgent.logout()
+
+ if self.agent_process_event is not None:
+ self.agent_process_event.set()
+
+ def bind_device(self):
+ # Login account
+ login_result = FedMLAccountManager.get_instance().login(
+ self.args.account_id, api_key=self.args.api_key, device_id=self.args.device_id,
+ os_name=self.args.os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM
+ )
+ if login_result is not None:
+ return login_result.edge_id
+ else:
+ return None
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
index 9204291c48..b1d0bebc47 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
@@ -12,7 +12,6 @@
from .device_model_msg_object import FedMLModelMsgObject
from .device_client_constants import ClientConstants
from .device_client_data_interface import FedMLClientDataInterface
-from ..scheduler_core.general_constants import GeneralConstants
from ..slave.base_slave_protocol_manager import FedMLBaseSlaveProtocolManager
from .worker_job_runner_manager import FedMLDeployJobRunnerManager
from .device_mqtt_inference_protocol import FedMLMqttInference
@@ -164,8 +163,7 @@ def callback_start_deployment(self, topic, payload):
run_id, request_json, args=self.args, edge_id=self.edge_id,
sender_message_queue=self.message_center.get_sender_message_queue(),
listener_message_queue=self.get_listener_message_queue(),
- status_center_queue=self.get_status_queue(),
- process_name=GeneralConstants.get_deploy_slave_job_process_name(run_id, self.edge_id)
+ status_center_queue=self.get_status_queue()
)
process = self._get_job_runner_manager().get_runner_process(run_id)
if process is not None:
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
index 8d73a42679..3b80511d12 100755
--- a/python/fedml/computing/scheduler/scheduler_core/account_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -25,7 +25,6 @@ class FedMLAccountManager(Singleton):
ROLE_CLOUD_SERVER = "cloud_server"
ROLE_EDGE_DEVICE = "client"
ROLE_GPU_PROVIDER = "gpu_supplier"
- ROLE_GPU_MASTER_SERVER = "gpu_master_server"
ROLE_DEPLOY_MASTER_ON_PREM = "md.on_premise_device.master"
ROLE_DEPLOY_WORKER_ON_PREM = "md.on_premise_device"
@@ -34,7 +33,6 @@ class FedMLAccountManager(Singleton):
DEVICE_ID_SUFFIX_CLOUD_SERVER = ".Public.Server"
DEVICE_ID_SUFFIX_EDGE_DEVICE = ".Edge.Device"
DEVICE_ID_SUFFIX_GPU_PROVIDER = ".Edge.GPU.Supplier"
- DEVICE_ID_SUFFIX_GPU_MASTER_SERVER = ".Edge.GPU.MasterServer"
DEVICE_ID_SUFFIX_DEPLOY = "MDA"
DEVICE_ID_SUFFIX_DEPLOY_MASTER_ON_PREM = ".OnPremise.Master.Device"
DEVICE_ID_SUFFIX_DEPLOY_WORKER_ON_PREM = ".OnPremise.Device"
@@ -43,7 +41,8 @@ class FedMLAccountManager(Singleton):
DEVICE_ID_DOCKER_HUB_TAG = ".DockerHub"
def __init__(self):
- pass
+ if not hasattr(self, "agent_args"):
+ self.agent_args = None
@staticmethod
def get_instance():
@@ -51,7 +50,7 @@ def get_instance():
def login(self, user_id, api_key="", device_id=None, os_name=None, role=None, runner_cmd=None):
# Build the agent args
- agent_args = self.build_agent_args(
+ self.build_agent_args(
user_id, api_key=api_key, device_id=device_id, os_name=os_name, role=role, runner_cmd=runner_cmd
)
@@ -94,8 +93,8 @@ def login(self, user_id, api_key="", device_id=None, os_name=None, role=None, ru
# noinspection PyBroadException
try:
edge_id, user_name, extra_url, general_edge_id = FedMLAccountManager.bind_account_and_device_id(
- service_config["ml_ops_config"]["EDGE_BINDING_URL"], agent_args.account_id,
- agent_args.unique_device_id, agent_args.os_name,
+ service_config["ml_ops_config"]["EDGE_BINDING_URL"], self.agent_args.account_id,
+ self.agent_args.unique_device_id, self.agent_args.os_name,
api_key=api_key, role=role
)
if edge_id > 0:
@@ -119,13 +118,13 @@ def login(self, user_id, api_key="", device_id=None, os_name=None, role=None, ru
return None
# Fill the bound result to agent args.
- agent_args = self.fill_argent_args(
- agent_args, log_server_url=log_server_url, server_id=edge_id,
+ self.fill_argent_args(
+ log_server_url=log_server_url, server_id=edge_id,
edge_id=edge_id, general_edge_id=general_edge_id,
user_name=user_name, extra_url=extra_url,
agent_config=service_config)
- return agent_args
+ return self.agent_args
def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None, role=None, runner_cmd=None):
# Generate the suffix for device based on the role
@@ -145,9 +144,6 @@ def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None,
device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_EDGE_DEVICE
elif role == FedMLAccountManager.ROLE_GPU_PROVIDER:
device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_GPU_PROVIDER
- elif role == FedMLAccountManager.ROLE_GPU_MASTER_SERVER:
- device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_GPU_MASTER_SERVER
- is_master = True
elif role == FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM:
device_id_suffix = FedMLAccountManager.DEVICE_ID_SUFFIX_DEPLOY_MASTER_ON_PREM
is_master = True
@@ -158,31 +154,32 @@ def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None,
# Build the agent args
version = fedml.get_env_version()
- agent_args = AgentArgs()
- agent_args.role = role
- agent_args.account_id = user_id
- agent_args.api_key = api_key
- agent_args.current_running_dir = GeneralConstants.get_deploy_fedml_home_dir(is_master=is_master) \
+ if self.agent_args is None:
+ self.agent_args = AgentArgs()
+ self.agent_args.role = role
+ self.agent_args.account_id = user_id
+ self.agent_args.api_key = api_key
+ self.agent_args.current_running_dir = GeneralConstants.get_deploy_fedml_home_dir(is_master=is_master) \
if is_deploy else GeneralConstants.get_launch_fedml_home_dir(is_master=is_master)
sys_name = platform.system()
if sys_name == "Darwin":
sys_name = "MacOS"
- agent_args.os_name = sys_name if os_name is None or os_name == "" else os_name
- agent_args.version = version
- agent_args.log_file_dir = GeneralConstants.get_deploy_log_file_dir(is_master=is_master) \
+ self.agent_args.os_name = sys_name if os_name is None or os_name == "" else os_name
+ self.agent_args.version = version
+ self.agent_args.log_file_dir = GeneralConstants.get_deploy_log_file_dir(is_master=is_master) \
if is_deploy else GeneralConstants.get_launch_log_file_dir(is_master=is_master)
is_from_docker = False
if device_id is not None and device_id != "0":
- agent_args.current_device_id = device_id
+ self.agent_args.current_device_id = device_id
else:
data_dir = GeneralConstants.get_deploy_data_dir(is_master=is_master) \
if is_deploy else GeneralConstants.get_launch_data_dir(is_master=is_master)
is_gpu_provider = True if role == FedMLAccountManager.ROLE_GPU_PROVIDER else False
- agent_args.current_device_id = FedMLAccountManager.get_device_id(
+ self.agent_args.current_device_id = FedMLAccountManager.get_device_id(
data_dir=data_dir, use_machine_id=is_gpu_provider)
- agent_args.device_id = agent_args.current_device_id
- agent_args.config_version = version
- agent_args.cloud_region = ""
+ self.agent_args.device_id = self.agent_args.current_device_id
+ self.agent_args.config_version = version
+ self.agent_args.cloud_region = ""
# Check if it is running in the fedml docker hub
is_from_fedml_docker_hub = False
@@ -194,29 +191,26 @@ def build_agent_args(self, user_id, api_key=None, device_id=None, os_name=None,
# Build unique device id
docker_tag = FedMLAccountManager.DEVICE_ID_DOCKER_TAG if is_from_docker else ""
docker_tag = FedMLAccountManager.DEVICE_ID_DOCKER_HUB_TAG if is_from_fedml_docker_hub else docker_tag
- unique_device_id = f"{agent_args.current_device_id}@{agent_args.os_name}" \
+ unique_device_id = f"{self.agent_args.current_device_id}@{self.agent_args.os_name}" \
f"{docker_tag}{device_id_suffix}"
if role == FedMLAccountManager.ROLE_CLOUD_SERVER:
- unique_device_id = agent_args.current_device_id
+ unique_device_id = self.agent_args.current_device_id
# Set the unique device id
- agent_args.is_from_docker = is_from_docker or is_from_fedml_docker_hub
- agent_args.unique_device_id = unique_device_id
- agent_args.runner_cmd = runner_cmd
-
- return agent_args
+ self.agent_args.is_from_docker = is_from_docker or is_from_fedml_docker_hub
+ self.agent_args.unique_device_id = unique_device_id
+ self.agent_args.runner_cmd = runner_cmd
def fill_argent_args(
- self, agent_args, log_server_url=None, server_id=None, edge_id=None,
+ self, log_server_url=None, server_id=None, edge_id=None,
user_name=None, extra_url=None, general_edge_id=None, agent_config=None):
- agent_args.log_server_url = log_server_url
- agent_args.server_id = server_id
- agent_args.edge_id = edge_id
- agent_args.user_name = user_name
- agent_args.extra_url = extra_url
- agent_args.general_edge_id = general_edge_id
- agent_args.agent_config = agent_config
- return agent_args
+ self.agent_args.log_server_url = log_server_url
+ self.agent_args.server_id = server_id
+ self.agent_args.edge_id = edge_id
+ self.agent_args.user_name = user_name
+ self.agent_args.extra_url = extra_url
+ self.agent_args.general_edge_id = general_edge_id
+ self.agent_args.agent_config = agent_config
@staticmethod
def write_login_failed_file(is_client=True):
diff --git a/python/fedml/computing/scheduler/scheduler_core/general_constants.py b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
index 0ab6f79577..8c60b17bdf 100755
--- a/python/fedml/computing/scheduler/scheduler_core/general_constants.py
+++ b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
@@ -65,20 +65,6 @@ class GeneralConstants:
FEDML_OTA_CMD_RESTART = "restart"
FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT = "MODEL_END_POINT"
- FEDML_PROCESS_NAME_PREFIX = "fedml-process-"
- FEDML_LAUNCH_MASTER_JOB_RUNNER_TAG = "launch-master-job-runner"
- FEDML_LAUNCH_SLAVE_JOB_RUNNER_TAG = "launch-slave-job-runner"
- FEDML_LAUNCH_MASTER_USER_JOB_TAG = "launch-master-user-job"
- FEDML_DEPLOY_MASTER_JOB_RUNNER_TAG = "deploy-master-job-runner"
- FEDML_DEPLOY_SLAVE_JOB_RUNNER_TAG = "deploy-slave-job-runner"
- FEDML_DEPLOY_MASTER_USER_JOB_TAG = "deploy-master-user-job"
- FEDML_MESSAGE_CENTER_LISTENER_TAG = "message-center-listener"
- FEDML_MESSAGE_CENTER_SENDER_TAG = "message-center-sender"
- FEDML_STATUS_CENTER_TAG = "status-center"
- FEDML_LOG_PROCESS_TAG = "log"
- FEDML_MONITOR_PROCESS_TAG = "monitor"
-
- FEDML_TOPIC_STATUS_CENTER_STOP = "anywhere/status_center/stop"
@staticmethod
def get_package_unzip_dir(package_download_dir):
@@ -230,65 +216,3 @@ def get_topic_complete_job(server_id):
def get_payload_complete_job(run_id, server_id):
payload_complete_job = {"runId": run_id, "serverId": server_id}
return payload_complete_job
-
- @staticmethod
- def get_process_name(process_tag, run_id=None, edge_id=None):
- return f'{GeneralConstants.FEDML_PROCESS_NAME_PREFIX}{process_tag}'\
- f'{"-run-" + str(run_id) if run_id is not None and int(run_id) != 0 else ""}'\
- f'{"-edge-" + str(edge_id) if edge_id is not None else ""}'
-
- @staticmethod
- def get_process_name_with_prefix(process_prefix, run_id=None, edge_id=None):
- return f"{process_prefix}-run-{run_id}-edge-{edge_id}"
-
- @staticmethod
- def get_launch_master_job_process_name(run_id, edge_id):
- return GeneralConstants.get_process_name(
- GeneralConstants.FEDML_LAUNCH_MASTER_JOB_RUNNER_TAG, run_id, edge_id)
-
- @staticmethod
- def get_launch_slave_job_process_name(run_id, edge_id):
- return GeneralConstants.get_process_name(
- GeneralConstants.FEDML_LAUNCH_SLAVE_JOB_RUNNER_TAG, run_id, edge_id)
-
- @staticmethod
- def get_launch_master_user_process_name(run_id, edge_id):
- return GeneralConstants.get_process_name(
- GeneralConstants.FEDML_LAUNCH_MASTER_USER_JOB_TAG, run_id, edge_id)
-
- @staticmethod
- def get_deploy_master_job_process_name(run_id, edge_id):
- return GeneralConstants.get_process_name(
- GeneralConstants.FEDML_DEPLOY_MASTER_JOB_RUNNER_TAG, run_id, edge_id)
-
- @staticmethod
- def get_deploy_slave_job_process_name(run_id, edge_id):
- return GeneralConstants.get_process_name(
- GeneralConstants.FEDML_DEPLOY_SLAVE_JOB_RUNNER_TAG, run_id, edge_id)
-
- @staticmethod
- def get_deploy_master_user_process_name(run_id, edge_id):
- return GeneralConstants.get_process_name(
- GeneralConstants.FEDML_DEPLOY_MASTER_USER_JOB_TAG, run_id, edge_id)
-
- @staticmethod
- def get_log_process_name(run_id, edge_id):
- return GeneralConstants.get_process_name(
- GeneralConstants.FEDML_LOG_PROCESS_TAG, run_id, edge_id)
-
- @staticmethod
- def get_message_center_listener_process_name(message_center_name):
- return f"{GeneralConstants.FEDML_PROCESS_NAME_PREFIX}{GeneralConstants.FEDML_MESSAGE_CENTER_LISTENER_TAG}-{message_center_name}"
-
- @staticmethod
- def get_message_center_sender_process_name(message_center_name):
- return f"{GeneralConstants.FEDML_PROCESS_NAME_PREFIX}{GeneralConstants.FEDML_MESSAGE_CENTER_SENDER_TAG}-{message_center_name}"
-
- @staticmethod
- def get_status_center_process_name(status_center_tag):
- return f"{GeneralConstants.FEDML_PROCESS_NAME_PREFIX}{GeneralConstants.FEDML_STATUS_CENTER_TAG}-{status_center_tag}"
-
- @staticmethod
- def get_monitor_process_name(monitor_tag, run_id, edge_id):
- return GeneralConstants.get_process_name(
- f"{GeneralConstants.FEDML_MONITOR_PROCESS_TAG}-{monitor_tag}", run_id, edge_id)
diff --git a/python/fedml/computing/scheduler/scheduler_core/message_center.py b/python/fedml/computing/scheduler/scheduler_core/message_center.py
index 5f414d1873..dbe11700a0 100755
--- a/python/fedml/computing/scheduler/scheduler_core/message_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/message_center.py
@@ -1,20 +1,16 @@
import json
import logging
import os
-import platform
import threading
import time
import traceback
import uuid
import multiprocessing
+from multiprocessing import Process, Queue
import queue
from os.path import expanduser
-import setproctitle
-
-import fedml
from fedml.core.distributed.communication.mqtt.mqtt_manager import MqttManager
-from .general_constants import GeneralConstants
from ..slave.client_constants import ClientConstants
from ....core.mlops.mlops_metrics import MLOpsMetrics
from operator import methodcaller
@@ -24,7 +20,6 @@
class FedMLMessageCenter(object):
FUNC_SETUP_MESSAGE_CENTER = "setup_message_center"
FUNC_REBUILD_MESSAGE_CENTER = "rebuild_message_center"
- FUNC_PROCESS_EXTRA_QUEUES = "process_extra_queues"
ENABLE_SAVE_MESSAGE_TO_FILE = True
PUBLISH_MESSAGE_RETRY_TIMEOUT = 60 * 1000.0
PUBLISH_MESSAGE_RETRY_COUNT = 3
@@ -32,12 +27,11 @@ class FedMLMessageCenter(object):
MESSAGE_SENT_SUCCESS_RECORDS_FILE = "message-sent-success-records.log"
MESSAGE_RECEIVED_RECORDS_FILE = "message-received-records.log"
- def __init__(self, agent_config=None, sender_message_queue=None,
- listener_message_queue=None, sender_message_event=None):
+ def __init__(self, agent_config=None, sender_message_queue=None, listener_message_queue=None):
self.sender_agent_config = agent_config
self.listener_agent_config = agent_config
self.sender_message_queue = sender_message_queue
- self.message_event = sender_message_event
+ self.message_event = None
self.message_center_process = None
self.sender_mqtt_mgr = None
self.sender_mlops_metrics = None
@@ -136,33 +130,21 @@ def release_sender_mqtt_mgr(self):
def get_sender_message_queue(self):
return self.sender_message_queue
- def get_sender_message_event(self):
- return self.message_event
-
def start_sender(self, message_center_name=None):
- self.sender_message_queue = multiprocessing.Manager().Queue()
+ self.sender_message_queue = Queue()
self.message_event = multiprocessing.Event()
self.message_event.clear()
- process_name = GeneralConstants.get_message_center_sender_process_name(message_center_name)
message_center = FedMLMessageCenter(agent_config=self.sender_agent_config,
sender_message_queue=self.sender_message_queue)
- if platform.system() == "Windows":
- self.message_center_process = multiprocessing.Process(
- target=message_center.run_sender, args=(
- self.message_event, self.sender_message_queue,
- message_center_name, process_name
- )
- )
- else:
- self.message_center_process = fedml.get_process(
- target=message_center.run_sender, args=(
- self.message_event, self.sender_message_queue,
- message_center_name, process_name
- )
+ self.message_center_process = Process(
+ target=message_center.run_sender, args=(
+ self.message_event, self.sender_message_queue,
+ message_center_name
)
+ )
self.message_center_process.start()
- def stop_message_center(self):
+ def stop(self):
if self.message_event is not None:
self.message_event.set()
@@ -174,10 +156,6 @@ def check_message_stop_event(self):
logging.info("Received message center stopping event.")
raise MessageCenterStoppedException("Message center stopped (for sender)")
- if self.listener_message_event is not None and self.listener_message_event.is_set():
- logging.info("Received message center stopping event.")
- raise MessageCenterStoppedException("Message center stopped (for listener)")
-
def send_message(self, topic, payload, run_id=None):
message_entity = FedMLMessageEntity(topic=topic, payload=payload, run_id=run_id)
self.sender_message_queue.put(message_entity.get_message_body())
@@ -215,13 +193,7 @@ def retry_sending_undelivered_message(self):
# Save the message
self.save_message_record(message_entity.run_id, message_entity.device_id, sent_message_record)
- def run_sender(self, message_event, message_queue, message_center_name, process_name=None):
- if process_name is not None:
- setproctitle.setproctitle(process_name)
-
- if platform.system() != "Windows":
- os.setsid()
-
+ def run_sender(self, message_event, message_queue, message_center_name):
self.message_event = message_event
self.sender_message_queue = message_queue
self.message_center_name = message_center_name
@@ -276,16 +248,10 @@ def run_sender(self, message_event, message_queue, message_center_name, process_
self.release_sender_mqtt_mgr()
- def get_protocol_communication_manager(self):
- return None
-
def setup_listener_mqtt_mgr(self):
if self.listener_mqtt_mgr is not None:
return
- # self.listener_mqtt_mgr = self.get_protocol_communication_manager()
- # return
-
self.listener_mqtt_mgr = MqttManager(
self.listener_agent_config["mqtt_config"]["BROKER_HOST"],
self.listener_agent_config["mqtt_config"]["BROKER_PORT"],
@@ -298,11 +264,7 @@ def setup_listener_mqtt_mgr(self):
self.listener_mqtt_mgr.connect()
self.listener_mqtt_mgr.loop_start()
- def get_listener_communication_manager(self):
- return self.listener_mqtt_mgr
-
def release_listener_mqtt_mgr(self):
- #return
try:
if self.listener_mqtt_mgr is not None:
self.listener_mqtt_mgr.loop_stop()
@@ -325,9 +287,6 @@ def remove_message_listener(self, topic):
self.listener_topics.remove(topic)
self.listener_handler_funcs.pop(topic)
- def get_listener_handler(self, topic):
- return self.listener_handler_funcs.get(topic)
-
def get_message_runner(self):
return None
@@ -335,42 +294,29 @@ def get_listener_message_queue(self):
return self.listener_message_queue
def setup_listener_message_queue(self):
- self.listener_message_queue = multiprocessing.Manager().Queue()
+ self.listener_message_queue = Queue()
- def start_listener(
- self, sender_message_queue=None, listener_message_queue=None,
- sender_message_event=None, agent_config=None, message_center_name=None, extra_queues=None
- ):
+ def start_listener(self, sender_message_queue=None, listener_message_queue=None, agent_config=None, message_center_name=None):
if self.listener_message_center_process is not None:
return
if listener_message_queue is None:
if self.listener_message_queue is None:
- self.listener_message_queue = multiprocessing.Manager().Queue()
+ self.listener_message_queue = Queue()
else:
self.listener_message_queue = listener_message_queue
self.listener_message_event = multiprocessing.Event()
self.listener_message_event.clear()
self.listener_agent_config = agent_config
- message_runner = self
+ message_runner = self.get_message_runner()
message_runner.listener_agent_config = agent_config
- process_name = GeneralConstants.get_message_center_listener_process_name(message_center_name)
- if platform.system() == "Windows":
- self.listener_message_center_process = multiprocessing.Process(
- target=message_runner.run_listener_dispatcher, args=(
- self.listener_message_event, self.listener_message_queue,
- self.listener_handler_funcs, sender_message_queue,
- sender_message_event, message_center_name, extra_queues, process_name
- )
- )
- else:
- self.listener_message_center_process = fedml.get_process(
- target=message_runner.run_listener_dispatcher, args=(
- self.listener_message_event, self.listener_message_queue,
- self.listener_handler_funcs, sender_message_queue,
- sender_message_event, message_center_name, extra_queues, process_name
- )
+ self.listener_message_center_process = Process(
+ target=message_runner.run_listener_dispatcher, args=(
+ self.listener_message_event, self.listener_message_queue,
+ self.listener_handler_funcs, sender_message_queue,
+ message_center_name
)
+ )
self.listener_message_center_process.start()
def check_listener_message_stop_event(self):
@@ -403,22 +349,13 @@ def unsubscribe_msg(self, topic):
self.listener_mqtt_mgr.unsubscribe_msg(topic)
def run_listener_dispatcher(
- self, listener_message_event, listener_message_queue,
- listener_funcs, sender_message_queue, sender_message_event,
- message_center_name, extra_queues, process_name=None
+ self, message_event, message_queue, listener_funcs, sender_message_queue,
+ message_center_name
):
- if process_name is not None:
- setproctitle.setproctitle(process_name)
-
- if platform.system() != "Windows":
- os.setsid()
-
- self.listener_message_event = listener_message_event
- self.listener_message_queue = listener_message_queue
+ self.listener_message_event = message_event
+ self.listener_message_queue = message_queue
self.listener_handler_funcs = listener_funcs
self.message_center_name = message_center_name
- self.sender_message_queue = sender_message_queue
- self.message_event = sender_message_event
self.setup_listener_mqtt_mgr()
@@ -427,9 +364,6 @@ def run_listener_dispatcher(
else:
methodcaller(FedMLMessageCenter.FUNC_REBUILD_MESSAGE_CENTER, sender_message_queue)(self)
- if extra_queues is not None:
- methodcaller(FedMLMessageCenter.FUNC_PROCESS_EXTRA_QUEUES, extra_queues)(self)
-
while True:
message_entity = None
try:
@@ -444,7 +378,7 @@ def run_listener_dispatcher(
# Get the message from the queue
try:
- message_body = listener_message_queue.get(block=False, timeout=0.1)
+ message_body = message_queue.get(block=False, timeout=0.1)
except queue.Empty as e: # If queue is empty, then break loop
message_body = None
if message_body is None:
@@ -468,11 +402,6 @@ def run_listener_dispatcher(
message_handler_func_name = self.listener_handler_funcs.get(message_entity.topic, None)
if message_handler_func_name is not None:
methodcaller(message_handler_func_name, message_entity.topic, message_entity.payload)(self)
- else:
- if hasattr(self, "callback_proxy_unknown_messages") and \
- self.callback_proxy_unknown_messages is not None:
- self.callback_proxy_unknown_messages(
- message_entity.run_id, message_entity.topic, message_entity.payload)
except Exception as e:
if message_entity is not None:
logging.info(
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 7175032375..6e0010f556 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -9,8 +9,6 @@
import traceback
import zipfile
import queue
-
-import fedml
from ..comm_utils.constants import SchedulerConstants
from ..comm_utils.job_utils import JobRunnerUtils, DockerArgs
from ..scheduler_entry.constants import Constants
@@ -75,7 +73,6 @@ def __init__(self, args, edge_id=0, request_json=None, agent_config=None, run_id
self.user_name = None
self.general_edge_id = None
self.message_center = None
- self.status_center = None
self.FEDML_DYNAMIC_CONSTRAIN_VARIABLES = {
"${FEDSYS.RUN_ID}": "",
"${FEDSYS.PRIVATE_LOCAL_DATA}": "",
@@ -211,15 +208,9 @@ def retrieve_and_unzip_package(self, package_name, package_url):
# Open a process to download the package so that we can avoid the request is blocked and check the timeout.
from multiprocessing import Process
completed_event = multiprocessing.Event()
- info_queue = multiprocessing.Manager().Queue()
- if platform.system() == "Windows":
- download_process = multiprocessing.Process(
- target=self.download_package_proc,
- args=(package_url, local_package_file, completed_event, info_queue))
- else:
- download_process = fedml.get_process(
- target=self.download_package_proc,
- args=(package_url, local_package_file, completed_event, info_queue))
+ info_queue = multiprocessing.Queue()
+ download_process = Process(target=self.download_package_proc,
+ args=(package_url, local_package_file, completed_event, info_queue))
download_process.start()
allowed_block_download_time = 60
download_finished = False
@@ -615,8 +606,7 @@ def job_error_processor(self, error_list):
def start_runner_process(
self, run_id, edge_id, request_json, cuda_visible_gpu_ids_str=None,
- sender_message_queue=None, listener_message_queue=None,
- status_center_queue=None, process_name=None
+ sender_message_queue=None, status_center_queue=None
):
return None
@@ -650,8 +640,8 @@ def rebuild_message_status_center(self, sender_message_queue, listener_message_q
self.mlops_metrics.set_messenger(self.message_center)
self.mlops_metrics.run_id = self.run_id
- self.status_center = FedMLStatusCenter.rebuild_status_center_from_queue(status_queue)
+ status_center = FedMLStatusCenter.rebuild_status_center_from_queue(status_queue)
if self.status_reporter is None:
self.status_reporter = MLOpsMetrics()
- self.status_reporter.set_messenger(self.status_center)
+ self.status_reporter.set_messenger(status_center)
self.status_reporter.run_id = self.run_id
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
index ad32f78631..dcc4045699 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
@@ -20,7 +20,7 @@ def start_job_runner(
self, run_id, request_json, args=None, edge_id=None, is_server_job=False,
sender_message_queue=None, listener_message_queue=None, status_center_queue=None,
should_start_cloud_server=False, use_local_process_as_cloud_server=False,
- cuda_visible_gpu_ids_str=None, process_name=None
+ cuda_visible_gpu_ids_str=None
):
run_id_str = str(run_id)
self.job_runners[run_id_str] = self._generate_job_runner_instance(
@@ -29,11 +29,9 @@ def start_job_runner(
)
self.job_runners[run_id_str].start_runner_process(
run_id, request_json, edge_id=edge_id,
- cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str,
sender_message_queue=sender_message_queue,
listener_message_queue=listener_message_queue,
- status_center_queue=status_center_queue,
- process_name=process_name
+ status_center_queue=status_center_queue
)
def stop_job_runner(self, run_id):
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
index 9970b1d3f6..19bb7e9882 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_protocol_manager.py
@@ -2,13 +2,11 @@
import json
import logging
import multiprocessing
-import os
import sys
import time
import traceback
import uuid
import fedml
-from ..comm_utils.run_process_utils import RunProcessUtils
from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
from ....core.distributed.communication.mqtt.mqtt_manager import MqttManager
from ....core.mlops.mlops_metrics import MLOpsMetrics
@@ -47,7 +45,6 @@ def __init__(self, args, agent_config=None, is_master=False):
self.mlops_metrics = None
self.status_reporter = None
self.user_name = args.user_name
- self.parent_agent = None
fedml._init_multiprocessing()
@@ -61,54 +58,38 @@ def add_protocol_handler(self):
# self.add_message_listener(self.topic_start_train, self.callback_start_train)
pass
- def initialize(
- self, communication_manager=None, sender_message_queue=None,
- status_center_queue=None, sender_message_event=None
- ):
+ def initialize(self):
# Generate the message topics
self.generate_topics()
# Setup MQTT connection
- if communication_manager is None:
- self.communication_mgr = MqttManager(
- self.agent_config["mqtt_config"]["BROKER_HOST"],
- self.agent_config["mqtt_config"]["BROKER_PORT"],
- self.agent_config["mqtt_config"]["MQTT_USER"],
- self.agent_config["mqtt_config"]["MQTT_PWD"],
- self.agent_config["mqtt_config"]["MQTT_KEEPALIVE"],
- f"FedML_Agent_Daemon_@{self.user_name}@_@{self.current_device_id}@_@{str(uuid.uuid4())}@",
- self.topic_last_will,
- json.dumps({"ID": self.edge_id, "status": GeneralConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE})
- )
- else:
- self.communication_mgr = communication_manager
+ self.communication_mgr = MqttManager(
+ self.agent_config["mqtt_config"]["BROKER_HOST"],
+ self.agent_config["mqtt_config"]["BROKER_PORT"],
+ self.agent_config["mqtt_config"]["MQTT_USER"],
+ self.agent_config["mqtt_config"]["MQTT_PWD"],
+ self.agent_config["mqtt_config"]["MQTT_KEEPALIVE"],
+ f"FedML_Agent_Daemon_@{self.user_name}@_@{self.current_device_id}@_@{str(uuid.uuid4())}@",
+ self.topic_last_will,
+ json.dumps({"ID": self.edge_id, "status": GeneralConstants.MSG_MLOPS_SERVER_STATUS_OFFLINE})
+ )
# Add the message listeners for all topics
self.add_protocol_handler()
# Start the message center to process edge related messages.
- if sender_message_queue is None:
- self.setup_message_center()
- sender_message_event = self.message_center.get_sender_message_event()
- else:
- self.rebuild_message_center(sender_message_queue)
+ self.setup_message_center()
# Setup the message listener queue
self.setup_listener_message_queue()
# Start the status center to process edge related status.
- if status_center_queue is None:
- self.start_status_listener_center(sender_message_event=sender_message_event)
- else:
- self.set_status_queue(status_center_queue)
- self.rebuild_status_center(status_center_queue)
+ self.start_status_listener_center()
# Start the message center for listener
self.start_listener(sender_message_queue=self.message_center.get_sender_message_queue(),
- sender_message_event=sender_message_event,
agent_config=self.agent_config,
- message_center_name=self.message_center_name,
- extra_queues=[self.get_status_queue()])
+ message_center_name=self.message_center_name)
# Init extra items, e.g. database, recovery, etc.
self._init_extra_items()
@@ -116,11 +97,11 @@ def initialize(
# Setup MQTT connected listener
self.communication_mgr.add_connected_listener(self.on_agent_communication_connected)
self.communication_mgr.add_disconnected_listener(self.on_agent_communication_disconnected)
+ self.communication_mgr.connect()
def start(self):
# Start MQTT message loop
try:
- self.communication_mgr.connect()
self.communication_mgr.loop_forever()
except Exception as e:
if str(e) == "Restarting after upgraded...":
@@ -129,8 +110,6 @@ def start(self):
logging.info("Server tracing: {}".format(traceback.format_exc()))
finally:
- logging.info(f"Protocol manager is about to exit, pid: {os.getpid()}")
-
FedMLAccountManager.write_login_failed_file(is_client=not self.is_master_agent)
self.stop()
@@ -141,7 +120,7 @@ def start(self):
clean_process_group=False)
sys.exit(1)
- def stop(self, kill_process=False):
+ def stop(self):
if self.communication_mgr is not None:
# noinspection PyBroadException
try:
@@ -153,10 +132,7 @@ def stop(self, kill_process=False):
self.communication_mgr.loop_stop()
self.communication_mgr.disconnect()
- if kill_process:
- self.post_status_center_stopping_message()
- self.release_message_center()
- RunProcessUtils.kill_process(os.getppid(), exclude_current_pid=True)
+ self.release_message_center()
@abstractmethod
def _init_extra_items(self):
@@ -220,37 +196,20 @@ def rebuild_message_center(self, message_center_queue):
def release_message_center(self):
try:
- self.stop_message_center()
-
if self.message_center is not None:
- self.message_center.stop_message_center()
+ self.message_center.stop()
self.message_center = None
except Exception as e:
logging.error(
- f"Failed to release the message center with Exception {e}. "
- f"Traceback: {traceback.format_exc()}")
- pass
-
- def release_status_center(self):
- try:
- self.stop_status_center()
-
- if self.status_center is not None:
- self.status_center.stop_status_center()
- self.status_center = None
-
- except Exception as e:
- logging.error(
- f"Failed to release the status center with Exception {e}. "
+ f"Failed to release slave communication manager with Exception {e}. "
f"Traceback: {traceback.format_exc()}")
pass
- def start_status_listener_center(self, sender_message_event=None):
+ def start_status_listener_center(self):
self.start_status_center(
sender_message_center_queue=self.message_center.get_sender_message_queue(),
listener_message_center_queue=self.get_listener_message_queue(),
- sender_message_event=sender_message_event,
is_slave_agent=not self.is_master_agent
)
@@ -272,9 +231,6 @@ def rebuild_status_center(self, status_center_queue):
self.status_reporter.edge_id = self.edge_id
self.status_reporter.server_agent_id = self.server_agent_id
- def process_extra_queues(self, extra_queues):
- pass
-
def generate_status_report(self, run_id, edge_id, server_agent_id=None):
status_reporter = MLOpsMetrics()
status_reporter.set_messenger(self, send_message_func=self.send_status_message)
@@ -310,29 +266,6 @@ def get_status_runner(self):
return None
- def get_protocol_communication_manager(self):
- return self.communication_mgr
-
- def get_protocol_sender_message_queue(self):
- return self.message_center.get_sender_message_queue()
-
- def get_protocol_sender_message_event(self):
- return self.message_center.get_sender_message_event()
-
- def get_protocol_status_center_queue(self):
- return self.get_status_queue()
-
- def get_subscribed_topics(self):
- return self.subscribed_topics
-
def send_agent_active_msg(self, edge_id):
active_msg = {"ID": edge_id, "status": GeneralConstants.MSG_MLOPS_SERVER_STATUS_IDLE}
self.message_center.send_message_json(self.topic_active, json.dumps(active_msg))
-
- def post_status_center_stopping_message(self, run_id=None):
- topic_status_center_stopping = GeneralConstants.FEDML_TOPIC_STATUS_CENTER_STOP
- payload = {"run_id": run_id}
- self.status_reporter.send_message(topic_status_center_stopping, json.dumps(payload))
-
- def set_parent_agent(self, parent_agent):
- self.parent_agent = parent_agent
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_center.py b/python/fedml/computing/scheduler/scheduler_core/status_center.py
index b1462d7ea9..97c2115e76 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_center.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_center.py
@@ -1,16 +1,10 @@
import logging
-import os
-import platform
import time
from enum import Enum, unique
import multiprocessing
+from multiprocessing import Process, Queue
import queue
-
-import setproctitle
-
-import fedml
-from .general_constants import GeneralConstants
from .message_common import FedMLMessageEntity, FedMLStatusEntity
from .message_center import FedMLMessageCenter
import traceback
@@ -87,7 +81,6 @@ class FedMLStatusCenter(object):
TOPIC_SLAVE_JOB_LAUNCH_SUFFIX = "/start_train"
TOPIC_SLAVE_JOB_STOP_PREFIX = "flserver_agent/"
TOPIC_SLAVE_JOB_STOP_SUFFIX = "/stop_train"
- TOPIC_STATUS_CENTER_STOP_PREFIX = GeneralConstants.FEDML_TOPIC_STATUS_CENTER_STOP
ALLOWED_MAX_JOB_STATUS_CACHE_NUM = 1000
def __init__(self, message_queue=None):
@@ -112,42 +105,25 @@ def get_status_runner(self):
return None
def start_status_center(self, sender_message_center_queue=None,
- listener_message_center_queue=None,
- sender_message_event=None,
- is_slave_agent=False):
- self.status_queue = multiprocessing.Manager().Queue()
+ listener_message_center_queue=None, is_slave_agent=False):
+ self.status_queue = Queue()
self.status_event = multiprocessing.Event()
self.status_event.clear()
self.status_sender_message_center_queue = sender_message_center_queue
self.status_listener_message_center_queue = listener_message_center_queue
- self.status_runner = self
- process_name = GeneralConstants.get_status_center_process_name(
- f'{"deploy" if self.is_deployment_status_center else "launch"}_'
- f'{"slave" if is_slave_agent else "master"}_agent')
+ self.status_runner = self.get_status_runner()
target_func = self.status_runner.run_status_dispatcher if not is_slave_agent else \
self.status_runner.run_status_dispatcher_in_slave
- if platform.system() == "Windows":
- self.status_center_process = multiprocessing.Process(
- target=target_func, args=(
- self.status_event, self.status_queue, self.status_sender_message_center_queue,
- self.status_listener_message_center_queue, sender_message_event, process_name
- )
- )
- else:
- self.status_center_process = fedml.get_process(
- target=target_func, args=(
- self.status_event, self.status_queue, self.status_sender_message_center_queue,
- self.status_listener_message_center_queue, sender_message_event, process_name
- )
+ self.status_center_process = Process(
+ target=target_func, args=(
+ self.status_event, self.status_queue, self.status_sender_message_center_queue,
+ self.status_listener_message_center_queue
)
+ )
self.status_center_process.start()
- def stop_status_center(self):
- if self.status_event is not None:
- self.status_event.set()
-
- def check_status_stop_event(self):
+ def check_message_stop_event(self):
if self.status_event is not None and self.status_event.is_set():
logging.info("Received status center stopping event.")
raise StatusCenterStoppedException("Status center stopped (for sender)")
@@ -166,9 +142,6 @@ def send_status_message(self, topic, payload):
def get_status_queue(self):
return self.status_queue
- def set_status_queue(self, status_queue):
- self.status_queue = status_queue
-
def status_center_process_master_status(self, topic, payload):
pass
@@ -183,14 +156,7 @@ def rebuild_status_center(self, status_queue):
def run_status_dispatcher(self, status_event, status_queue,
sender_message_center_queue,
- listener_message_center_queue,
- sender_message_event, process_name=None):
- if process_name is not None:
- setproctitle.setproctitle(process_name)
-
- if platform.system() != "Windows":
- os.setsid()
-
+ listener_message_center_queue):
# Save the parameters
self.status_event = status_event
self.status_queue = status_queue
@@ -203,11 +169,10 @@ def run_status_dispatcher(self, status_event, status_queue,
self.rebuild_message_center(sender_message_center_queue)
message_center = FedMLMessageCenter(
sender_message_queue=sender_message_center_queue,
- listener_message_queue=listener_message_center_queue,
- sender_message_event=sender_message_event
+ listener_message_queue=listener_message_center_queue
)
- if status_queue is not None:
+ if sender_message_center_queue is not None:
self.rebuild_status_center(status_queue)
# Init status manager instances
@@ -218,7 +183,7 @@ def run_status_dispatcher(self, status_event, status_queue,
# Check if we should stop status dispatcher
try:
- self.check_status_stop_event()
+ self.check_message_stop_event()
except StatusCenterStoppedException as e:
break
@@ -238,12 +203,6 @@ def run_status_dispatcher(self, status_event, status_queue,
message_entity = FedMLMessageEntity(message_body=message_body)
status_entity = FedMLStatusEntity(status_msg_body=message_body)
- if message_entity.topic.startswith(FedMLStatusCenter.TOPIC_STATUS_CENTER_STOP_PREFIX):
- # Process the stop message for message center and status center
- message_center.stop_message_center()
- self.stop_status_center()
- continue
-
# Generate status manager instance
run_id_str = str(status_entity.run_id)
run_id_int = int(status_entity.run_id)
@@ -293,14 +252,7 @@ def run_status_dispatcher(self, status_event, status_queue,
def run_status_dispatcher_in_slave(self, status_event, status_queue,
sender_message_center_queue,
- listener_message_center_queue,
- sender_message_event, process_name=None):
- if process_name is not None:
- setproctitle.setproctitle(process_name)
-
- if platform.system() != "Windows":
- os.setsid()
-
+ listener_message_center_queue):
# Save the parameters
self.status_event = status_event
self.status_queue = status_queue
@@ -313,11 +265,10 @@ def run_status_dispatcher_in_slave(self, status_event, status_queue,
self.rebuild_message_center(sender_message_center_queue)
message_center = FedMLMessageCenter(
sender_message_queue=sender_message_center_queue,
- listener_message_queue=listener_message_center_queue,
- sender_message_event=sender_message_event
+ listener_message_queue=listener_message_center_queue
)
- if status_queue is not None:
+ if sender_message_center_queue is not None:
self.rebuild_status_center(status_queue)
# Init status manager instances
@@ -329,7 +280,7 @@ def run_status_dispatcher_in_slave(self, status_event, status_queue,
# Check if we should stop status dispatcher
try:
- self.check_status_stop_event()
+ self.check_message_stop_event()
except StatusCenterStoppedException as e:
break
diff --git a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
index ec98cc7906..e045458db5 100755
--- a/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
+++ b/python/fedml/computing/scheduler/scheduler_core/status_manager_protocols.py
@@ -136,14 +136,12 @@ def process_job_completed_status(self, master_id, status):
# self.stop_cloud_server()
# self.remove_listener_for_run_metrics(self.run_id)
# self.remove_listener_for_run_logs(self.run_id)
-
self.message_center.receive_message(
GeneralConstants.get_topic_complete_job(master_id),
json.dumps(GeneralConstants.get_payload_complete_job(self.run_id, master_id)))
- if self.status_center.is_deployment_status_center:
- if status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
- self.report_deployment_status(self.run_id, GeneralConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
+ if self.status_center.is_deployment_status_center and status == ServerConstants.MSG_MLOPS_SERVER_STATUS_FAILED:
+ self.report_deployment_status(self.run_id, GeneralConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED)
def process_job_exception_status(self, master_id, status):
# Report exception job status
@@ -189,17 +187,16 @@ def process_job_status_consensus(self, run_id, master_id, status):
status = self.get_entire_job_status()
# Set the device status based on the job status
- if self.edge_status_dict is not None:
- for edge_id_item, edge_status_item in self.edge_status_dict.items():
- if edge_id_item == "server":
- continue
-
- # Calc the device status based on the job status
- consensus_device_status = FedMLStatusManager.get_device_consensus_status_in_job(
- status, edge_status_item)
- if consensus_device_status is not None:
- self.message_reporter.report_client_training_status(
- edge_id_item, consensus_device_status, run_id=run_id, update_db=False)
+ for edge_id_item, edge_status_item in self.edge_status_dict.items():
+ if edge_id_item == "server":
+ continue
+
+ # Calc the device status based on the job status
+ consensus_device_status = FedMLStatusManager.get_device_consensus_status_in_job(
+ status, edge_status_item)
+ if consensus_device_status is not None:
+ self.message_reporter.report_client_training_status(
+ edge_id_item, consensus_device_status, run_id=run_id, update_db=False)
# Save the job status to local storage
FedMLServerDataInterface.get_instance().save_job_status(run_id, master_id, status, status)
diff --git a/python/fedml/computing/scheduler/slave/base_slave_agent.py b/python/fedml/computing/scheduler/slave/base_slave_agent.py
index 9876ac9912..01c0a39195 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_agent.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_agent.py
@@ -24,9 +24,7 @@ def __init__(self):
def login(
self, userid, api_key=None, device_id=None,
- os_name=None, need_to_check_gpu=False, role=None,
- communication_manager=None, sender_message_queue=None,
- status_center_queue=None, sender_message_event=None
+ os_name=None, need_to_check_gpu=False, role=None
):
# Preprocess the login args
if need_to_check_gpu:
@@ -35,7 +33,7 @@ def login(
print("We can't find any gpu device on your machine. \n"
"With the gpu_supplier(-g) option, you need to check if your machine "
"has nvidia GPUs and installs CUDA related drivers.")
- return None
+ return
# Login account
login_result = FedMLAccountManager.get_instance().login(
@@ -59,22 +57,17 @@ def login(
# Initialize the protocol manager
# noinspection PyBoardException
try:
- self._initialize_protocol_manager(
- communication_manager=communication_manager,
- sender_message_queue=sender_message_queue,
- status_center_queue=status_center_queue,
- sender_message_event=sender_message_event)
+ self._initialize_protocol_manager()
except Exception as e:
FedMLAccountManager.write_login_failed_file(is_client=True)
self.protocol_mgr.stop()
raise e
- return login_result
-
- def start(self):
# Start the protocol manager to process the messages from MLOps and slave agents.
self.protocol_mgr.start()
+ return login_result
+
@staticmethod
def logout():
GeneralConstants.cleanup_run_process(None)
@@ -91,20 +84,12 @@ def _create_protocol_manager(self, login_result):
self.protocol_mgr.user_name = login_result.user_name
self.protocol_mgr.agent_config = login_result.agent_config
- def _initialize_protocol_manager(
- self, communication_manager=None, sender_message_queue=None,
- status_center_queue=None, sender_message_event=None
- ):
+ def _initialize_protocol_manager(self):
# Init local database
self._init_database()
# Initialize the master protocol
- self.protocol_mgr.set_parent_agent(self)
- self.protocol_mgr.initialize(
- communication_manager=communication_manager,
- sender_message_queue=sender_message_queue,
- status_center_queue=status_center_queue,
- sender_message_event=sender_message_event)
+ self.protocol_mgr.initialize()
# Start the client API process
self._start_slave_api()
@@ -137,9 +122,6 @@ def _start_slave_api(self):
should_capture_stderr=False
)
- def get_protocol_manager(self):
- return self.protocol_mgr
-
@abstractmethod
def _get_log_file_dir(self):
pass
@@ -155,8 +137,3 @@ def _init_database(self):
@abstractmethod
def _generate_protocol_manager_instance(self, args, agent_config=None):
return None
-
- def save_deploy_ids(self, deploy_master_edge_id=None, deploy_slave_edge_id=None):
- self.protocol_mgr.save_deploy_ids(
- deploy_master_edge_id=deploy_master_edge_id, deploy_slave_edge_id=deploy_slave_edge_id)
-
diff --git a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
index 0486b131a6..5e530dbba7 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
@@ -7,9 +7,6 @@
import traceback
from abc import ABC, abstractmethod
-import setproctitle
-
-import fedml
from ....core.mlops.mlops_runtime_log import MLOpsRuntimeLog
from ....core.mlops.mlops_runtime_log_daemon import MLOpsRuntimeLogDaemon
from .client_data_interface import FedMLClientDataInterface
@@ -50,12 +47,8 @@ def __repr__(self):
)
def run(self, process_event, completed_event, run_extend_queue_list,
- sender_message_center, listener_message_queue, status_center_queue,
- process_name=None):
- if process_name is not None:
- setproctitle.setproctitle(process_name)
-
- print(f"Client runner process id {os.getpid()}, name {process_name}, run id {self.run_id}")
+ sender_message_center, listener_message_queue, status_center_queue):
+ print(f"Client runner process id {os.getpid()}, run id {self.run_id}")
if platform.system() != "Windows":
os.setsid()
@@ -251,7 +244,7 @@ def reset_devices_status(self, edge_id, status):
def start_runner_process(
self, run_id, request_json, edge_id=None,
sender_message_queue=None, listener_message_queue=None,
- status_center_queue=None, cuda_visible_gpu_ids_str=None, process_name=None
+ status_center_queue=None, cuda_visible_gpu_ids_str=None
):
client_runner = self._generate_job_runner_instance(
self.args, run_id=run_id, request_json=request_json,
@@ -266,17 +259,9 @@ def start_runner_process(
client_runner.server_id = request_json.get("server_id", "0")
self.run_extend_queue_list = self._generate_extend_queue_list()
logging.info("start the runner process.")
-
- if platform.system() == "Windows":
- self.run_process = multiprocessing.Process(
- target=client_runner.run, args=(
- self.run_process_event, self.run_process_completed_event, self.run_extend_queue_list,
- sender_message_queue, listener_message_queue, status_center_queue, process_name
- ))
- else:
- self.run_process = fedml.get_process(target=client_runner.run, args=(
- self.run_process_event, self.run_process_completed_event, self.run_extend_queue_list,
- sender_message_queue, listener_message_queue, status_center_queue, process_name
- ))
+ self.run_process = Process(target=client_runner.run, args=(
+ self.run_process_event, self.run_process_completed_event, self.run_extend_queue_list,
+ sender_message_queue, listener_message_queue, status_center_queue
+ ))
self.run_process.start()
return self.run_process
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index 534ee2f7d0..447bd05cd9 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -62,6 +62,8 @@ def __init__(self, args, agent_config=None):
self.server_id = args.server_id
self.model_device_server_id = None
self.model_device_client_edge_id_list = None
+ self.model_device_server = None
+ self.model_device_client_list = None
@abstractmethod
def generate_topics(self):
@@ -145,9 +147,15 @@ def add_subscribe_topic(self, topic):
self.subscribed_topics.append(topic)
def stop(self):
- if self.model_device_client_edge_id_list is not None:
- self.model_device_client_edge_id_list.clear()
- self.model_device_client_edge_id_list = None
+ if self.model_device_server is not None:
+ self.model_device_server.stop()
+ self.model_device_server = None
+
+ if self.model_device_client_list is not None:
+ for model_client in self.model_device_client_list:
+ model_client.stop()
+ self.model_device_client_list.clear()
+ self.model_device_client_list = None
super().stop()
@@ -257,8 +265,6 @@ def callback_start_train(self, topic, payload):
# Report the run status with finished status and return
self.generate_status_report(run_id, edge_id, server_agent_id=server_agent_id).report_client_id_status(
edge_id, GeneralConstants.MSG_MLOPS_CLIENT_STATUS_FINISHED, run_id=run_id)
-
- MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, edge_id)
return
logging.info(
f"Run started, available gpu ids: {JobRunnerUtils.get_instance().get_available_gpu_id_list(edge_id)}")
@@ -276,7 +282,6 @@ def callback_start_train(self, topic, payload):
listener_message_queue=self.get_listener_message_queue(),
status_center_queue=self.get_status_queue(),
cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str,
- process_name=GeneralConstants.get_launch_slave_job_process_name(run_id, edge_id)
)
run_process = self._get_job_runner_manager().get_runner_process(run_id)
if run_process is not None:
diff --git a/python/fedml/computing/scheduler/slave/client_data_interface.py b/python/fedml/computing/scheduler/slave/client_data_interface.py
index 74bf7a64a3..0e9e84381a 100755
--- a/python/fedml/computing/scheduler/slave/client_data_interface.py
+++ b/python/fedml/computing/scheduler/slave/client_data_interface.py
@@ -343,15 +343,6 @@ def handle_database_compatibility(self):
self.close_job_db()
- def check_if_table_exist(self, current_db_cursor):
- results = current_db_cursor.execute("select * from sqlite_master where type='table' and name='jobs';")
- if results is None:
- return False
- result_len = 0
- for row in results:
- result_len += 1
- return False if result_len == 0 else True
-
def get_agent_status(self, edge_id=0):
self.open_job_db()
enabled = 1
diff --git a/python/fedml/computing/scheduler/slave/client_login.py b/python/fedml/computing/scheduler/slave/client_login.py
index 7a1c759410..95c772a225 100755
--- a/python/fedml/computing/scheduler/slave/client_login.py
+++ b/python/fedml/computing/scheduler/slave/client_login.py
@@ -1,11 +1,11 @@
import argparse
import os
import fedml
-from fedml.computing.scheduler.slave.united_agents import FedMLUnitedAgent
+from fedml.computing.scheduler.slave.slave_agent import FedMLLaunchSlaveAgent
def logout():
- FedMLUnitedAgent.get_instance().logout()
+ FedMLLaunchSlaveAgent.logout()
if __name__ == "__main__":
@@ -18,7 +18,6 @@ def logout():
parser.add_argument("--version", "-v", type=str, default="release")
parser.add_argument("--local_server", "-ls", type=str, default="127.0.0.1")
parser.add_argument("--role", "-r", type=str, default="client")
- parser.add_argument("--runner_cmd", "-rc", type=str, default="{}")
parser.add_argument("--device_id", "-id", type=str, default="0")
parser.add_argument("--os_name", "-os", type=str, default="")
parser.add_argument("--api_key", "-k", type=str, default="")
@@ -37,10 +36,9 @@ def logout():
fedml.set_local_on_premise_platform_port(args.local_on_premise_platform_port)
fedml.set_env_version(args.version)
- united_agents = FedMLUnitedAgent.get_instance()
+ slave_agent = FedMLLaunchSlaveAgent()
if args.type == 'login':
- united_agents.login(
- args.api_key, api_key=args.api_key, device_id=args.device_id,
- os_name=args.os_name, role=args.role, runner_cmd=args.runner_cmd)
+ slave_agent.login(args.api_key, api_key=args.api_key, device_id=args.device_id,
+ os_name=args.os_name, role=args.role)
else:
- united_agents.logout()
+ FedMLLaunchSlaveAgent.logout()
diff --git a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
index 449cd7c29c..a1067a0d96 100755
--- a/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/slave_protocol_manager.py
@@ -1,15 +1,18 @@
-
+import copy
+import json
import os
+import fedml
from ..comm_utils.job_cleanup import JobCleanup
from .base_slave_protocol_manager import FedMLBaseSlaveProtocolManager
from .launch_job_runner_manager import FedMLLaunchJobRunnerManager
+from ..model_scheduler.model_device_server import FedMLModelDeviceServerRunner
+from ..model_scheduler.model_device_client import FedMLModelDeviceClientRunner
class FedMLLaunchSlaveProtocolManager(FedMLBaseSlaveProtocolManager):
def __init__(self, args, agent_config=None):
FedMLBaseSlaveProtocolManager.__init__(self, args, agent_config=agent_config)
- self.message_center_name = "launch_slave_agent"
# Override
def generate_topics(self):
@@ -31,8 +34,7 @@ def _get_job_runner_manager(self):
def _process_connection_ready(self):
from fedml.core.mlops import sync_deploy_id
sync_deploy_id(
- self.edge_id, self.model_device_server_id, self.model_device_client_edge_id_list,
- message_center=self.message_center)
+ self.edge_id, self.model_device_server.edge_id, self.model_device_client_edge_id_list)
# Override
def _process_connection_lost(self):
@@ -45,19 +47,59 @@ def _init_extra_items(self):
# Sync the data when startup
JobCleanup.get_instance().sync_data_on_startup(self.args.edge_id)
- # Start the monitor process
- self.mlops_metrics.stop_device_realtime_perf()
- self.mlops_metrics.report_device_realtime_perf(self.args, self.args.agent_config["mqtt_config"])
-
- def save_deploy_ids(self, deploy_master_edge_id=None, deploy_slave_edge_id=None):
- if deploy_master_edge_id is not None:
- self.model_device_server_id = deploy_master_edge_id
-
- if deploy_slave_edge_id is not None:
- if self.model_device_client_edge_id_list is None:
- self.model_device_client_edge_id_list = list()
- self.model_device_client_edge_id_list.append(deploy_slave_edge_id)
+ # Get the environment variables
+ infer_host = os.getenv("FEDML_INFER_HOST", None)
+ infer_redis_addr = os.getenv("FEDML_INFER_REDIS_ADDR", None)
+ infer_redis_port = os.getenv("FEDML_INFER_REDIS_PORT", None)
+ infer_redis_password = os.getenv("FEDML_INFER_REDIS_PASSWORD", None)
+ model_client_num = os.getenv("FEDML_MODEL_WORKER_NUM", None)
+
+ # Start deploy master agent and slave agent
+ in_args = copy.deepcopy(self.args)
+ if self.model_device_client_edge_id_list is None:
+ self.model_device_client_edge_id_list = list()
+ if self.model_device_client_list is None:
+ model_client_num = 1 if model_client_num is None else int(model_client_num)
+ self.model_device_client_list = list()
+ for client_index in range(model_client_num):
+ model_device_client = FedMLModelDeviceClientRunner(
+ in_args, f"{in_args.current_device_id}_{client_index + 1}", in_args.os_name,
+ in_args.is_from_docker, self.agent_config)
+ if infer_host is not None:
+ model_device_client.infer_host = infer_host
+ if infer_redis_addr is not None:
+ model_device_client.redis_addr = infer_redis_addr
+ if infer_redis_port is not None:
+ model_device_client.redis_port = infer_redis_port
+ if infer_redis_password is not None:
+ model_device_client.redis_password = infer_redis_password
+ model_device_client.start()
+ self.model_device_client_list.append(model_device_client)
+ self.model_device_client_edge_id_list.append(model_device_client.get_edge_id())
+
+ self.args = copy.deepcopy(in_args)
+ if self.model_device_server is None:
+ self.model_device_server = FedMLModelDeviceServerRunner(in_args, in_args.current_device_id,
+ in_args.os_name, in_args.is_from_docker,
+ self.agent_config)
+ if infer_host is not None:
+ self.model_device_server.infer_host = infer_host
+ if infer_redis_addr is not None:
+ self.model_device_server.redis_addr = infer_redis_addr
+ if infer_redis_port is not None:
+ self.model_device_server.redis_port = infer_redis_port
+ if infer_redis_password is not None:
+ self.model_device_server.redis_password = infer_redis_password
+
+ self.model_device_server.start()
+ self.model_device_server_id = self.model_device_server.get_edge_id()
# Save the deployed master and worker id list to the environment variable.
os.environ["FEDML_DEPLOY_MASTER_ID"] = str(self.model_device_server_id)
os.environ["FEDML_DEPLOY_WORKER_IDS"] = str(self.model_device_client_edge_id_list)
+
+ # Start the monitor process
+ self.args = copy.deepcopy(in_args)
+ self.mlops_metrics.stop_device_realtime_perf()
+ self.mlops_metrics.report_device_realtime_perf(self.args, self.args.agent_config["mqtt_config"])
+ pass
\ No newline at end of file
diff --git a/python/fedml/computing/scheduler/slave/united_agents.py b/python/fedml/computing/scheduler/slave/united_agents.py
deleted file mode 100755
index 3c8549c06a..0000000000
--- a/python/fedml/computing/scheduler/slave/united_agents.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from fedml.computing.scheduler.model_scheduler.master_agent import FedMLDeployMasterAgent
-from fedml.computing.scheduler.model_scheduler.worker_agent import FedMLDeployWorkerAgent
-from fedml.computing.scheduler.scheduler_core.account_manager import FedMLAccountManager
-from fedml.computing.scheduler.slave.slave_agent import FedMLLaunchSlaveAgent
-from fedml.computing.scheduler.master.master_agent import FedMLLaunchMasterAgent
-from fedml.core.common.singleton import Singleton
-
-
-class FedMLUnitedAgent(Singleton):
-
- @staticmethod
- def get_instance():
- return FedMLUnitedAgent()
-
- def logout(self):
- FedMLLaunchSlaveAgent.logout()
-
- def login(self, userid, api_key=None, device_id=None,
- os_name=None, need_to_check_gpu=False, role=None, runner_cmd=None):
- # Create the launch master/slave and deploy master/slave agents.
- launch_slave_agent = FedMLLaunchSlaveAgent()
- launch_master_agent = FedMLLaunchMasterAgent()
- deploy_slave_agent = FedMLDeployWorkerAgent()
- deploy_master_agent = FedMLDeployMasterAgent()
-
- # Login with the launch slave role
- login_result = launch_slave_agent.login(
- api_key, api_key=api_key, device_id=device_id,
- os_name=os_name, role=role
- )
-
- # Get the communication manager, sender message queue
- shared_communication_mgr = launch_slave_agent.get_protocol_manager().get_protocol_communication_manager()
- shared_slave_sender_message_queue = launch_slave_agent.get_protocol_manager().get_protocol_sender_message_queue()
- shared_slave_sender_message_event = launch_slave_agent.get_protocol_manager().get_protocol_sender_message_event()
-
- # Login with the launch master role based on
- # the shared communication manager, sender message center
- launch_master_agent.login(
- api_key, api_key=api_key, device_id=login_result.device_id,
- os_name=os_name, runner_cmd=runner_cmd,
- role=FedMLAccountManager.ROLE_GPU_MASTER_SERVER,
- communication_manager=shared_communication_mgr,
- sender_message_queue=None
- )
-
- # Get the status center queue
- shared_slave_status_center_queue = launch_slave_agent.get_protocol_manager().get_protocol_status_center_queue()
- shared_master_status_center_queue = launch_master_agent.get_protocol_manager().get_protocol_status_center_queue()
- shared_master_sender_message_queue = launch_master_agent.get_protocol_manager().get_protocol_sender_message_queue()
- shared_master_sender_message_event = launch_master_agent.get_protocol_manager().get_protocol_sender_message_event()
-
- # Login with the deployment master role based on
- # the shared communication manager, sender message center, status center
- deploy_master_login_result = deploy_master_agent.login(
- userid, api_key=api_key, device_id=login_result.device_id,
- os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_MASTER_ON_PREM,
- communication_manager=shared_communication_mgr
- )
-
- # Login with the deployment slave role based on
- # the shared communication manager, sender message center, status center
- deploy_slave_login_result = deploy_slave_agent.login(
- userid, api_key=api_key, device_id=login_result.device_id,
- os_name=os_name, role=FedMLAccountManager.ROLE_DEPLOY_WORKER_ON_PREM,
- communication_manager=shared_communication_mgr
- )
-
- # Set the deployment ids to launch agent so that we can report the related device info to MLOps.
- launch_slave_agent.save_deploy_ids(
- deploy_master_edge_id=deploy_master_login_result.edge_id,
- deploy_slave_edge_id=deploy_slave_login_result.edge_id)
-
- # Start the slave agent to connect to servers and loop forever.
- launch_slave_agent.start()
diff --git a/python/fedml/core/mlops/__init__.py b/python/fedml/core/mlops/__init__.py
index 121c8e26bb..148427fe1f 100644
--- a/python/fedml/core/mlops/__init__.py
+++ b/python/fedml/core/mlops/__init__.py
@@ -1453,14 +1453,12 @@ def release_resources(run_id, device_id):
MLOpsConstants.MSG_TOPIC_LAUNCH_RELEASE_GPU_IDS, json.dumps(payload))
-def sync_deploy_id(device_id, master_deploy_id, worker_deploy_id_list, message_center=None):
- payload = {"device_id": device_id, "master_deploy_id": master_deploy_id, "worker_deploy_ids": worker_deploy_id_list}
- if message_center is None:
- fedml_args = get_fedml_args()
- setup_log_mqtt_mgr()
- MLOpsStore.mlops_log_mqtt_mgr.send_message_json(
- MLOpsConstants.MSG_TOPIC_LAUNCH_SYNC_DEPLOY_IDS, json.dumps(payload))
- else:
- message_center.send_message( MLOpsConstants.MSG_TOPIC_LAUNCH_SYNC_DEPLOY_IDS, json.dumps(payload))
+def sync_deploy_id(device_id, master_deploy_id, worker_deploy_id_list):
+ fedml_args = get_fedml_args()
+ setup_log_mqtt_mgr()
+
+ payload = {"device_id": device_id, "master_deploy_id": master_deploy_id, "worker_deploy_ids": worker_deploy_id_list}
+ MLOpsStore.mlops_log_mqtt_mgr.send_message_json(
+ MLOpsConstants.MSG_TOPIC_LAUNCH_SYNC_DEPLOY_IDS, json.dumps(payload))
diff --git a/python/fedml/core/mlops/mlops_device_perfs.py b/python/fedml/core/mlops/mlops_device_perfs.py
index 61da372d97..4bb41df73f 100644
--- a/python/fedml/core/mlops/mlops_device_perfs.py
+++ b/python/fedml/core/mlops/mlops_device_perfs.py
@@ -1,7 +1,6 @@
import json
import logging
import os
-import platform
import time
import traceback
import uuid
@@ -9,15 +8,12 @@
import multiprocessing
import psutil
-import setproctitle
-import fedml
from fedml.computing.scheduler.comm_utils import sys_utils
from .device_info_report_protocol import FedMLDeviceInfoReportProtocol
from .mlops_utils import MLOpsUtils
from .system_stats import SysStats
from ...computing.scheduler.comm_utils.job_monitor import JobMonitor
-from ...computing.scheduler.scheduler_core.general_constants import GeneralConstants
from ...core.distributed.communication.mqtt.mqtt_manager import MqttManager
@@ -32,17 +28,6 @@
ROLE_ENDPOINT_REPLICA_NUM = 8
ROLE_ENDPOINT_REPLICA_PERF = 9
-ROLE_DEVICE_JOB_TOTAL_MONITOR_STR = "device_job_total"
-ROLE_DEVICE_INFO_REPORTER_STR = "device_info"
-ROLE_ENDPOINT_MASTER_STR = "endpoint_master"
-ROLE_ENDPOINT_SLAVE_STR = "endpoint_slave"
-ROLE_RUN_MASTER_STR = "run_master"
-ROLE_RUN_SLAVE_STR = "run_slave"
-ROLE_ENDPOINT_LOGS_STR = "endpoint_logs"
-ROLE_AUTO_SCALER_STR = "autoscaler"
-ROLE_ENDPOINT_REPLICA_NUM_STR = "endpoint_replica_num"
-ROLE_ENDPOINT_REPLICA_PERF_STR = "endpoint_replica_perf"
-
class MLOpsDevicePerfStats(object):
def __init__(self):
@@ -91,161 +76,58 @@ def setup_realtime_stats_process(self, sys_args):
self.device_realtime_stats_event.clear()
perf_stats.device_realtime_stats_event = self.device_realtime_stats_event
- if platform.system() == "Windows":
- self.device_realtime_stats_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_DEVICE_INFO_REPORTER, self.is_client,
- GeneralConstants.get_monitor_process_name(
- ROLE_DEVICE_INFO_REPORTER_STR, perf_stats.run_id, perf_stats.edge_id)))
- else:
- self.device_realtime_stats_process = fedml.get_process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_DEVICE_INFO_REPORTER, self.is_client,
- GeneralConstants.get_monitor_process_name(
- ROLE_DEVICE_INFO_REPORTER_STR, perf_stats.run_id, perf_stats.edge_id)))
+ self.device_realtime_stats_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_DEVICE_INFO_REPORTER, self.is_client))
self.device_realtime_stats_process.start()
if self.enable_job_total_monitor:
- if platform.system() == "Windows":
- self.job_total_monitor_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_DEVICE_JOB_TOTAL_MONITOR, self.is_client,
- GeneralConstants.get_monitor_process_name(
- ROLE_DEVICE_JOB_TOTAL_MONITOR_STR, perf_stats.run_id, perf_stats.edge_id)))
- else:
- self.job_total_monitor_process = fedml.get_process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_DEVICE_JOB_TOTAL_MONITOR, self.is_client,
- GeneralConstants.get_monitor_process_name(
- ROLE_DEVICE_JOB_TOTAL_MONITOR_STR, perf_stats.run_id, perf_stats.edge_id)))
+ self.job_total_monitor_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_DEVICE_JOB_TOTAL_MONITOR, self.is_client))
self.job_total_monitor_process.start()
else:
if self.is_client:
- # Register endpoint master process
- if platform.system() == "Windows":
- self.monitor_endpoint_master_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_MASTER, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_ENDPOINT_MASTER_STR, perf_stats.run_id, perf_stats.edge_id)))
- else:
- self.monitor_endpoint_master_process = fedml.get_process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_MASTER, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_ENDPOINT_MASTER_STR, perf_stats.run_id, perf_stats.edge_id)))
+ self.monitor_endpoint_master_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_MASTER))
self.monitor_endpoint_master_process.start()
- # Register endpoint slave process
- if platform.system() == "Windows":
- self.monitor_endpoint_slave_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_SLAVE, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_ENDPOINT_SLAVE_STR, perf_stats.run_id, perf_stats.edge_id)))
- else:
- self.monitor_endpoint_slave_process = fedml.get_process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_SLAVE, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_ENDPOINT_SLAVE_STR, perf_stats.run_id, perf_stats.edge_id)))
- self.monitor_endpoint_slave_process.start()
-
- # Register run slave process
- if platform.system() == "Windows":
- self.monitor_run_slave_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_RUN_SLAVE, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_RUN_SLAVE_STR, perf_stats.run_id, perf_stats.edge_id)))
- else:
- self.monitor_run_slave_process = fedml.get_process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_RUN_SLAVE, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_RUN_SLAVE_STR, perf_stats.run_id, perf_stats.edge_id)))
+ self.monitor_run_slave_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_RUN_SLAVE))
self.monitor_run_slave_process.start()
- # Register endpoint logs process
- if platform.system() == "Windows":
- self.monitor_endpoint_logs_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_LOGS, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_ENDPOINT_LOGS_STR, perf_stats.run_id, perf_stats.edge_id)))
- else:
- self.monitor_endpoint_logs_process = fedml.get_process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_LOGS, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_ENDPOINT_LOGS_STR, perf_stats.run_id, perf_stats.edge_id)))
+ self.monitor_endpoint_logs_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_LOGS))
self.monitor_endpoint_logs_process.start()
# Register auto-scaler process
- if platform.system() == "Windows":
- self.monitor_auto_scaler_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_AUTO_SCALER, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_AUTO_SCALER_STR, perf_stats.run_id, perf_stats.edge_id)))
- else:
- self.monitor_auto_scaler_process = fedml.get_process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_AUTO_SCALER, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_AUTO_SCALER_STR, perf_stats.run_id, perf_stats.edge_id)))
+ self.monitor_auto_scaler_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_AUTO_SCALER))
self.monitor_auto_scaler_process.start()
# Register replica number report channel
- if platform.system() == "Windows":
- self.monitor_replica_num_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_NUM, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_ENDPOINT_REPLICA_NUM_STR, perf_stats.run_id, perf_stats.edge_id)))
- else:
- self.monitor_replica_num_process = fedml.get_process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_NUM, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_ENDPOINT_REPLICA_NUM_STR, perf_stats.run_id, perf_stats.edge_id)))
+ self.monitor_replica_num_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_NUM))
self.monitor_replica_num_process.start()
# Register replica performance report channel
- if platform.system() == "Windows":
- self.monitor_replica_perf_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_PERF, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_ENDPOINT_REPLICA_PERF_STR, perf_stats.run_id, perf_stats.edge_id)))
-
- else:
- self.monitor_replica_perf_process = fedml.get_process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_PERF, True,
- GeneralConstants.get_monitor_process_name(
- ROLE_ENDPOINT_REPLICA_PERF_STR, perf_stats.run_id, perf_stats.edge_id)))
+ self.monitor_replica_perf_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_ENDPOINT_REPLICA_PERF))
self.monitor_replica_perf_process.start()
else:
- if platform.system() == "Windows":
- self.monitor_run_master_process = multiprocessing.Process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_RUN_MASTER, False,
- GeneralConstants.get_monitor_process_name(
- ROLE_RUN_MASTER_STR, perf_stats.run_id, perf_stats.edge_id)))
- else:
- self.monitor_run_master_process = fedml.get_process(
- target=perf_stats.report_device_realtime_stats_entry,
- args=(self.device_realtime_stats_event, ROLE_RUN_MASTER, False,
- GeneralConstants.get_monitor_process_name(
- ROLE_RUN_MASTER_STR, perf_stats.run_id, perf_stats.edge_id)))
+ self.monitor_run_master_process = multiprocessing.Process(
+ target=perf_stats.report_device_realtime_stats_entry,
+ args=(self.device_realtime_stats_event, ROLE_RUN_MASTER))
self.monitor_run_master_process.start()
- def report_device_realtime_stats_entry(self, sys_event, role, is_client=False, process_name=None):
- if process_name is not None:
- setproctitle.setproctitle(process_name)
-
- # print(f"Report device realtime stats, process id {os.getpid()}, name {process_name}")
+ def report_device_realtime_stats_entry(self, sys_event, role, is_client=False):
+ # print(f"Report device realtime stats, process id {os.getpid()}")
self.device_realtime_stats_event = sys_event
mqtt_mgr = MqttManager(
diff --git a/python/fedml/core/mlops/mlops_job_perfs.py b/python/fedml/core/mlops/mlops_job_perfs.py
index 429e32ff1d..fe3d921558 100644
--- a/python/fedml/core/mlops/mlops_job_perfs.py
+++ b/python/fedml/core/mlops/mlops_job_perfs.py
@@ -1,25 +1,19 @@
import json
import logging
import os
-import platform
import time
import traceback
import uuid
import multiprocess as multiprocessing
import psutil
-import setproctitle
-import fedml
from .mlops_utils import MLOpsUtils
from .system_stats import SysStats
-from ...computing.scheduler.scheduler_core.general_constants import GeneralConstants
from ...core.distributed.communication.mqtt.mqtt_manager import MqttManager
class MLOpsJobPerfStats(object):
- JOB_PERF_PROCESS_TAG = "job_perf"
-
def __init__(self):
self.job_stats_process = None
self.job_stats_event = None
@@ -144,26 +138,16 @@ def setup_job_stats_process(self, sys_args):
self.job_stats_event.clear()
perf_stats.job_stats_event = self.job_stats_event
perf_stats.job_process_id_map = self.job_process_id_map
- if platform.system() == "Windows":
- self.job_stats_process = multiprocessing.Process(
- target=perf_stats.report_job_stats_entry,
- args=(self.job_stats_event, GeneralConstants.get_monitor_process_name(
- MLOpsJobPerfStats.JOB_PERF_PROCESS_TAG, perf_stats.run_id, perf_stats.edge_id)))
- else:
- self.job_stats_process = fedml.get_process(
- target=perf_stats.report_job_stats_entry,
- args=(self.job_stats_event, GeneralConstants.get_monitor_process_name(
- MLOpsJobPerfStats.JOB_PERF_PROCESS_TAG, perf_stats.run_id, perf_stats.edge_id)))
+
+ self.job_stats_process = multiprocessing.Process(target=perf_stats.report_job_stats_entry,
+ args=(self.job_stats_event,))
self.job_stats_process.start()
def report_job_stats(self, sys_args):
self.setup_job_stats_process(sys_args)
- def report_job_stats_entry(self, sys_event, process_name):
- if process_name is not None:
- setproctitle.setproctitle(process_name)
-
- # print(f"Report job realtime stats, process id {os.getpid()}, name {process_name}")
+ def report_job_stats_entry(self, sys_event):
+ # print(f"Report job realtime stats, process id {os.getpid()}")
self.job_stats_event = sys_event
mqtt_mgr = MqttManager(
diff --git a/python/fedml/core/mlops/mlops_runtime_log_daemon.py b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
index bf136a36c9..ff06dc91b3 100644
--- a/python/fedml/core/mlops/mlops_runtime_log_daemon.py
+++ b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
@@ -1,19 +1,16 @@
import argparse
import logging
import os
-import platform
import shutil
import threading
import time
import multiprocess as multiprocessing
import requests
-import setproctitle
import yaml
import fedml
from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
-from fedml.computing.scheduler.scheduler_core.general_constants import GeneralConstants
from fedml.core.mlops.mlops_utils import MLOpsLoggingUtils
from ...core.mlops.mlops_configs import MLOpsConfigs
@@ -258,11 +255,8 @@ def should_ignore_log_line(log_line):
return False
- def log_process(self, process_event, process_name=None):
- if process_name is not None:
- setproctitle.setproctitle(process_name)
-
- logging.info(f"Log uploading process id {os.getpid()}, run id {self.run_id}, name {process_name}, edge id {self.device_id}")
+ def log_process(self, process_event):
+ logging.info(f"Log uploading process id {os.getpid()}, run id {self.run_id}, edge id {self.device_id}")
self.log_process_event = process_event
only_push_artifact = False
@@ -424,8 +418,6 @@ def set_log_source(self, source):
self.log_source = source
def start_log_processor(self, log_run_id, log_device_id, log_source=None, log_file_prefix=None):
- if log_run_id == "-1" or int(log_run_id) <= 0:
- return
log_processor = MLOpsRuntimeLogProcessor(self.args.using_mlops, log_run_id,
log_device_id, self.log_file_dir,
self.log_server_url,
@@ -439,13 +431,8 @@ def start_log_processor(self, log_run_id, log_device_id, log_source=None, log_fi
self.log_process_event_map[event_map_id] = multiprocessing.Event()
self.log_process_event_map[event_map_id].clear()
log_processor.log_process_event = self.log_process_event_map[event_map_id]
- process_name = GeneralConstants.get_log_process_name(log_run_id, log_device_id)
- if platform.system() == "Windows":
- log_child_process = multiprocessing.Process(
- target=log_processor.log_process, args=(self.log_process_event_map[event_map_id], process_name))
- else:
- log_child_process = fedml.get_process(
- target=log_processor.log_process, args=(self.log_process_event_map[event_map_id], process_name))
+ log_child_process = multiprocessing.Process(target=log_processor.log_process,
+ args=(self.log_process_event_map[event_map_id],))
# process = threading.Thread(target=log_processor.log_process)
# process.start()
if log_child_process is not None:
diff --git a/python/setup.py b/python/setup.py
index 262fc060c4..4757c10a17 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -20,7 +20,7 @@ def finalize_options(self):
requirements = [
'GPUtil',
- 'PyYAML==5.3.1',
+ 'PyYAML',
'aiohttp>=3.8.1',
'attrdict',
'attrs',
@@ -69,8 +69,7 @@ def finalize_options(self):
'python-dotenv',
'protobuf>=3.20.2,<4.0dev',
'typer<0.10.0,>=0.3.0',
- 'fastapi-cli==0.0.1',
- 'setproctitle'
+ 'fastapi-cli==0.0.1'
]
requirements_extra_mpi = [
@@ -127,7 +126,7 @@ def finalize_options(self):
setup(
name="fedml",
- version="0.8.51b1",
+ version="0.9.0",
author="FedML Team",
author_email="ch@fedml.ai",
description="A research and production integrated edge-cloud library for "
diff --git a/python/tests/cross-silo/run_cross_silo.sh b/python/tests/cross-silo/run_cross_silo.sh
index 0beaaffc52..2ccdbff15b 100644
--- a/python/tests/cross-silo/run_cross_silo.sh
+++ b/python/tests/cross-silo/run_cross_silo.sh
@@ -1,10 +1,10 @@
#!/bin/bash
set -e
WORKSPACE=$(pwd)
-# PROJECT_HOME=$WORKSPACE/../../
-# cd $PROJECT_HOME
+PROJECT_HOME=$WORKSPACE/../../
+cd $PROJECT_HOME
-cd examples/federate/cross_silo/mqtt_s3_fedavg_mnist_lr_example/custom_data_and_model
+cd examples/cross_silo/mqtt_s3_fedavg_mnist_lr_example/custom_data_and_model
# run client(s)
RUN_ID="$(python -c "import uuid; print(uuid.uuid4().hex)")"
diff --git a/python/tests/smoke_test/cli/build.sh b/python/tests/smoke_test/cli/build.sh
index de956692f1..98fdb05244 100644
--- a/python/tests/smoke_test/cli/build.sh
+++ b/python/tests/smoke_test/cli/build.sh
@@ -16,7 +16,7 @@
# --help Show this message and exit.
# build client package
-cd ../../../examples/federate/cross_silo/mqtt_s3_fedavg_mnist_lr_example/one_line
+cd ../../../examples/cross_silo/mqtt_s3_fedavg_mnist_lr_example/one_line
echo "$PWD"
SOURCE=client
@@ -30,4 +30,4 @@ SOURCE=server
ENTRY=torch_server.py
CONFIG=config
DEST=./mlops
-fedml build -t server -sf $SOURCE -ep $ENTRY -cf $CONFIG -df $DEST
+fedml build -t server -sf $SOURCE -ep $ENTRY -cf $CONFIG -df $DEST
\ No newline at end of file
diff --git a/python/tests/test_deploy/test_deploy.py b/python/tests/test_deploy/test_deploy.py
deleted file mode 100644
index d7243c68de..0000000000
--- a/python/tests/test_deploy/test_deploy.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import os.path
-import time
-import fedml
-# Login
-API_KEY = os.getenv("API_KEY")
-fedml.set_env_version("test")
-fedml.set_local_on_premise_platform_port(18080)
-error_code, error_msg = fedml.api.fedml_login(api_key=API_KEY)
-if error_code != 0:
- raise Exception("API Key is invalid!")
-
-# Yaml file
-cur_dir = os.path.dirname(__file__)
-fedml_dir = os.path.dirname(cur_dir)
-python_dir = os.path.dirname(fedml_dir)
-yaml_file = os.path.join(python_dir, "examples", "launch", "serve_job_mnist.yaml")
-
-# Launch job
-launch_result_dict = {}
-launch_result_status = {}
-
-launch_result = fedml.api.launch_job(yaml_file)
-print("Endpoint id is", launch_result.inner_id)
-
-cnt = 0
-while 1:
- try:
- r = fedml.api.get_endpoint(endpoint_id=launch_result.inner_id)
- except Exception as e:
- raise Exception(f"FAILED to get endpoint:{launch_result.inner_id}. {e}")
- if r.status == "DEPLOYED":
- print("Deployment has been successfully!")
- break
- elif r.status == "FAILED":
- raise Exception("FAILED to deploy.")
- time.sleep(1)
- cnt += 1
- if cnt %3 ==0:
- print('Deployment status is', r.status)
\ No newline at end of file
diff --git a/python/tests/test_federate/test_federate.sh b/python/tests/test_federate/test_federate.sh
deleted file mode 100644
index ebfcb60330..0000000000
--- a/python/tests/test_federate/test_federate.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-
-WORKSPACE=`pwd`
-echo $WORKSPACE
-cd $WORKSPACE/examples/federate/quick_start/parrot
-python torch_fedavg_mnist_lr_one_line_example.py --cf fedml_config.yaml
-python torch_fedavg_mnist_lr_custum_data_and_model_example.py --cf fedml_config.yaml
-
-cd $WORKSPACE/examples/federate/simulation/sp_decentralized_mnist_lr_example
-python torch_fedavg_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
-
-cd $WORKSPACE/examples/federate/simulation/sp_fednova_mnist_lr_example
-python torch_fednova_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
-
-cd $WORKSPACE/examples/federate/simulation/sp_fedopt_mnist_lr_example
-python torch_fedopt_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
-
-cd $WORKSPACE/examples/federate/simulation/sp_hierarchicalfl_mnist_lr_example
-python torch_hierarchicalfl_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
-
-
-cd $WORKSPACE/examples/federate/simulation/sp_turboaggregate_mnist_lr_example
-python torch_turboaggregate_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
-
-
-cd $WORKSPACE/examples/federate/simulation/sp_vertical_mnist_lr_example
-python torch_vertical_mnist_lr_step_by_step_example.py --cf fedml_config.yaml
diff --git a/python/tests/test_launch/test_launch.py b/python/tests/test_launch/test_launch.py
deleted file mode 100644
index a6b6ffb9cf..0000000000
--- a/python/tests/test_launch/test_launch.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import os.path
-import time
-import fedml
-from fedml.api.constants import RunStatus
-
-API_KEY = os.getenv("API_KEY")
-# Login
-fedml.set_env_version("test")
-fedml.set_local_on_premise_platform_port(18080)
-error_code, error_msg = fedml.api.fedml_login(api_key=API_KEY)
-if error_code != 0:
- raise Exception("API Key is invalid!")
-
-# Yaml file
-cur_dir = os.path.dirname(__file__)
-fedml_dir = os.path.dirname(cur_dir)
-python_dir = os.path.dirname(fedml_dir)
-yaml_file = os.path.join(python_dir, "examples", "launch", "hello_job.yaml")
-
-# Launch job
-
-launch_result = fedml.api.launch_job(yaml_file)
-
-# launch_result = fedml.api.launch_job_on_cluster(yaml_file, "alex-cluster")
-if launch_result.result_code != 0:
- raise Exception(f"Failed to launch job. Reason: {launch_result.result_message}")
-
-# check job status
-while 1:
- time.sleep(1)
- # if
- # if launch_result_status[run_id] == RunStatus.FINISHED:
- # continue
- log_result = fedml.api.run_logs(launch_result.run_id, 1, 5)
- if log_result is None or log_result.run_status is None:
- raise Exception(f"Failed to get job status.")
-
- print(f"run_id: {launch_result.run_id} run_status: {log_result.run_status}")
-
- if log_result.run_status in [RunStatus.ERROR, RunStatus.FAILED]:
- log_result = fedml.api.run_logs(launch_result.run_id, 1, 100)
- if log_result is None or log_result.run_status is None:
- raise Exception(f"run_id:{launch_result.run_id} run_status:{log_result.run_status} and failed to get run logs.")
-
- raise Exception(f"run_id:{launch_result.run_id} run_status:{log_result.run_status} run logs: {log_result.log_line_list}")
- if log_result.run_status == RunStatus.FINISHED:
- print(f"Job finished successfully.")
- break
-
-
diff --git a/python/tests/test_train/test_train.py b/python/tests/test_train/test_train.py
deleted file mode 100644
index 039d3b81d2..0000000000
--- a/python/tests/test_train/test_train.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import os.path
-import time
-import fedml
-from fedml.api.constants import RunStatus
-
-API_KEY = os.getenv("API_KEY")
-# Login
-fedml.set_env_version("test")
-fedml.set_local_on_premise_platform_port(18080)
-error_code, error_msg = fedml.api.fedml_login(api_key=API_KEY)
-if error_code != 0:
- raise Exception("API Key is invalid!")
-
-# Yaml file
-cur_dir = os.path.dirname(__file__)
-fedml_dir = os.path.dirname(cur_dir)
-python_dir = os.path.dirname(fedml_dir)
-yaml_file = os.path.join(python_dir, "examples", "train", "mnist_train", "train.yaml")
-
-# Launch job
-
-launch_result = fedml.api.launch_job(yaml_file)
-
-# launch_result = fedml.api.launch_job_on_cluster(yaml_file, "alex-cluster")
-if launch_result.result_code != 0:
- raise Exception(f"Failed to launch job. Reason: {launch_result.result_message}")
-
-# check job status
-while 1:
- time.sleep(1)
- # if
- # if launch_result_status[run_id] == RunStatus.FINISHED:
- # continue
- log_result = fedml.api.run_logs(launch_result.run_id, 1, 5)
- if log_result is None or log_result.run_status is None:
- raise Exception(f"Failed to get job status.")
-
- print(f"run_id: {launch_result.run_id} run_status: {log_result.run_status}")
-
- if log_result.run_status in [RunStatus.ERROR, RunStatus.FAILED]:
- log_result = fedml.api.run_logs(launch_result.run_id, 1, 100)
- if log_result is None or log_result.run_status is None:
- raise Exception(f"run_id:{launch_result.run_id} run_status:{log_result.run_status} and failed to get run logs.")
-
- raise Exception(f"run_id:{launch_result.run_id} run_status:{log_result.run_status} run logs: {log_result.log_line_list}")
- if log_result.run_status == RunStatus.FINISHED:
- print(f"Job finished successfully.")
- break
-
From f5ad35b6b2cf15cf39a183e587e465bc8eba0439 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Mon, 8 Jul 2024 12:03:39 -0700
Subject: [PATCH 198/282] [Deploy] Fix round-robin algorithm; Format code.
---
.../model_scheduler/device_model_cache.py | 197 +++++++++---------
1 file changed, 101 insertions(+), 96 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
index 292af4e659..7a47c1961e 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cache.py
@@ -250,7 +250,7 @@ def delete_deployment_result(self, element: str, end_point_id, end_point_name, m
device_id, replica_no, _ = self.get_result_item_info(element)
self.model_deployment_db.delete_deployment_result_with_device_id_and_rank(
- end_point_id, end_point_name, model_name, device_id, replica_rank=replica_no-1)
+ end_point_id, end_point_name, model_name, device_id, replica_rank=replica_no - 1)
return
@@ -286,8 +286,9 @@ def get_deployment_result_list(self, end_point_id, end_point_name, model_name):
result_list = self.model_deployment_db.get_deployment_result_list(end_point_id, end_point_name, model_name)
try:
for result in result_list:
- self.redis_connection.rpush(self.get_deployment_result_key(end_point_id, end_point_name, model_name),
- json.dumps(result))
+ self.redis_connection.rpush(
+ self.get_deployment_result_key(end_point_id, end_point_name, model_name),
+ json.dumps(result))
except Exception as e:
logging.info(e)
pass
@@ -332,7 +333,8 @@ def get_deployment_result_list_size(self, end_point_id, end_point_name, model_na
def get_deployment_status_list(self, end_point_id, end_point_name, model_name):
try:
- status_list = self.redis_connection.lrange(self.get_deployment_status_key(end_point_id, end_point_name, model_name), 0, -1)
+ status_list = self.redis_connection.lrange(
+ self.get_deployment_status_key(end_point_id, end_point_name, model_name), 0, -1)
except Exception as e:
status_list = None
@@ -340,8 +342,9 @@ def get_deployment_status_list(self, end_point_id, end_point_name, model_name):
status_list = self.model_deployment_db.get_deployment_status_list(end_point_id, end_point_name, model_name)
try:
for status in status_list:
- self.redis_connection.rpush(self.get_deployment_status_key(end_point_id, end_point_name, model_name),
- json.dumps(status))
+ self.redis_connection.rpush(
+ self.get_deployment_status_key(end_point_id, end_point_name, model_name),
+ json.dumps(status))
except Exception as e:
pass
return status_list
@@ -369,7 +372,7 @@ def get_result_item_info(result_item):
result_item_json = json.loads(result_item_json)
device_id = result_item_json["cache_device_id"]
- replica_no = result_item_json.get("cache_replica_no", 1) # Compatible with the old version
+ replica_no = result_item_json.get("cache_replica_no", 1) # Compatible with the old version
if isinstance(result_item_json["result"], str):
result_payload = json.loads(result_item_json["result"])
@@ -400,7 +403,7 @@ def get_idle_device(self,
found_model_name == model_name and
(not limit_specific_model_version or found_model_version == model_version)):
if "model_status" in result_payload and result_payload["model_status"] == "DEPLOYED":
- idle_device_list.append({"device_id": device_id, "end_point_id": end_point_id})
+ idle_device_list.append({"device_id": device_id, "result_payload": result_payload.copy()})
logging.debug(f"{len(idle_device_list)} devices this model has on it: {idle_device_list}")
@@ -416,54 +419,21 @@ def get_idle_device(self,
# Round Robin
total_device_num = len(idle_device_list)
- redis_round_robin_key = self.get_round_robin_prev_device(end_point_id, end_point_name, model_name, model_version)
-
- selected_device_index = 0
- try:
- if self.redis_connection.exists(redis_round_robin_key):
- selected_device_index = int(self.redis_connection.get(redis_round_robin_key))
- selected_device_index %= total_device_num
- else:
- selected_device_index = 0
- next_selected_device_index = (selected_device_index + 1) % total_device_num
- self.redis_connection.set(redis_round_robin_key, str(next_selected_device_index))
- except Exception as e:
- logging.info("Inference Device selection Failed:")
- logging.info(e)
-
- logging.debug(f"Using Round Robin, the device index is {selected_device_index}")
- idle_device_dict = idle_device_list[selected_device_index]
+ redis_round_robin_key = self.get_round_robin_prev_device(end_point_id, end_point_name, model_name,
+ model_version)
+ if self.redis_connection.exists(redis_round_robin_key):
+ selected_device_index = int(self.redis_connection.get(redis_round_robin_key))
+ selected_device_index %= total_device_num
+ else:
+ selected_device_index = 0
- # Note that within the same endpoint_id, there could be one device with multiple same models
- same_model_device_rank = 0
- start = selected_device_index
- while(start != 0 and idle_device_list[start]["device_id"] == idle_device_list[start-1]["device_id"]):
- start -= 1
- same_model_device_rank += 1
+ next_idx = (selected_device_index + 1) % total_device_num
+ self.redis_connection.set(redis_round_robin_key, next_idx)
- # Find deployment result from the target idle device.
- try:
- for result_item in result_list:
- logging.debug("enter the for loop")
- device_id, _, result_payload = self.get_result_item_info(result_item)
- found_end_point_id = result_payload["end_point_id"]
- found_end_point_name = result_payload["end_point_name"]
- found_model_status = result_payload["model_status"]
-
- if found_model_status != "DEPLOYED":
- continue
-
- if str(found_end_point_id) == str(idle_device_dict["end_point_id"]) \
- and device_id == idle_device_dict["device_id"]:
- if same_model_device_rank > 0:
- same_model_device_rank -= 1
- continue
- logging.debug(f"The chosen device is {device_id}")
- return result_payload, device_id
- except Exception as e:
- logging.info(str(e))
+ idle_device_info = idle_device_list[selected_device_index]
+ payload = idle_device_info["result_payload"]
- return None, None
+ return payload, idle_device_info["device_id"]
def get_latest_version(self, status_list):
latest_version = None
@@ -472,8 +442,8 @@ def get_latest_version(self, status_list):
try:
_, status_payload = self.get_status_item_info(status_item)
model_version = status_payload["model_version"]
- prefix = model_version.split("-")[0] # version-date
- prefix_int = int(prefix[1:]) # v12 -> 12
+ prefix = model_version.split("-")[0] # version-date
+ prefix_int = int(prefix[1:]) # v12 -> 12
if latest_version is None:
latest_version = model_version
@@ -568,32 +538,47 @@ def delete_end_point(self, end_point_id, end_point_name, model_name, model_versi
# Device id is either deploy master or deploy worker
try:
logging.info("Will Delete the related redis keys permanently")
- self.redis_connection.expire(self.get_deployment_result_key(end_point_id, end_point_name, model_name), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
- self.redis_connection.expire(self.get_deployment_status_key(end_point_id, end_point_name, model_name), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
- self.redis_connection.expire(self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
- self.redis_connection.expire(self.get_deployment_token_key(end_point_id, end_point_name, model_name), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
-
- any_version_round_robin_key = self.get_round_robin_prev_device_any_version(end_point_id, end_point_name, model_name)
+ self.redis_connection.expire(self.get_deployment_result_key(end_point_id, end_point_name, model_name),
+ ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
+ self.redis_connection.expire(self.get_deployment_status_key(end_point_id, end_point_name, model_name),
+ ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
+ self.redis_connection.expire(
+ self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version),
+ ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
+ self.redis_connection.expire(self.get_deployment_token_key(end_point_id, end_point_name, model_name),
+ ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
+
+ any_version_round_robin_key = self.get_round_robin_prev_device_any_version(end_point_id, end_point_name,
+ model_name)
for key in self.redis_connection.scan_iter(any_version_round_robin_key + "*"):
self.redis_connection.expire(key, ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
- self.redis_connection.expire(self.get_deployment_device_info_key(end_point_id), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
- self.redis_connection.expire(self.get_end_point_activation_key(end_point_id), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
- self.redis_connection.expire(self.get_end_point_status_key(end_point_id), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
- self.redis_connection.expire(self.get_user_setting_replica_num_key(end_point_id), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
+ self.redis_connection.expire(self.get_deployment_device_info_key(end_point_id),
+ ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
+ self.redis_connection.expire(self.get_end_point_activation_key(end_point_id),
+ ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
+ self.redis_connection.expire(self.get_end_point_status_key(end_point_id),
+ ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
+ self.redis_connection.expire(self.get_user_setting_replica_num_key(end_point_id),
+ ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
# Delete all replicas gpu ids
- matched_prefix_replica = self.get_replica_gpu_ids_key_all_replicas(end_point_id, end_point_name, model_name, device_id)
+ matched_prefix_replica = self.get_replica_gpu_ids_key_all_replicas(end_point_id, end_point_name, model_name,
+ device_id)
for key in self.redis_connection.scan_iter(matched_prefix_replica + "*"):
self.redis_connection.expire(key, ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
logging.info(f"Those keys are deleted: {key}")
# Delete the compute gpu cache
- self.redis_connection.expire(ComputeGpuCache.get_run_total_num_gpus_key(end_point_id), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
- self.redis_connection.expire(ComputeGpuCache.get_run_total_num_gpus_key(end_point_id), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
- self.redis_connection.expire(ComputeGpuCache.get_run_device_ids_key(end_point_id), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
- self.redis_connection.expire(ComputeGpuCache.get_edge_model_id_map_key(end_point_id), ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
+ self.redis_connection.expire(ComputeGpuCache.get_run_total_num_gpus_key(end_point_id),
+ ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
+ self.redis_connection.expire(ComputeGpuCache.get_run_total_num_gpus_key(end_point_id),
+ ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
+ self.redis_connection.expire(ComputeGpuCache.get_run_device_ids_key(end_point_id),
+ ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
+ self.redis_connection.expire(ComputeGpuCache.get_edge_model_id_map_key(end_point_id),
+ ServerConstants.MODEL_CACHE_KEY_EXPIRE_TIME)
logging.info(f"Those keys are deleted:"
f"{ComputeGpuCache.get_endpoint_run_id_map_key(end_point_id)}, "
@@ -703,7 +688,8 @@ def get_end_point_token(self, end_point_id, end_point_name, model_name):
token = None
try:
if self.redis_connection.exists(self.get_deployment_token_key(end_point_id, end_point_name, model_name)):
- token = self.redis_connection.get(self.get_deployment_token_key(end_point_id, end_point_name, model_name))
+ token = self.redis_connection.get(
+ self.get_deployment_token_key(end_point_id, end_point_name, model_name))
except Exception as e:
token = None
@@ -711,7 +697,8 @@ def get_end_point_token(self, end_point_id, end_point_name, model_name):
token = self.model_deployment_db.get_end_point_token(end_point_id, end_point_name, model_name)
if token is not None:
try:
- self.redis_connection.set(self.get_deployment_token_key(end_point_id, end_point_name, model_name), token)
+ self.redis_connection.set(self.get_deployment_token_key(end_point_id, end_point_name, model_name),
+ token)
except Exception as e:
pass
@@ -743,32 +730,41 @@ def get_endpoint_devices_replica_num(self, end_point_id):
return replica_num
- def get_deployment_result_key(self, end_point_id, end_point_name, model_name):
- return "{}-{}-{}-{}".format(FedMLModelCache.FEDML_MODEL_DEPLOYMENT_RESULT_TAG, end_point_id, end_point_name, model_name)
+ @staticmethod
+ def get_deployment_result_key(end_point_id, end_point_name, model_name):
+ return "{}-{}-{}-{}".format(FedMLModelCache.FEDML_MODEL_DEPLOYMENT_RESULT_TAG, end_point_id, end_point_name,
+ model_name)
- def get_deployment_status_key(self, end_point_id, end_point_name, model_name):
- return "{}-{}-{}-{}".format(FedMLModelCache.FEDML_MODEL_DEPLOYMENT_STATUS_TAG, end_point_id, end_point_name, model_name)
+ @staticmethod
+ def get_deployment_status_key(end_point_id, end_point_name, model_name):
+ return "{}-{}-{}-{}".format(FedMLModelCache.FEDML_MODEL_DEPLOYMENT_STATUS_TAG, end_point_id, end_point_name,
+ model_name)
- def get_end_point_status_key(self, end_point_id):
+ @staticmethod
+ def get_end_point_status_key(end_point_id):
return "{}{}".format(FedMLModelCache.FEDML_MODEL_END_POINT_STATUS_TAG, end_point_id)
@staticmethod
def get_end_point_activation_key(end_point_id):
return "{}{}".format(FedMLModelCache.FEDML_MODEL_END_POINT_ACTIVATION_TAG, end_point_id)
- def get_deployment_device_info_key(self, end_point_id):
+ @staticmethod
+ def get_deployment_device_info_key(end_point_id):
return "{}{}".format(FedMLModelCache.FEDML_MODEL_DEVICE_INFO_TAG, end_point_id)
@staticmethod
def get_deployment_token_key(end_point_id, end_point_name, model_name):
- return "{}-{}-{}-{}".format(FedMLModelCache.FEDML_MODEL_END_POINT_TOKEN_TAG, end_point_id, end_point_name, model_name)
+ return "{}-{}-{}-{}".format(FedMLModelCache.FEDML_MODEL_END_POINT_TOKEN_TAG, end_point_id, end_point_name,
+ model_name)
@staticmethod
def get_deployment_token_key_eid(end_point_id):
return "{}-{}".format(FedMLModelCache.FEDML_MODEL_END_POINT_TOKEN_TAG, end_point_id)
- def get_round_robin_prev_device(self, end_point_id, end_point_name, model_name, version):
- return "{}-{}-{}-{}-{}".format(FedMLModelCache.FEDML_MODEL_ROUND_ROBIN_PREVIOUS_DEVICE_TAG, end_point_id, end_point_name, model_name, version)
+ @staticmethod
+ def get_round_robin_prev_device(end_point_id, end_point_name, model_name, version):
+ return "{}-{}-{}-{}-{}".format(FedMLModelCache.FEDML_MODEL_ROUND_ROBIN_PREVIOUS_DEVICE_TAG, end_point_id,
+ end_point_name, model_name, version)
@staticmethod
def get_round_robin_prev_device_any_version(endpoint_id, endpoint_name, model_name):
@@ -797,8 +793,9 @@ def set_monitor_metrics(self, end_point_id, end_point_name,
"total_request_num": total_request_num, "current_qps": current_qps,
"avg_qps": avg_qps, "timestamp": timestamp, "device_id": device_id}
try:
- self.redis_connection.rpush(self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version),
- json.dumps(metrics_dict))
+ self.redis_connection.rpush(
+ self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version),
+ json.dumps(metrics_dict))
except Exception as e:
pass
self.model_deployment_db.set_monitor_metrics(end_point_id, end_point_name,
@@ -809,16 +806,20 @@ def set_monitor_metrics(self, end_point_id, end_point_name,
def get_latest_monitor_metrics(self, end_point_id, end_point_name, model_name, model_version):
try:
- if self.redis_connection.exists(self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version)):
- return self.redis_connection.lindex(self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version), -1)
+ if self.redis_connection.exists(
+ self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version)):
+ return self.redis_connection.lindex(
+ self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version), -1)
except Exception as e:
pass
- metrics_dict = self.model_deployment_db.get_latest_monitor_metrics(end_point_id, end_point_name, model_name, model_version)
+ metrics_dict = self.model_deployment_db.get_latest_monitor_metrics(end_point_id, end_point_name, model_name,
+ model_version)
if metrics_dict is not None:
try:
- self.redis_connection.rpush(self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version),
- metrics_dict)
+ self.redis_connection.rpush(
+ self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version),
+ metrics_dict)
except Exception as e:
pass
@@ -826,21 +827,25 @@ def get_latest_monitor_metrics(self, end_point_id, end_point_name, model_name, m
def get_monitor_metrics_item(self, end_point_id, end_point_name, model_name, model_version, index):
try:
- if self.redis_connection.exists(self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version)):
- metrics_item = self.redis_connection.lindex(self.get_monitor_metrics_key(end_point_id, end_point_name, model_name,
- model_version), index)
- return metrics_item, index+1
+ if self.redis_connection.exists(
+ self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version)):
+ metrics_item = self.redis_connection.lindex(
+ self.get_monitor_metrics_key(end_point_id, end_point_name, model_name,
+ model_version), index)
+ return metrics_item, index + 1
except Exception as e:
pass
- metrics_dict = self.model_deployment_db.get_monitor_metrics_item(end_point_id, end_point_name, model_name, model_version, index)
+ metrics_dict = self.model_deployment_db.get_monitor_metrics_item(end_point_id, end_point_name, model_name,
+ model_version, index)
if metrics_dict is not None:
try:
- self.redis_connection.rpush(self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version),
- metrics_dict)
+ self.redis_connection.rpush(
+ self.get_monitor_metrics_key(end_point_id, end_point_name, model_name, model_version),
+ metrics_dict)
except Exception as e:
pass
- return metrics_dict, index+1
+ return metrics_dict, index + 1
return None, 0
From c8e575503be9265b3abd49a09871eb9aef22376e Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Mon, 22 Jul 2024 15:51:14 -0700
Subject: [PATCH 199/282] [Deploy] Use terminology expose_subdomains.
---
.../examples/deploy/custom_inference_image/template.yaml | 2 +-
.../custom_inference_image/tensorrt_llm/tensorrtllm.yaml | 2 +-
.../template/custom_inference_image.yaml | 2 +-
.../scheduler/model_scheduler/device_client_constants.py | 2 +-
.../scheduler/model_scheduler/device_model_deployment.py | 8 ++++----
.../scheduler/model_scheduler/device_server_constants.py | 2 +-
.../scheduler/model_scheduler/master_job_runner.py | 2 +-
7 files changed, 10 insertions(+), 10 deletions(-)
diff --git a/python/examples/deploy/custom_inference_image/template.yaml b/python/examples/deploy/custom_inference_image/template.yaml
index 10e6580bcf..1dd13e1530 100644
--- a/python/examples/deploy/custom_inference_image/template.yaml
+++ b/python/examples/deploy/custom_inference_image/template.yaml
@@ -1,6 +1,6 @@
# Required
workspace: "./" # We will pacakge all the files in the workspace directory
-enable_serverless_container: true # Identify whether to use serverless container
+expose_subdomains: true # For customized image, if you want to route all the subdomains, set to true. e.g. localhost:2345/{all-subdomain}
inference_image_name: "" # Container image name
container_run_command: "" # str or list, similar to CMD in the dockerfile
port: 80 # Service port, currently you can only indicate one arbitrary port
diff --git a/python/examples/deploy/custom_inference_image/tensorrt_llm/tensorrtllm.yaml b/python/examples/deploy/custom_inference_image/tensorrt_llm/tensorrtllm.yaml
index d41dba7983..a72c1f7753 100644
--- a/python/examples/deploy/custom_inference_image/tensorrt_llm/tensorrtllm.yaml
+++ b/python/examples/deploy/custom_inference_image/tensorrt_llm/tensorrtllm.yaml
@@ -1,6 +1,6 @@
workspace: "./"
-enable_serverless_container: true
+expose_subdomains: true
inference_image_name: "fedml/llama3-8b-tensorrtllm"
# If you put the model repository in $workspace/model_repository, it will be mounted to /home/fedml/models_serving/model_repository
diff --git a/python/examples/deploy/custom_inference_image/triton_inference_server/template/custom_inference_image.yaml b/python/examples/deploy/custom_inference_image/triton_inference_server/template/custom_inference_image.yaml
index eb02e3904a..11ae9f82ff 100644
--- a/python/examples/deploy/custom_inference_image/triton_inference_server/template/custom_inference_image.yaml
+++ b/python/examples/deploy/custom_inference_image/triton_inference_server/template/custom_inference_image.yaml
@@ -1,6 +1,6 @@
workspace: "./"
-enable_serverless_container: true
+expose_subdomains: true
inference_image_name: "nvcr.io/nvidia/tritonserver:24.05-py3"
volumes:
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
index 4006e50726..3bb2e12aed 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
@@ -159,7 +159,7 @@ class ClientConstants(object):
DEPLOY_TIMEOUT_SEC_KEY = "deploy_timeout_sec"
DEPLOY_TIMEOUT_SEC_DEFAULT = 600
- ENABLE_SERVERLESS_CONTAINER_KEY = "enable_serverless_container"
+ EXPOSE_SUBDOMAINS_KEY = "expose_subdomains"
CUSTOMIZED_VOLUMES_MOUNT_KEY = "volumes"
CUSTOMIZED_VOLUMES_PATH_FROM_WORKSPACE_KEY = "workspace_path"
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 665bb4082e..6a637653fc 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -88,7 +88,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
registry_user_name, registry_user_password = parse_image_registry_related_config(config)
# Service app related
- dst_bootstrap_dir, dst_model_serving_dir, relative_entry_fedml_format, enable_serverless_container, \
+ dst_bootstrap_dir, dst_model_serving_dir, relative_entry_fedml_format, expose_subdomains, \
customized_image_entry_cmd, customized_readiness_check, customized_liveliness_check, customized_uri = \
handle_container_service_app(config, model_storage_local_path)
@@ -255,7 +255,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
model_metadata = ret_model_metadata
model_metadata["liveliness_check"] = customized_liveliness_check
model_metadata["readiness_check"] = customized_readiness_check
- model_metadata[ClientConstants.ENABLE_SERVERLESS_CONTAINER_KEY] = enable_serverless_container
+ model_metadata[ClientConstants.EXPOSE_SUBDOMAINS_KEY] = expose_subdomains
logging.info(f"[Worker][Replica{replica_rank}] Model deployment is successful with inference_output_url: "
f"{inference_output_url}, model_metadata: {model_metadata}, model_config: {ret_model_config}")
@@ -616,13 +616,13 @@ def handle_container_service_app(config, model_storage_local_path):
relative_entry_fedml_format = config.get('entry_point', "")
# User indicate either fedml format python main entry filename or entry command
- enable_serverless_container = config.get(ClientConstants.ENABLE_SERVERLESS_CONTAINER_KEY, False)
+ expose_subdomains = config.get(ClientConstants.EXPOSE_SUBDOMAINS_KEY, False)
customized_image_entry_cmd = config.get('container_run_command', None) # Could be str or list
customized_readiness_check = config.get('readiness_probe', ClientConstants.READINESS_PROBE_DEFAULT)
customized_liveliness_check = config.get('liveness_probe', ClientConstants.LIVENESS_PROBE_DEFAULT)
customized_uri = config.get(ClientConstants.CUSTOMIZED_SERVICE_KEY, "")
- return (dst_bootstrap_dir, dst_model_serving_dir, relative_entry_fedml_format, enable_serverless_container,
+ return (dst_bootstrap_dir, dst_model_serving_dir, relative_entry_fedml_format, expose_subdomains,
customized_image_entry_cmd, customized_readiness_check, customized_liveliness_check, customized_uri)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
index 00f0fe73bf..44eaeb9371 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_server_constants.py
@@ -151,7 +151,7 @@ class ServerConstants(object):
DEVICE_DIFF_REPLACE_OPERATION = "op: replace"
# Worker comfig yaml related
- ENABLE_SERVERLESS_CONTAINER_KEY = "enable_serverless_container"
+ EXPOSE_SUBDOMAINS_KEY = "expose_subdomains"
@staticmethod
def get_fedml_home_dir():
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index ab6bc4c895..00b08acfb8 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -763,7 +763,7 @@ def construct_final_gateway_url(self, end_point_id):
if self.deployed_replica_payload is not None:
payload_json = self.deployed_replica_payload
enable_custom_path = payload_json["model_metadata"].get(
- ServerConstants.ENABLE_SERVERLESS_CONTAINER_KEY, False)
+ ServerConstants.EXPOSE_SUBDOMAINS_KEY, False)
if enable_custom_path:
identifier = "custom_inference"
From 281f8c0a2f573b77986f872c09266b7b5c33300b Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Thu, 1 Aug 2024 13:04:27 -0700
Subject: [PATCH 200/282] Add marketplace_type, price_per_hour as optional
login parameters
---
python/fedml/api/__init__.py | 15 ++++++-----
python/fedml/api/constants.py | 17 ++++++++++++-
python/fedml/api/modules/device.py | 17 +++++++------
python/fedml/cli/modules/login.py | 25 +++++++++++++++++--
.../scheduler_core/account_manager.py | 16 ++++++++----
.../scheduler/slave/base_slave_agent.py | 6 +++--
.../scheduler/slave/client_daemon.py | 10 +++++++-
.../computing/scheduler/slave/client_login.py | 8 ++++--
8 files changed, 88 insertions(+), 26 deletions(-)
diff --git a/python/fedml/api/__init__.py b/python/fedml/api/__init__.py
index b03c72b675..b1c56403f1 100755
--- a/python/fedml/api/__init__.py
+++ b/python/fedml/api/__init__.py
@@ -15,7 +15,7 @@
"""
from typing import List, Tuple
-from fedml.api.constants import RunStatus
+from fedml.api.constants import RunStatus, MarketplaceType
from fedml.api.fedml_response import FedMLResponse
from fedml.api.modules import launch, utils, build, device, logs, diagnosis, cluster, run, train, federate, storage, \
model as model_module # Since "model" has conflict with one of the input parameters, we need to rename it
@@ -214,9 +214,10 @@ def fedml_build(platform, type, source_folder, entry_point, config_folder, dest_
def login(api_key, computing, server, supplier,
master_inference_gateway_port: int = ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
worker_inference_proxy_port: int = ClientConstants.LOCAL_CLIENT_API_PORT,
- worker_connection_type: str = ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT):
+ worker_connection_type: str = ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT,
+ marketplace_type: str = MarketplaceType.SECURE.value, price_per_hour: float = 0.0):
device_bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port,
- worker_connection_type)
+ worker_connection_type, marketplace_type, price_per_hour)
def logout(computing, server):
@@ -224,9 +225,11 @@ def logout(computing, server):
def device_bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port,
- worker_connection_type):
- device.bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port,
- worker_connection_type)
+ worker_connection_type, marketplace_type, price_per_hour):
+ device.bind(api_key=api_key, computing=computing, server=server, supplier=supplier,
+ master_inference_gateway_port=master_inference_gateway_port,
+ worker_inference_proxy_port=worker_inference_proxy_port, worker_connection_type=worker_connection_type,
+ marketplace_type=marketplace_type, price_per_hour=price_per_hour)
def device_unbind(computing, server):
diff --git a/python/fedml/api/constants.py b/python/fedml/api/constants.py
index e804775f74..d4c59898a8 100755
--- a/python/fedml/api/constants.py
+++ b/python/fedml/api/constants.py
@@ -1,6 +1,22 @@
from enum import Enum, unique
+class MarketplaceType(Enum):
+ SECURE = 1
+ COMMUNITY = 2
+
+ def __str__(self):
+ return self.name
+
+ @classmethod
+ def from_str(cls, name: str):
+ """Get the enum member from a string."""
+ if name.upper() in cls.__members__:
+ return cls[name.upper()]
+ else:
+ raise ValueError(f"Invalid marketplace type: {name}")
+
+
class ApiConstants:
RESOURCE_MATCHED_STATUS_MATCHED = "MATCHED"
RESOURCE_MATCHED_STATUS_JOB_URL_ERROR = "ERROR_JOB_URL"
@@ -106,4 +122,3 @@ def get_run_enum_from_str(cls, run_status_str: str):
if run_status.value == run_status_str:
return run_status
return cls.UNDETERMINED
-
diff --git a/python/fedml/api/modules/device.py b/python/fedml/api/modules/device.py
index 7c4e52c8b5..4a932ac631 100644
--- a/python/fedml/api/modules/device.py
+++ b/python/fedml/api/modules/device.py
@@ -19,11 +19,10 @@
def bind(
- api_key, computing, server, supplier,
+ api_key, computing, server, supplier, marketplace_type, price_per_hour,
master_inference_gateway_port=DeviceServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
worker_inference_proxy_port=DeviceClientConstants.LOCAL_CLIENT_API_PORT,
- worker_connection_type=DeviceClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT
-):
+ worker_connection_type=DeviceClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT):
userid = api_key
runner_cmd = "{}"
device_id = "0"
@@ -48,13 +47,13 @@ def bind(
_bind(
userid, computing, server,
api_key, role, runner_cmd, device_id, os_name,
- docker, master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type)
+ docker, master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type, marketplace_type,
+ price_per_hour)
def _bind(
- userid, computing, server,
- api_key, role, runner_cmd, device_id, os_name,
- docker, master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type):
+ userid, computing, server, api_key, role, runner_cmd, device_id, os_name, docker, master_inference_gateway_port,
+ worker_inference_proxy_port, worker_connection_type, marketplace_type, price_per_hour):
fedml.load_env()
if os.getenv(ModuleConstants.ENV_FEDML_INFER_HOST) is None:
fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_HOST, SchedulerConstants.REDIS_INFER_HOST)
@@ -179,6 +178,10 @@ def _bind(
user_api_key,
"-ngc",
"1"
+ "-mpt",
+ marketplace_type,
+ "-pph",
+ price_per_hour
]
).pid
sys_utils.save_login_process(ClientConstants.LOCAL_HOME_RUNNER_DIR_NAME,
diff --git a/python/fedml/cli/modules/login.py b/python/fedml/cli/modules/login.py
index 7ec4191a3e..2692a89177 100644
--- a/python/fedml/cli/modules/login.py
+++ b/python/fedml/cli/modules/login.py
@@ -1,8 +1,10 @@
import os
+from enum import Enum
import click
import fedml.api
+from fedml.api import MarketplaceType
from fedml.api.modules.utils import authenticate
from fedml.computing.scheduler.model_scheduler.device_server_constants import ServerConstants
from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants
@@ -74,10 +76,29 @@
default=ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT,
help="The connection type for worker inference proxy.",
)
+@click.option(
+ "--marketplace_type",
+ "-mpt",
+ type=click.Choice([marketplace_type for marketplace_type in MarketplaceType.__members__]),
+ default=MarketplaceType.SECURE.name,
+ help="Specify the marketplace type: 'SECURE' for Secure Cloud or 'COMMUNITY' for Community Cloud. "
+ "Defaults to Secure Cloud.",
+)
+@click.option(
+ "--price_per_hour",
+ "-pph",
+ type=click.FLOAT,
+ default=0.0,
+ help="Enter the price per GPU per hour as a floating-point number. For example, if the cost of using an H100 node "
+ "for one hour is $1.5 per GPU, then you would input 1.5. Do not multiply this number by the total number of "
+ "GPUs in the node, as the system will automatically detect the number of GPUs and include it in the cost "
+ "calculation. Default is 0.0."
+ "Optionally, you can also set this price later through supplier page on the FEDML® Nexus AI Platform."
+)
def fedml_login(
api_key, version, compute_node, server, provider, deploy_worker_num,
local_on_premise_platform, local_on_premise_platform_port,
- master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type
+ master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type, marketplace_type, cost
):
fedml.set_env_version(version)
fedml.set_local_on_premise_platform_host(local_on_premise_platform)
@@ -92,4 +113,4 @@ def fedml_login(
pass
os.environ["FEDML_MODEL_WORKER_NUM"] = str(deploy_worker_num)
fedml.api.login(api_key, compute_node, server, provider, master_inference_gateway_port,
- worker_inference_proxy_port, worker_connection_type)
+ worker_inference_proxy_port, worker_connection_type, marketplace_type, cost)
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
index 3b80511d12..3b3f1efc75 100755
--- a/python/fedml/computing/scheduler/scheduler_core/account_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -48,7 +48,8 @@ def __init__(self):
def get_instance():
return FedMLAccountManager()
- def login(self, user_id, api_key="", device_id=None, os_name=None, role=None, runner_cmd=None):
+ def login(self, user_id, api_key="", device_id=None, os_name=None, role=None, runner_cmd=None, marketplace_type=None,
+ price_per_hour=None):
# Build the agent args
self.build_agent_args(
user_id, api_key=api_key, device_id=device_id, os_name=os_name, role=role, runner_cmd=runner_cmd
@@ -93,9 +94,9 @@ def login(self, user_id, api_key="", device_id=None, os_name=None, role=None, ru
# noinspection PyBroadException
try:
edge_id, user_name, extra_url, general_edge_id = FedMLAccountManager.bind_account_and_device_id(
- service_config["ml_ops_config"]["EDGE_BINDING_URL"], self.agent_args.account_id,
- self.agent_args.unique_device_id, self.agent_args.os_name,
- api_key=api_key, role=role
+ url=service_config["ml_ops_config"]["EDGE_BINDING_URL"], account_id=self.agent_args.account_id,
+ device_id=self.agent_args.unique_device_id, os_name=self.agent_args.os_name,
+ api_key=api_key, role=role, marketplace_type=marketplace_type, price_per_hour=price_per_hour
)
if edge_id > 0:
break
@@ -308,7 +309,7 @@ def get_machine_id():
@staticmethod
def bind_account_and_device_id(
- url, account_id, device_id, os_name, api_key="",
+ url, account_id, device_id, marketplace_type, price_per_hour, os_name,api_key="",
role=ROLE_EDGE_SERVER):
ip = requests.get('https://checkip.amazonaws.com').text.strip()
fedml_ver, exec_path, os_ver, cpu_info, python_ver, torch_ver, mpi_installed, \
@@ -335,6 +336,11 @@ def bind_account_and_device_id(
"available_mem": available_mem, "total_mem": total_mem,
"cpu_count": cpu_count, "gpu_count": 0, "host_name": host_name}
}
+
+ if role == FedMLAccountManager.ROLE_GPU_PROVIDER:
+ json_params["marketplace_type"] = marketplace_type
+ json_params["price_per_hour"] = price_per_hour
+
if gpu_count > 0:
if gpu_total_mem is not None:
json_params["gpu"] = gpu_info if gpu_info is not None else "" + ", Total GPU Memory: " + gpu_total_mem
diff --git a/python/fedml/computing/scheduler/slave/base_slave_agent.py b/python/fedml/computing/scheduler/slave/base_slave_agent.py
index 01c0a39195..61ef6bf4c5 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_agent.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_agent.py
@@ -24,7 +24,8 @@ def __init__(self):
def login(
self, userid, api_key=None, device_id=None,
- os_name=None, need_to_check_gpu=False, role=None
+ os_name=None, need_to_check_gpu=False, role=None,
+ marketplace_type=None, price_per_hour=None
):
# Preprocess the login args
if need_to_check_gpu:
@@ -38,7 +39,8 @@ def login(
# Login account
login_result = FedMLAccountManager.get_instance().login(
userid, api_key=api_key, device_id=device_id,
- os_name=os_name, role=role
+ os_name=os_name, role=role, marketplace_type=marketplace_type,
+ price_per_hour=price_per_hour
)
if login_result is not None:
self.agent_args = login_result
diff --git a/python/fedml/computing/scheduler/slave/client_daemon.py b/python/fedml/computing/scheduler/slave/client_daemon.py
index e543115b4c..6d83952e1f 100755
--- a/python/fedml/computing/scheduler/slave/client_daemon.py
+++ b/python/fedml/computing/scheduler/slave/client_daemon.py
@@ -11,6 +11,7 @@
daemon_ota_upgrade
from fedml.computing.scheduler.slave.client_constants import ClientConstants
from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
+from fedml.api.constants import MarketplaceType
if __name__ == "__main__":
@@ -26,6 +27,8 @@
parser.add_argument("--no_gpu_check", "-ngc", type=int, default=1)
parser.add_argument("--local_on_premise_platform_host", "-lp", type=str, default="127.0.0.1")
parser.add_argument("--local_on_premise_platform_port", "-lpp", type=int, default=80)
+ parser.add_argument("--market_place_type", "-mpt", type=int, default=MarketplaceType.SECURE.value)
+ parser.add_argument("--price_per_hour", "-pph", type=float, default=0.0)
args = parser.parse_args()
args.user = args.user
@@ -84,7 +87,12 @@
"-k",
args.api_key,
"-ngc",
- str(args.no_gpu_check)
+ str(args.no_gpu_check),
+ "-mpt",
+ args.marketplace_type,
+ "-pph",
+ args.price_per_hour
+
]
)
ret_code, exec_out, exec_err = ClientConstants.get_console_sys_out_pipe_err_results(login_pid)
diff --git a/python/fedml/computing/scheduler/slave/client_login.py b/python/fedml/computing/scheduler/slave/client_login.py
index 95c772a225..1ecc581f0d 100755
--- a/python/fedml/computing/scheduler/slave/client_login.py
+++ b/python/fedml/computing/scheduler/slave/client_login.py
@@ -1,6 +1,7 @@
import argparse
import os
import fedml
+from fedml.api import MarketplaceType
from fedml.computing.scheduler.slave.slave_agent import FedMLLaunchSlaveAgent
@@ -24,6 +25,8 @@ def logout():
parser.add_argument("--no_gpu_check", "-ngc", type=int, default=1)
parser.add_argument("--local_on_premise_platform_host", "-lp", type=str, default="127.0.0.1")
parser.add_argument("--local_on_premise_platform_port", "-lpp", type=int, default=80)
+ parser.add_argument("--market_place_type", "-mpt", type=int, default=MarketplaceType.SECURE.value)
+ parser.add_argument("--price_per_hour", "-pph", type=float, default=0.0)
args = parser.parse_args()
args.user = args.user
@@ -38,7 +41,8 @@ def logout():
fedml.set_env_version(args.version)
slave_agent = FedMLLaunchSlaveAgent()
if args.type == 'login':
- slave_agent.login(args.api_key, api_key=args.api_key, device_id=args.device_id,
- os_name=args.os_name, role=args.role)
+ slave_agent.login(userid=args.api_key, api_key=args.api_key, device_id=args.device_id,
+ os_name=args.os_name, role=args.role, marketplace_type=args.market_place_type,
+ price_per_hours=args.price_per_hour)
else:
FedMLLaunchSlaveAgent.logout()
From 24e4ce4e338bd37598dc40102ee01a66bdd1bb45 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Mon, 5 Aug 2024 12:57:45 -0700
Subject: [PATCH 201/282] Fixes
---
python/fedml/api/__init__.py | 2 +-
python/fedml/api/constants.py | 16 ----------------
python/fedml/api/modules/device.py | 4 ++--
python/fedml/cli/modules/login.py | 6 +++---
.../scheduler/scheduler_core/account_manager.py | 6 +++---
.../scheduler_core/general_constants.py | 17 +++++++++++++++++
.../computing/scheduler/slave/client_daemon.py | 4 ++--
.../computing/scheduler/slave/client_login.py | 6 +++---
8 files changed, 31 insertions(+), 30 deletions(-)
diff --git a/python/fedml/api/__init__.py b/python/fedml/api/__init__.py
index b1c56403f1..cd8dd378d5 100755
--- a/python/fedml/api/__init__.py
+++ b/python/fedml/api/__init__.py
@@ -215,7 +215,7 @@ def login(api_key, computing, server, supplier,
master_inference_gateway_port: int = ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
worker_inference_proxy_port: int = ClientConstants.LOCAL_CLIENT_API_PORT,
worker_connection_type: str = ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT,
- marketplace_type: str = MarketplaceType.SECURE.value, price_per_hour: float = 0.0):
+ marketplace_type: str = MarketplaceType.SECURE.name, price_per_hour: float = 0.0):
device_bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port,
worker_connection_type, marketplace_type, price_per_hour)
diff --git a/python/fedml/api/constants.py b/python/fedml/api/constants.py
index d4c59898a8..b284d7a056 100755
--- a/python/fedml/api/constants.py
+++ b/python/fedml/api/constants.py
@@ -1,22 +1,6 @@
from enum import Enum, unique
-class MarketplaceType(Enum):
- SECURE = 1
- COMMUNITY = 2
-
- def __str__(self):
- return self.name
-
- @classmethod
- def from_str(cls, name: str):
- """Get the enum member from a string."""
- if name.upper() in cls.__members__:
- return cls[name.upper()]
- else:
- raise ValueError(f"Invalid marketplace type: {name}")
-
-
class ApiConstants:
RESOURCE_MATCHED_STATUS_MATCHED = "MATCHED"
RESOURCE_MATCHED_STATUS_JOB_URL_ERROR = "ERROR_JOB_URL"
diff --git a/python/fedml/api/modules/device.py b/python/fedml/api/modules/device.py
index 4a932ac631..41380cc51e 100644
--- a/python/fedml/api/modules/device.py
+++ b/python/fedml/api/modules/device.py
@@ -177,11 +177,11 @@ def _bind(
"-k",
user_api_key,
"-ngc",
- "1"
+ "1",
"-mpt",
marketplace_type,
"-pph",
- price_per_hour
+ str(price_per_hour)
]
).pid
sys_utils.save_login_process(ClientConstants.LOCAL_HOME_RUNNER_DIR_NAME,
diff --git a/python/fedml/cli/modules/login.py b/python/fedml/cli/modules/login.py
index 2692a89177..d05106d04f 100644
--- a/python/fedml/cli/modules/login.py
+++ b/python/fedml/cli/modules/login.py
@@ -4,10 +4,10 @@
import click
import fedml.api
-from fedml.api import MarketplaceType
from fedml.api.modules.utils import authenticate
from fedml.computing.scheduler.model_scheduler.device_server_constants import ServerConstants
from fedml.computing.scheduler.model_scheduler.device_client_constants import ClientConstants
+from fedml.computing.scheduler.scheduler_core.general_constants import MarketplaceType
@click.command("login", help="Login the FedML® Nexus AI Platform")
@@ -98,7 +98,7 @@
def fedml_login(
api_key, version, compute_node, server, provider, deploy_worker_num,
local_on_premise_platform, local_on_premise_platform_port,
- master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type, marketplace_type, cost
+ master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type, marketplace_type, price_per_hour
):
fedml.set_env_version(version)
fedml.set_local_on_premise_platform_host(local_on_premise_platform)
@@ -113,4 +113,4 @@ def fedml_login(
pass
os.environ["FEDML_MODEL_WORKER_NUM"] = str(deploy_worker_num)
fedml.api.login(api_key, compute_node, server, provider, master_inference_gateway_port,
- worker_inference_proxy_port, worker_connection_type, marketplace_type, cost)
+ worker_inference_proxy_port, worker_connection_type, marketplace_type, price_per_hour)
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
index 3b3f1efc75..b29dd1bec2 100755
--- a/python/fedml/computing/scheduler/scheduler_core/account_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -12,7 +12,7 @@
from fedml.computing.scheduler.comm_utils import sys_utils, security_utils
from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
from fedml.computing.scheduler.comm_utils.sys_utils import get_sys_runner_info
-from fedml.computing.scheduler.scheduler_core.general_constants import GeneralConstants
+from fedml.computing.scheduler.scheduler_core.general_constants import GeneralConstants, MarketplaceType
from fedml.core.common.singleton import Singleton
from fedml.core.mlops import MLOpsConfigs
@@ -338,8 +338,8 @@ def bind_account_and_device_id(
}
if role == FedMLAccountManager.ROLE_GPU_PROVIDER:
- json_params["marketplace_type"] = marketplace_type
- json_params["price_per_hour"] = price_per_hour
+ json_params["marketplace_type"] = MarketplaceType.from_str(marketplace_type).value
+ json_params["price_per_hour"] = float(price_per_hour)
if gpu_count > 0:
if gpu_total_mem is not None:
diff --git a/python/fedml/computing/scheduler/scheduler_core/general_constants.py b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
index 8c60b17bdf..0fbd4881d9 100755
--- a/python/fedml/computing/scheduler/scheduler_core/general_constants.py
+++ b/python/fedml/computing/scheduler/scheduler_core/general_constants.py
@@ -1,5 +1,6 @@
import logging
import os
+from enum import Enum
from fedml.computing.scheduler.comm_utils.constants import SchedulerConstants
from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
@@ -9,6 +10,22 @@
from fedml.computing.scheduler.model_scheduler import device_server_constants
+class MarketplaceType(Enum):
+ SECURE = 1
+ COMMUNITY = 2
+
+ def __str__(self):
+ return self.name
+
+ @classmethod
+ def from_str(cls, name: str):
+ """Get the enum member from a string."""
+ if name.upper() in cls.__members__:
+ return cls[name.upper()]
+ else:
+ raise ValueError(f"Invalid marketplace type: {name}")
+
+
class GeneralConstants:
MSG_TOPIC_REQUEST_JOB_STATUS_PREFIX = f"anywhere/master_agent/request_job_status/"
MSG_TOPIC_REPORT_DEVICE_STATUS_IN_JOB = f"slave_job/slave_agent/report_device_status_in_job"
diff --git a/python/fedml/computing/scheduler/slave/client_daemon.py b/python/fedml/computing/scheduler/slave/client_daemon.py
index 6d83952e1f..14cf1ce23a 100755
--- a/python/fedml/computing/scheduler/slave/client_daemon.py
+++ b/python/fedml/computing/scheduler/slave/client_daemon.py
@@ -27,8 +27,8 @@
parser.add_argument("--no_gpu_check", "-ngc", type=int, default=1)
parser.add_argument("--local_on_premise_platform_host", "-lp", type=str, default="127.0.0.1")
parser.add_argument("--local_on_premise_platform_port", "-lpp", type=int, default=80)
- parser.add_argument("--market_place_type", "-mpt", type=int, default=MarketplaceType.SECURE.value)
- parser.add_argument("--price_per_hour", "-pph", type=float, default=0.0)
+ parser.add_argument("--market_place_type", "-mpt", type=str, default=MarketplaceType.SECURE.name)
+ parser.add_argument("--price_per_hour", "-pph", type=str, default="0.0")
args = parser.parse_args()
args.user = args.user
diff --git a/python/fedml/computing/scheduler/slave/client_login.py b/python/fedml/computing/scheduler/slave/client_login.py
index 1ecc581f0d..4464443089 100755
--- a/python/fedml/computing/scheduler/slave/client_login.py
+++ b/python/fedml/computing/scheduler/slave/client_login.py
@@ -25,8 +25,8 @@ def logout():
parser.add_argument("--no_gpu_check", "-ngc", type=int, default=1)
parser.add_argument("--local_on_premise_platform_host", "-lp", type=str, default="127.0.0.1")
parser.add_argument("--local_on_premise_platform_port", "-lpp", type=int, default=80)
- parser.add_argument("--market_place_type", "-mpt", type=int, default=MarketplaceType.SECURE.value)
- parser.add_argument("--price_per_hour", "-pph", type=float, default=0.0)
+ parser.add_argument("--market_place_type", "-mpt", type=str, default=MarketplaceType.SECURE.name)
+ parser.add_argument("--price_per_hour", "-pph", type=str, default="0.0")
args = parser.parse_args()
args.user = args.user
@@ -43,6 +43,6 @@ def logout():
if args.type == 'login':
slave_agent.login(userid=args.api_key, api_key=args.api_key, device_id=args.device_id,
os_name=args.os_name, role=args.role, marketplace_type=args.market_place_type,
- price_per_hours=args.price_per_hour)
+ price_per_hour=args.price_per_hour)
else:
FedMLLaunchSlaveAgent.logout()
From b692734ff70141c25bd69bdc94e97e7a1aea7e14 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Mon, 5 Aug 2024 13:06:19 -0700
Subject: [PATCH 202/282] Nits
---
python/fedml/api/__init__.py | 4 ++--
python/fedml/computing/scheduler/slave/client_daemon.py | 2 +-
python/fedml/computing/scheduler/slave/client_login.py | 2 +-
3 files changed, 4 insertions(+), 4 deletions(-)
diff --git a/python/fedml/api/__init__.py b/python/fedml/api/__init__.py
index cd8dd378d5..70b4b1c547 100755
--- a/python/fedml/api/__init__.py
+++ b/python/fedml/api/__init__.py
@@ -15,12 +15,12 @@
"""
from typing import List, Tuple
-from fedml.api.constants import RunStatus, MarketplaceType
+from fedml.api.constants import RunStatus
from fedml.api.fedml_response import FedMLResponse
from fedml.api.modules import launch, utils, build, device, logs, diagnosis, cluster, run, train, federate, storage, \
model as model_module # Since "model" has conflict with one of the input parameters, we need to rename it
-from fedml.api.modules.launch import FeatureEntryPoint
from fedml.api.modules.storage import StorageMetadata
+from fedml.computing.scheduler.scheduler_core.general_constants import MarketplaceType
from fedml.computing.scheduler.scheduler_entry.cluster_manager import FedMLClusterModelList
from fedml.computing.scheduler.scheduler_entry.run_manager import FedMLRunStartedModel, FedMLGpuDevices, \
FedMLRunModelList, FeatureEntryPoint
diff --git a/python/fedml/computing/scheduler/slave/client_daemon.py b/python/fedml/computing/scheduler/slave/client_daemon.py
index 14cf1ce23a..2ded72f15b 100755
--- a/python/fedml/computing/scheduler/slave/client_daemon.py
+++ b/python/fedml/computing/scheduler/slave/client_daemon.py
@@ -9,9 +9,9 @@
from fedml.computing.scheduler.comm_utils.sys_utils import cleanup_all_fedml_client_api_processes, \
cleanup_all_fedml_client_learning_processes, cleanup_all_fedml_client_login_processes, get_python_program, \
daemon_ota_upgrade
+from fedml.computing.scheduler.scheduler_core.general_constants import MarketplaceType
from fedml.computing.scheduler.slave.client_constants import ClientConstants
from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
-from fedml.api.constants import MarketplaceType
if __name__ == "__main__":
diff --git a/python/fedml/computing/scheduler/slave/client_login.py b/python/fedml/computing/scheduler/slave/client_login.py
index 4464443089..d08984e5ba 100755
--- a/python/fedml/computing/scheduler/slave/client_login.py
+++ b/python/fedml/computing/scheduler/slave/client_login.py
@@ -1,7 +1,7 @@
import argparse
import os
import fedml
-from fedml.api import MarketplaceType
+from fedml.computing.scheduler.scheduler_core.general_constants import MarketplaceType
from fedml.computing.scheduler.slave.slave_agent import FedMLLaunchSlaveAgent
From 043fa6e93c81f79f3be0844c9f3c0705c2ac2669 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Mon, 5 Aug 2024 17:18:21 -0700
Subject: [PATCH 203/282] Bugfix
---
.../scheduler/scheduler_core/account_manager.py | 4 ++--
python/fedml/computing/scheduler/slave/client_daemon.py | 9 +++------
python/fedml/computing/scheduler/slave/client_login.py | 4 ++--
.../core/distributed/communication/mqtt/mqtt_manager.py | 1 +
4 files changed, 8 insertions(+), 10 deletions(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
index b29dd1bec2..fd27836d4b 100755
--- a/python/fedml/computing/scheduler/scheduler_core/account_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -338,8 +338,8 @@ def bind_account_and_device_id(
}
if role == FedMLAccountManager.ROLE_GPU_PROVIDER:
- json_params["marketplace_type"] = MarketplaceType.from_str(marketplace_type).value
- json_params["price_per_hour"] = float(price_per_hour)
+ json_params["marketplaceType"] = MarketplaceType.from_str(marketplace_type).value
+ json_params["providerPricePerHour"] = float(price_per_hour)
if gpu_count > 0:
if gpu_total_mem is not None:
diff --git a/python/fedml/computing/scheduler/slave/client_daemon.py b/python/fedml/computing/scheduler/slave/client_daemon.py
index 2ded72f15b..93dc7e65e1 100755
--- a/python/fedml/computing/scheduler/slave/client_daemon.py
+++ b/python/fedml/computing/scheduler/slave/client_daemon.py
@@ -1,4 +1,3 @@
-
import argparse
import os
import time
@@ -13,7 +12,6 @@
from fedml.computing.scheduler.slave.client_constants import ClientConstants
from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
-
if __name__ == "__main__":
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--type", "-t", help="Login or logout to MLOps platform")
@@ -27,7 +25,7 @@
parser.add_argument("--no_gpu_check", "-ngc", type=int, default=1)
parser.add_argument("--local_on_premise_platform_host", "-lp", type=str, default="127.0.0.1")
parser.add_argument("--local_on_premise_platform_port", "-lpp", type=int, default=80)
- parser.add_argument("--market_place_type", "-mpt", type=str, default=MarketplaceType.SECURE.name)
+ parser.add_argument("--marketplace_type", "-mpt", type=str, default=MarketplaceType.SECURE.name)
parser.add_argument("--price_per_hour", "-pph", type=str, default="0.0")
args = parser.parse_args()
@@ -62,7 +60,6 @@
logging.error(f"Cleanup failed | Exception: {e}")
pass
-
# daemon_ota_upgrade(args)
if platform.system() == "Windows":
@@ -92,7 +89,6 @@
args.marketplace_type,
"-pph",
args.price_per_hour
-
]
)
ret_code, exec_out, exec_err = ClientConstants.get_console_sys_out_pipe_err_results(login_pid)
@@ -101,7 +97,8 @@
login_logs = os.path.join(ClientConstants.get_log_file_dir(), "login.log")
run_login_cmd = f"nohup {get_python_program()} -W ignore {login_cmd} -t login -u {args.user} " \
f"-v {args.version} -r {args.role} -id {args.device_id} " \
- f"-k {args.api_key} -ngc {str(args.no_gpu_check)} > {login_logs} 2>&1 &"
+ f"-k {args.api_key} -ngc {str(args.no_gpu_check)} -mpt {args.marketplace_type} " \
+ f"-pph {args.price_per_hour} > {login_logs} 2>&1 &"
if args.os_name != "":
run_login_cmd += f" -os {args.os_name}"
os.system(run_login_cmd)
diff --git a/python/fedml/computing/scheduler/slave/client_login.py b/python/fedml/computing/scheduler/slave/client_login.py
index d08984e5ba..e3f2378132 100755
--- a/python/fedml/computing/scheduler/slave/client_login.py
+++ b/python/fedml/computing/scheduler/slave/client_login.py
@@ -25,7 +25,7 @@ def logout():
parser.add_argument("--no_gpu_check", "-ngc", type=int, default=1)
parser.add_argument("--local_on_premise_platform_host", "-lp", type=str, default="127.0.0.1")
parser.add_argument("--local_on_premise_platform_port", "-lpp", type=int, default=80)
- parser.add_argument("--market_place_type", "-mpt", type=str, default=MarketplaceType.SECURE.name)
+ parser.add_argument("--marketplace_type", "-mpt", type=str, default=MarketplaceType.SECURE.name)
parser.add_argument("--price_per_hour", "-pph", type=str, default="0.0")
args = parser.parse_args()
@@ -42,7 +42,7 @@ def logout():
slave_agent = FedMLLaunchSlaveAgent()
if args.type == 'login':
slave_agent.login(userid=args.api_key, api_key=args.api_key, device_id=args.device_id,
- os_name=args.os_name, role=args.role, marketplace_type=args.market_place_type,
+ os_name=args.os_name, role=args.role, marketplace_type=args.marketplace_type,
price_per_hour=args.price_per_hour)
else:
FedMLLaunchSlaveAgent.logout()
diff --git a/python/fedml/core/distributed/communication/mqtt/mqtt_manager.py b/python/fedml/core/distributed/communication/mqtt/mqtt_manager.py
index 937e9f6644..401f2e7521 100644
--- a/python/fedml/core/distributed/communication/mqtt/mqtt_manager.py
+++ b/python/fedml/core/distributed/communication/mqtt/mqtt_manager.py
@@ -11,6 +11,7 @@
import fedml
+
class MqttManager(object):
def __init__(self, host, port, user, pwd, keepalive_time,
client_id, last_will_topic=None, last_will_msg=None,
From bbf24933565fc39c81163e4dea43c7201e040a6a Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Thu, 8 Aug 2024 14:18:33 -0700
Subject: [PATCH 204/282] Adding validation and price range restriction
---
python/fedml/cli/modules/login.py | 24 ++++++++++++++++++++++--
1 file changed, 22 insertions(+), 2 deletions(-)
diff --git a/python/fedml/cli/modules/login.py b/python/fedml/cli/modules/login.py
index d05106d04f..0e44159692 100644
--- a/python/fedml/cli/modules/login.py
+++ b/python/fedml/cli/modules/login.py
@@ -89,7 +89,8 @@
"-pph",
type=click.FLOAT,
default=0.0,
- help="Enter the price per GPU per hour as a floating-point number. For example, if the cost of using an H100 node "
+ help="Enter the price per GPU per hour as a non-negative floating-point number between 0.0 and 1000.0. For "
+ "example, if the cost of using an H100 node"
"for one hour is $1.5 per GPU, then you would input 1.5. Do not multiply this number by the total number of "
"GPUs in the node, as the system will automatically detect the number of GPUs and include it in the cost "
"calculation. Default is 0.0."
@@ -98,12 +99,20 @@
def fedml_login(
api_key, version, compute_node, server, provider, deploy_worker_num,
local_on_premise_platform, local_on_premise_platform_port,
- master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type, marketplace_type, price_per_hour
+ master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type, marketplace_type,
+ price_per_hour
):
fedml.set_env_version(version)
fedml.set_local_on_premise_platform_host(local_on_premise_platform)
fedml.set_local_on_premise_platform_port(local_on_premise_platform_port)
+ try:
+ price_per_hour = float(price_per_hour)
+ except ValueError as e:
+ raise click.BadParameter(str(e), param_hint="price_per_hour")
+
+ __validate_mpt_pph(marketplace_type, price_per_hour)
+
api_key = api_key[0] if len(api_key) > 0 else None
try:
authenticate(api_key)
@@ -114,3 +123,14 @@ def fedml_login(
os.environ["FEDML_MODEL_WORKER_NUM"] = str(deploy_worker_num)
fedml.api.login(api_key, compute_node, server, provider, master_inference_gateway_port,
worker_inference_proxy_port, worker_connection_type, marketplace_type, price_per_hour)
+
+
+def __validate_mpt_pph(marketplace_type, price_per_hour):
+ try:
+ MarketplaceType.from_str(marketplace_type)
+ except ValueError as e:
+ raise click.BadParameter(str(e), param_hint="marketplace_type")
+
+ if price_per_hour < 0 or price_per_hour > 1000:
+ raise click.BadParameter(f"Price per hour should be a non-negative float ranging between 0 and 1000. Current "
+ f"input value {price_per_hour} is not valid", param_hint="price_per_hour")
From a990a6013336b0ea665168529cc6ae28367bc143 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Mon, 12 Aug 2024 10:54:38 -0700
Subject: [PATCH 205/282] [Deploy] Automatically mount the workspace to
container in the default setting.
---
.../device_client_constants.py | 5 +-
.../device_model_deployment.py | 66 ++++++-------------
2 files changed, 24 insertions(+), 47 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
index 3bb2e12aed..fbe7a95ab9 100644
--- a/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_client_constants.py
@@ -162,8 +162,9 @@ class ClientConstants(object):
EXPOSE_SUBDOMAINS_KEY = "expose_subdomains"
CUSTOMIZED_VOLUMES_MOUNT_KEY = "volumes"
- CUSTOMIZED_VOLUMES_PATH_FROM_WORKSPACE_KEY = "workspace_path"
- CUSTOMIZED_VOLUMES_PATH_FROM_CONTAINER_KEY = "mount_path"
+
+ CUSTOMIZED_WORKSPACE_MOUNT_PATH_KEY = "workspace_mount_path"
+
CUSTOMIZED_SERVICE_KEY = "service"
ENV_USER_ENCRYPTED_API_KEY = "FEDML_USER_ENCRYPTED_API_KEY"
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 6a637653fc..5aa58ae520 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -95,7 +95,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
# Storage related
src_code_dir = os.path.join(model_storage_local_path, config.get('source_code_dir', ""))
data_cache_dir_input = config.get('data_cache_dir', "")
- usr_customized_mount_rule = config.get(ClientConstants.CUSTOMIZED_VOLUMES_MOUNT_KEY, None)
+ usr_customized_workspace_dst = config.get(ClientConstants.CUSTOMIZED_WORKSPACE_MOUNT_PATH_KEY, "")
# Others
extra_envs = config.get('environment_variables', None)
@@ -170,7 +170,7 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
# Handle the default volume mount
handle_volume_mount(volumes, binds, environment, relative_entry_fedml_format, src_code_dir,
- dst_model_serving_dir, usr_customized_mount_rule, host_workspace_root=model_storage_local_path)
+ dst_model_serving_dir, usr_customized_workspace_dst)
# Host config
host_config_dict = {
@@ -541,52 +541,22 @@ def _handle_union_volume_mount(binds, volumes, environment, data_cache_dir_input
def handle_volume_mount(volumes, binds, environment, relative_entry_fedml_format="", src_code_dir="",
- dst_model_serving_dir="", customized_volumes_mount_rule=None, host_workspace_root=""):
+ dst_model_serving_dir="", usr_customized_workspace_dst=""):
# If fedml format entry point is specified, inject the source code, e.g., main.py (FedMLPredictor inside)
+ volumes.append(src_code_dir)
+ dst_mount_dir = dst_model_serving_dir
+
+ if usr_customized_workspace_dst != "" and relative_entry_fedml_format == "":
+ # We only allow user to indicate the workspace mount rule when they are using the custom image
+ dst_mount_dir = usr_customized_workspace_dst
+
+ binds[src_code_dir] = {
+ "bind": dst_mount_dir,
+ "mode": "rw"
+ }
+
if relative_entry_fedml_format != "":
- logging.info("Using FedML format entry point, mounting the source code...")
- volumes.append(src_code_dir)
- binds[src_code_dir] = {
- "bind": dst_model_serving_dir,
- "mode": "rw"
- }
environment["MAIN_ENTRY"] = relative_entry_fedml_format
- return # The reason we return here is that we don't need to mount the source code again
-
- # If customized volume mount rule is specified, just follow the mount rule
- """
- e.g.,
- volumes:
- - workspace_path: "./model_repository"
- mount_path: "/repo_inside_container"
- """
- mount_list = []
- if not isinstance(customized_volumes_mount_rule, list):
- if not isinstance(customized_volumes_mount_rule, dict):
- logging.warning("customized_volumes_mount_rule is not a list or a dictionary, "
- "skip mounting it to the container")
- return
-
- # transform the dict to list
- for k, v in customized_volumes_mount_rule.items():
- mount_list.append({ClientConstants.CUSTOMIZED_VOLUMES_PATH_FROM_WORKSPACE_KEY: k,
- ClientConstants.CUSTOMIZED_VOLUMES_PATH_FROM_CONTAINER_KEY: v})
- else:
- mount_list = customized_volumes_mount_rule if customized_volumes_mount_rule is not None else []
-
- for mount in mount_list:
- workspace_relative_path = mount[ClientConstants.CUSTOMIZED_VOLUMES_PATH_FROM_WORKSPACE_KEY]
- mount_path = mount[ClientConstants.CUSTOMIZED_VOLUMES_PATH_FROM_CONTAINER_KEY]
-
- workspace_path = os.path.join(host_workspace_root, workspace_relative_path)
- if os.path.exists(workspace_path):
- volumes.append(workspace_path)
- binds[workspace_path] = {
- "bind": mount_path,
- "mode": "rw"
- }
- else:
- logging.warning(f"{workspace_path} does not exist, skip mounting it to the container")
def handle_container_service_app(config, model_storage_local_path):
@@ -618,6 +588,12 @@ def handle_container_service_app(config, model_storage_local_path):
# User indicate either fedml format python main entry filename or entry command
expose_subdomains = config.get(ClientConstants.EXPOSE_SUBDOMAINS_KEY, False)
customized_image_entry_cmd = config.get('container_run_command', None) # Could be str or list
+
+ if customized_image_entry_cmd is not None and relative_entry_fedml_format != "":
+ logging.warning("Both entry_point and container_run_command are specified, "
+ "entry_point will be ignored")
+ relative_entry_fedml_format = ""
+
customized_readiness_check = config.get('readiness_probe', ClientConstants.READINESS_PROBE_DEFAULT)
customized_liveliness_check = config.get('liveness_probe', ClientConstants.LIVENESS_PROBE_DEFAULT)
customized_uri = config.get(ClientConstants.CUSTOMIZED_SERVICE_KEY, "")
From dfd83088c96e78c4848dce63f591dd4896e87391 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Mon, 12 Aug 2024 15:08:58 -0700
Subject: [PATCH 206/282] [Deploy] Support bootstrap and CMD be indicated
together.
---
.../device_model_deployment.py | 27 +++++++++++++++++++
1 file changed, 27 insertions(+)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 5aa58ae520..ce4da9553c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -594,6 +594,33 @@ def handle_container_service_app(config, model_storage_local_path):
"entry_point will be ignored")
relative_entry_fedml_format = ""
+ # if usr indicates both the customized_image_entry_cmd and bootstrap, we will inject the bootstrap into the entry
+ # Using /bin/bash to run the bootstrap script, there are three legal formats for the customized_image_entry_cmd
+ # However, only the third one is supported in this function
+ """
+ 1. CMD ["executable","param1","param2"] (exec form)
+ e.g.
+ CMD ["python3", "/app/app.py", "--port", "8080"]
+
+ 2. CMD ["param1","param2"] (exec form, as default parameters to ENTRYPOINT)
+ e.g.
+ ENTRYPOINT ["nginx"]
+ CMD ["-g", "daemon off;"]
+
+ 3. CMD command param1 param2
+ e.g.
+ echo "Container is running" && curl http://example.com
+ """
+ if dst_bootstrap_dir != "" and customized_image_entry_cmd is not None:
+ if isinstance(customized_image_entry_cmd, str):
+ if customized_image_entry_cmd == "":
+ # We do not know the original CMD in the Dockerfile and do not want to overwrite it
+ pass
+ else:
+ customized_image_entry_cmd = f"/bin/bash {dst_bootstrap_dir} && {customized_image_entry_cmd}"
+ else:
+ logging.warning("The customized_image_entry_cmd is not a string, skip injecting the bootstrap script")
+
customized_readiness_check = config.get('readiness_probe', ClientConstants.READINESS_PROBE_DEFAULT)
customized_liveliness_check = config.get('liveness_probe', ClientConstants.LIVENESS_PROBE_DEFAULT)
customized_uri = config.get(ClientConstants.CUSTOMIZED_SERVICE_KEY, "")
From 1a09b0edc210f51e05190c7b54ad51b9c45a549f Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Mon, 12 Aug 2024 16:58:12 -0700
Subject: [PATCH 207/282] [Deploy] Nit.
---
.../device_model_deployment.py | 19 +++++++++++++++----
1 file changed, 15 insertions(+), 4 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index ce4da9553c..2fbbbfcb0d 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -555,13 +555,16 @@ def handle_volume_mount(volumes, binds, environment, relative_entry_fedml_format
"mode": "rw"
}
+ logging.info(f"Mounting the source code to the container..., target: {dst_mount_dir}")
+
if relative_entry_fedml_format != "":
environment["MAIN_ENTRY"] = relative_entry_fedml_format
def handle_container_service_app(config, model_storage_local_path):
# Bootstrap, job and entrypoint related
- dst_model_serving_dir = "/home/fedml/models_serving"
+ dst_model_serving_dir = config.get(ClientConstants.CUSTOMIZED_WORKSPACE_MOUNT_PATH_KEY,
+ "/home/fedml/models_serving")
bootstrap_cmds_str_frm_yaml = config.get('bootstrap', "")
job_cmds_str_frm_yaml = config.get('job', "")
@@ -569,16 +572,21 @@ def handle_container_service_app(config, model_storage_local_path):
if bootstrap_cmds_str_frm_yaml != "" or job_cmds_str_frm_yaml != "":
src_bootstrap_file_path = os.path.join(model_storage_local_path, auto_gen_bootstrap_file_name)
with open(src_bootstrap_file_path, 'w') as f:
- f.write("cd /home/fedml/models_serving/\n")
+ f.write(f"cd {dst_model_serving_dir}/\n")
f.write(bootstrap_cmds_str_frm_yaml)
f.write("\n")
- f.write("cd /home/fedml/models_serving/\n")
+ f.write(f"cd {dst_model_serving_dir}/\n")
f.write(job_cmds_str_frm_yaml)
else:
src_bootstrap_file_path = ""
if src_bootstrap_file_path != "":
dst_bootstrap_dir = os.path.join(dst_model_serving_dir, auto_gen_bootstrap_file_name)
+
+ # User could specify "workspace_mount_path", override the default path
+ if ClientConstants.CUSTOMIZED_WORKSPACE_MOUNT_PATH_KEY in config:
+ dst_bootstrap_dir = os.path.join(config[ClientConstants.CUSTOMIZED_WORKSPACE_MOUNT_PATH_KEY],
+ auto_gen_bootstrap_file_name)
else:
dst_bootstrap_dir = ""
@@ -617,7 +625,10 @@ def handle_container_service_app(config, model_storage_local_path):
# We do not know the original CMD in the Dockerfile and do not want to overwrite it
pass
else:
- customized_image_entry_cmd = f"/bin/bash {dst_bootstrap_dir} && {customized_image_entry_cmd}"
+ # TODO(Raphael): Try to fix the compatibility issue with the first two formats and
+ # also the restriction of /bin/bash
+ customized_image_entry_cmd = \
+ f"/bin/bash -c '/bin/bash {dst_bootstrap_dir} && {customized_image_entry_cmd}'"
else:
logging.warning("The customized_image_entry_cmd is not a string, skip injecting the bootstrap script")
From 7e5f6a177afd5767f020890d19d38814cafdc40b Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Mon, 12 Aug 2024 17:05:44 -0700
Subject: [PATCH 208/282] [Deploy] Add example.
---
.../quickstart/config.yaml | 12 ++++++++
.../quickstart/main_entry.py | 28 +++++++++++++++++++
2 files changed, 40 insertions(+)
create mode 100644 python/examples/deploy/custom_inference_image/quickstart/config.yaml
create mode 100644 python/examples/deploy/custom_inference_image/quickstart/main_entry.py
diff --git a/python/examples/deploy/custom_inference_image/quickstart/config.yaml b/python/examples/deploy/custom_inference_image/quickstart/config.yaml
new file mode 100644
index 0000000000..382209ad1b
--- /dev/null
+++ b/python/examples/deploy/custom_inference_image/quickstart/config.yaml
@@ -0,0 +1,12 @@
+workspace: "."
+inference_image: "your_docker_hub_repo/your_image_name"
+
+workspace_mount_path: "/my_workspace" # Default is "/home/fedml/models_serving"
+
+container_run_command: "echo hello && python3 /my_workspace/main_entry.py"
+
+# If you want to install some packages
+# Please write the command in the bootstrap.sh
+bootstrap: |
+ echo "Install some packages..."
+ echo "Install finished!"
diff --git a/python/examples/deploy/custom_inference_image/quickstart/main_entry.py b/python/examples/deploy/custom_inference_image/quickstart/main_entry.py
new file mode 100644
index 0000000000..7b7caee87b
--- /dev/null
+++ b/python/examples/deploy/custom_inference_image/quickstart/main_entry.py
@@ -0,0 +1,28 @@
+from fedml.serving import FedMLPredictor
+from fedml.serving import FedMLInferenceRunner
+import uuid
+
+
+class Bot(FedMLPredictor): # Inherit FedMLClientPredictor
+ def __init__(self):
+ super().__init__()
+
+ # --- Your model initialization code here, here is a example ---
+ self.uuid = uuid.uuid4()
+ # -------------------------------------------
+
+ def predict(self, request: dict):
+ input_dict = request
+ question: str = input_dict.get("text", "").strip()
+
+ # --- Your model inference code here ---
+ response = f"I am a replica, my id is {self.uuid}"
+ # ---------------------------------------
+
+ return {"v1_generated_text": f"V1: The answer to your question {question} is: {response}"}
+
+
+if __name__ == "__main__":
+ chatbot = Bot()
+ fedml_inference_runner = FedMLInferenceRunner(chatbot)
+ fedml_inference_runner.run()
From 47efcde55a7e8fd669dd88ce505b74c197e5f6e8 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Thu, 22 Aug 2024 14:26:44 -0700
Subject: [PATCH 209/282] feat: Add name parameter to the bindingEdge method
---
python/fedml/api/__init__.py | 8 ++++----
python/fedml/api/modules/device.py | 10 ++++++----
python/fedml/cli/modules/login.py | 9 ++++++++-
.../scheduler/scheduler_core/account_manager.py | 9 ++++++---
.../computing/scheduler/slave/base_slave_agent.py | 4 ++--
.../fedml/computing/scheduler/slave/client_daemon.py | 7 +++++--
python/fedml/computing/scheduler/slave/client_login.py | 3 ++-
7 files changed, 33 insertions(+), 17 deletions(-)
diff --git a/python/fedml/api/__init__.py b/python/fedml/api/__init__.py
index 70b4b1c547..6c82c9b9b3 100755
--- a/python/fedml/api/__init__.py
+++ b/python/fedml/api/__init__.py
@@ -215,9 +215,9 @@ def login(api_key, computing, server, supplier,
master_inference_gateway_port: int = ServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
worker_inference_proxy_port: int = ClientConstants.LOCAL_CLIENT_API_PORT,
worker_connection_type: str = ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT,
- marketplace_type: str = MarketplaceType.SECURE.name, price_per_hour: float = 0.0):
+ marketplace_type: str = MarketplaceType.SECURE.name, price_per_hour: float = 0.0, name=""):
device_bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port,
- worker_connection_type, marketplace_type, price_per_hour)
+ worker_connection_type, marketplace_type, price_per_hour, name)
def logout(computing, server):
@@ -225,11 +225,11 @@ def logout(computing, server):
def device_bind(api_key, computing, server, supplier, master_inference_gateway_port, worker_inference_proxy_port,
- worker_connection_type, marketplace_type, price_per_hour):
+ worker_connection_type, marketplace_type, price_per_hour, name):
device.bind(api_key=api_key, computing=computing, server=server, supplier=supplier,
master_inference_gateway_port=master_inference_gateway_port,
worker_inference_proxy_port=worker_inference_proxy_port, worker_connection_type=worker_connection_type,
- marketplace_type=marketplace_type, price_per_hour=price_per_hour)
+ marketplace_type=marketplace_type, price_per_hour=price_per_hour, name=name)
def device_unbind(computing, server):
diff --git a/python/fedml/api/modules/device.py b/python/fedml/api/modules/device.py
index 41380cc51e..9f39419a74 100644
--- a/python/fedml/api/modules/device.py
+++ b/python/fedml/api/modules/device.py
@@ -19,7 +19,7 @@
def bind(
- api_key, computing, server, supplier, marketplace_type, price_per_hour,
+ api_key, computing, server, supplier, marketplace_type, price_per_hour, name,
master_inference_gateway_port=DeviceServerConstants.MODEL_INFERENCE_DEFAULT_PORT,
worker_inference_proxy_port=DeviceClientConstants.LOCAL_CLIENT_API_PORT,
worker_connection_type=DeviceClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT):
@@ -48,12 +48,12 @@ def bind(
userid, computing, server,
api_key, role, runner_cmd, device_id, os_name,
docker, master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type, marketplace_type,
- price_per_hour)
+ price_per_hour, name)
def _bind(
userid, computing, server, api_key, role, runner_cmd, device_id, os_name, docker, master_inference_gateway_port,
- worker_inference_proxy_port, worker_connection_type, marketplace_type, price_per_hour):
+ worker_inference_proxy_port, worker_connection_type, marketplace_type, price_per_hour, name):
fedml.load_env()
if os.getenv(ModuleConstants.ENV_FEDML_INFER_HOST) is None:
fedml.set_env_kv(ModuleConstants.ENV_FEDML_INFER_HOST, SchedulerConstants.REDIS_INFER_HOST)
@@ -181,7 +181,9 @@ def _bind(
"-mpt",
marketplace_type,
"-pph",
- str(price_per_hour)
+ str(price_per_hour),
+ "-n",
+ name
]
).pid
sys_utils.save_login_process(ClientConstants.LOCAL_HOME_RUNNER_DIR_NAME,
diff --git a/python/fedml/cli/modules/login.py b/python/fedml/cli/modules/login.py
index 0e44159692..fc1cd408c4 100644
--- a/python/fedml/cli/modules/login.py
+++ b/python/fedml/cli/modules/login.py
@@ -96,11 +96,18 @@
"calculation. Default is 0.0."
"Optionally, you can also set this price later through supplier page on the FEDML® Nexus AI Platform."
)
+@click.option(
+ "--name",
+ "-n",
+ type=str,
+ default="",
+ help="Name of the node.",
+)
def fedml_login(
api_key, version, compute_node, server, provider, deploy_worker_num,
local_on_premise_platform, local_on_premise_platform_port,
master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type, marketplace_type,
- price_per_hour
+ price_per_hour, name
):
fedml.set_env_version(version)
fedml.set_local_on_premise_platform_host(local_on_premise_platform)
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
index fd27836d4b..1a378d54cd 100755
--- a/python/fedml/computing/scheduler/scheduler_core/account_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -49,7 +49,7 @@ def get_instance():
return FedMLAccountManager()
def login(self, user_id, api_key="", device_id=None, os_name=None, role=None, runner_cmd=None, marketplace_type=None,
- price_per_hour=None):
+ price_per_hour=None, name=""):
# Build the agent args
self.build_agent_args(
user_id, api_key=api_key, device_id=device_id, os_name=os_name, role=role, runner_cmd=runner_cmd
@@ -96,7 +96,8 @@ def login(self, user_id, api_key="", device_id=None, os_name=None, role=None, ru
edge_id, user_name, extra_url, general_edge_id = FedMLAccountManager.bind_account_and_device_id(
url=service_config["ml_ops_config"]["EDGE_BINDING_URL"], account_id=self.agent_args.account_id,
device_id=self.agent_args.unique_device_id, os_name=self.agent_args.os_name,
- api_key=api_key, role=role, marketplace_type=marketplace_type, price_per_hour=price_per_hour
+ api_key=api_key, role=role, marketplace_type=marketplace_type, price_per_hour=price_per_hour,
+ name=name
)
if edge_id > 0:
break
@@ -310,7 +311,7 @@ def get_machine_id():
@staticmethod
def bind_account_and_device_id(
url, account_id, device_id, marketplace_type, price_per_hour, os_name,api_key="",
- role=ROLE_EDGE_SERVER):
+ role=ROLE_EDGE_SERVER, name=""):
ip = requests.get('https://checkip.amazonaws.com').text.strip()
fedml_ver, exec_path, os_ver, cpu_info, python_ver, torch_ver, mpi_installed, \
cpu_usage, available_mem, total_mem, gpu_info, gpu_available_mem, gpu_total_mem, \
@@ -340,6 +341,8 @@ def bind_account_and_device_id(
if role == FedMLAccountManager.ROLE_GPU_PROVIDER:
json_params["marketplaceType"] = MarketplaceType.from_str(marketplace_type).value
json_params["providerPricePerHour"] = float(price_per_hour)
+ if name:
+ json_params["name"] = name
if gpu_count > 0:
if gpu_total_mem is not None:
diff --git a/python/fedml/computing/scheduler/slave/base_slave_agent.py b/python/fedml/computing/scheduler/slave/base_slave_agent.py
index 61ef6bf4c5..a149dfe046 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_agent.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_agent.py
@@ -25,7 +25,7 @@ def __init__(self):
def login(
self, userid, api_key=None, device_id=None,
os_name=None, need_to_check_gpu=False, role=None,
- marketplace_type=None, price_per_hour=None
+ marketplace_type=None, price_per_hour=None, name=""
):
# Preprocess the login args
if need_to_check_gpu:
@@ -40,7 +40,7 @@ def login(
login_result = FedMLAccountManager.get_instance().login(
userid, api_key=api_key, device_id=device_id,
os_name=os_name, role=role, marketplace_type=marketplace_type,
- price_per_hour=price_per_hour
+ price_per_hour=price_per_hour, name=name
)
if login_result is not None:
self.agent_args = login_result
diff --git a/python/fedml/computing/scheduler/slave/client_daemon.py b/python/fedml/computing/scheduler/slave/client_daemon.py
index 93dc7e65e1..a82c446c0d 100755
--- a/python/fedml/computing/scheduler/slave/client_daemon.py
+++ b/python/fedml/computing/scheduler/slave/client_daemon.py
@@ -27,6 +27,7 @@
parser.add_argument("--local_on_premise_platform_port", "-lpp", type=int, default=80)
parser.add_argument("--marketplace_type", "-mpt", type=str, default=MarketplaceType.SECURE.name)
parser.add_argument("--price_per_hour", "-pph", type=str, default="0.0")
+ parser.add_argument("--name", "-n", type=str, default="")
args = parser.parse_args()
args.user = args.user
@@ -88,7 +89,9 @@
"-mpt",
args.marketplace_type,
"-pph",
- args.price_per_hour
+ args.price_per_hour,
+ "-n",
+ args.name
]
)
ret_code, exec_out, exec_err = ClientConstants.get_console_sys_out_pipe_err_results(login_pid)
@@ -98,7 +101,7 @@
run_login_cmd = f"nohup {get_python_program()} -W ignore {login_cmd} -t login -u {args.user} " \
f"-v {args.version} -r {args.role} -id {args.device_id} " \
f"-k {args.api_key} -ngc {str(args.no_gpu_check)} -mpt {args.marketplace_type} " \
- f"-pph {args.price_per_hour} > {login_logs} 2>&1 &"
+ f"-pph {args.price_per_hour} -n {args.name} > {login_logs} 2>&1 &"
if args.os_name != "":
run_login_cmd += f" -os {args.os_name}"
os.system(run_login_cmd)
diff --git a/python/fedml/computing/scheduler/slave/client_login.py b/python/fedml/computing/scheduler/slave/client_login.py
index e3f2378132..c830f9e8c2 100755
--- a/python/fedml/computing/scheduler/slave/client_login.py
+++ b/python/fedml/computing/scheduler/slave/client_login.py
@@ -27,6 +27,7 @@ def logout():
parser.add_argument("--local_on_premise_platform_port", "-lpp", type=int, default=80)
parser.add_argument("--marketplace_type", "-mpt", type=str, default=MarketplaceType.SECURE.name)
parser.add_argument("--price_per_hour", "-pph", type=str, default="0.0")
+ parser.add_argument("--name", "-n", type=str, default="")
args = parser.parse_args()
args.user = args.user
@@ -43,6 +44,6 @@ def logout():
if args.type == 'login':
slave_agent.login(userid=args.api_key, api_key=args.api_key, device_id=args.device_id,
os_name=args.os_name, role=args.role, marketplace_type=args.marketplace_type,
- price_per_hour=args.price_per_hour)
+ price_per_hour=args.price_per_hour, name=args.name)
else:
FedMLLaunchSlaveAgent.logout()
From 135c55b77a1631a78cd0027fb6f2fdf2a2e57a68 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Thu, 22 Aug 2024 14:51:02 -0700
Subject: [PATCH 210/282] Pass name into login
---
python/fedml/cli/modules/login.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/cli/modules/login.py b/python/fedml/cli/modules/login.py
index fc1cd408c4..b76346ec1b 100644
--- a/python/fedml/cli/modules/login.py
+++ b/python/fedml/cli/modules/login.py
@@ -129,7 +129,7 @@ def fedml_login(
pass
os.environ["FEDML_MODEL_WORKER_NUM"] = str(deploy_worker_num)
fedml.api.login(api_key, compute_node, server, provider, master_inference_gateway_port,
- worker_inference_proxy_port, worker_connection_type, marketplace_type, price_per_hour)
+ worker_inference_proxy_port, worker_connection_type, marketplace_type, price_per_hour, name)
def __validate_mpt_pph(marketplace_type, price_per_hour):
From 4cf806621ac5d2387720b8de47cb2a380ca6b4c4 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@tensoropera.com>
Date: Wed, 4 Sep 2024 00:20:00 +0000
Subject: [PATCH 211/282] Fixing grpc and trpc ipconfig from 127.0.0.0 to
0.0.0.0
---
.../custom_data_and_model/config/grpc_ipconfig.csv | 6 +++---
.../one_line/config/grpc_ipconfig.csv | 6 +++---
.../step_by_step/config/grpc_ipconfig.csv | 6 +++---
.../one_line/config/trpc_master_config.csv | 2 +-
4 files changed, 10 insertions(+), 10 deletions(-)
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/config/grpc_ipconfig.csv b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/config/grpc_ipconfig.csv
index f97ee8f910..4618a98b7a 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/config/grpc_ipconfig.csv
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/config/grpc_ipconfig.csv
@@ -1,4 +1,4 @@
receiver_id,ip
-0,127.0.0.1
-1,127.0.0.1
-2,127.0.0.1
\ No newline at end of file
+0,0.0.0.0
+1,0.0.0.0
+2,0.0.0.0
\ No newline at end of file
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/config/grpc_ipconfig.csv b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/config/grpc_ipconfig.csv
index f97ee8f910..4618a98b7a 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/config/grpc_ipconfig.csv
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/config/grpc_ipconfig.csv
@@ -1,4 +1,4 @@
receiver_id,ip
-0,127.0.0.1
-1,127.0.0.1
-2,127.0.0.1
\ No newline at end of file
+0,0.0.0.0
+1,0.0.0.0
+2,0.0.0.0
\ No newline at end of file
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/config/grpc_ipconfig.csv b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/config/grpc_ipconfig.csv
index f97ee8f910..4618a98b7a 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/config/grpc_ipconfig.csv
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/config/grpc_ipconfig.csv
@@ -1,4 +1,4 @@
receiver_id,ip
-0,127.0.0.1
-1,127.0.0.1
-2,127.0.0.1
\ No newline at end of file
+0,0.0.0.0
+1,0.0.0.0
+2,0.0.0.0
\ No newline at end of file
diff --git a/python/examples/federate/cross_silo/trpc_fedavg_mnist_lr_example/one_line/config/trpc_master_config.csv b/python/examples/federate/cross_silo/trpc_fedavg_mnist_lr_example/one_line/config/trpc_master_config.csv
index dbfb0c6a4b..9708cd18e6 100644
--- a/python/examples/federate/cross_silo/trpc_fedavg_mnist_lr_example/one_line/config/trpc_master_config.csv
+++ b/python/examples/federate/cross_silo/trpc_fedavg_mnist_lr_example/one_line/config/trpc_master_config.csv
@@ -1,2 +1,2 @@
master_ip, master_port
-127.0.0.1,29600
+0.0.0.0,29600
From 277f4caefe31478f2a5accbfb24170307cb73035 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Thu, 5 Sep 2024 15:46:19 -0700
Subject: [PATCH 212/282] Remove if condition, add log
---
.../computing/scheduler/scheduler_core/account_manager.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
index 1a378d54cd..85d76d5973 100755
--- a/python/fedml/computing/scheduler/scheduler_core/account_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -341,8 +341,8 @@ def bind_account_and_device_id(
if role == FedMLAccountManager.ROLE_GPU_PROVIDER:
json_params["marketplaceType"] = MarketplaceType.from_str(marketplace_type).value
json_params["providerPricePerHour"] = float(price_per_hour)
- if name:
- json_params["name"] = name
+ json_params["name"] = name
+ logging.info(f"[DEBUG] marketplaceType: {marketplace_type}, price_per_hour: {price_per_hour}, name: {name}")
if gpu_count > 0:
if gpu_total_mem is not None:
From 17dd2b743c779a36f7aa4353723d3b7e9fd73108 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Thu, 12 Sep 2024 11:43:14 -0700
Subject: [PATCH 213/282] Stringify name
---
python/fedml/api/modules/device.py | 2 +-
python/fedml/computing/scheduler/slave/client_daemon.py | 3 +--
2 files changed, 2 insertions(+), 3 deletions(-)
diff --git a/python/fedml/api/modules/device.py b/python/fedml/api/modules/device.py
index 9f39419a74..27b2d0d198 100644
--- a/python/fedml/api/modules/device.py
+++ b/python/fedml/api/modules/device.py
@@ -183,7 +183,7 @@ def _bind(
"-pph",
str(price_per_hour),
"-n",
- name
+ str(name)
]
).pid
sys_utils.save_login_process(ClientConstants.LOCAL_HOME_RUNNER_DIR_NAME,
diff --git a/python/fedml/computing/scheduler/slave/client_daemon.py b/python/fedml/computing/scheduler/slave/client_daemon.py
index a82c446c0d..5e346a0901 100755
--- a/python/fedml/computing/scheduler/slave/client_daemon.py
+++ b/python/fedml/computing/scheduler/slave/client_daemon.py
@@ -6,8 +6,7 @@
import fedml
from fedml.computing.scheduler.comm_utils.sys_utils import cleanup_all_fedml_client_api_processes, \
- cleanup_all_fedml_client_learning_processes, cleanup_all_fedml_client_login_processes, get_python_program, \
- daemon_ota_upgrade
+ cleanup_all_fedml_client_learning_processes, cleanup_all_fedml_client_login_processes, get_python_program
from fedml.computing.scheduler.scheduler_core.general_constants import MarketplaceType
from fedml.computing.scheduler.slave.client_constants import ClientConstants
from fedml.computing.scheduler.comm_utils.run_process_utils import RunProcessUtils
From 4a198ebd8385a0c7fb83222eb5d5162c2f777604 Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Thu, 12 Sep 2024 14:31:52 -0700
Subject: [PATCH 214/282] Set name arg required to True
---
python/fedml/computing/scheduler/slave/client_daemon.py | 2 +-
python/fedml/computing/scheduler/slave/client_login.py | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/slave/client_daemon.py b/python/fedml/computing/scheduler/slave/client_daemon.py
index 5e346a0901..4c69210346 100755
--- a/python/fedml/computing/scheduler/slave/client_daemon.py
+++ b/python/fedml/computing/scheduler/slave/client_daemon.py
@@ -26,7 +26,7 @@
parser.add_argument("--local_on_premise_platform_port", "-lpp", type=int, default=80)
parser.add_argument("--marketplace_type", "-mpt", type=str, default=MarketplaceType.SECURE.name)
parser.add_argument("--price_per_hour", "-pph", type=str, default="0.0")
- parser.add_argument("--name", "-n", type=str, default="")
+ parser.add_argument("--name", "-n", type=str, required=True)
args = parser.parse_args()
args.user = args.user
diff --git a/python/fedml/computing/scheduler/slave/client_login.py b/python/fedml/computing/scheduler/slave/client_login.py
index c830f9e8c2..e551639409 100755
--- a/python/fedml/computing/scheduler/slave/client_login.py
+++ b/python/fedml/computing/scheduler/slave/client_login.py
@@ -27,7 +27,7 @@ def logout():
parser.add_argument("--local_on_premise_platform_port", "-lpp", type=int, default=80)
parser.add_argument("--marketplace_type", "-mpt", type=str, default=MarketplaceType.SECURE.name)
parser.add_argument("--price_per_hour", "-pph", type=str, default="0.0")
- parser.add_argument("--name", "-n", type=str, default="")
+ parser.add_argument("--name", "-n", type=str, required=True)
args = parser.parse_args()
args.user = args.user
From 16417d57d29e73303ab0bf9023c6dc7e48f82b3a Mon Sep 17 00:00:00 2001
From: Alay Shah <alay11shah@gmail.com>
Date: Thu, 12 Sep 2024 14:36:28 -0700
Subject: [PATCH 215/282] Making name optional
---
python/fedml/computing/scheduler/slave/client_daemon.py | 2 +-
python/fedml/computing/scheduler/slave/client_login.py | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/slave/client_daemon.py b/python/fedml/computing/scheduler/slave/client_daemon.py
index 4c69210346..a3dff06e2d 100755
--- a/python/fedml/computing/scheduler/slave/client_daemon.py
+++ b/python/fedml/computing/scheduler/slave/client_daemon.py
@@ -26,7 +26,7 @@
parser.add_argument("--local_on_premise_platform_port", "-lpp", type=int, default=80)
parser.add_argument("--marketplace_type", "-mpt", type=str, default=MarketplaceType.SECURE.name)
parser.add_argument("--price_per_hour", "-pph", type=str, default="0.0")
- parser.add_argument("--name", "-n", type=str, required=True)
+ parser.add_argument("--name", "-n", type=str, nargs='?', default="")
args = parser.parse_args()
args.user = args.user
diff --git a/python/fedml/computing/scheduler/slave/client_login.py b/python/fedml/computing/scheduler/slave/client_login.py
index e551639409..6d8b9d1ae1 100755
--- a/python/fedml/computing/scheduler/slave/client_login.py
+++ b/python/fedml/computing/scheduler/slave/client_login.py
@@ -27,7 +27,7 @@ def logout():
parser.add_argument("--local_on_premise_platform_port", "-lpp", type=int, default=80)
parser.add_argument("--marketplace_type", "-mpt", type=str, default=MarketplaceType.SECURE.name)
parser.add_argument("--price_per_hour", "-pph", type=str, default="0.0")
- parser.add_argument("--name", "-n", type=str, required=True)
+ parser.add_argument("--name", "-n", type=str, nargs='?', default="")
args = parser.parse_args()
args.user = args.user
From 53aead35928fedfaff68cfc863ea47c34888694d Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Fri, 13 Sep 2024 11:11:27 +0800
Subject: [PATCH 216/282] add the new certs.
---
python/fedml/core/mlops/mlops_configs.py | 38 ++++++++---
.../mlops/ssl/open.chainopera.ai.local.crt | 63 +++++++++++++++++++
.../ssl/open.chainopera.ai.local.rootca.crt | 29 +++++++++
3 files changed, 121 insertions(+), 9 deletions(-)
create mode 100644 python/fedml/core/mlops/ssl/open.chainopera.ai.local.crt
create mode 100644 python/fedml/core/mlops/ssl/open.chainopera.ai.local.rootca.crt
diff --git a/python/fedml/core/mlops/mlops_configs.py b/python/fedml/core/mlops/mlops_configs.py
index 1ed2e0476d..338f59e697 100644
--- a/python/fedml/core/mlops/mlops_configs.py
+++ b/python/fedml/core/mlops/mlops_configs.py
@@ -8,6 +8,7 @@
import fedml
from fedml.core.mlops.mlops_utils import MLOpsUtils
+from urllib.parse import urlparse
class Configs(Enum):
@@ -49,9 +50,15 @@ def get_request_params():
cert_path = None
if str(url).startswith("https://"):
cur_source_dir = os.path.dirname(__file__)
- cert_path = os.path.join(
- cur_source_dir, "ssl", "open-" + fedml.get_env_version() + ".fedml.ai_bundle.crt"
- )
+ version = fedml.get_env_version()
+ if version == "local":
+ cert_path = os.path.join(
+ cur_source_dir, "ssl", f"{urlparse(url).hostname}.{version}.crt"
+ )
+ else:
+ cert_path = os.path.join(
+ cur_source_dir, "ssl", "open-" + fedml.get_env_version() + ".fedml.ai_bundle.crt"
+ )
return url, cert_path
@@ -88,17 +95,30 @@ def get_cert_path_with_version():
cert_path = None
if str(url).startswith("https://"):
cur_source_dir = os.path.dirname(__file__)
- cert_path = os.path.join(
- cur_source_dir, "ssl", "open-" + version + ".fedml.ai_bundle.crt"
- )
+ if version == "local":
+ cert_path = os.path.join(
+ cur_source_dir, "ssl", f"{urlparse(url).hostname}.{version}.crt"
+ )
+ else:
+ cert_path = os.path.join(
+ cur_source_dir, "ssl", "open-" + version + ".fedml.ai_bundle.crt"
+ )
+
return cert_path
@staticmethod
def get_root_ca_path():
cur_source_dir = os.path.dirname(__file__)
- cert_path = os.path.join(
- cur_source_dir, "ssl", "open-root-ca.crt"
- )
+ version = fedml.get_env_version()
+ if version == "local":
+ url = fedml._get_backend_service()
+ cert_path = os.path.join(
+ cur_source_dir, "ssl", f"{urlparse(url).hostname}.{version}.rootca.crt"
+ )
+ else:
+ cert_path = os.path.join(
+ cur_source_dir, "ssl", "open-root-ca.crt"
+ )
return cert_path
@staticmethod
diff --git a/python/fedml/core/mlops/ssl/open.chainopera.ai.local.crt b/python/fedml/core/mlops/ssl/open.chainopera.ai.local.crt
new file mode 100644
index 0000000000..400c30aaa0
--- /dev/null
+++ b/python/fedml/core/mlops/ssl/open.chainopera.ai.local.crt
@@ -0,0 +1,63 @@
+-----BEGIN CERTIFICATE-----
+MIIF8zCCBFugAwIBAgIQKyZVUxZMMiOwsUN8tL5DgjANBgkqhkiG9w0BAQwFADBZ
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+Ds2c6MGRaNxrmYx90fzyudF7w40AX9PMgBLWKuuX+qiEpab9MHhI
+-----END CERTIFICATE-----
+-----BEGIN CERTIFICATE-----
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+-----END CERTIFICATE-----
\ No newline at end of file
diff --git a/python/fedml/core/mlops/ssl/open.chainopera.ai.local.rootca.crt b/python/fedml/core/mlops/ssl/open.chainopera.ai.local.rootca.crt
new file mode 100644
index 0000000000..e87d0c5783
--- /dev/null
+++ b/python/fedml/core/mlops/ssl/open.chainopera.ai.local.rootca.crt
@@ -0,0 +1,29 @@
+-----BEGIN CERTIFICATE-----
+MIIFBzCCA++gAwIBAgIRALIM7VUuMaC/NDp1KHQ76aswDQYJKoZIhvcNAQELBQAw
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+Zale8OMckn/BU3O/3aFNLHLfET2H2hT6Wb3nwxjpLIfXmSVcVd8A58XH0g==
+-----END CERTIFICATE-----
From f46cd1e2e5920d3be5c1edcbbb5f19a9c7ba0b38 Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Fri, 13 Sep 2024 11:13:52 +0800
Subject: [PATCH 217/282] update new certs.
---
python/setup.py | 2 ++
1 file changed, 2 insertions(+)
diff --git a/python/setup.py b/python/setup.py
index 4757c10a17..3847f360fa 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -173,6 +173,8 @@ def finalize_options(self):
"fedml/core/mlops/ssl/open-test.fedml.ai_bundle.crt",
"fedml/core/mlops/ssl/open-release.fedml.ai_bundle.crt",
"fedml/core/mlops/ssl/open-root-ca.crt",
+ "fedml/core/mlops/ssl/open.chainopera.ai.local.crt",
+ "fedml/core/mlops/ssl/open.chainopera.ai.local.rootca.crt",
],
)
],
From e046f5b6dd7a70e7b928f4778d32cb99e19a7e41 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@tensoropera.com>
Date: Fri, 13 Sep 2024 23:30:27 +0000
Subject: [PATCH 218/282] Fixing grpc compatibility with the fedml.ai platform
and simplifying the user-end requirements to define the grpc server ip and
port. The values defined in the grpc config file will override the default ip
and port values.
---
.../custom_data_and_model/README.md | 22 ++-
.../config/grpc_ipconfig.csv | 6 +-
.../one_line/README.md | 22 ++-
.../one_line/config/grpc_ipconfig.csv | 6 +-
.../step_by_step/README.md | 22 ++-
.../step_by_step/config/grpc_ipconfig.csv | 6 +-
.../model_scheduler/autoscaler/autoscaler.py | 2 +-
.../communication/grpc/grpc_comm_manager.py | 138 ++++++++++--------
.../communication/grpc/ip_config_utils.py | 14 --
.../core/distributed/communication/message.py | 17 ++-
.../core/distributed/fedml_comm_manager.py | 8 +-
.../client/fedml_client_master_manager.py | 5 +-
.../fedml/cross_silo/client/message_define.py | 1 +
13 files changed, 158 insertions(+), 111 deletions(-)
delete mode 100644 python/fedml/core/distributed/communication/grpc/ip_config_utils.py
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/README.md b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/README.md
index d125847dd6..da37ebda43 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/README.md
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/README.md
@@ -1,7 +1,21 @@
-## Training Script
-At the client side, the client ID (a.k.a rank) starts from 1.
-Please also modify config/fedml_config.yaml, changing the `worker_num` the as the number of clients you plan to run.
+## Prerequisites
+At the client side, the client ID (a.k.a rank) starts from 1.
+Please also modify `config/fedml_config.yaml` as you see fit. Changing the `worker_num` the as the number of clients you plan to run.
+The default ip of every groc server is set to `0.0.0.0`, and all grpc ports start from 8890 and increase based on the client's rank.
+
+> **_NOTE:_**
+> The `config/grpc_ipconfig.csv` file contains only one record referring to the grpc server of
+> the aggregator (rank: 0). This record is mandatory. However, you can change the values of the `ip` and `port`
+> attributes as you see fit, and more records for grpc server of the rest of clients. For instance:
+```
+eid,rank,ip,port
+0,0,0.0.0.0,8890
+1,1,0.0.0.0,8899
+2,2,0.0.0.0,8898
+```
+
+## Start Script
At the server side, run the following script:
```
@@ -18,7 +32,7 @@ bash run_client.sh 2 your_run_id
```
Note: please run the server first.
-## A Better User-experience with FedML MLOps (open.fedml.ai)
+## A Better User-experience with FedML FLOps (fedml.ai)
To reduce the difficulty and complexity of these CLI commands. We recommend you to use our MLOps (open.fedml.ai).
FedML MLOps provides:
- Install Client Agent and Login
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/config/grpc_ipconfig.csv b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/config/grpc_ipconfig.csv
index 4618a98b7a..c92391a46a 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/config/grpc_ipconfig.csv
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/config/grpc_ipconfig.csv
@@ -1,4 +1,2 @@
-receiver_id,ip
-0,0.0.0.0
-1,0.0.0.0
-2,0.0.0.0
\ No newline at end of file
+eid,rank,ip,port
+0,0,0.0.0.0,8890
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/README.md b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/README.md
index d125847dd6..da37ebda43 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/README.md
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/README.md
@@ -1,7 +1,21 @@
-## Training Script
-At the client side, the client ID (a.k.a rank) starts from 1.
-Please also modify config/fedml_config.yaml, changing the `worker_num` the as the number of clients you plan to run.
+## Prerequisites
+At the client side, the client ID (a.k.a rank) starts from 1.
+Please also modify `config/fedml_config.yaml` as you see fit. Changing the `worker_num` the as the number of clients you plan to run.
+The default ip of every groc server is set to `0.0.0.0`, and all grpc ports start from 8890 and increase based on the client's rank.
+
+> **_NOTE:_**
+> The `config/grpc_ipconfig.csv` file contains only one record referring to the grpc server of
+> the aggregator (rank: 0). This record is mandatory. However, you can change the values of the `ip` and `port`
+> attributes as you see fit, and more records for grpc server of the rest of clients. For instance:
+```
+eid,rank,ip,port
+0,0,0.0.0.0,8890
+1,1,0.0.0.0,8899
+2,2,0.0.0.0,8898
+```
+
+## Start Script
At the server side, run the following script:
```
@@ -18,7 +32,7 @@ bash run_client.sh 2 your_run_id
```
Note: please run the server first.
-## A Better User-experience with FedML MLOps (open.fedml.ai)
+## A Better User-experience with FedML FLOps (fedml.ai)
To reduce the difficulty and complexity of these CLI commands. We recommend you to use our MLOps (open.fedml.ai).
FedML MLOps provides:
- Install Client Agent and Login
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/config/grpc_ipconfig.csv b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/config/grpc_ipconfig.csv
index 4618a98b7a..b46eb6d33b 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/config/grpc_ipconfig.csv
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/config/grpc_ipconfig.csv
@@ -1,4 +1,2 @@
-receiver_id,ip
-0,0.0.0.0
-1,0.0.0.0
-2,0.0.0.0
\ No newline at end of file
+eid,rank,ip,port
+0,0,0.0.0.0,8890
\ No newline at end of file
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/README.md b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/README.md
index d125847dd6..da37ebda43 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/README.md
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/README.md
@@ -1,7 +1,21 @@
-## Training Script
-At the client side, the client ID (a.k.a rank) starts from 1.
-Please also modify config/fedml_config.yaml, changing the `worker_num` the as the number of clients you plan to run.
+## Prerequisites
+At the client side, the client ID (a.k.a rank) starts from 1.
+Please also modify `config/fedml_config.yaml` as you see fit. Changing the `worker_num` the as the number of clients you plan to run.
+The default ip of every groc server is set to `0.0.0.0`, and all grpc ports start from 8890 and increase based on the client's rank.
+
+> **_NOTE:_**
+> The `config/grpc_ipconfig.csv` file contains only one record referring to the grpc server of
+> the aggregator (rank: 0). This record is mandatory. However, you can change the values of the `ip` and `port`
+> attributes as you see fit, and more records for grpc server of the rest of clients. For instance:
+```
+eid,rank,ip,port
+0,0,0.0.0.0,8890
+1,1,0.0.0.0,8899
+2,2,0.0.0.0,8898
+```
+
+## Start Script
At the server side, run the following script:
```
@@ -18,7 +32,7 @@ bash run_client.sh 2 your_run_id
```
Note: please run the server first.
-## A Better User-experience with FedML MLOps (open.fedml.ai)
+## A Better User-experience with FedML FLOps (fedml.ai)
To reduce the difficulty and complexity of these CLI commands. We recommend you to use our MLOps (open.fedml.ai).
FedML MLOps provides:
- Install Client Agent and Login
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/config/grpc_ipconfig.csv b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/config/grpc_ipconfig.csv
index 4618a98b7a..b46eb6d33b 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/config/grpc_ipconfig.csv
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/config/grpc_ipconfig.csv
@@ -1,4 +1,2 @@
-receiver_id,ip
-0,0.0.0.0
-1,0.0.0.0
-2,0.0.0.0
\ No newline at end of file
+eid,rank,ip,port
+0,0,0.0.0.0,8890
\ No newline at end of file
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
index dd6ca67706..3f3da8d656 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
@@ -8,7 +8,7 @@
from enum import Enum
from fedml.computing.scheduler.model_scheduler.device_model_cache import FedMLModelCache
from fedml.computing.scheduler.model_scheduler.autoscaler.policies import *
-from utils.singleton import Singleton
+from fedml.computing.scheduler.model_scheduler.autoscaler.utils.singleton import Singleton
class ScaleOp(Enum):
diff --git a/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py b/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py
index 6eb9fe613e..a2cc255437 100644
--- a/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py
+++ b/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py
@@ -1,52 +1,50 @@
+import csv
+import grpc
+import logging
import os
import pickle
import threading
-from concurrent import futures
-from typing import List
-
-import grpc
-
-from ..grpc import grpc_comm_manager_pb2_grpc, grpc_comm_manager_pb2
+import time
lock = threading.Lock()
+from concurrent import futures
+from fedml.core.mlops.mlops_profiler_event import MLOpsProfilerEvent
+from typing import List
+
from ...communication.base_com_manager import BaseCommunicationManager
from ...communication.message import Message
from ...communication.observer import Observer
from ..constants import CommunicationConstants
-
-from fedml.core.mlops.mlops_profiler_event import MLOpsProfilerEvent
-
-import time
+from ..grpc import grpc_comm_manager_pb2_grpc, grpc_comm_manager_pb2
# Check Service or serve?
from ...communication.grpc.grpc_server import GRPCCOMMServicer
-import logging
-
-import csv
-
class GRPCCommManager(BaseCommunicationManager):
+ MSG_ARG_KEY_SENDER_RANK = "sender_rank"
+ MSG_ARG_KEY_SENDER_IP = "sender_ip"
+ MSG_ARG_KEY_SENDER_PORT = "sender_port"
+
def __init__(
- self,
- host,
- port,
- ip_config_path,
- topic="fedml",
- client_id=0,
- client_num=0,
+ self,
+ grpc_ipconfig_path,
+ topic="fedml",
+ client_rank=0,
+ client_num=0,
+ args=None
):
- # host is the ip address of server
- self.host = host
- self.port = str(port)
+
self._topic = topic
- self.client_id = client_id
- self.client_num = client_num
self._observers: List[Observer] = []
- self.rank = client_id
+ self.grpc_ipconfig_path = grpc_ipconfig_path
+ self.client_rank = client_rank
+ self.client_id = self.client_rank
+ self.client_num = client_num
+ self.args = args
- if client_id == 0:
+ if self.client_rank == 0:
self.node_type = "server"
logging.info("############# THIS IS FL SERVER ################")
else:
@@ -61,24 +59,43 @@ def __init__(
futures.ThreadPoolExecutor(max_workers=client_num),
options=self.opts,
)
- self.grpc_servicer = GRPCCOMMServicer(host, port, client_num, client_id)
+
+ self.grpc_mappings = self._init_grpc_mappings() # Load input mappings.
+ if self.client_id not in self.grpc_mappings:
+ # if no record exists for the current client id, then
+ # default ip and rank to "0.0.0.0" and BASE + RANK.
+ self.ip = "0.0.0.0"
+ self.port = CommunicationConstants.GRPC_BASE_PORT + self.client_rank
+ self.grpc_mappings[self.client_id] = (self.client_rank, self.ip, self.port)
+ else:
+ _, self.ip, self.port = self.grpc_mappings[self.client_id]
+
+ self.grpc_servicer = GRPCCOMMServicer(
+ self.ip,
+ self.port,
+ self.client_num,
+ self.client_rank
+ )
grpc_comm_manager_pb2_grpc.add_gRPCCommManagerServicer_to_server(
self.grpc_servicer, self.grpc_server
)
logging.info(os.getcwd())
- self.ip_config = self._build_ip_table(ip_config_path)
- # starts a grpc_server on local machine using ip address "0.0.0.0"
- self.grpc_server.add_insecure_port("{}:{}".format("0.0.0.0", port))
+ self.grpc_server.add_insecure_port("{}:{}".format(self.ip, self.port))
self.grpc_server.start()
+ # Wait for 100 milliseconds to make sure the grpc
+ # server has started before proceeding.
+ time.sleep(0.01)
self.is_running = True
- logging.info("grpc server started. Listening on port " + str(port))
+ logging.info("grpc server started. Listening on port " + str(self.port))
def send_message(self, msg: Message):
- logging.info("msg = {}".format(msg))
- # payload = msg.to_json()
-
+ # Register the sender rank, ip and port attribute on the message.
+ msg.add_params(GRPCCommManager.MSG_ARG_KEY_SENDER_RANK, self.client_rank)
+ msg.add_params(GRPCCommManager.MSG_ARG_KEY_SENDER_IP, self.ip)
+ msg.add_params(GRPCCommManager.MSG_ARG_KEY_SENDER_PORT, self.port)
+ logging.info("sending msg = {}".format(msg.get_params_wout_model()))
logging.info("pickle.dumps(msg) START")
pickle_dump_start_time = time.time()
msg_pkl = pickle.dumps(msg)
@@ -86,10 +103,8 @@ def send_message(self, msg: Message):
logging.info("pickle.dumps(msg) END")
receiver_id = msg.get_receiver_id()
- PORT_BASE = CommunicationConstants.GRPC_BASE_PORT
- # lookup ip of receiver from self.ip_config table
- receiver_ip = self.ip_config[str(receiver_id)]
- channel_url = "{}:{}".format(receiver_ip, str(PORT_BASE + receiver_id))
+ receiver_rank, receiver_ip, receiver_port = self.grpc_mappings[int(receiver_id)]
+ channel_url = "{}:{}".format(receiver_ip, receiver_port)
channel = grpc.insecure_channel(channel_url, options=self.opts)
stub = grpc_comm_manager_pb2_grpc.gRPCCommManagerStub(channel)
@@ -98,7 +113,6 @@ def send_message(self, msg: Message):
logging.info("sending message to {}".format(channel_url))
request.client_id = self.client_id
-
request.message = msg_pkl
tick = time.time()
@@ -116,10 +130,8 @@ def remove_observer(self, observer: Observer):
def handle_receive_message(self):
self._notify_connection_ready()
self.message_handling_subroutine()
-
- # Cannont run message_handling_subroutine in new thread
+ # Cannot run message_handling_subroutine in new thread
# Related https://stackoverflow.com/a/70705165
-
# thread = threading.Thread(target=self.message_handling_subroutine)
# thread.start()
@@ -131,19 +143,28 @@ def message_handling_subroutine(self):
lock.acquire()
busy_time_start_time = time.time()
msg_pkl = self.grpc_servicer.message_q.get()
- logging.info("unpickle START")
+ logging.info("Unpickle START.")
unpickle_start_time = time.time()
msg = pickle.loads(msg_pkl)
MLOpsProfilerEvent.log_to_wandb({"UnpickleTime": time.time() - unpickle_start_time})
- logging.info("unpickle END")
+ logging.info("Unpickle END.")
msg_type = msg.get_type()
+
+ sender_id = int(msg.get_sender_id())
+ if sender_id not in self.grpc_mappings:
+ sender_rank = int(msg.get_params()[GRPCCommManager.MSG_ARG_KEY_SENDER_RANK])
+ sender_ip = str(msg.get_params()[GRPCCommManager.MSG_ARG_KEY_SENDER_IP])
+ sender_port = int(msg.get_params()[GRPCCommManager.MSG_ARG_KEY_SENDER_PORT])
+ self.grpc_mappings[sender_id] = (sender_rank, sender_ip, sender_port)
+
for observer in self._observers:
_message_handler_start_time = time.time()
observer.receive_message(msg_type, msg)
MLOpsProfilerEvent.log_to_wandb({"MessageHandlerTime": time.time() - _message_handler_start_time})
MLOpsProfilerEvent.log_to_wandb({"BusyTime": time.time() - busy_time_start_time})
lock.release()
- time.sleep(0.0001)
+ time.sleep(0.0001)
+
MLOpsProfilerEvent.log_to_wandb({"TotalTime": time.time() - start_listening_time})
return
@@ -158,20 +179,17 @@ def notify(self, message: Message):
def _notify_connection_ready(self):
msg_params = Message()
- msg_params.sender_id = self.rank
- msg_params.receiver_id = self.rank
+ msg_params.sender_id = self.client_rank
+ msg_params.receiver_id = self.client_rank
msg_type = CommunicationConstants.MSG_TYPE_CONNECTION_IS_READY
for observer in self._observers:
observer.receive_message(msg_type, msg_params)
- def _build_ip_table(self, path):
- ip_config = dict()
- with open(path, newline="") as csv_file:
- csv_reader = csv.reader(csv_file)
- # skip header line
- next(csv_reader)
-
- for row in csv_reader:
- receiver_id, receiver_ip = row
- ip_config[receiver_id] = receiver_ip
- return ip_config
+ def _init_grpc_mappings(self):
+ mappings = dict()
+ csv_reader = csv.reader(open(self.grpc_ipconfig_path, "r"))
+ next(csv_reader) # skip header line
+ for row in csv_reader:
+ eid, rank, ip, port = row
+ mappings[int(eid)] = (int(rank), str(ip), int(port))
+ return mappings
diff --git a/python/fedml/core/distributed/communication/grpc/ip_config_utils.py b/python/fedml/core/distributed/communication/grpc/ip_config_utils.py
deleted file mode 100644
index 1ebedfd73a..0000000000
--- a/python/fedml/core/distributed/communication/grpc/ip_config_utils.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import csv
-
-
-def build_ip_table(path):
- ip_config = dict()
- with open(path, newline="") as csv_file:
- csv_reader = csv.reader(csv_file)
- # skip header line
- next(csv_reader)
-
- for row in csv_reader:
- receiver_id, receiver_ip = row
- ip_config[receiver_id] = receiver_ip
- return ip_config
diff --git a/python/fedml/core/distributed/communication/message.py b/python/fedml/core/distributed/communication/message.py
index 7d465461e5..7b1bc63cec 100644
--- a/python/fedml/core/distributed/communication/message.py
+++ b/python/fedml/core/distributed/communication/message.py
@@ -4,16 +4,10 @@
class Message(object):
- MSG_ARG_KEY_OPERATION = "operation"
MSG_ARG_KEY_TYPE = "msg_type"
MSG_ARG_KEY_SENDER = "sender"
MSG_ARG_KEY_RECEIVER = "receiver"
- MSG_OPERATION_SEND = "send"
- MSG_OPERATION_RECEIVE = "receive"
- MSG_OPERATION_BROADCAST = "broadcast"
- MSG_OPERATION_REDUCE = "reduce"
-
MSG_ARG_KEY_MODEL_PARAMS = "model_params"
MSG_ARG_KEY_MODEL_PARAMS_URL = "model_params_url"
MSG_ARG_KEY_MODEL_PARAMS_KEY = "model_params_key"
@@ -54,6 +48,15 @@ def add_params(self, key, value):
def get_params(self):
return self.msg_params
+ def get_params_wout_model(self):
+ # We explicitly return the message triple, because the msg params
+ # dictionary is populated at different stages during execution,
+ # e.g., Message.MSG_ARG_KEY_MODEL_PARAMS
+ return {
+ k: v for k, v in self.msg_params.items()
+ if k != Message.MSG_ARG_KEY_MODEL_PARAMS
+ }
+
def add(self, key, value):
self.msg_params[key] = value
@@ -65,7 +68,7 @@ def get(self, key):
def get_type(self):
return self.msg_params[Message.MSG_ARG_KEY_TYPE]
- def to_string(self):
+ def to_string(self, include_model_params=True):
return self.msg_params
def to_json(self):
diff --git a/python/fedml/core/distributed/fedml_comm_manager.py b/python/fedml/core/distributed/fedml_comm_manager.py
index d358b0b7cd..29cd498bdc 100644
--- a/python/fedml/core/distributed/fedml_comm_manager.py
+++ b/python/fedml/core/distributed/fedml_comm_manager.py
@@ -188,11 +188,11 @@ def _init_manager(self):
)
elif self.backend == "GRPC":
from .communication.grpc.grpc_comm_manager import GRPCCommManager
-
- HOST = "0.0.0.0"
- PORT = CommunicationConstants.GRPC_BASE_PORT + self.rank
self.com_manager = GRPCCommManager(
- HOST, PORT, ip_config_path=self.args.grpc_ipconfig_path, client_id=self.rank, client_num=self.size,
+ grpc_ipconfig_path=self.args.grpc_ipconfig_path,
+ client_rank=self.rank,
+ client_num=self.size,
+ args=self.args,
)
elif self.backend == "TRPC":
from .communication.trpc.trpc_comm_manager import TRPCCommManager
diff --git a/python/fedml/cross_silo/client/fedml_client_master_manager.py b/python/fedml/cross_silo/client/fedml_client_master_manager.py
index f03f7f03c1..fa333af2cc 100644
--- a/python/fedml/cross_silo/client/fedml_client_master_manager.py
+++ b/python/fedml/cross_silo/client/fedml_client_master_manager.py
@@ -78,7 +78,10 @@ def register_message_receive_handlers(self):
MyMessage.MSG_TYPE_S2C_CHECK_CLIENT_STATUS, self.handle_message_check_status
)
- self.register_message_receive_handler(MyMessage.MSG_TYPE_S2C_INIT_CONFIG, self.handle_message_init)
+ self.register_message_receive_handler(
+ MyMessage.MSG_TYPE_S2C_INIT_CONFIG, self.handle_message_init
+ )
+
self.register_message_receive_handler(
MyMessage.MSG_TYPE_S2C_SYNC_MODEL_TO_CLIENT, self.handle_message_receive_model_from_server,
)
diff --git a/python/fedml/cross_silo/client/message_define.py b/python/fedml/cross_silo/client/message_define.py
index ef482e102b..0b694c68c0 100644
--- a/python/fedml/cross_silo/client/message_define.py
+++ b/python/fedml/cross_silo/client/message_define.py
@@ -1,3 +1,4 @@
+
class MyMessage(object):
"""
message type definition
From 303f29bbce2740103039dd787764b1242d584290 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@tensoropera.com>
Date: Sat, 14 Sep 2024 00:54:11 +0000
Subject: [PATCH 219/282] Removing empty line.
---
.../computing/scheduler/model_scheduler/autoscaler/autoscaler.py | 1 -
1 file changed, 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
index 4da3d1135d..4cab1e133c 100644
--- a/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
+++ b/python/fedml/computing/scheduler/model_scheduler/autoscaler/autoscaler.py
@@ -8,7 +8,6 @@
from enum import Enum
from fedml.computing.scheduler.model_scheduler.device_model_cache import FedMLModelCache
from fedml.computing.scheduler.model_scheduler.autoscaler.policies import *
-
from fedml.computing.scheduler.comm_utils.singleton import Singleton
From 142089860238d4a577e56d208deb008cc736503c Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Mon, 16 Sep 2024 09:36:23 +0800
Subject: [PATCH 220/282] [CoreEngine] set the cuda visible id into the docker
container when training.
---
python/fedml/computing/scheduler/slave/base_slave_job_runner.py | 1 +
1 file changed, 1 insertion(+)
diff --git a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
index 5e530dbba7..9ea2c4beaf 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_job_runner.py
@@ -251,6 +251,7 @@ def start_runner_process(
agent_config=None, edge_id=edge_id
)
client_runner.start_request_json = request_json
+ client_runner.cuda_visible_gpu_ids_str = cuda_visible_gpu_ids_str
run_id_str = str(run_id)
self.run_process_event = multiprocessing.Event()
client_runner.run_process_event = self.run_process_event
From 30cfe0228c7794bfb2fc65fbadf9a1b531956f3a Mon Sep 17 00:00:00 2001
From: Alex <alex.gpt.llm@gmail.com>
Date: Wed, 18 Sep 2024 19:38:59 +0800
Subject: [PATCH 221/282] set the gpu ids when training.
---
.../scheduler_core/scheduler_base_job_runner_manager.py | 1 +
1 file changed, 1 insertion(+)
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
index dcc4045699..0e30beeab4 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner_manager.py
@@ -29,6 +29,7 @@ def start_job_runner(
)
self.job_runners[run_id_str].start_runner_process(
run_id, request_json, edge_id=edge_id,
+ cuda_visible_gpu_ids_str=cuda_visible_gpu_ids_str,
sender_message_queue=sender_message_queue,
listener_message_queue=listener_message_queue,
status_center_queue=status_center_queue
From 16a79d9f1f431e687cf387b2383eb9a0b5ecbfef Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@tensoropera.com>
Date: Wed, 16 Oct 2024 21:45:51 +0000
Subject: [PATCH 222/282] Adding simple local env docker client checker.
---
.../scheduler/comm_utils/job_utils.py | 9 +++++++++
.../slave/base_slave_protocol_manager.py | 18 +++++++++---------
2 files changed, 18 insertions(+), 9 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_utils.py b/python/fedml/computing/scheduler/comm_utils/job_utils.py
index 8a917e539d..ae50239d25 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_utils.py
@@ -574,6 +574,15 @@ def get_run_container_name(run_id: int) -> str:
container_name = f"{container_prefix}__{run_id}"
return container_name
+ @staticmethod
+ def docker_client_exists() -> bool:
+ try:
+ client = docker.from_env()
+ client.ping()
+ return True
+ except docker.errors.DockerException:
+ return False
+
@staticmethod
def get_docker_client(docker_args: DockerArgs) -> DockerClient:
try:
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index 447bd05cd9..bb2215bf95 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -509,16 +509,16 @@ def process_status(self, run_id, status, edge_id, master_id=None):
if run_process is not None:
if run_process.pid is not None:
RunProcessUtils.kill_process(run_process.pid)
-
# Terminate the run docker container if exists
- try:
- container_name = JobRunnerUtils.get_run_container_name(run_id)
- docker_client = JobRunnerUtils.get_docker_client(DockerArgs())
- logging.info(f"Terminating the run docker container {container_name} if exists...")
- JobRunnerUtils.remove_run_container_if_exists(container_name, docker_client)
- except Exception as e:
- logging.error(f"Error occurred when terminating docker container."
- f"Exception: {e}, Traceback: {traceback.format_exc()}.")
+ if JobRunnerUtils.docker_client_exists():
+ try:
+ docker_client = JobRunnerUtils.get_docker_client(DockerArgs())
+ container_name = JobRunnerUtils.get_run_container_name(run_id)
+ logging.info(f"Terminating the run docker container {container_name} if exists...")
+ JobRunnerUtils.remove_run_container_if_exists(container_name, docker_client)
+ except Exception as e:
+ logging.error(f"Error occurred when terminating docker container."
+ f"Exception: {e}, Traceback: {traceback.format_exc()}.")
# Stop log processor for current run
MLOpsRuntimeLogDaemon.get_instance(self.args).stop_log_processor(run_id, edge_id)
From f299a8e451a4148f4d3dd62694efc116bcfadf48 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@tensoropera.com>
Date: Wed, 16 Oct 2024 22:27:36 +0000
Subject: [PATCH 223/282] Adding more docker client existence checkpoints.
---
.../scheduler_base_job_runner.py | 20 ++++++++++---------
.../slave/base_slave_protocol_manager.py | 3 ++-
2 files changed, 13 insertions(+), 10 deletions(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index 6e0010f556..f5e08107f2 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -616,15 +616,17 @@ def cleanup_containers_and_release_gpus(run_id, edge_id, job_type=SchedulerConst
if not (job_type == SchedulerConstants.JOB_TASK_TYPE_SERVE or
job_type == SchedulerConstants.JOB_TASK_TYPE_DEPLOY):
- # Terminate the run docker container if exists
- try:
- container_name = JobRunnerUtils.get_run_container_name(run_id)
- docker_client = JobRunnerUtils.get_docker_client(DockerArgs())
- logging.info(f"Terminating the run docker container {container_name} if exists...")
- JobRunnerUtils.remove_run_container_if_exists(container_name, docker_client)
- except Exception as e:
- logging.error(f"Exception {e} occurred when terminating docker container. "
- f"Traceback: {traceback.format_exc()}")
+ # Check if docker client exists and then terminate containers.
+ if JobRunnerUtils.docker_client_exists():
+ try:
+ # Terminate docker container.
+ docker_client = JobRunnerUtils.get_docker_client(DockerArgs())
+ container_name = JobRunnerUtils.get_run_container_name(run_id)
+ logging.info(f"Terminating the run docker container {container_name} if exists...")
+ JobRunnerUtils.remove_run_container_if_exists(container_name, docker_client)
+ except Exception as e:
+ logging.error(f"Exception {e} occurred when terminating docker container. "
+ f"Traceback: {traceback.format_exc()}")
# Release the GPU ids and update the GPU availability in the persistent store
JobRunnerUtils.get_instance().release_gpu_ids(run_id, edge_id)
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index bb2215bf95..05469e78ff 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -509,9 +509,10 @@ def process_status(self, run_id, status, edge_id, master_id=None):
if run_process is not None:
if run_process.pid is not None:
RunProcessUtils.kill_process(run_process.pid)
- # Terminate the run docker container if exists
+ # Check if docker client exists and then terminate containers.
if JobRunnerUtils.docker_client_exists():
try:
+ # Terminate docker container.
docker_client = JobRunnerUtils.get_docker_client(DockerArgs())
container_name = JobRunnerUtils.get_run_container_name(run_id)
logging.info(f"Terminating the run docker container {container_name} if exists...")
From 3349667322b72fc4a2ff8d614e8185659e7d3421 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@tensoropera.com>
Date: Thu, 17 Oct 2024 00:12:22 +0000
Subject: [PATCH 224/282] Fixing grpc readme file.
---
.../grpc_fedavg_mnist_lr_example/README.md | 17 ++++++++++-------
1 file changed, 10 insertions(+), 7 deletions(-)
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/README.md b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/README.md
index 47250ef894..037747c8ee 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/README.md
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/README.md
@@ -8,22 +8,25 @@ comm_args:
grpc_ipconfig_path: config/grpc_ipconfig.csv
```
-`grpc_ipconfig_path` specifies the path of the config for gRPC communication. Config file specifies an ip address for each process through with they can communicate with each other. The config file should have the folliwng format:
+`grpc_ipconfig_path` specifies the path of the config for gRPC communication. Config file specifies an ip address for each process through with they can communicate with each other. The config file should have the following format:
```csv
-receiver_id,ip
-0,127.0.0.1
-1,127.0.0.1
-2,127.0.0.1
+eid,rank,ip,port
+0,0,0.0.0.0,8890
+1,1,0.0.0.0,8899
+2,2,0.0.0.0,8898
```
-Here the `receiver_id` is the rank of the process.
+Here, `eid, rank, ip, port` are the id, rank, ip address and port of the server or client process. For server processes the rank is always set to 0, while for clients is always set to 1 or above.
## One Line API Example
-Example is provided at:
+Examples are provided at:
`python/examples/cross_silo/grpc_fedavg_mnist_lr_example/one_line`
+`python/examples/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step`
+`python/examples/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model`
+
### Training Script
At the client side, the client ID (a.k.a rank) starts from 1.
From d2484fad6c30cc8fbd2f1c89de9437377d702b9f Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@tensoropera.com>
Date: Thu, 17 Oct 2024 02:01:09 +0000
Subject: [PATCH 225/282] Remove circular dependency.
---
python/fedml/computing/scheduler/comm_utils/sys_utils.py | 3 +--
1 file changed, 1 insertion(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/sys_utils.py b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
index aaa37bc4db..2e6ba8a08c 100644
--- a/python/fedml/computing/scheduler/comm_utils/sys_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
@@ -20,8 +20,6 @@
import sys
import subprocess
-from fedml.computing.scheduler.slave.client_constants import ClientConstants
-
FETAL_ERROR_START_CODE = 128
SYS_ERR_CODE_MAP = {"0": "Successful exit without errors.",
@@ -817,6 +815,7 @@ def daemon_ota_upgrade_with_version(in_version="release"):
def run_cmd(command, show_local_console=False):
+ from fedml.computing.scheduler.slave.client_constants import ClientConstants
process = ClientConstants.exec_console_with_script(command, should_capture_stdout=True,
should_capture_stderr=True)
ret_code, out, err = ClientConstants.get_console_pipe_out_err_results(process)
From a959802390c8c2dfb6869084544ff41afa08ce67 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@tensoropera.com>
Date: Thu, 17 Oct 2024 22:27:39 +0000
Subject: [PATCH 226/282] Extending grpc support to also consider docker
container ips.
---
.../scheduler/comm_utils/sys_utils.py | 1 +
.../communication/grpc/grpc_comm_manager.py | 76 +++++++++++++------
2 files changed, 54 insertions(+), 23 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/sys_utils.py b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
index 2e6ba8a08c..a0ee3402f0 100644
--- a/python/fedml/computing/scheduler/comm_utils/sys_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
@@ -815,6 +815,7 @@ def daemon_ota_upgrade_with_version(in_version="release"):
def run_cmd(command, show_local_console=False):
+ # Had to import ClientConstans here because otherwise it was raising circular import errors.
from fedml.computing.scheduler.slave.client_constants import ClientConstants
process = ClientConstants.exec_console_with_script(command, should_capture_stdout=True,
should_capture_stderr=True)
diff --git a/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py b/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py
index a2cc255437..a92d3abbc8 100644
--- a/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py
+++ b/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py
@@ -8,9 +8,10 @@
lock = threading.Lock()
+from collections import namedtuple
from concurrent import futures
from fedml.core.mlops.mlops_profiler_event import MLOpsProfilerEvent
-from typing import List
+from typing import List, TypedDict
from ...communication.base_com_manager import BaseCommunicationManager
from ...communication.message import Message
@@ -22,6 +23,11 @@
from ...communication.grpc.grpc_server import GRPCCOMMServicer
+GRPCMapping = \
+ namedtuple("GRPCMapping",
+ ["eid", "rank", "grpc_server_ip", "grpc_server_port", "ingress_ip"])
+
+
class GRPCCommManager(BaseCommunicationManager):
MSG_ARG_KEY_SENDER_RANK = "sender_rank"
MSG_ARG_KEY_SENDER_IP = "sender_ip"
@@ -39,11 +45,14 @@ def __init__(
self._topic = topic
self._observers: List[Observer] = []
self.grpc_ipconfig_path = grpc_ipconfig_path
+ self.grpc_mappings = dict()
self.client_rank = client_rank
self.client_id = self.client_rank
self.client_num = client_num
self.args = args
+ self._init_grpc_mappings() # Initialize self.grpc_mappings variable.
+
if self.client_rank == 0:
self.node_type = "server"
logging.info("############# THIS IS FL SERVER ################")
@@ -60,19 +69,19 @@ def __init__(
options=self.opts,
)
- self.grpc_mappings = self._init_grpc_mappings() # Load input mappings.
if self.client_id not in self.grpc_mappings:
# if no record exists for the current client id, then
# default ip and rank to "0.0.0.0" and BASE + RANK.
- self.ip = "0.0.0.0"
- self.port = CommunicationConstants.GRPC_BASE_PORT + self.client_rank
- self.grpc_mappings[self.client_id] = (self.client_rank, self.ip, self.port)
- else:
- _, self.ip, self.port = self.grpc_mappings[self.client_id]
+ self.grpc_mappings[self.client_id] = GRPCMapping(
+ eid=self.client_id,
+ rank=self.client_id,
+ grpc_server_ip="0.0.0.0",
+ grpc_server_port=CommunicationConstants.GRPC_BASE_PORT + self.client_rank,
+ ingress_ip=None)
self.grpc_servicer = GRPCCOMMServicer(
- self.ip,
- self.port,
+ self.grpc_mappings[self.client_id].grpc_server_ip,
+ self.grpc_mappings[self.client_id].grpc_server_port,
self.client_num,
self.client_rank
)
@@ -81,20 +90,22 @@ def __init__(
)
logging.info(os.getcwd())
- self.grpc_server.add_insecure_port("{}:{}".format(self.ip, self.port))
+ grpc_insecure_ip_port = "{}:{}".format(self.grpc_mappings[self.client_id].grpc_server_ip,
+ self.grpc_mappings[self.client_id].grpc_server_port)
+ self.grpc_server.add_insecure_port(grpc_insecure_ip_port)
self.grpc_server.start()
# Wait for 100 milliseconds to make sure the grpc
# server has started before proceeding.
time.sleep(0.01)
self.is_running = True
- logging.info("grpc server started. Listening on port " + str(self.port))
+ logging.info("Started gRPC server: {}.".format(grpc_insecure_ip_port))
def send_message(self, msg: Message):
# Register the sender rank, ip and port attribute on the message.
msg.add_params(GRPCCommManager.MSG_ARG_KEY_SENDER_RANK, self.client_rank)
- msg.add_params(GRPCCommManager.MSG_ARG_KEY_SENDER_IP, self.ip)
- msg.add_params(GRPCCommManager.MSG_ARG_KEY_SENDER_PORT, self.port)
+ msg.add_params(GRPCCommManager.MSG_ARG_KEY_SENDER_IP, self.grpc_mappings[self.client_id].grpc_server_ip)
+ msg.add_params(GRPCCommManager.MSG_ARG_KEY_SENDER_PORT, self.grpc_mappings[self.client_id].grpc_server_port)
logging.info("sending msg = {}".format(msg.get_params_wout_model()))
logging.info("pickle.dumps(msg) START")
pickle_dump_start_time = time.time()
@@ -103,8 +114,13 @@ def send_message(self, msg: Message):
logging.info("pickle.dumps(msg) END")
receiver_id = msg.get_receiver_id()
- receiver_rank, receiver_ip, receiver_port = self.grpc_mappings[int(receiver_id)]
- channel_url = "{}:{}".format(receiver_ip, receiver_port)
+ receiver_grpc_mappings = self.grpc_mappings[int(receiver_id)]
+ if receiver_grpc_mappings.ingress_ip:
+ channel_url = "{}:{}".format(receiver_grpc_mappings.ingress_ip,
+ receiver_grpc_mappings.grpc_server_port)
+ else:
+ channel_url = "{}:{}".format(receiver_grpc_mappings.grpc_server_ip,
+ receiver_grpc_mappings.grpc_server_port)
channel = grpc.insecure_channel(channel_url, options=self.opts)
stub = grpc_comm_manager_pb2_grpc.gRPCCommManagerStub(channel)
@@ -155,7 +171,12 @@ def message_handling_subroutine(self):
sender_rank = int(msg.get_params()[GRPCCommManager.MSG_ARG_KEY_SENDER_RANK])
sender_ip = str(msg.get_params()[GRPCCommManager.MSG_ARG_KEY_SENDER_IP])
sender_port = int(msg.get_params()[GRPCCommManager.MSG_ARG_KEY_SENDER_PORT])
- self.grpc_mappings[sender_id] = (sender_rank, sender_ip, sender_port)
+ self.grpc_mappings[sender_id] = GRPCMapping(
+ eid=sender_id,
+ rank=sender_rank,
+ grpc_server_ip=sender_ip,
+ grpc_server_port=sender_port,
+ ingress_ip=sender_ip)
for observer in self._observers:
_message_handler_start_time = time.time()
@@ -186,10 +207,19 @@ def _notify_connection_ready(self):
observer.receive_message(msg_type, msg_params)
def _init_grpc_mappings(self):
- mappings = dict()
- csv_reader = csv.reader(open(self.grpc_ipconfig_path, "r"))
- next(csv_reader) # skip header line
- for row in csv_reader:
- eid, rank, ip, port = row
- mappings[int(eid)] = (int(rank), str(ip), int(port))
- return mappings
+ csv_dict_reader = csv.DictReader(open(self.grpc_ipconfig_path, "r"))
+ data_dict = list(csv_dict_reader)
+ for row in data_dict:
+ eid = int(row["eid"])
+ rank = int(row["rank"])
+ grpc_server_ip = str(row["grpc_server_ip"])
+ grpc_server_port = int(row["grpc_server_port"])
+ ingress_ip = None
+ if "ingress_ip" in row:
+ ingress_ip = row["ingress_ip"]
+ self.grpc_mappings[int(eid)] = GRPCMapping(
+ eid=eid,
+ rank=rank,
+ grpc_server_ip=grpc_server_ip,
+ grpc_server_port=grpc_server_port,
+ ingress_ip=ingress_ip)
From aa691223264bd94fa23661ee5534d4d05f8ef0b5 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@tensoropera.com>
Date: Thu, 17 Oct 2024 22:32:06 +0000
Subject: [PATCH 227/282] Fixing notation and attribute names in grpc config
files.
---
.../federate/cross_silo/grpc_fedavg_mnist_lr_example/README.md | 2 +-
.../custom_data_and_model/README.md | 2 +-
.../custom_data_and_model/config/grpc_ipconfig.csv | 2 +-
.../cross_silo/grpc_fedavg_mnist_lr_example/one_line/README.md | 2 +-
.../one_line/config/grpc_ipconfig.csv | 2 +-
.../grpc_fedavg_mnist_lr_example/step_by_step/README.md | 2 +-
.../step_by_step/config/grpc_ipconfig.csv | 2 +-
.../core/distributed/communication/grpc/grpc_comm_manager.py | 2 +-
8 files changed, 8 insertions(+), 8 deletions(-)
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/README.md b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/README.md
index 037747c8ee..3ee1850a03 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/README.md
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/README.md
@@ -11,7 +11,7 @@ comm_args:
`grpc_ipconfig_path` specifies the path of the config for gRPC communication. Config file specifies an ip address for each process through with they can communicate with each other. The config file should have the following format:
```csv
-eid,rank,ip,port
+eid,rank,grpc_server_ip,grpc_server_port
0,0,0.0.0.0,8890
1,1,0.0.0.0,8899
2,2,0.0.0.0,8898
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/README.md b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/README.md
index da37ebda43..dd787b9141 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/README.md
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/README.md
@@ -9,7 +9,7 @@ The default ip of every groc server is set to `0.0.0.0`, and all grpc ports star
> the aggregator (rank: 0). This record is mandatory. However, you can change the values of the `ip` and `port`
> attributes as you see fit, and more records for grpc server of the rest of clients. For instance:
```
-eid,rank,ip,port
+eid,rank,grpc_server_ip,grpc_server_port
0,0,0.0.0.0,8890
1,1,0.0.0.0,8899
2,2,0.0.0.0,8898
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/config/grpc_ipconfig.csv b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/config/grpc_ipconfig.csv
index c92391a46a..9729b81833 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/config/grpc_ipconfig.csv
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/config/grpc_ipconfig.csv
@@ -1,2 +1,2 @@
-eid,rank,ip,port
+eid,rank,grpc_server_ip,grpc_server_port
0,0,0.0.0.0,8890
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/README.md b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/README.md
index da37ebda43..dd787b9141 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/README.md
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/README.md
@@ -9,7 +9,7 @@ The default ip of every groc server is set to `0.0.0.0`, and all grpc ports star
> the aggregator (rank: 0). This record is mandatory. However, you can change the values of the `ip` and `port`
> attributes as you see fit, and more records for grpc server of the rest of clients. For instance:
```
-eid,rank,ip,port
+eid,rank,grpc_server_ip,grpc_server_port
0,0,0.0.0.0,8890
1,1,0.0.0.0,8899
2,2,0.0.0.0,8898
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/config/grpc_ipconfig.csv b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/config/grpc_ipconfig.csv
index b46eb6d33b..8d082a9613 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/config/grpc_ipconfig.csv
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/config/grpc_ipconfig.csv
@@ -1,2 +1,2 @@
-eid,rank,ip,port
+eid,rank,grpc_server_ip,grpc_server_port
0,0,0.0.0.0,8890
\ No newline at end of file
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/README.md b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/README.md
index da37ebda43..dd787b9141 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/README.md
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/README.md
@@ -9,7 +9,7 @@ The default ip of every groc server is set to `0.0.0.0`, and all grpc ports star
> the aggregator (rank: 0). This record is mandatory. However, you can change the values of the `ip` and `port`
> attributes as you see fit, and more records for grpc server of the rest of clients. For instance:
```
-eid,rank,ip,port
+eid,rank,grpc_server_ip,grpc_server_port
0,0,0.0.0.0,8890
1,1,0.0.0.0,8899
2,2,0.0.0.0,8898
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/config/grpc_ipconfig.csv b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/config/grpc_ipconfig.csv
index b46eb6d33b..8d082a9613 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/config/grpc_ipconfig.csv
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/config/grpc_ipconfig.csv
@@ -1,2 +1,2 @@
-eid,rank,ip,port
+eid,rank,grpc_server_ip,grpc_server_port
0,0,0.0.0.0,8890
\ No newline at end of file
diff --git a/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py b/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py
index a92d3abbc8..399b2d44b9 100644
--- a/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py
+++ b/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py
@@ -11,7 +11,7 @@
from collections import namedtuple
from concurrent import futures
from fedml.core.mlops.mlops_profiler_event import MLOpsProfilerEvent
-from typing import List, TypedDict
+from typing import List
from ...communication.base_com_manager import BaseCommunicationManager
from ...communication.message import Message
From c302749f8f539bcbdf692109db17dec43a2d3e8b Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@tensoropera.com>
Date: Fri, 18 Oct 2024 00:32:30 +0000
Subject: [PATCH 228/282] testing with ingress ip.
---
.../core/distributed/communication/grpc/grpc_comm_manager.py | 5 ++++-
1 file changed, 4 insertions(+), 1 deletion(-)
diff --git a/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py b/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py
index 399b2d44b9..347f449937 100644
--- a/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py
+++ b/python/fedml/core/distributed/communication/grpc/grpc_comm_manager.py
@@ -104,7 +104,10 @@ def __init__(
def send_message(self, msg: Message):
# Register the sender rank, ip and port attribute on the message.
msg.add_params(GRPCCommManager.MSG_ARG_KEY_SENDER_RANK, self.client_rank)
- msg.add_params(GRPCCommManager.MSG_ARG_KEY_SENDER_IP, self.grpc_mappings[self.client_id].grpc_server_ip)
+ if self.grpc_mappings[self.client_id].ingress_ip:
+ msg.add_params(GRPCCommManager.MSG_ARG_KEY_SENDER_IP, self.grpc_mappings[self.client_id].ingress_ip)
+ else:
+ msg.add_params(GRPCCommManager.MSG_ARG_KEY_SENDER_IP, self.grpc_mappings[self.client_id].grpc_server_ip)
msg.add_params(GRPCCommManager.MSG_ARG_KEY_SENDER_PORT, self.grpc_mappings[self.client_id].grpc_server_port)
logging.info("sending msg = {}".format(msg.get_params_wout_model()))
logging.info("pickle.dumps(msg) START")
From 292bfb360b48568e72bbf355d7b7cd43877a028e Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@tensoropera.com>
Date: Fri, 18 Oct 2024 01:52:30 +0000
Subject: [PATCH 229/282] Polishing grpc + docker examples.
---
.../custom_data_and_model/README.md | 4 +-
.../grpc_docker_fedmlai/README.md | 51 ++++++++++++++++++
.../grpc_docker_fedmlai/__init__.py | 0
.../grpc_docker_fedmlai/config/bootstrap.bat | 12 +++++
.../grpc_docker_fedmlai/config/bootstrap.sh | 7 +++
.../config/fedml_config.yaml | 52 +++++++++++++++++++
.../config/grpc_ipconfig.csv | 3 ++
.../grpc_docker_fedmlai/run_client.sh | 3 ++
.../grpc_docker_fedmlai/run_server.sh | 3 ++
.../grpc_docker_fedmlai/torch_client.py | 18 +++++++
.../grpc_docker_fedmlai/torch_server.py | 18 +++++++
.../one_line/README.md | 4 +-
.../step_by_step/README.md | 4 +-
13 files changed, 173 insertions(+), 6 deletions(-)
create mode 100644 python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/README.md
create mode 100644 python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/__init__.py
create mode 100755 python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/bootstrap.bat
create mode 100644 python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/bootstrap.sh
create mode 100644 python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/fedml_config.yaml
create mode 100644 python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/grpc_ipconfig.csv
create mode 100644 python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/run_client.sh
create mode 100644 python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/run_server.sh
create mode 100644 python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/torch_client.py
create mode 100644 python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/torch_server.py
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/README.md b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/README.md
index dd787b9141..22e628e502 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/README.md
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/custom_data_and_model/README.md
@@ -11,8 +11,8 @@ The default ip of every groc server is set to `0.0.0.0`, and all grpc ports star
```
eid,rank,grpc_server_ip,grpc_server_port
0,0,0.0.0.0,8890
-1,1,0.0.0.0,8899
-2,2,0.0.0.0,8898
+1,1,0.0.0.0,8891
+2,2,0.0.0.0,8892
```
## Start Script
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/README.md b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/README.md
new file mode 100644
index 0000000000..174309aa55
--- /dev/null
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/README.md
@@ -0,0 +1,51 @@
+
+# Introduction
+In this working example, we will run 1 aggregation server and 2 clients on the same machine using Docker + gRPC and we will use the FEDML.ai platform to run the FL job.
+
+# gRPC Configuration File
+The content of the gRPC configuration file is as follows:
+```
+eid,rank,grpc_server_ip,grpc_server_port,ingress_ip
+0,0,0.0.0.0,8890,fedml_server
+1,1,0.0.0.0,8899,fedml_client_1
+2,2,0.0.0.0,8898,fedml_client_2
+```
+The ingress_ip variable refers to the name of the container that we assign to either the server or the client, as we discuss in detail below:
+
+
+# Docker Configuration
+Before creating any docker container one our machine, we need to pull the latest fedml image (e.g., `fedml:v090`) and ensure that all spawned containers can communicate to each other through a network bridge (e.g., `fedml_grpc_network`).
+Specifically, what you need to do is:
+```bash
+docker pull fedml:v090
+docker network create fedml_grpc_network
+```
+
+Once these two steps are configured we can start 1 aggregation server and 2 clients (without using a GPU) and register them using our <FEDML_API_KEY> with the fedml platform as follows:
+
+```bash
+# Server
+docker run -it -p 8890:8890 --entrypoint /bin/bash --name fedml_server --network fedml_grpc_network fedml:dev090
+redis-server --daemonize yes
+source /fedml/bin/activate
+fedml login -s <FEDML_API_KEY>
+```
+
+```bash
+# Client 1
+docker run -it -p 8891:8891 --entrypoint /bin/bash --name fedml_client_1 --network fedml_grpc_network fedml:dev090
+redis-server --daemonize yes
+source /fedml/bin/activate
+fedml login -c <FEDML_API_KEY>
+```
+
+```bash
+# Client-2
+docker run -it -p 8892:8892 --entrypoint /bin/bash --name fedml_client_2 --network fedml_grpc_network fedml:dev090
+redis-server --daemonize yes
+source /fedml/bin/activate
+fedml login -c <FEDML_API_KEY>
+```
+
+Then we only need to compile our job and submit to our dockerb-based cluster as it is also discussed in detail in the official FEDML documentation: https://fedml.ai/octopus/userGuides
+
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/__init__.py b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/bootstrap.bat b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/bootstrap.bat
new file mode 100755
index 0000000000..fb0dd54d6d
--- /dev/null
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/bootstrap.bat
@@ -0,0 +1,12 @@
+:: ### don't modify this part ###
+:: ##############################
+
+
+:: ### please customize your script in this region ####
+set DATA_PATH=%userprofile%\fedml_data
+if exist %DATA_PATH% (echo Exist %DATA_PATH%) else mkdir %DATA_PATH%
+
+
+:: ### don't modify this part ###
+echo [FedML]Bootstrap Finished
+:: ##############################
\ No newline at end of file
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/bootstrap.sh b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/bootstrap.sh
new file mode 100644
index 0000000000..3d969974b0
--- /dev/null
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/bootstrap.sh
@@ -0,0 +1,7 @@
+
+# pip install fedml==0.7.15
+#pip install --upgrade fedml
+
+### don't modify this part ###
+echo "[FedML]Bootstrap Finished"
+##############################
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/fedml_config.yaml b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/fedml_config.yaml
new file mode 100644
index 0000000000..d7183b6ada
--- /dev/null
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/fedml_config.yaml
@@ -0,0 +1,52 @@
+common_args:
+ training_type: "cross_silo"
+ scenario: "horizontal"
+ using_mlops: false
+ random_seed: 0
+
+environment_args:
+ bootstrap: config/bootstrap.sh
+
+data_args:
+ dataset: "mnist"
+ data_cache_dir: "../../../../data/mnist"
+ partition_method: "hetero"
+ partition_alpha: 0.5
+
+model_args:
+ model: "lr"
+ model_file_cache_folder: "./model_file_cache" # will be filled by the server automatically
+ global_model_file_path: "./model_file_cache/global_model.pt"
+
+train_args:
+ federated_optimizer: "FedAvg"
+ client_id_list:
+ client_num_in_total: 1000
+ client_num_per_round: 2
+ comm_round: 50
+ epochs: 1
+ batch_size: 10
+ client_optimizer: sgd
+ learning_rate: 0.03
+ weight_decay: 0.001
+
+validation_args:
+ frequency_of_the_test: 5
+
+device_args:
+ worker_num: 2
+ using_gpu: false
+ gpu_mapping_file: config/gpu_mapping.yaml
+ gpu_mapping_key: mapping_default
+
+comm_args:
+ backend: "GRPC"
+ grpc_ipconfig_path: config/grpc_ipconfig.csv
+
+tracking_args:
+ # When running on MLOps platform(open.fedml.ai), the default log path is at ~/.fedml/fedml-client/fedml/logs/ and ~/.fedml/fedml-server/fedml/logs/
+ local_log_output_path: ./log
+ enable_wandb: false
+ wandb_key: ee0b5f53d949c84cee7decbe7a619e63fb1f8408
+ wandb_project: fedml
+ wandb_name: fedml_torch_fedavg_mnist_lr
\ No newline at end of file
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/grpc_ipconfig.csv b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/grpc_ipconfig.csv
new file mode 100644
index 0000000000..8f461936dd
--- /dev/null
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/config/grpc_ipconfig.csv
@@ -0,0 +1,3 @@
+eid,rank,grpc_server_ip,grpc_server_port,ingress_ip
+0,0,0.0.0.0,8890,fedml_server
+1,1,0.0.0.0,8891,fedml_client_1
\ No newline at end of file
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/run_client.sh b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/run_client.sh
new file mode 100644
index 0000000000..18d3cea9fe
--- /dev/null
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/run_client.sh
@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+RANK=$1
+python3 torch_client.py --cf config/fedml_config.yaml --rank $RANK --role client
\ No newline at end of file
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/run_server.sh b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/run_server.sh
new file mode 100644
index 0000000000..08007b7e81
--- /dev/null
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/run_server.sh
@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+
+python3 torch_server.py --cf config/fedml_config.yaml --rank 0 --role server
\ No newline at end of file
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/torch_client.py b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/torch_client.py
new file mode 100644
index 0000000000..9085c85ebe
--- /dev/null
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/torch_client.py
@@ -0,0 +1,18 @@
+import fedml
+from fedml import FedMLRunner
+
+if __name__ == "__main__":
+ args = fedml.init()
+
+ # init device
+ device = fedml.device.get_device(args)
+
+ # load data
+ dataset, output_dim = fedml.data.load(args)
+
+ # load model
+ model = fedml.model.create(args, output_dim)
+
+ # start training
+ fedml_runner = FedMLRunner(args, device, dataset, model)
+ fedml_runner.run()
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/torch_server.py b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/torch_server.py
new file mode 100644
index 0000000000..9085c85ebe
--- /dev/null
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/torch_server.py
@@ -0,0 +1,18 @@
+import fedml
+from fedml import FedMLRunner
+
+if __name__ == "__main__":
+ args = fedml.init()
+
+ # init device
+ device = fedml.device.get_device(args)
+
+ # load data
+ dataset, output_dim = fedml.data.load(args)
+
+ # load model
+ model = fedml.model.create(args, output_dim)
+
+ # start training
+ fedml_runner = FedMLRunner(args, device, dataset, model)
+ fedml_runner.run()
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/README.md b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/README.md
index dd787b9141..22e628e502 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/README.md
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/one_line/README.md
@@ -11,8 +11,8 @@ The default ip of every groc server is set to `0.0.0.0`, and all grpc ports star
```
eid,rank,grpc_server_ip,grpc_server_port
0,0,0.0.0.0,8890
-1,1,0.0.0.0,8899
-2,2,0.0.0.0,8898
+1,1,0.0.0.0,8891
+2,2,0.0.0.0,8892
```
## Start Script
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/README.md b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/README.md
index dd787b9141..22e628e502 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/README.md
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/step_by_step/README.md
@@ -11,8 +11,8 @@ The default ip of every groc server is set to `0.0.0.0`, and all grpc ports star
```
eid,rank,grpc_server_ip,grpc_server_port
0,0,0.0.0.0,8890
-1,1,0.0.0.0,8899
-2,2,0.0.0.0,8898
+1,1,0.0.0.0,8891
+2,2,0.0.0.0,8892
```
## Start Script
From 55ff447dc34b758fffdf8f74fd3769c4a3980ec7 Mon Sep 17 00:00:00 2001
From: fedml-dimitris <dimitris@fedml.ai>
Date: Mon, 4 Nov 2024 14:31:37 -0500
Subject: [PATCH 230/282] Parameterizing deploy host, port.
---
python/fedml/serving/fedml_inference_runner.py | 7 ++++---
1 file changed, 4 insertions(+), 3 deletions(-)
diff --git a/python/fedml/serving/fedml_inference_runner.py b/python/fedml/serving/fedml_inference_runner.py
index 30dd9a6fca..64a8a2d596 100644
--- a/python/fedml/serving/fedml_inference_runner.py
+++ b/python/fedml/serving/fedml_inference_runner.py
@@ -6,8 +6,10 @@
class FedMLInferenceRunner(ABC):
- def __init__(self, client_predictor):
+ def __init__(self, client_predictor, host="0.0.0.0", port=2345):
self.client_predictor = client_predictor
+ self.host = host
+ self.port = port
def run(self):
api = FastAPI()
@@ -43,5 +45,4 @@ async def ready():
return Response(status_code=status.HTTP_202_ACCEPTED)
import uvicorn
- port = 2345
- uvicorn.run(api, host="0.0.0.0", port=port)
+ uvicorn.run(api, host=self.host, port=self.port)
From a108a8a63c4bf93f3251456c1f5d7fc47d8b2d78 Mon Sep 17 00:00:00 2001
From: Raphael Jin <kimheavy@yahoo.com>
Date: Sun, 10 Nov 2024 23:00:40 -0800
Subject: [PATCH 231/282] [Deploy] Edge Case Handling.
---
python/fedml/__init__.py | 5 +-
.../scheduler/comm_utils/run_process_utils.py | 53 ++-
.../scheduler/comm_utils/sys_utils.py | 450 ++++++++++--------
.../scheduler/slave/client_daemon.py | 4 +
python/fedml/core/mlops/mlops_configs.py | 19 +-
5 files changed, 308 insertions(+), 223 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index c96d65adc5..d6341c25c9 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -511,7 +511,8 @@ def _get_mqtt_service():
def set_local_on_premise_platform_host(local_on_premise_platform_host):
- os.environ['FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_HOST'] = local_on_premise_platform_host
+ # Should Also update the .env file
+ set_env_kv("FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_HOST", local_on_premise_platform_host)
def get_local_on_premise_platform_host():
@@ -519,7 +520,7 @@ def get_local_on_premise_platform_host():
def set_local_on_premise_platform_port(local_on_premise_platform_port):
- os.environ['FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_PORT'] = str(local_on_premise_platform_port)
+ set_env_kv("FEDML_ENV_LOCAL_ON_PREMISE_PLATFORM_PORT", str(local_on_premise_platform_port))
def get_local_on_premise_platform_port():
diff --git a/python/fedml/computing/scheduler/comm_utils/run_process_utils.py b/python/fedml/computing/scheduler/comm_utils/run_process_utils.py
index 05cc342e36..a84b078b54 100644
--- a/python/fedml/computing/scheduler/comm_utils/run_process_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/run_process_utils.py
@@ -166,26 +166,39 @@ def generate_yaml_doc(run_config_object, yaml_file):
@staticmethod
def get_pid_from_cmd_line(cmd_line, break_on_first=True):
ret_pids = list()
- pids = psutil.process_iter()
- for pid in pids:
- try:
- for cmd in pid.cmdline():
- if cmd.find(cmd_line) != -1:
- is_running = False
- try:
- process = psutil.Process(pid.pid)
- if process.status() == psutil.STATUS_RUNNING or \
- process.status() == psutil.STATUS_SLEEPING or \
- process.status() == psutil.STATUS_IDLE:
- is_running = True
- except Exception as e:
- pass
- if is_running:
- ret_pids.append(pid.pid)
- if break_on_first:
- return ret_pids
- except Exception as e:
- pass
+ try:
+ for pid in psutil.process_iter():
+ try:
+ try:
+ _ = pid.as_dict(attrs=['cpu_times', 'name', 'pid', 'status'])
+ except psutil.ZombieProcess:
+ # Filter out zombie processes
+ continue
+ except psutil.NoSuchProcess:
+ continue
+
+ for cmd in pid.cmdline():
+ if cmd.find(cmd_line) != -1:
+ is_running = False
+ try:
+ process = psutil.Process(pid.pid)
+ if process.status() == psutil.STATUS_RUNNING or \
+ process.status() == psutil.STATUS_SLEEPING or \
+ process.status() == psutil.STATUS_IDLE:
+ is_running = True
+ except Exception as e:
+ print(f"Error in get_pid_from_cmd_line inner loop: {e}")
+ pass
+ if is_running:
+ ret_pids.append(pid.pid)
+ if break_on_first:
+ return ret_pids
+ except Exception as e:
+ # print(f"Error in get_pid_from_cmd_line inner loop: {e}")
+ continue
+ except Exception as e:
+ print(f"Error in get_pid_from_cmd_line outer loop: {e}")
+ pass
return ret_pids
diff --git a/python/fedml/computing/scheduler/comm_utils/sys_utils.py b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
index a0ee3402f0..6dbef9bde3 100644
--- a/python/fedml/computing/scheduler/comm_utils/sys_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
@@ -301,249 +301,303 @@ def save_login_process(runner_home_dir, runner_info_dir, edge_process_id):
def cleanup_all_fedml_client_learning_processes():
# Cleanup all fedml client learning processes.
- for process in psutil.process_iter():
- try:
- pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
- found_learning_process = False
- found_client_process = False
- for cmd in pinfo["cmdline"]:
- if str(cmd).find("fedml_config.yaml") != -1:
- found_learning_process = True
-
- if str(cmd).find("client") != -1:
- found_client_process = True
-
- if found_learning_process and found_client_process:
- # click.echo("find client learning process at {}.".format(process.pid))
- if platform.system() == 'Windows':
- os.system("taskkill /PID {} /T /F".format(process.pid))
- else:
- os.killpg(os.getpgid(process.pid), signal.SIGKILL)
- except Exception as e:
- pass
+ try:
+ for process in psutil.process_iter():
+ try:
+ pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
+ found_learning_process = False
+ found_client_process = False
+ for cmd in pinfo["cmdline"]:
+ if str(cmd).find("fedml_config.yaml") != -1:
+ found_learning_process = True
+
+ if str(cmd).find("client") != -1:
+ found_client_process = True
+
+ if found_learning_process and found_client_process:
+ # click.echo("find client learning process at {}.".format(process.pid))
+ if platform.system() == 'Windows':
+ os.system("taskkill /PID {} /T /F".format(process.pid))
+ else:
+ os.killpg(os.getpgid(process.pid), signal.SIGKILL)
+ except Exception as e:
+ print(f"Failed to cleanup the client learning process due to {e}.")
+ pass
+ except Exception as e:
+ print(f"Failed to cleanup the client learning process due to {e}.")
+ pass
def cleanup_all_fedml_client_diagnosis_processes():
# Cleanup all fedml client learning processes.
- for process in psutil.process_iter():
- try:
- pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
- found_client_diagnosis_process = False
- for cmd in pinfo["cmdline"]:
- if str(cmd).find("client_diagnosis") != -1:
- found_client_diagnosis_process = True
-
- if found_client_diagnosis_process:
- # click.echo("find client diagnosis process at {}.".format(process.pid))
- if platform.system() == 'Windows':
- os.system("taskkill /PID {} /T /F".format(process.pid))
- else:
- os.killpg(os.getpgid(process.pid), signal.SIGKILL)
- except Exception as e:
- pass
+ try:
+ for process in psutil.process_iter():
+ try:
+ pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
+ found_client_diagnosis_process = False
+ for cmd in pinfo["cmdline"]:
+ if str(cmd).find("client_diagnosis") != -1:
+ found_client_diagnosis_process = True
+
+ if found_client_diagnosis_process:
+ # click.echo("find client diagnosis process at {}.".format(process.pid))
+ if platform.system() == 'Windows':
+ os.system("taskkill /PID {} /T /F".format(process.pid))
+ else:
+ os.killpg(os.getpgid(process.pid), signal.SIGKILL)
+ except Exception as e:
+ print(f"Failed to cleanup the client diagnosis process due to {e}.")
+ pass
+ except Exception as e:
+ print(f"Failed to cleanup the client diagnosis process due to {e}.")
+ pass
def cleanup_all_fedml_client_login_processes(login_program, clean_process_group=True):
# Cleanup all fedml client login processes.
- for process in psutil.process_iter():
- try:
- pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
- for cmd in pinfo["cmdline"]:
- if str(cmd).find(login_program) != -1:
- if os.path.basename(cmd) == login_program:
- # click.echo("find client login process at {}.".format(process.pid))
- if platform.system() == "Windows":
- os.system("taskkill /PID {} /T /F".format(process.pid))
- else:
- os.kill(process.pid, signal.SIGKILL)
- if clean_process_group:
- os.killpg(os.getpgid(process.pid), signal.SIGKILL)
- except Exception as e:
- pass
+ try:
+ for process in psutil.process_iter():
+ try:
+ pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
+ for cmd in pinfo["cmdline"]:
+ if str(cmd).find(login_program) != -1:
+ if os.path.basename(cmd) == login_program:
+ # click.echo("find client login process at {}.".format(process.pid))
+ if platform.system() == "Windows":
+ os.system("taskkill /PID {} /T /F".format(process.pid))
+ else:
+ os.kill(process.pid, signal.SIGKILL)
+ if clean_process_group:
+ os.killpg(os.getpgid(process.pid), signal.SIGKILL)
+ except Exception as e:
+ print(f"Failed to cleanup the client login process due to {e}.")
+ pass
+ except Exception as e:
+ print(f"Failed to cleanup the client login process since psutil.process_iter() failed.")
+ pass
def cleanup_all_fedml_server_learning_processes():
# Cleanup all fedml server learning processes.
- for process in psutil.process_iter():
- try:
- pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
- found_learning_process = False
- found_server_process = False
- for cmd in pinfo["cmdline"]:
- if str(cmd).find("fedml_config.yaml") != -1:
- found_learning_process = True
-
- if str(cmd).find("server") != -1:
- found_server_process = True
-
- if found_learning_process and found_server_process:
- # click.echo("find server learning process at {}.".format(process.pid))
- if platform.system() == 'Windows':
- os.system("taskkill /PID {} /T /F".format(process.pid))
- else:
- os.killpg(os.getpgid(process.pid), signal.SIGKILL)
- except Exception as e:
- pass
+ try:
+ for process in psutil.process_iter():
+ try:
+ pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
+ found_learning_process = False
+ found_server_process = False
+ for cmd in pinfo["cmdline"]:
+ if str(cmd).find("fedml_config.yaml") != -1:
+ found_learning_process = True
+
+ if str(cmd).find("server") != -1:
+ found_server_process = True
+
+ if found_learning_process and found_server_process:
+ # click.echo("find server learning process at {}.".format(process.pid))
+ if platform.system() == 'Windows':
+ os.system("taskkill /PID {} /T /F".format(process.pid))
+ else:
+ os.killpg(os.getpgid(process.pid), signal.SIGKILL)
+ except Exception as e:
+ print(f"Failed to cleanup the server learning process due to {e}.")
+ pass
+ except Exception as e:
+ print(f"Failed to cleanup the server learning process due to {e}.")
+ pass
def cleanup_all_fedml_client_api_processes(kill_all=False, is_model_device=False):
# Cleanup all fedml client api processes.
- for process in psutil.process_iter():
- try:
- pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
- find_api_process = False
- for cmd in pinfo["cmdline"]:
- if is_model_device:
- if str(cmd).find("model_scheduler.device_client_api:api") != -1:
- find_api_process = True
- else:
- if str(cmd).find("slave.client_api:api") != -1:
- find_api_process = True
+ try:
+ for process in psutil.process_iter():
+ try:
+ pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
+ find_api_process = False
+ for cmd in pinfo["cmdline"]:
+ if is_model_device:
+ if str(cmd).find("model_scheduler.device_client_api:api") != -1:
+ find_api_process = True
+ else:
+ if str(cmd).find("slave.client_api:api") != -1:
+ find_api_process = True
- if find_api_process:
- # click.echo("find client api process at {}.".format(process.pid))
- if platform.system() == 'Windows':
- os.system("taskkill /PID {} /T /F".format(process.pid))
- else:
- if kill_all:
- os.killpg(os.getpgid(process.pid), signal.SIGKILL)
+ if find_api_process:
+ # click.echo("find client api process at {}.".format(process.pid))
+ if platform.system() == 'Windows':
+ os.system("taskkill /PID {} /T /F".format(process.pid))
else:
- os.kill(process.pid, signal.SIGKILL)
- except Exception as e:
- pass
+ if kill_all:
+ os.killpg(os.getpgid(process.pid), signal.SIGKILL)
+ else:
+ os.kill(process.pid, signal.SIGKILL)
+ except Exception as e:
+ print(f"Failed to cleanup the client api process due to {e}.")
+ pass
+ except Exception as e:
+ print(f"Failed to cleanup the client api process due to {e}.")
+ pass
def cleanup_all_fedml_server_api_processes(kill_all=False, is_model_device=False):
# Cleanup all fedml server api processes.
- for process in psutil.process_iter():
- try:
- pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
- find_api_process = False
- for cmd in pinfo["cmdline"]:
- if is_model_device:
- if str(cmd).find("model_scheduler.device_server_api:api") != -1:
- find_api_process = True
-
- if str(cmd).find("model_scheduler.device_model_inference:api") != -1:
- find_api_process = True
- else:
- if str(cmd).find("master.server_api:api") != -1:
- find_api_process = True
-
- if find_api_process:
- # click.echo("find server api process at {}.".format(process.pid))
- if platform.system() == 'Windows':
- os.system("taskkill /PID {} /T /F".format(process.pid))
- else:
- if kill_all:
- os.killpg(os.getpgid(process.pid), signal.SIGKILL)
+ try:
+ for process in psutil.process_iter():
+ try:
+ pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
+ find_api_process = False
+ for cmd in pinfo["cmdline"]:
+ if is_model_device:
+ if str(cmd).find("model_scheduler.device_server_api:api") != -1:
+ find_api_process = True
+
+ if str(cmd).find("model_scheduler.device_model_inference:api") != -1:
+ find_api_process = True
else:
- os.kill(process.pid, signal.SIGKILL)
- except Exception as e:
- pass
+ if str(cmd).find("master.server_api:api") != -1:
+ find_api_process = True
+ if find_api_process:
+ # click.echo("find server api process at {}.".format(process.pid))
+ if platform.system() == 'Windows':
+ os.system("taskkill /PID {} /T /F".format(process.pid))
+ else:
+ if kill_all:
+ os.killpg(os.getpgid(process.pid), signal.SIGKILL)
+ else:
+ os.kill(process.pid, signal.SIGKILL)
+ except Exception as e:
+ print(f"Failed to cleanup the server api process due to {e}.")
+ pass
+ except Exception as e:
+ print(f"Failed to cleanup the server api process due to {e}.")
+ pass
def cleanup_all_fedml_server_login_processes(login_program, clean_process_group=False):
# Cleanup all fedml client login processes.
- for process in psutil.process_iter():
- try:
- pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
- for cmd in pinfo["cmdline"]:
- if str(cmd).find(login_program) != -1:
- if os.path.basename(cmd) == login_program:
- # click.echo("find server login process at {}.".format(process.pid))
- if platform.system() == 'Windows':
- os.system("taskkill /PID {} /T /F".format(process.pid))
- else:
- os.kill(process.pid, signal.SIGKILL)
- if clean_process_group:
- os.killpg(os.getpgid(process.pid), signal.SIGKILL)
- except Exception as e:
- pass
+ try:
+ for process in psutil.process_iter():
+ try:
+ pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
+ for cmd in pinfo["cmdline"]:
+ if str(cmd).find(login_program) != -1:
+ if os.path.basename(cmd) == login_program:
+ # click.echo("find server login process at {}.".format(process.pid))
+ if platform.system() == 'Windows':
+ os.system("taskkill /PID {} /T /F".format(process.pid))
+ else:
+ os.kill(process.pid, signal.SIGKILL)
+ if clean_process_group:
+ os.killpg(os.getpgid(process.pid), signal.SIGKILL)
+ except Exception as e:
+ print(f"Failed to cleanup the server login process due to {e}.")
+ pass
+ except Exception as e:
+ print(f"Failed to cleanup the server login process due to {e}.")
+ pass
def cleanup_all_bootstrap_processes(bootstrap_program, clean_process_group=False):
# Cleanup all fedml bootstrap processes.
- for process in psutil.process_iter():
- try:
- pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
- for cmd in pinfo["cmdline"]:
- if str(cmd).find(bootstrap_program) != -1:
- if os.path.basename(cmd) == bootstrap_program:
- # click.echo("find server login process at {}.".format(process.pid))
- if platform.system() == 'Windows':
- os.system("taskkill /PID {} /T /F".format(process.pid))
- else:
- os.kill(process.pid, signal.SIGKILL)
- if clean_process_group:
- os.killpg(os.getpgid(process.pid), signal.SIGKILL)
- except Exception as e:
- pass
+ try:
+ for process in psutil.process_iter():
+ try:
+ pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
+ for cmd in pinfo["cmdline"]:
+ if str(cmd).find(bootstrap_program) != -1:
+ if os.path.basename(cmd) == bootstrap_program:
+ # click.echo("find server login process at {}.".format(process.pid))
+ if platform.system() == 'Windows':
+ os.system("taskkill /PID {} /T /F".format(process.pid))
+ else:
+ os.kill(process.pid, signal.SIGKILL)
+ if clean_process_group:
+ os.killpg(os.getpgid(process.pid), signal.SIGKILL)
+ except Exception as e:
+ print(f"Failed to cleanup the bootstrap process due to {e}.")
+ pass
+ except Exception as e:
+ print(f"Failed to cleanup the bootstrap process due to {e}.")
+ pass
def cleanup_model_monitor_processes(run_id, end_point_name, model_id, model_name, model_version):
# Cleanup all fedml server api processes.
- for process in psutil.process_iter():
- try:
- pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
- find_monitor_process = False
- find_monitor_name_arg = False
- find_endpoint_id_name_arg = False
- for cmd in pinfo["cmdline"]:
- if str(cmd).endswith("device_model_monitor.py"):
- find_monitor_name_arg = True
-
- if find_monitor_name_arg and str(cmd) == f"-ep":
- find_endpoint_id_name_arg = True
-
- if find_monitor_name_arg and find_endpoint_id_name_arg and str(cmd) == f"{run_id}":
- find_monitor_process = True
- break
+ try:
+ for process in psutil.process_iter():
+ try:
+ pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
+ find_monitor_process = False
+ find_monitor_name_arg = False
+ find_endpoint_id_name_arg = False
+ for cmd in pinfo["cmdline"]:
+ if str(cmd).endswith("device_model_monitor.py"):
+ find_monitor_name_arg = True
+
+ if find_monitor_name_arg and str(cmd) == f"-ep":
+ find_endpoint_id_name_arg = True
+
+ if find_monitor_name_arg and find_endpoint_id_name_arg and str(cmd) == f"{run_id}":
+ find_monitor_process = True
+ break
- if find_monitor_process:
- # click.echo("find the monitor process at {}.".format(process.pid))
- if platform.system() == 'Windows':
- os.system("taskkill /PID {} /T /F".format(process.pid))
- else:
- os.kill(process.pid, signal.SIGKILL)
- break
- except Exception as e:
- pass
+ if find_monitor_process:
+ # click.echo("find the monitor process at {}.".format(process.pid))
+ if platform.system() == 'Windows':
+ os.system("taskkill /PID {} /T /F".format(process.pid))
+ else:
+ os.kill(process.pid, signal.SIGKILL)
+ break
+ except Exception as e:
+ logging.error(f"Failed to cleanup the model monitor process due to {e}.")
+ pass
+ except Exception as e:
+ logging.error(f"For loop failed to stop the model inference monitor due to {e}.")
+ pass
def get_process_running_count(process_name):
count = 0
- for process in psutil.process_iter():
- try:
- pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
- for cmd in pinfo["cmdline"]:
- if str(cmd).find(process_name) != -1:
- if os.path.basename(cmd) == process_name:
- count += 1
- except Exception as e:
- pass
+ try:
+ for process in psutil.process_iter():
+ try:
+ pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
+ for cmd in pinfo["cmdline"]:
+ if str(cmd).find(process_name) != -1:
+ if os.path.basename(cmd) == process_name:
+ count += 1
+ except Exception as e:
+ print(f"Error in get_process_running_count: {e}")
+ pass
+ except Exception as e:
+ print(f"Error in get_process_running_count: {e}")
+ pass
return count
def edge_simulator_has_login(login_program="client_login.py"):
- for process in psutil.process_iter():
- try:
- pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
- found_login_process = False
- found_simulator_process = False
- for cmd in pinfo["cmdline"]:
- if str(cmd).find(login_program) != -1:
- if os.path.basename(cmd) == login_program:
- found_login_process = True
-
- if str(cmd).find("edge_simulator") != -1:
- found_simulator_process = True
-
- if found_login_process and found_simulator_process:
- return True
- except Exception as e:
- pass
+ try:
+ for process in psutil.process_iter():
+ try:
+ pinfo = process.as_dict(attrs=["pid", "name", "cmdline"])
+ found_login_process = False
+ found_simulator_process = False
+ for cmd in pinfo["cmdline"]:
+ if str(cmd).find(login_program) != -1:
+ if os.path.basename(cmd) == login_program:
+ found_login_process = True
+
+ if str(cmd).find("edge_simulator") != -1:
+ found_simulator_process = True
+
+ if found_login_process and found_simulator_process:
+ return True
+ except Exception as e:
+ print(f"Error in edge_simulator_has_login: {e}")
+ pass
+ except Exception as e:
+ print(f"Error in edge_simulator_has_login: {e}")
+ pass
return False
diff --git a/python/fedml/computing/scheduler/slave/client_daemon.py b/python/fedml/computing/scheduler/slave/client_daemon.py
index a3dff06e2d..14b841707f 100755
--- a/python/fedml/computing/scheduler/slave/client_daemon.py
+++ b/python/fedml/computing/scheduler/slave/client_daemon.py
@@ -97,6 +97,7 @@
time.sleep(3)
else:
login_logs = os.path.join(ClientConstants.get_log_file_dir(), "login.log")
+ # If we use this kind of command, we cannot penetrate the environment variables to the subprocess
run_login_cmd = f"nohup {get_python_program()} -W ignore {login_cmd} -t login -u {args.user} " \
f"-v {args.version} -r {args.role} -id {args.device_id} " \
f"-k {args.api_key} -ngc {str(args.no_gpu_check)} -mpt {args.marketplace_type} " \
@@ -106,6 +107,9 @@
os.system(run_login_cmd)
login_pids = RunProcessUtils.get_pid_from_cmd_line(login_cmd)
+ if len(login_pids) == 0:
+ print(f"[Client] Cannot find login pid {login_pids}, check the log file {login_logs}")
+ retry_count += 1
while len(login_pids) > 0:
with open(login_logs, "r") as f:
log_list = f.readlines()
diff --git a/python/fedml/core/mlops/mlops_configs.py b/python/fedml/core/mlops/mlops_configs.py
index 338f59e697..891f721c9d 100644
--- a/python/fedml/core/mlops/mlops_configs.py
+++ b/python/fedml/core/mlops/mlops_configs.py
@@ -137,8 +137,20 @@ def _fetch_configs(configs) -> dict:
request_configs = request_configs.union(configs)
json_params = {"config_name": [config.value for config in request_configs],
"device_send_time": int(time.time() * 1000)}
- response = MLOpsConfigs._request(request_url=url, request_json=json_params, cert_path=cert_path)
- status_code = response.json().get("code")
+ try:
+ response = MLOpsConfigs._request(request_url=url, request_json=json_params, cert_path=cert_path)
+ except Exception as e:
+ print(f"Fetch configs failed due to {e} "
+ f"please check the network connection and try again.")
+ return {}
+
+ msg_str = ""
+ if response:
+ status_code = response.json().get("code")
+ msg_str = response.json()
+ else:
+ status_code = "FAILED"
+
result = {}
if status_code == "SUCCESS":
data = response.json().get("data")
@@ -147,7 +159,8 @@ def _fetch_configs(configs) -> dict:
mlops_config = data.get(Configs.ML_OPS_CONFIG.value)
MLOpsUtils.calc_ntp_from_config(mlops_config)
else:
- raise Exception("failed to fetch device configurations!")
+ raise Exception(f"failed to fetch device configs from server, with status code: {status_code} "
+ f"and response: {msg_str}")
return result
@staticmethod
From 698e95e1b9281d387ead72075a3a6d417d2f7c29 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Mon, 2 Dec 2024 16:14:11 +0800
Subject: [PATCH 232/282] [fixbug] 1.The work inference proxy port needs to be
read from the configuration file 2.occupy_gpu_ids fail,get gpu ids from cache
may return [] instead of None
---
python/fedml/computing/scheduler/comm_utils/job_utils.py | 2 +-
.../model_scheduler/device_http_proxy_inference_protocol.py | 3 ++-
2 files changed, 3 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_utils.py b/python/fedml/computing/scheduler/comm_utils/job_utils.py
index ae50239d25..0fca06a11b 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_utils.py
@@ -93,7 +93,7 @@ def occupy_gpu_ids(self, run_id, request_gpu_num, device_id, inner_id=None,
f" for run {run_id}: {available_gpu_ids}")
# If the available GPU list is not in the cache, set it to the current system available GPU list
- if available_gpu_ids is None:
+ if available_gpu_ids is None or available_gpu_ids == []:
# Get realtime GPU availability list from the system
available_gpu_ids = JobRunnerUtils.get_realtime_gpu_available_ids().copy()
logging.info(f"Cache not set yet, fetching realtime available GPU Ids: {available_gpu_ids}")
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_http_proxy_inference_protocol.py b/python/fedml/computing/scheduler/model_scheduler/device_http_proxy_inference_protocol.py
index 746d17bb7c..180b10994b 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_http_proxy_inference_protocol.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_http_proxy_inference_protocol.py
@@ -53,7 +53,8 @@ async def run_http_proxy_inference_with_request(
# TODO(Raphael): Add support for GET and other methods
):
inference_response = {}
- http_proxy_url = f"http://{urlparse(inference_url).hostname}:{ClientConstants.LOCAL_CLIENT_API_PORT}/api/v1/predict"
+ worker_proxy_port = ClientConstants.get_inference_worker_proxy_port()
+ http_proxy_url = f"http://{urlparse(inference_url).hostname}:{worker_proxy_port}/api/v1/predict"
if inference_type == "default":
model_api_headers = {'Content-Type': 'application/json', 'Connection': 'close',
'Accept': 'application/json'}
From 757e5f0faa7f737973ba2638d35dc6680ab7ff11 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Tue, 10 Dec 2024 15:00:01 +0800
Subject: [PATCH 233/282] add log in get_available_gpu_ids[hardware_utils.py]
---
python/fedml/computing/scheduler/comm_utils/hardware_utils.py | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index 56a75fe3e1..70f6e3d114 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -62,7 +62,9 @@ def get_gpus() -> List[GPUCard]:
def get_available_gpu_ids(order: str = "memory", limit: int = 1, max_load: float = 0.01,
max_memory: float = 0.01) -> List[int]:
gpu_util = HardwareUtil.__get_util()
- return gpu_util.get_available_gpu_card_ids(order, limit, max_load, max_memory) if gpu_util is not None else []
+ card_ids = gpu_util.get_available_gpu_card_ids(order, limit, max_load, max_memory) if gpu_util is not None else []
+ logging.info(f"Available GPU card ids ---> {card_ids}")
+ return card_ids
@staticmethod
def get_docker_gpu_device_mapping(gpu_ids: Optional[List[int]], num_gpus: int = 0) -> Optional[Dict]:
From c5c22c830b35eca26cac857fc7492d7f962c4f80 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Tue, 10 Dec 2024 17:18:09 +0800
Subject: [PATCH 234/282] add logs
---
.../computing/scheduler/slave/base_slave_protocol_manager.py | 2 ++
1 file changed, 2 insertions(+)
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index 05469e78ff..1af75d936e 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -379,6 +379,8 @@ def callback_report_device_info(self, topic, payload):
"edge_info": device_info_json}
if context is not None:
response_payload["context"] = context
+
+ logging.info(f"Response payload --> {response_payload}")
self.message_center.send_message(response_topic, json.dumps(response_payload), run_id=run_id)
def callback_request_device_info_from_mlops(self, topic, payload):
From ffa54a374783e600af4b52707b3f8b4f41fc3e7b Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Tue, 10 Dec 2024 18:04:37 +0800
Subject: [PATCH 235/282] add logs
---
python/fedml/computing/scheduler/comm_utils/job_utils.py | 1 +
python/fedml/computing/scheduler/comm_utils/sys_utils.py | 1 +
.../computing/scheduler/slave/base_slave_protocol_manager.py | 3 +++
3 files changed, 5 insertions(+)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_utils.py b/python/fedml/computing/scheduler/comm_utils/job_utils.py
index 0fca06a11b..fa143510cd 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_utils.py
@@ -351,6 +351,7 @@ def get_realtime_gpu_available_ids():
gpu_list = sys_utils.get_gpu_list()
gpu_count = len(gpu_list)
realtime_available_gpu_ids = sys_utils.get_available_gpu_id_list(limit=gpu_count)
+ logging.info(f"get_available_gpu_id_list limit:{gpu_count}, available_gpu_ids:{realtime_available_gpu_ids}")
return realtime_available_gpu_ids
@staticmethod
diff --git a/python/fedml/computing/scheduler/comm_utils/sys_utils.py b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
index 6dbef9bde3..fe518e4ba4 100644
--- a/python/fedml/computing/scheduler/comm_utils/sys_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
@@ -191,6 +191,7 @@ def get_available_gpu_id_list(limit=1) -> List[int]:
gpu_available_list = HardwareUtil.get_available_gpu_ids(order='memory', limit=limit, max_load=0.01,
max_memory=0.01)
+ logging.info(f"GPU available ids from HardwareUtil.get_available_gpu_ids, limit --> {limit}")
return gpu_available_list
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index 1af75d936e..9ba8c26e15 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -340,10 +340,13 @@ def callback_report_device_info(self, topic, payload):
else:
total_mem, free_mem, total_disk_size, free_disk_size, cup_utilization, cpu_cores, gpu_cores_total, \
gpu_cores_available, sent_bytes, recv_bytes, gpu_available_ids = sys_utils.get_sys_realtime_stats()
+ logging.info(f"GPU available ids from get_sys_realtime_stats --> {gpu_available_ids}")
host_ip = sys_utils.get_host_ip()
host_port = sys_utils.get_available_port()
gpu_available_ids = JobRunnerUtils.get_available_gpu_id_list(self.edge_id)
+ logging.info(f"GPU available ids from get_available_gpu_id_list(device_id) --> {gpu_available_ids}")
gpu_available_ids = JobRunnerUtils.trim_unavailable_gpu_ids(gpu_available_ids)
+ logging.info(f"GPU available ids from trim_unavailable_gpu_ids --> {gpu_available_ids}")
gpu_cores_available = len(gpu_available_ids)
gpu_list = sys_utils.get_gpu_list()
device_info_json = {
From 589dc47a551c0317a7ab34616b6847ef496cdc0b Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Tue, 10 Dec 2024 19:01:39 +0800
Subject: [PATCH 236/282] add logs
---
.../fedml/computing/scheduler/comm_utils/hardware_utils.py | 7 +++++--
1 file changed, 5 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index 70f6e3d114..96aeecfc4a 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -56,14 +56,17 @@ def __get_util(cls) -> Optional[GPUCardUtil]:
@staticmethod
def get_gpus() -> List[GPUCard]:
gpu_util = HardwareUtil.__get_util()
- return gpu_util.get_gpu_cards() if gpu_util is not None else []
+ cards = gpu_util.get_gpu_cards() if gpu_util is not None else []
+ logging.info(f"hardware_utils Available GPU cards len ---> { len(cards)}")
+ logging.info(f"hardware_utils Available GPU cards ---> {cards}")
+ return cards
@staticmethod
def get_available_gpu_ids(order: str = "memory", limit: int = 1, max_load: float = 0.01,
max_memory: float = 0.01) -> List[int]:
gpu_util = HardwareUtil.__get_util()
card_ids = gpu_util.get_available_gpu_card_ids(order, limit, max_load, max_memory) if gpu_util is not None else []
- logging.info(f"Available GPU card ids ---> {card_ids}")
+ logging.info(f"hardware_utils get_available_gpu_ids ids ---> {card_ids}")
return card_ids
@staticmethod
From 40735d96f98ec8dcdcd03f8a24bc39838ca2c2d3 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Tue, 10 Dec 2024 19:48:16 +0800
Subject: [PATCH 237/282] add logs
---
python/fedml/computing/scheduler/comm_utils/hardware_utils.py | 3 +--
1 file changed, 1 insertion(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index 96aeecfc4a..f50f5d73f5 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -58,7 +58,6 @@ def get_gpus() -> List[GPUCard]:
gpu_util = HardwareUtil.__get_util()
cards = gpu_util.get_gpu_cards() if gpu_util is not None else []
logging.info(f"hardware_utils Available GPU cards len ---> { len(cards)}")
- logging.info(f"hardware_utils Available GPU cards ---> {cards}")
return cards
@staticmethod
@@ -66,7 +65,7 @@ def get_available_gpu_ids(order: str = "memory", limit: int = 1, max_load: float
max_memory: float = 0.01) -> List[int]:
gpu_util = HardwareUtil.__get_util()
card_ids = gpu_util.get_available_gpu_card_ids(order, limit, max_load, max_memory) if gpu_util is not None else []
- logging.info(f"hardware_utils get_available_gpu_ids ids ---> {card_ids}")
+ logging.info(f"hardware_utils get_available_gpu_ids ids ---> {card_ids}, limit ---> {limit}")
return card_ids
@staticmethod
From 90c1191ddf942d580715715eddab334836ee80ea Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 11 Dec 2024 11:11:08 +0800
Subject: [PATCH 238/282] [bugfix]Enhance GPU management(need compare the
readtime availabe gpu with init gpu ids because of the system gpu resource
may change) and logging in hardware and job utilities
---
.../scheduler/comm_utils/job_utils.py | 23 +++++++++++---
.../scheduler_core/compute_gpu_cache.py | 31 +++++++++++++++++++
2 files changed, 49 insertions(+), 5 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_utils.py b/python/fedml/computing/scheduler/comm_utils/job_utils.py
index fa143510cd..21becad68e 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_utils.py
@@ -86,8 +86,7 @@ def occupy_gpu_ids(self, run_id, request_gpu_num, device_id, inner_id=None,
# Get the available GPU list, FEDML_GLOBAL_DEVICE_AVAILABLE_GPU_IDS_TAG-${device_id}
available_gpu_ids = ComputeCacheManager.get_instance().get_gpu_cache().get_device_available_gpu_ids(
device_id)
- logging.info(
- f"Available GPU Ids fetched from cache: {available_gpu_ids}")
+ logging.info(f"Available GPU Ids fetched from cache: {available_gpu_ids}")
logging.info(f"Check worker({device_id})'s realtime gpu availability in DB"
f" for run {run_id}: {available_gpu_ids}")
@@ -99,8 +98,20 @@ def occupy_gpu_ids(self, run_id, request_gpu_num, device_id, inner_id=None,
logging.info(f"Cache not set yet, fetching realtime available GPU Ids: {available_gpu_ids}")
else:
available_gpu_ids = JobRunnerUtils.trim_unavailable_gpu_ids(available_gpu_ids)
- logging.info(
- f"Trimmed available GPU Ids: {available_gpu_ids}")
+ logging.info(f"Trimmed available GPU Ids: {available_gpu_ids}")
+
+ # check if the initial available GPU ids is changed
+ initial_available_gpu_ids = ComputeCacheManager.get_instance().get_gpu_cache().get_device_initial_available_gpu_ids(device_id)
+ # logging.info(f"Initial available GPU Ids: {initial_available_gpu_ids}")
+ if initial_available_gpu_ids and (not all(gpu_id in available_gpu_ids for gpu_id in initial_available_gpu_ids)):
+ # then add the initial_available_gpu_ids to the available_gpu_ids
+ available_gpu_ids.extend(initial_available_gpu_ids)
+ available_gpu_ids = list(set(available_gpu_ids))
+ # sort the available_gpu_ids
+ available_gpu_ids.sort()
+ logging.info(f"Device {device_id} available GPU ids is changed because of the system gpu resource change, "
+ f"initial available GPU ids: {initial_available_gpu_ids}, "
+ f"current available GPU ids: {available_gpu_ids}")
# Get the matched gpu ids string by the request gpu num
cuda_visible_gpu_ids_str, matched_gpu_num = JobRunnerUtils.request_gpu_ids(request_gpu_num,
@@ -317,10 +328,12 @@ def get_available_gpu_id_list(device_id):
device_id)
# If the available GPU list is not in the cache, set it to the current system available GPU list
- if available_gpu_ids is None:
+ if available_gpu_ids is None or available_gpu_ids == []:
# Get realtime GPU availability list from the system
gpu_ids = JobRunnerUtils.get_realtime_gpu_available_ids().copy()
ComputeCacheManager.get_instance().get_gpu_cache().set_device_available_gpu_ids(device_id, gpu_ids)
+ # Set the initial available GPU ids to the cache, use to check if the device all available GPU ids is changed because of the system resource change
+ ComputeCacheManager.get_instance().get_gpu_cache().set_device_initial_available_gpu_ids(device_id, gpu_ids)
available_gpu_ids = gpu_ids
return available_gpu_ids
diff --git a/python/fedml/computing/scheduler/scheduler_core/compute_gpu_cache.py b/python/fedml/computing/scheduler/scheduler_core/compute_gpu_cache.py
index 7bab71212e..6b3addc320 100755
--- a/python/fedml/computing/scheduler/scheduler_core/compute_gpu_cache.py
+++ b/python/fedml/computing/scheduler/scheduler_core/compute_gpu_cache.py
@@ -10,6 +10,7 @@ class ComputeGpuCache(object):
FEDML_GLOBAL_DEVICE_RUN_NUM_GPUS_TAG = "FEDML_GLOBAL_DEVICE_RUN_NUM_GPUS_TAG-"
FEDML_GLOBAL_DEVICE_RUN_GPU_IDS_TAG = "FEDML_GLOBAL_DEVICE_RUN_GPU_IDS_TAG-"
FEDML_GLOBAL_DEVICE_AVAILABLE_GPU_IDS_TAG = "FEDML_GLOBAL_DEVICE_AVAILABLE_GPU_IDS_TAG-"
+ FEDML_GLOBAL_DEVICE_INITIAL_AVAILABLE_GPU_IDS_TAG = "FEDML_GLOBAL_DEVICE_INITIAL_AVAILABLE_GPU_IDS_TAG-"
FEDML_GLOBAL_DEVICE_TOTAL_NUM_GPUS_TAG = "FEDML_GLOBAL_DEVICE_TOTAL_NUM_GPUS_TAG-"
FEDML_GLOBAL_RUN_TOTAL_NUM_GPUS_TAG = "FEDML_GLOBAL_RUN_TOTAL_NUM_GPUS_TAG-"
FEDML_GLOBAL_RUN_DEVICE_IDS_TAG = "FEDML_GLOBAL_RUN_DEVICE_IDS_TAG-"
@@ -107,6 +108,25 @@ def get_device_available_gpu_ids(self, device_id):
return []
return device_available_gpu_ids
+
+ def get_device_initial_available_gpu_ids(self, device_id):
+ # Get the initial available GPU ids from the cache, for checking if the device all available GPU ids is changed
+ device_initial_available_gpu_ids = None
+ try:
+ if self.redis_connection.exists(self.get_device_initial_available_gpu_ids_key(device_id)):
+ device_initial_available_gpu_ids = self.redis_connection.get(self.get_device_initial_available_gpu_ids_key(device_id))
+ if str(device_initial_available_gpu_ids).strip() == "":
+ return []
+ except Exception as e:
+ pass
+
+ if device_initial_available_gpu_ids is not None and str(device_initial_available_gpu_ids).strip() != "":
+ device_initial_available_gpu_ids = device_initial_available_gpu_ids.split(',')
+ device_initial_available_gpu_ids = self.map_str_list_to_int_list(device_initial_available_gpu_ids)
+ else:
+ return []
+
+ return device_initial_available_gpu_ids
def get_device_total_num_gpus(self, device_id):
device_total_num_gpus = None
@@ -241,6 +261,14 @@ def set_device_available_gpu_ids(self, device_id, gpu_ids):
pass
ComputeGpuDatabase.get_instance().set_device_available_gpu_ids(device_id, gpu_ids)
+
+ def set_device_initial_available_gpu_ids(self, device_id, gpu_ids):
+ # Set the initial available GPU ids to the cache, use to check if the device all available GPU ids is changed
+ try:
+ str_gpu_ids = self.map_list_to_str(gpu_ids)
+ self.redis_connection.set(self.get_device_initial_available_gpu_ids_key(device_id), str_gpu_ids)
+ except Exception as e:
+ pass
def set_device_total_num_gpus(self, device_id, num_gpus):
try:
@@ -311,6 +339,9 @@ def get_device_run_gpu_ids_key(device_id, run_id):
def get_device_available_gpu_ids_key(self, device_id):
return f"{ComputeGpuCache.FEDML_GLOBAL_DEVICE_AVAILABLE_GPU_IDS_TAG}{device_id}"
+
+ def get_device_initial_available_gpu_ids_key(self, device_id):
+ return f"{ComputeGpuCache.FEDML_GLOBAL_DEVICE_INITIAL_AVAILABLE_GPU_IDS_TAG}{device_id}"
def get_device_total_num_gpus_key(self, device_id):
return f"{ComputeGpuCache.FEDML_GLOBAL_DEVICE_TOTAL_NUM_GPUS_TAG}{device_id}"
From 30e1f70de68ed60efb38f0bf77083b97ac556949 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 11 Dec 2024 11:32:47 +0800
Subject: [PATCH 239/282] [debug]add logs
---
python/fedml/computing/scheduler/comm_utils/job_utils.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_utils.py b/python/fedml/computing/scheduler/comm_utils/job_utils.py
index 21becad68e..ff5a2dd478 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_utils.py
@@ -102,7 +102,7 @@ def occupy_gpu_ids(self, run_id, request_gpu_num, device_id, inner_id=None,
# check if the initial available GPU ids is changed
initial_available_gpu_ids = ComputeCacheManager.get_instance().get_gpu_cache().get_device_initial_available_gpu_ids(device_id)
- # logging.info(f"Initial available GPU Ids: {initial_available_gpu_ids}")
+ logging.info(f"Initial available GPU Ids: {initial_available_gpu_ids}")
if initial_available_gpu_ids and (not all(gpu_id in available_gpu_ids for gpu_id in initial_available_gpu_ids)):
# then add the initial_available_gpu_ids to the available_gpu_ids
available_gpu_ids.extend(initial_available_gpu_ids)
@@ -334,6 +334,7 @@ def get_available_gpu_id_list(device_id):
ComputeCacheManager.get_instance().get_gpu_cache().set_device_available_gpu_ids(device_id, gpu_ids)
# Set the initial available GPU ids to the cache, use to check if the device all available GPU ids is changed because of the system resource change
ComputeCacheManager.get_instance().get_gpu_cache().set_device_initial_available_gpu_ids(device_id, gpu_ids)
+ logging.info(f"Set device {device_id} initial available GPU ids: {gpu_ids}")
available_gpu_ids = gpu_ids
return available_gpu_ids
From 21a374ce82e028153b31473d6e127637d43a322e Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 11 Dec 2024 12:50:05 +0800
Subject: [PATCH 240/282] [bugfix] Enhance GPU cache management by setting
initial available GPU IDs when reset available gpus
---
python/fedml/computing/scheduler/comm_utils/job_utils.py | 3 +++
1 file changed, 3 insertions(+)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_utils.py b/python/fedml/computing/scheduler/comm_utils/job_utils.py
index ff5a2dd478..de25a8e342 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_utils.py
@@ -353,6 +353,9 @@ def reset_available_gpu_id_list(device_id):
current_available_gpu_ids = JobRunnerUtils.get_realtime_gpu_available_ids().copy()
ComputeCacheManager.get_instance().get_gpu_cache().set_device_available_gpu_ids(device_id,
current_available_gpu_ids)
+ # Set the initial available GPU ids to the cache, use to check if the device all available GPU ids is changed because of the system resource change
+ ComputeCacheManager.get_instance().get_gpu_cache().set_device_initial_available_gpu_ids(device_id, current_available_gpu_ids)
+
gpu_list = sys_utils.get_gpu_list()
ComputeCacheManager.get_instance().get_gpu_cache().set_device_total_num_gpus(device_id, len(gpu_list))
except Exception as e:
From 02b87f44facee475aeb4f933dd02a349e8f241df Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 11 Dec 2024 13:18:08 +0800
Subject: [PATCH 241/282] [bugfix]calculate the difference between
realtime_available_gpu_ids and initial_available_gpu_ids
---
.../scheduler/comm_utils/job_utils.py | 29 +++++++++++--------
1 file changed, 17 insertions(+), 12 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_utils.py b/python/fedml/computing/scheduler/comm_utils/job_utils.py
index de25a8e342..654e7d2ea2 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_utils.py
@@ -90,28 +90,33 @@ def occupy_gpu_ids(self, run_id, request_gpu_num, device_id, inner_id=None,
logging.info(f"Check worker({device_id})'s realtime gpu availability in DB"
f" for run {run_id}: {available_gpu_ids}")
+
+ # Get realtime GPU availability list from the system
+ realtime_available_gpu_ids = JobRunnerUtils.get_realtime_gpu_available_ids().copy()
+ logging.info(f"Cache not set yet, fetching realtime available GPU Ids: {realtime_available_gpu_ids}")
# If the available GPU list is not in the cache, set it to the current system available GPU list
if available_gpu_ids is None or available_gpu_ids == []:
# Get realtime GPU availability list from the system
- available_gpu_ids = JobRunnerUtils.get_realtime_gpu_available_ids().copy()
- logging.info(f"Cache not set yet, fetching realtime available GPU Ids: {available_gpu_ids}")
+ available_gpu_ids = realtime_available_gpu_ids
else:
available_gpu_ids = JobRunnerUtils.trim_unavailable_gpu_ids(available_gpu_ids)
logging.info(f"Trimmed available GPU Ids: {available_gpu_ids}")
-
- # check if the initial available GPU ids is changed
- initial_available_gpu_ids = ComputeCacheManager.get_instance().get_gpu_cache().get_device_initial_available_gpu_ids(device_id)
- logging.info(f"Initial available GPU Ids: {initial_available_gpu_ids}")
- if initial_available_gpu_ids and (not all(gpu_id in available_gpu_ids for gpu_id in initial_available_gpu_ids)):
- # then add the initial_available_gpu_ids to the available_gpu_ids
- available_gpu_ids.extend(initial_available_gpu_ids)
+
+ initial_available_gpu_ids = ComputeCacheManager.get_instance().get_gpu_cache().get_device_initial_available_gpu_ids(
+ device_id)
+ # calculate the difference between realtime_available_gpu_ids and initial_available_gpu_ids
+ # if the difference is not empty, then add to available gpu ids
+ diff_gpu_ids = list(set(realtime_available_gpu_ids) - set(initial_available_gpu_ids))
+ if diff_gpu_ids:
+ available_gpu_ids.extend(diff_gpu_ids)
available_gpu_ids = list(set(available_gpu_ids))
- # sort the available_gpu_ids
available_gpu_ids.sort()
logging.info(f"Device {device_id} available GPU ids is changed because of the system gpu resource change, "
- f"initial available GPU ids: {initial_available_gpu_ids}, "
- f"current available GPU ids: {available_gpu_ids}")
+ f"initial available gpu ids: {initial_available_gpu_ids}, "
+ f"realtime available gpu ids: {realtime_available_gpu_ids}, "
+ f"diff gpu ids: {diff_gpu_ids}, "
+ f"new available gpu ids: {available_gpu_ids}")
# Get the matched gpu ids string by the request gpu num
cuda_visible_gpu_ids_str, matched_gpu_num = JobRunnerUtils.request_gpu_ids(request_gpu_num,
From 9ff4a560776205abcf18cb3405ef599fc6060778 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 11 Dec 2024 16:23:41 +0800
Subject: [PATCH 242/282] [bugfix]set shm_size to 8G if not specified
---
python/fedml/computing/scheduler/comm_utils/job_utils.py | 4 ++--
.../scheduler/model_scheduler/device_model_deployment.py | 4 ++++
2 files changed, 6 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_utils.py b/python/fedml/computing/scheduler/comm_utils/job_utils.py
index 654e7d2ea2..b1ecd9b11b 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_utils.py
@@ -96,7 +96,7 @@ def occupy_gpu_ids(self, run_id, request_gpu_num, device_id, inner_id=None,
logging.info(f"Cache not set yet, fetching realtime available GPU Ids: {realtime_available_gpu_ids}")
# If the available GPU list is not in the cache, set it to the current system available GPU list
- if available_gpu_ids is None or available_gpu_ids == []:
+ if available_gpu_ids is None:
# Get realtime GPU availability list from the system
available_gpu_ids = realtime_available_gpu_ids
else:
@@ -333,7 +333,7 @@ def get_available_gpu_id_list(device_id):
device_id)
# If the available GPU list is not in the cache, set it to the current system available GPU list
- if available_gpu_ids is None or available_gpu_ids == []:
+ if available_gpu_ids is None:
# Get realtime GPU availability list from the system
gpu_ids = JobRunnerUtils.get_realtime_gpu_available_ids().copy()
ComputeCacheManager.get_instance().get_gpu_cache().set_device_available_gpu_ids(device_id, gpu_ids)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 2fbbbfcb0d..baee7a2973 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -398,6 +398,10 @@ def parse_resource_related_config(config, gpu_num_frm_platform=0):
num_gpus = 0
shm_size = config.get('shm_size', None)
+ # set shm_size to 8G if not specified
+ if not shm_size:
+ shm_size = "8G"
+
storage_opt = config.get('storage_opt', None)
tmpfs = config.get('tmpfs', None)
cpus = config.get('cpus', None)
From d5831b956c5b6d2e60033e47559d21b668b41d25 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Tue, 17 Dec 2024 17:10:45 +0800
Subject: [PATCH 243/282] Revert "Merge pull request #2233 from
FedML-AI/charlie/dev/v0.7.0"
From bb31c93a66539608333c58867503b89acfebb6ae Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 18 Dec 2024 17:19:31 +0800
Subject: [PATCH 244/282] remove debug logs
---
python/fedml/computing/scheduler/comm_utils/hardware_utils.py | 4 ++--
python/fedml/computing/scheduler/comm_utils/sys_utils.py | 1 -
2 files changed, 2 insertions(+), 3 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
index f50f5d73f5..8e0763753f 100644
--- a/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/hardware_utils.py
@@ -57,7 +57,7 @@ def __get_util(cls) -> Optional[GPUCardUtil]:
def get_gpus() -> List[GPUCard]:
gpu_util = HardwareUtil.__get_util()
cards = gpu_util.get_gpu_cards() if gpu_util is not None else []
- logging.info(f"hardware_utils Available GPU cards len ---> { len(cards)}")
+ # logging.info(f"hardware_utils Available GPU cards len ---> { len(cards)}")
return cards
@staticmethod
@@ -65,7 +65,7 @@ def get_available_gpu_ids(order: str = "memory", limit: int = 1, max_load: float
max_memory: float = 0.01) -> List[int]:
gpu_util = HardwareUtil.__get_util()
card_ids = gpu_util.get_available_gpu_card_ids(order, limit, max_load, max_memory) if gpu_util is not None else []
- logging.info(f"hardware_utils get_available_gpu_ids ids ---> {card_ids}, limit ---> {limit}")
+ # logging.info(f"hardware_utils get_available_gpu_ids ids ---> {card_ids}, limit ---> {limit}")
return card_ids
@staticmethod
diff --git a/python/fedml/computing/scheduler/comm_utils/sys_utils.py b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
index fe518e4ba4..6dbef9bde3 100644
--- a/python/fedml/computing/scheduler/comm_utils/sys_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/sys_utils.py
@@ -191,7 +191,6 @@ def get_available_gpu_id_list(limit=1) -> List[int]:
gpu_available_list = HardwareUtil.get_available_gpu_ids(order='memory', limit=limit, max_load=0.01,
max_memory=0.01)
- logging.info(f"GPU available ids from HardwareUtil.get_available_gpu_ids, limit --> {limit}")
return gpu_available_list
From e27b830a7080331ed8f46d4e9ff0ceea32d4be2c Mon Sep 17 00:00:00 2001
From: "alex.liang" <alexliang.kh@gmail.com>
Date: Fri, 20 Dec 2024 11:15:48 +0800
Subject: [PATCH 245/282] check the gpu avaiablity using the random api to
adapte the rental gpus.
---
python/fedml/__init__.py | 2 +-
.../computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py | 3 ++-
.../computing/scheduler/slave/base_slave_protocol_manager.py | 2 +-
python/fedml/core/mlops/mlops_device_perfs.py | 2 +-
python/setup.py | 2 +-
5 files changed, 6 insertions(+), 5 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index d6341c25c9..677d06b4e5 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -37,7 +37,7 @@
_global_training_type = None
_global_comm_backend = None
-__version__ = "0.9.0"
+__version__ = "0.9.2"
# This is the deployment environment used for different roles (RD/PM/BD/Public Developers). Potential VALUE: local, dev, test, release
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
index 34d0c3be1c..a6717de8cb 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
@@ -25,7 +25,8 @@ def get_gpu_cards() -> List[GPUCard]:
@staticmethod
def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memory: float) -> List[int]:
- return GPUtil.getAvailable(order=order, limit=limit, maxLoad=max_load, maxMemory=max_memory)
+ # return GPUtil.getAvailable(order=order, limit=limit, maxLoad=max_load, maxMemory=max_memory)
+ return GPUtil.getAvailable(order='random', limit=limit)
@staticmethod
def get_docker_gpu_device_mapping(gpu_ids: List[int], num_gpus: int = 0) -> Optional[Dict]:
diff --git a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
index 9ba8c26e15..3acd9f2488 100755
--- a/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
+++ b/python/fedml/computing/scheduler/slave/base_slave_protocol_manager.py
@@ -321,7 +321,7 @@ def callback_report_device_info(self, topic, payload):
run_id = payload_json.get("run_id", 0)
listen_edge_id = str(topic).split("/")[-1]
context = payload_json.get("context", None)
- need_gpu_info = payload_json.get("need_gpu_info", False)
+ need_gpu_info = payload_json.get("need_gpu_info", True)
need_running_process_list = payload_json.get("need_running_process_list", False)
model_master_device_id = payload_json.get("model_master_device_id", None)
model_slave_device_id_list = payload_json.get("model_slave_device_id_list", None)
diff --git a/python/fedml/core/mlops/mlops_device_perfs.py b/python/fedml/core/mlops/mlops_device_perfs.py
index 4bb41df73f..0c2bde6785 100644
--- a/python/fedml/core/mlops/mlops_device_perfs.py
+++ b/python/fedml/core/mlops/mlops_device_perfs.py
@@ -233,7 +233,7 @@ def report_gpu_device_info(edge_id, mqtt_mgr=None):
# Do not use the following two lines as the realtime available gpu ids.
# gpu_available_ids = JobRunnerUtils.get_available_gpu_id_list(edge_id)
# gpu_available_ids = JobRunnerUtils.trim_unavailable_gpu_ids(gpu_available_ids)
- gpu_cores_available = len(gpu_available_ids)
+ gpu_cores_available = len(gpu_available_ids) if gpu_available_ids is not None else 0
deploy_worker_id_list = list()
try:
deploy_worker_id_list = json.loads(os.environ.get("FEDML_DEPLOY_WORKER_IDS", "[]"))
diff --git a/python/setup.py b/python/setup.py
index 3847f360fa..032bdb4eed 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -126,7 +126,7 @@ def finalize_options(self):
setup(
name="fedml",
- version="0.9.0",
+ version="0.9.2",
author="FedML Team",
author_email="ch@fedml.ai",
description="A research and production integrated edge-cloud library for "
From 77c69066507aac2ba791e239b63dd3dd3c32ecd9 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Fri, 20 Dec 2024 12:41:41 +0800
Subject: [PATCH 246/282] [bugfix]Adapt log method(in transformers/trainer.py)
parameters
---
python/fedml/train/llm/hf_trainer.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/train/llm/hf_trainer.py b/python/fedml/train/llm/hf_trainer.py
index aaf27340fe..e287a8518f 100644
--- a/python/fedml/train/llm/hf_trainer.py
+++ b/python/fedml/train/llm/hf_trainer.py
@@ -64,7 +64,7 @@ def __init__(
if TYPE_CHECKING:
self.args: ExperimentArguments = self.args # noqa
- def log(self, logs: Dict[str, float]) -> None:
+ def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
# Adapted from https://github.com/huggingface/transformers/blob/b71f20a7c9f3716d30f6738501559acf863e2c5c/examples/pytorch/language-modeling/run_clm.py#L630-L634
# compute perplexity
for key in tuple(logs.keys()):
@@ -77,7 +77,7 @@ def log(self, logs: Dict[str, float]) -> None:
perplexity = math.inf
logs[f"{prefix}perplexity"] = perplexity
- super().log(logs)
+ super().log(logs, start_time)
def has_callback(self, callback: Union[Type[TrainerCallback], TrainerCallback]) -> bool:
# Adapted from https://github.com/huggingface/transformers/blob/a7da2996a00c0ea083012ac86ab70f0bc4799f33/src/transformers/trainer_callback.py#L332
From 74803feed153298eec990347b48fa66203fd936f Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Fri, 20 Dec 2024 14:48:50 +0800
Subject: [PATCH 247/282] [bugfix]Add the. zip suffix to the s3 key of the
model card
---
.../computing/scheduler/model_scheduler/device_model_cards.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cards.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cards.py
index c2f11a2917..8697d0a62c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cards.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cards.py
@@ -784,7 +784,7 @@ def push_model_to_s3(self, model_name, model_zip_path, user_id, show_progress=Tr
args = {"config_version": self.config_version}
_, s3_config = ModelOpsConfigs.get_instance(args).fetch_configs(self.config_version)
s3_storage = S3Storage(s3_config)
- model_dst_key = "{}@{}@{}".format(user_id, model_name, str(uuid.uuid4()))
+ model_dst_key = "{}@{}@{}.zip".format(user_id, model_name, str(uuid.uuid4()))
model_storage_url = s3_storage.upload_file_with_progress(model_zip_path, model_dst_key,
show_progress=show_progress,
out_progress_to_err=True,
From 93f9760de9dbbc389889942b6ba1f148c03aa9ff Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Fri, 20 Dec 2024 16:39:45 +0800
Subject: [PATCH 248/282] =?UTF-8?q?[update]Upgrade=20official=20website=20?=
=?UTF-8?q?address:=20https://tensoropera.ai=20,=20an=20the=20brand:=20Ten?=
=?UTF-8?q?sorOpera=20=C2=AE=20=20AI?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
python/README.md | 4 +-
python/examples/README.md | 14 +++----
.../examples/deploy/complex_example/README.md | 2 +-
.../deploy/complex_example/config.yaml | 2 +-
python/examples/deploy/mnist/README.md | 4 +-
python/examples/deploy/mnist/mnist.yaml | 2 +-
.../examples/deploy/multi_service/README.md | 6 +--
python/examples/deploy/quick_start/README.md | 2 +-
.../deploy/scalellm-multi-engine/README.md | 2 +-
python/examples/deploy/scalellm/README.md | 2 +-
.../deploy/streaming_response/README.md | 2 +-
.../deploy/streaming_response/config.yaml | 2 +-
python/examples/deploy/triton/README.md | 6 +--
python/examples/deploy/your_own_llm/README.md | 4 +-
python/examples/deploy/your_own_llm/llm.yaml | 2 +-
.../grpc_docker_fedmlai/README.md | 4 +-
python/examples/launch/README.md | 4 +-
.../launch/federate_build_package/README.md | 2 +-
.../launch/train_build_package/README.md | 2 +-
python/examples/train/README.md | 2 +-
python/examples/train/llm_train/job.yaml | 2 +-
python/fedml/api/constants.py | 2 +-
python/fedml/api/modules/build.py | 4 +-
python/fedml/api/modules/device.py | 6 +--
python/fedml/api/modules/model.py | 4 +-
python/fedml/api/modules/utils.py | 2 +-
python/fedml/cli/README.md | 20 +++++-----
python/fedml/cli/modules/build.py | 4 +-
python/fedml/cli/modules/cluster.py | 8 ++--
python/fedml/cli/modules/device.py | 18 ++++-----
python/fedml/cli/modules/federate.py | 4 +-
python/fedml/cli/modules/launch.py | 4 +-
python/fedml/cli/modules/login.py | 12 +++---
python/fedml/cli/modules/logout.py | 4 +-
python/fedml/cli/modules/run.py | 40 +++++++++----------
python/fedml/cli/modules/storage.py | 16 ++++----
python/fedml/cli/modules/train.py | 4 +-
.../computing/scheduler/env/collect_env.py | 2 +-
.../scheduler_core/account_manager.py | 2 +-
.../scheduler/scheduler_entry/README.md | 4 +-
.../scheduler/scheduler_entry/app_manager.py | 4 +-
python/fedml/core/mlops/__init__.py | 8 ++--
.../serving/templates/hf_template/config.yaml | 2 +-
.../customized_job_example/README.md | 4 +-
python/spotlight_prj/unitedllm/README.md | 2 +-
45 files changed, 126 insertions(+), 126 deletions(-)
diff --git a/python/README.md b/python/README.md
index ad85023e6d..1e30f2b167 100644
--- a/python/README.md
+++ b/python/README.md
@@ -43,5 +43,5 @@ Other low-level APIs related to security and privacy are also supported. All alg
**utils**: Common utilities shared by other modules.
-## About FedML, Inc.
-https://FedML.ai
+## About TensorOpera, Inc.
+https://tensoropera.ai
diff --git a/python/examples/README.md b/python/examples/README.md
index 45086c27cf..32831a63f3 100644
--- a/python/examples/README.md
+++ b/python/examples/README.md
@@ -2,14 +2,14 @@
# FEDML Examples (Including Prebuilt Jobs in Jobs Store)
- `FedML/python/examples` -- examples for training, deployment, and federated learning
- - `FedML/python/examples/launch` -- examples for FEDML®Launch
- - `FedML/python/examples/serving` -- examples for FEDML®Deploy
- - `FedML/python/examples/train` -- examples for FEDML®Train
- - `FedML/python/examples/cross_cloud` -- examples for FEDML®Train cross-cloud distributed training
+ - `FedML/python/examples/launch` -- examples for TensorOpera®Launch
+ - `FedML/python/examples/serving` -- examples for TensorOpera®Deploy
+ - `FedML/python/examples/train` -- examples for TensorOpera®Train
+ - `FedML/python/examples/cross_cloud` -- examples for TensorOpera®Train cross-cloud distributed training
- `FedML/python/examples/federate/prebuilt_jobs` -- examples for federated learning prebuilt jobs (FedCV, FedNLP, FedGraphNN, Healthcare, etc.)
- `FedML/python/examples/federate/cross_silo` -- examples for cross-silo federated learning
- `FedML/python/examples/federate/cross_device` -- examples for cross-device federated learning
- `FedML/python/examples/federate/simulation` -- examples for federated learning simulation
- - `FedML/python/examples/federate/security` -- examples for FEDML®Federate security related features
- - `FedML/python/examples/federate/privacy` -- examples for FEDML®Federate privacy related features
- - `FedML/python/examples/federate/federated_analytics` -- examples for FEDML®Federate federated analytics (FA)
+ - `FedML/python/examples/federate/security` -- examples for TensorOpera®Federate security related features
+ - `FedML/python/examples/federate/privacy` -- examples for TensorOpera®Federate privacy related features
+ - `FedML/python/examples/federate/federated_analytics` -- examples for TensorOpera®Federate federated analytics (FA)
diff --git a/python/examples/deploy/complex_example/README.md b/python/examples/deploy/complex_example/README.md
index 1f67f587fd..b7a03aeea6 100644
--- a/python/examples/deploy/complex_example/README.md
+++ b/python/examples/deploy/complex_example/README.md
@@ -16,7 +16,7 @@ Use -cf to indicate the configuration file.
curl -XPOST localhost:2345/predict -d '{"text": "Hello"}'
```
-## Option 2: Deploy to the Cloud (Using fedml®launch platform)
+## Option 2: Deploy to the Cloud (Using TensorOpera®launch platform)
- Uncomment the following line in config.yaml
For information about the configuration, please refer to fedml ® launch.
diff --git a/python/examples/deploy/complex_example/config.yaml b/python/examples/deploy/complex_example/config.yaml
index 037183a066..cd658aae33 100644
--- a/python/examples/deploy/complex_example/config.yaml
+++ b/python/examples/deploy/complex_example/config.yaml
@@ -15,7 +15,7 @@ environment_variables:
LOCAL_RANK: "0"
# If you do not have any GPU resource but want to serve the model
-# Try FedML® Nexus AI Platform, and Uncomment the following lines.
+# Try TensorOpera® Nexus AI Platform, and Uncomment the following lines.
# ------------------------------------------------------------
computing:
minimum_num_gpus: 1 # minimum # of GPUs to provision
diff --git a/python/examples/deploy/mnist/README.md b/python/examples/deploy/mnist/README.md
index 11dd696234..b64b4bd70e 100644
--- a/python/examples/deploy/mnist/README.md
+++ b/python/examples/deploy/mnist/README.md
@@ -11,9 +11,9 @@ curl -XPOST localhost:2345/predict -d '{"arr":[$DATA]}'
#For $DATA, please check the request_input_example, it is a 28*28=784 float array
#Output:{"generated_text":"tensor([0.2333, 0.5296, 0.4350, 0.4537, 0.5424, 0.4583, 0.4803, 0.2862, 0.5507,\n 0.8683], grad_fn=<SigmoidBackward0>)"}
```
-## Option 2: Deploy to the Cloud (Using fedml® launch platform)
+## Option 2: Deploy to the Cloud (Using TensorOpera® launch platform)
Uncomment the following line in mnist.yaml,
-for infomation about the configuration, please refer to fedml® launch.
+for infomation about the configuration, please refer to TensorOpera® launch.
```yaml
# computing:
# minimum_num_gpus: 1
diff --git a/python/examples/deploy/mnist/mnist.yaml b/python/examples/deploy/mnist/mnist.yaml
index fe419abb1c..cae8050674 100644
--- a/python/examples/deploy/mnist/mnist.yaml
+++ b/python/examples/deploy/mnist/mnist.yaml
@@ -5,7 +5,7 @@ data_cache_dir: ""
bootstrap: ""
# If you do not have any GPU resource but want to serve the model
-# Try FedML® Nexus AI Platform, and Uncomment the following lines.
+# Try TensorOpera® Nexus AI Platform, and Uncomment the following lines.
# ------------------------------------------------------------
computing:
minimum_num_gpus: 1 # minimum # of GPUs to provision
diff --git a/python/examples/deploy/multi_service/README.md b/python/examples/deploy/multi_service/README.md
index 2b897d087a..59bd7429f3 100644
--- a/python/examples/deploy/multi_service/README.md
+++ b/python/examples/deploy/multi_service/README.md
@@ -15,7 +15,7 @@ fedml model create --name $model_name --config_file config.yaml
```
## On-premsie Deploy
-Register an account on FedML website: https://fedml.ai
+Register an account on TensorOpera website: https://tensoropera.ai
You will have a user id and api key, which can be found in the profile page.
@@ -44,8 +44,8 @@ You will have a user id and api key, which can be found in the profile page.
```
- Result
- See the deployment result in https://fedml.ai
+ See the deployment result in https://tensoropera.ai
- OPT2: Deploy - UI
- Follow the instructions on https://fedml.ai
+ Follow the instructions on https://tensoropera.ai
diff --git a/python/examples/deploy/quick_start/README.md b/python/examples/deploy/quick_start/README.md
index 1f67f587fd..b7a03aeea6 100644
--- a/python/examples/deploy/quick_start/README.md
+++ b/python/examples/deploy/quick_start/README.md
@@ -16,7 +16,7 @@ Use -cf to indicate the configuration file.
curl -XPOST localhost:2345/predict -d '{"text": "Hello"}'
```
-## Option 2: Deploy to the Cloud (Using fedml®launch platform)
+## Option 2: Deploy to the Cloud (Using TensorOpera®launch platform)
- Uncomment the following line in config.yaml
For information about the configuration, please refer to fedml ® launch.
diff --git a/python/examples/deploy/scalellm-multi-engine/README.md b/python/examples/deploy/scalellm-multi-engine/README.md
index 4de6058c95..b65ad7dd5c 100644
--- a/python/examples/deploy/scalellm-multi-engine/README.md
+++ b/python/examples/deploy/scalellm-multi-engine/README.md
@@ -40,7 +40,7 @@ computing:
#device_type: CPU # options: GPU, CPU, hybrid
resource_type: A100-80G # e.g., A100-80G,
# please check the resource type list by "fedml show-resource-type"
- # or visiting URL: https://fedml.ai/accelerator_resource_type
+ # or visiting URL: https://tensoropera.ai/accelerator_resource_type
```
```bash
diff --git a/python/examples/deploy/scalellm/README.md b/python/examples/deploy/scalellm/README.md
index 4de6058c95..b65ad7dd5c 100644
--- a/python/examples/deploy/scalellm/README.md
+++ b/python/examples/deploy/scalellm/README.md
@@ -40,7 +40,7 @@ computing:
#device_type: CPU # options: GPU, CPU, hybrid
resource_type: A100-80G # e.g., A100-80G,
# please check the resource type list by "fedml show-resource-type"
- # or visiting URL: https://fedml.ai/accelerator_resource_type
+ # or visiting URL: https://tensoropera.ai/accelerator_resource_type
```
```bash
diff --git a/python/examples/deploy/streaming_response/README.md b/python/examples/deploy/streaming_response/README.md
index f91cda5278..b190b50dc7 100644
--- a/python/examples/deploy/streaming_response/README.md
+++ b/python/examples/deploy/streaming_response/README.md
@@ -16,7 +16,7 @@ Use -cf to indicate the configuration file.
curl -XPOST localhost:2345/predict -d '{"text": "Hello"}'
```
-## Option 2: Deploy to the Cloud (Using fedml®launch platform)
+## Option 2: Deploy to the Cloud (Using TensorOpera®launch platform)
- Uncomment the following line in config.yaml
For information about the configuration, please refer to fedml ® launch.
diff --git a/python/examples/deploy/streaming_response/config.yaml b/python/examples/deploy/streaming_response/config.yaml
index 83479068e6..1a18b9d85b 100644
--- a/python/examples/deploy/streaming_response/config.yaml
+++ b/python/examples/deploy/streaming_response/config.yaml
@@ -8,7 +8,7 @@ bootstrap: |
echo "Bootstrap finished"
# If you do not have any GPU resource but want to serve the model
-# Try FedML® Nexus AI Platform, and Uncomment the following lines.
+# Try TensorOpera® Nexus AI Platform, and Uncomment the following lines.
# ------------------------------------------------------------
computing:
minimum_num_gpus: 1 # minimum # of GPUs to provision
diff --git a/python/examples/deploy/triton/README.md b/python/examples/deploy/triton/README.md
index 4d861fb7ff..5430939d28 100644
--- a/python/examples/deploy/triton/README.md
+++ b/python/examples/deploy/triton/README.md
@@ -39,7 +39,7 @@ fedml model create --name $model_name --config_file config.yaml
```
## On-premsie Deploy
-Register an account on FedML website: https://fedml.ai
+Register an account on TensorOpera website: https://tensoropera.ai
You will have a user id and api key, which can be found in the profile page.
@@ -68,8 +68,8 @@ You will have a user id and api key, which can be found in the profile page.
```
- Result
- See the deployment result in https://fedml.ai
+ See the deployment result in https://tensoropera.ai
- OPT2: Deploy - UI
- Follow the instructions on https://fedml.ai
+ Follow the instructions on https://tensoropera.ai
diff --git a/python/examples/deploy/your_own_llm/README.md b/python/examples/deploy/your_own_llm/README.md
index fc7234293b..415db7fe92 100644
--- a/python/examples/deploy/your_own_llm/README.md
+++ b/python/examples/deploy/your_own_llm/README.md
@@ -9,9 +9,9 @@ fedml model deploy --name llm --local
#INFO: Uvicorn running on http://0.0.0.0:2345 (Press CTRL+C to quit)
curl -XPOST localhost:2345/predict -d '{"text": "Hello"}'
```
-## Option 2: Deploy to the Cloud (Using fedml®launch platform)
+## Option 2: Deploy to the Cloud (Using TensorOpera®launch platform)
Uncomment the following line in llm.yaml,
-for infomation about the configuration, please refer to fedml®launch.
+for infomation about the configuration, please refer to TensorOpera®launch.
```yaml
# computing:
# minimum_num_gpus: 1
diff --git a/python/examples/deploy/your_own_llm/llm.yaml b/python/examples/deploy/your_own_llm/llm.yaml
index 5e5e09730b..b3b3d5da15 100644
--- a/python/examples/deploy/your_own_llm/llm.yaml
+++ b/python/examples/deploy/your_own_llm/llm.yaml
@@ -11,7 +11,7 @@ bootstrap: |
echo "Bootstrap finished"
# If you do not have any GPU resource but want to serve the model
-# Try FedML® Nexus AI Platform, and Uncomment the following lines.
+# Try TensorOpera® Nexus AI Platform, and Uncomment the following lines.
# ------------------------------------------------------------
# computing:
# minimum_num_gpus: 1 # minimum # of GPUs to provision
diff --git a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/README.md b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/README.md
index 174309aa55..8c56622d06 100644
--- a/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/README.md
+++ b/python/examples/federate/cross_silo/grpc_fedavg_mnist_lr_example/grpc_docker_fedmlai/README.md
@@ -1,6 +1,6 @@
# Introduction
-In this working example, we will run 1 aggregation server and 2 clients on the same machine using Docker + gRPC and we will use the FEDML.ai platform to run the FL job.
+In this working example, we will run 1 aggregation server and 2 clients on the same machine using Docker + gRPC and we will use the TensorOpera.ai platform to run the FL job.
# gRPC Configuration File
The content of the gRPC configuration file is as follows:
@@ -47,5 +47,5 @@ source /fedml/bin/activate
fedml login -c <FEDML_API_KEY>
```
-Then we only need to compile our job and submit to our dockerb-based cluster as it is also discussed in detail in the official FEDML documentation: https://fedml.ai/octopus/userGuides
+Then we only need to compile our job and submit to our dockerb-based cluster as it is also discussed in detail in the official TensorOpera documentation: https://tensoropera.ai/octopus/userGuides
diff --git a/python/examples/launch/README.md b/python/examples/launch/README.md
index 1ded267276..fc79cbfe26 100644
--- a/python/examples/launch/README.md
+++ b/python/examples/launch/README.md
@@ -132,7 +132,7 @@ You just need to customize the following config items.
3. `bootstrap`, It is the bootstrap shell command which will be executed before running entry commands.
-Then you can use the following example CLI to launch the job at FedML® Nexus AI Platform
+Then you can use the following example CLI to launch the job at TensorOpera® Nexus AI Platform
(Replace $YourApiKey with your own account API key from open.fedml.ai)
Example:
@@ -142,7 +142,7 @@ fedml launch hello_job.yaml
After the launch CLI is executed, the output is as follows. Here you may open the job url to confirm and actually start the job.
```
-Submitting your job to FedML® Nexus AI Platform: 100%|████████████████████████████████████████████████████████████████████████████████████████| 6.07k/6.07k [00:01<00:00, 4.94kB/s]
+Submitting your job to TensorOpera® Nexus AI Platform: 100%|████████████████████████████████████████████████████████████████████████████████████████| 6.07k/6.07k [00:01<00:00, 4.94kB/s]
Searched and matched the following GPU resource for your job:
+-----------+-------------------+---------+------------+-------------------------+---------+-------+----------+
diff --git a/python/examples/launch/federate_build_package/README.md b/python/examples/launch/federate_build_package/README.md
index c0d3356150..325258407e 100644
--- a/python/examples/launch/federate_build_package/README.md
+++ b/python/examples/launch/federate_build_package/README.md
@@ -3,7 +3,7 @@
```
Usage: fedml federate build [OPTIONS] [YAML_FILE]
- Build federate packages for the FedML® Nexus AI Platform.
+ Build federate packages for the TensorOpera® Nexus AI Platform.
Options:
-h, --help Show this message and exit.
diff --git a/python/examples/launch/train_build_package/README.md b/python/examples/launch/train_build_package/README.md
index 03c8dbe71b..f0f1dff857 100644
--- a/python/examples/launch/train_build_package/README.md
+++ b/python/examples/launch/train_build_package/README.md
@@ -3,7 +3,7 @@
```
Usage: fedml train build [OPTIONS] [YAML_FILE]
- Build training packages for the FedML® Nexus AI Platform.
+ Build training packages for the TensorOpera® Nexus AI Platform.
Options:
-h, --help Show this message and exit.
diff --git a/python/examples/train/README.md b/python/examples/train/README.md
index 9a6853d740..0e301c86b2 100644
--- a/python/examples/train/README.md
+++ b/python/examples/train/README.md
@@ -1 +1 @@
-# Examples (Prebuilt Jobs) for FEDML®Train
\ No newline at end of file
+# Examples (Prebuilt Jobs) for TensorOpera®Train
\ No newline at end of file
diff --git a/python/examples/train/llm_train/job.yaml b/python/examples/train/llm_train/job.yaml
index d1ba08ed4c..a9e81c91f7 100644
--- a/python/examples/train/llm_train/job.yaml
+++ b/python/examples/train/llm_train/job.yaml
@@ -44,4 +44,4 @@ computing:
allow_cross_cloud_resources: false # true, false
device_type: GPU # options: GPU, CPU, hybrid
- resource_type: A100-80G # e.g., A100-80G, please check the resource type list by "fedml show-resource-type" or visiting URL: https://fedml.ai/accelerator_resource_type
+ resource_type: A100-80G # e.g., A100-80G, please check the resource type list by "fedml show-resource-type" or visiting URL: https://tensoropera.ai/accelerator_resource_type
diff --git a/python/fedml/api/constants.py b/python/fedml/api/constants.py
index b284d7a056..313da61798 100755
--- a/python/fedml/api/constants.py
+++ b/python/fedml/api/constants.py
@@ -18,7 +18,7 @@ class ApiConstants:
RESOURCE_MATCHED_STATUS_BIND_CREDIT_CARD_FIRST = \
"""
- Before we can start a job, please add a credit card to your FEDML account at https://fedml.ai/billing/home.
+ Before we can start a job, please add a credit card to your FEDML account at https://tensoropera.ai/billing.
Once it's added, please try to run the launch command again
"""
diff --git a/python/fedml/api/modules/build.py b/python/fedml/api/modules/build.py
index 7d23bc02ed..9299944bb0 100644
--- a/python/fedml/api/modules/build.py
+++ b/python/fedml/api/modules/build.py
@@ -22,7 +22,7 @@ def build(platform, type, source_folder, entry_point, config_folder, dest_folder
if type == "client" or type == "server":
click.echo(
- "Now, you are building the fedml packages which will be used in the FedML® Nexus AI Platform "
+ "Now, you are building the fedml packages which will be used in the TensorOpera® Nexus AI Platform "
"platform."
)
click.echo(
@@ -34,7 +34,7 @@ def build(platform, type, source_folder, entry_point, config_folder, dest_folder
+ "."
)
click.echo(
- "Then you may upload the packages on the configuration page in the FedML® Nexus AI Platform to "
+ "Then you may upload the packages on the configuration page in the TensorOpera® Nexus AI Platform to "
"start your training flow."
)
click.echo("Building...")
diff --git a/python/fedml/api/modules/device.py b/python/fedml/api/modules/device.py
index 27b2d0d198..1b578aa903 100644
--- a/python/fedml/api/modules/device.py
+++ b/python/fedml/api/modules/device.py
@@ -78,7 +78,7 @@ def _bind(
else:
docker_install_url = "https://docs.docker.com/engine/install/"
docker_config_text = " Moreover, you need to config the docker engine to run as a non-root user. Here is the docs. https://docs.docker.com/engine/install/linux-postinstall/"
- print("\n Welcome to FedML.ai! \n Start to login the current device to the FedML® Nexus AI Platform\n")
+ print("\n Welcome toTensorOpera.ai! \n Start to login the current device to the TensorOpera® Nexus AI Platform\n")
print(" If you want to deploy models into this computer, you need to install the docker engine to serve your models.")
print(f" Here is the docs for installation docker engine. {docker_install_url}")
if docker_config_text is not None:
@@ -137,7 +137,7 @@ def _bind(
client_daemon_cmd = "client_daemon.py"
client_daemon_pids = RunProcessUtils.get_pid_from_cmd_line(client_daemon_cmd)
if client_daemon_pids is not None and len(client_daemon_pids) > 0:
- print("Your computer has been logged into the FedML® Nexus AI Platform. "
+ print("Your computer has been logged into the TensorOpera® Nexus AI Platform. "
"Before logging in again, please log out of the previous login using the command "
"'fedml logout -c'. If it still doesn't work, run the command 'fedml logout -c' "
"using your computer's administrator account.")
@@ -193,7 +193,7 @@ def _bind(
server_daemon_cmd = "server_daemon.py"
server_daemon_pids = RunProcessUtils.get_pid_from_cmd_line(server_daemon_cmd)
if server_daemon_pids is not None and len(server_daemon_pids) > 0:
- print("Your computer has been logged into the FedML® Nexus AI Platform. "
+ print("Your computer has been logged into the TensorOpera® Nexus AI Platform. "
"Before logging in again, please log out of the previous login using the command "
"'fedml logout -s'. If it still doesn't work, run the command 'fedml logout -s' "
"using your computer's administrator account.")
diff --git a/python/fedml/api/modules/model.py b/python/fedml/api/modules/model.py
index a02e674f47..3b4a7afd0b 100644
--- a/python/fedml/api/modules/model.py
+++ b/python/fedml/api/modules/model.py
@@ -252,9 +252,9 @@ def deploy(name: str, endpoint_name: str = "", endpoint_id: str = None, local: b
return FedMLModelCards.get_instance().serve_model_on_premise(
name, endpoint_name, master_ids, worker_ids, use_remote, endpoint_id)
else:
- # FedML® Launch deploy mode
+ # TensorOpera® Launch deploy mode
click.echo("Warning: You did not indicate the master device id and worker device id\n\
- Do you want to use FedML® Nexus AI Platform to find GPU Resources deploy your model?")
+ Do you want to use TensorOpera® Nexus AI Platform to find GPU Resources deploy your model?")
answer = click.prompt("Please input your answer: (y/n)")
if answer == "y" or answer == "Y":
api_key = get_api_key()
diff --git a/python/fedml/api/modules/utils.py b/python/fedml/api/modules/utils.py
index 76801ffe81..abbea71f9f 100644
--- a/python/fedml/api/modules/utils.py
+++ b/python/fedml/api/modules/utils.py
@@ -21,7 +21,7 @@ def _check_api_key(api_key=None):
if api_key is None or api_key == "":
saved_api_key = get_api_key()
if saved_api_key is None or saved_api_key == "":
- api_key = click.prompt("FedML® Launch API Key is not set yet, please input your API key")
+ api_key = click.prompt("TensorOpera® Launch API Key is not set yet, please input your API key")
else:
api_key = saved_api_key
diff --git a/python/fedml/cli/README.md b/python/fedml/cli/README.md
index f94200f258..425bf0c5de 100644
--- a/python/fedml/cli/README.md
+++ b/python/fedml/cli/README.md
@@ -27,7 +27,7 @@ fedml build \
--ignore __pycache__,*.git
```
-## 2. Login into the FedML® Nexus AI Platform (fedml.ai)
+## 2. Login into the TensorOpera® Nexus AI Platform (fedml.ai)
login as general computing device with local pip mode:
```
fedml login $YourApiKey
@@ -38,7 +38,7 @@ login as federated-learning server with local pip mode:
fedml login $YourApiKey -s
```
-### 2.1. Examples for Logining into the FedML® Nexus AI Platform (fedml.ai)
+### 2.1. Examples for Logining into the TensorOpera® Nexus AI Platform (fedml.ai)
```
fedml login 113343dad999933
@@ -48,7 +48,7 @@ fedml login 113343dad999933
fedml login 113343dad999933 -s
```
-## 3. Logout from the FedML FedML® Nexus AI Platform (fedml.ai)
+## 3. Logout from the FedML TensorOpera® Nexus AI Platform (fedml.ai)
logout from computing device with local pip mode:
```
fedml logout
@@ -81,17 +81,17 @@ fedml diagnosis --open --s3 --mqtt
```
## 7. Jobs
-Start a job at FedML® Nexus AI Platform
+Start a job at TensorOpera® Nexus AI Platform
```
Usage: fedml jobs start [OPTIONS]
-Start a job at FedML® Nexus AI Platform
+Start a job at TensorOpera® Nexus AI Platform
Options:
-pf, --platform TEXT The platform name at the MLOps platform(options: octopus, parrot, spider, beehive).
--prj, --project_name TEXT The project name at FedML® Nexus AI Platform
--app, --application_name TEXT Application name in the My Application list at FedML® Nexus AI Platform
--jn, --job_name TEXT The job name at FedML® Nexus AI Platform If you don't specify here, the job name from the job yaml file will be used.
+-prj, --project_name TEXT The project name at TensorOpera® Nexus AI Platform
+-app, --application_name TEXT Application name in the My Application list at TensorOpera® Nexus AI Platform
+-jn, --job_name TEXT The job name at TensorOpera® Nexus AI Platform If you don't specify here, the job name from the job yaml file will be used.
-ds, --devices_server TEXT The server to run the launching job, for the launch platform, we do not need to set this option.
-de, --devices_edges TEXT The edge devices to run the launching job. Separated with ',', e.g. 705,704. For the launch platform, we do not need to set this option.
-u, --user TEXT user id or api key.
@@ -238,7 +238,7 @@ You just need to customize the following config items.
3. `bootstrap`, It is the bootstrap shell command which will be executed before running entry commands.
-Then you can use the following example CLI to launch the job at FedML® Nexus AI Platform
+Then you can use the following example CLI to launch the job at TensorOpera® Nexus AI Platform
(Replace $YourApiKey with your own account API key from open.fedml.ai)
Example:
@@ -248,7 +248,7 @@ fedml launch hello_job.yaml
After the launch CLI is executed, the output is as follows. Here you may open the job url to confirm and actually start the job.
```
-Submitting your job to FedML® Nexus AI Platform: 100%|████████████████████████████████████████████████████████████████████████████████████████| 6.07k/6.07k [00:01<00:00, 4.94kB/s]
+Submitting your job to TensorOpera® Nexus AI Platform: 100%|████████████████████████████████████████████████████████████████████████████████████████| 6.07k/6.07k [00:01<00:00, 4.94kB/s]
Searched and matched the following GPU resource for your job:
+-----------+-------------------+---------+------------+-------------------------+---------+-------+----------+
diff --git a/python/fedml/cli/modules/build.py b/python/fedml/cli/modules/build.py
index 4674a88e9e..2fd68492fd 100644
--- a/python/fedml/cli/modules/build.py
+++ b/python/fedml/cli/modules/build.py
@@ -3,14 +3,14 @@
import fedml.api
-@click.command("build", help="Build packages for the FedML® Nexus AI Platform")
+@click.command("build", help="Build packages for the TensorOpera® AI Platform")
@click.help_option("--help", "-h")
@click.option(
"--platform",
"-pf",
type=str,
default="octopus",
- help="The platform name at the FedML® Nexus AI Platform (options: octopus, parrot, spider, beehive, falcon, launch).",
+ help="The platform name at the TensorOpera® AI Platform (options: octopus, parrot, spider, beehive, falcon, launch).",
)
@click.option(
"--type",
diff --git a/python/fedml/cli/modules/cluster.py b/python/fedml/cli/modules/cluster.py
index 95822e1c18..47617b1f12 100644
--- a/python/fedml/cli/modules/cluster.py
+++ b/python/fedml/cli/modules/cluster.py
@@ -7,10 +7,10 @@
# Message strings constants
confirmation_message: str = "Are you sure you want to {} these clusters?"
failure_message: str = ("Failed to {} the clusters, please check the arguments are valid and your network "
- "connection and make sure be able to access the FedML® Nexus AI Platform.")
-version_help: str = "specify version of FedML® Nexus AI Platform. It should be dev, test or release"
+ "connection and make sure be able to access the TensorOpera® AI Platform.")
+version_help: str = "specify version of TensorOpera® AI Platform. It should be dev, test or release"
api_key_help: str = "user api key."
-cluster_action_help: str = "{} clusters from FedML® Nexus AI Platform"
+cluster_action_help: str = "{} clusters from TensorOpera® AI Platform"
@click.group("cluster")
@@ -27,7 +27,7 @@
)
def fedml_clusters(api_key, version):
"""
- Manage clusters on FedML® Nexus AI Platform
+ Manage clusters on TensorOpera® AI Platform
"""
pass
diff --git a/python/fedml/cli/modules/device.py b/python/fedml/cli/modules/device.py
index 5c4804fa69..b21b3d09d2 100644
--- a/python/fedml/cli/modules/device.py
+++ b/python/fedml/cli/modules/device.py
@@ -7,12 +7,12 @@
@click.help_option("--help", "-h")
def fedml_device():
"""
- Bind/unbind devices to the FedML® Nexus AI Platform
+ Bind/unbind devices to the TensorOpera® AI Platform
"""
pass
-@fedml_device.command("bind", help="Bind to the FedML® Nexus AI Platform")
+@fedml_device.command("bind", help="Bind to the TensorOpera® AI Platform")
@click.help_option("--help", "-h")
@click.argument("api_key", nargs=-1)
@click.option(
@@ -20,13 +20,13 @@ def fedml_device():
"-v",
type=str,
default="release",
- help="Bind to which version of FedML® Nexus AI Platform. It should be dev, test or release.",
+ help="Bind to which version of TensorOpera® AI Platform. It should be dev, test or release.",
)
@click.option(
"--compute_node", "-c", default=None, is_flag=True,
help="Bind as the general compute node in FEDML Nexus AI compute network. This is enabled by default. "
- "After binding, you can view and manage the device in the FEDML® Nexus AI Platform: https://fedml.ai/compute. "
- "It can be grouped as a cluster and then you can use FEDML®Launch to schedule any job (training, deployment, federated learning) to it. "
+ "After binding, you can view and manage the device in the TensorOpera® AI Platform: https://tensoropera.ai/gpu/local?label=Private. "
+ "It can be grouped as a cluster and then you can use TensorOpera®Launch to schedule any job (training, deployment, federated learning) to it. "
"You can not specify the option -c and -s simultaneously.",
)
@click.option(
@@ -36,7 +36,7 @@ def fedml_device():
)
@click.option(
"--provider", "-p", default=None, is_flag=True,
- help="Bind as the FedML compute node (GPU) provider (supplier). This is used by Nexus AI Platform - Share and Earn: https://fedml.ai/gpu-supplier. You can share your GPUs in this way and earn money. "
+ help="Bind as the FedML compute node (GPU) provider (supplier). This is used by Nexus AI Platform - Share and Earn: https://tensoropera.ai/share-and-earn. You can share your GPUs in this way and earn money. "
"You can specify the option -p and -c simultaneously (can be used as provider for others as well compute node for your own jobs), but you can not specify -p and -s simultaneously.",
)
def fedml_device_bind(api_key, version, compute_node, server, provider):
@@ -47,14 +47,14 @@ def fedml_device_bind(api_key, version, compute_node, server, provider):
fedml.api.device_bind(api_key, compute_node, server, provider)
-@fedml_device.command("unbind", help="Unbind from the FedML® Nexus AI Platform")
+@fedml_device.command("unbind", help="Unbind from the TensorOpera® AI Platform")
@click.help_option("--help", "-h")
@click.option(
"--version",
"-v",
type=str,
default="release",
- help="Unbind which backend environment version of FedML® Nexus AI Platform. It should be dev, test, or release.",
+ help="Unbind which backend environment version of TensorOpera® AI Platform. It should be dev, test, or release.",
)
@click.option(
"--compute_node", "-c", default=None, is_flag=True, help="Unbind from the FedML general compute node.",
@@ -75,7 +75,7 @@ def fedml_device_unbind(version, computing, server):
"-v",
type=str,
default="release",
- help="show resource type at which version of FedML® Nexus AI Platform. It should be dev, test or release",
+ help="show resource type at which version of TensorOpera® AI Platform. It should be dev, test or release",
)
def resource_type(version):
fedml.set_env_version(version)
diff --git a/python/fedml/cli/modules/federate.py b/python/fedml/cli/modules/federate.py
index 6f26b2bea8..ff4fd6c791 100644
--- a/python/fedml/cli/modules/federate.py
+++ b/python/fedml/cli/modules/federate.py
@@ -7,12 +7,12 @@
@click.help_option("--help", "-h")
def fedml_federate():
"""
- Manage federated learning resources on FedML® Nexus AI Platform
+ Manage federated learning resources on TensorOpera® AI Platform
"""
pass
-@fedml_federate.command("build", help="Build federate packages for the FedML® Nexus AI Platform.")
+@fedml_federate.command("build", help="Build federate packages for the TensorOpera® AI Platform.")
@click.help_option("--help", "-h")
@click.option(
"--dest_folder",
diff --git a/python/fedml/cli/modules/launch.py b/python/fedml/cli/modules/launch.py
index 16450e08a9..c14bbac353 100644
--- a/python/fedml/cli/modules/launch.py
+++ b/python/fedml/cli/modules/launch.py
@@ -13,7 +13,7 @@
from fedml.computing.scheduler.scheduler_entry.run_manager import FedMLRunStartedModel, FeatureEntryPoint
-@click.command("launch", help="Launch job at the FedML® Nexus AI Platform")
+@click.command("launch", help="Launch job at the TensorOpera® AI Platform")
@click.help_option("--help", "-h")
@click.option(
"--api_key", "-k", type=str, help="user api key.",
@@ -56,7 +56,7 @@
@click.argument("yaml_file", nargs=-1)
def fedml_launch(yaml_file, cluster, version, api_key, group, local_on_premise_platform, local_on_premise_platform_port):
"""
- Manage resources on the FedML® Nexus AI Platform.
+ Manage resources on the TensorOpera® AI Platform.
"""
set_env_version(version)
fedml.set_local_on_premise_platform_host(local_on_premise_platform)
diff --git a/python/fedml/cli/modules/login.py b/python/fedml/cli/modules/login.py
index b76346ec1b..5e77910cbb 100644
--- a/python/fedml/cli/modules/login.py
+++ b/python/fedml/cli/modules/login.py
@@ -10,7 +10,7 @@
from fedml.computing.scheduler.scheduler_core.general_constants import MarketplaceType
-@click.command("login", help="Login the FedML® Nexus AI Platform")
+@click.command("login", help="Login the TensorOpera® AI Platform")
@click.help_option("--help", "-h")
@click.argument("api_key", nargs=-1)
@click.option(
@@ -18,13 +18,13 @@
"-v",
type=str,
default="release",
- help="Login which backend environment version of FedML® Nexus AI Platform. It should be dev, test, or release.",
+ help="Login which backend environment version of TensorOpera® AI Platform. It should be dev, test, or release.",
)
@click.option(
"--compute_node", "-c", default=None, is_flag=True,
help="Login as the general compute node in FEDML Nexus AI compute network. This is enabled by default. "
- "After login, you can view and manage the device in the FEDML® Nexus AI Platform: https://fedml.ai/compute. "
- "It can be grouped as a cluster and then you can use FEDML®Launch to schedule any job (training, deployment, federated learning) to it. "
+ "After login, you can view and manage the device in the TensorOpera® AI Platform: https://tensoropera.ai/gpu/local?label=Private. "
+ "It can be grouped as a cluster and then you can use TensorOpera®Launch to schedule any job (training, deployment, federated learning) to it. "
"You can not specify the option -c and -s simultaneously.",
)
@click.option(
@@ -34,7 +34,7 @@
)
@click.option(
"--provider", "-p", default=None, is_flag=True,
- help="Login as the FedML compute node (GPU) provider (supplier). This is used by Nexus AI Platform - Share and Earn: https://fedml.ai/gpu-supplier. You can share your GPUs in this way and earn money. "
+ help="Login as the FedML compute node (GPU) provider (supplier). This is used by Nexus AI Platform - Share and Earn: https://tensoropera.ai/share-and-earn. You can share your GPUs in this way and earn money. "
"You can specify the option -p and -c simultaneously (can be used as provider for others as well compute node for your own jobs), but you can not specify -p and -s simultaneously.",
)
@click.option(
@@ -94,7 +94,7 @@
"for one hour is $1.5 per GPU, then you would input 1.5. Do not multiply this number by the total number of "
"GPUs in the node, as the system will automatically detect the number of GPUs and include it in the cost "
"calculation. Default is 0.0."
- "Optionally, you can also set this price later through supplier page on the FEDML® Nexus AI Platform."
+ "Optionally, you can also set this price later through supplier page on the TensorOpera® AI Platform."
)
@click.option(
"--name",
diff --git a/python/fedml/cli/modules/logout.py b/python/fedml/cli/modules/logout.py
index 94a51b395a..ab2abfde95 100644
--- a/python/fedml/cli/modules/logout.py
+++ b/python/fedml/cli/modules/logout.py
@@ -3,7 +3,7 @@
import fedml.api
-@click.command("logout", help="Logout from the FedML® Nexus AI Platform")
+@click.command("logout", help="Logout from the TensorOpera® AI Platform")
@click.help_option("--help", "-h")
@click.option(
"--computing", "-c", default=None, is_flag=True, help="Logout from the FedML general compute node.",
@@ -16,7 +16,7 @@
"-v",
type=str,
default="release",
- help="Logout which backend environment version of FedML® Nexus AI Platform. It should be dev, test, or release.",
+ help="Logout which backend environment version of TensorOpera® AI Platform. It should be dev, test, or release.",
)
def fedml_logout(computing, server, version):
fedml.set_env_version(version)
diff --git a/python/fedml/cli/modules/run.py b/python/fedml/cli/modules/run.py
index f2c24b445a..a2c479897b 100644
--- a/python/fedml/cli/modules/run.py
+++ b/python/fedml/cli/modules/run.py
@@ -15,24 +15,24 @@
"-v",
type=str,
default="release",
- help="version of FedML® Nexus AI Platform. It should be dev, test or release",
+ help="version of TensorOpera® AI Platform. It should be dev, test or release",
)
@click.option(
"--platform",
"-pf",
type=str,
default="falcon",
- help="The platform name at the FedML® Nexus AI Platform (options: octopus, parrot, spider, beehive, falcon, launch,"
+ help="The platform name at the TensorOpera® AI Platform (options: octopus, parrot, spider, beehive, falcon, launch,"
"default is falcon).",
)
def fedml_run(api_key, version, platform):
"""
- Manage runs on the FedML® Nexus AI Platform.
+ Manage runs on the TensorOpera® AI Platform.
"""
pass
-@fedml_run.command("stop", help="Stop a run from the FedML® Nexus AI Platform.")
+@fedml_run.command("stop", help="Stop a run from the TensorOpera® AI Platform.")
@click.help_option("--help", "-h")
@click.option(
"--run_id",
@@ -49,14 +49,14 @@ def fedml_run(api_key, version, platform):
"-v",
type=str,
default="release",
- help="stop a run at which version of FedML® Nexus AI Platform. It should be dev, test or release",
+ help="stop a run at which version of TensorOpera® AI Platform. It should be dev, test or release",
)
@click.option(
"--platform",
"-pf",
type=str,
default="falcon",
- help="The platform name at the FedML® Nexus AI Platform (options: octopus, parrot, spider, beehive, falcon, launch, "
+ help="The platform name at the TensorOpera® AI Platform (options: octopus, parrot, spider, beehive, falcon, launch, "
"default is falcon).",
)
def stop_run(platform, run_id, api_key, version):
@@ -68,14 +68,14 @@ def stop_run(platform, run_id, api_key, version):
click.echo(f"Failed to stop Run {run_id}. Please check if the run id is valid.")
-@fedml_run.command("list", help="List runs from the FedML® Nexus AI Platform.")
+@fedml_run.command("list", help="List runs from the TensorOpera® AI Platform.")
@click.help_option("--help", "-h")
@click.option(
"--platform",
"-pf",
type=str,
default="falcon",
- help="The platform name at the FedML® Nexus AI Platform (options: octopus, parrot, spider, beehive, falcon, launch, "
+ help="The platform name at the TensorOpera® AI Platform (options: octopus, parrot, spider, beehive, falcon, launch, "
"default is falcon).",
)
@click.option(
@@ -83,14 +83,14 @@ def stop_run(platform, run_id, api_key, version):
"-r",
type=str,
default="",
- help="Run name at the FedML® Nexus AI Platform.",
+ help="Run name at the TensorOpera® AI Platform.",
)
@click.option(
"--run_id",
"-rid",
type=str,
default="",
- help="Run id at the FedML® Nexus AI Platform.",
+ help="Run id at the TensorOpera® AI Platform.",
)
@click.option(
"--api_key", "-k", type=str, help="user api key.",
@@ -100,7 +100,7 @@ def stop_run(platform, run_id, api_key, version):
"-v",
type=str,
default="release",
- help="list runs at which version of FedML® Nexus AI Platform. It should be dev, test or release",
+ help="list runs at which version of TensorOpera® AI Platform. It should be dev, test or release",
)
def list_runs(platform, run_name, run_id, api_key, version):
fedml.set_env_version(version)
@@ -109,14 +109,14 @@ def list_runs(platform, run_name, run_id, api_key, version):
_print_run_table(run_list_obj)
-@fedml_run.command("status", help="Get status of run from the FedML® Nexus AI Platform.")
+@fedml_run.command("status", help="Get status of run from the TensorOpera® AI Platform.")
@click.help_option("--help", "-h")
@click.option(
"--platform",
"-pf",
type=str,
default="falcon",
- help="The platform name at the FedML® Nexus AI Platform (options: octopus, parrot, spider, beehive, falcon, launch, "
+ help="The platform name at the TensorOpera® AI Platform (options: octopus, parrot, spider, beehive, falcon, launch, "
"default is falcon).",
)
@click.option(
@@ -124,14 +124,14 @@ def list_runs(platform, run_name, run_id, api_key, version):
"-r",
type=str,
default=None,
- help="Run name at the FedML® Nexus AI Platform.",
+ help="Run name at the TensorOpera® AI Platform.",
)
@click.option(
"--run_id",
"-rid",
type=str,
default=None,
- help="Run id at the FedML® Nexus AI Platform.",
+ help="Run id at the TensorOpera® AI Platform.",
)
@click.option(
"--api_key", "-k", type=str, help="user api key.",
@@ -141,7 +141,7 @@ def list_runs(platform, run_name, run_id, api_key, version):
"-v",
type=str,
default="release",
- help="get status of run at which version of FedML® Nexus AI Platform. It should be dev, test or release",
+ help="get status of run at which version of TensorOpera® AI Platform. It should be dev, test or release",
)
def status(platform, run_name, run_id, api_key, version):
fedml.set_env_version(version)
@@ -153,14 +153,14 @@ def status(platform, run_name, run_id, api_key, version):
_print_run_table(run_list_obj)
-@fedml_run.command("logs", help="Get logs of run from the FedML® Nexus AI Platform.")
+@fedml_run.command("logs", help="Get logs of run from the TensorOpera® AI Platform.")
@click.help_option("--help", "-h")
@click.option(
"--platform",
"-pf",
type=str,
default="falcon",
- help="The platform name at the FedML® Nexus AI Platform (options: octopus, parrot, spider, beehive, falcon, launch, "
+ help="The platform name at the TensorOpera® AI Platform (options: octopus, parrot, spider, beehive, falcon, launch, "
"default is falcon).",
)
@click.option(
@@ -168,7 +168,7 @@ def status(platform, run_name, run_id, api_key, version):
"-rid",
type=str,
default=None,
- help="Run id at the FedML® Nexus AI Platform.",
+ help="Run id at the TensorOpera® AI Platform.",
)
@click.option(
"--api_key", "-k", type=str, help="user api key.",
@@ -178,7 +178,7 @@ def status(platform, run_name, run_id, api_key, version):
"-v",
type=str,
default="release",
- help="get logs of run at which version of FedML® Nexus AI Platform. It should be dev, test or release",
+ help="get logs of run at which version of TensorOpera® AI Platform. It should be dev, test or release",
)
@click.option(
"--page_num",
diff --git a/python/fedml/cli/modules/storage.py b/python/fedml/cli/modules/storage.py
index 7e060fc12e..8b75075289 100644
--- a/python/fedml/cli/modules/storage.py
+++ b/python/fedml/cli/modules/storage.py
@@ -12,7 +12,7 @@
from fedml.api.fedml_response import ResponseCode
# Message strings constants
-version_help: str = "specify version of FedML® Nexus AI Platform. It should be dev, test or release"
+version_help: str = "specify version of TensorOpera® AI Platform. It should be dev, test or release"
api_key_help: str = "user api key."
@@ -31,7 +31,7 @@
)
def fedml_storage(api_key, version):
"""
- Manage storage on FedML® Nexus AI Platform
+ Manage storage on TensorOpera® AI Platform
"""
pass
@@ -43,7 +43,7 @@ def validate_argument(ctx, param, value):
return value
-@fedml_storage.command("upload", help="Upload data on FedML® Nexus AI Platform")
+@fedml_storage.command("upload", help="Upload data on TensorOpera® AI Platform")
@click.help_option("--help", "-h")
@click.argument("data_path", nargs=1, callback=validate_argument)
@click.option("--name", "-n", type=str, help="Name your data to store. If not provided, the name will be the same as "
@@ -78,7 +78,7 @@ def upload(data_path: str, name: str, user_metadata: str, description: str, vers
click.echo(f"Failed to upload data. Error message: {response.message}")
-@fedml_storage.command("list", help="List data stored on FedML® Nexus AI Platform")
+@fedml_storage.command("list", help="List data stored on TensorOpera® AI Platform")
@click.help_option("--help", "-h")
@click.option(
"--api_key", "-k", type=str, help=api_key_help,
@@ -108,7 +108,7 @@ def list_data(version, api_key):
f"Error message: {response.message}")
-@fedml_storage.command("get-user-metadata", help="Get user-defined metadata of data object stored on FedML® Nexus AI "
+@fedml_storage.command("get-user-metadata", help="Get user-defined metadata of data object stored on TensorOpera® AI "
"Platform")
@click.help_option("--help", "-h")
@click.argument("data_name", nargs=1, callback=validate_argument)
@@ -136,7 +136,7 @@ def get_user_metadata(data_name, version, api_key):
click.echo(f"Failed to fetch user-metadata for {data_name}. Error message: {response.message}")
-@fedml_storage.command("get-metadata", help="Get metadata of data object stored on FedML® Nexus AI Platform")
+@fedml_storage.command("get-metadata", help="Get metadata of data object stored on TensorOpera® AI Platform")
@click.help_option("--help", "-h")
@click.argument("data_name", nargs=1, callback=validate_argument)
@click.option(
@@ -167,7 +167,7 @@ def get_metadata(data_name, version, api_key):
click.echo(f"Fetching metadata failed. Error message: {response.message}")
-@fedml_storage.command("download", help="Download data stored on FedML® Nexus AI Platform")
+@fedml_storage.command("download", help="Download data stored on TensorOpera® AI Platform")
@click.help_option("--help", "-h")
@click.argument("data_name", nargs=1, callback=validate_argument)
@click.option("--dest_path", "-d", default=None, type=str, help="Destination path to download data. By default, "
@@ -194,7 +194,7 @@ def download(data_name, dest_path, version, api_key, service):
click.echo(f"Failed to download data {data_name}. Error message: {response.message}")
-@fedml_storage.command("delete", help="Delete data stored on FedML® Nexus AI Platform")
+@fedml_storage.command("delete", help="Delete data stored on TensorOpera® AI Platform")
@click.argument("data_name", nargs=1, callback=validate_argument)
@click.help_option("--help", "-h")
@click.option(
diff --git a/python/fedml/cli/modules/train.py b/python/fedml/cli/modules/train.py
index b4c36d1663..ae9c5fcbb1 100644
--- a/python/fedml/cli/modules/train.py
+++ b/python/fedml/cli/modules/train.py
@@ -7,12 +7,12 @@
@click.help_option("--help", "-h")
def fedml_train():
"""
- Manage training resources on FedML® Nexus AI Platform
+ Manage training resources on TensorOpera® AI Platform
"""
pass
-@fedml_train.command("build", help="Build training packages for the FedML® Nexus AI Platform.")
+@fedml_train.command("build", help="Build training packages for the TensorOpera® AI Platform.")
@click.help_option("--help", "-h")
@click.option(
"--dest_folder",
diff --git a/python/fedml/computing/scheduler/env/collect_env.py b/python/fedml/computing/scheduler/env/collect_env.py
index da4d54e7a0..39654eac6c 100644
--- a/python/fedml/computing/scheduler/env/collect_env.py
+++ b/python/fedml/computing/scheduler/env/collect_env.py
@@ -9,7 +9,7 @@
def collect_env():
- print("\n======== FedML (https://fedml.ai) ========")
+ print("\n======== FedML (https://tensoropera.ai) ========")
print("FedML version: " + str(fedml.__version__))
env_version = fedml.get_env_version()
print("FedML ENV version: " + str(env_version))
diff --git a/python/fedml/computing/scheduler/scheduler_core/account_manager.py b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
index 85d76d5973..4b6a628b43 100755
--- a/python/fedml/computing/scheduler/scheduler_core/account_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_core/account_manager.py
@@ -84,7 +84,7 @@ def login(self, user_id, api_key="", device_id=None, os_name=None, role=None, ru
print("Please check whether your network is normal!")
return None
- # Bind account id to FedML® Nexus AI Platform
+ # Bind account id to TensorOpera® Nexus AI Platform
register_try_count = 0
edge_id = -1
user_name = None
diff --git a/python/fedml/computing/scheduler/scheduler_entry/README.md b/python/fedml/computing/scheduler/scheduler_entry/README.md
index 0d1da81950..41d32ff399 100644
--- a/python/fedml/computing/scheduler/scheduler_entry/README.md
+++ b/python/fedml/computing/scheduler/scheduler_entry/README.md
@@ -132,7 +132,7 @@ You just need to customize the following config items.
3. `bootstrap`, It is the bootstrap shell command which will be executed before running entry commands.
-Then you can use the following example CLI to launch the job at FedML® Nexus AI Platform
+Then you can use the following example CLI to launch the job at TensorOpera® Nexus AI Platform
(Replace $YourApiKey with your own account API key from open.fedml.ai)
Example:
@@ -142,7 +142,7 @@ fedml launch hello_job.yaml
After the launch CLI is executed, the output is as follows. Here you may open the job url to confirm and actually start the job.
```
-Submitting your job to FedML® Nexus AI Platform: 100%|████████████████████████████████████████████████████████████████████████████████████████| 6.07k/6.07k [00:01<00:00, 4.94kB/s]
+Submitting your job to TensorOpera® Nexus AI Platform: 100%|████████████████████████████████████████████████████████████████████████████████████████| 6.07k/6.07k [00:01<00:00, 4.94kB/s]
Searched and matched the following GPU resource for your job:
+-----------+-------------------+---------+------------+-------------------------+---------+-------+----------+
diff --git a/python/fedml/computing/scheduler/scheduler_entry/app_manager.py b/python/fedml/computing/scheduler/scheduler_entry/app_manager.py
index 91b5ff64cc..267db08901 100755
--- a/python/fedml/computing/scheduler/scheduler_entry/app_manager.py
+++ b/python/fedml/computing/scheduler/scheduler_entry/app_manager.py
@@ -278,7 +278,7 @@ def push_app_package_to_s3(self, app_name, app_package_path):
app_storage_url = s3_storage.upload_file_with_progress(app_package_path, app_dst_key,
out_progress_to_err=True,
progress_desc="Submitting your job to "
- "FedML® Nexus AI Platform")
+ "TensorOpera® Nexus AI Platform")
return app_storage_url
def pull_app_package_from_s3(self, model_storage_url, model_name):
@@ -315,7 +315,7 @@ def push_model_to_s3(self, model_name, model_zip_path):
return FedMLModelCards.get_instance().push_model_to_s3(
model_name, model_zip_path, "FedMLLaunchServe",
show_progress=False,
- progress_desc="Submitting your job to FedML® Nexus AI Platform")
+ progress_desc="Submitting your job to TensorOpera® Nexus AI Platform")
def check_model_package(self, workspace):
model_config_file = os.path.join(
diff --git a/python/fedml/core/mlops/__init__.py b/python/fedml/core/mlops/__init__.py
index 148427fe1f..4d60534547 100644
--- a/python/fedml/core/mlops/__init__.py
+++ b/python/fedml/core/mlops/__init__.py
@@ -107,7 +107,7 @@ def init(args, should_init_logs=True):
return
else:
if hasattr(args, "simulator_daemon"):
- # Bind local device as simulation device on FedML® Nexus AI Platform
+ # Bind local device as simulation device on TensorOpera® Nexus AI Platform
setattr(args, "using_mlops", True)
setattr(args, "rank", 1)
MLOpsStore.mlops_bind_result = bind_simulation_device(args, args.user)
@@ -125,7 +125,7 @@ def init(args, should_init_logs=True):
if project_name is None or api_key is None:
raise Exception("Please check mlops_project_name and mlops_api_key params.")
- # Bind local device as simulation device on FedML® Nexus AI Platform
+ # Bind local device as simulation device on TensorOpera® Nexus AI Platform
setattr(args, "using_mlops", True)
setattr(args, "rank", 1)
MLOpsStore.mlops_bind_result = bind_simulation_device(args, api_key, args.config_version)
@@ -753,7 +753,7 @@ def push_artifact_to_s3(artifact: fedml.mlops.Artifact, version="release", show_
show_progress=show_progress,
out_progress_to_err=True,
progress_desc="Submitting your artifact to "
- "FedML® Nexus AI Platform")
+ "TensorOpera® Nexus AI Platform")
artifact_storage_url = str(artifact_storage_url).split("?")[0]
except Exception as e:
pass
@@ -1289,7 +1289,7 @@ def bind_simulation_device(args, userid):
device_role = "Edge.Simulator"
unique_device_id = "{}@{}.{}".format(args.device_id, args.os_name, device_role)
- # Bind account id to FedML® Nexus AI Platform
+ # Bind account id to TensorOpera® Nexus AI Platform
register_try_count = 0
edge_id = -1
while register_try_count < 5:
diff --git a/python/fedml/serving/templates/hf_template/config.yaml b/python/fedml/serving/templates/hf_template/config.yaml
index da512f4a46..72551635d4 100644
--- a/python/fedml/serving/templates/hf_template/config.yaml
+++ b/python/fedml/serving/templates/hf_template/config.yaml
@@ -62,7 +62,7 @@ environment_variables:
VERBOSE: "True"
# If you do not have any GPU resource but want to serve the model
-# Try fedml® launch platform, and uncomment the following lines.
+# Try TensorOpera® launch platform, and uncomment the following lines.
# ------------------------------------------------------------
computing:
minimum_num_gpus: 1 # minimum # of GPUs to provision
diff --git a/python/fedml/workflow/driver_example/customized_job_example/README.md b/python/fedml/workflow/driver_example/customized_job_example/README.md
index 647cddc290..cd95ef5c75 100644
--- a/python/fedml/workflow/driver_example/customized_job_example/README.md
+++ b/python/fedml/workflow/driver_example/customized_job_example/README.md
@@ -1,6 +1,6 @@
# Make your own workflow with multiple jobs
-## Define the job yaml based on the FEDML® Launch docs (https://doc.fedml.ai/launch)
+## Define the job yaml based on the TensorOpera® Launch docs (https://doc.fedml.ai/launch)
```
working_directory = os.path.dirname(os.path.abspath(__file__))
deploy_image_job_yaml = os.path.join(working_directory, "deploy_image_job.yaml")
@@ -119,7 +119,7 @@
```
The output of the above deploy workflow is as follows.
```
-Submitting your job to FedML® Nexus AI Platform: 100%|██████████| 3.00k/3.00k [00:00<00:00, 3.10kB/s]
+Submitting your job to TensorOpera® Nexus AI Platform: 100%|██████████| 3.00k/3.00k [00:00<00:00, 3.10kB/s]
Final status of the workflow is as follows. JobStatus.FINISHED
Output of the workflow is as follows. {'endpoint_id': 2131, 'endpoint_name': 'endpoint_test1', 'inference_url': 'https://open-test.fedml.ai/inference', 'request_body': {'arr': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0100005, -0.0100005, -0.0100005, -0.013973799, -0.0189315247, -0.023184301, -0.0360728861, -0.0392619154, -0.0380269994, -0.0390143887, -0.0346046778, -0.0257765396, -0.0209733754, -0.0217809993, -0.0144984527, -0.0118807892, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0178081425, -0.0232058779, -0.0298662898, -0.0414395151, -0.0586512813, -0.0812643979, -0.105997038, -0.121704878, -0.134457288, -0.139756261, -0.141562422, -0.135229133, -0.120246727, -0.104490087, -0.0870044931, -0.0716699334, -0.0485892545, -0.0324260775, -0.0216926329, -0.0100005, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0132956624, -0.0225936238, -0.0383702224, -0.0598206019, -0.0842014426, -0.118390816, -0.154266827, -0.188282524, -0.219803054, -0.242936317, -0.255020324, -0.259481423, -0.249404582, -0.226727106, -0.200418885, -0.16716117, -0.134317009, -0.0958717755, -0.0736565245, -0.0503983075, -0.0269783475, -0.0168919, -0.0100005, 0.0, 0.0, 0.0, 0.0, -0.0147795885, -0.025122101, -0.0381226487, -0.0786317321, -0.119593671, -0.165704529, -0.228814281, -0.288620224, -0.354491034, -0.421140618, -0.480243669, -0.527064646, -0.540807419, -0.521388017, -0.474446021, -0.403948632, -0.336571539, -0.271580657, -0.20666741, -0.154539645, -0.108856709, -0.0677589146, -0.0340327281, -0.0215091205, 0.0, 0.0, -0.0100005, -0.0107381289, -0.0260253876, -0.0570600482, -0.0914378767, -0.143000013, -0.199005834, -0.266034404, -0.353401549, -0.450251488, -0.551598332, -0.647939202, -0.743171364, -0.818162561, -0.851073275, -0.83112168, -0.763764496, -0.659992784, -0.547527626, -0.439376979, -0.33557659, -0.254856553, -0.183933732, -0.126755715, -0.0706477667, -0.0388818206, 0.0, 0.0, 0.0, -0.0134176155, -0.0390612132, -0.0873974922, -0.133107017, -0.194532142, -0.27478633, -0.369886454, -0.482920333, -0.605294063, -0.735621386, -0.869509827, -0.989564738, -1.09132506, -1.13182948, -1.09408349, -0.996373436, -0.868781173, -0.717778845, -0.570649327, -0.439021868, -0.326889344, -0.235934504, -0.167697996, -0.0995100269, -0.0479392976, -0.0187851186, 0.0, -0.0117322667, -0.0288274493, -0.0646532861, -0.118956716, -0.17783758, 1.53795878, 2.57176245, 1.53212043, 1.00392168, -0.179355647, -0.591732991, -1.05273662, -1.15378689, -1.22142979, -1.2388156, -1.21321586, -1.14302847, -1.02018313, -0.857098743, -0.676706697, -0.516203262, -0.379287244, -0.271402545, -0.189934521, -0.119940614, -0.0556340911, -0.0145752163, 0.0, -0.0206611389, -0.0437166621, -0.0808756237, -0.140488164, -0.207699245, 3.7747726, 3.14033146, 2.28939169, 1.76127332, 1.4318542, 1.1313135, 0.679164893, 0.665484747, 0.666043389, 0.680680095, 0.677305174, 0.665508286, 0.721340316, 0.883661589, 0.91751869, 0.0282541074, -0.401002939, -0.283099723, -0.194831338, -0.123075256, -0.066612686, -0.0161462821, -0.0112546885, -0.0293918605, -0.0484646663, -0.093178326, -0.146682925, -0.218121209, 0.830460131, 1.04725853, 0.147086928, 0.259684517, 0.495679969, 0.998953721, 1.29535061, 1.12204782, 1.41528197, 1.4259952, 1.36416372, 1.22805443, 1.03395727, 1.40874227, 1.73166837, 1.00260058, -0.401823716, -0.275049233, -0.181713744, -0.107567122, -0.0566041118, -0.0189159236, -0.0121427928, -0.0243168731, -0.050270377, -0.0887358114, -0.138806025, -0.212706019, -0.321729999, -0.462313723, -0.652442841, -0.845524923, -0.961258323, -0.793125052, -0.226359955, -0.640468216, -0.12372009, -0.167157468, -0.255843161, -0.441448335, -0.792766628, 1.30597044, 1.81460411, 0.691054579, -0.383665051, -0.26310513, -0.166473946, -0.0799663431, -0.0455007946, -0.0195541446, -0.0100005, -0.0186206584, -0.0414986832, -0.0722615997, -0.123238725, -0.212256343, -0.331309824, -0.491126078, -0.687704902, -0.86260267, -0.939124713, -0.869991467, -0.758168797, -0.722198511, -0.739826964, -0.809980626, -0.911188613, -1.00032001, -0.221550751, 1.53134484, 1.47605194, -0.273150738, -0.363157263, -0.252975575, -0.157152039, -0.0652009258, -0.0335283586, -0.0124209728, 0.0, -0.014849279, -0.0329699917, -0.0601451792, -0.118353377, -0.219271688, -0.354392407, -0.523006773, -0.71568287, -0.862626101, -0.90524289, -0.831592288, -0.751312636, -0.762948163, -0.825877849, -0.930232292, -1.04727288, -0.879016953, 1.11455708, 1.61660969, 0.264000765, -0.464282235, -0.354907482, -0.256014147, -0.158427696, -0.0620647188, -0.0242921899, 0.0, 0.0, -0.0117874599, -0.0252632841, -0.0502423656, -0.115068847, -0.235195531, -0.377531303, -0.547311188, -0.723069536, -0.848981953, -0.878897369, -0.826469482, -0.795496372, -0.883536617, -0.994814123, -1.13364619, -1.20871511, 5.60198157e-05, 1.28700658, 1.50082995, -0.122561277, -0.462110102, -0.360151562, -0.263898374, -0.166295096, -0.0568635009, -0.0105441394, 0.0, 0.0, 0.0, -0.016636779, -0.0423254862, -0.119931644, -0.252550583, -0.39191634, -0.556171069, -0.717849905, -0.829516019, -0.854549188, -0.84598967, -0.889246054, -1.03761315, -1.16457617, -1.30025654, -0.740699086, 1.05188993, 1.3036988, -0.163440609, -0.59058464, -0.474233049, -0.368789557, -0.274082099, -0.174264813, -0.0696188843, -0.018003151, 0.0, 0.0, 0.0, -0.0168610568, -0.0451688568, -0.131668459, -0.267838929, -0.398906806, -0.548202377, -0.690077015, -0.789823563, -0.831599129, -0.861314493, -0.95681566, -1.11036634, -1.22743073, -1.31006468, -0.02573686, 1.14239899, 0.761423491, -0.706825874, -0.608999426, -0.492457882, -0.380502867, -0.279282191, -0.173984018, -0.0767235054, -0.0195871373, -0.0100005, 0.0, -0.0100005, -0.024817808, -0.0552275065, -0.148243512, -0.283202341, -0.4022125, -0.534598048, -0.656007943, -0.738083794, -0.781657503, -0.824620535, -0.918824463, -1.04078449, -1.13391454, -1.09212795, 0.70592031, 1.17679031, -0.37378182, -0.758547572, -0.62868064, -0.501492113, -0.381043892, -0.270505206, -0.168251255, -0.0784168728, -0.022799968, -0.0157856413, 0.0, 0.0, -0.0269850288, -0.0676999793, -0.167498207, -0.298089736, -0.411096027, -0.522810883, -0.625838621, -0.693423683, -0.731704263, -0.767086709, -0.82998003, -0.921590434, -1.00562716, 0.0779492952, 1.22959017, 0.636500653, -0.901400043, -0.769630793, -0.635363773, -0.494618472, -0.369117095, -0.255794246, -0.156732083, -0.0783809414, -0.0267109338, -0.0148726634, 0.0, -0.0100005, -0.0348385687, -0.0869311199, -0.185622432, -0.311777198, -0.427690033, -0.530457702, -0.612837575, -0.669073252, -0.706628103, -0.737178903, -0.779583917, -0.866698428, -0.288157768, 1.2193059, 1.10500698, -0.50413989, -0.909137779, -0.774520432, -0.619405771, -0.472096102, -0.344822207, -0.235626373, -0.144455008, -0.0769092863, -0.0286146987, -0.0100005, 0.0, -0.0100005, -0.0342628198, -0.101174053, -0.195711272, -0.324606261, -0.442716711, -0.545960978, -0.637281741, -0.703742928, -0.753441795, -0.788772419, -0.829773267, -0.745526297, 0.949893727, 1.18293215, 0.385795002, -1.023299, -0.89872884, -0.736858006, -0.575258663, -0.430322485, -0.30912025, -0.209889823, -0.13189517, -0.0731506415, -0.0276674735, -0.0100005, 0.0, -0.0100005, -0.0400234981, -0.10709374, -0.194645695, -0.316981297, -0.440895564, -0.560086039, -0.667605659, -0.763806998, -0.843535003, -0.903604039, -0.938010529, 0.763887624, 1.12176928, 0.784111, -0.818046093, -0.991046672, -0.828340182, -0.652780006, -0.495325185, -0.364891317, -0.261772085, -0.17529887, -0.112966586, -0.0617374486, -0.0270715466, 0.0, 0.0, 0.0, -0.0406825662, -0.0978606438, -0.177848987, -0.287783481, -0.412614752, -0.543271605, -0.671018812, -0.798159188, -0.916686263, -1.02499517, -0.773682132, 1.09355574, 1.05041156, -0.498209852, -1.05256459, -0.870980804, -0.688431167, -0.523166414, -0.391308572, -0.282035183, -0.199071147, -0.13652517, -0.0893688913, -0.041317086, -0.016850831, 0.0, 0.0, 0.0, -0.0283386899, -0.0765120563, -0.141969555, -0.232658498, -0.341261378, -0.469723228, -0.606194512, -0.747366354, -0.880786554, -0.729389144, 0.895224865, 1.11943124, -0.105438374, -1.00783177, -0.859696548, -0.683890026, -0.531181637, -0.395889778, -0.289956123, -0.203267966, -0.14295145, -0.0963532989, -0.0643914026, -0.0337070214, -0.0111853003, 0.0, 0.0, -0.0100005, -0.0151722732, -0.0480051146, -0.0951161616, -0.160643556, -0.245453283, -0.353245922, -0.474265429, -0.598667391, -0.729305101, 0.389322873, 1.38694264, 1.37486731, -0.403963644, -0.77444593, -0.638730244, -0.502999283, -0.387339921, -0.279971294, -0.198381814, -0.135822721, -0.0965383286, -0.0633365644, -0.0427549534, -0.0257581657, -0.0100005, 0.0, 0.0, 0.0, 0.0, -0.0237543896, -0.0522032466, -0.0858749627, -0.140703979, -0.208515621, -0.290149335, -0.368567087, 0.334201602, 2.33307288, 2.27286258, 2.23777229, 0.0412218057, -0.494890333, -0.422342015, -0.339048837, -0.257069088, -0.185534152, -0.136577185, -0.0860242391, -0.0578259874, -0.033636416, -0.0181122384, -0.0100005, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0136274661, -0.0285803164, -0.0474793553, -0.0779785591, -0.118532172, -0.167201555, -0.214787719, 2.22171299, 4.30500754, 4.03125111, 3.36505818, 0.379953648, -0.284269948, -0.247694588, -0.205869945, -0.155925102, -0.116435448, -0.0857647974, -0.0546508166, -0.0401800073, -0.023758997, -0.0165780693, -0.0100005, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0115748833, -0.0284271584, -0.0506655656, -0.0740332846, -0.100455604, -0.124744578, 4.17363552, 7.81243004, 5.7896979, 0.322149281, -0.181506609, -0.160333393, -0.139182079, -0.118875455, -0.0873316648, -0.0700227708, -0.0540690537, -0.0384297037, -0.0265616274, -0.0161844507, -0.0119683967, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0132918601, -0.0159980455, -0.0207236291, -0.0266997366, -0.0284703819, -0.0343035092, -0.0410336906, -0.0488886427, -0.0548357917, -0.0551988782, -0.0469971082, -0.0388769026, -0.0316010302, -0.0285226846, -0.021736589, -0.0100005, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'model': 'fedml-qa-customer-0219/endpoint_test1'}, 'key_token': '5d427244128c45f58a74f3ecdb09b1e0'}
Output of all jobs is as follows. {'deploy_image_job': {'endpoint_id': 2131, 'endpoint_name': 'endpoint_test1', 'inference_url': 'https://open-test.fedml.ai/inference', 'request_body': {'arr': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0100005, -0.0100005, -0.0100005, -0.013973799, -0.0189315247, -0.023184301, -0.0360728861, -0.0392619154, -0.0380269994, -0.0390143887, -0.0346046778, -0.0257765396, -0.0209733754, -0.0217809993, -0.0144984527, -0.0118807892, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0178081425, -0.0232058779, -0.0298662898, -0.0414395151, -0.0586512813, -0.0812643979, -0.105997038, -0.121704878, -0.134457288, -0.139756261, -0.141562422, -0.135229133, -0.120246727, -0.104490087, -0.0870044931, -0.0716699334, -0.0485892545, -0.0324260775, -0.0216926329, -0.0100005, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0132956624, -0.0225936238, -0.0383702224, -0.0598206019, -0.0842014426, -0.118390816, -0.154266827, -0.188282524, -0.219803054, -0.242936317, -0.255020324, -0.259481423, -0.249404582, -0.226727106, -0.200418885, -0.16716117, -0.134317009, -0.0958717755, -0.0736565245, -0.0503983075, -0.0269783475, -0.0168919, -0.0100005, 0.0, 0.0, 0.0, 0.0, -0.0147795885, -0.025122101, -0.0381226487, -0.0786317321, -0.119593671, -0.165704529, -0.228814281, -0.288620224, -0.354491034, -0.421140618, -0.480243669, -0.527064646, -0.540807419, -0.521388017, -0.474446021, -0.403948632, -0.336571539, -0.271580657, -0.20666741, -0.154539645, -0.108856709, -0.0677589146, -0.0340327281, -0.0215091205, 0.0, 0.0, -0.0100005, -0.0107381289, -0.0260253876, -0.0570600482, -0.0914378767, -0.143000013, -0.199005834, -0.266034404, -0.353401549, -0.450251488, -0.551598332, -0.647939202, -0.743171364, -0.818162561, -0.851073275, -0.83112168, -0.763764496, -0.659992784, -0.547527626, -0.439376979, -0.33557659, -0.254856553, -0.183933732, -0.126755715, -0.0706477667, -0.0388818206, 0.0, 0.0, 0.0, -0.0134176155, -0.0390612132, -0.0873974922, -0.133107017, -0.194532142, -0.27478633, -0.369886454, -0.482920333, -0.605294063, -0.735621386, -0.869509827, -0.989564738, -1.09132506, -1.13182948, -1.09408349, -0.996373436, -0.868781173, -0.717778845, -0.570649327, -0.439021868, -0.326889344, -0.235934504, -0.167697996, -0.0995100269, -0.0479392976, -0.0187851186, 0.0, -0.0117322667, -0.0288274493, -0.0646532861, -0.118956716, -0.17783758, 1.53795878, 2.57176245, 1.53212043, 1.00392168, -0.179355647, -0.591732991, -1.05273662, -1.15378689, -1.22142979, -1.2388156, -1.21321586, -1.14302847, -1.02018313, -0.857098743, -0.676706697, -0.516203262, -0.379287244, -0.271402545, -0.189934521, -0.119940614, -0.0556340911, -0.0145752163, 0.0, -0.0206611389, -0.0437166621, -0.0808756237, -0.140488164, -0.207699245, 3.7747726, 3.14033146, 2.28939169, 1.76127332, 1.4318542, 1.1313135, 0.679164893, 0.665484747, 0.666043389, 0.680680095, 0.677305174, 0.665508286, 0.721340316, 0.883661589, 0.91751869, 0.0282541074, -0.401002939, -0.283099723, -0.194831338, -0.123075256, -0.066612686, -0.0161462821, -0.0112546885, -0.0293918605, -0.0484646663, -0.093178326, -0.146682925, -0.218121209, 0.830460131, 1.04725853, 0.147086928, 0.259684517, 0.495679969, 0.998953721, 1.29535061, 1.12204782, 1.41528197, 1.4259952, 1.36416372, 1.22805443, 1.03395727, 1.40874227, 1.73166837, 1.00260058, -0.401823716, -0.275049233, -0.181713744, -0.107567122, -0.0566041118, -0.0189159236, -0.0121427928, -0.0243168731, -0.050270377, -0.0887358114, -0.138806025, -0.212706019, -0.321729999, -0.462313723, -0.652442841, -0.845524923, -0.961258323, -0.793125052, -0.226359955, -0.640468216, -0.12372009, -0.167157468, -0.255843161, -0.441448335, -0.792766628, 1.30597044, 1.81460411, 0.691054579, -0.383665051, -0.26310513, -0.166473946, -0.0799663431, -0.0455007946, -0.0195541446, -0.0100005, -0.0186206584, -0.0414986832, -0.0722615997, -0.123238725, -0.212256343, -0.331309824, -0.491126078, -0.687704902, -0.86260267, -0.939124713, -0.869991467, -0.758168797, -0.722198511, -0.739826964, -0.809980626, -0.911188613, -1.00032001, -0.221550751, 1.53134484, 1.47605194, -0.273150738, -0.363157263, -0.252975575, -0.157152039, -0.0652009258, -0.0335283586, -0.0124209728, 0.0, -0.014849279, -0.0329699917, -0.0601451792, -0.118353377, -0.219271688, -0.354392407, -0.523006773, -0.71568287, -0.862626101, -0.90524289, -0.831592288, -0.751312636, -0.762948163, -0.825877849, -0.930232292, -1.04727288, -0.879016953, 1.11455708, 1.61660969, 0.264000765, -0.464282235, -0.354907482, -0.256014147, -0.158427696, -0.0620647188, -0.0242921899, 0.0, 0.0, -0.0117874599, -0.0252632841, -0.0502423656, -0.115068847, -0.235195531, -0.377531303, -0.547311188, -0.723069536, -0.848981953, -0.878897369, -0.826469482, -0.795496372, -0.883536617, -0.994814123, -1.13364619, -1.20871511, 5.60198157e-05, 1.28700658, 1.50082995, -0.122561277, -0.462110102, -0.360151562, -0.263898374, -0.166295096, -0.0568635009, -0.0105441394, 0.0, 0.0, 0.0, -0.016636779, -0.0423254862, -0.119931644, -0.252550583, -0.39191634, -0.556171069, -0.717849905, -0.829516019, -0.854549188, -0.84598967, -0.889246054, -1.03761315, -1.16457617, -1.30025654, -0.740699086, 1.05188993, 1.3036988, -0.163440609, -0.59058464, -0.474233049, -0.368789557, -0.274082099, -0.174264813, -0.0696188843, -0.018003151, 0.0, 0.0, 0.0, -0.0168610568, -0.0451688568, -0.131668459, -0.267838929, -0.398906806, -0.548202377, -0.690077015, -0.789823563, -0.831599129, -0.861314493, -0.95681566, -1.11036634, -1.22743073, -1.31006468, -0.02573686, 1.14239899, 0.761423491, -0.706825874, -0.608999426, -0.492457882, -0.380502867, -0.279282191, -0.173984018, -0.0767235054, -0.0195871373, -0.0100005, 0.0, -0.0100005, -0.024817808, -0.0552275065, -0.148243512, -0.283202341, -0.4022125, -0.534598048, -0.656007943, -0.738083794, -0.781657503, -0.824620535, -0.918824463, -1.04078449, -1.13391454, -1.09212795, 0.70592031, 1.17679031, -0.37378182, -0.758547572, -0.62868064, -0.501492113, -0.381043892, -0.270505206, -0.168251255, -0.0784168728, -0.022799968, -0.0157856413, 0.0, 0.0, -0.0269850288, -0.0676999793, -0.167498207, -0.298089736, -0.411096027, -0.522810883, -0.625838621, -0.693423683, -0.731704263, -0.767086709, -0.82998003, -0.921590434, -1.00562716, 0.0779492952, 1.22959017, 0.636500653, -0.901400043, -0.769630793, -0.635363773, -0.494618472, -0.369117095, -0.255794246, -0.156732083, -0.0783809414, -0.0267109338, -0.0148726634, 0.0, -0.0100005, -0.0348385687, -0.0869311199, -0.185622432, -0.311777198, -0.427690033, -0.530457702, -0.612837575, -0.669073252, -0.706628103, -0.737178903, -0.779583917, -0.866698428, -0.288157768, 1.2193059, 1.10500698, -0.50413989, -0.909137779, -0.774520432, -0.619405771, -0.472096102, -0.344822207, -0.235626373, -0.144455008, -0.0769092863, -0.0286146987, -0.0100005, 0.0, -0.0100005, -0.0342628198, -0.101174053, -0.195711272, -0.324606261, -0.442716711, -0.545960978, -0.637281741, -0.703742928, -0.753441795, -0.788772419, -0.829773267, -0.745526297, 0.949893727, 1.18293215, 0.385795002, -1.023299, -0.89872884, -0.736858006, -0.575258663, -0.430322485, -0.30912025, -0.209889823, -0.13189517, -0.0731506415, -0.0276674735, -0.0100005, 0.0, -0.0100005, -0.0400234981, -0.10709374, -0.194645695, -0.316981297, -0.440895564, -0.560086039, -0.667605659, -0.763806998, -0.843535003, -0.903604039, -0.938010529, 0.763887624, 1.12176928, 0.784111, -0.818046093, -0.991046672, -0.828340182, -0.652780006, -0.495325185, -0.364891317, -0.261772085, -0.17529887, -0.112966586, -0.0617374486, -0.0270715466, 0.0, 0.0, 0.0, -0.0406825662, -0.0978606438, -0.177848987, -0.287783481, -0.412614752, -0.543271605, -0.671018812, -0.798159188, -0.916686263, -1.02499517, -0.773682132, 1.09355574, 1.05041156, -0.498209852, -1.05256459, -0.870980804, -0.688431167, -0.523166414, -0.391308572, -0.282035183, -0.199071147, -0.13652517, -0.0893688913, -0.041317086, -0.016850831, 0.0, 0.0, 0.0, -0.0283386899, -0.0765120563, -0.141969555, -0.232658498, -0.341261378, -0.469723228, -0.606194512, -0.747366354, -0.880786554, -0.729389144, 0.895224865, 1.11943124, -0.105438374, -1.00783177, -0.859696548, -0.683890026, -0.531181637, -0.395889778, -0.289956123, -0.203267966, -0.14295145, -0.0963532989, -0.0643914026, -0.0337070214, -0.0111853003, 0.0, 0.0, -0.0100005, -0.0151722732, -0.0480051146, -0.0951161616, -0.160643556, -0.245453283, -0.353245922, -0.474265429, -0.598667391, -0.729305101, 0.389322873, 1.38694264, 1.37486731, -0.403963644, -0.77444593, -0.638730244, -0.502999283, -0.387339921, -0.279971294, -0.198381814, -0.135822721, -0.0965383286, -0.0633365644, -0.0427549534, -0.0257581657, -0.0100005, 0.0, 0.0, 0.0, 0.0, -0.0237543896, -0.0522032466, -0.0858749627, -0.140703979, -0.208515621, -0.290149335, -0.368567087, 0.334201602, 2.33307288, 2.27286258, 2.23777229, 0.0412218057, -0.494890333, -0.422342015, -0.339048837, -0.257069088, -0.185534152, -0.136577185, -0.0860242391, -0.0578259874, -0.033636416, -0.0181122384, -0.0100005, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0136274661, -0.0285803164, -0.0474793553, -0.0779785591, -0.118532172, -0.167201555, -0.214787719, 2.22171299, 4.30500754, 4.03125111, 3.36505818, 0.379953648, -0.284269948, -0.247694588, -0.205869945, -0.155925102, -0.116435448, -0.0857647974, -0.0546508166, -0.0401800073, -0.023758997, -0.0165780693, -0.0100005, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0115748833, -0.0284271584, -0.0506655656, -0.0740332846, -0.100455604, -0.124744578, 4.17363552, 7.81243004, 5.7896979, 0.322149281, -0.181506609, -0.160333393, -0.139182079, -0.118875455, -0.0873316648, -0.0700227708, -0.0540690537, -0.0384297037, -0.0265616274, -0.0161844507, -0.0119683967, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0132918601, -0.0159980455, -0.0207236291, -0.0266997366, -0.0284703819, -0.0343035092, -0.0410336906, -0.0488886427, -0.0548357917, -0.0551988782, -0.0469971082, -0.0388769026, -0.0316010302, -0.0285226846, -0.021736589, -0.0100005, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'model': 'fedml-qa-customer-0219/endpoint_test1'}, 'key_token': '5d427244128c45f58a74f3ecdb09b1e0'}}
diff --git a/python/spotlight_prj/unitedllm/README.md b/python/spotlight_prj/unitedllm/README.md
index 5d5972cef1..5a300f2cfc 100644
--- a/python/spotlight_prj/unitedllm/README.md
+++ b/python/spotlight_prj/unitedllm/README.md
@@ -4,7 +4,7 @@
# UnitedLLM: Training and Serving LLM Collaboratively on Decentralized GPU Clouds
-[FEDML® UnitedLLM](https://blog.fedml.ai/releasing-fedllm-build-your-own-large-language-models-on-proprietary-data-using-the-fedml-platform/)
+[TensorOpera® UnitedLLM](https://blog.fedml.ai/releasing-fedllm-build-your-own-large-language-models-on-proprietary-data-using-the-fedml-platform/)
is an MLOps-supported training pipeline for decentralized pretraining and finetuning of large language models.
## Getting Started
From 4acb0f051705b0479d38faedbf03ab37b854accc Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Fri, 20 Dec 2024 18:36:53 +0800
Subject: [PATCH 249/282] undo "Welcome to FedML.ai!"
---
python/fedml/api/modules/device.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/api/modules/device.py b/python/fedml/api/modules/device.py
index 1b578aa903..4ca5695523 100644
--- a/python/fedml/api/modules/device.py
+++ b/python/fedml/api/modules/device.py
@@ -78,7 +78,7 @@ def _bind(
else:
docker_install_url = "https://docs.docker.com/engine/install/"
docker_config_text = " Moreover, you need to config the docker engine to run as a non-root user. Here is the docs. https://docs.docker.com/engine/install/linux-postinstall/"
- print("\n Welcome toTensorOpera.ai! \n Start to login the current device to the TensorOpera® Nexus AI Platform\n")
+ print("\n Welcome to FedML.ai! \n Start to login the current device to the TensorOpera® Nexus AI Platform\n")
print(" If you want to deploy models into this computer, you need to install the docker engine to serve your models.")
print(f" Here is the docs for installation docker engine. {docker_install_url}")
if docker_config_text is not None:
From 07ae5ec68f2848a0a8fb12fc89bea8bc9d8c4023 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 25 Dec 2024 12:08:41 +0800
Subject: [PATCH 250/282] [bugfix]start_job_perf on execute_job_task
---
.../scheduler/scheduler_core/scheduler_base_job_runner.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
index f5e08107f2..cf700e9a9d 100755
--- a/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
+++ b/python/fedml/computing/scheduler/scheduler_core/scheduler_base_job_runner.py
@@ -594,7 +594,7 @@ def callback_start_fl_job(self, job_pid):
def start_job_perf(self, job_pid):
GeneralConstants.save_learning_process(self.run_id, job_pid, data_dir=self.agent_data_dir)
- #self.mlops_metrics.report_job_perf(self.args, self.agent_config["mqtt_config"], job_pid)
+ self.mlops_metrics.report_job_perf(self.args, self.agent_config["mqtt_config"], job_pid)
def job_error_processor(self, error_list):
self.check_runner_stop_event()
From 46d766a2ba697bd76dcb68ed16090cc9c4427a9c Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Sat, 28 Dec 2024 13:09:45 +0800
Subject: [PATCH 251/282] [logs]add deploy param
---
.../device_model_deployment.py | 23 +++++++++++++++++++
.../master_protocol_manager.py | 5 ++++
2 files changed, 28 insertions(+)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index baee7a2973..647882c84f 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -65,6 +65,10 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
request_json = dict()
logging.info("[Worker] Model deployment is starting...")
+ logging.info("=" * 80)
+ logging.info("[Device Model Deployment] Received start deployment request: {}".format(request_json))
+ logging.info("=" * 80)
+
# Real gpu per replica (container-level)
num_gpus = gpu_per_replica
gpu_ids, gpu_attach_cmd = None, ""
@@ -213,6 +217,25 @@ def start_deployment(end_point_id, end_point_name, model_id, model_version,
detach=True,
command=customized_image_entry_cmd,
)
+
+ logging.info("=" * 80)
+ logging.info("[Device Model Deployment] Creating container with following parameters:")
+ logging.info("=" * 80)
+ logging.info("Image: {}".format(inference_image_name))
+ logging.info("Container name: {}".format(default_server_container_name))
+ logging.info("Volumes:")
+ for vol in volumes:
+ logging.info(" - {}".format(vol))
+ logging.info("Ports: [{}]".format(port_inside_container))
+ logging.info("Environment variables:")
+ for key, value in environment.items():
+ logging.info(" {} = {}".format(key, value))
+ logging.info("Host config:")
+ for key, value in host_config_dict.items():
+ logging.info(" {} = {}".format(key, value))
+ logging.info("Command: {}".format(customized_image_entry_cmd))
+ logging.info("=" * 80)
+
client.api.start(container=new_container.get("Id"))
except Exception as e:
logging.error(f"Failed to create the container with exception {e}, traceback : {traceback.format_exc()}")
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index 9e0d51b588..e06c5162eb 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -155,6 +155,11 @@ def callback_start_deployment(self, topic, payload):
# Get deployment params
request_json = json.loads(payload)
+
+ logging.info("=" * 80)
+ logging.info("[Master Protocol Manager] Received start deployment request: {}".format(request_json))
+ logging.info("=" * 80)
+
run_id = request_json["end_point_id"]
end_point_name = request_json["end_point_name"]
token = request_json["token"]
From 0f1a37ef20426fc3e6378effc19a3ebf93016f52 Mon Sep 17 00:00:00 2001
From: "alex.liang" <alexliang.kh@gmail.com>
Date: Tue, 11 Feb 2025 00:51:42 +0800
Subject: [PATCH 252/282] add the login cli for the service provider named
chainopera.
---
python/fedml/cli/modules/login.py | 14 +++++++++++++-
1 file changed, 13 insertions(+), 1 deletion(-)
diff --git a/python/fedml/cli/modules/login.py b/python/fedml/cli/modules/login.py
index 5e77910cbb..d17b0d0125 100644
--- a/python/fedml/cli/modules/login.py
+++ b/python/fedml/cli/modules/login.py
@@ -103,16 +103,28 @@
default="",
help="Name of the node.",
)
+@click.option(
+ "--service_provider",
+ "-sp",
+ type=str,
+ default="",
+ help="Service provider.",
+)
def fedml_login(
api_key, version, compute_node, server, provider, deploy_worker_num,
local_on_premise_platform, local_on_premise_platform_port,
master_inference_gateway_port, worker_inference_proxy_port, worker_connection_type, marketplace_type,
- price_per_hour, name
+ price_per_hour, name, service_provider
):
fedml.set_env_version(version)
fedml.set_local_on_premise_platform_host(local_on_premise_platform)
fedml.set_local_on_premise_platform_port(local_on_premise_platform_port)
+ if service_provider == "chainopera" or service_provider == "co":
+ fedml.set_env_version('local')
+ fedml.set_local_on_premise_platform_host('open.chainopera.ai')
+ fedml.set_local_on_premise_platform_port(443)
+
try:
price_per_hour = float(price_per_hour)
except ValueError as e:
From a912c5738a90992fd06fd2394e068dcde9139aa0 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Tue, 11 Feb 2025 12:53:28 +0800
Subject: [PATCH 253/282] [bugfix] Handle deployment failure by deleting
deployed replicas and releasing GPU
---
.../computing/scheduler/model_scheduler/master_job_runner.py | 4 ++++
1 file changed, 4 insertions(+)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 00b08acfb8..bc943307f2 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -11,6 +11,7 @@
import fedml
from fedml.core.mlops import MLOpsRuntimeLog, MLOpsConfigs
from fedml.core.mlops.mlops_runtime_log import MLOpsFormatter
+from .device_model_msg_object import FedMLModelMsgObject
from .device_client_constants import ClientConstants
from .device_model_cache import FedMLModelCache
from .device_server_constants import ServerConstants
@@ -278,6 +279,9 @@ def process_deployment_result_message(self, topic=None, payload=None):
end_point_id, end_point_name, payload_json["model_name"], "",
ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
message_center=self.message_center)
+ # when report failed to the MLOps, need to delete the replica has successfully deployed and release the gpu
+ model_msg_object = FedMLModelMsgObject(topic, payload)
+ self.send_deployment_delete_request_to_edges(payload, model_msg_object, message_center=self.message_center)
return
# Failure handler, send the rollback message to the worker devices only if it has not been rollback
From c6bfe20e84915c8d692961a1c9ad6e26f3fdbb0c Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 12 Feb 2025 11:11:47 +0800
Subject: [PATCH 254/282] [refactor] Disable request timeout middleware in
device model inference
---
.../model_scheduler/device_model_inference.py | 80 +++++++++----------
1 file changed, 40 insertions(+), 40 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 9adc17538d..6c37f10322 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -46,46 +46,46 @@ class Settings:
redis_password=Settings.redis_password)
-@api.middleware("http")
-async def auth_middleware(request: Request, call_next):
- if "/inference" in request.url.path or "/api/v1/predict" in request.url.path:
- try:
- # Attempt to parse the JSON body.
- request_json = await request.json()
- except json.JSONDecodeError:
- return JSONResponse(
- {"error": True, "message": "Invalid JSON."},
- status_code=status.HTTP_400_BAD_REQUEST)
-
- # Get endpoint's total pending requests.
- end_point_id = request_json.get("end_point_id", None)
- pending_requests_num = FEDML_MODEL_CACHE.get_pending_requests_counter(end_point_id)
- if pending_requests_num:
- # Fetch metrics of the past k=3 requests.
- pask_k_metrics = FEDML_MODEL_CACHE.get_endpoint_metrics(
- end_point_id=end_point_id,
- k_recent=3)
-
- # Get the request timeout from the endpoint settings.
- request_timeout_s = FEDML_MODEL_CACHE.get_endpoint_settings(end_point_id) \
- .get(ServerConstants.INFERENCE_REQUEST_TIMEOUT_KEY, ServerConstants.INFERENCE_REQUEST_TIMEOUT_DEFAULT)
-
- # Only proceed if the past k metrics collection is not empty.
- if pask_k_metrics:
- # Measure the average latency in seconds(!), hence the 0.001 multiplier.
- past_k_latencies_sec = \
- [float(j_obj["current_latency"]) * 0.001 for j_obj in pask_k_metrics]
- mean_latency = sum(past_k_latencies_sec) / len(past_k_latencies_sec)
-
- # If timeout threshold is exceeded then cancel and return time out error.
- should_block = (mean_latency * pending_requests_num) > request_timeout_s
- if should_block:
- return JSONResponse(
- {"error": True, "message": "Request timed out."},
- status_code=status.HTTP_504_GATEWAY_TIMEOUT)
-
- response = await call_next(request)
- return response
+# @api.middleware("http")
+# async def auth_middleware(request: Request, call_next):
+# if "/inference" in request.url.path or "/api/v1/predict" in request.url.path:
+# try:
+# # Attempt to parse the JSON body.
+# request_json = await request.json()
+# except json.JSONDecodeError:
+# return JSONResponse(
+# {"error": True, "message": "Invalid JSON."},
+# status_code=status.HTTP_400_BAD_REQUEST)
+
+# # Get endpoint's total pending requests.
+# end_point_id = request_json.get("end_point_id", None)
+# pending_requests_num = FEDML_MODEL_CACHE.get_pending_requests_counter(end_point_id)
+# if pending_requests_num:
+# # Fetch metrics of the past k=3 requests.
+# pask_k_metrics = FEDML_MODEL_CACHE.get_endpoint_metrics(
+# end_point_id=end_point_id,
+# k_recent=3)
+
+# # Get the request timeout from the endpoint settings.
+# request_timeout_s = FEDML_MODEL_CACHE.get_endpoint_settings(end_point_id) \
+# .get(ServerConstants.INFERENCE_REQUEST_TIMEOUT_KEY, ServerConstants.INFERENCE_REQUEST_TIMEOUT_DEFAULT)
+
+# # Only proceed if the past k metrics collection is not empty.
+# if pask_k_metrics:
+# # Measure the average latency in seconds(!), hence the 0.001 multiplier.
+# past_k_latencies_sec = \
+# [float(j_obj["current_latency"]) * 0.001 for j_obj in pask_k_metrics]
+# mean_latency = sum(past_k_latencies_sec) / len(past_k_latencies_sec)
+
+# # If timeout threshold is exceeded then cancel and return time out error.
+# should_block = (mean_latency * pending_requests_num) > request_timeout_s
+# if should_block:
+# return JSONResponse(
+# {"error": True, "message": "Request timed out."},
+# status_code=status.HTTP_504_GATEWAY_TIMEOUT)
+
+# response = await call_next(request)
+# return response
@api.on_event("startup")
From 7134c15c38bb0f06bfa3ddc9d7491dbb3996dcb3 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 12 Feb 2025 12:25:22 +0800
Subject: [PATCH 255/282] add logs
---
.../device_http_inference_protocol.py | 12 ++++++++++++
.../model_scheduler/device_model_inference.py | 11 +++++++++++
2 files changed, 23 insertions(+)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
index 28d50d5a50..f71caa110c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
@@ -1,4 +1,6 @@
import logging
+import time
+import uuid
import httpx
import traceback
@@ -99,12 +101,22 @@ async def stream_generator(inference_url, input_json, method="POST"):
async def redirect_non_stream_req_to_worker(inference_type, inference_url, model_api_headers, model_inference_json,
timeout=None, method="POST"):
response_ok = True
+ request_id = str(uuid.uuid4())[:8] # 生成短UUID作为请求ID
+ start_time = time.time()
+ logging.info(f"[Request-{request_id}] Starting HTTP request to {inference_url}")
+
try:
async with httpx.AsyncClient() as client:
response = await client.request(
method=method, url=inference_url, headers=model_api_headers, json=model_inference_json, timeout=timeout
)
+ end_time = time.time()
+ elapsed_time = end_time - start_time
+ logging.info(f"[Request-{request_id}] Completed HTTP request. Time taken: {elapsed_time:.3f} seconds")
except Exception as e:
+ end_time = time.time()
+ elapsed_time = end_time - start_time
+ logging.error(f"[Request-{request_id}] Failed HTTP request after {elapsed_time:.3f} seconds. Error: {str(e)}")
response_ok = False
model_inference_result = {"error": e}
return response_ok, model_inference_result
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 6c37f10322..35d47f8dbd 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -4,6 +4,7 @@
import time
import traceback
import os
+import uuid
from typing import Any, Mapping, MutableMapping, Union
from urllib.parse import urlparse
@@ -198,6 +199,7 @@ async def _predict(
# Always increase the pending requests counter on a new incoming request.
FEDML_MODEL_CACHE.update_pending_requests_counter(end_point_id, increase=True)
inference_response = {}
+ request_uuid = str(uuid.uuid4()) # Generate unique request ID
try:
in_end_point_id = end_point_id
@@ -260,6 +262,10 @@ async def _predict(
output_list = input_json.get("outputs", [])
# main execution of redirecting the inference request to the idle device
+ inference_start_time = time.time()
+ start_time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(inference_start_time))
+ logging.info(f"[Request {request_uuid}] Starting send_inference_request at {start_time_str}")
+
inference_response = await send_inference_request(
idle_device,
end_point_id,
@@ -269,6 +275,11 @@ async def _predict(
inference_type=in_return_type,
connectivity_type=connectivity_type,
path=path, request_method=request_method)
+
+ inference_end_time = time.time()
+ end_time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(inference_end_time))
+ inference_duration = inference_end_time - inference_start_time
+ logging.info(f"[Request {request_uuid}] Completed send_inference_request at {end_time_str}, duration: {inference_duration:.3f} seconds")
# Calculate model metrics
try:
From f7552c49aa6da10a3cf3148a7982745b9e3f6090 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 12 Feb 2025 13:57:39 +0800
Subject: [PATCH 256/282] [logs] Always enable log file
---
python/fedml/core/mlops/mlops_runtime_log.py | 25 ++++++++++----------
1 file changed, 13 insertions(+), 12 deletions(-)
diff --git a/python/fedml/core/mlops/mlops_runtime_log.py b/python/fedml/core/mlops/mlops_runtime_log.py
index 0fc5db3d23..258f413448 100644
--- a/python/fedml/core/mlops/mlops_runtime_log.py
+++ b/python/fedml/core/mlops/mlops_runtime_log.py
@@ -139,10 +139,11 @@ def __init__(self, args):
self.stdout_handle = None
self.logger = None
self.args = args
- if hasattr(args, "using_mlops"):
- self.should_write_log_file = args.using_mlops
- else:
- self.should_write_log_file = False
+ # if hasattr(args, "using_mlops"):
+ # self.should_write_log_file = args.using_mlops
+ # else:
+ # self.should_write_log_file = False
+ self.should_write_log_file = True
if not hasattr(args, "log_file_dir"):
setattr(args, "log_file_dir", "./logs")
self.log_file_dir = args.log_file_dir
@@ -175,14 +176,14 @@ def init_logs(self, log_level=None):
self.logger.setLevel(log_level)
self.logger.handlers.clear()
self.logger.addHandler(self.stdout_handle)
- if hasattr(self, "should_write_log_file") and self.should_write_log_file:
- run_id, edge_id = self.args.run_id, MLOpsLoggingUtils.get_edge_id_from_args(self.args)
- log_config_file = os.path.join(self.log_file_dir, MLOpsLoggingUtils.LOG_CONFIG_FILE)
- file_handle = MLOpsFileHandler(filepath=log_file_path, log_config_file=log_config_file, run_id=run_id,
- edge_id=edge_id)
- file_handle.setFormatter(self.format_str)
- file_handle.setLevel(logging.INFO)
- self.logger.addHandler(file_handle)
+ # if hasattr(self, "should_write_log_file") and self.should_write_log_file:
+ run_id, edge_id = self.args.run_id, MLOpsLoggingUtils.get_edge_id_from_args(self.args)
+ log_config_file = os.path.join(self.log_file_dir, MLOpsLoggingUtils.LOG_CONFIG_FILE)
+ file_handle = MLOpsFileHandler(filepath=log_file_path, log_config_file=log_config_file, run_id=run_id,
+ edge_id=edge_id)
+ file_handle.setFormatter(self.format_str)
+ file_handle.setLevel(logging.INFO)
+ self.logger.addHandler(file_handle)
logging.root = self.logger
# Rewrite sys.stdout to redirect stdout (i.e print()) to Logger
sys.stdout.write = self.logger.info
From d3b447a1e0abfa728e413335e8a5decec8c255ba Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 12 Feb 2025 14:16:10 +0800
Subject: [PATCH 257/282] [refactor] Optimize HTTP inference client and log
file handling
---
.../device_http_inference_protocol.py | 25 +++++++++++++------
python/fedml/core/mlops/mlops_runtime_log.py | 25 +++++++++----------
2 files changed, 29 insertions(+), 21 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
index f71caa110c..ef98b085b0 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
@@ -14,6 +14,15 @@
class FedMLHttpInference:
+ _http_client = None # Class variable for shared HTTP client
+
+ @classmethod
+ async def get_http_client(cls):
+ if cls._http_client is None:
+ limits = httpx.Limits(max_keepalive_connections=100, max_connections=100)
+ cls._http_client = httpx.AsyncClient(limits=limits)
+ return cls._http_client
+
def __init__(self):
pass
@@ -101,18 +110,18 @@ async def stream_generator(inference_url, input_json, method="POST"):
async def redirect_non_stream_req_to_worker(inference_type, inference_url, model_api_headers, model_inference_json,
timeout=None, method="POST"):
response_ok = True
- request_id = str(uuid.uuid4())[:8] # 生成短UUID作为请求ID
+ request_id = str(uuid.uuid4())[:8]
start_time = time.time()
logging.info(f"[Request-{request_id}] Starting HTTP request to {inference_url}")
try:
- async with httpx.AsyncClient() as client:
- response = await client.request(
- method=method, url=inference_url, headers=model_api_headers, json=model_inference_json, timeout=timeout
- )
- end_time = time.time()
- elapsed_time = end_time - start_time
- logging.info(f"[Request-{request_id}] Completed HTTP request. Time taken: {elapsed_time:.3f} seconds")
+ client = await FedMLHttpInference.get_http_client()
+ response = await client.request(
+ method=method, url=inference_url, headers=model_api_headers, json=model_inference_json, timeout=timeout
+ )
+ end_time = time.time()
+ elapsed_time = end_time - start_time
+ logging.info(f"[Request-{request_id}] Completed HTTP request. Time taken: {elapsed_time:.3f} seconds")
except Exception as e:
end_time = time.time()
elapsed_time = end_time - start_time
diff --git a/python/fedml/core/mlops/mlops_runtime_log.py b/python/fedml/core/mlops/mlops_runtime_log.py
index 258f413448..0fc5db3d23 100644
--- a/python/fedml/core/mlops/mlops_runtime_log.py
+++ b/python/fedml/core/mlops/mlops_runtime_log.py
@@ -139,11 +139,10 @@ def __init__(self, args):
self.stdout_handle = None
self.logger = None
self.args = args
- # if hasattr(args, "using_mlops"):
- # self.should_write_log_file = args.using_mlops
- # else:
- # self.should_write_log_file = False
- self.should_write_log_file = True
+ if hasattr(args, "using_mlops"):
+ self.should_write_log_file = args.using_mlops
+ else:
+ self.should_write_log_file = False
if not hasattr(args, "log_file_dir"):
setattr(args, "log_file_dir", "./logs")
self.log_file_dir = args.log_file_dir
@@ -176,14 +175,14 @@ def init_logs(self, log_level=None):
self.logger.setLevel(log_level)
self.logger.handlers.clear()
self.logger.addHandler(self.stdout_handle)
- # if hasattr(self, "should_write_log_file") and self.should_write_log_file:
- run_id, edge_id = self.args.run_id, MLOpsLoggingUtils.get_edge_id_from_args(self.args)
- log_config_file = os.path.join(self.log_file_dir, MLOpsLoggingUtils.LOG_CONFIG_FILE)
- file_handle = MLOpsFileHandler(filepath=log_file_path, log_config_file=log_config_file, run_id=run_id,
- edge_id=edge_id)
- file_handle.setFormatter(self.format_str)
- file_handle.setLevel(logging.INFO)
- self.logger.addHandler(file_handle)
+ if hasattr(self, "should_write_log_file") and self.should_write_log_file:
+ run_id, edge_id = self.args.run_id, MLOpsLoggingUtils.get_edge_id_from_args(self.args)
+ log_config_file = os.path.join(self.log_file_dir, MLOpsLoggingUtils.LOG_CONFIG_FILE)
+ file_handle = MLOpsFileHandler(filepath=log_file_path, log_config_file=log_config_file, run_id=run_id,
+ edge_id=edge_id)
+ file_handle.setFormatter(self.format_str)
+ file_handle.setLevel(logging.INFO)
+ self.logger.addHandler(file_handle)
logging.root = self.logger
# Rewrite sys.stdout to redirect stdout (i.e print()) to Logger
sys.stdout.write = self.logger.info
From 591011120ed6dda2935dd6915e69566bcc59503a Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 12 Feb 2025 14:45:47 +0800
Subject: [PATCH 258/282] [perf] Optimize Uvicorn server configuration for
improved inference gateway performance
---
.../device_http_inference_protocol.py | 2 +-
.../model_scheduler/master_job_runner.py | 23 +++++++++++--------
2 files changed, 15 insertions(+), 10 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
index ef98b085b0..7f248ee281 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
@@ -19,7 +19,7 @@ class FedMLHttpInference:
@classmethod
async def get_http_client(cls):
if cls._http_client is None:
- limits = httpx.Limits(max_keepalive_connections=100, max_connections=100)
+ limits = httpx.Limits(max_keepalive_connections=50, max_connections=1000)
cls._http_client = httpx.AsyncClient(limits=limits)
return cls._http_client
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index bc943307f2..c61e1d4374 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -452,15 +452,20 @@ def start_device_inference_gateway():
if inference_gateway_pids is None or len(inference_gateway_pids) <= 0:
cur_dir = os.path.dirname(__file__)
fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- inference_gateway_process = ServerConstants.exec_console_with_script(f"{python_program} "
- f"-m uvicorn {inference_gw_cmd} "
- f"--host 0.0.0.0 "
- f"--port {str(inference_port)} "
- f"--reload --reload-delay 3 "
- f"--reload-dir {fedml_base_dir} "
- f"--log-level info",
- should_capture_stdout=False,
- should_capture_stderr=False)
+ inference_gateway_process = ServerConstants.exec_console_with_script(
+ f"{python_program} -m uvicorn {inference_gw_cmd} "
+ f"--host 0.0.0.0 "
+ f"--port {str(inference_port)} "
+ f"--workers 10 "
+ f"--loop uvloop "
+ f"--http httptools "
+ f"--limit-concurrency 1000 "
+ f"--backlog 2048 "
+ f"--timeout-keep-alive 75 "
+ f"--log-level warning ",
+ should_capture_stdout=False,
+ should_capture_stderr=False
+ )
return inference_gateway_process
else:
return inference_gateway_pids[0]
From a7280376a69f631a63909844f0dd7157e4cd3c59 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 12 Feb 2025 16:18:10 +0800
Subject: [PATCH 259/282] [perf] Disable uvloop and httptools in model
inference gateway
---
.../scheduler/model_scheduler/master_job_runner.py | 9 +++++----
1 file changed, 5 insertions(+), 4 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index c61e1d4374..4fb585756c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -446,19 +446,20 @@ def start_device_inference_gateway():
python_program = get_python_program()
inference_port = ServerConstants.get_inference_master_gateway_port()
if not ServerConstants.is_running_on_k8s():
- logging.info(f"start the model inference gateway...")
inference_gw_cmd = "fedml.computing.scheduler.model_scheduler.device_model_inference:api"
inference_gateway_pids = RunProcessUtils.get_pid_from_cmd_line(inference_gw_cmd)
if inference_gateway_pids is None or len(inference_gateway_pids) <= 0:
cur_dir = os.path.dirname(__file__)
fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
+ workers = 10
+ logging.info(f"start the model inference gateway workers[{workers}] no uvloop/httptools...")
inference_gateway_process = ServerConstants.exec_console_with_script(
f"{python_program} -m uvicorn {inference_gw_cmd} "
f"--host 0.0.0.0 "
f"--port {str(inference_port)} "
- f"--workers 10 "
- f"--loop uvloop "
- f"--http httptools "
+ f"--workers {workers} "
+ # f"--loop uvloop "
+ # f"--http httptools "
f"--limit-concurrency 1000 "
f"--backlog 2048 "
f"--timeout-keep-alive 75 "
From 3e1ae10b0785f2cb5bd9f054f37064ad53badb0d Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 12 Feb 2025 16:35:40 +0800
Subject: [PATCH 260/282] [perf] Reduce model inference gateway workers from 10
to 2
---
.../computing/scheduler/model_scheduler/master_job_runner.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index 4fb585756c..bfc3b5d354 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -451,7 +451,7 @@ def start_device_inference_gateway():
if inference_gateway_pids is None or len(inference_gateway_pids) <= 0:
cur_dir = os.path.dirname(__file__)
fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- workers = 10
+ workers = 2
logging.info(f"start the model inference gateway workers[{workers}] no uvloop/httptools...")
inference_gateway_process = ServerConstants.exec_console_with_script(
f"{python_program} -m uvicorn {inference_gw_cmd} "
From d6d67e82e80b6c6ed20f96a6e6eff392df37f504 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 12 Feb 2025 16:55:20 +0800
Subject: [PATCH 261/282] [perf] Optimize HTTP inference client and Uvicorn
server configuration
---
.../device_http_inference_protocol.py | 25 ++++++++++++-------
.../model_scheduler/master_job_runner.py | 6 ++---
2 files changed, 19 insertions(+), 12 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
index 7f248ee281..4f4c19aaee 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
@@ -19,7 +19,11 @@ class FedMLHttpInference:
@classmethod
async def get_http_client(cls):
if cls._http_client is None:
- limits = httpx.Limits(max_keepalive_connections=50, max_connections=1000)
+ limits = httpx.Limits(
+ max_keepalive_connections=100,
+ max_connections=1000,
+ keepalive_expiry=60
+ )
cls._http_client = httpx.AsyncClient(limits=limits)
return cls._http_client
@@ -39,8 +43,9 @@ async def is_inference_ready(inference_url, path="ready", timeout=None):
# TODO (Raphael): Support more methods and return codes rules.
try:
- async with httpx.AsyncClient() as client:
- ready_response = await client.get(url=ready_url, timeout=timeout)
+ # async with httpx.AsyncClient() as client:
+ client = await FedMLHttpInference.get_http_client()
+ ready_response = await client.get(url=ready_url, timeout=timeout)
if isinstance(ready_response, (Response, StreamingResponse)):
error_code = ready_response.status_code
@@ -99,12 +104,13 @@ async def run_http_inference_with_curl_request(
async def stream_generator(inference_url, input_json, method="POST"):
- async with httpx.AsyncClient() as client:
- async with client.stream(method, inference_url, json=input_json,
- timeout=ClientConstants.WORKER_STREAM_API_TIMEOUT) as response:
- async for chunk in response.aiter_lines():
- # we consumed a newline, need to put it back
- yield f"{chunk}\n"
+ # async with httpx.AsyncClient() as client:
+ client = await FedMLHttpInference.get_http_client()
+ async with client.stream(method, inference_url, json=input_json,
+ timeout=ClientConstants.WORKER_STREAM_API_TIMEOUT) as response:
+ async for chunk in response.aiter_lines():
+ # we consumed a newline, need to put it back
+ yield f"{chunk}\n"
async def redirect_non_stream_req_to_worker(inference_type, inference_url, model_api_headers, model_inference_json,
@@ -115,6 +121,7 @@ async def redirect_non_stream_req_to_worker(inference_type, inference_url, model
logging.info(f"[Request-{request_id}] Starting HTTP request to {inference_url}")
try:
+ # async with httpx.AsyncClient() as client:
client = await FedMLHttpInference.get_http_client()
response = await client.request(
method=method, url=inference_url, headers=model_api_headers, json=model_inference_json, timeout=timeout
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index bfc3b5d354..aa4abeef81 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -451,7 +451,7 @@ def start_device_inference_gateway():
if inference_gateway_pids is None or len(inference_gateway_pids) <= 0:
cur_dir = os.path.dirname(__file__)
fedml_base_dir = os.path.dirname(os.path.dirname(os.path.dirname(cur_dir)))
- workers = 2
+ workers = 4
logging.info(f"start the model inference gateway workers[{workers}] no uvloop/httptools...")
inference_gateway_process = ServerConstants.exec_console_with_script(
f"{python_program} -m uvicorn {inference_gw_cmd} "
@@ -460,9 +460,9 @@ def start_device_inference_gateway():
f"--workers {workers} "
# f"--loop uvloop "
# f"--http httptools "
- f"--limit-concurrency 1000 "
+ f"--limit-concurrency 1024 "
f"--backlog 2048 "
- f"--timeout-keep-alive 75 "
+ f"--timeout-keep-alive 60 "
f"--log-level warning ",
should_capture_stdout=False,
should_capture_stderr=False
From 3bc06666a51b911fc0f3412f3f141a9047976bc1 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Thu, 13 Feb 2025 12:36:06 +0800
Subject: [PATCH 262/282] [perf] Remove verbose logging in model inference
request handling
---
.../device_http_inference_protocol.py | 18 +++++++++---------
.../model_scheduler/device_model_inference.py | 19 +++++++++----------
2 files changed, 18 insertions(+), 19 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
index 4f4c19aaee..00f18a78e4 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_http_inference_protocol.py
@@ -116,9 +116,9 @@ async def stream_generator(inference_url, input_json, method="POST"):
async def redirect_non_stream_req_to_worker(inference_type, inference_url, model_api_headers, model_inference_json,
timeout=None, method="POST"):
response_ok = True
- request_id = str(uuid.uuid4())[:8]
- start_time = time.time()
- logging.info(f"[Request-{request_id}] Starting HTTP request to {inference_url}")
+ # request_id = str(uuid.uuid4())[:8]
+ # start_time = time.time()
+ # logging.info(f"[Request-{request_id}] Starting HTTP request to {inference_url}")
try:
# async with httpx.AsyncClient() as client:
@@ -126,13 +126,13 @@ async def redirect_non_stream_req_to_worker(inference_type, inference_url, model
response = await client.request(
method=method, url=inference_url, headers=model_api_headers, json=model_inference_json, timeout=timeout
)
- end_time = time.time()
- elapsed_time = end_time - start_time
- logging.info(f"[Request-{request_id}] Completed HTTP request. Time taken: {elapsed_time:.3f} seconds")
+ # end_time = time.time()
+ # elapsed_time = end_time - start_time
+ # logging.info(f"[Request-{request_id}] Completed HTTP request. Time taken: {elapsed_time:.3f} seconds")
except Exception as e:
- end_time = time.time()
- elapsed_time = end_time - start_time
- logging.error(f"[Request-{request_id}] Failed HTTP request after {elapsed_time:.3f} seconds. Error: {str(e)}")
+ # end_time = time.time()
+ # elapsed_time = end_time - start_time
+ # logging.error(f"[Request-{request_id}] Failed HTTP request after {elapsed_time:.3f} seconds. Error: {str(e)}")
response_ok = False
model_inference_result = {"error": e}
return response_ok, model_inference_result
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
index 35d47f8dbd..feabbf321b 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_inference.py
@@ -199,8 +199,6 @@ async def _predict(
# Always increase the pending requests counter on a new incoming request.
FEDML_MODEL_CACHE.update_pending_requests_counter(end_point_id, increase=True)
inference_response = {}
- request_uuid = str(uuid.uuid4()) # Generate unique request ID
-
try:
in_end_point_id = end_point_id
in_end_point_name = input_json.get("end_point_name", None)
@@ -261,11 +259,12 @@ async def _predict(
input_list["stream"] = input_list.get("stream", stream_flag)
output_list = input_json.get("outputs", [])
- # main execution of redirecting the inference request to the idle device
- inference_start_time = time.time()
- start_time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(inference_start_time))
- logging.info(f"[Request {request_uuid}] Starting send_inference_request at {start_time_str}")
+ # request_uuid = str(uuid.uuid4()) # Generate unique request ID
+ # inference_start_time = time.time()
+ # start_time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(inference_start_time))
+ # logging.info(f"[Request {request_uuid}] Starting send_inference_request at {start_time_str}")
+ # main execution of redirecting the inference request to the idle device
inference_response = await send_inference_request(
idle_device,
end_point_id,
@@ -276,10 +275,10 @@ async def _predict(
connectivity_type=connectivity_type,
path=path, request_method=request_method)
- inference_end_time = time.time()
- end_time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(inference_end_time))
- inference_duration = inference_end_time - inference_start_time
- logging.info(f"[Request {request_uuid}] Completed send_inference_request at {end_time_str}, duration: {inference_duration:.3f} seconds")
+ # inference_end_time = time.time()
+ # end_time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(inference_end_time))
+ # inference_duration = inference_end_time - inference_start_time
+ # logging.info(f"[Request {request_uuid}] Completed send_inference_request at {end_time_str}, duration: {inference_duration:.3f} seconds")
# Calculate model metrics
try:
From ad22de4e398cffe7ef22fec428d73e0a77f59f9b Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Thu, 13 Feb 2025 14:56:07 +0800
Subject: [PATCH 263/282] [bugfix-combination] Add model configuration details
for deployment failure handling
---
.../device_model_msg_object.py | 39 ++++++++++---------
.../model_scheduler/master_job_runner.py | 12 +++++-
.../model_scheduler/worker_job_runner.py | 25 ++++++------
.../worker_protocol_manager.py | 2 +-
python/setup.py | 2 +-
5 files changed, 46 insertions(+), 34 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_msg_object.py b/python/fedml/computing/scheduler/model_scheduler/device_model_msg_object.py
index 6ec05f64ed..5d2ac8319b 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_msg_object.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_msg_object.py
@@ -43,31 +43,34 @@ def __init__(self, topic, payload):
request_json = json.loads(payload)
self.msg_topic = topic
self.request_json = request_json
- self.run_id = request_json["end_point_id"]
- self.end_point_name = request_json["end_point_name"]
- self.token = request_json["token"]
- self.user_id = request_json["user_id"]
- self.user_name = request_json["user_name"]
- self.device_ids = request_json["device_ids"]
- self.device_objs = request_json["device_objs"]
+ self.run_id = request_json.get("end_point_id")
+ self.end_point_name = request_json.get("end_point_name", "")
+ self.token = request_json.get("token", "")
+ self.user_id = request_json.get("user_id")
+ self.user_name = request_json.get("user_name", "")
+ self.device_ids = request_json.get("device_ids", [])
+ self.device_objs = request_json.get("device_objs", [])
- self.model_config = request_json["model_config"]
- self.model_name = self.model_config["model_name"]
- self.model_id = self.model_config["model_id"]
- self.model_version = self.model_config["model_version"]
- self.model_storage_url = self.model_config["model_storage_url"]
- self.scale_min = self.model_config.get("instance_scale_min", 0)
- self.scale_max = self.model_config.get("instance_scale_max", 0)
- self.inference_engine = self.model_config.get("inference_engine", 0)
- self.inference_end_point_id = self.run_id
+ # check if model_config is in request_json and is not None
+ self.scale_min = 1
+ self.max_unavailable_rate = 0.1
+ if "model_config" in request_json and request_json["model_config"] is not None:
+ self.model_config = request_json["model_config"]
+ self.model_name = self.model_config["model_name"]
+ self.model_id = self.model_config["model_id"]
+ self.model_version = self.model_config["model_version"]
+ self.model_storage_url = self.model_config["model_storage_url"]
+ self.scale_min = self.model_config.get("instance_scale_min", 0)
+ self.scale_max = self.model_config.get("instance_scale_max", 0)
+ self.inference_engine = self.model_config.get("inference_engine", 0)
+ self.max_unavailable_rate = self.model_config.get("max_unavailable_rate", 0.1)
+ self.inference_end_point_id = self.run_id
self.request_json["run_id"] = self.run_id
self.gpu_topology = self.get_devices_avail_gpus()
self.gpu_per_replica = self.get_gpu_per_replica()
- self.max_unavailable_rate = self.model_config.get("max_unavailable_rate", 0.1)
-
def get_devices_avail_gpus(self):
"""
{
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
index aa4abeef81..d6829719ad 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_job_runner.py
@@ -279,9 +279,17 @@ def process_deployment_result_message(self, topic=None, payload=None):
end_point_id, end_point_name, payload_json["model_name"], "",
ServerConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
message_center=self.message_center)
+
# when report failed to the MLOps, need to delete the replica has successfully deployed and release the gpu
- model_msg_object = FedMLModelMsgObject(topic, payload)
- self.send_deployment_delete_request_to_edges(payload, model_msg_object, message_center=self.message_center)
+ model_config = dict()
+ model_config["model_name"] = payload_json["model_name"]
+ model_config["model_id"] = payload_json["model_id"]
+ model_config["model_version"] = payload_json["model_version"]
+ # add model_config to the payload for the delete request
+ payload_json["model_config"] = model_config
+ payload_for_del_deploy = json.dumps(payload_json)
+ model_msg_object = FedMLModelMsgObject(topic, payload_for_del_deploy)
+ self.send_deployment_delete_request_to_edges(payload_for_del_deploy, model_msg_object, message_center=self.message_center)
return
# Failure handler, send the rollback message to the worker devices only if it has not been rollback
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
index c73630fb65..42c53b549a 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_job_runner.py
@@ -261,8 +261,8 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
# Send failed result back to master
_ = self.send_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
- model_id, model_name, inference_output_url, inference_model_version, inference_port,
+ end_point_name, self.edge_id, device_ids, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
+ model_id, model_name, inference_output_url, model_version, inference_port,
inference_engine, model_metadata, model_config)
self.status_reporter.run_id = self.run_id
@@ -272,7 +272,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
# Send failed successful result back to master
logging.info("Finished deployment, continue to send results to master...")
result_payload = self.send_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
+ end_point_name, self.edge_id, device_ids, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
model_id, model_name, inference_output_url, model_version, inference_port_external,
inference_engine, model_metadata, model_config, replica_no=rank + 1,
connectivity=connectivity
@@ -283,7 +283,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
logging.info("inference_port_external {} != inference_port {}".format(
inference_port_external, inference_port))
result_payload = self.construct_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
+ end_point_name, self.edge_id, device_ids, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
model_id, model_name, inference_output_url, model_version, inference_port,
inference_engine, model_metadata, model_config, replica_no=rank + 1,
connectivity=connectivity
@@ -317,7 +317,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
# Report the deletion msg to master
result_payload = self.send_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DELETED,
+ end_point_name, self.edge_id, device_ids, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DELETED,
model_id, model_name, inference_output_url, model_version, inference_port_external,
inference_engine, model_metadata, model_config, replica_no=rank_to_delete + 1)
@@ -395,8 +395,8 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
run_id, self.edge_id, replica_occupied_gpu_ids)
self.send_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
- model_id, model_name, inference_output_url, inference_model_version, inference_port,
+ end_point_name, self.edge_id, device_ids, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_FAILED,
+ model_id, model_name, inference_output_url, model_version, inference_port,
inference_engine, model_metadata, model_config)
self.status_reporter.run_id = self.run_id
@@ -407,7 +407,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
else:
logging.info("Finished deployment, continue to send results to master...")
result_payload = self.send_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
+ end_point_name, self.edge_id, device_ids, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
model_id, model_name, inference_output_url, model_version, inference_port_external,
inference_engine, model_metadata, model_config, replica_no=rank + 1,
connectivity=connectivity
@@ -417,7 +417,7 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
logging.info("inference_port_external {} != inference_port {}".format(
inference_port_external, inference_port))
result_payload = self.construct_deployment_results(
- end_point_name, self.edge_id, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
+ end_point_name, self.edge_id, device_ids, ClientConstants.MSG_MODELOPS_DEPLOYMENT_STATUS_DEPLOYED,
model_id, model_name, inference_output_url, model_version, inference_port,
inference_engine, model_metadata, model_config, replica_no=rank + 1,
connectivity=connectivity
@@ -441,12 +441,13 @@ def run_impl(self, run_extend_queue_list, sender_message_center,
logging.error(f"Unsupported op {op} with op num {op_num}")
return False
- def construct_deployment_results(self, end_point_name, device_id, model_status,
+ def construct_deployment_results(self, end_point_name, device_id, device_ids, model_status,
model_id, model_name, model_inference_url,
model_version, inference_port, inference_engine,
model_metadata, model_config, replica_no=1,
connectivity=ClientConstants.WORKER_CONNECTIVITY_TYPE_DEFAULT):
deployment_results_payload = {"end_point_id": self.run_id, "end_point_name": end_point_name,
+ "device_ids": device_ids,
"model_id": model_id, "model_name": model_name,
"model_url": model_inference_url, "model_version": model_version,
"port": inference_port,
@@ -460,7 +461,7 @@ def construct_deployment_results(self, end_point_name, device_id, model_status,
}
return deployment_results_payload
- def send_deployment_results(self, end_point_name, device_id, model_status,
+ def send_deployment_results(self, end_point_name, device_id, device_ids, model_status,
model_id, model_name, model_inference_url,
model_version, inference_port, inference_engine,
model_metadata, model_config, replica_no=1,
@@ -469,7 +470,7 @@ def send_deployment_results(self, end_point_name, device_id, model_status,
self.run_id, device_id)
deployment_results_payload = self.construct_deployment_results(
- end_point_name, device_id, model_status,
+ end_point_name, device_id, device_ids, model_status,
model_id, model_name, model_inference_url,
model_version, inference_port, inference_engine,
model_metadata, model_config, replica_no=replica_no, connectivity=connectivity)
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
index b1d0bebc47..7f7b041b77 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
@@ -170,7 +170,7 @@ def callback_start_deployment(self, topic, payload):
ClientConstants.save_run_process(run_id, process.pid)
def callback_delete_deployment(self, topic, payload):
- logging.info("[Worker] callback_delete_deployment")
+ logging.info("[Worker] callback_delete_deployment, topic: {}, payload: {}".format(topic, payload))
# Parse payload as the model message object.
model_msg_object = FedMLModelMsgObject(topic, payload)
diff --git a/python/setup.py b/python/setup.py
index 032bdb4eed..d09bed3c3f 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -126,7 +126,7 @@ def finalize_options(self):
setup(
name="fedml",
- version="0.9.2",
+ version="0.9.6-dev",
author="FedML Team",
author_email="ch@fedml.ai",
description="A research and production integrated edge-cloud library for "
From 2528f4f082a69333ee66582e69492cf0ed0ba335 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Thu, 13 Feb 2025 17:02:30 +0800
Subject: [PATCH 264/282] [bugfix] Update default model configuration
parameters for safer deployment
---
.../scheduler/model_scheduler/device_model_msg_object.py | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_msg_object.py b/python/fedml/computing/scheduler/model_scheduler/device_model_msg_object.py
index 5d2ac8319b..6a21c880b0 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_msg_object.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_msg_object.py
@@ -59,10 +59,10 @@ def __init__(self, topic, payload):
self.model_name = self.model_config["model_name"]
self.model_id = self.model_config["model_id"]
self.model_version = self.model_config["model_version"]
- self.model_storage_url = self.model_config["model_storage_url"]
- self.scale_min = self.model_config.get("instance_scale_min", 0)
- self.scale_max = self.model_config.get("instance_scale_max", 0)
- self.inference_engine = self.model_config.get("inference_engine", 0)
+ self.model_storage_url = self.model_config.get("model_storage_url", "")
+ self.scale_min = self.model_config.get("instance_scale_min", 1)
+ self.scale_max = self.model_config.get("instance_scale_max", 1)
+ self.inference_engine = self.model_config.get("inference_engine")
self.max_unavailable_rate = self.model_config.get("max_unavailable_rate", 0.1)
self.inference_end_point_id = self.run_id
From 60be0f71ad3cec4bdcc690b9cd091ce8db19ab13 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Fri, 14 Feb 2025 17:47:22 +0800
Subject: [PATCH 265/282] [feature] Add endpoint_name parameter to model
deployment method
---
.../computing/scheduler/model_scheduler/device_model_cards.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_cards.py b/python/fedml/computing/scheduler/model_scheduler/device_model_cards.py
index 8697d0a62c..1600b58bd2 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_cards.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_cards.py
@@ -59,7 +59,8 @@ def serve_model_on_premise(self, model_name, endpoint_name, master_device_ids,
if use_remote:
if not self.deploy_model(model_name, device_type, target_devices, "", user_api_key,
- additional_params_dict, use_local_deployment, endpoint_id=endpoint_id):
+ additional_params_dict, use_local_deployment, endpoint_name=endpoint_name,
+ endpoint_id=endpoint_id):
print("Failed to deploy model")
return False
return True
From 7853c9064d8c8a45ceee04a50d8d1b57c7d7f690 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Mon, 17 Feb 2025 16:58:41 +0800
Subject: [PATCH 266/282] [bugfix] Restore full GPU card selection parameters
in NvidiaGPUtil
---
.../computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py | 3 +--
1 file changed, 1 insertion(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
index a6717de8cb..34d0c3be1c 100644
--- a/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
+++ b/python/fedml/computing/scheduler/comm_utils/gpu_utils/nvidia_utils.py
@@ -25,8 +25,7 @@ def get_gpu_cards() -> List[GPUCard]:
@staticmethod
def get_available_gpu_card_ids(order: str, limit: int, max_load: float, max_memory: float) -> List[int]:
- # return GPUtil.getAvailable(order=order, limit=limit, maxLoad=max_load, maxMemory=max_memory)
- return GPUtil.getAvailable(order='random', limit=limit)
+ return GPUtil.getAvailable(order=order, limit=limit, maxLoad=max_load, maxMemory=max_memory)
@staticmethod
def get_docker_gpu_device_mapping(gpu_ids: List[int], num_gpus: int = 0) -> Optional[Dict]:
From 068ed5141d7fff84b3271a40a0fb4672f3bb86ba Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Mon, 17 Feb 2025 18:26:42 +0800
Subject: [PATCH 267/282] [perf] Reduce job metrics reporting sleep interval to
15 seconds and disable MQTT debug logging
---
python/fedml/core/mlops/mlops_job_perfs.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/core/mlops/mlops_job_perfs.py b/python/fedml/core/mlops/mlops_job_perfs.py
index fe3d921558..fe205323ec 100644
--- a/python/fedml/core/mlops/mlops_job_perfs.py
+++ b/python/fedml/core/mlops/mlops_job_perfs.py
@@ -175,7 +175,7 @@ def report_job_stats_entry(self, sys_event):
logging.debug("exception when reporting job pref: {}.".format(traceback.format_exc()))
pass
- time.sleep(10)
+ time.sleep(15)
logging.info("Job metrics process is about to exit.")
mqtt_mgr.loop_stop()
From 8ac783d7e1f552769f2c7b7c5e24cd67d9c23df9 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Tue, 18 Feb 2025 10:40:21 +0800
Subject: [PATCH 268/282] [chore] Bump version to 0.9.6-dev202502181030
---
python/fedml/__init__.py | 2 +-
python/setup.py | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index 677d06b4e5..2ddeac361c 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -37,7 +37,7 @@
_global_training_type = None
_global_comm_backend = None
-__version__ = "0.9.2"
+__version__ = "0.9.6-dev202502181030"
# This is the deployment environment used for different roles (RD/PM/BD/Public Developers). Potential VALUE: local, dev, test, release
diff --git a/python/setup.py b/python/setup.py
index d09bed3c3f..31d2483237 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -126,7 +126,7 @@ def finalize_options(self):
setup(
name="fedml",
- version="0.9.6-dev",
+ version="0.9.6-dev202502181030",
author="FedML Team",
author_email="ch@fedml.ai",
description="A research and production integrated edge-cloud library for "
From b11558b3cd09150d4ae60422afa4f2b1b2732eb9 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Tue, 18 Feb 2025 11:12:43 +0800
Subject: [PATCH 269/282] [feature] Enhance container log retrieval for exited
containers
---
.../device_model_deployment.py | 30 +++++++++++++++----
1 file changed, 24 insertions(+), 6 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
index 647882c84f..7fab64d9b9 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_deployment.py
@@ -342,10 +342,15 @@ def log_deployment_output(end_point_id, model_id, cmd_container_name, cmd_type,
if container_obj is not None:
out_logs, err_logs = None, None
try:
- out_logs = container_obj.logs(stdout=True, stderr=False, stream=False, follow=False,
- since=last_log_time)
- err_logs = container_obj.logs(stdout=False, stderr=True, stream=False, follow=False,
- since=last_log_time)
+ if container_obj.status == "exited":
+ # If the container has exited, we need to get the whole logs from the container
+ out_logs = container_obj.logs(stdout=True, stderr=False, stream=False, follow=False)
+ err_logs = container_obj.logs(stdout=False, stderr=True, stream=False, follow=False)
+ else:
+ out_logs = container_obj.logs(stdout=True, stderr=False, stream=False, follow=False,
+ since=last_log_time)
+ err_logs = container_obj.logs(stdout=False, stderr=True, stream=False, follow=False,
+ since=last_log_time)
except Exception as e:
logging.error(f"Failed to get the logs from the container with exception {e}")
pass
@@ -355,16 +360,29 @@ def log_deployment_output(end_point_id, model_id, cmd_container_name, cmd_type,
if err_logs is not None:
err_logs = sys_utils.decode_our_err_result(err_logs)
if len(err_logs) > 0:
- logging.error(f"{format(err_logs)}")
+ logging.error(f"[-- Container Error Logs Start --]\n{format(err_logs)}\n[-- Container Error Logs End --]")
if out_logs is not None:
out_logs = sys_utils.decode_our_err_result(out_logs)
if len(out_logs) > 0:
- logging.info(f"{format(out_logs)}")
+ logging.info(f"[-- Container Stdout Logs Start --]\n{format(out_logs)}\n[-- Container Stdout Logs End --]")
if container_obj.status == "exited":
logging.info("Container {} has exited, automatically remove it".format(cmd_container_name))
+ # try to get the logs from the filesystem
+ if out_logs is None or err_logs is None:
+ try:
+ logs_path = f"/var/lib/docker/containers/{container_obj.id}/{container_obj.id}-json.log"
+ if os.path.exists(logs_path):
+ with open(logs_path, 'r') as f:
+ raw_logs = f.readlines()
+ out_logs = '\n'.join([line for line in raw_logs if '"stream":"stdout"' in line])
+ err_logs = '\n'.join([line for line in raw_logs if '"stream":"stderr"' in line])
+ logging.error(f"read Container Error Logs from log file: {err_logs}")
+ except Exception as e:
+ logging.warning(f"Failed to read logs from filesystem: {str(e)}")
+
# Save the failed log into ~/.fedml/fedml-model-client/fedml/logs/failed_logs/
# $run_id/$container_name.log
try:
From f376824cca0c3a31ab02f011834b2e3856078ac0 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 19 Feb 2025 11:19:43 +0800
Subject: [PATCH 270/282] [bugfix] Improve lock handling in MLOps logging
utilities
---
python/fedml/core/mlops/mlops_runtime_log.py | 8 +++-
python/fedml/core/mlops/mlops_utils.py | 45 +++++++++++++++++---
2 files changed, 44 insertions(+), 9 deletions(-)
diff --git a/python/fedml/core/mlops/mlops_runtime_log.py b/python/fedml/core/mlops/mlops_runtime_log.py
index 0fc5db3d23..6b8f5fb9d2 100644
--- a/python/fedml/core/mlops/mlops_runtime_log.py
+++ b/python/fedml/core/mlops/mlops_runtime_log.py
@@ -60,8 +60,11 @@ def update_config_and_rotate(self, source, dest):
MLOpsLoggingUtils.release_lock()
def __initialize_config(self):
+ lock_acquired = False
try:
- MLOpsLoggingUtils.acquire_lock()
+ lock_acquired = MLOpsLoggingUtils.acquire_lock(block=True)
+ if not lock_acquired:
+ raise RuntimeError("Failed to acquire lock")
config_data = MLOpsLoggingUtils.load_log_config(run_id=self.run_id, device_id=self.edge_id,
log_config_file=self.log_config_file)
if not config_data:
@@ -72,7 +75,8 @@ def __initialize_config(self):
except Exception as e:
raise ValueError("Error initializing log config: {}".format(e))
finally:
- MLOpsLoggingUtils.release_lock()
+ if lock_acquired:
+ MLOpsLoggingUtils.release_lock()
class MLOpsFormatter(logging.Formatter):
diff --git a/python/fedml/core/mlops/mlops_utils.py b/python/fedml/core/mlops/mlops_utils.py
index 8bde9e4299..f18a88e21f 100644
--- a/python/fedml/core/mlops/mlops_utils.py
+++ b/python/fedml/core/mlops/mlops_utils.py
@@ -1,4 +1,5 @@
import json
+import logging
import multiprocessing
import os
import time
@@ -75,13 +76,37 @@ class MLOpsLoggingUtils:
@staticmethod
def acquire_lock(block=True):
- return MLOpsLoggingUtils._lock.acquire(block)
+ logging.info("acquire_lock start, block: {}".format(block))
+ lock_acquired = MLOpsLoggingUtils._lock.acquire(block)
+ logging.info("acquire_lock end, lock_acquired: {}".format(lock_acquired))
+ return lock_acquired
@staticmethod
def release_lock():
- # Purposefully acquire lock with non-blocking call to make it idempotent
- MLOpsLoggingUtils._lock.acquire(block=False)
- MLOpsLoggingUtils._lock.release()
+ # # Purposefully acquire lock with non-blocking call to make it idempotent
+ # MLOpsLoggingUtils._lock.acquire(block=False)
+ # MLOpsLoggingUtils._lock.release()
+
+ # modify by charlie
+ # release_lock method may have incorrect implementation:
+ # -> The acquire(block=False) in release_lock may incorrectly acquire and release the lock, especially in a multi-threaded environment.
+ # -> If the current thread already holds the lock, acquire(block=False) will fail (return False) in multiprocessing.Lock,
+ # because the lock is not re-entrant, and cross-thread use may lead to undefined behavior.
+ # -> Therefore, the acquire call in release_lock may fail, causing subsequent release() to throw an exception,
+ # or incorrectly release the lock held by other threads.
+
+ # modify by charlie
+ # acquire the lock and release it in old lock implementation
+ # perhaps cause the lock is released in the wrong place
+ # so we need to release the lock directly
+ try:
+ logging.info("release_lock start")
+ MLOpsLoggingUtils._lock.release()
+ logging.info("release_lock end")
+ except ValueError as e:
+ # The lock is not acquired, ignore it
+ logging.warning("release_lock error: {}".format(e))
+ pass
@staticmethod
def build_log_file_path_with_run_params(
@@ -185,9 +210,15 @@ def save_log_config(run_id, device_id, log_config_file, config_data):
@staticmethod
def load_yaml_config(log_config_file):
"""Helper function to load a yaml config file"""
- if MLOpsLoggingUtils._lock.acquire(block=False):
- MLOpsLoggingUtils._lock.release()
- raise ValueError("Able to acquire lock. This means lock was not acquired by the caller")
+
+ # modify by charlie
+ # the lock is acquired in the caller, so the check is not necessary,
+ # it should be removed to avoid potential exceptions and logical conflicts.
+
+ # if MLOpsLoggingUtils._lock.acquire(block=False):
+ # MLOpsLoggingUtils._lock.release()
+ # raise ValueError("Able to acquire lock. This means lock was not acquired by the caller")
+
if not os.path.exists(log_config_file):
MLOpsLoggingUtils.generate_yaml_doc({}, log_config_file)
with open(log_config_file, "r") as stream:
From cd976a58bd29d94c44030d6890b501e781452745 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 19 Feb 2025 12:37:49 +0800
Subject: [PATCH 271/282] [refactor] Simplify lock handling in MLOps logging
utilities
---
python/fedml/core/mlops/mlops_utils.py | 32 ++++++++++++++------------
1 file changed, 17 insertions(+), 15 deletions(-)
diff --git a/python/fedml/core/mlops/mlops_utils.py b/python/fedml/core/mlops/mlops_utils.py
index f18a88e21f..f56e10b52c 100644
--- a/python/fedml/core/mlops/mlops_utils.py
+++ b/python/fedml/core/mlops/mlops_utils.py
@@ -76,16 +76,18 @@ class MLOpsLoggingUtils:
@staticmethod
def acquire_lock(block=True):
- logging.info("acquire_lock start, block: {}".format(block))
- lock_acquired = MLOpsLoggingUtils._lock.acquire(block)
- logging.info("acquire_lock end, lock_acquired: {}".format(lock_acquired))
- return lock_acquired
+ return MLOpsLoggingUtils._lock.acquire(block)
+
+ # logging.info("acquire_lock start, block: {}".format(block))
+ # lock_acquired = MLOpsLoggingUtils._lock.acquire(block)
+ # logging.info("acquire_lock end, lock_acquired: {}".format(lock_acquired))
+ # return lock_acquired
@staticmethod
def release_lock():
- # # Purposefully acquire lock with non-blocking call to make it idempotent
- # MLOpsLoggingUtils._lock.acquire(block=False)
- # MLOpsLoggingUtils._lock.release()
+ # Purposefully acquire lock with non-blocking call to make it idempotent
+ MLOpsLoggingUtils._lock.acquire(block=False)
+ MLOpsLoggingUtils._lock.release()
# modify by charlie
# release_lock method may have incorrect implementation:
@@ -99,14 +101,14 @@ def release_lock():
# acquire the lock and release it in old lock implementation
# perhaps cause the lock is released in the wrong place
# so we need to release the lock directly
- try:
- logging.info("release_lock start")
- MLOpsLoggingUtils._lock.release()
- logging.info("release_lock end")
- except ValueError as e:
- # The lock is not acquired, ignore it
- logging.warning("release_lock error: {}".format(e))
- pass
+ # try:
+ # logging.info("release_lock start")
+ # MLOpsLoggingUtils._lock.release()
+ # logging.info("release_lock end")
+ # except ValueError as e:
+ # # The lock is not acquired, ignore it
+ # logging.warning("release_lock error: {}".format(e))
+ # pass
@staticmethod
def build_log_file_path_with_run_params(
From add749a2cb7f257b3cf20e5e9abc70d569039345 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 19 Feb 2025 15:48:29 +0800
Subject: [PATCH 272/282] [feature] Add robust database operation error
handling decorator
---
.../model_scheduler/device_model_db.py | 69 ++++++++++++++++++-
1 file changed, 68 insertions(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_db.py b/python/fedml/computing/scheduler/model_scheduler/device_model_db.py
index 606d8c010b..5be1b55ae5 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_db.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_db.py
@@ -11,6 +11,8 @@
from fedml.core.common.singleton import Singleton
from sqlalchemy.sql import text
from typing import List, Dict
+import functools
+from sqlalchemy import exc
Base = declarative_base()
@@ -25,7 +27,57 @@ def __init__(self):
self.db_engine = None
if not hasattr(self, "db_base_dir"):
self.db_base_dir = None
-
+
+ @staticmethod
+ def db_operation(func):
+ """decorator: handle the database operation exceptions"""
+ @functools.wraps(func)
+ def wrapper(self, *args, **kwargs):
+ try:
+ # open the database connection
+ self.open_job_db()
+ # execute the function
+ return func(self, *args, **kwargs)
+ except (
+ # session state error
+ exc.InvalidRequestError, # including "prepared state" error
+ exc.StatementError, # SQL statement execution error
+ # connection error
+ exc.DBAPIError, # base class of database API error
+ exc.OperationalError, # database operation error (e.g. connection failure)
+ exc.DisconnectionError, # connection disconnected
+ # transaction error
+ exc.InvalidatePoolError, # connection pool invalid
+ exc.TimeoutError, # connection timeout
+ exc.ResourceClosedError, # resource (e.g. cursor) closed
+ # concurrent error
+ exc.PendingRollbackError, # pending rollback transaction
+ exc.IntegrityError # integrity constraint violation
+ ) as e:
+ logging.error(f"Database error in {func.__name__}, rebuilding session: {e}")
+ # rollback any unfinished transactions
+ if self.db_connection:
+ try:
+ self.db_connection.rollback()
+ except:
+ pass
+ try:
+ self.db_connection.close()
+ except:
+ pass
+ # set the db connection to None, then open again in open_job_db method
+ self.db_connection = None
+ # retry open the database connection
+ self.open_job_db()
+ # retry execute the function
+ return func(self, *args, **kwargs)
+ except Exception as e:
+ # other unexpected errors, record logs and raise
+ logging.error(f"Unexpected error in {func.__name__}: {e}")
+ self.db_connection = None
+ raise
+ return wrapper
+
@staticmethod
def get_instance():
return FedMLModelDatabase()
@@ -134,6 +186,7 @@ def get_deployment_status_with_device_id(self, end_point_id, end_point_name, mod
return None
+ @db_operation
def delete_deployment_status(self, end_point_id, end_point_name, model_name, model_version=None):
self.open_job_db()
if model_version is None:
@@ -149,6 +202,7 @@ def delete_deployment_status(self, end_point_id, end_point_name, model_name, mod
FedMLDeploymentResultInfoModel.model_version == f'{model_version}')).delete()
self.db_connection.commit()
+ @db_operation
def delete_deployment_result(self, end_point_id, end_point_name, model_name, model_version=None):
self.open_job_db()
if model_version is None:
@@ -164,6 +218,7 @@ def delete_deployment_result(self, end_point_id, end_point_name, model_name, mod
FedMLDeploymentResultInfoModel.model_version == f'{model_version}')).delete()
self.db_connection.commit()
+ @db_operation
def delete_deployment_result_with_device_id(self, end_point_id, end_point_name, model_name, device_id):
self.open_job_db()
self.db_connection.query(FedMLDeploymentResultInfoModel).filter(
@@ -173,6 +228,7 @@ def delete_deployment_result_with_device_id(self, end_point_id, end_point_name,
FedMLDeploymentResultInfoModel.device_id == f'{device_id}')).delete()
self.db_connection.commit()
+ @db_operation
def delete_deployment_result_with_device_id_and_rank(self, end_point_id, end_point_name, model_name,
device_id, replica_rank):
replica_no = replica_rank + 1
@@ -185,6 +241,7 @@ def delete_deployment_result_with_device_id_and_rank(self, end_point_id, end_poi
FedMLDeploymentResultInfoModel.replica_no == f'{replica_no}')).delete()
self.db_connection.commit()
+ @db_operation
def delete_deployment_run_info(self, end_point_id):
# db / table -> model-deployment.db / "deployment_run_info"
self.open_job_db()
@@ -343,6 +400,7 @@ def drop_table(self):
except Exception as e:
pass
+ @db_operation
def get_deployment_results_info(self, end_point_id, end_point_name, model_name, model_version):
self.open_job_db()
if model_version is None:
@@ -358,11 +416,13 @@ def get_deployment_results_info(self, end_point_id, end_point_name, model_name,
FedMLDeploymentResultInfoModel.model_version == f'{model_version}')).all()
return result_info
+ @db_operation
def _get_all_deployment_results_info(self):
self.open_job_db()
result_info = self.db_connection.query(FedMLDeploymentResultInfoModel).all()
return result_info
+ @db_operation
def set_deployment_results_info(self, end_point_id, end_point_name,
model_name, model_version, device_id,
deployment_result=None, deployment_status=None, replica_no=None):
@@ -402,12 +462,14 @@ def set_deployment_results_info(self, end_point_id, end_point_name,
self.db_connection.commit()
+ @db_operation
def get_deployment_run_info(self, end_point_id):
self.open_job_db()
run_info = self.db_connection.query(FedMLDeploymentRunInfoModel). \
filter_by(end_point_id=f'{end_point_id}').first()
return run_info
+ @db_operation
def set_deployment_run_info(self, end_point_id, end_point_name,
end_point_status=None, device_info=None,
activated=None, token=None):
@@ -435,6 +497,7 @@ def set_deployment_run_info(self, end_point_id, end_point_name,
self.db_connection.commit()
+ @db_operation
def get_deployment_auth_info(self, end_point_id, end_point_name, model_name):
self.open_job_db()
run_info = self.db_connection.query(FedMLDeploymentAuthInfoModel). \
@@ -443,6 +506,7 @@ def get_deployment_auth_info(self, end_point_id, end_point_name, model_name):
FedMLDeploymentAuthInfoModel.model_name == f'{model_name}')).first()
return run_info
+ @db_operation
def set_deployment_auth_info(self, end_point_id, end_point_name, model_name, token):
self.open_job_db()
auth_info = self.db_connection.query(FedMLDeploymentAuthInfoModel). \
@@ -462,6 +526,7 @@ def set_deployment_auth_info(self, end_point_id, end_point_name, model_name, tok
self.db_connection.commit()
+ @db_operation
def get_latest_end_point_metrics(self, end_point_id, end_point_name, model_name, model_version):
self.open_job_db()
endpoint_metric = self.db_connection.query(FedMLEndPointMetricsModel). \
@@ -473,6 +538,7 @@ def get_latest_end_point_metrics(self, end_point_id, end_point_name, model_name,
return endpoint_metric[-1]
return None
+ @db_operation
def get_end_point_metrics_by_index(self, end_point_id, end_point_name, model_name, model_version, index):
self.open_job_db()
endpoint_metric = self.db_connection.query(FedMLEndPointMetricsModel). \
@@ -483,6 +549,7 @@ def get_end_point_metrics_by_index(self, end_point_id, end_point_name, model_nam
offset(index).limit(1).first()
return endpoint_metric
+ @db_operation
def set_end_point_metrics(self, end_point_id, end_point_name,
model_name, model_version,
total_latency=None, avg_latency=None, current_latency=None,
From 46b8ce7459374dafcfc3095e86bf91eacffb5771 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 19 Feb 2025 16:09:22 +0800
Subject: [PATCH 273/282] [upd] Bump version to 0.9.6-dev202502191600
---
python/fedml/__init__.py | 2 +-
python/setup.py | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index 2ddeac361c..6ceaecc368 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -37,7 +37,7 @@
_global_training_type = None
_global_comm_backend = None
-__version__ = "0.9.6-dev202502181030"
+__version__ = "0.9.6-dev202502191600"
# This is the deployment environment used for different roles (RD/PM/BD/Public Developers). Potential VALUE: local, dev, test, release
diff --git a/python/setup.py b/python/setup.py
index 31d2483237..7cd9a2591c 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -126,7 +126,7 @@ def finalize_options(self):
setup(
name="fedml",
- version="0.9.6-dev202502181030",
+ version="0.9.6-dev202502191600",
author="FedML Team",
author_email="ch@fedml.ai",
description="A research and production integrated edge-cloud library for "
From 38a930a073bad0d72ae948718193544abb3bb66f Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Wed, 19 Feb 2025 19:16:21 +0800
Subject: [PATCH 274/282] [debug] Add logging for endpoint replica information
in job monitor
---
.../fedml/computing/scheduler/comm_utils/job_monitor.py | 9 +++++++--
1 file changed, 7 insertions(+), 2 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index b8237d93ba..966d28e435 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -207,6 +207,7 @@ def monitor_replicas_number():
res_to_mlops = {} # endpoint_id -> num_replica
for endpoint_detail in res_frm_db:
+ logging.info(f"endpoint_detail: {endpoint_detail}")
endpoint_replicas_details = {}
if isinstance(endpoint_detail, str):
endpoint_replicas_details = json.loads(endpoint_detail)
@@ -218,11 +219,13 @@ def monitor_replicas_number():
endpoint_replica_details = {}
if isinstance(endpoint_replicas_details["result"], str):
endpoint_replica_details = json.loads(endpoint_replicas_details["result"])
-
+
+ logging.info(f"endpoint_replica_details: {endpoint_replica_details}")
res_to_mlops[endpoint_replica_details["end_point_id"]] = res_to_mlops.get(
endpoint_replica_details["end_point_id"], 0) + 1
for endpoint_id, num_replica in res_to_mlops.items():
+ logging.info(f"endpoint_id: {endpoint_id}, num_replica: {num_replica}")
num_replica_url_path = "fedmlModelServer/api/v1/endpoint/replica-info"
mlops_prefix = fedml._get_backend_service()
url = f"{mlops_prefix}/{num_replica_url_path}"
@@ -240,13 +243,15 @@ def monitor_replicas_number():
"replicaNumber": int(num_replica),
"timestamp": int(time.time() * 1000)
}
-
+ logging.info(f"req_header: {req_header}")
+ logging.info(f"req_body: {req_body}")
try:
response = requests.post(
url,
headers=req_header,
json=req_body
)
+ logging.info(f"endpoint_id: {endpoint_id}, response: {response}")
if response.status_code != 200:
logging.error(f"Failed to send the replica number request to MLOps platform.")
else:
From 69c0d844c554bd57e26590d646fc97dc956490fd Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Thu, 20 Feb 2025 18:14:41 +0800
Subject: [PATCH 275/282] [debug] Re-enable logging for model monitoring
metrics
---
.../computing/scheduler/model_scheduler/device_model_monitor.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_monitor.py b/python/fedml/computing/scheduler/model_scheduler/device_model_monitor.py
index 472cab84aa..5c49011204 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_monitor.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_monitor.py
@@ -141,7 +141,7 @@ def send_monitoring_metrics(self, index):
"total_request_num": int(total_request_num),
"timestamp": timestamp,
"edgeId": device_id}
- # logging.info("send monitor metrics {}".format(json.dumps(deployment_monitoring_payload)))
+ logging.info("send monitor metrics {}".format(json.dumps(deployment_monitoring_payload)))
self.monitor_mqtt_mgr.send_message_json(deployment_monitoring_topic, json.dumps(deployment_monitoring_payload))
self.monitor_mqtt_mgr.send_message_json(deployment_monitoring_topic_prefix,
From dcfe268f3d111465044a011f461d9c303383015f Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Thu, 20 Feb 2025 19:59:13 +0800
Subject: [PATCH 276/282] [refactor] Improve lock handling and error management
in MLOps logging utilities
---
python/fedml/core/mlops/mlops_runtime_log.py | 59 +++++++-------
.../core/mlops/mlops_runtime_log_daemon.py | 80 +++++++++++++------
python/fedml/core/mlops/mlops_utils.py | 8 +-
3 files changed, 92 insertions(+), 55 deletions(-)
diff --git a/python/fedml/core/mlops/mlops_runtime_log.py b/python/fedml/core/mlops/mlops_runtime_log.py
index 6b8f5fb9d2..4d2d68f50b 100644
--- a/python/fedml/core/mlops/mlops_runtime_log.py
+++ b/python/fedml/core/mlops/mlops_runtime_log.py
@@ -33,38 +33,39 @@ def __init__(self, run_id, edge_id, log_config_file, filepath):
def update_config_and_rotate(self, source, dest):
# source = current log file name
# dest = log file name (dated)
- MLOpsLoggingUtils.acquire_lock()
-
- # Check if the source and destination files exist. If it does, return
- if os.path.exists(source):
- # Copy the contents of the source file to the destination file
- shutil.copy(source, dest)
- # Clear everything in the source file
- with open(source, 'w') as src_file:
- src_file.truncate(0)
- src_file.close()
-
- config_data = MLOpsLoggingUtils.load_log_config(self.run_id, self.edge_id,
- self.log_config_file)
-
- # Update file name of current log file
- config_data[self.rotate_count].file_path = dest
- self.rotate_count += 1
-
- # Store the rotate count, and corresponding log file name in the config file
- rotated_log_file = LogFile(file_path=source)
- config_data[self.rotate_count] = rotated_log_file
- MLOpsLoggingUtils.save_log_config(run_id=self.run_id, device_id=self.edge_id,
- log_config_file=self.log_config_file,
- config_data=config_data)
- MLOpsLoggingUtils.release_lock()
+ lock_acquired = MLOpsLoggingUtils.acquire_lock()
+ try:
+ # Check if the source and destination files exist. If it does, return
+ if os.path.exists(source):
+ # Copy the contents of the source file to the destination file
+ shutil.copy(source, dest)
+ # Clear everything in the source file
+ with open(source, 'w') as src_file:
+ src_file.truncate(0)
+ src_file.close()
+
+ config_data = MLOpsLoggingUtils.load_log_config(self.run_id, self.edge_id,
+ self.log_config_file)
+
+ # Update file name of current log file
+ config_data[self.rotate_count].file_path = dest
+ self.rotate_count += 1
+
+ # Store the rotate count, and corresponding log file name in the config file
+ rotated_log_file = LogFile(file_path=source)
+ config_data[self.rotate_count] = rotated_log_file
+ MLOpsLoggingUtils.save_log_config(run_id=self.run_id, device_id=self.edge_id,
+ log_config_file=self.log_config_file,
+ config_data=config_data)
+ except Exception as e:
+ raise ValueError("Error updating log config: {}".format(e))
+ finally:
+ if lock_acquired:
+ MLOpsLoggingUtils.release_lock()
def __initialize_config(self):
- lock_acquired = False
+ lock_acquired = MLOpsLoggingUtils.acquire_lock(block=True)
try:
- lock_acquired = MLOpsLoggingUtils.acquire_lock(block=True)
- if not lock_acquired:
- raise RuntimeError("Failed to acquire lock")
config_data = MLOpsLoggingUtils.load_log_config(run_id=self.run_id, device_id=self.edge_id,
log_config_file=self.log_config_file)
if not config_data:
diff --git a/python/fedml/core/mlops/mlops_runtime_log_daemon.py b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
index ff06dc91b3..9218519659 100644
--- a/python/fedml/core/mlops/mlops_runtime_log_daemon.py
+++ b/python/fedml/core/mlops/mlops_runtime_log_daemon.py
@@ -120,15 +120,19 @@ def log_upload(self, run_id, device_id):
line_start_req = line_end_req
# Update the uploaded file index
- MLOpsLoggingUtils.acquire_lock()
- config_data = MLOpsLoggingUtils.load_log_config(run_id, device_id,
+ lock_acquired = MLOpsLoggingUtils.acquire_lock()
+ try:
+ config_data = MLOpsLoggingUtils.load_log_config(run_id, device_id,
self.log_config_file)
-
- config_data[self.file_rotate_count].uploaded_file_index = uploaded_file_index
- MLOpsLoggingUtils.save_log_config(run_id=run_id, device_id=device_id,
- log_config_file=self.log_config_file,
- config_data=config_data)
- MLOpsLoggingUtils.release_lock()
+ config_data[self.file_rotate_count].uploaded_file_index = uploaded_file_index
+ MLOpsLoggingUtils.save_log_config(run_id=run_id, device_id=device_id,
+ log_config_file=self.log_config_file,
+ config_data=config_data)
+ except Exception as e:
+ raise ValueError("Error updating log config: {}".format(e))
+ finally:
+ if lock_acquired:
+ MLOpsLoggingUtils.release_lock()
@staticmethod
def __format_log_lines(log_lines: list, line_start_req: int, line_end_req: int):
@@ -296,37 +300,65 @@ def log_process(self, process_event):
print("Log Process exits normally.")
def fetch_file_path_and_index(self) -> (str, int):
+ lock_acquired = False
try:
upload_file_index = None
- MLOpsLoggingUtils.acquire_lock()
- config_data = MLOpsLoggingUtils.load_log_config(run_id=self.run_id, device_id=self.device_id,
- log_config_file=self.log_config_file)
- MLOpsLoggingUtils.release_lock()
+ # Acquire lock for initial config read
+ lock_acquired = MLOpsLoggingUtils.acquire_lock()
+ try:
+ config_data = MLOpsLoggingUtils.load_log_config(
+ run_id=self.run_id,
+ device_id=self.device_id,
+ log_config_file=self.log_config_file
+ )
+ finally:
+ if lock_acquired:
+ MLOpsLoggingUtils.release_lock()
+ lock_acquired = False
+
if config_data is not None:
config_len = len(config_data)
upload_file_config = config_data.get(self.file_rotate_count, None)
if upload_file_config is not None:
- file_path, uploaded_file_index = upload_file_config.file_path, upload_file_config.uploaded_file_index
+ file_path = upload_file_config.file_path
+ uploaded_file_index = upload_file_config.uploaded_file_index
shutil.copyfile(file_path, self.log_file_path)
- if MLOpsRuntimeLogProcessor.is_file_rotated(self.log_file_path, uploaded_file_index, config_len,
- self.file_rotate_count):
- MLOpsLoggingUtils.acquire_lock()
- config_data = MLOpsLoggingUtils.load_log_config(run_id=self.run_id, device_id=self.device_id,
- log_config_file=self.log_config_file)
- config_data[self.file_rotate_count].upload_complete = True
- MLOpsLoggingUtils.save_log_config(run_id=self.run_id, device_id=self.device_id,
- log_config_file=self.log_config_file, config_data=config_data)
- MLOpsLoggingUtils.release_lock()
+
+ if MLOpsRuntimeLogProcessor.is_file_rotated(
+ self.log_file_path, uploaded_file_index, config_len, self.file_rotate_count
+ ):
+ # Acquire new lock for config update
+ lock_acquired = MLOpsLoggingUtils.acquire_lock()
+ try:
+ config_data = MLOpsLoggingUtils.load_log_config(
+ run_id=self.run_id,
+ device_id=self.device_id,
+ log_config_file=self.log_config_file
+ )
+ config_data[self.file_rotate_count].upload_complete = True
+ MLOpsLoggingUtils.save_log_config(
+ run_id=self.run_id,
+ device_id=self.device_id,
+ log_config_file=self.log_config_file,
+ config_data=config_data
+ )
+ finally:
+ if lock_acquired:
+ MLOpsLoggingUtils.release_lock()
+ lock_acquired = False
+
self.file_rotate_count += 1
- # Re-fetch file path and index if file is rotated
+ # Recursive call without holding any locks
return self.fetch_file_path_and_index()
return uploaded_file_index
return upload_file_index
+
except Exception as e:
raise ValueError(f"Failed to open log file. Exception: {e}")
finally:
- MLOpsLoggingUtils.release_lock()
+ if lock_acquired:
+ MLOpsLoggingUtils.release_lock()
@staticmethod
def is_file_rotated(file_path, uploaded_file_index, config_len, rotate_count):
diff --git a/python/fedml/core/mlops/mlops_utils.py b/python/fedml/core/mlops/mlops_utils.py
index f56e10b52c..5fab10dd09 100644
--- a/python/fedml/core/mlops/mlops_utils.py
+++ b/python/fedml/core/mlops/mlops_utils.py
@@ -203,10 +203,14 @@ def save_log_config(run_id, device_id, log_config_file, config_data):
log_config_key = "log_config_{}_{}".format(run_id, device_id)
log_config = MLOpsLoggingUtils.load_yaml_config(log_config_file)
log_config[log_config_key] = MLOpsLoggingUtils.__convert_to_dict(config_data)
+ # Use with statement to ensure file is properly closed
with open(log_config_file, "w") as stream:
- yaml.dump(log_config, stream)
+ # use safe_dump to avoid the problem of the lock
+ yaml.safe_dump(log_config, stream)
except Exception as e:
- MLOpsLoggingUtils.release_lock()
+ # modify by charlie
+ # Don't release lock here - let caller handle it
+ # MLOpsLoggingUtils.release_lock()
raise ValueError("Error saving log config: {}".format(e))
@staticmethod
From a26e1b3b412582c2f95f4d8624675cd55b61b3b9 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Thu, 20 Feb 2025 20:00:30 +0800
Subject: [PATCH 277/282] [upd] Bump version to 0.9.6-dev202502202000
---
python/fedml/__init__.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index 6ceaecc368..108ea8ae0b 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -37,7 +37,7 @@
_global_training_type = None
_global_comm_backend = None
-__version__ = "0.9.6-dev202502191600"
+__version__ = "0.9.6-dev202502202000"
# This is the deployment environment used for different roles (RD/PM/BD/Public Developers). Potential VALUE: local, dev, test, release
From cdb32e982193d3c14172354dcb046cb99205c37d Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Thu, 20 Feb 2025 20:01:51 +0800
Subject: [PATCH 278/282] [upd] Bump version to 0.9.6-dev202502202000-2
---
python/setup.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/setup.py b/python/setup.py
index 7cd9a2591c..59be71c5e0 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -126,7 +126,7 @@ def finalize_options(self):
setup(
name="fedml",
- version="0.9.6-dev202502191600",
+ version="0.9.6-dev202502202000",
author="FedML Team",
author_email="ch@fedml.ai",
description="A research and production integrated edge-cloud library for "
From 4dd13dffb9bd189433eefb78ffb18c9a62c56eef Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Thu, 20 Feb 2025 20:09:19 +0800
Subject: [PATCH 279/282] [debug] Remove verbose logging in job monitor and
model metrics
---
python/fedml/computing/scheduler/comm_utils/job_monitor.py | 2 --
.../computing/scheduler/model_scheduler/device_model_monitor.py | 2 +-
2 files changed, 1 insertion(+), 3 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index 966d28e435..a1d0cc25ec 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -225,7 +225,6 @@ def monitor_replicas_number():
endpoint_replica_details["end_point_id"], 0) + 1
for endpoint_id, num_replica in res_to_mlops.items():
- logging.info(f"endpoint_id: {endpoint_id}, num_replica: {num_replica}")
num_replica_url_path = "fedmlModelServer/api/v1/endpoint/replica-info"
mlops_prefix = fedml._get_backend_service()
url = f"{mlops_prefix}/{num_replica_url_path}"
@@ -243,7 +242,6 @@ def monitor_replicas_number():
"replicaNumber": int(num_replica),
"timestamp": int(time.time() * 1000)
}
- logging.info(f"req_header: {req_header}")
logging.info(f"req_body: {req_body}")
try:
response = requests.post(
diff --git a/python/fedml/computing/scheduler/model_scheduler/device_model_monitor.py b/python/fedml/computing/scheduler/model_scheduler/device_model_monitor.py
index 5c49011204..472cab84aa 100755
--- a/python/fedml/computing/scheduler/model_scheduler/device_model_monitor.py
+++ b/python/fedml/computing/scheduler/model_scheduler/device_model_monitor.py
@@ -141,7 +141,7 @@ def send_monitoring_metrics(self, index):
"total_request_num": int(total_request_num),
"timestamp": timestamp,
"edgeId": device_id}
- logging.info("send monitor metrics {}".format(json.dumps(deployment_monitoring_payload)))
+ # logging.info("send monitor metrics {}".format(json.dumps(deployment_monitoring_payload)))
self.monitor_mqtt_mgr.send_message_json(deployment_monitoring_topic, json.dumps(deployment_monitoring_payload))
self.monitor_mqtt_mgr.send_message_json(deployment_monitoring_topic_prefix,
From 9516a2e28258f6e5b2c5a2268c45f9c780c91d0f Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Fri, 21 Feb 2025 17:51:51 +0800
Subject: [PATCH 280/282] [debug] Reorder logging initialization in deployment
protocol managers
---
.../master_protocol_manager.py | 24 ++++++++++---------
.../worker_protocol_manager.py | 6 +++++
2 files changed, 19 insertions(+), 11 deletions(-)
diff --git a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
index e06c5162eb..d88224b1f2 100755
--- a/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/master_protocol_manager.py
@@ -156,9 +156,7 @@ def callback_start_deployment(self, topic, payload):
# Get deployment params
request_json = json.loads(payload)
- logging.info("=" * 80)
- logging.info("[Master Protocol Manager] Received start deployment request: {}".format(request_json))
- logging.info("=" * 80)
+
run_id = request_json["end_point_id"]
end_point_name = request_json["end_point_name"]
@@ -183,7 +181,19 @@ def callback_start_deployment(self, topic, payload):
inference_end_point_id = run_id
+ # Start log processor for current run
+ self.args.run_id = run_id
+ self.args.edge_id = self.edge_id
+ MLOpsRuntimeLog(args=self.args).init_logs()
+ MLOpsRuntimeLogDaemon.get_instance(self.args).set_log_source(
+ ServerConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT)
+ MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(run_id, self.edge_id)
+
+ # print the log after the log had initialized
logging.info("[Master] received start deployment request for end point {}.".format(run_id))
+ logging.info("=" * 80)
+ logging.info("[Master Protocol Manager] Received start deployment request: {}".format(request_json))
+ logging.info("=" * 80)
# Set redis config
FedMLModelCache.get_instance().set_redis_params(self.redis_addr, self.redis_port, self.redis_password)
@@ -210,14 +220,6 @@ def callback_start_deployment(self, topic, payload):
timeout_s=timeout_s, user_encrypted_api_key=user_encrypted_api_key
)
- # Start log processor for current run
- self.args.run_id = run_id
- self.args.edge_id = self.edge_id
- MLOpsRuntimeLog(args=self.args).init_logs()
- MLOpsRuntimeLogDaemon.get_instance(self.args).set_log_source(
- ServerConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT)
- MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(run_id, self.edge_id)
-
# Add additional parameters to the request_json
run_id = inference_end_point_id
self.args.run_id = run_id
diff --git a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
index 7f7b041b77..0e56e2ce5c 100755
--- a/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
+++ b/python/fedml/computing/scheduler/model_scheduler/worker_protocol_manager.py
@@ -154,6 +154,12 @@ def callback_start_deployment(self, topic, payload):
ClientConstants.FEDML_LOG_SOURCE_TYPE_MODEL_END_POINT)
MLOpsRuntimeLogDaemon.get_instance(self.args).start_log_processor(run_id, self.edge_id)
+ # print the log after the log had initialized
+ logging.info("[Worker] received start deployment request for end point {}.".format(run_id))
+ logging.info("=" * 80)
+ logging.info("[Worker Protocol Manager] Received start deployment request: {}".format(request_json))
+ logging.info("=" * 80)
+
# Start the job runner
request_json["run_id"] = run_id
run_id_str = str(run_id)
From dac284e757f83f57742591c0cb82e172b51ca56f Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Mon, 24 Feb 2025 10:44:16 +0800
Subject: [PATCH 281/282] [debug] Remove verbose logging in job monitor
---
.../fedml/computing/scheduler/comm_utils/job_monitor.py | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/python/fedml/computing/scheduler/comm_utils/job_monitor.py b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
index a1d0cc25ec..15e3230055 100644
--- a/python/fedml/computing/scheduler/comm_utils/job_monitor.py
+++ b/python/fedml/computing/scheduler/comm_utils/job_monitor.py
@@ -207,7 +207,7 @@ def monitor_replicas_number():
res_to_mlops = {} # endpoint_id -> num_replica
for endpoint_detail in res_frm_db:
- logging.info(f"endpoint_detail: {endpoint_detail}")
+ # logging.info(f"endpoint_detail: {endpoint_detail}")
endpoint_replicas_details = {}
if isinstance(endpoint_detail, str):
endpoint_replicas_details = json.loads(endpoint_detail)
@@ -220,7 +220,7 @@ def monitor_replicas_number():
if isinstance(endpoint_replicas_details["result"], str):
endpoint_replica_details = json.loads(endpoint_replicas_details["result"])
- logging.info(f"endpoint_replica_details: {endpoint_replica_details}")
+ # logging.info(f"endpoint_replica_details: {endpoint_replica_details}")
res_to_mlops[endpoint_replica_details["end_point_id"]] = res_to_mlops.get(
endpoint_replica_details["end_point_id"], 0) + 1
@@ -242,14 +242,14 @@ def monitor_replicas_number():
"replicaNumber": int(num_replica),
"timestamp": int(time.time() * 1000)
}
- logging.info(f"req_body: {req_body}")
+ # logging.info(f"req_body: {req_body}")
try:
response = requests.post(
url,
headers=req_header,
json=req_body
)
- logging.info(f"endpoint_id: {endpoint_id}, response: {response}")
+ # logging.info(f"endpoint_id: {endpoint_id}, response: {response}")
if response.status_code != 200:
logging.error(f"Failed to send the replica number request to MLOps platform.")
else:
From d5b81503afbe6e287e417e76d5e84db140d7d788 Mon Sep 17 00:00:00 2001
From: charlieyl <charlie@tensoropera.com>
Date: Tue, 25 Feb 2025 10:38:18 +0800
Subject: [PATCH 282/282] Release version 0.9.6
---
python/fedml/__init__.py | 2 +-
python/setup.py | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/fedml/__init__.py b/python/fedml/__init__.py
index 108ea8ae0b..c9dda8911e 100644
--- a/python/fedml/__init__.py
+++ b/python/fedml/__init__.py
@@ -37,7 +37,7 @@
_global_training_type = None
_global_comm_backend = None
-__version__ = "0.9.6-dev202502202000"
+__version__ = "0.9.6"
# This is the deployment environment used for different roles (RD/PM/BD/Public Developers). Potential VALUE: local, dev, test, release
diff --git a/python/setup.py b/python/setup.py
index 59be71c5e0..8f5fe25219 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -126,7 +126,7 @@ def finalize_options(self):
setup(
name="fedml",
- version="0.9.6-dev202502202000",
+ version="0.9.6",
author="FedML Team",
author_email="ch@fedml.ai",
description="A research and production integrated edge-cloud library for "