run local & federated mlp training with job api

examples/advanced/bionemo/task_fitting/split_data.py

@@ -159,6 +159,6 @@ def split(proteins, num_sites, split_dir=".", alpha=1.0, seed=0, concat=False):
             )
 
         print(
-            f"Saved {len(df_split_train_proteins)} training and {len(test_proteins)} testing proteins for {client_name}, "
+            f"Saved {len(df_split_train_proteins)} training and {len(df_test_proteins)} testing proteins for {client_name}, "
             f"({len(set(df_split_train_proteins['labels']))}/{len(set(df_test_proteins['labels']))}) unique train/test classes."
         )

examples/advanced/bionemo/task_fitting/src/bionemo_mlp_job.py

@@ -0,0 +1,111 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import List, Optional
+from torch import nn as nn
+
+from bionemo_mlp_model_persistor import BioNeMoMLPModelPersistor
+
+from nvflare.app_opt.pt.file_model_locator import PTFileModelLocator
+from nvflare.app_common.abstract.model_locator import ModelLocator
+from nvflare.app_common.abstract.model_persistor import ModelPersistor
+from nvflare.app_common.tracking.tracker_types import ANALYTIC_EVENT_TYPE
+from nvflare.app_common.widgets.convert_to_fed_event import ConvertToFedEvent
+from nvflare.app_common.widgets.intime_model_selector import IntimeModelSelector
+from nvflare.app_common.widgets.streaming import AnalyticsReceiver
+from nvflare.app_common.widgets.validation_json_generator import ValidationJsonGenerator
+from nvflare.app_opt.pt.job_config.model import PTModel
+from nvflare.app_opt.tracking.tb.tb_receiver import TBAnalyticsReceiver
+from nvflare.job_config.api import FedJob, validate_object_for_job
+
+
+class BioNeMoMLPJob(FedJob):
+    def __init__(
+        self,
+        initial_model: nn.Module = None,
+        name: str = "fed_job",
+        min_clients: int = 1,
+        mandatory_clients: Optional[List[str]] = None,
+        key_metric: str = "accuracy",
+        validation_json_generator: Optional[ValidationJsonGenerator] = None,
+        intime_model_selector: Optional[IntimeModelSelector] = None,
+        convert_to_fed_event: Optional[ConvertToFedEvent] = None,
+        analytics_receiver: Optional[AnalyticsReceiver] = None,
+    ):
+        """PyTorch BaseFedJob.
+
+        Configures ValidationJsonGenerator, IntimeModelSelector, AnalyticsReceiver, ConvertToFedEvent.
+
+        User must add controllers and executors.
+
+        Args:
+            initial_model (nn.Module): initial PyTorch Model. Defaults to None.
+            name (name, optional): name of the job. Defaults to "fed_job".
+            min_clients (int, optional): the minimum number of clients for the job. Defaults to 1.
+            mandatory_clients (List[str], optional): mandatory clients to run the job. Default None.
+            key_metric (str, optional): Metric used to determine if the model is globally best.
+                if metrics are a `dict`, `key_metric` can select the metric used for global model selection.
+                Defaults to "accuracy".
+            validation_json_generator (ValidationJsonGenerator, optional): A component for generating validation results.
+                if not provided, a ValidationJsonGenerator will be configured.
+            intime_model_selector: (IntimeModelSelector, optional): A component for select the model.
+                if not provided, an IntimeModelSelector will be configured.
+            convert_to_fed_event: (ConvertToFedEvent, optional): A component to covert certain events to fed events.
+                if not provided, a ConvertToFedEvent object will be created.
+            analytics_receiver (AnlyticsReceiver, optional): Receive analytics.
+                If not provided, a TBAnalyticsReceiver will be configured.
+        """
+        super().__init__(
+            name=name,
+            min_clients=min_clients,
+            mandatory_clients=mandatory_clients,
+        )
+
+        self.initial_model = initial_model
+        self.comp_ids = {}
+
+        if validation_json_generator:
+            validate_object_for_job("validation_json_generator", validation_json_generator, ValidationJsonGenerator)
+        else:
+            validation_json_generator = ValidationJsonGenerator()
+        self.to_server(id="json_generator", obj=validation_json_generator)
+
+        if intime_model_selector:
+            validate_object_for_job("intime_model_selector", intime_model_selector, IntimeModelSelector)
+            self.to_server(id="model_selector", obj=intime_model_selector)
+        elif key_metric:
+            self.to_server(id="model_selector", obj=IntimeModelSelector(key_metric=key_metric))
+
+        if convert_to_fed_event:
+            validate_object_for_job("convert_to_fed_event", convert_to_fed_event, ConvertToFedEvent)
+        else:
+            convert_to_fed_event = ConvertToFedEvent(events_to_convert=[ANALYTIC_EVENT_TYPE])
+        self.convert_to_fed_event = convert_to_fed_event
+
+        if analytics_receiver:
+            validate_object_for_job("analytics_receiver", analytics_receiver, AnalyticsReceiver)
+        else:
+            analytics_receiver = TBAnalyticsReceiver()
+
+        self.to_server(
+            id="receiver",
+            obj=analytics_receiver,
+        )
+
+        self.to_server(id="persistor", obj=BioNeMoMLPModelPersistor())
+
+        self.to_server(id="locator", obj=PTFileModelLocator(pt_persistor_id="persistor"))
+
+    def set_up_client(self, target: str):
+        self.to(id="event_to_fed", obj=self.convert_to_fed_event, target=target)