Remove old references to seeds, clean up objects

configs/distributed_tasks/example_arrival_config.json

@@ -1,13 +1,4 @@
 {
-  "experimentConfiguration": {
-    "randomSeed": [
-      1,
-      41,
-      42,
-      43,
-      430
-    ]
-  },
   "trainTasks": [
     {
       "type": "distributed",

fltk/launch.py

@@ -98,7 +98,7 @@ def exec_orchestrator(args: Namespace = None, conf: DistributedConfig = None):
     logging.info("Starting Orchestrator, initializing resources....")
     if args.local:
         logging.info("Loading local configuration file")
-        config.load_kube_config()
+        # config.load_kube_config()
     else:
         logging.info("Loading in cluster configuration file")
         config.load_incluster_config()
@@ -333,6 +333,10 @@ def launch_cluster(arg_path: Path, conf_path: Path, rank: Rank, nic: Optional[NI
     logging.info(f"Starting in cluster mode{' (locally)' if args.local else ''}.")
     logging.basicConfig(level=logging.DEBUG,
                         datefmt='%m-%d %H:%M')
+
+    # TODO: Set arrival seeds correctly, and launch experiments.
+    # TODO: Make sure to wait for experiments to complete.
+
     # Set the seed for arrivals, torch seed is mostly ignored. Set the `arrival_seed` to a different value
     # for each repetition that you want to run an experiment with.
     init_reproducibility(conf.execution_config)

fltk/util/task/config/parameter.py

@@ -280,7 +280,6 @@ class ExperimentDescription:
     configurations for repetitions. See also JobDescription to define types of jobs, and
     ExperimentConfiguration to set the configuration of experiments.
     """
-    experiment_configuration: ExperimentConfiguration
     train_tasks: List[JobDescription]
 
 

fltk/util/task/generator/arrival_generator.py

@@ -246,6 +246,7 @@ def run(self, duration: float):
         description: JobDescription
         for job_name, description in self.job_dict.items():
             # TODO: Ensure seeds are set properly
+            raise NotImplementedError("Run is to be re-implemented for BatchedArrivals in an upcomming release")
             for repl, seed in enumerate(description.job_class_parameters.experiment_configuration.random_seed):
                 replication_name = f"{job_name}_{repl}_{seed}"
                 train_task = TrainTask(identity=replication_name,

fltk/util/task/task.py

@@ -233,7 +233,6 @@ class FederatedArrivalTask(ArrivalTask):
 
     @staticmethod
     def build(arrival: Arrival, u_id: uuid.UUID, replication: int) -> T:
-        # TODO: Check type-maps
         task = FederatedArrivalTask(
                 id=u_id,
                 network=arrival.get_network(),