switch to 2.4.1.rc1

integration/monai/examples/mednist/code/monai_mednist_train.py

@@ -109,15 +109,15 @@ def main():
         print(f"current_round={input_model.current_round}")
 
         # (4) loads model from NVFlare and sends it to GPU
-        trainer.network.load_state_dict(input_model.params)
+        trainer.network.load_state_dict(input_model.params, strict=False)  # TODO: enable strict
         trainer.network.to(DEVICE)
 
         trainer.run()
 
         # (5) wraps evaluation logic into a method to re-use for
         #       evaluation on both trained and received model
         def evaluate(input_weights):
-            model.load_state_dict(input_weights)
+            model.load_state_dict(input_weights, strict=False)  # TODO: enable strict
 
             # Check the prediction on the test dataset
             dataset_dir = Path(root_dir, "MedNIST")

integration/monai/examples/mednist/job_templates/fedavg_mednist/config_fed_client.conf

@@ -3,11 +3,10 @@
   format_version = 2
 
   # This is the application script which will be invoked. Client can replace this script with user's own training script.
-  app_script = "monai_mednist_train.py"
+  app_script = "cifar10.py"
 
   # Additional arguments needed by the training code. For example, in lightning, these can be --trainer.batch_size=xxx.
-    # Additional arguments needed by the training code. For example, in lightning, these can be --trainer.batch_size=xxx.
-  app_config = ""
+  app_config = "monai_mednist_train.py"
 
   # Client Computing Executors.
   executors = [
@@ -18,33 +17,32 @@
       # This particular executor
       executor {
 
-        path = "nvflare.app_opt.pt.in_process_client_api_executor.PTInProcessClientAPIExecutor"
+        # This is an executor for Client API. The underline data exchange is using Pipe.
+        path = "nvflare.app_opt.pt.client_api_launcher_executor.PTClientAPILauncherExecutor"
+
         args {
-              task_script_path = "{app_script}"
-              task_script_args = "{app_config}"
-
-              # if the transfer_type is FULL, then it will be sent directly
-              # if the transfer_type is DIFF, then we will calculate the
-              # difference VS received parameters and send the difference
-              params_transfer_type = "DIFF"
-
-              # if train_with_evaluation is true, the executor will expect
-              # the custom code need to send back both the trained parameters and the evaluation metric
-              # otherwise only trained parameters are expected
-              train_with_evaluation = true
-
-              # time interval in seconds. Time interval to wait before check if the local task has submitted the result
-              # if the local task takes long time, you can increase this interval to larger number
-              # uncomment to overwrite the default, default is 0.5 seconds
-              result_pull_interval = 0.5
-
-              # time interval in seconds. Time interval to wait before check if the trainig code has log metric (such as
-              # Tensorboard log, MLFlow log or Weights & Biases logs. The result will be streanmed to the server side
-              # then to the corresponding tracking system
-              # if the log is not needed, you can set this to a larger number
-              # uncomment to overwrite the default, default is None, which disable the log streaming feature.
-              log_pull_interval = 0.1
+          # launcher_id is used to locate the Launcher object in "components"
+          launcher_id = "launcher"
+
+          # pipe_id is used to locate the Pipe object in "components"
+          pipe_id = "pipe"
+
+          # Timeout in seconds for waiting for a heartbeat from the training script. Defaults to 30 seconds.
+          # Please refer to the class docstring for all available arguments
+          heartbeat_timeout = 60
+
+          # format of the exchange parameters
+          params_exchange_format =  "pytorch"
+
+          # if the transfer_type is FULL, then it will be sent directly
+          # if the transfer_type is DIFF, then we will calculate the
+          # difference VS received parameters and send the difference
+          params_transfer_type = "DIFF"
 
+          # if train_with_evaluation is true, the executor will expect
+          # the custom code need to send back both the trained parameters and the evaluation metric
+          # otherwise only trained parameters are expected
+          train_with_evaluation = true
         }
       }
     }
@@ -56,12 +54,63 @@
   # this defined an array of task result filters. If provided, it will control the result from client executor to server controller
   task_result_filters = []
 
-  # define this component that will help relay local metrics log to FL server.
   components =  [
-      {
-        "id": "event_to_fed",
-        "name": "ConvertToFedEvent",
-        "args": {"events_to_convert": ["analytix_log_stats"], "fed_event_prefix": "fed."}
+    {
+      # component id is "launcher"
+      id = "launcher"
+
+      # the class path of this component
+      path = "nvflare.app_common.launchers.subprocess_launcher.SubprocessLauncher"
+
+      args {
+        # the launcher will invoke the script
+        script = "python3 custom/{app_script}  {app_config} "
+        # if launch_once is true, the SubprocessLauncher will launch once for the whole job
+        # if launch_once is false, the SubprocessLauncher will launch a process for each task it receives from server
+        launch_once = true
       }
-   ]
-}
+    }
+    {
+      id = "pipe"
+      path = "nvflare.fuel.utils.pipe.cell_pipe.CellPipe"
+      args {
+        mode = "PASSIVE"
+        site_name = "{SITE_NAME}"
+        token = "{JOB_ID}"
+        root_url = "{ROOT_URL}"
+        secure_mode = "{SECURE_MODE}"
+        workspace_dir = "{WORKSPACE}"
+      }
+    }
+    {
+      id = "metrics_pipe"
+      path = "nvflare.fuel.utils.pipe.cell_pipe.CellPipe"
+      args {
+        mode = "PASSIVE"
+        site_name = "{SITE_NAME}"
+        token = "{JOB_ID}"
+        root_url = "{ROOT_URL}"
+        secure_mode = "{SECURE_MODE}"
+        workspace_dir = "{WORKSPACE}"
+      }
+    },
+    {
+      id = "metric_relay"
+      path = "nvflare.app_common.widgets.metric_relay.MetricRelay"
+      args {
+        pipe_id = "metrics_pipe"
+        event_type = "fed.analytix_log_stats"
+        # how fast should it read from the peer
+        read_interval = 0.1
+      }
+    },
+    {
+      # we use this component so the client api `flare.init()` can get required information
+      id = "config_preparer"
+      path = "nvflare.app_common.widgets.external_configurator.ExternalConfigurator"
+      args {
+        component_ids = ["metric_relay"]
+      }
+    }
+  ]
+}