Refactor/SK-1144 | Simplify on_train and on_validate (#731)

fedn/network/clients/README.rst

@@ -26,111 +26,95 @@ Step-by-Step Instructions
 
     import argparse
     import json
-    import threading
     import uuid
-    
-    import fedn.network.grpc.fedn_pb2 as fedn
+
     from fedn.network.clients.client_api import ClientAPI, ConnectToApiResult
-    
-    client_api = ClientAPI()
-    
-    
+
+
+    def get_api_url(api_url: str, api_port: int):
+        url = f"{api_url}:{api_port}" if api_port else api_url
+        if not url.endswith("/"):
+            url += "/"
+        return url
+
+    def on_train(in_model):
+        training_metadata = {
+            "num_examples": 1,
+            "batch_size": 1,
+            "epochs": 1,
+            "lr": 1,
+        }
+
+        config = {
+            "round_id": 1,
+        }
+
+        metadata = {
+            "training_metadata": training_metadata,
+            "config": json.dumps(config),
+        }
+
+        # Do your training here, out_model is your result...
+        out_model = in_model
+
+        return out_model, metadata
+
+    def on_validate(in_model):
+
+        # Calculate metrics here...
+        metrics = {
+            "test_accuracy": 0.9,
+            "test_loss": 0.1,
+            "train_accuracy": 0.8,
+            "train_loss": 0.2,
+        }
+        return metrics
+
     def main(api_url: str, api_port: int, token: str = None):
         print(f"API URL: {api_url}")
         print(f"API Token: {token or "-"}")
         print(f"API Port: {api_port or "-"}")
-    
+
+        client_api = ClientAPI(train_callback=on_train, validate_callback=on_validate)
+
+        url = get_api_url(api_url, api_port)
+
         name = input("Enter Client Name: ")
-    
-        url = f"{api_url}:{api_port}" if api_port else api_url
-    
-        if not url.endswith("/"):
-            url += "/"
-    
-        print(f"Client Name: {name}")
-    
+        client_api.set_name(name)
+
         client_id = str(uuid.uuid4())
-    
-        print("Connecting to API...")
-    
-        client_options = {
-            "name": "client_example",
+        client_api.set_client_id(client_id)
+
+        controller_config = {
+            "name": name,
             "client_id": client_id,
             "package": "local",
             "preferred_combiner": "",
         }
-    
-        result, combiner_config = client_api.connect_to_api(url, token, client_options)
-    
+
+        result, combiner_config = client_api.connect_to_api(url, token, controller_config)
+
         if result != ConnectToApiResult.Assigned:
             print("Failed to connect to API, exiting.")
             return
-    
-        print("Connected to API")
-    
+
         result: bool = client_api.init_grpchandler(config=combiner_config, client_name=client_id, token=token)
-    
+
         if not result:
             return
-    
-        threading.Thread(target=client_api.send_heartbeats, kwargs={"client_name": name, "client_id": client_id}, daemon=True).start()
-    
-        def on_train(request):
-            print("Received train request")
-            model_id: str = request.model_id
-    
-            model = client_api.get_model_from_combiner(id=str(model_id), client_name=name)
-    
-            # Do your training here, out_model is your result...
-            out_model = model
-            updated_model_id = uuid.uuid4()
-            client_api.send_model_to_combiner(out_model, str(updated_model_id))
-    
-            training_metadata = {
-                "num_examples": 1,
-                "batch_size": 1,
-                "epochs": 1,
-                "lr": 1,
-            }
-    
-            config = {
-                "round_id": 1,
-            }
-    
-            client_api.send_model_update(
-                sender_name=name,
-                sender_role=fedn.WORKER,
-                client_id=client_id,
-                model_id=model_id,
-                model_update_id=str(updated_model_id),
-                receiver_name=request.sender.name,
-                receiver_role=request.sender.role,
-                meta={
-                    "training_metadata": training_metadata,
-                    "config": json.dumps(config),
-                },
-            )
-    
-        client_api.subscribe("train", on_train)
-    
-        threading.Thread(target=client_api.listen_to_task_stream, kwargs={"client_name": name, "client_id": client_id}, daemon=True).start()
-    
-        stop_event = threading.Event()
-        try:
-            stop_event.wait()
-        except KeyboardInterrupt:
-            print("Client stopped by user.")
-    
-    
+
+        client_api.run()
+
     if __name__ == "__main__":
         parser = argparse.ArgumentParser(description="Client Example")
         parser.add_argument("--api-url", type=str, required=True, help="The API URL")
         parser.add_argument("--api-port", type=int, required=False, help="The API Port")
         parser.add_argument("--token", type=str, required=False, help="The API Token")
-    
+
         args = parser.parse_args()
         main(args.api_url, args.api_port, args.token)
 
+
 4. **Run the client**: Run the client by executing the following command:
 
 .. code-block:: bash