update cifar10 and gnn examples (#2340)

examples/advanced/cifar10/cifar10-sim/figs/plot_tensorboard_events.py

@@ -30,7 +30,7 @@
 
 # 4.1 Central vs. FedAvg
 experiments = {
-    "cifar10_central": {"tag": "val_acc_local_model"},
+    "cifar10_central": {"tag": "val_acc_local_model", "alpha": 0.0},
     "cifar10_fedavg": {"tag": "val_acc_global_model", "alpha": 1.0},
 }
 
@@ -95,6 +95,8 @@ def main():
         alpha = exp.get("alpha", None)
         if alpha:
             config_name = config_name + f"*alpha{alpha}"
+        else:
+            raise ValueError(f"Expected an alpha value to be provided but got alpha={alpha}")
         eventfile = glob.glob(
             os.path.join(client_results_root, config_name, "**", "app_site-1", "events.*"), recursive=True
         )
@@ -116,7 +118,8 @@ def main():
                 try:
                     xsite_data[k].append(xsite_results["site-1"][k]["val_accuracy"])
                 except Exception as e:
-                    raise ValueError(f"No val_accuracy for {k} in {xsite_file}!")
+                    xsite_data[k].append(None)
+                    print(f"Warning: No val_accuracy for {k} in {xsite_file}!")
 
     print("Training TB data:")
     print(pd.DataFrame(data))

examples/advanced/cifar10/cifar10-sim/run_simulator.sh

@@ -8,12 +8,7 @@ n_clients=$4
 
 # specify output workdir
 RESULT_ROOT=/tmp/nvflare/sim_cifar10
-if [ 1 -eq "$(echo "${alpha} > 0" | bc)" ]
-then
-  out_workspace=${RESULT_ROOT}/${job}_alpha${alpha}
-else
-  out_workspace=${RESULT_ROOT}/${job}
-fi
+out_workspace=${RESULT_ROOT}/${job}_alpha${alpha}
 
 # run FL simulator
 ./set_alpha.sh "${job}" "${alpha}"

examples/advanced/gnn/README.md

@@ -31,7 +31,7 @@ python3 -m pip install -r requirements.txt
 ```
 To support functions of PyTorch Geometric necessary for this example, we need extra dependencies. Please refer to [installation guide](https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html) and install accordingly:
 ```
-pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.1.0+cpu.html
+python3 -m pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.1.0+cpu.html
 ```
 
 #### Job Template
@@ -46,8 +46,8 @@ nvflare job list_templates
 We can see the "sag_gnn" template is available
 
 #### Protein Classification
-The PPI dataset is directly available via torch_geometric library, we randomly split the dataset to 2 subsets, one for each client.
-First, we run the local training on each client, as well as the whole dataset.
+The PPI dataset is directly available via torch_geometric library, we randomly split the dataset to 2 subsets, one for each client (`--client_id 1` and `--client_id 2`).
+First, we run the local training on each client, as well as the whole dataset with `--client_id 0`.
 ```
 python3 code/graphsage_protein_local.py --client_id 0
 python3 code/graphsage_protein_local.py --client_id 1
@@ -64,7 +64,7 @@ For client configs, we set client_ids for each client, and the number of local e
 
 For server configs, we set the number of rounds for federated training, the key metric for model selection, and the model class path with model hyperparameters.
 
-With the produced job, we run the federated training on both clients via FedAvg using NVFlare Simulator.
+With the produced job, we run the federated training on both clients via FedAvg using the NVFlare Simulator.
 ```
 nvflare simulator -w /tmp/nvflare/gnn/protein_fl_workspace -n 2 -t 2 /tmp/nvflare/jobs/gnn_protein
 ```
@@ -74,7 +74,7 @@ We first download the Elliptic++ dataset to `/tmp/nvflare/datasets/elliptic_pp`
 - `txs_classes.csv`: transaction id and its class (licit or illicit)
 - `txs_edgelist.csv`: connections for transaction ids 
 - `txs_features.csv`: transaction id and its features
-Then, we run the local training on each client, as well as the whole dataset.
+Then, we run the local training on each client, as well as the whole dataset. Again, `--client_id 0` uses all data.
 ```
 python3 code/graphsage_finance_local.py --client_id 0
 python3 code/graphsage_finance_local.py --client_id 1
@@ -87,7 +87,7 @@ nvflare job create -force -j "/tmp/nvflare/jobs/gnn_finance" -w "sag_gnn" -sd "c
   -f app_2/config_fed_client.conf app_script="graphsage_finance_fl.py" app_config="--client_id 2 --epochs 10" \
   -f app_server/config_fed_server.conf num_rounds=7 key_metric="validation_auc" model_class_path="pyg_sage.SAGE" components[0].args.model.args.in_channels=165  components[0].args.model.args.hidden_channels=256 components[0].args.model.args.num_layers=3 components[0].args.model.args.num_classes=2  
 ```
-And with the produced job, we run the federated training on both clients via FedAvg using NVFlare Simulator.
+And with the produced job, we run the federated training on both clients via FedAvg using the NVFlare Simulator.
 ```
 nvflare simulator -w /tmp/nvflare/gnn/finance_fl_workspace -n 2 -t 2 /tmp/nvflare/jobs/gnn_finance
 ```

nvflare/app_common/workflows/base_fedavg.py

@@ -17,6 +17,7 @@
 
 from nvflare.apis.fl_constant import FLMetaKey
 from nvflare.app_common.abstract.fl_model import FLModel
+from nvflare.app_common.abstract.model import make_model_learnable
 from nvflare.app_common.aggregators.weighted_aggregation_helper import WeightedAggregationHelper
 from nvflare.app_common.app_constant import AppConstants
 from nvflare.app_common.app_event_type import AppEventType
@@ -142,5 +143,8 @@ def update_model(self, aggr_result):
 
         self.model = FLModelUtils.update_model(self.model, aggr_result)
 
-        self.fl_ctx.set_prop(AppConstants.GLOBAL_MODEL, self.model, private=True, sticky=True)
+        # persistor uses Learnable format to save model
+        ml = make_model_learnable(weights=self.model.params, meta_props=self.model.meta)
+        self.fl_ctx.set_prop(AppConstants.GLOBAL_MODEL, ml, private=True, sticky=True)
+
         self.event(AppEventType.AFTER_SHAREABLE_TO_LEARNABLE)

nvflare/app_common/workflows/model_controller.py

@@ -138,7 +138,9 @@ def start_controller(self, fl_ctx: FLContext) -> None:
         else:
             self.model = FLModel(params_type=ParamsType.FULL, params={})
 
-        self.fl_ctx.set_prop(AppConstants.GLOBAL_MODEL, self.model, private=True, sticky=True)
+        # persistor uses Learnable format to save model
+        ml = make_model_learnable(weights=self.model.params, meta_props=self.model.meta)
+        self.fl_ctx.set_prop(AppConstants.GLOBAL_MODEL, ml, private=True, sticky=True)
         self.event(AppEventType.INITIAL_MODEL_LOADED)
 
         self.engine = self.fl_ctx.get_engine()
@@ -231,7 +233,11 @@ def _process_result(self, client_task: ClientTask, fl_ctx: FLContext) -> None:
         result = client_task.result
         client_name = client_task.client.name
 
+        self.fl_ctx.set_prop(AppConstants.CURRENT_ROUND, self._current_round, private=True, sticky=True)
+
+        self.event(AppEventType.BEFORE_CONTRIBUTION_ACCEPT)
         self._accept_train_result(client_name=client_name, result=result, fl_ctx=fl_ctx)
+        self.event(AppEventType.AFTER_CONTRIBUTION_ACCEPT)
 
         # Turn result into FLModel
         result_model = FLModelUtils.from_shareable(result)
@@ -270,7 +276,6 @@ def _accept_train_result(self, client_name: str, result: Shareable, fl_ctx: FLCo
                 )
                 return
 
-        self.fl_ctx.set_prop(AppConstants.CURRENT_ROUND, self._current_round, private=True, sticky=True)
         self.fl_ctx.set_prop(AppConstants.TRAINING_RESULT, result, private=True, sticky=False)
 
     @abstractmethod
@@ -307,6 +312,7 @@ def save_model(self):
             ) or self._current_round == self._num_rounds - 1:
                 self.info("Start persist model on server.")
                 self.event(AppEventType.BEFORE_LEARNABLE_PERSIST)
+                # persistor uses Learnable format to save model
                 ml = make_model_learnable(weights=self.model.params, meta_props=self.model.meta)
                 self.persistor.save(ml, self.fl_ctx)
                 self.event(AppEventType.AFTER_LEARNABLE_PERSIST)