Fixed Workflow_Interface_Mnist_Implementation_2.py

openfl-tutorials/experimental/workflow/Global_DP/Workflow_Interface_Mnist_Implementation_1.py

@@ -38,13 +38,15 @@
 
 # Fixing the seed for result repeatation: remove below to stop repeatable runs
 # ----------------------------------
+# Fixing the seed for result reproducibility
 random_seed = 5495300300540669060
-g_device = torch.Generator(device="cuda")
-# Uncomment the line below to use g_cpu if not using cuda
-# g_device = torch.Generator() # noqa: E800
-# NOTE: remove below to stop repeatable runs
+# Determine the device to use (CUDA if available, otherwise CPU)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# Create a generator for the specified device
+g_device = torch.Generator(device=device)
+# Set the seed for the generator
 g_device.manual_seed(random_seed)
-print(f"\n\nWe are using seed: {random_seed}")
+print(f"\n\nWe are using seed: {random_seed} on device: {device}")
 # ----------------------------------
 
 # Loading torchvision MNIST datasets
@@ -152,6 +154,11 @@ def FedAvg(models, previous_global_model=None, dp_params=None):  # NOQA: N802
             state_dict[key] = np.sum(
                 np.array([state[key] for state in state_dicts], dtype=object), axis=0
             ) / len(models)
+            # Convert numpy arrays to torch tensors
+            if isinstance(state_dict[key], np.ndarray):
+                if state_dict[key].dtype == np.object_:
+                    state_dict[key] = state_dict[key].astype(np.float32)
+                state_dict[key] = torch.tensor(state_dict[key])
     new_model.load_state_dict(state_dict)
     return new_model
 
@@ -619,7 +626,7 @@ def end(self):
 
     # Setup participants
     # Set `num_gpus=0.09` to `num_gpus=0.0` in order to run this tutorial on CPU
-    aggregator = Aggregator(num_gpus=0.09)
+    aggregator = Aggregator(num_gpus=0.0)
 
     # Collaborator names
     collaborator_names = [
@@ -662,7 +669,7 @@ def callable_to_initialize_collaborator_private_attributes(
                 private_attributes_callable=callable_to_initialize_collaborator_private_attributes,
                 # Set `num_gpus=0.09` to `num_gpus=0.0` in order to run this tutorial on CPU
                 num_cpus=0.0,
-                num_gpus=0.09,  # Assuming GPU(s) is available in the machine
+                num_gpus=0.0,  # Assuming GPU(s) is available in the machine
                 index=idx,
                 n_collaborators=len(collaborator_names),
                 batch_size=batch_size_train,

openfl-tutorials/experimental/workflow/Global_DP/Workflow_Interface_Mnist_Implementation_2.py

@@ -35,12 +35,18 @@
 
 random_seed = 5495300300540669060
 
-g_device = torch.Generator(device="cuda")
-# Uncomment the line below to use g_cpu if not using cuda
-# g_device = torch.Generator() # noqa: E800
-# NOTE: remove below to stop repeatable runs
+# Fixing the seed for result repeatation: remove below to stop repeatable runs
+# ----------------------------------
+# Fixing the seed for result reproducibility
+random_seed = 5495300300540669060
+# Determine the device to use (CUDA if available, otherwise CPU)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# Create a generator for the specified device
+g_device = torch.Generator(device=device)
+# Set the seed for the generator
 g_device.manual_seed(random_seed)
-print(f"\n\nWe are using seed: {random_seed}")
+print(f"\n\nWe are using seed: {random_seed} on device: {device}")
+# ----------------------------------
 
 mnist_train = torchvision.datasets.MNIST(
     "files/",
@@ -134,6 +140,33 @@ def populate_model_params_and_gradients(
                 # use only the first state
                 params.grad = states_for_gradients[0][name]
 
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        # Remove the optimizer and privacy engine from the state
+        state['global_optimizer'] = None
+        state['privacy_engine'] = None
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        # Recreate the optimizer and privacy engine
+        self.global_optimizer = torch.optim.SGD(
+            params=self.global_model.parameters(), lr=1.0
+        )
+        self.privacy_engine = PrivacyEngine()
+        if self.dp_params is not None:
+            (
+                self.global_model,
+                self.global_optimizer,
+                self.global_data_loader,
+            ) = self.privacy_engine.make_private(
+                module=self.global_model,
+                optimizer=self.global_optimizer,
+                data_loader=self.global_data_loader,
+                noise_multiplier=self.dp_params["noise_multiplier"],
+                max_grad_norm=self.dp_params["clip_norm"],
+            )
+
 
 class Net(nn.Module):
     def __init__(self):
@@ -202,7 +235,7 @@ def FedAvg(models, global_model_tools, previous_global_state, dp_params):  # NOQ
     # global_model_tools.global_model state dict
     new_model.load_state_dict(
         {
-            key[8:]: value
+            key.replace('_module.', ''): value
             for key, value in global_model_tools.global_model.state_dict().items()
         }
     )
@@ -223,7 +256,7 @@ def inference(network, test_loader, device):
             correct += pred.eq(target.data.view_as(pred)).sum()
     test_loss /= len(test_loader.dataset)
     print(
-        "\nTest set: Avg. loss: {test_loss:.4f},"
+        f"\nTest set: Avg. loss: {test_loss:.4f},"
         f" Accuracy: {correct}/{len(test_loader.dataset)}"
         f" ({(100.0 * correct / len(test_loader.dataset)):.0f})\n"
     )
@@ -338,12 +371,6 @@ def __init__(
             self.dp_params = config["differential_privacy"]
             print(f"Here are dp_params: {self.dp_params}")
             validate_dp_params(self.dp_params)
-        self.global_model_tools = GlobalModelTools(
-            global_model=self.global_model,
-            example_model_state=self.model.state_dict(),
-            collaborator_names=self.collaborator_names,
-            dp_params=self.dp_params,
-        )
 
     @aggregator
     def start(self):
@@ -596,9 +623,10 @@ def end(self):
     else:
         device = torch.device("cpu")
 
+    # Initialize the model
+    initial_model = Net()
+
     # Setup participants
-    # Set `num_gpus=0.09` to `num_gpus=0.0` in order to run this tutorial on CPU
-    aggregator = Aggregator(num_gpus=0.09)
 
     # Setup collaborators with private attributes
     collaborator_names = [
@@ -614,6 +642,28 @@ def end(self):
         "Guadalajara",
     ]
 
+    # Initialize the aggregator with the callable
+    # Set `num_gpus=0.09` to `num_gpus=0.0` in order to run this tutorial on CPU
+
+    def callable_to_initialize_aggregator_private_attributes():
+        # Load the configuration file to get dp_params
+        with open(args.config_path, "rb") as _file:
+            config = yaml.safe_load(_file)
+        dp_params = config["differential_privacy"]
+
+        return {'global_model_tools': GlobalModelTools(
+            global_model=initial_model,
+            example_model_state=initial_model.state_dict(),
+            collaborator_names=collaborator_names,
+            dp_params=dp_params
+        )}
+
+    aggregator = Aggregator(
+        name="agg",
+        private_attributes_callable= callable_to_initialize_aggregator_private_attributes,
+        num_gpus=0.0
+    )
+
     def callable_to_initialize_collaborator_private_attributes(
         index, n_collaborators, batch_size, train_dataset, test_dataset
     ):
@@ -641,7 +691,7 @@ def callable_to_initialize_collaborator_private_attributes(
                 private_attributes_callable=callable_to_initialize_collaborator_private_attributes,
                 # Set `num_gpus=0.09` to `num_gpus=0.0` in order to run this tutorial on CPU
                 num_cpus=0.0,
-                num_gpus=0.09,  # Assuming GPU(s) is available in the machine
+                num_gpus=0.0,  # Assuming GPU(s) is available in the machine
                 index=idx,
                 n_collaborators=len(collaborator_names),
                 batch_size=batch_size_train,
@@ -656,7 +706,6 @@ def callable_to_initialize_collaborator_private_attributes(
     print(f"Local runtime collaborators = {local_runtime.collaborators}")
 
     best_model = None
-    initial_model = Net()
     top_model_accuracy = 0
     total_rounds = 10
 

openfl-tutorials/experimental/workflow/Global_DP/requirements_global_dp.txt

@@ -1,9 +1,9 @@
 cloudpickle
-matplotlib==3.6.0
-numpy==1.23.3
-opacus==1.5.1
-pillow==10.3.0
-pyyaml==6.0
-torch==2.2.0
-torchaudio==2.2.0
-torchvision==0.17.0
+matplotlib==3.10.0
+numpy==1.26.4
+opacus==1.5.2
+pillow==11.0.0
+pyyaml==6.0.2
+torch==2.5.1
+torchaudio==2.5.1
+torchvision==0.20.1