Feature/SK-838 | Ruff linting (#599)

examples/async-clients/client/entrypoint.py

@@ -8,7 +8,7 @@
 
 from fedn.utils.helpers.helpers import get_helper, save_metadata, save_metrics
 
-HELPER_MODULE = 'numpyhelper'
+HELPER_MODULE = "numpyhelper"
 ARRAY_SIZE = 10000
 
 
@@ -22,7 +22,7 @@ def compile_model(max_iter=1):
 
 
 def save_parameters(model, out_path):
-    """ Save model to disk.
+    """Save model to disk.
 
     :param model: The model to save.
     :type model: torch.nn.Module
@@ -36,7 +36,7 @@ def save_parameters(model, out_path):
 
 
 def load_parameters(model_path):
-    """ Load model from disk.
+    """Load model from disk.
 
     param model_path: The path to load from.
     :type model_path: str
@@ -49,8 +49,8 @@ def load_parameters(model_path):
     return parameters
 
 
-def init_seed(out_path='seed.npz'):
-    """ Initialize seed model.
+def init_seed(out_path="seed.npz"):
+    """Initialize seed model.
 
     :param out_path: The path to save the seed model to.
     :type out_path: str
@@ -61,7 +61,7 @@ def init_seed(out_path='seed.npz'):
 
 
 def make_data(n_min=50, n_max=100):
-    """ Generate / simulate a random number n data points.
+    """Generate / simulate a random number n data points.
 
     n will fall in the interval (n_min, n_max)
 
@@ -78,14 +78,12 @@ def make_data(n_min=50, n_max=100):
 
 
 def train(in_model_path, out_model_path):
-    """ Train model.
-
-    """
+    """Train model."""
 
     # Load model
     parameters = load_parameters(in_model_path)
     model = compile_model()
-    n = len(parameters)//2
+    n = len(parameters) // 2
     model.coefs_ = parameters[:n]
     model.intercepts_ = parameters[n:]
 
@@ -97,7 +95,7 @@ def train(in_model_path, out_model_path):
 
     # Metadata needed for aggregation server side
     metadata = {
-        'num_examples': len(X_train),
+        "num_examples": len(X_train),
     }
 
     # Save JSON metadata file
@@ -108,7 +106,7 @@ def train(in_model_path, out_model_path):
 
 
 def validate(in_model_path, out_json_path):
-    """ Validate model.
+    """Validate model.
 
     :param in_model_path: The path to the input model.
     :type in_model_path: str
@@ -119,7 +117,7 @@ def validate(in_model_path, out_json_path):
     """
     parameters = load_parameters(in_model_path)
     model = compile_model()
-    n = len(parameters)//2
+    n = len(parameters) // 2
     model.coefs_ = parameters[:n]
     model.intercepts_ = parameters[n:]
 
@@ -134,9 +132,5 @@ def validate(in_model_path, out_json_path):
     save_metrics(report, out_json_path)
 
 
-if __name__ == '__main__':
-    fire.Fire({
-        'init_seed': init_seed,
-        'train': train,
-        'validate': validate
-    })
+if __name__ == "__main__":
+    fire.Fire({"init_seed": init_seed, "train": train, "validate": validate})

examples/async-clients/init_fedn.py

@@ -1,8 +1,8 @@
 from fedn import APIClient
 
-DISCOVER_HOST = '127.0.0.1'
+DISCOVER_HOST = "127.0.0.1"
 DISCOVER_PORT = 8092
 
 client = APIClient(DISCOVER_HOST, DISCOVER_PORT)
-client.set_active_package('package.tgz', 'numpyhelper')
-client.set_active_model('seed.npz')
+client.set_active_package("package.tgz", "numpyhelper")
+client.set_active_model("seed.npz")

examples/async-clients/run_clients.py

@@ -26,24 +26,39 @@
 
 # Use with a local deployment
 settings = {
-    'DISCOVER_HOST': '127.0.0.1',
-    'DISCOVER_PORT': 8092,
-    'TOKEN': None,
-    'N_CLIENTS': 10,
-    'N_CYCLES': 100,
-    'CLIENTS_MAX_DELAY': 10,
-    'CLIENTS_ONLINE_FOR_SECONDS': 120
+    "DISCOVER_HOST": "127.0.0.1",
+    "DISCOVER_PORT": 8092,
+    "TOKEN": None,
+    "N_CLIENTS": 10,
+    "N_CYCLES": 100,
+    "CLIENTS_MAX_DELAY": 10,
+    "CLIENTS_ONLINE_FOR_SECONDS": 120,
 }
 
-client_config = {'discover_host': settings['DISCOVER_HOST'], 'discover_port': settings['DISCOVER_PORT'], 'token': settings['TOKEN'], 'name': 'testclient',
-                 'client_id': 1, 'remote_compute_context': True, 'force_ssl': False, 'dry_run': False, 'secure': False,
-                 'preshared_cert': False, 'verify': False, 'preferred_combiner': False,
-                 'validator': True, 'trainer': True, 'init': None, 'logfile': 'test.log', 'heartbeat_interval': 2,
-                 'reconnect_after_missed_heartbeat': 30}
+client_config = {
+    "discover_host": settings["DISCOVER_HOST"],
+    "discover_port": settings["DISCOVER_PORT"],
+    "token": settings["TOKEN"],
+    "name": "testclient",
+    "client_id": 1,
+    "remote_compute_context": True,
+    "force_ssl": False,
+    "dry_run": False,
+    "secure": False,
+    "preshared_cert": False,
+    "verify": False,
+    "preferred_combiner": False,
+    "validator": True,
+    "trainer": True,
+    "init": None,
+    "logfile": "test.log",
+    "heartbeat_interval": 2,
+    "reconnect_after_missed_heartbeat": 30,
+}
 
 
-def run_client(online_for=120, name='client'):
-    """ Simulates a client that starts and stops
+def run_client(online_for=120, name="client"):
+    """Simulates a client that starts and stops
     at random intervals.
 
     The client will start after a radom time 'mean_delay',
@@ -55,23 +70,28 @@ def run_client(online_for=120, name='client'):
     """
 
     conf = copy.deepcopy(client_config)
-    conf['name'] = name
+    conf["name"] = name
 
-    for i in range(settings['N_CYCLES']):
+    for i in range(settings["N_CYCLES"]):
         # Sample a delay until the client starts
-        t_start = np.random.randint(0, settings['CLIENTS_MAX_DELAY'])
+        t_start = np.random.randint(0, settings["CLIENTS_MAX_DELAY"])
         time.sleep(t_start)
         fl_client = Client(conf)
         time.sleep(online_for)
         fl_client.disconnect()
 
 
-if __name__ == '__main__':
-
+if __name__ == "__main__":
     # We start N_CLIENTS independent client processes
     processes = []
-    for i in range(settings['N_CLIENTS']):
-        p = Process(target=run_client, args=(settings['CLIENTS_ONLINE_FOR_SECONDS'], 'client{}'.format(i),))
+    for i in range(settings["N_CLIENTS"]):
+        p = Process(
+            target=run_client,
+            args=(
+                settings["CLIENTS_ONLINE_FOR_SECONDS"],
+                "client{}".format(i),
+            ),
+        )
         processes.append(p)
         p.start()
 

examples/async-clients/run_experiment.py

@@ -3,16 +3,14 @@
 
 from fedn import APIClient
 
-DISCOVER_HOST = '127.0.0.1'
+DISCOVER_HOST = "127.0.0.1"
 DISCOVER_PORT = 8092
 client = APIClient(DISCOVER_HOST, DISCOVER_PORT)
 
-if __name__ == '__main__':
-
+if __name__ == "__main__":
     # Run six sessions, each with 100 rounds.
     num_sessions = 6
     for s in range(num_sessions):
-
         session_config = {
             "helper": "numpyhelper",
             "id": str(uuid.uuid4()),
@@ -23,12 +21,12 @@
         }
 
         session = client.start_session(**session_config)
-        if session['success'] is False:
-            print(session['message'])
+        if session["success"] is False:
+            print(session["message"])
             exit(0)
 
         print("Started session: {}".format(session))
 
         # Wait for session to finish
-        while not client.session_is_finished(session_config['id']):
+        while not client.session_is_finished(session_config["id"]):
             time.sleep(2)

examples/flower-client/client/entrypoint.py

@@ -56,9 +56,7 @@ def train(in_model_path, out_model_path):
     parameters_np = helper.load(in_model_path)
 
     # Train on flower client
-    params, num_examples = flwr_adapter.train(
-        parameters=parameters_np, partition_id=_get_node_id(), config={}
-    )
+    params, num_examples = flwr_adapter.train(parameters=parameters_np, partition_id=_get_node_id(), config={})
 
     # Metadata needed for aggregation server side
     metadata = {

examples/flower-client/client/flwr_client.py

@@ -3,18 +3,15 @@
 """
 
 from flwr.client import ClientApp, NumPyClient
-from flwr_task import (DEVICE, Net, get_weights, load_data, set_weights, test,
-                       train)
+from flwr_task import DEVICE, Net, get_weights, load_data, set_weights, test, train
 
 
 # Define FlowerClient and client_fn
 class FlowerClient(NumPyClient):
     def __init__(self, cid) -> None:
         super().__init__()
         self.net = Net().to(DEVICE)
-        self.trainloader, self.testloader = load_data(
-            partition_id=int(cid), num_clients=10
-        )
+        self.trainloader, self.testloader = load_data(partition_id=int(cid), num_clients=10)
 
     def get_parameters(self, config):
         return [val.cpu().numpy() for _, val in self.net.state_dict().items()]

examples/flower-client/init_fedn.py

@@ -1,8 +1,8 @@
 from fedn import APIClient
 
-DISCOVER_HOST = '127.0.0.1'
+DISCOVER_HOST = "127.0.0.1"
 DISCOVER_PORT = 8092
 
 client = APIClient(DISCOVER_HOST, DISCOVER_PORT)
-client.set_package('package.tgz', 'numpyhelper')
-client.set_initial_model('seed.npz')
+client.set_package("package.tgz", "numpyhelper")
+client.set_initial_model("seed.npz")