ruff linting

examples/load-test/client/model.py

@@ -1,12 +1,10 @@
 # /bin/python
-import sys
-import time
 
 import numpy as np
 
-from fedn.utils.helpers.helpers import get_helper, save_metadata, save_metrics
+from fedn.utils.helpers.helpers import get_helper
 
-HELPER_MODULE = 'numpyhelper'
+HELPER_MODULE = "numpyhelper"
 ARRAY_SIZE = 20000000
 
 
@@ -35,7 +33,7 @@ def load_model(model_path):
     return weights
 
 
-def init_seed(out_path='seed.npz'):
+def init_seed(out_path="seed.npz"):
     """ Initialize seed model.
 
     :param out_path: The path to save the seed model to.

examples/load-test/client/train.py

@@ -5,17 +5,16 @@
 import numpy as np
 from model import load_model, save_model
 
-from fedn.utils.helpers.helpers import get_helper, save_metadata, save_metrics
+from fedn.utils.helpers.helpers import save_metadata
 
-HELPER_MODULE = 'numpyhelper'
+HELPER_MODULE = "numpyhelper"
 ARRAY_SIZE = 10000
 
 
 def train(in_model_path, out_model_path):
     """ Train model.
 
     """
-
     # Load model
     weights = load_model(in_model_path)
 
@@ -24,7 +23,7 @@ def train(in_model_path, out_model_path):
 
     # Metadata needed for aggregation server side
     metadata = {
-        'num_examples': ARRAY_SIZE,
+        "num_examples": ARRAY_SIZE,
     }
 
     # Save JSON metadata file

examples/load-test/client/validate.py

@@ -1,13 +1,12 @@
 # /bin/python
 import sys
-import time
 
 import numpy as np
-from model import load_model, save_model
+from model import load_model
 
-from fedn.utils.helpers.helpers import get_helper, save_metadata, save_metrics
+from fedn.utils.helpers.helpers import save_metrics
 
-HELPER_MODULE = 'numpyhelper'
+HELPER_MODULE = "numpyhelper"
 ARRAY_SIZE = 1000000
 
 

fedn/network/combiner/aggregators/fedavg.py

@@ -58,7 +58,7 @@ def combine_models(self, helper=None, delete_models=True, parameters=None):
                 tic = time.time()
                 model_next, metadata = self.update_handler.load_model_update(
                     model_update, helper)
-                data['time_model_load'] += time.time()-tic
+                data["time_model_load"] += time.time()-tic
 
                 logger.info("AGGREGATOR({}): Processing model update {}, metadata: {}  ".format(
                     self.name, model_update.model_update_id, metadata))
@@ -72,7 +72,7 @@ def combine_models(self, helper=None, delete_models=True, parameters=None):
                 else:
                     model = helper.increment_average(
                         model, model_next, metadata["num_examples"], total_examples)
-                data['time_model_aggregation'] += time.time()-tic
+                data["time_model_aggregation"] += time.time()-tic
 
                 nr_aggregated_models += 1
                 # Delete model from storage

fedn/network/combiner/aggregators/fedopt.py

@@ -111,7 +111,7 @@ def combine_models(self, helper=None, delete_models=True, parameters=None):
                 tic = time.time()
                 model_next, metadata = self.update_handler.load_model_update(
                     model_update, helper)
-                data['time_model_load'] += time.time()-tic
+                data["time_model_load"] += time.time()-tic
 
                 logger.info("AGGREGATOR({}): Processing model update {}".format(
                     self.name, model_update.model_update_id))
@@ -129,7 +129,7 @@ def combine_models(self, helper=None, delete_models=True, parameters=None):
                         model_next, model_old)
                     pseudo_gradient = helper.increment_average(
                         pseudo_gradient, pseudo_gradient_next, metadata["num_examples"], total_examples)
-                data['time_model_aggregation'] += time.time()-tic
+                data["time_model_aggregation"] += time.time()-tic
 
                 nr_aggregated_models += 1
                 # Delete model from storage