fixing ruff linting

examples/splitlearning_titanic/api.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,16 +22,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<fedn.network.api.client.APIClient at 0x107cd93d0>"
+       "<fedn.network.api.client.APIClient at 0x1162a05e0>"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -42,34 +42,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'count': 2,\n",
-       " 'result': [{'client_id': 'eb2bcf55-17c5-4e34-9b6f-d53b41e38cd4',\n",
-       "   'combiner': 'combinerd3486ca7',\n",
-       "   'id': '6763339936d0caaa05834846',\n",
+       " 'result': [{'client_id': 'ef5dd2e4-0660-400e-b883-2301366f3bd5',\n",
+       "   'combiner': 'combiner93d66f64',\n",
+       "   'id': '67641f327c2d2ddecce002c8',\n",
        "   'ip': '127.0.0.1',\n",
-       "   'last_seen': 'Wed, 18 Dec 2024 21:42:03 GMT',\n",
-       "   'name': 'clientb0abc7ea',\n",
+       "   'last_seen': 'Thu, 19 Dec 2024 14:27:16 GMT',\n",
+       "   'name': 'client4e582282',\n",
        "   'package': 'local',\n",
        "   'status': 'online',\n",
-       "   'updated_at': '2024-12-18 21:42:01.464088'},\n",
-       "  {'client_id': '8b4a21f9-c1e6-4632-a196-6aa29b268f61',\n",
-       "   'combiner': 'combinerd3486ca7',\n",
-       "   'id': '676332f636d0caaa058346f8',\n",
+       "   'updated_at': '2024-12-19 14:27:14.648611'},\n",
+       "  {'client_id': '7d50d8eb-24df-4a91-aa98-737c06a9d455',\n",
+       "   'combiner': 'combiner93d66f64',\n",
+       "   'id': '67641f267c2d2ddecce002ab',\n",
        "   'ip': '127.0.0.1',\n",
-       "   'last_seen': 'Wed, 18 Dec 2024 21:41:07 GMT',\n",
-       "   'name': 'client1aafa234',\n",
+       "   'last_seen': 'Thu, 19 Dec 2024 14:27:04 GMT',\n",
+       "   'name': 'client5c2c168f',\n",
        "   'package': 'local',\n",
        "   'status': 'online',\n",
-       "   'updated_at': '2024-12-18 21:39:18.504766'}]}"
+       "   'updated_at': '2024-12-19 14:27:02.270562'}]}"
       ]
      },
-     "execution_count": 136,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 172,
    "metadata": {},
    "outputs": [
     {
@@ -89,7 +89,7 @@
        "{'message': 'Compute package set.', 'success': True}"
       ]
      },
-     "execution_count": 137,
+     "execution_count": 172,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 173,
    "metadata": {},
    "outputs": [
     {
@@ -110,6 +110,7 @@
        "  'buffer_size': -1,\n",
        "  'clients_requested': 8,\n",
        "  'clients_required': 1,\n",
+       "  'committed_at': 'Thu, 19 Dec 2024 14:29:30 GMT',\n",
        "  'delete_models_storage': True,\n",
        "  'helper_type': 'splitlearninghelper',\n",
        "  'round_timeout': 60,\n",
@@ -122,7 +123,7 @@
        " 'success': True}"
       ]
      },
-     "execution_count": 138,
+     "execution_count": 173,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -141,6 +142,13 @@
     "\n",
     "client.start_splitlearning_session(**session_config)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

examples/splitlearning_titanic/client/backward.py

@@ -7,7 +7,7 @@
 from torch import optim
 
 from fedn.common.log_config import logger
-from fedn.utils.helpers.helpers import get_helper, save_metadata
+from fedn.utils.helpers.helpers import get_helper
 
 dir_path = os.path.dirname(os.path.realpath(__file__))
 abs_path = os.path.abspath(dir_path)

fedn/network/api/interface.py

@@ -7,7 +7,8 @@
 from werkzeug.security import safe_join
 from werkzeug.utils import secure_filename
 
-from fedn.common.config import FEDN_COMPUTE_PACKAGE_DIR, get_controller_config, get_network_config
+from fedn.common.config import (FEDN_COMPUTE_PACKAGE_DIR,
+                                get_controller_config, get_network_config)
 from fedn.common.log_config import logger
 from fedn.network.combiner.interfaces import CombinerUnavailableError
 from fedn.network.state import ReducerState, ReducerStateToString

fedn/network/clients/client_v2.py

@@ -8,7 +8,8 @@
 
 from fedn.common.config import FEDN_CUSTOM_URL_PREFIX
 from fedn.common.log_config import logger
-from fedn.network.clients.fedn_client import ConnectToApiResult, FednClient, GrpcConnectionOptions
+from fedn.network.clients.fedn_client import (ConnectToApiResult, FednClient,
+                                              GrpcConnectionOptions)
 from fedn.network.combiner.modelservice import get_tmp_path
 from fedn.utils.helpers.helpers import get_helper
 

fedn/network/combiner/aggregators/splitlearningagg.py

@@ -54,7 +54,7 @@ def __init__(self, update_handler):
     def combine_models(self, helper=None, delete_models=True):
         """Concatenates client embeddings in the queue by aggregating them.
 
-        NOTE: After all embeddings are received, the embeddings need to be sorted 
+        After all embeddings are received, the embeddings need to be sorted 
         (consistently) by client ID.
 
         :param helper: An instance of :class: `fedn.utils.helpers.helpers.HelperBase`, ML framework specific helper, defaults to None
@@ -78,7 +78,7 @@ def combine_models(self, helper=None, delete_models=True):
             try:
                 logger.info("AGGREGATOR({}): Getting next embedding from queue.".format(self.name))
                 new_embedding = self.update_handler.next_model_update() # NOTE: should return in format {client_id: embedding}
-                                
+
                 # Load model parameters and metadata
                 logger.info("AGGREGATOR({}): Loading embedding metadata.".format(self.name))
                 embedding_next, metadata = self.update_handler.load_model_update(new_embedding, helper)
@@ -103,7 +103,7 @@ def combine_models(self, helper=None, delete_models=True):
                 logger.error(tb)
 
         logger.info("splitlearning aggregator: starting calculation of gradients")
-        
+
         # NOTE: When aggregating the embeddings in SplitLearning, they always need to be sorted consistently
         client_order = sorted(embeddings.keys())
 
@@ -114,19 +114,19 @@ def combine_models(self, helper=None, delete_models=True):
         concatenated_embeddings = torch.cat(ordered_embeddings, dim=1) # to 1d tensor
 
         self.optimizer.zero_grad()
-        
+
         output = self.model(concatenated_embeddings)
 
         # TODO: need to match indices of data samples to target indices in order to calculate gradients.
         # NOTE: For one epoch, depending on the batch size, multiple communications are necessary.
         # use dummy target for now
         # batch_size = concatenated_embeddings.shape[0] # TODO: check
-        
+
         targets = helper.load_targets()
-        
+
         loss = self.criterion(output, targets)
         loss.backward()
-        
+
         self.optimizer.step()
 
         logger.info("AGGREGATOR({}): Loss: {}".format(self.name, loss))
@@ -135,6 +135,6 @@ def combine_models(self, helper=None, delete_models=True):
         gradients = {}
         for client_id, embedding in zip(client_order, ordered_embeddings):
             gradients[str(client_id)] = embedding.grad.numpy()
-        
+
         logger.info("AGGREGATOR({}): Gradients are calculated.".format(self.name))
-        return gradients, data
+        return gradients, data

fedn/network/combiner/combiner.py

@@ -14,9 +14,13 @@
 import fedn.network.grpc.fedn_pb2 as fedn
 import fedn.network.grpc.fedn_pb2_grpc as rpc
 from fedn.common.certificate.certificate import Certificate
-from fedn.common.log_config import logger, set_log_level_from_string, set_log_stream
+from fedn.common.log_config import (logger, set_log_level_from_string,
+                                    set_log_stream)
 from fedn.network.combiner.roundhandler import RoundConfig, RoundHandler
-from fedn.network.combiner.shared import client_store, combiner_store, prediction_store, repository, statestore, status_store, validation_store
+from fedn.network.combiner.shared import (client_store, combiner_store,
+                                          prediction_store, repository,
+                                          statestore, status_store,
+                                          validation_store)
 from fedn.network.grpc.server import Server, ServerConfig
 from fedn.network.storage.statestore.stores.shared import EntityNotFound
 
@@ -289,11 +293,9 @@ def _send_request_type(self, request_type, session_id, model_id=None, config=Non
             elif request_type == fedn.StatusType.MODEL_PREDICTION:
                 # TODO: add prediction clients type
                 clients = self.get_active_validators()
-            elif request_type == fedn.StatusType.FORWARD:
+            elif request_type == fedn.StatusType.FORWARD or request_type == fedn.StatusType.BACKWARD:
                 clients = self.get_active_trainers()
-            elif request_type == fedn.StatusType.BACKWARD:
-                clients = self.get_active_trainers()
-
+
         for client in clients:
             request = fedn.TaskRequest()
             request.timestamp = str(datetime.now())
@@ -309,15 +311,15 @@ def _send_request_type(self, request_type, session_id, model_id=None, config=Non
                 # TODO: in prediction, request.data should also contain user-defined data/parameters
                 request.data = json.dumps({"presigned_url": presigned_url})
             elif request_type == fedn.StatusType.MODEL_UPDATE:  # noqa: SIM114
-                request.model_id = model_id 
+                request.model_id = model_id
                 request.correlation_id = str(uuid.uuid4())
                 request.data = json.dumps(config)
             elif request_type == fedn.StatusType.FORWARD:  # noqa: SIM114
-                # request.model_id = model_id 
+                # request.model_id = model_id
                 # request.correlation_id = str(uuid.uuid4()) # NOTE: necessary or not? TODO: rename model_id
                 request.data = json.dumps(config)
             elif request_type == fedn.StatusType.BACKWARD:
-                request.model_id = model_id 
+                request.model_id = model_id
                 request.correlation_id = str(uuid.uuid4())
                 request.data = json.dumps(config)
             self._put_request_to_client_queue(request, fedn.Queue.TASK_QUEUE)

fedn/network/combiner/roundhandler.py

@@ -283,7 +283,8 @@ def _backward_pass(self, config: dict, clients: list):
         self.server.request_backward_pass(session_id=config["session_id"], gradient_id=config["model_id"], config=config, clients=clients)
 
         time.sleep(1) # TODO: this is an easy hack for now. There needs to be some waiting time for the backward pass to complete.
-        # the above mechanism cannot be used, as the backward pass is not returning any model updates (update_handler.waitforit checks for aggregation on the queue)
+        # the above mechanism cannot be used, as the backward pass is not returning any model updates (update_handler.waitforit checks for aggregation on the
+        #  queue)
         return meta
 
     def stage_model(self, model_id, timeout_retry=3, retry=2):
@@ -439,7 +440,7 @@ def execute_forward_pass(self, config):
         data = {}
         data["config"] = config
         data["round_id"] = config["round_id"]
-        
+
         data["model_id"] = None # TODO: checking
 
         clients = self._assign_round_clients(self.server.max_clients)
@@ -479,7 +480,7 @@ def execute_backward_pass(self, config):
         data["round_id"] = config["round_id"]
 
         logger.info("roundhandler execute_backward_pass: downloading model/gradient with id: {}".format(config["model_id"]))
-        
+
         # Download gradients and set in temp storage.
         self.stage_model(config["model_id"]) # Download a model from persistent storage and set in modelservice
 

fedn/network/controller/control.py

@@ -3,7 +3,8 @@
 import time
 import uuid
 
-from tenacity import retry, retry_if_exception_type, stop_after_delay, wait_random
+from tenacity import (retry, retry_if_exception_type, stop_after_delay,
+                      wait_random)
 
 from fedn.common.log_config import logger
 from fedn.network.combiner.interfaces import CombinerUnavailableError

fedn/utils/helpers/plugins/splitlearninghelper.py

@@ -53,7 +53,6 @@ def load(self, path):
             data = np.load(path)
             logger.info("SPLIT LEARNING HELPER: loaded data from {}".format(path))
             logger.info("data type: {}".format(type(data)))
-
             result_dict = {k: data[k] for k in data.files}
             return result_dict
         except Exception as e:
@@ -65,7 +64,7 @@ def load_targets(self):
         try:
             data_path = os.environ.get("FEDN_LABELS_PATH")
         except Exception as e:
-            logger.error("FEDN_LABELS_PATH environment variable is not set. Set via export FEDN_LABELS_PATH='path/to/labels.pt'")
+            logger.error(f"FEDN_LABELS_PATH environment variable is not set. Set via export FEDN_LABELS_PATH='path/to/labels.pt', {e}")
             raise
 
         try: