Cleaned up pre deploy

src/participation-hub/federated-learning-microservice/app/routes/developers.py

@@ -27,13 +27,17 @@ def upload_and_train(use_case: str, developer_id: int):
 
 
     #FORWARD TO GPU SERVER WITH IP AND PORT 
+    #data = {'use_case' : use_case,
+#           'developer_id' : developer_id}
 
     url = f'https://{network_constants.FEDERATED_TRAINING_HOSTNAME}:{network_constants.FEDERATED_TRAINING_REST_PORT}/api/Developers/use_cases/{use_case}/developer_id/{developer_id}/upload_and_train:'
+    #url= 'gpu3.itec.aau.at/home/itec/bogdan/Articonf/smart/tools/federated-training/app/routes/developers'
     response = requests.post(
             url,
             verify = False, 
             proxies = { "http":None, "https":None },
-            files= request.files
+            files= request.files,
+            #data = data
     )
 
     return response

tools/federated-training/app/configs/swagger.yml

 swagger: "2.0"
 info:
-  title: Federated Learning microservice
-  description: This is the documentation for the federated learning microservice.
+  title: Federated Training microservice
+  description: This is the documentation for the federated Training microservice.
   version: "1.0.0"
 
 consumes:

tools/federated-training/app/configs/swagger_local.yml

 swagger: "2.0"
 info:
-  title: Federated Learning microservice
+  title: Federated Training microservice
   description: This is the documentation for the federated learning microservice.
   version: "1.0.0"
 

tools/federated-training/app/routes/developers.py

@@ -34,7 +34,6 @@ def upload_and_train(use_case: str, developer_id: int):
     #file_dict = request.files
     #db_File_True = file_dict["dataset_file1"]
     #db_File_False = file_dict["dataset_file2"]
-    #TODO IMPLEMENT HERE
 
     #THEN start processing
     last_train_metrics = main_proc.start_processing(use_case,developer_id)

tools/federated-training/app/routes/owners.py

@@ -12,7 +12,7 @@ def last(use_case: str):
         bottom = df.tail(1)
         bottom = bottom.to_json(orient ="records")
         print(bottom[1:-1])
-        metricsJson = bottom[1:-1] #remove the list [  { a: "1"} ...  ]
+        metricsJson = bottom[1:-1] #remove the paranthesis of the list to make a dict [  { a: "1" , ... } ]
         return Response(status=200, response=metricsJson)
     except Exception as e:
         print(e)
@@ -22,6 +22,7 @@ def last(use_case: str):
 def upload_and_train(use_case: str):
 
     use_case_path = './processing/'+use_case+'/'
+    default_path = 'processing/default/'
     #Remove old files
     try:
         if os.path.exists(use_case_path):
@@ -31,20 +32,23 @@ def upload_and_train(use_case: str):
     except OSError as error:
         print(error)
         return Response(status=400, response="Error occured when deleteing the old use_case directory")
+
     #Start a new implementation of the model.
-    #TODO: get the python files and create them locally in the {use_case} folder
-     
     try:
         os.mkdir(use_case_path)
-        #COPY DEFAULT FILES
-        default_path = 'processing/default/'
-        use_case_path +='/'
+        #COPY DEFAULT FILES       
 
         shutil.copyfile(default_path+'main_proc.py',use_case_path+'main_proc.py')
         shutil.copyfile(default_path+'__init__.py',use_case_path+'__init__.py')
         shutil.copyfile(default_path+'checkpoint_manager.py',use_case_path+'checkpoint_manager.py')
         shutil.copyfile(default_path+'federated_algorithm.py',use_case_path+'federated_algorithm.py')
         
+        #TODO: get the python files and create them locally in the {use_case} folder
+        # copy dataset_file1 into use_case_path+'db/'
+        # copy dataset_file1 into use_case_path+'db/'
+        # copy preprocessing into use_case_path
+        # copy global_hyperparams into use_case_path
+        # copy model into use_case_path
 
         #COPY flask files
         #use flask? request.files.getlist('filename')[0] ???
@@ -53,9 +57,9 @@ def upload_and_train(use_case: str):
         return Response(status=400, response="Error occured when creating/copying the use_case files")
 
     
-    
-    #TODO: after the files are copied, start training
-
+    #after the files are copied, start training
+    #TODO: check if training is working
+    # should write to ledger.csv the last train metrics after 2-3 mins
     use_case_path = 'processing/'+use_case+'/'
     sys.path.append(use_case_path)
     import main_proc