Merge branch 'feature/federated-learning' into develop

src/participation-hub/federated-learning-microservice/app/configs/swagger_local.yml

 swagger: "2.0"
 info:
-  title: Business Logic microservice
+  title: Federated Learning Microservice
   description: This is the documentation for the business logic microservice.
   version: "1.0.0"
 

src/participation-hub/federated-learning-microservice/app/main.py

@@ -35,4 +35,4 @@ else:
 
 # start app
 if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=5000, debug=False, ssl_context=context) #<-- IF running locally comment ss_context
+    app.run(host='0.0.0.0', port=5000, debug=False, ssl_context=context) #<-- IF running locally comment ss_context and change port to5000 from 30422

src/participation-hub/federated-learning-microservice/app/requirements.txt

+#pandas==1.2.4
 astroid==2.4.2
 attrs==19.3.0
 autopep8==1.5.4
@@ -22,7 +23,6 @@ lazy-object-proxy==1.4.3
 MarkupSafe==1.1.1
 mccabe==0.6.1
 openapi-spec-validator==0.2.9
-pandas==1.2.4
 prance==0.19.0
 pycodestyle==2.6.0
 pycparser==2.20
@@ -35,10 +35,7 @@ rope==0.17.0
 semver==2.10.2
 six==1.15.0
 swagger-ui-bundle==0.0.8
-tensorflow==2.3.0
-tensorflow-federated==0.17.0
 toml==0.10.1
-typed-ast==1.4.1
 urllib3==1.25.10
 Werkzeug==1.0.1
 wrapt==1.12.1

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

@@ -2,7 +2,7 @@ import json
 import os
 from flask import Response, request
 import requests
-import pandas as pd
+#import pandas as pd
 import sys
 import network_constants
 import time

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

@@ -4,12 +4,12 @@ import sys
 import shutil
 from flask import Response, request
 import requests
-import pandas as pd
+#import pandas as pd
 import network_constants
 
 def last(use_case: str):    
     #FORWARD TO GPU SERVER WITH IP AND POR
-    url = f'https://{network_constants.FEDERATED_TRAINING_HOSTNAME}:{network_constants.FEDERATED_TRAINING_REST_PORT}/api/Owners/use_case/{use_case}/last_train:'
+    url = f'http://{network_constants.FEDERATED_TRAINING_HOSTNAME}:{network_constants.FEDERATED_TRAINING_REST_PORT}/api/Owners/use_case/{use_case}/last_train'
     response = requests.get(
             url,
             verify = False, 
@@ -24,7 +24,7 @@ def upload_and_train(use_case: str):
     #TODO FORWARD FILES, for some reason  the files are received in this microservice (Federated-LEARNING), but after forwarding they are empty in Federated-TRAINING
     file_dict = request.files
     
-    url = f'https://{network_constants.FEDERATED_TRAINING_HOSTNAME}:{network_constants.FEDERATED_TRAINING_REST_PORT}/api/Owners/use_cases/{use_case}/upload_and_train:'
+    url = f'http://{network_constants.FEDERATED_TRAINING_HOSTNAME}:{network_constants.FEDERATED_TRAINING_REST_PORT}/api/Owners/use_cases/{use_case}/upload_and_train'
     response = requests.post(
             url,
             verify = False, 

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

@@ -9,9 +9,9 @@ import json
 def check_article(use_case: str,data_entry: str):
 
     #FORWARD TO GPU SERVER WITH IP AND PORT
-    url = f'https://{network_constants.FEDERATED_TRAINING_HOSTNAME}:{network_constants.FEDERATED_TRAINING_REST_PORT}/api/Users/use_case/{use_case}/data_entry/{data_entry}/check_article:'
+    url = f'http://{network_constants.FEDERATED_TRAINING_HOSTNAME}:{network_constants.FEDERATED_TRAINING_REST_PORT}/api/Users/use_case/{use_case}/data_entry/{data_entry}/check_article'
     #url = "google.com"#API ENDPOINT WITH IP AND PORT OF GPU SERVER
-    response = requests.get(
+    response = requests.post(
             url,
             verify = False, 
             proxies = { "http":None, "https":None }

tools/federated-training/app/main.py

@@ -37,6 +37,7 @@ app = connexion.App(__name__, specification_dir='configs/')
 app.add_api('routes.yml')
 
 
+
 # start app
 if __name__ == '__main__':
     app.run(host='0.0.0.0', port=30424, debug=False)

tools/federated-training/app/processing/default/federated_algorithm.py

@@ -18,7 +18,8 @@ def model_fn():
       metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
 
 def federated_computation_new(train_dataset,test_dataset):
-
+    #print("###TEEEST")git 
+    #tff.backends.native.set_local_python_execution_context()
     if(globals.INPUT_SPEC == None):
         #should never reach this place because INPUT_SPEC is instantiated inside get_preprocessed_train_test_data.
         #however, if in the future, processed data is provided without hte preprocessing function it will be none -> therefore assign it here

tools/federated-training/app/processing/text_processing/federated_algorithm.py

@@ -7,17 +7,20 @@ import tensorflow as tf
 import tensorflow_federated as tff
 
 
-
 def model_fn():
-  keras_model = get_simple_LSTM_model()
+    keras_model = get_simple_LSTM_model()
 
-  return tff.learning.from_keras_model(
-      keras_model,
-      input_spec=globals.INPUT_SPEC,
-      loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
-      metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
+    return tff.learning.from_keras_model(
+        keras_model,
+        input_spec=globals.INPUT_SPEC,
+        loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
+        metrics=[tf.keras.metrics.BinaryAccuracy()])
+        #loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+        #metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
 
 def federated_computation_new(train_dataset,test_dataset):
+    tff.backends.native.set_local_execution_context
+    #TFF must be imported here, to guarantee that the context is initialised
 
     if(globals.INPUT_SPEC == None):
         #should never reach this place because INPUT_SPEC is instantiated inside get_preprocessed_train_test_data.

tools/federated-training/app/processing/text_processing/global_hyperparams.py

@@ -6,7 +6,7 @@ def initialize(use_case,trainer_id = 0,dataset_id = 0):
     global VOCAB_SIZE #tells how many words it memorises. each word is stored as an int. 
     VOCAB_SIZE = 6000
     global NUM_CLIENTS #number of clients in the federated dataset
-    NUM_CLIENTS = 4
+    NUM_CLIENTS = 10
     global SHUFFLE_BUFFER 
     SHUFFLE_BUFFER = 5000 #used in preprocessing
     global BATCH_SIZE

tools/federated-training/app/processing/text_processing/ledger.csv

@@ -17,3 +17,24 @@ Trainer_id,Model_id,Dataset_id,Accuracy,Loss
 3,1624889350,1624889350,0.5,nan
 3,1624890858,1624890858,0.75,nan
 3,1624891629,1624891629,0.75,nan
+3,1633515464,1633515464,0.75,nan
+3,1633518451,1633518451,0.25,nan
+4,1633525163,1633525163,0.75,nan
+4,1633528455,1633528455,0.5,nan
+3,1634034576,1634034576,0.75,nan
+0,1634036764,1634036764,0.75,nan
+0,1634553320,1634553320,0.5,nan
+3,1634567728,1634567728,0.75,nan
+3,1634568735,1634568735,0.5,nan
+3,1634569432,1634569432,0.5,nan
+3,1634570407,1634570407,0.75,nan
+3,1635859162,1635859162,0.0,0.3336388
+3,1635859677,1635859677,0.75,0.7262713
+3,1635860773,1635860773,0.5,0.7085196
+3,1635945006,1635945006,0.5,0.7163227
+3,1635945114,1635945114,0.75,0.7200562
+3,1635945197,1635945197,0.75,0.5879684
+3,1635945506,1635945506,0.5,0.70786154
+3,1635945786,1635945786,0.5,0.7161836
+3,1635945886,1635945886,0.6,0.7275145
+3,1635946190,1635946190,0.8,0.53487456

tools/federated-training/app/processing/text_processing/main_proc.py

@@ -20,15 +20,16 @@ def start_processing(use_case, developer_id:int = 0):
     timestamp = int(time.time())
     globals.DATASET_ID = timestamp
     
-    written_row = save_to_file_CSV(use_case,globals.TRAINER_ID,timestamp,globals.DATASET_ID,trained_metrics['sparse_categorical_accuracy'],trained_metrics['loss'])
+    written_row = save_to_file_CSV(use_case,globals.TRAINER_ID,timestamp,globals.DATASET_ID,trained_metrics['binary_accuracy'],trained_metrics['loss'])
     return written_row
 
 def start_prediction(use_case, developer_id:int = -1):
+  #TODO
     globals.initialize(use_case,developer_id)
     
     raw_input_prediction_data = "Test sentence. And another sentence which is going to be used as a mean for checking if this article is true or not. Also Santa is real"
     raw_input_prediction_data = "Donald Trump Sends Out Embarrassing New Year’s Eve Message. This is Disturbing,'Donald Trump just couldn t wish all Americans a Happy New Year and leave it at that. Instead, he had to give a shout out to his enemies, haters and  the very dishonest fake news media.  The former reality show star had just one job to do and he couldn t do it. As our Country rapidly grows stronger and smarter, I want to wish all of my friends, supporters, enemies, haters, and even the very dishonest Fake News Media, a Happy and Healthy New Year,  President Angry Pants tweeted.  2018 will be a great year for America! As our Country rapidly grows stronger and smarter, I want to wish all of my friends, supporters, enemies, haters, and even the very dishonest Fake News Media, a Happy and Healthy New Year. 2018 will be a great year for America!  Donald J. Trump (@realDonaldTrump) December 31, 2017Trump s tweet went down about as welll as you d expect.What kind of president sends a New Year s greeting like this despicable, petty, infantile gibberish? Only Trump! His lack of decency won t even allow him to rise above the gutter long enough to wish the American citizens a happy new year!  Bishop Talbert Swan (@TalbertSwan) December 31, 2017no one likes you  Calvin (@calvinstowell) December 31, 2017Your impeachment would make 2018 a great year for America, but I ll also accept regaining control of Congress.  Miranda Yaver (@mirandayaver) December 31, 2017Do you hear yourself talk? When you have to include that many people that hate you you have to wonder? Why do the they all hate me?  Alan Sandoval (@AlanSandoval13) December 31, 2017Who uses the word Haters in a New Years wish??  Marlene (@marlene399) December 31, 2017You can t just say happy new year?  Koren pollitt (@Korencarpenter) December 31, 2017Here s Trump s New Year s Eve tweet from 2016.Happy New Year to all, including to my many enemies and those who have fought me and lost so badly they just don t know what to do. Love!  Donald J. Trump (@realDonaldTrump) December 31, 2016This is nothing new for Trump. He s been doing this for years.Trump has directed messages to his  enemies  and  haters  for New Year s, Easter, Thanksgiving, and the anniversary of 9/11. pic.twitter.com/4FPAe2KypA  Daniel Dale (@ddale8) December 31, 2017Trump s holiday tweets are clearly not presidential.How long did he work at Hallmark before becoming President?  Steven Goodine (@SGoodine) December 31, 2017He s always been like this . . . the only difference is that in the last few years, his filter has been breaking down.  Roy Schulze (@thbthttt) December 31, 2017Who, apart from a teenager uses the term haters?  Wendy (@WendyWhistles) December 31, 2017he s a fucking 5 year old  Who Knows (@rainyday80) December 31, 2017So, to all the people who voted for this a hole thinking he would change once he got into power, you were wrong! 70-year-old men don t change and now he s a year older.Photo by Andrew Burton/Getty Images."
-    input_prediction_data= preprocess_single_train_data(raw_input_prediction_data)
+    input_prediction_data = preprocess_single_train_data(raw_input_prediction_data)
     prediction_result = make_prediction(input_prediction_data)
     res = prediction_result[0]
     if (res[0]>=0.50):

tools/federated-training/app/requirements.txt

-astroid==2.4.2
+absl-py==0.15.0
+astunparse==1.6.3
 attrs==19.3.0
-autopep8==1.5.4
-certifi==2020.6.20
-cffi==1.14.2
-chardet==3.0.4
-click==7.1.2
-clickclick==1.2.2
-colorama==0.4.3
+cachetools==3.1.1
+certifi==2021.10.8
+charset-normalizer==2.0.7
+click==8.0.3
+clickclick==20.10.2
 connexion==2.7.0
 coverage==5.3.1
-cryptography==3.1
+dm-tree==0.1.6
 Flask==1.1.2
-idna==2.10
-importlib-metadata==1.7.0
-inflection==0.5.0
-isort==4.3.21
-itsdangerous==1.1.0
-Jinja2==2.11.2
+flatbuffers==1.12
+gast==0.4.0
+google-auth==2.3.3
+google-auth-oauthlib==0.4.6
+google-pasta==0.2.0
+grpcio==1.34.1
+h5py==3.1.0
+idna==3.3
+inflection==0.5.1
+isodate==0.6.0
+itsdangerous==2.0.1
+jax==0.2.24
+jaxlib==0.1.73
+Jinja2==3.0.2
+joblib==1.1.0
 jsonschema==3.2.0
-lazy-object-proxy==1.4.3
-MarkupSafe==1.1.1
-mccabe==0.6.1
+keras-nightly==2.5.0.dev2021032900
+Keras-Preprocessing==1.1.2
+Markdown==3.3.4
+MarkupSafe==2.0.1
+mpmath==1.2.1
+numpy==1.19.5
+oauthlib==3.1.1
+openapi-schema-validator==0.1.5
 openapi-spec-validator==0.2.9
+opt-einsum==3.3.0
 pandas==1.2.4
-prance==0.19.0
-pycodestyle==2.6.0
-pycparser==2.20
-pylint==2.5.3
-pymongo==3.11.0
-pyrsistent==0.16.0
-PyYAML==5.3.1
-requests==2.24.0
+portpicker==1.3.9
+protobuf==3.19.1
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pyrsistent==0.18.0
+python-dateutil==2.8.2
+pytz==2021.3
+PyYAML==6.0
+requests==2.26.0
+requests-oauthlib==1.3.0
+retrying==1.3.3
 rope==0.17.0
-semver==2.10.2
+rsa==4.7.2
+scikit-learn==1.0.1
+scipy==1.7.1
+semantic-version==2.8.5
 six==1.15.0
+sklearn==0.0
 swagger-ui-bundle==0.0.8
-tensorflow==2.3.0
-tensorflow-federated==0.17.0
-toml==0.10.1
-typed-ast==1.4.1
-urllib3==1.25.10
-Werkzeug==1.0.1
+tensorboard==2.7.0
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.0
+tensorflow==2.5.2
+tensorflow-estimator==2.5.0
+tensorflow-federated==0.19.0
+tensorflow-model-optimization==0.5.0
+tensorflow-privacy==0.5.2
+termcolor==1.1.0
+threadpoolctl==3.0.0
+tqdm==4.28.1
+typing-extensions==3.7.4.3
+urllib3==1.26.7
+Werkzeug==2.0.2
 wrapt==1.12.1
-zipp==3.1.0
-

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

@@ -6,6 +6,7 @@ import pandas as pd
 import sys
 from os.path import dirname, abspath
 import time
+import threading
 modules_path = './'
 if os.path.exists(modules_path):
     sys.path.insert(1, modules_path)
@@ -25,12 +26,12 @@ def last(use_case: str):
 
 
 def upload_and_train(use_case: str, developer_id: int):
-
-
+    
+    
     use_case_path = 'processing/'+use_case+'/'
     sys.path.append(use_case_path)
     import main_proc
-
+    
     #COPY THE NEW DB TO THE FOLDER
     #TODO IMPLEMENT HERE
     app_path = dirname(dirname(abspath(__file__)))
@@ -41,7 +42,7 @@ def upload_and_train(use_case: str, developer_id: int):
     fake_csv_path = os.path.join(app_path+"/"+use_case_path+"db/", "Fake.csv")
     db_File_True.save(true_csv_path)
     db_File_Fake.save(fake_csv_path)
-    time.sleep(5) #wait for the files to be copied before proceeding with the processing (they are copied in a separate thread, i think?)
+    time.sleep(10) #wait for the files to be copied before proceeding with the processing (they are copied in a separate thread, i think?)
     #THEN start processing
     last_train_metrics = main_proc.start_processing(use_case,developer_id)
     print("## Last train metrics")
@@ -50,7 +51,9 @@ def upload_and_train(use_case: str, developer_id: int):
     #0,1623160388,0,0.25,nan
     metricsJson = trainMetricsToJSON(last_train_metrics)
     
-
+    #START A THREAD to process after the response is sent
+    thread1 = threading.Thread(target=reload_connection,args=[])
+    thread1.start()
     return Response(status=200, response=metricsJson)
 
 def trainMetricsToJSON(last_train_metrics : list):
@@ -62,6 +65,11 @@ def trainMetricsToJSON(last_train_metrics : list):
     metricsDict["Loss"] = last_train_metrics[4]
     return json.dumps(metricsDict)
 
-#upload_and_train("text_processing",1)
+#upload_and_train("text_processing",3)
 #last("text_processing")
 
+def reload_connection():
+    print("In the thread")
+    time.sleep(5)
+    print("Closing Server")
+    os._exit(0)

tools/federated-training/app/slurm-1791.out

-python: can't open file 'processing/main_processing.py': [Errno 2] No such file or directory

tools/federated-training/app/startup.py → tools/federated-training/app/startup.sh

-/home/itec/bogdan/Articonf/smart/tools/federated-training/app/venv/bin/python3 /home/itec/bogdan/Articonf/smart/tools/federated-training/app/print_test.py
+while true
\ No newline at end of file
+do
+/home/itec/bogdan/Articonf/smart/tools/federated-training/app/venv/bin/python3 /home/itec/bogdan/Articonf/smart/tools/federated-training/app/main.py
+sleep 3
+done
\ No newline at end of file