Merge branch 'performaance-model-profiler' into 'morphemic-rc1.5'

update performance model profiler

See merge request melodic/morphemic-preprocessor!47

morphemic-performance-model/ml_code/src/initial_ML_module.py

@@ -18,7 +18,7 @@ import sqlite3
 
 path_ml_model = os.environ.get("MLMODELPATH","./models")
 log_folder = os.environ.get("LOGS_FOLDER","./logs")
-time_sklearn_training = int(os.environ.get("SKLEARNTIME","600")) #in seconde
+time_sklearn_training = int(os.environ.get("SKLEARNTIME","600"))
 local_database_path = os.environ.get("LOCAL_DATABASE_PATH","./db/")
 
 logFile = log_folder + '/ml.log'

morphemic-performance-model/ml_code/src/service.py

@@ -35,73 +35,6 @@ _process_stop = False
 db_evaluation_period = 60*2
 db_last_evaluation = time.time()
 
-"""
-class ServerRPC(Thread):
-    def __init__(self):
-        self.port = 8767
-        self.max_workers = 10
-        super(ServerRPC, self).__init__()
-
-    def run(self):
-        server = grpc.server(futures.ThreadPoolExecutor(max_workers=self.max_workers))
-        service_pb2_grpc.add_PredictServicer_to_server(PredictRPC(), server)
-        server.add_insecure_port('[::]:{0}'.format(self.port))
-        server.start()
-        print("RPC server started on {0}".format(self.port))
-        #server.wait_for_termination()
-        while True:
-            if _process_stop:
-                break
-            time.sleep(5)
-        print("RPC server stopped normally")
-            
-rpc_server = ServerRPC()
-rpc_server.start() 
-"""
-
-"""
-def DBEvaluationRoutine():
-    print("DB Evaluator routine started")
-    global db_last_evaluation
-    while True:
-        if _process_stop:
-            break
-        if time.time() - db_last_evaluation >= db_evaluation_period:
-            try:
-                conn = sqlite3.connect(local_database_path + "prediction.db")
-                cursor = conn.cursor()
-                data_to_send = []
-                for row in cursor.execute("SELECT * FROM Prediction"):
-                    _json = {'application': row[1], 'target': row[2], 'features': json.loads(row[4]), 'prediction': row[3], 'variant': row[4]}
-                    data_to_send.append(_json)
-                conn.close()
-                if data_to_send != []:
-                    conn = stomp.Connection(host_and_ports = [(activemq_hostname, activemq_port)])
-                    conn.connect(login=activemq_username,passcode=activemq_password)
-                    time.sleep(2)
-                    conn.send(body=json.dumps(data_to_send), destination=subscription_topic, persistent='false')
-                    conn.disconnect()
-                    print("Messages pushed to activemq")
-                    print("Removing message to Local DB")
-                    conn = sqlite3.connect(local_database_path + "prediction.db")
-                    cursor = conn.cursor()
-                    cursor.execute("DELETE FROM Prediction")
-                    conn.commit()
-                    conn.close()
-                    
-                else:
-                    print("Nothing found")
-            except Exception as e:
-                print("An error occured")
-                print(e)
-            db_last_evaluation = time.time()
-        time.sleep(5)
-    print("DB Evaluation stopped")
-
-thread = Thread(target=DBEvaluationRoutine, args=())
-thread.start()
-"""
-
 class TrainRequest(BaseModel):
     url_file: Optional[str] = None
     application: str