python 3.x - TypeError: can't pickle _thread.lock objects

Question

Trying to run two different functions at the same time with shared queue and get an error...how can I run two functions at the same time with a shared queue? This is Python version 3.6 on Windows 7.

from multiprocessing import Process
from queue import Queue
import logging

def main():
    x = DataGenerator()
    try:
        x.run()
    except Exception as e:
        logging.exception("message")


class DataGenerator:

    def __init__(self):
        logging.basicConfig(filename='testing.log', level=logging.INFO)

    def run(self):
        logging.info("Running Generator")
        queue = Queue()
        Process(target=self.package, args=(queue,)).start()
        logging.info("Process started to generate data")
        Process(target=self.send, args=(queue,)).start()
        logging.info("Process started to send data.")

    def package(self, queue): 
        while True:
            for i in range(16):
                datagram = bytearray()
                datagram.append(i)
                queue.put(datagram)

    def send(self, queue):
        byte_array = bytearray()
        while True:
            size_of__queue = queue.qsize()
            logging.info(" queue size %s", size_of_queue)
            if size_of_queue > 7:
                for i in range(1, 8):
                    packet = queue.get()
                    byte_array.append(packet)
                logging.info("Sending datagram ")
                print(str(datagram))
                byte_array(0)

if __name__ == "__main__":
    main()

The logs indicate an error, I tried running console as administrator and I get the same message...

INFO:root:Running Generator
ERROR:root:message
Traceback (most recent call last):
  File "test.py", line 8, in main
    x.run()
  File "test.py", line 20, in run
    Process(target=self.package, args=(queue,)).start()
  File "C:ProgramDataMiniconda3libmultiprocessingprocess.py", line 105, in start
    self._popen = self._Popen(self)
  File "C:ProgramDataMiniconda3libmultiprocessingcontext.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "C:ProgramDataMiniconda3libmultiprocessingcontext.py", line 322, in _Popen
    return Popen(process_obj)
  File "C:ProgramDataMiniconda3libmultiprocessingpopen_spawn_win32.py", line 65, in __init__
    reduction.dump(process_obj, to_child)
  File "C:ProgramDataMiniconda3libmultiprocessing
eduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
TypeError: can't pickle _thread.lock objects

Answer 1

I had the same problem with Pool() in Python 3.6.3.

Error received: TypeError: can't pickle _thread.RLock objects

Let's say we want to add some number num_to_add to each element of some list num_list in parallel. The code is schematically like this:

class DataGenerator:
    def __init__(self, num_list, num_to_add)
        self.num_list = num_list # e.g. [4,2,5,7]
        self.num_to_add = num_to_add # e.g. 1 

        self.run()

    def run(self):
        new_num_list = Manager().list()

        pool = Pool(processes=50)
        results = [pool.apply_async(run_parallel, (num, new_num_list)) 
                      for num in num_list]
        roots = [r.get() for r in results]
        pool.close()
        pool.terminate()
        pool.join()

    def run_parallel(self, num, shared_new_num_list):
        new_num = num + self.num_to_add # uses class parameter
        shared_new_num_list.append(new_num)

The problem here is that self in function run_parallel() can't be pickled as it is a class instance. Moving this parallelized function run_parallel() out of the class helped. But it's not the best solution as this function probably needs to use class parameters like self.num_to_add and then you have to pass it as an argument.

Solution:

def run_parallel(num, shared_new_num_list, to_add): # to_add is passed as an argument
    new_num = num + to_add
    shared_new_num_list.append(new_num)

class DataGenerator:
    def __init__(self, num_list, num_to_add)
        self.num_list = num_list # e.g. [4,2,5,7]
        self.num_to_add = num_to_add # e.g. 1

        self.run()

    def run(self):
        new_num_list = Manager().list()

        pool = Pool(processes=50)
        results = [pool.apply_async(run_parallel, (num, new_num_list, self.num_to_add)) # num_to_add is passed as an argument
                      for num in num_list]
        roots = [r.get() for r in results]
        pool.close()
        pool.terminate()
        pool.join()

Other suggestions above didn't help me.