RuntimeError on windows trying python multiprocessing

I am trying my very first formal python program using Threading and Multiprocessing on a windows machine. I am unable to launch the processes though, with python giving the following message. The thing is, I am not launching my threads in the main module. The threads are handled in a separate module inside a class.

EDIT: By the way this code runs fine on ubuntu. Not quite on windows

RuntimeError: 
            Attempt to start a new process before the current process
            has finished its bootstrapping phase.
            This probably means that you are on Windows and you have
            forgotten to use the proper idiom in the main module:
                if __name__ == '__main__':
                    freeze_support()
                    ...
            The "freeze_support()" line can be omitted if the program
            is not going to be frozen to produce a Windows executable.

My original code is pretty long, but I was able to reproduce the error in an abridged version of the code. It is split in two files, the first is the main module and does very little other than import the module which handles processes/threads and calls a method. The second module is where the meat of the code is.

---

testMain.py:

import parallelTestModule

extractor = parallelTestModule.ParallelExtractor()
extractor.runInParallel(numProcesses=2, numThreads=4)

---

parallelTestModule.py:

import multiprocessing
from multiprocessing import Process
import threading
        
class ThreadRunner(threading.Thread):
    """ This class represents a single instance of a running thread"""
    def __init__(self, name):
        threading.Thread.__init__(self)
        self.name = name
    def run(self):
        print self.name,'\n'
                
class ProcessRunner:
    """ This class represents a single instance of a running process """
    def runp(self, pid, numThreads):
        mythreads = []
        for tid in range(numThreads):
            name = "Proc-"+str(pid)+"-Thread-"+str(tid)
            th = ThreadRunner(name)
            mythreads.append(th) 
        for i in mythreads:
            i.start()
        for i in mythreads:
            i.join()
        
class ParallelExtractor:    
    def runInParallel(self, numProcesses, numThreads):
        myprocs = []
        prunner = ProcessRunner()
        for pid in range(numProcesses):
            pr = Process(target=prunner.runp, args=(pid, numThreads)) 
            myprocs.append(pr) 
#        if __name__ == 'parallelTestModule':    #This didnt work
#        if __name__ == '__main__':              #This obviously doesnt work
#        multiprocessing.freeze_support()        #added after seeing error to no avail
        for i in myprocs:
            i.start()

        for i in myprocs:
            i.join()

On Windows the subprocesses will import (i.e. execute) the main module at start. You need to insert an if __name__ == '__main__': guard in the main module to avoid creating subprocesses recursively.

Modified testMain.py:

import parallelTestModule

if __name__ == '__main__':    
    extractor = parallelTestModule.ParallelExtractor()
    extractor.runInParallel(numProcesses=2, numThreads=4)

Try putting your code inside a main function in testMain.py

import parallelTestModule

if __name__ ==  '__main__':
  extractor = parallelTestModule.ParallelExtractor()
  extractor.runInParallel(numProcesses=2, numThreads=4)

See the docs:

"For an explanation of why (on Windows) the if __name__ == '__main__' 
part is necessary, see Programming guidelines."

which say

> "Make sure that the main module can be safely imported by a new Python > interpreter without causing unintended side effects (such a starting a > new process)."

... by using if __name__ == '__main__'

Though the earlier answers are correct, there's a small complication it would help to remark on.

In case your main module imports another module in which global variables or class member variables are defined and initialized to (or using) some new objects, you may have to condition that import in the same way:

if __name__ ==  '__main__':
  import my_module

As @Ofer said, when you are using another libraries or modules, you should import all of them inside the if __name__ == '__main__':

So, in my case, ended like this:

if __name__ == '__main__':       
    import librosa
    import os
    import pandas as pd
    run_my_program()

hello here is my structure for multi process

from multiprocessing import Process
import time


start = time.perf_counter()


def do_something(time_for_sleep):
    print(f'Sleeping {time_for_sleep} second...')
    time.sleep(time_for_sleep)
    print('Done Sleeping...')



p1 = Process(target=do_something, args=[1])
p2 = Process(target=do_something, args=[2])


if __name__ == '__main__':
    p1.start()
    p2.start()

    p1.join()
    p2.join()

    finish = time.perf_counter()
    print(f'Finished in {round(finish-start,2 )} second(s)')

you don't have to put imports in the if __name__ == '__main__':, just running the program you wish to running inside

In my case it was a simple bug in the code, using a variable before it was created. Worth checking that out before trying the above solutions. Why I got this particular error message, Lord knows.

The below solution should work for both python multiprocessing and pytorch multiprocessing.

As other answers mentioned that the fix is to have if __name__ == '__main__': but I faced several issues in identifying where to start because I am using several scripts and modules. When I can call my first function inside main then everything before it started to create multiple processes (not sure why).

Putting it at the very first line (even before the import) worked. Only calling the first function return timeout error. The below is the first file of my code and multiprocessing is used after calling several functions but putting main in the first seems to be the only fix here.

if __name__ == '__main__':
    from mjrl.utils.gym_env import GymEnv
    from mjrl.policies.gaussian_mlp import MLP
    from mjrl.baselines.quadratic_baseline import QuadraticBaseline
    from mjrl.baselines.mlp_baseline import MLPBaseline
    from mjrl.algos.npg_cg import NPG
    from mjrl.algos.dapg import DAPG
    from mjrl.algos.behavior_cloning import BC
    from mjrl.utils.train_agent import train_agent
    from mjrl.samplers.core import sample_paths
    import os
    import json
    import mjrl.envs
    import mj_envs
    import time as timer
    import pickle
    import argparse

    import numpy as np 

    # ===============================================================================
    # Get command line arguments
    # ===============================================================================

    parser = argparse.ArgumentParser(description='Policy gradient algorithms with demonstration data.')
    parser.add_argument('--output', type=str, required=True, help='location to store results')
    parser.add_argument('--config', type=str, required=True, help='path to config file with exp params')
    args = parser.parse_args()
    JOB_DIR = args.output
    if not os.path.exists(JOB_DIR):
        os.mkdir(JOB_DIR)
    with open(args.config, 'r') as f:
        job_data = eval(f.read())
    assert 'algorithm' in job_data.keys()
    assert any([job_data['algorithm'] == a for a in ['NPG', 'BCRL', 'DAPG']])
    job_data['lam_0'] = 0.0 if 'lam_0' not in job_data.keys() else job_data['lam_0']
    job_data['lam_1'] = 0.0 if 'lam_1' not in job_data.keys() else job_data['lam_1']
    EXP_FILE = JOB_DIR + '/job_config.json'
    with open(EXP_FILE, 'w') as f:
        json.dump(job_data, f, indent=4)

    # ===============================================================================
    # Train Loop
    # ===============================================================================

    e = GymEnv(job_data['env'])
    policy = MLP(e.spec, hidden_sizes=job_data['policy_size'], seed=job_data['seed'])
    baseline = MLPBaseline(e.spec, reg_coef=1e-3, batch_size=job_data['vf_batch_size'],
                           epochs=job_data['vf_epochs'], learn_rate=job_data['vf_learn_rate'])

    # Get demonstration data if necessary and behavior clone
    if job_data['algorithm'] != 'NPG':
        print("========================================")
        print("Collecting expert demonstrations")
        print("========================================")
        demo_paths = pickle.load(open(job_data['demo_file'], 'rb'))

        ########################################################################################
        demo_paths = demo_paths[0:3]
        print (job_data['demo_file'], len(demo_paths))
        for d in range(len(demo_paths)):
            feats = demo_paths[d]['features']
            feats = np.vstack(feats)
            demo_paths[d]['observations'] = feats

        ########################################################################################

        bc_agent = BC(demo_paths, policy=policy, epochs=job_data['bc_epochs'], batch_size=job_data['bc_batch_size'],
                      lr=job_data['bc_learn_rate'], loss_type='MSE', set_transforms=False)

        in_shift, in_scale, out_shift, out_scale = bc_agent.compute_transformations()
        bc_agent.set_transformations(in_shift, in_scale, out_shift, out_scale)
        bc_agent.set_variance_with_data(out_scale)

        ts = timer.time()
        print("========================================")
        print("Running BC with expert demonstrations")
        print("========================================")
        bc_agent.train()
        print("========================================")
        print("BC training complete !!!")
        print("time taken = %f" % (timer.time() - ts))
        print("========================================")

        # if job_data['eval_rollouts'] >= 1:
        #     score = e.evaluate_policy(policy, num_episodes=job_data['eval_rollouts'], mean_action=True)
        #     print("Score with behavior cloning = %f" % score[0][0])

    if job_data['algorithm'] != 'DAPG':
        # We throw away the demo data when training from scratch or fine-tuning with RL without explicit augmentation
        demo_paths = None

    # ===============================================================================
    # RL Loop
    # ===============================================================================

    rl_agent = DAPG(e, policy, baseline, demo_paths,
                    normalized_step_size=job_data['rl_step_size'],
                    lam_0=job_data['lam_0'], lam_1=job_data['lam_1'],
                    seed=job_data['seed'], save_logs=True
                    )

    print("========================================")
    print("Starting reinforcement learning phase")
    print("========================================")


    ts = timer.time()
    train_agent(job_name=JOB_DIR,
                agent=rl_agent,
                seed=job_data['seed'],
                niter=job_data['rl_num_iter'],
                gamma=job_data['rl_gamma'],
                gae_lambda=job_data['rl_gae'],
                num_cpu=job_data['num_cpu'],
                sample_mode='trajectories',
                num_traj=job_data['rl_num_traj'],
                num_samples= job_data['rl_num_samples'],
                save_freq=job_data['save_freq'],
                evaluation_rollouts=job_data['eval_rollouts'])
    print("time taken = %f" % (timer.time()-ts))

I ran into the same problem. @ofter method is correct because there are some details to pay attention to. The following is the successful debugging code I modified for your reference:

if __name__ == '__main__':
    import matplotlib.pyplot as plt
    import numpy as np
    def imgshow(img):
        img = img / 2 + 0.5
        np_img = img.numpy()
        plt.imshow(np.transpose(np_img, (1, 2, 0)))
        plt.show()

    dataiter = iter(train_loader)
    images, labels = dataiter.next()

    imgshow(torchvision.utils.make_grid(images))
    print(' '.join('%5s' % classes[labels[i]] for i in range(4)))

For the record, I don't have a subroutine, I just have a main program, but I have the same problem as you. This demonstrates that when importing a Python library file in the middle of a program segment, we should add:

if __name__ == '__main__':