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 yolo v5 with python 3.8.5
if __name__ == '__main__':
from yolov5 import train
train.run()
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__':
I tried the tricks mentioned above on the following very simple code. but I still cannot stop it from resetting on any of my Window machines with Python 3.8/3.10. I would very much appreciate it if you could tell me where I am wrong.
print('script reset')
def do_something(inp):
print('Done!')
if __name__ == '__main__':
from multiprocessing import Process, get_start_method
print('main reset')
print(get_start_method())
Process(target=do_something, args=[1]).start()
print('Finished')
output displays:
script reset
main reset
spawn
Finished
script reset
Done!
Update:
As far as I understand, you guys are not preventing either the script containing the __main__ or the .start() from resetting (which doesn't happen in Linux), rather you are suggesting workarounds so that we don't see the reset. One has to make all imports minimal and put them in each function separately, but it is still, relative to Linux, slow.
So I got an idea to build something like mp3 player. To build that, I used multiprocessing module provided in Python. Here is my code
import multiprocessing
from playsound import playsound
def cp():
playsound('Music.mp3') # play the music
x = multiprocessing.Process(target = cp, daemon = True)
def main():
x.start()
while True and x.is_alive:
u = input("Input: ")
print(u)
if u == "S":
x.terminate()
print("Terminated process")
break
elif u == 'P':
# questioned code
print("Process paused")
elif u == 'R':
# questioned code
print("Process resumed")
if __name__ == '__main__':
main()
The idea is, when the program is executed, the program will automatically play the Music.mp3 file. To control the program, the user must input specific keyword into it.
S to stop the music and exit the program
P to pause the music
R to resuming playing the music
For now, I only know how to code the S option. For the others, I don't have any idea how to code it. So my question is: Is there any idea how to complete the P and R options? Or maybe, is there any idea about how to build the program using another method besides using multiprocessing module?
Thanks for the help
I'm trying to write code that create sub-process using another module(demo_2.py),
and exit program if i get wanted value on sub-processes.
But result looks like this.
It seems that demo_1 makes two sub-process that run demo_1 and load demo_2.
I want to make sub-process only runs demo_2.
What did i missed?
demo_1.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from multiprocessing import Process,Queue
import sys
import demo_2 as A
def multi_process():
print ("Function multi_process called!")
process_status_A = Queue()
process_status_B = Queue()
A_Process = Process(target = A.process_A, args = (process_status_A,))
B_Process = Process(target = A.process_A, args = (process_status_B,))
A_Process.start()
B_Process.start()
while True:
process_status_output_A = process_status_A.get()
process_status_output_B = process_status_B.get()
if process_status_output_A == 'exit' and process_status_output_B == 'exit':
print ("Success!")
break
process_status_A.close()
process_status_B.close()
A_Process.join()
B_Process.join()
sys.exit()
print ("demo_1 started")
if __name__ == "__main__":
multi_process()
demo_2.py
class process_A(object):
def __init__(self, process_status):
print ("demo_2 called!")
process_status.put('exit')
def call_exit(self):
pass
if process_status_A == 'exit' and process_status_B == 'exit':
should be
if process_status_A_output == 'exit' and process_status_B_output == 'exit':
Conclusion: The naming of variables is important.
Avoid long variable names which are almost the same (such as process_status_A and process_status_A_output).
Placing the distinguishing part of the variable name first helps clarify the meaning of the variable.
So instead of
process_status_A_output
process_status_B_output
perhaps use
output_A
output_B
Because Windows lacks os.fork,
on Windows every time a new subprocess is spawned, a new Python interpreter is started and the calling module is imported.
Therefore, code that you do not wish to be run in the spawned subprocess must be "protected" inside the if-statement (see in particular the section entitled "Safe importing of main module"):
Thus use
if __name__ == "__main__":
print ("demo_1 started")
multi_process()
to avoid printing the extra "demo_1 started" messages.
I'm working on something that reads commands from a stream of data. I got stuck trying to work out making a re-usable non-blocking countdown in Python that works off a trigger. So I started a small program w/ just keyboard input and some basic threading to work out the logic. I found a few posts, and this post (How to create a trigger with threading.Timer?) and it was very helpful. But I need some help with another part.
Right now my logic is along the lines of: "Each time the value of command is 1 call start"
How do I update my logic to be:
"if value of command is 1 call start, do not call start again as long as the value of command remains 1.
So its more of a value change detection than a normal if/else, or I have to track a boolean somewhere. I'm just not sure how to approach it.
#! /usr/bin/env python
import time
import threading
import random
from random import randint
import logging
from sys import argv
logging.basicConfig(level=logging.DEBUG, format='[%(levelname)s] (%(threadName)-10s) %(message)s')
def countdown(pName,command):
print("{0} countdown - command{1} ".format(pName,command))
retry = 0
while True:
print("{0}:{1}".format(pName,retry))
retry += 1
if retry > randint(5,10):
break
time.sleep(1)
print("{0} ended".format(pName))
def start(pName,command):
print("starting countdown for: ",pName)
t = threading.Thread(target=countdown,args=(pName,command))
t.setName(pName)
t.setDaemon(True)
t.start()
if __name__ == "__main__":
while 1:
command = int(input("[1 or 2] >"))
if command == 1:
start("Salad",command)
elif command == 2:
start("Bingo",command)
This is pretty brute right now, but its just a first past to try and puzzle it out.
Thanks!
You want the function isAlive. First, you'll have to move your thread variable to the main function, so that main has the appropriate scope to call thread.isAlive().
if __name__ == "__main__":
tSalad = threading.Thread()
tBingo = threading.Thread()
while 1:
command = int(input("[1 or 2] >"))
if command == 1 and not tSalad.isAlive():
tSalad = threading.Thread(target = countdown, args=("Salad", 1))
start("Salad", tSalad)
elif command == 2 and not tBingo.isAlive():
tBingo = threading.Thread(target = countdown, args=("Bingo", 2))
start("Bingo", tBingo)
Then you modify your 'start' function to take a thread argument:
def start(pName, t):
print("starting countdown for: ",pName)
t.setName(pName)
t.setDaemon(True)
t.start()
That should do the trick for you.
I'm struggling with a issue for some time now.
I'm building a little script which uses a main loop. This is a process that needs some attention from the users. The user responds on the steps and than some magic happens with use of some functions
Beside this I want to spawn another process which monitors the computer system for some specific events like pressing specif keys. If these events occur then it will launch the same functions as when the user gives in the right values.
So I need to make two processes:
-The main loop (which allows user interaction)
-The background "event scanner", which searches for specific events and then reacts on it.
I try this by launching a main loop and a daemon multiprocessing process. The problem is that when I launch the background process it starts, but after that I does not launch the main loop.
I simplified everything a little to make it more clear:
import multiprocessing, sys, time
def main_loop():
while 1:
input = input('What kind of food do you like?')
print(input)
def test():
while 1:
time.sleep(1)
print('this should run in the background')
if __name__ == '__main__':
try:
print('hello!')
mProcess = multiprocessing.Process(target=test())
mProcess.daemon = True
mProcess.start()
#after starting main loop does not start while it prints out the test loop fine.
main_loop()
except:
sys.exit(0)
You should do
mProcess = multiprocessing.Process(target=test)
instead of
mProcess = multiprocessing.Process(target=test())
Your code actually calls test in the parent process, and that call never returns.
You can use the locking synchronization to have a better control over your program's flow. Curiously, the input function raise an EOF error, but I'm sure you can find a workaround.
import multiprocessing, sys, time
def main_loop(l):
time.sleep(4)
l.acquire()
# raise an EOFError, I don't know why .
#_input = input('What kind of food do you like?')
print(" raw input at 4 sec ")
l.release()
return
def test(l):
i=0
while i<8:
time.sleep(1)
l.acquire()
print('this should run in the background : ', i+1, 'sec')
l.release()
i+=1
return
if __name__ == '__main__':
lock = multiprocessing.Lock()
#try:
print('hello!')
mProcess = multiprocessing.Process(target=test, args = (lock, ) ).start()
inputProcess = multiprocessing.Process(target=main_loop, args = (lock,)).start()
#except:
#sys.exit(0)