def fun:
path = "C:\\Users\\siya\\Desktop\\data"
if filesnames.endswith('.xlsm'):
file_path = Path(os.path.join(path, filesnames))
sheet = pd.read_excel(file_path, 'sheetname', index_col=False)
print(sheet)
def func_process:
with Pool(processes=12) as pool:
filesnames = os.listdir(path)
pool.map(fun, filesnames)
func_process()
getting an error when i try use the pool in func_process instead of
if __name__ == '__main__'
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes 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 an executable.
can any one help how execute the pool with out using main function
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.
import time
import multiprocessing
def mydef(arg):
time.sleep(arg)
print("Seleeped for 1 Sec")
pro=[]
for _ in range(15):
p=multiprocessing.Process(target=mydef,args=[1])
p.start()
pro.append(p)
for proo in pro:
proo.join()
error
RuntimeError File "C:\Users\admin\AppData\Local\Programs\Python\Python38\lib\concurrent\futures\process.py", line 608, in _adjust_process_count
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes 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 an executable.
results = executor.map(do_something, secs)
I am using the module Inputs to get key input in Python when I run this code below
import inputs
events = inputs.get_key()
if __name__ == '__main__':
freeze_support()
I get this error:
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes 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 an executable.
Exception ignored in: <bound method InputDevice.__del__ of inputs.Keyboard("/dev/input/by-id/usb-A_Nice_Keyboard-event-kbd")>
Traceback (most recent call last):
File "C:\Users\26099\AppData\Local\Programs\Python\Python36\lib\site-packages\inputs.py", line 2541, in __del__
File "C:\Users\26099\AppData\Local\Programs\Python\Python36\lib\multiprocessing\process.py", line 113, in terminate
AttributeError: 'NoneType' object has no attribute 'terminate'
This only happens when i run it in a python file. If i run it in the python shell i don't get this error.
freeze_support() has to be properly imported and be the first thing run. That would look like this:
from multiprocessing import freeze_support
import inputs
freeze_support()
if __name__ == '__main__':
events = inputs.get_key()
So the answer given by Nathaniel Taulbut was what I needed but it didn't run in a loop which is what I needed so I changed the code a bit to run in a loop.
from multiprocessing import freeze_support
import inputs
freeze_support()
def Keys():
if __name__ == '__main__':
while True:
events = inputs.get_key()
for event in events:
print(event.code)
Keys()
As far as I have tested it works but is there a way I could get this code working without if __name__ == '__main__'
I want to execute a Python file from another Python file and show all print() outputs and error outputs without waiting (realtime).
The simplified version of my code is as follows and I would like to show "start" and an error message without waiting for "end" (the end of the script).
def main():
# Function that takes a long time (in my actual code)
x += 1 # this raises an error
if __name__ == "main":
print("start")
main()
print("end")
I also have run.py:
import subprocess
def run():
subprocess.run(["python", "main.py"])
if __name__ == '__main__':
run()
I tried this blog post and several other similar answers on stackoverflow, but none of them worked, so I decided to put my original code here, which is above.
The following seems to work for me (on Windows). It uses subprocess.Popen() to execute the other script because this gives more control over what goes on. It turns buffering off to eliminate any delays that might cause, plus it redirects stderr to stdout to so all output can be retrieved from a single source.. Also note it also includes the correction #Ketan Mukadam mentions is his answer tregarding the value of __name__ in your first script.
main_script.py:
def main():
# Function that takes a long time (in my actual code)
x += 1 # this raises an error
if __name__ == '__main__':
print("start")
main()
print("end")
run.py:
import subprocess
import sys
def run():
kwargs = dict(bufsize=0, # No buffering.
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT, # Redirect stderr to stdout.
universal_newlines=True)
args = [sys.executable, 'main_script.py']
with subprocess.Popen(args, **kwargs).stdout as output:
for line in output:
print(line, end='') # Process the output...
if __name__ == '__main__':
run()
Output from executing run.py:
start
Traceback (most recent call last):
File "main_script.py", line 10, in <module>
main()
File "main_script.py", line 6, in main
x += 1 # this raises an error
UnboundLocalError: local variable 'x' referenced before assignment
Is this line a mistake?
if __name__ == "main":
The symbol is __main__ set by interpreter and not main. It is possible that because of this typo error no code is running from main script. Try first executing the main script directly on command shell.