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How to use PyTorch multiprocessing?

Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about How to use PyTorch multiprocessing in Python. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

How to use PyTorch multiprocessing?

  1. How to use PyTorch multiprocessing?

    As stated in pytorch documentation the best practice to handle multiprocessing is to use torch.multiprocessing instead of multiprocessing.

  2. use PyTorch multiprocessing

    As stated in pytorch documentation the best practice to handle multiprocessing is to use torch.multiprocessing instead of multiprocessing.

Method 1

As stated in pytorch documentation the best practice to handle multiprocessing is to use torch.multiprocessing instead of multiprocessing.

Be aware that sharing CUDA tensors between processes is supported only in Python 3, either with spawn or forkserver as start method.

Without touching your code, a workaround for the error you got is replacing

from multiprocessing import Process, Pool

with:

from torch.multiprocessing import Pool, Process, set_start_method
try:
     set_start_method('spawn')
except RuntimeError:
    pass

Method 2

I suggest you read the docs for the multiprocessing module, especially this section. You will have to change the way subprocesses are created by calling set_start_method. Taken from those quoted docs:

import multiprocessing as mp

def foo(q):
    q.put('hello')

if __name__ == '__main__':
    mp.set_start_method('spawn')
    q = mp.Queue()
    p = mp.Process(target=foo, args=(q,))
    p.start()
    print(q.get())
    p.join()

Summery

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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