Help Center/ ModelArts/ Troubleshooting/ Training Jobs/ GPU Issues/ Error Message "RuntimeError: Cannot re-initialize CUDA in forked subprocess" Displayed in Logs
Updated on 2024-06-11 GMT+08:00
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Error Message "RuntimeError: Cannot re-initialize CUDA in forked subprocess" Displayed in Logs

Symptom

When PyTorch is used to start multiple processes, the following error message is displayed: 
RuntimeError: Cannot re-initialize CUDA in forked subprocess

Possible Causes

The multi-processing startup mode is incorrect.

Solution

For details, see Writing Distributed Applications with PyTorch. 
"""run.py:"""
#!/usr/bin/env python
import os
import torch
import torch.distributed as dist
import torch.multiprocessing as mp

def run(rank, size):
    """ Distributed function to be implemented later. """
    pass

def init_process(rank, size, fn, backend='gloo'):
    """ Initialize the distributed environment. """
    os.environ['MASTER_ADDR'] = '127.0.0.1'
    os.environ['MASTER_PORT'] = '29500'
    dist.init_process_group(backend, rank=rank, world_size=size)
    fn(rank, size)


if __name__ == "__main__":
    size = 2
    processes = []
    mp.set_start_method("spawn")
    for rank in range(size):
        p = mp.Process(target=init_process, args=(rank, size, run))
        p.start()
        processes.append(p)

    for p in processes:
        p.join()

Summary and Suggestions

Before creating a training job, use the ModelArts development environment to debug the training code to maximally eliminate errors in code migration.

Parent topic: GPU Issues