Distributed.init_process_group failure

MehdiA · July 24, 2019, 12:44am

Hello,

I am trying to get started with torch.distributed with the following toy example, on a multi-gpu cluster :

narumiruna/pytorch-distributed-example/blob/master/toy/main.py

import argparse
from random import randint
from time import sleep

import torch
import torch.distributed as dist


def run(world_size, rank, steps):
    for step in range(1, steps + 1):
        # get random int
        value = randint(0, 10)

        # group all ranks
        ranks = list(range(world_size))
        group = dist.new_group(ranks=ranks)

        # compute reduced sum
        tensor = torch.tensor(value, dtype=torch.int)
        dist.all_reduce(tensor, op=dist.ReduceOp.SUM, group=group)

This file has been truncated. show original

After running the program with the following command :

python3 main.py --init-method tcp://127.0.0.1:23456 --rank 0 --world-size 2

The program gets stuck in an the dist.init_process_group on line 42. I am not really sure about the reason as no message gets displayed.

Thanks,

SimonW · July 25, 2019, 12:28am

it’s waiting for both ranks to reach that line to actually initialize the proc group.

pietern · July 24, 2019, 8:50am

Also see the docs for the torch.distributed.launch tool.

meilu_zhu · August 19, 2019, 12:33pm

I have launched all the node, but the program still gets stuck in the init_process_group.

meilu_zhu · August 19, 2019, 1:12pm

have solved. it is the problem about communication between nodes.

alchemi5t · September 6, 2019, 12:00pm

How did you solve this?

pietern · September 10, 2019, 1:27pm

@alchemi5t If you’re running processes on two machines, they won’t be able to talk if you’re using localhost (127.0.0.1) for the address of rank 0 in the initialization method. It must be an IP that’s reachable from all other ranks. In the example here, rank 1 was trying to connect to rank 0 over 127.0.0.1.

alchemi5t · September 10, 2019, 12:47pm

Hi @pietern,

I was running it on one machine with 4 cards in it( trying to train only on 2). I fixed my problem by installing and using nvidia Apex(apex.parallel.multiproc).

Not sure why I had to do this, because I’ve seen people use the same script without any hacks like this.

pietern · September 10, 2019, 1:18pm

Very odd. Especially since Apex also uses torch.distributed under the hood.