DistributedDataParallel : what did I wrong? with simple code

Hi, I’m new to DistributedDataParallel(DDP). I have lots of problem with this module…

I tried to run code like below.(from Getting Started with Distributed Data Parallel — PyTorch Tutorials 1.9.0+cu102 documentation)

import os
import tempfile
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
import torch.optim as optim

from torch.nn.parallel import DistributedDataParallel as DDP

def setup(rank, world_size):
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '12355'

    # initialize the process group
    dist.init_process_group("gloo", rank=rank, world_size=world_size)

def cleanup():

class ToyModel(nn.Module):
    def __init__(self):
        super(ToyModel, self).__init__()
        self.net1 = nn.Linear(10, 10)
        self.relu = nn.ReLU()
        self.net2 = nn.Linear(10, 5)

    def forward(self, x):
        return self.net2(self.relu(self.net1(x)))

def demo_basic(rank, world_size):
    print(f"Running basic DDP example on rank {rank}.")
    setup(rank, world_size)

    # create model and move it to GPU with id rank
    for x in range( 1, 10000):
        model = ToyModel().to(rank)
        ddp_model = DDP(model, device_ids=[rank])

        loss_fn = nn.MSELoss()
        optimizer = optim.SGD(ddp_model.parameters(), lr=0.001)

        outputs = ddp_model(torch.randn(20, 10))
        labels = torch.randn(20, 5).to(rank)
        loss_fn(outputs, labels).backward()

def run_demo(demo_fn, world_size):

if __name__ == "__main__":
    n_gpus = torch.cuda.device_count()
    if n_gpus < 4:
        print(f"Requires at least 4 GPUs to run, but got {n_gpus}.")
        run_demo(demo_basic, 4)

run with

python3 new.py

However, when I monitor ‘nvidia-smi’. There are 4 process in GPU 0. Why not one process for each GPU device?

Add this line right after the setup(rank, world_size)

setup(rank, world_size)
for x in range(1, 10000):
1 Like

Thanks! Everything become good!