Multiprocessing failed with Torch.distributed.launch module

leo-mao: <p>During training MNIST example dataset in PyTorch, I met the this RuntimeError on the Master node</p>
<pre><code class="lang-auto">File "./torch-dist/mnist-dist.py", line 201, in &lt;module&gt;
    init_processes(args.rank, args.world_size, run, args.batch_size, backend=args.backend)
  File "./torch-dist/mnist-dist.py", line 196, in init_processes
    dist.init_process_group(backend=backend, world_size=world_size, rank=rank, init_method="env://")
  File "/home/dl/anaconda2/envs/torch-dist-py3.6/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 354, in init_process_group
    store, rank, world_size = next(rendezvous(url))
  File "/home/dl/anaconda2/envs/torch-dist-py3.6/lib/python3.6/site-packages/torch/distributed/rendezvous.py", line 143, in _env_rendezvous_handler
    store = TCPStore(master_addr, master_port, start_daemon)
RuntimeError: Address already in use

</code></pre>
<p>It seemed that multiprocessing with launch utility had problem<br>
I ran the code by invoking the launch utility as <a href="https://pytorch.org/docs/stable/distributed.html#launch-utility" rel="nofollow noopener">documentation</a> suggested</p>
<pre><code class="lang-auto">python -m torch.distributed.launch --nproc_per_node=2 --nnode=2 --node_rank=0 --master_addr='10.0.3.29' --master_port=9901 ./torch-dist/mnist-dist.py 
</code></pre>

leo-mao: <p>Here is my code, hope it help</p>
<pre><code class="lang-auto">import argparse
import time
import torch

import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.utils.data
import torch.utils.data.distributed
import torch.optim as optim

import torch.distributed


from torchvision import datasets, transforms
from torch.autograd import Variable

parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=256, metavar='N',
                    help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                    help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=5, metavar='N', help='number of epochs to train (default: 10)')

parser.add_argument('--lr', type=float, default=0.01, metavar='LR', help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M', help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                    help='how many batches to wait before logging training status')
parser.add_argument('--backend', type=str, default='nccl')
parser.add_argument('--rank', type=int, default=0)
parser.add_argument('--world-size', type=int, default=1)
parser.add_argument('--local_rank', type=int)
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=0)


def average_gradients(model):
    """ Gradient averaging"""
    size = float(dist.get_world_size())

    for param in model.parameters():
        dist.all_reduce_multigpu(param.grad.data, op=dist.ReduceOp.SUM)
        param.grad.data /= size


def summary_print(rank, loss, accuracy, average_epoch_time, tot_time):
    import logging
    size = float(dist.get_world_size())
    summaries = torch.tensor([loss, accuracy, average_epoch_time, tot_time], requires_grad=False, device='cuda')
    dist.reduce_multigpu(summaries, 0, op=dist.ReduceOp.SUM)
    if rank == 0:
        summaries /= size
        logging.critical('\n[Summary]System : Average epoch time(ex. 1.): {:.2f}s, Average total time : {:.2f}s '
                         'Average loss: {:.4f}\n, Average accuracy: {:.2f}%'
                         .format(summaries[2], summaries[3], summaries[0], summaries[1] * 100))


def train(model, optimizer, train_loader, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if args.world_size &gt; 1:
            average_gradients(model)
        if batch_idx % args.log_interval == 0:
            print('Train Epoch {} - {} / {:3.0f} \tLoss  {:.6f}'.format(
                epoch, batch_idx, 1.0 * len(train_loader.dataset) / len(data), loss))


def test(test_loader, model):
    model.eval()
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        # Varibale(data, volatile=True)
        data, target = Variable(data, requires_grad=False), Variable(target)
        output = model(data)
        test_loss += F.nll_loss(output, target, reduction='sum')
        pred = output.data.max(1, keepdim=True)[1]
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    test_loss /= len(test_loader.dataset)
    print('\nTest set : Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'
          .format(test_loss, correct, len(test_loader.dataset),
                  100. * correct / len(test_loader.dataset)))
    return test_loss, float(correct) / len(test_loader.dataset)


def config_print(rank, batch_size, world_size):
    print('----Torch Config----')
    print('rank : {}'.format(rank))
    print('mini batch-size : {}'.format(batch_size))
    print('world-size : {}'.format(world_size))
    print('backend : {}'.format(args.backend))
    print('--------------------')


def run(rank, batch_size, world_size):
    """ Distributed Synchronous SGD Example """
    config_print(rank, batch_size, world_size)

    train_dataset = datasets.MNIST('../MNIST_data/', train=True,
                                   transform=transforms.Compose([transforms.ToTensor(),
                                                                 transforms.Normalize((0.1307,), (0.3081,))]))

    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset, num_replicas=world_size,
                                                                    rank=rank)

    kwargs = {'num_workers': args.world_size, 'pin_memory': True} if args.cuda else {}

    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, **kwargs)
    test_loader = torch.utils.data.DataLoader(datasets.MNIST('../MNIST_data/', train=False,
                                                             transform=transforms.Compose(
                                                                 [transforms.ToTensor(),
                                                                  transforms.Normalize((0.1307,), (0.3081,))])),
                                              batch_size=args.test_batch_size, shuffle=True, **kwargs)

    model = Net()

    if args.cuda:
        torch.cuda.manual_seed(args.seed)
        torch.cuda.set_device(args.local_rank)
        device = torch.device('cuda', args.local_rank)
        model.cuda(device=device)
        model = nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank)
        cudnn.benchmark = True
    else:
        device = torch.device('cpu')

    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)

    torch.manual_seed(args.seed)
    tot_time = 0
    first_epoch = 0

    for epoch in range(1, args.epochs + 1):
        train_sampler.set_epoch(epoch)
        start_cpu_secs = time.time()
        train(model, optimizer, train_loader, epoch)
        end_cpu_secs = time.time()
        # print('start_cpu_secs {}'.format())
        print("Epoch {} of took {:.3f}s".format(
            epoch, end_cpu_secs - start_cpu_secs))

        tot_time += end_cpu_secs - start_cpu_secs
        print('Current Total time : {:.3f}s'.format(tot_time))
        if epoch == 1:
            first_epoch = tot_time

    test_loss, accuracy = test(test_loader, model)

    if args.epochs &gt; 1:
        average_epoch_time = float(tot_time - first_epoch) / (args.epochs - 1)
        print('Average epoch time(ex. 1.) : {:.3f}s'.format(average_epoch_time))
        print("Total time : {:.3f}s".format(tot_time))
        if args.world_size &gt; 1:
            summary_print(rank, test_loss, accuracy, average_epoch_time, tot_time)


def init_processes(rank, world_size, fn, batch_size, backend='gloo'):
    import os
    os.environ['MASTER_ADDR'] = '10.0.3.29'
    os.environ['MASTER_PORT'] = '9901'
    os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
    os.environ['NCCL_DEBUG'] = 'INFO'
    os.environ['GLOO_SOCKET_IFNAME'] = 'enp0s31f6'
    dist.init_process_group(backend=backend, world_size=world_size, rank=rank, init_method="env://")
    fn(rank, batch_size, world_size)


if __name__ == '__main__':
    init_processes(args.rank, args.world_size, run, args.batch_size, backend=args.backend)
    torch.multiprocessing.set_start_method('spawn')
    # processes = []
    # for rank in range(1):
    #     p = Process(target=init_processes,
    #                 args=(rank, args.world_size, run, args.batch_size, args.backend))
    #     p.start()
    #     processes.append(p)
    #
    # for p in processes:
    #     p.join()

</code></pre>

smth: <p>check <code>ps -elf | grep python</code>, and see if you have any processes from previous runs that still have not been killed. Maybe they are occupying that port and are still alive.</p>

leo-mao: <p>Thx for reply, no background process was found and the port was always available.</p>
<p>I have fixed a typo in my command, where <code>--master_addr ='10.0.3.29' --master_port=9901</code> had been typed as <code>--master_addr ='10.0.3.29' --master_port='10.0.3.29'</code>.</p>
<p>However the error has remained and at least one process has launched with the output</p>
<pre><code class="lang-auto">Traceback (most recent call last):
  File "mnist-dist.py", line 203, in &lt;module&gt;
    init_processes(args.rank, args.world_size, run, args.batch_size, backend=args.backend)
  File "mnist-dist.py", line 198, in init_processes
    dist.init_process_group(backend=backend, world_size=world_size, rank=rank, init_method="env://")
  File "/home/dl/anaconda2/envs/torch-dist-py3.6/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 354, in init_process_group
    store, rank, world_size = next(rendezvous(url))
  File "/home/dl/anaconda2/envs/torch-dist-py3.6/lib/python3.6/site-packages/torch/distributed/rendezvous.py", line 143, in _env_rendezvous_handler
    store = TCPStore(master_addr, master_port, start_daemon)
RuntimeError: Address already in use
dl-Server:10648:10648 [1] NCCL INFO NET : Using interface enp0s31f6:10.0.13.29&lt;0&gt;
dl-Server:10648:10648 [1] NCCL INFO NET/IB : Using interface enp0s31f6 for sideband communication
dl-Server:10648:10648 [1] NCCL INFO Using internal Network Socket
dl-Server:10648:10648 [1] NCCL INFO NET : Using interface enp0s31f6:10.0.13.29&lt;0&gt;
dl-Server:10648:10648 [1] NCCL INFO NET/Socket : 1 interfaces found
NCCL version 2.3.7+cuda9.0
dl-Server:10648:10648 [1] NCCL INFO rank 0 nranks 1
dl-Server:10648:10675 [1] NCCL INFO comm 0x7fd38c00d3f0 rank 0 nranks 1
dl-Server:10648:10675 [1] NCCL INFO CUDA Dev 1, IP Interfaces : enp0s31f6(PHB) 
dl-Server:10648:10675 [1] NCCL INFO Using 256 threads
dl-Server:10648:10675 [1] NCCL INFO Min Comp Cap 6
dl-Server:10648:10675 [1] NCCL INFO comm 0x7fd38c00d3f0 rank 0 nranks 1 - COMPLETE
Train Epoch 1 - 0 / 234         Loss  5.576234
Train Epoch 1 - 10 / 234        Loss  5.541703
Train Epoch 1 - 20 / 234        Loss  5.515630
Train Epoch 1 - 30 / 234        Loss  5.514045
Train Epoch 1 - 40 / 234        Loss  5.485974
Train Epoch 1 - 50 / 234        Loss  5.462833
Train Epoch 1 - 60 / 234        Loss  5.422739
Train Epoch 1 - 70 / 234        Loss  5.374931
Train Epoch 1 - 80 / 234        Loss  5.342307
Train Epoch 1 - 90 / 234        Loss  5.291063
Train Epoch 1 - 100 / 234       Loss  5.220443
Train Epoch 1 - 110 / 234       Loss  5.083968
Train Epoch 1 - 120 / 234       Loss  5.002171
Train Epoch 1 - 130 / 234       Loss  4.953607
Train Epoch 1 - 140 / 234       Loss  4.894170
Train Epoch 1 - 150 / 234       Loss  4.805832
Train Epoch 1 - 160 / 234       Loss  4.792961
Train Epoch 1 - 170 / 234       Loss  4.732522
Train Epoch 1 - 180 / 234       Loss  4.770869
Train Epoch 1 - 190 / 234       Loss  4.688779
Train Epoch 1 - 200 / 234       Loss  4.725927
Train Epoch 1 - 210 / 234       Loss  4.620460
Train Epoch 1 - 220 / 234       Loss  4.605740
Train Epoch 1 - 230 / 234       Loss  4.563363
Epoch 1 of took 5.263s
Current Total time : 5.263s
Train Epoch 2 - 0 / 234         Loss  4.555817
Train Epoch 2 - 10 / 234        Loss  4.603082
Train Epoch 2 - 20 / 234        Loss  4.618500
Train Epoch 2 - 30 / 234        Loss  4.520389
Train Epoch 2 - 40 / 234        Loss  4.531864
Train Epoch 2 - 50 / 234        Loss  4.467782
Train Epoch 2 - 60 / 234        Loss  4.447100
Train Epoch 2 - 70 / 234        Loss  4.424728
Train Epoch 2 - 80 / 234        Loss  4.433639
Train Epoch 2 - 90 / 234        Loss  4.372109
Train Epoch 2 - 100 / 234       Loss  4.435561
Train Epoch 2 - 110 / 234       Loss  4.351253
Train Epoch 2 - 120 / 234       Loss  4.306677
Train Epoch 2 - 130 / 234       Loss  4.343150
Train Epoch 2 - 140 / 234       Loss  4.243150
Train Epoch 2 - 150 / 234       Loss  4.347620
Train Epoch 2 - 160 / 234       Loss  4.217095
Train Epoch 2 - 170 / 234       Loss  4.255800
Train Epoch 2 - 180 / 234       Loss  4.282191
Train Epoch 2 - 190 / 234       Loss  4.249407
Train Epoch 2 - 200 / 234       Loss  4.209113
Train Epoch 2 - 210 / 234       Loss  4.194527
Train Epoch 2 - 220 / 234       Loss  4.220213
Train Epoch 2 - 230 / 234       Loss  4.201759
Epoch 2 of took 5.524s
Current Total time : 10.787s
Train Epoch 3 - 0 / 234         Loss  4.158279
Train Epoch 3 - 10 / 234        Loss  4.111032
Train Epoch 3 - 20 / 234        Loss  4.147989
Train Epoch 3 - 30 / 234        Loss  4.255434
Train Epoch 3 - 40 / 234        Loss  4.111946
Train Epoch 3 - 50 / 234        Loss  4.111733
Train Epoch 3 - 60 / 234        Loss  4.176547
Train Epoch 3 - 70 / 234        Loss  4.063233
Train Epoch 3 - 80 / 234        Loss  4.079793
Train Epoch 3 - 90 / 234        Loss  4.042555
Train Epoch 3 - 100 / 234       Loss  4.050662
Train Epoch 3 - 110 / 234       Loss  4.066662
Train Epoch 3 - 120 / 234       Loss  4.090621
Train Epoch 3 - 130 / 234       Loss  4.015823
Train Epoch 3 - 140 / 234       Loss  4.092526
Train Epoch 3 - 150 / 234       Loss  4.045942
Train Epoch 3 - 160 / 234       Loss  4.048071
Train Epoch 3 - 170 / 234       Loss  3.984233
Train Epoch 3 - 180 / 234       Loss  3.942847
Train Epoch 3 - 190 / 234       Loss  3.943717
Train Epoch 3 - 200 / 234       Loss  3.959996
Train Epoch 3 - 210 / 234       Loss  4.059554
Train Epoch 3 - 220 / 234       Loss  3.918130
Train Epoch 3 - 230 / 234       Loss  4.074725
Epoch 3 of took 5.308s
Current Total time : 16.095s
Train Epoch 4 - 0 / 234         Loss  3.944645
Train Epoch 4 - 10 / 234        Loss  3.923414
Train Epoch 4 - 20 / 234        Loss  3.944232
Train Epoch 4 - 30 / 234        Loss  3.978234
Train Epoch 4 - 40 / 234        Loss  3.950741
Train Epoch 4 - 50 / 234        Loss  3.913695
Train Epoch 4 - 60 / 234        Loss  3.907088
Train Epoch 4 - 70 / 234        Loss  4.026055
Train Epoch 4 - 80 / 234        Loss  3.854659
Train Epoch 4 - 90 / 234        Loss  3.954557
Train Epoch 4 - 100 / 234       Loss  3.880200
Train Epoch 4 - 110 / 234       Loss  3.911777
Train Epoch 4 - 120 / 234       Loss  3.866536
Train Epoch 4 - 130 / 234       Loss  3.957554
Train Epoch 4 - 140 / 234       Loss  3.930515
Train Epoch 4 - 150 / 234       Loss  3.950871
Train Epoch 4 - 160 / 234       Loss  3.845739
Train Epoch 4 - 170 / 234       Loss  3.905876
Train Epoch 4 - 180 / 234       Loss  3.884211
Train Epoch 4 - 190 / 234       Loss  4.034623
Train Epoch 4 - 200 / 234       Loss  3.863284
Train Epoch 4 - 210 / 234       Loss  3.899471
Train Epoch 4 - 220 / 234       Loss  3.837218
Train Epoch 4 - 230 / 234       Loss  3.862398
Epoch 4 of took 5.271s
Current Total time : 21.366s
Train Epoch 5 - 0 / 234         Loss  3.878444
Train Epoch 5 - 10 / 234        Loss  3.919256
Train Epoch 5 - 20 / 234        Loss  3.872842
Train Epoch 5 - 30 / 234        Loss  3.926296
Train Epoch 5 - 40 / 234        Loss  3.787506
Train Epoch 5 - 50 / 234        Loss  3.959824
Train Epoch 5 - 60 / 234        Loss  3.830777
Train Epoch 5 - 70 / 234        Loss  3.883856
Train Epoch 5 - 80 / 234        Loss  3.877614
Train Epoch 5 - 90 / 234        Loss  3.846863
Train Epoch 5 - 100 / 234       Loss  3.908530
Train Epoch 5 - 110 / 234       Loss  3.819784
Train Epoch 5 - 120 / 234       Loss  3.798816
Train Epoch 5 - 130 / 234       Loss  3.757388
Train Epoch 5 - 140 / 234       Loss  3.837136
Train Epoch 5 - 150 / 234       Loss  3.855000
Train Epoch 5 - 160 / 234       Loss  3.821057
Train Epoch 5 - 170 / 234       Loss  3.777124
Train Epoch 5 - 180 / 234       Loss  3.714392
Train Epoch 5 - 190 / 234       Loss  3.776406
Train Epoch 5 - 200 / 234       Loss  3.886733
Train Epoch 5 - 210 / 234       Loss  3.927509
Train Epoch 5 - 220 / 234       Loss  3.719052
Train Epoch 5 - 230 / 234       Loss  3.785564
Epoch 5 of took 5.216s
Current Total time : 26.582s

Test set : Average loss: 4.8523, Accuracy: 9453/10000 (94%)

Average epoch time(ex. 1.) : 5.330s
Total time : 26.582s

</code></pre>
<p>Would you like to have a look <img src="https://discuss.pytorch.org/images/emoji/apple/grin.png?v=6" title=":grin:" class="emoji" alt=":grin:"><img src="https://discuss.pytorch.org/images/emoji/apple/grin.png?v=6" title=":grin:" class="emoji" alt=":grin:"><img src="https://discuss.pytorch.org/images/emoji/apple/grin.png?v=6" title=":grin:" class="emoji" alt=":grin:">?</p>

leo-mao: <p>Maybe the way I was using launch utility was to blame, do you have any idea? Was I suppose to type same command on the master node and other worker nodes?</p>
<p>Any suggestion would be welcome, since I’ve been stuck for too long. <img src="https://discuss.pytorch.org/images/emoji/apple/zipper_mouth_face.png?v=6" title=":zipper_mouth_face:" class="emoji" alt=":zipper_mouth_face:"><img src="https://discuss.pytorch.org/images/emoji/apple/zipper_mouth_face.png?v=6" title=":zipper_mouth_face:" class="emoji" alt=":zipper_mouth_face:"><img src="https://discuss.pytorch.org/images/emoji/apple/zipper_mouth_face.png?v=6" title=":zipper_mouth_face:" class="emoji" alt=":zipper_mouth_face:"></p>

smth: <p><a class="mention" href="/u/leo-mao">@leo-mao</a> actually I have reproduced your issue, I am taking a look.</p>

teng-li: <p><a class="mention" href="/u/leo-mao">@leo-mao</a>, you should not set world_size and rank in torch.distributed.init_process_group, they are automatically set by torch.distributed.launch.</p>
<p>So please change that to dist.init_process_group(backend=backend, init_method=“env://”)</p>
<p>Also, you should not set WORLD_SIZE, RANK env variables in your code either since they will be set by launch utility.</p>

leo-mao: <p>you are right, it works after I delete <code>rank</code> and <code>world_size</code> parameter in <code>torch.distributed.init_process_group</code>, thanks a lot</p>

Lausanne: <p>Hi, I have met a situation which was little different with <a class="mention" href="/u/leo-mao">@leo-mao</a>. I want to train my model using one machine (node) which has multi-GPUs with torch.distributed. Since each time I just used 2 GPUs, I want to run several models at the same time. The problem is when I have started one model running with torch.distributed, others will get an error info " RuntimeError: Address already in use ". I set the initial way as <a class="mention" href="/u/teng-li">@teng-li</a> says. Maybe I ignore something like port setting ? I am confused about it. I will be very appreciate if someone can give me some suggestions.</p>

memray: <p>Got the same problem as <a class="mention" href="/u/lausanne">@Lausanne</a> has. Any thought into this?</p>

Lausanne: <p><a class="mention" href="/u/memray">@memray</a> Hi, I have solved my questions. The reason why this bug happened is that two programme used the same port. So my solution is using random port in your command line.<br>
For example, you can write your sh command as " python -m torch.distributed.launch --nproc_per_node=$NGPUS --master_port=$RANDOM  train.py ". Just use random number to occupy port. Hope my finding can solve your problem.</p>

zeal: <p>Hi, I am working on distributed.launch module recently, I have some question.</p>
<ol>
<li>I think with the launch and distributedDataParallel(model), you don’t need to average grads manually.<br>
2.During your training, does your gpu0 have more memory usage than the other gpus? I found that the other gpus have extra memory usage in gpu0, it’s annoying.</li>
</ol>

pietern: <p><a class="mention" href="/u/zeal">@zeal</a> Regarding 1, yes you don’t need to manually average gradients. Regarding 2, this is possible if you have some code that somehow uses GPU 0 at some time during execution of your program. This is not an invariant of using the distributed data parallel module.</p>

zeal: <p>It’s weird. I found out it seems like the Gpu cache release problem of pytorch. I add ‘torch.cuda.empty_cached’ in somewhere of my code and every gpu have same memory usage. But the program runs rather slower since the empty_cached was add in a for loop.<br>
I still cannot found out what’s wrong. Does it in theory, if you use distributed training, every gpu will have the same memory usage? I know that if you use dataparallel module, the gpu0 will have more memory consumtion.</p>

cognitiverobot: <p>Thanks. worked for me.</p>

Cyril-JZ: <p>Worked for me! Thanks!! <img src="https://discuss.pytorch.org/images/emoji/apple/smile.png?v=9" title=":smile:" class="emoji" alt=":smile:"></p>

ginobilinie: <p>Worked for me. Thanks.</p>

Liangqiong_Qu: <p>I have tried to donot set the rank and world_size, but it shows that “ValueError: Error initializing torch.distributed using env:// rendezvous: environment variable RANK expected, but not set”</p>
<pre><code>os.environ['MASTER_ADDR'] = '171.65.34.137'
os.environ['MASTER_PORT'] = '2000901'

#dist.init_process_group(backend, rank=rank, world_size=size)
dist.init_process_group(backend)
</code></pre>
<p>Do you have any idea what’s this comes from?</p>

rvarm1: <p>Could you please provide an example script to reproduce this, and the arguments that you’re passing in to DDP? thanks!</p>

Liangqiong_Qu: <p>Hi sorry for the late reply. I have solved the issues.</p>