Try to use Docker Cluster without GPU to run distributed training，but connect refused

molybdenum: <ol>
<li>
<p>2 nodes ，1 container/node</p>
</li>
<li>
<p>only cpu</p>
</li>
<li>
<p>code run in container</p>
</li>
<li>
<p>connect by tcp</p>
</li>
<li>
<pre><code class="lang-bash">docker run -it  run --rm -it --ipc=host --network=host xxx
</code></pre>
</li>
<li>
<pre><code class="lang-bash">python mnist.py --init-method tcp://ip:port --rank 0 --world-size 2
python mnist.py --init-method tcp://ip:port --rank 1 --world-size 2
</code></pre>
</li>
</ol>
<p>my code is here</p>
<pre><code class="lang-auto">from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import time

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

# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=1024, 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=20, 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_false', 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('--init-method', type=str, default='tcp://127.0.0.1:23456')
parser.add_argument('--rank', type=int)
parser.add_argument('--world-size',type=int)

args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()


dist.init_process_group(init_method=args.init_method,backend="gloo",world_size=args.world_size,rank=args.rank,group_name="pytorch_test")

torch.manual_seed(args.seed)
if args.cuda:
    torch.cuda.manual_seed(args.seed)

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

train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)

kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}

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


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)

model = Net()

model = torch.nn.parallel.DistributedDataParallelCPU(model)
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)

def train(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 batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

def test():
    model.eval()
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)
        test_loss += F.nll_loss(output, target, size_average=False).item() # sum up batch loss
        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
        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)))

tot_time=0;

for epoch in range(1, args.epochs + 1):

    train_sampler.set_epoch(epoch)
    start_cpu_secs = time.time()
    #long running
    train(epoch)
    end_cpu_secs = time.time()
    print("Epoch {} of {} took {:.3f}s".format(
        epoch , args.epochs , end_cpu_secs - start_cpu_secs))
    tot_time+=end_cpu_secs - start_cpu_secs
    test()

print("Total time= {:.3f}s".format(tot_time))
</code></pre>
<p>and then i got problem</p>
<pre><code class="lang-auto">
  File "mnsit.py", line 43, in &lt;module&gt;
    dist.init_process_group(init_method=args.init_method,backend="gloo",world_size=args.world_size,rank=args.rank,group_name="pytorch_test")
  File "/opt/conda/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 416, in init_process_group
    timeout=timeout)
  File "/opt/conda/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 484, in _new_process_group_helper
    timeout=timeout)
RuntimeError: [/pytorch/third_party/gloo/gloo/transport/tcp/pair.cc:760] connect [127.0.1.1]:10129: Connection refused
root@pcl2-2288H-V5:/workspace/recommendation# python mnsit.py --init-method tcp://10.10.16.62:45795 --rank 0 --world-size 2
Traceback (most recent call last):
  File "mnsit.py", line 43, in &lt;module&gt;
    dist.init_process_group(init_method=args.init_method,backend="gloo",world_size=args.world_size,rank=args.rank,group_name="pytorch_test")
  File "/opt/conda/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 416, in init_process_group
    timeout=timeout)
  File "/opt/conda/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 484, in _new_process_group_helper
    timeout=timeout)
RuntimeError: [/pytorch/third_party/gloo/gloo/transport/tcp/pair.cc:760] connect [127.0.1.1]:39850: Connection refused
</code></pre>

pietern: <p>The Gloo backend tries to resolve each process’ IP address by looking at the host name. This likely resolves to the loopback address for you, looking at the error message.</p>
<p>You can set <code>GLOO_SOCKET_IFNAME</code> to the network interface name you want to use for communication and it will resolve the right IP address.</p>
<p>Also see the <a href="https://pytorch.org/docs/stable/distributed.html#choosing-the-network-interface-to-use" rel="nofollow noopener"><code>torch.distributed</code> documentation</a>.</p>

molybdenum: <p>Thanks,it fixed,too many network interface the machine have,i tried all of then.</p>