main.py

from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable

#=====START: ADDED FOR DISTRIBUTED======
'''Add custom module for distributed'''

try:
    from apex.parallel import DistributedDataParallel as DDP
except ImportError:
    raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this example.")

'''Import distributed data loader'''
import torch.utils.data
import torch.utils.data.distributed

'''Import torch.distributed'''
import torch.distributed as dist

#=====END:   ADDED FOR DISTRIBUTED======

# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, 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=10, 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')

#======START: ADDED FOR DISTRIBUTED======
'''
Add some distributed options. For explanation of dist-url and dist-backend please see
http://pytorch.org/tutorials/intermediate/dist_tuto.html

--world-size and --rank are required parameters as they will be used by the multiproc.py launcher
but do not have to be set explicitly.
'''

parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
                    help='distributed backend')
parser.add_argument('--world-size', default=1, type=int,
                    help='Number of GPUs to use. Can either be manually set ' +
                    'or automatically set by using \'python -m multiproc\'.')
parser.add_argument('--rank', default=0, type=int,
                    help='Used for multi-process training. Can either be manually set ' +
                    'or automatically set by using \'python -m multiproc\'.')
#=====END:   ADDED FOR DISTRIBUTED======

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

#======START: ADDED FOR DISTRIBUTED======
'''Add a convenience flag to see if we are running distributed'''
args.distributed = args.world_size > 1

'''Check that we are running with cuda, as distributed is only supported for cuda.'''
if args.distributed:
    assert args.cuda, "Distributed mode requires running with CUDA."

if args.distributed:
    '''
    Set cuda device so everything is done on the right GPU.
    THIS MUST BE DONE AS SOON AS POSSIBLE.
    '''
    torch.cuda.set_device(args.rank % torch.cuda.device_count())

    '''Initialize distributed communication'''
    dist.init_process_group(args.dist_backend, init_method=args.dist_url,
                            world_size=args.world_size)

#=====END:   ADDED FOR DISTRIBUTED======

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


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

#=====START: ADDED FOR DISTRIBUTED======
'''
Change sampler to distributed if running distributed.
Shuffle data loader only if distributed.
'''
train_dataset = datasets.MNIST('../data', train=True, download=True,
                               transform=transforms.Compose([
                                   transforms.ToTensor(),
                                   transforms.Normalize((0.1307,), (0.3081,))
                               ]))

if args.distributed:
    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
    train_sampler = None

train_loader = torch.utils.data.DataLoader(
    train_dataset, sampler=train_sampler,
    batch_size=args.batch_size, shuffle=(train_sampler is None), **kwargs
)

#=====END:   ADDED FOR DISTRIBUTED======

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()
if args.cuda:
    model.cuda()

#=====START: ADDED FOR DISTRIBUTED======
'''
Wrap model in our version of DistributedDataParallel.
This must be done AFTER the model is converted to cuda.
'''

if args.distributed:
    model = DDP(model)
#=====END:   ADDED FOR DISTRIBUTED======

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.data[0]))

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).data[0] # 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)))


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