I am fresh here…I want to use 8 gpu for  DataParallel in both forward(success) and backward(failed).I don’t know why. If I only use one GPU for backward like that( 72.criterion = nn.CrossEntropyLoss().cuda()), it can work. However ,I want to play with 8 gpu.Here is my code.


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


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')



args = parser.parse_args()


args.cuda = not args.no_cuda


torch.manual_seed(args.seed)


if args.cuda:


torch.cuda.manual_seed(args.seed)



kwargs = {‘num_workers’: 1, ‘pin_memory’: False} if args.cuda else {}


train_loader = torch.utils.data.DataLoader(


datasets.MNIST('../data', train=True, download=True,



               transform=transforms.Compose([



                   transforms.ToTensor(),



                   transforms.Normalize((0.1307,), (0.3081,))



               ])),



batch_size=args.batch_size, shuffle=True, **kwargs)



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.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,target):



    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, self.num_flat_features(x))



    x = F.relu(self.fc1(x))



    x = F.dropout(x, training=self.training)



    x = self.fc2(x)



    output = F.log_softmax(x)



    return output



def num_flat_features(self, x):



    size = x.size()[1:]  # all dimensions except the batch dimension



    num_features = 1



    for s in size:



        num_features *= s



    return num_features



model = Net()


if args.cuda:


model=torch.nn.DataParallel(model, device_ids=[0,1,2,3,4,5,6,7]).cuda()



criterion = torch.nn.DataParallel(nn.CrossEntropyLoss(), device_ids=[0,1,2,3,4,5,6,7]).cuda()



optimizer = optim.SGD(model.parameters(), lr=args.lr 1, 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 = torch.autograd.Variable(data), torch.autograd.Variable(target)



    optimizer.zero_grad()



    output = model(data,target)





    loss = criterion(output, target)



    loss.backward()



    optimizer.step()



    if batch_idx % args.log_interval == 0:



        correct = 0



        pred = output.data.max(1)[1]  # get the index of the max log-probability



        correct += pred.eq(target.data).sum()



        print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\t Accuracy: {}/{} ({:.0f}%)'.format(



            epoch, batch_idx * len(data), len(train_loader.dataset),



            100. * batch_idx / len(train_loader), loss.data[0],



            correct, len(target),



            100. * correct / len(target)))



def test(epoch):


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).data[0]



    pred = output.data.max(1)[1] # get the index of the max log-probability



    correct += pred.eq(target.data).cpu().sum()



test_loss = test_loss



test_loss /= len(test_loader) # loss function already averages over batch size



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(epoch)