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Adding distributed example with summaries (#118)
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from __future__ import print_function | ||
import argparse | ||
import torch | ||
import torch.distributed as dist | ||
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 | ||
from tensorboardX import SummaryWriter | ||
# 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') | ||
parser.add_argument('--dir', default='logs', metavar='L', | ||
help='directory where summary logs are stored') | ||
args = parser.parse_args() | ||
args.cuda = not args.no_cuda and torch.cuda.is_available() | ||
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torch.manual_seed(args.seed) | ||
if args.cuda: | ||
torch.cuda.manual_seed(args.seed) | ||
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kwargs = {'num_workers': 1, 'pin_memory': True} 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) | ||
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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) | ||
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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) | ||
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model = Net() | ||
if args.cuda: | ||
model.cuda() | ||
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print('Learning rate: {} Momentum: {} Logs dir: {}'.format(args.lr, args.momentum, args.dir)) | ||
writer = SummaryWriter(args.dir) | ||
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) | ||
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def average_gradients(): | ||
world_size = dist.get_world_size() | ||
for param in model.parameters(): | ||
dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM) | ||
param.grad.data /= float(world_size) | ||
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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() | ||
average_gradients() | ||
optimizer.step() | ||
if batch_idx % args.log_interval == 0: | ||
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tloss={:.4f}'.format( | ||
epoch, batch_idx * len(data), len(train_loader.dataset), | ||
100. * batch_idx / len(train_loader), loss.item())) | ||
niter = epoch*len(train_loader)+batch_idx | ||
writer.add_scalar('loss', loss.item(), niter) | ||
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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, size_average=False).item() # sum up batch loss | ||
pred = output.data.max(1)[1] # get the index of the max log-probability | ||
correct += pred.eq(target.data).cpu().sum() | ||
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test_loss /= len(test_loader.dataset) | ||
print('\naccuracy={:.4f}\n'.format(float(correct) / len(test_loader.dataset))) | ||
writer.add_scalar('accuracy', float(correct) / len(test_loader.dataset), epoch) | ||
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def init_processes(backend='tcp'): | ||
""" Initialize the distributed environment. """ | ||
dist.init_process_group(backend) | ||
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if __name__ == "__main__": | ||
init_processes() | ||
for epoch in range(1, args.epochs + 1): | ||
train(epoch) | ||
test(epoch) |
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