Here is my code, hope it help
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 > 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 > 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 > 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()
