103 lines
4 KiB
Python
103 lines
4 KiB
Python
import os
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from datetime import datetime
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import argparse
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import torch.multiprocessing as mp
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import torchvision
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import torchvision.transforms as transforms
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import torch
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import torch.nn as nn
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import torch.distributed as dist
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from apex.parallel import DistributedDataParallel as DDP
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from apex import amp
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('-n', '--nodes', default=1, type=int, metavar='N')
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parser.add_argument('-g', '--gpus', default=1, type=int,
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help='number of gpus per node')
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parser.add_argument('-nr', '--nr', default=0, type=int,
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help='ranking within the nodes')
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parser.add_argument('--epochs', default=2, type=int, metavar='N',
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help='number of total epochs to run')
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args = parser.parse_args()
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args.world_size = args.gpus * args.nodes
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os.environ['MASTER_ADDR'] = '10.0.115.33'
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os.environ['MASTER_PORT'] = '8888'
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mp.spawn(train, nprocs=args.gpus, args=(args,))
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class ConvNet(nn.Module):
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def __init__(self, num_classes=10):
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super(ConvNet, self).__init__()
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self.layer1 = nn.Sequential(
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nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
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nn.BatchNorm2d(16),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=2))
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self.layer2 = nn.Sequential(
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nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
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nn.BatchNorm2d(32),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=2))
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self.fc = nn.Linear(7*7*32, num_classes)
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def forward(self, x):
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out = self.layer1(x)
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out = self.layer2(out)
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out = out.reshape(out.size(0), -1)
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out = self.fc(out)
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return out
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def train(gpu, args):
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rank = args.nr * args.gpus + gpu
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dist.init_process_group(backend='nccl', init_method='env://', world_size=args.world_size, rank=rank)
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torch.manual_seed(0)
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model = ConvNet()
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torch.cuda.set_device(gpu)
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model.cuda(gpu)
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batch_size = 100
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# define loss function (criterion) and optimizer
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criterion = nn.CrossEntropyLoss().cuda(gpu)
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optimizer = torch.optim.SGD(model.parameters(), 1e-4)
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# Wrap the model
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model = nn.parallel.DistributedDataParallel(model, device_ids=[gpu])
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# Data loading code
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train_dataset = torchvision.datasets.MNIST(root='./data',
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train=True,
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transform=transforms.ToTensor(),
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download=True)
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train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset,
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num_replicas=args.world_size,
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rank=rank)
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train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
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batch_size=batch_size,
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shuffle=False,
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num_workers=0,
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pin_memory=True,
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sampler=train_sampler)
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start = datetime.now()
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total_step = len(train_loader)
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for epoch in range(args.epochs):
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for i, (images, labels) in enumerate(train_loader):
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images = images.cuda(non_blocking=True)
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labels = labels.cuda(non_blocking=True)
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# Forward pass
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outputs = model(images)
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loss = criterion(outputs, labels)
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# Backward and optimize
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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if (i + 1) % 100 == 0 and gpu == 0:
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print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, args.epochs, i + 1, total_step,
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loss.item()))
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if gpu == 0:
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print("Training complete in: " + str(datetime.now() - start))
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if __name__ == '__main__':
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main()
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