单机多卡训练卷积神经网络

基于引用[1]提供的信息，我们可以使用PyTorch实现单机多卡训练卷积神经网络。具体步骤如下：

1.导入必要的库和模块

import torch
import torch.nn as nn
import torch.optim as optim
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP

2.定义模型

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84,10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

3.定义训练函数

def train(gpu, args):
    rank = args.nr * args.gpus + gpu
    dist.init_process_group(backend='nccl', init_method='env://', world_size=args.world_size, rank=rank)
    torch.manual_seed(0)
    model = Net().cuda(gpu)
    ddp_model = DDP(model, device_ids=[gpu])
    criterion = nn.CrossEntropyLoss().cuda(gpu)
    optimizer = optim.SGD(ddp_model.parameters(), lr=args.lr, momentum=args.momentum)

    train_sampler = torch.utils.data.distributed.DistributedSampler(args.train_dataset, num_replicas=args.world_size, rank=rank)
    train_loader = torch.utils.data.DataLoader(args.train_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True, sampler=train_sampler)

    for epoch in range(args.epochs):
        train_sampler.set_epoch(epoch)
        for batch_idx, (data, target) in enumerate(train_loader):
            data, target = data.cuda(gpu, non_blocking=True), target.cuda(gpu, non_blocking=True)
            optimizer.zero_grad()
            output = ddp_model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()
            if batch_idx % args.log_interval == 0:
                print('Rank [{}] Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(rank, epoch, batch_idx * len(data), len(train_sampler), 100. * batch_idx / len(train_loader), loss.item()))

4.定义主函数

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch-size', type=int, default=64)
    parser.add_argument('--epochs', type=int, default=10)
    parser.add_argument('--lr', type=float, default=0.01)
    parser.add_argument('--momentum', type=float, default=0.5)
    parser.add_argument('--num-workers', type=int, default=4)
    parser.add_argument('--gpus', type=int, default=2)
    parser.add_argument('--nr', type=int, default=0)
    parser.add_argument('--world-size', type=int, default=2)
    parser.add_argument('--dist-url', default='tcp://127.0.0.1:23456', type=str)
    parser.add_argument('--dist-backend', default='nccl', type=str)
    args = parser.parse_args()

    args.world_size = args.gpus * args.world_size
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '8888'
    mp.spawn(train, nprocs=args.gpus, args=(args,))

5.运行主函数

if __name__ == '__main__':
    main()

以上就是使用PyTorch实现单机多卡训练卷积神经网络的步骤。需要注意的是，这里使用了分布式数据并行（DDP）来实现单机多卡训练，因此需要在每个进程中初始化分布式环境。