Machine learning 系统在pytorch的第一次历元训练后挂起

所以，我尝试在PyTorch中使用GitHub存储库中的ImageNet示例来训练ResNet模型

下面是我的训练方法的样子（它与示例中的几乎相似）

系统信息： GPU:Nvidia Titan XP 内存：32 Gb

PyTorch:0.4.0

当我运行此代码时，训练从历元0开始

Epoch: [0][0/108]   Time 5.644 (5.644)  Data 1.929 (1.929)  Loss 6.9052 (6.9052)    Prec@1 0.000 (0.000)

然后远程服务器自动断开连接。事情发生了五次

这是数据加载器：

#Load the Data --> TRAIN
    traindir = 'train'
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    train_dataset = datasets.ImageFolder(traindir, transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))
    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers,
        pin_memory=cuda
    )

    # Load the data --> Validation
    valdir = 'valid'
    valid_loader = torch.utils.data.DataLoader(
        datasets.ImageFolder(valdir, transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ])),
        batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers,
        pin_memory=cuda
    )

    if args.evaluate:
        validate(valid_loader, model, criterion, epoch=0)
        return

    # Start
    for epoch in range(args.start_epoch, args.epochs):
        adjust_learning_rate(optimizer, epoch)

        # train for epoch
        train(train_loader, model, criterion, optimizer, epoch)

        # evaluate on valid
        prec1 = validate(valid_loader, model, criterion, epoch)

        # remember best prec1 and save checkpoint
        is_best = prec1 > best_prec1
        best_prec1 = max(prec1, best_prec1)
        save_checkpoint({
            'epoch': epoch + 1,
            'arch': args.arch,
            'state_dict': model.state_dict(),
            'best_prec1': best_prec1,
            'optimizer': optimizer.state_dict()
        }, is_best)

对于加载程序，使用以下参数：

args.num_workers = 4
args.batch_size = 32
pin_memory = torch.cuda.is_available()

---

我的方法有什么问题吗？

似乎pytorch的数据加载程序中有一个bug

尝试args.num\u workers=0

args.num_workers = 4
args.batch_size = 32
pin_memory = torch.cuda.is_available()