Parallel processing 如何在pytorch中进行并行处理

我正在研究一个深度学习问题。我正在用Pytork解决它。我有两个GPU在同一台机器上（16273MiB、12193MiB）。我想使用这两个GPU进行培训（视频数据集）

我得到一个警告：

您的GPU之间存在不平衡。您可能希望排除GPU 1，因为 少于GPU 0的75%的内存或内核。您可以通过设置 将device_id参数设置为DataParallel，或设置CUDA_VISIBLE_DEVICES 环境变量。 warnings.warn（不平衡警告格式（设备标识[min\u pos]，设备标识[max\u pos]））

我还得到一个错误：

raise TypeError（'未为CPU张量实现广播功能'） TypeError:未为CPU张量实现广播功能

if __name__ == '__main__':

    opt.scales = [opt.initial_scale]
    for i in range(1, opt.n_scales):
        opt.scales.append(opt.scales[-1] * opt.scale_step)
    opt.arch = '{}-{}'.format(opt.model, opt.model_depth)
    opt.mean = get_mean(opt.norm_value)
    opt.std = get_std(opt.norm_value)
    print("opt",opt)
    with open(os.path.join(opt.result_path, 'opts.json'), 'w') as opt_file:
        json.dump(vars(opt), opt_file)

    torch.manual_seed(opt.manual_seed)

    model, parameters = generate_model(opt)
    #print(model)

    pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    print("Total number of trainable parameters: ", pytorch_total_params)

    # Define Class weights
    if opt.weighted:
        print("Weighted Loss is created")
        if opt.n_finetune_classes == 2:
            weight = torch.tensor([1.0, 3.0])
        else:
            weight = torch.ones(opt.n_finetune_classes)
    else:
        weight = None

    criterion = nn.CrossEntropyLoss()
    if not opt.no_cuda:



        criterion = nn.DataParallel(criterion.cuda())




    if opt.no_mean_norm and not opt.std_norm:
        norm_method = Normalize([0, 0, 0], [1, 1, 1])
    elif not opt.std_norm:
        norm_method = Normalize(opt.mean, [1, 1, 1])
    else:
        norm_method = Normalize(opt.mean, opt.std)

        train_loader = torch.utils.data.DataLoader(
            training_data,
            batch_size=opt.batch_size,
            shuffle=True,
            num_workers=opt.n_threads,
            pin_memory=True)
        train_logger = Logger(
            os.path.join(opt.result_path, 'train.log'),
            ['epoch', 'loss', 'acc', 'precision','recall','lr'])
        train_batch_logger = Logger(
            os.path.join(opt.result_path, 'train_batch.log'),
            ['epoch', 'batch', 'iter', 'loss', 'acc', 'precision', 'recall', 'lr'])

        if opt.nesterov:
            dampening = 0
        else:
            dampening = opt.dampening
        optimizer = optim.SGD(
            parameters,
            lr=opt.learning_rate,
            momentum=opt.momentum,
            dampening=dampening,
            weight_decay=opt.weight_decay,
            nesterov=opt.nesterov)
        # scheduler = lr_scheduler.ReduceLROnPlateau(
        #     optimizer, 'min', patience=opt.lr_patience)
    if not opt.no_val:
        spatial_transform = Compose([
            Scale(opt.sample_size),
            CenterCrop(opt.sample_size),
            ToTensor(opt.norm_value), norm_method
        ])




    print('run')
    for i in range(opt.begin_epoch, opt.n_epochs + 1):
        if not opt.no_train:
            adjust_learning_rate(optimizer, i, opt.lr_steps)
            train_epoch(i, train_loader, model, criterion, optimizer, opt,
                        train_logger, train_batch_logger)

我还对我的列车文件进行了更改：

      model = nn.DataParallel(model(),device_ids=[0,1]).cuda() 
        outputs = model(inputs)

它似乎工作不正常，并且出现了错误。请告诉我，我是pytorch的新手

---

谢谢

如本链接所述，您必须先执行model.cuda（），然后才能将其传递给nn.DataParallel

net = nn.DataParallel(model.cuda(), device_ids=[0,1])