def _train()

in bring-your-own-container/pytorch_extending_our_containers/container/cifar10/cifar10.py [0:0]

• 58 lines of code
• 8 McCabe index (conditional complexity)

def _train(args):
    is_distributed = len(args.hosts) > 1 and args.dist_backend is not None
    logger.debug("Distributed training - {}".format(is_distributed))

    if is_distributed:
        # Initialize the distributed environment.
        world_size = len(args.hosts)
        os.environ["WORLD_SIZE"] = str(world_size)
        host_rank = args.hosts.index(args.current_host)
        dist.init_process_group(backend=args.dist_backend, rank=host_rank, world_size=world_size)
        logger.info(
            "Initialized the distributed environment: '{}' backend on {} nodes. ".format(
                args.dist_backend, dist.get_world_size()
            )
            + "Current host rank is {}. Using cuda: {}. Number of gpus: {}".format(
                dist.get_rank(), torch.cuda.is_available(), args.num_gpus
            )
        )

    device = "cuda" if torch.cuda.is_available() else "cpu"
    logger.info("Device Type: {}".format(device))

    logger.info("Loading Cifar10 dataset")
    transform = transforms.Compose(
        [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
    )

    trainset = torchvision.datasets.CIFAR10(
        root=args.data_dir, train=True, download=False, transform=transform
    )
    train_loader = torch.utils.data.DataLoader(
        trainset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers
    )

    testset = torchvision.datasets.CIFAR10(
        root=args.data_dir, train=False, download=False, transform=transform
    )
    test_loader = torch.utils.data.DataLoader(
        testset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers
    )

    logger.info("Model loaded")
    model = Net()

    if torch.cuda.device_count() > 1:
        logger.info("Gpu count: {}".format(torch.cuda.device_count()))
        model = nn.DataParallel(model)

    model = model.to(device)

    criterion = nn.CrossEntropyLoss().to(device)
    optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)

    for epoch in range(0, args.epochs):
        running_loss = 0.0
        for i, data in enumerate(train_loader):
            # get the inputs
            inputs, labels = data
            inputs, labels = inputs.to(device), labels.to(device)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            if i % 2000 == 1999:  # print every 2000 mini-batches
                print("[%d, %5d] loss: %.3f" % (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0
    print("Finished Training")
    return _save_model(model, args.model_dir)