def _train()

in pytorch_managed_spot_training_checkpointing/source_dir/cifar10.py [0:0]

• 63 lines of code
• 11 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 os.path.isdir(args.checkpoint_path):
        print("Checkpointing directory {} exists".format(args.checkpoint_path))
    else:
        print("Creating Checkpointing directory {}".format(args.checkpoint_path))
        os.mkdir(args.checkpoint_path)
    
    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)
        os.environ['RANK'] = str(host_rank)
        dist.init_process_group(backend=args.dist_backend, rank=host_rank, world_size=world_size)
        print(
            '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'
    print("Device Type: {}".format(device))

    print("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)

    print("Model loaded")
    model = Net()

    if torch.cuda.device_count() > 1:
        print("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)

    # Check if checkpoints exists
    if not os.path.isfile(args.checkpoint_path + '/checkpoint.pth'):
        epoch_number = 0
    else:    
        model, optimizer, epoch_number = _load_checkpoint(model, optimizer, args)        
    
    for epoch in range(epoch_number, 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
            
        _save_checkpoint(model, optimizer, epoch, loss, args)
            
    print('Finished Training')
    return _save_model(model, args.model_dir)