NOT using multiprocessing but get CUDA error on google colab while using PyTorch DataLoader
I've cloned my GitHub repo into google colab and trying to load data using PyTorch's DataLoader.

global gpu, device
if torch.cuda.is_available():
    gpu = True
    device = 'cuda:0'
    torch.set_default_tensor_type('torch.cuda.FloatTensor')
    print("Using GPU")
else:
    gpu = False
    device = 'cpu'
    print("Using CPU")

data_transforms = transforms.Compose([
    #transforms.Resize(224),
    transforms.ToTensor(),
    transforms.Normalize([0.3112, 0.2636, 0.2047], [0.2419, 0.1972, 0.1554])
    ])
train_path = '/content/convLSTM/code/data/train/'
val_path = '/content/convLSTM/code/data/val/'
test_path = '/content/convLSTM/code/data/test/'

train_data = datasets.ImageFolder(root=train_path, transform=data_transforms)
val_data = datasets.ImageFolder(root=val_path, transform=data_transforms)
test_data = datasets.ImageFolder(root=test_path, transform=data_transforms)

train_loader = torch.utils.data.DataLoader(
    train_data,
    batch_size=18,
    num_workers=4,
    shuffle=False,
    pin_memory=True
    )

val_loader = torch.utils.data.DataLoader(
    val_data,
    batch_size=18,
    shuffle=False,
    num_workers=4,
    pin_memory=True
    )

test_loader = torch.utils.data.DataLoader(
    test_data,
    batch_size=18,
    shuffle=False,
    num_workers=4,
    pin_memory=True
    )
for batch_idx, (data, target) in enumerate(train_loader):
  print(batch_idx)
  if batch_idx==3:
    break


I'm getting the following error when I run the last for loop:

RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method


I tried num_workers = 1 instead of 4 but the error persists. I'm not using any multiprocessing. 

I also tried without setting torch.set_default_tensor_type('torch.cuda.FloatTensor') but the error persists.

Python  : 3.6.8 | PyTorch : 1.3.1 

What seems to be the problem?

Not sure if you fixed it already but just in case someone else reads this, using n number of works activates pytorch multi processing. To disable it you need to have the default number of workers which is 0, not 1.

Try setting num_workers to 0 or using the Torch Multiprocessing submodule.
