can't read data, the kernel dies

[2naff]

Please don't ask for help here. Use the forums device : Mac M1 Pro

I have practiced through colab so far, but I wanted to implement fastai locally after I recently encountered Pythorch's gpu support.

I succeeded in making blocks, but I found the kernel down when I loaded the dataset from the block.

|| code ||

pets = DataBlock(blocks = (ImageBlock, CategoryBlock), get_items = get_image_files, splitter = RandomSplitter(seed=42), get_y = usingattr(RegexLabeller(r'(.+)\d+.jpg$'), 'name'), item_tfms = Resize(460), batch_tfms=aug_transforms(size=244, min_scale=0.75))

no problems until here

dls = pets.dataloaders(path/'images') << when run this line, the kernel down

I also modified the memory capacity, but it didn't change.

fastai 2.7.9 pypi_0 pypi fastbook 0.0.26 pypi_0 pypi torch 1.13.0.dev20220807 pypi_0 pypi torchaudio 0.14.0.dev20220603 pypi_0 pypi torchvision 0.14.0.dev20220807 pypi_0 pypi this is version what i installed using conda.

what can i do? or Is the support still unstable?

[TomPham97]

dls = pets.dataloaders(path/'images') << when run this line, the kernel down

Was there an error prompt at all before the kernel shut down? And why did you assume it was the memory limit?

[TomPham97]

Apparently, torch.device("mps") on M1 GPU is analogous to torch.device("cuda") on an Nvidia GPU. I recommend running these codes from the official Pytorch site first to see if it helps.

# Check that MPS is available
if not torch.backends.mps.is_available():
    if not torch.backends.mps.is_built():
        print("MPS not available because the current PyTorch install was not "
              "built with MPS enabled.")
    else:
        print("MPS not available because the current MacOS version is not 12.3+ "
              "and/or you do not have an MPS-enabled device on this machine.")

else:
    mps_device = torch.device("mps")

    # Create a Tensor directly on the mps device
    x = torch.ones(5, device=mps_device)
    # Or
    x = torch.ones(5, device="mps")

    # Any operation happens on the GPU
    y = x * 2

    # Move your model to mps just like any other device
    model = YourFavoriteNet()
    model.to(mps_device)

    # Now every call runs on the GPU
    pred = model(x)

If not, I suggest the following alternatives:

# Use GPU
dls = pets.dataloaders(path/'images', device = 'mps')

# Or use CPU
dls = pets.dataloaders(path/'images', device = 'cpu')