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pythonpytorch
G. Ramistella
G. Ramistella

Reputation: 1407

Using CUDA with pytorch?

Is there a way to reliably enable CUDA on the whole model?

I want to run the training on my GPU. I found on some forums that I need to apply .cuda() on anything I want to use CUDA with (I've applied it to everything I could without making the program crash). Surprisingly, this makes the training even slower.

Then, I found that you could use this torch.set_default_tensor_type('torch.cuda.FloatTensor') to use CUDA. With both enabled, nothing changes. What is happening?

Upvotes: 43

Views: 204366

Answers (2)

iacob
iacob

Reputation: 24201

With both enabled, nothing changes.

That is because you have already set every tensor to GPU.

Is there a way to reliably enable CUDA on the whole model?

model.to('cuda')

I've applied it to everything I could

You only need to apply it to tensors the model will be interacting with, generally:

  • the model's pramaters model.to('cuda')
  • the features data features = features.to('cuda')
  • the target data targets = targets.to('cuda')

Upvotes: 3

M. Deckers
M. Deckers

Reputation: 1293

You can use the tensor.to(device) command to move a tensor to a device.

The .to() command is also used to move a whole model to a device, like in the post you linked to.

Another possibility is to set the device of a tensor during creation using the device= keyword argument, like in t = torch.tensor(some_list, device=device)

To set the device dynamically in your code, you can use 

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

to set cuda as your device if possible.

There are various code examples on PyTorch Tutorials and in the documentation linked above that could help you.

Upvotes: 89

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