I have searched on here but I found only outdated posts.
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?
Is there a way to reliably enable CUDA on the whole model?
Also, what does MyModel() mean? I need more tangible examples, like code examples. (This is the post I am referring to)
Answer
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.