How to check if pytorch is using the GPU?

This should work:

import torch

torch.cuda.is_available()
>>> True

torch.cuda.current_device()
>>> 0

torch.cuda.device(0)
>>> <torch.cuda.device at 0x7efce0b03be0>

torch.cuda.device_count()
>>> 1

torch.cuda.get_device_name(0)
>>> 'GeForce GTX 950M'

This tells me CUDA is available and can be used in one of your devices (GPUs). And currently, Device 0 or the GPU GeForce GTX 950M is being used by PyTorch.

As it hasn't been proposed here, I'm adding a method using torch.device, as this is quite handy, also when initializing tensors on the correct device.

# setting device on GPU if available, else CPU
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Using device:', device)
print()

#Additional Info when using cuda
if device.type == 'cuda':
    print(torch.cuda.get_device_name(0))
    print('Memory Usage:')
    print('Allocated:', round(torch.cuda.memory_allocated(0)/1024**3,1), 'GB')
    print('Cached:   ', round(torch.cuda.memory_reserved(0)/1024**3,1), 'GB')

Edit: torch.cuda.memory_cached has been renamed to torch.cuda.memory_reserved. So use memory_cached for older versions.

Output:

Using device: cuda

Tesla K80
Memory Usage:
Allocated: 0.3 GB
Cached:    0.6 GB

As mentioned above, using device it is possible to:

To move tensors to the respective device:
```
  torch.rand(10).to(device)
```
To create a tensor directly on the device:
```
  torch.rand(10, device=device)
```

Which makes switching between CPU and GPU comfortable without changing the actual code.

Edit:

As there has been some questions and confusion about the cached and allocated memory I'm adding some additional information about it:

torch.cuda.max_memory_cached(device=None)

Returns the maximum GPU memory managed by the caching allocator in bytes for a given device.
torch.cuda.memory_allocated(device=None)

Returns the current GPU memory usage by tensors in bytes for a given device.

You can either directly hand over a device as specified further above in the post or you can leave it None and it will use the current_device().

Additional note: Old graphic cards with Cuda compute capability 3.0 or lower may be visible but cannot be used by Pytorch!
Thanks to hekimgil for pointing this out! - "Found GPU0 GeForce GT 750M which is of cuda capability 3.0. PyTorch no longer supports this GPU because it is too old. The minimum cuda capability that we support is 3.5."

How to check if pytorch is using the GPU?

Edit:

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