Google Colaboratory PyTorch GPU/TPU Setup - Automatically switch between GPUs and CPUs. I make the code here the second cell to run on all the Colab notebooks.

colab_gpu_pytorch.py

'''
MIT License

Copyright (c) 2020 Ronald Seoh

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'''

# torch.device / CUDA Setup
import torch

use_cuda = True
use_colab_tpu = False
colab_tpu_available = False

if use_colab_tpu:
    try:
        assert os.environ['COLAB_TPU_ADDR']
        colab_tpu_available = True
    except:
        colab_tpu_available = True

if use_cuda and torch.cuda.is_available():
    torch_device = torch.device('cuda')

    # Set this to True to make your output immediately reproducible
    # Note: https://pytorch.org/docs/stable/notes/randomness.html
    torch.backends.cudnn.deterministic = False
    
    # Disable 'benchmark' mode: Set this False if you want to measure running times more fairly
    # Note: https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936
    torch.backends.cudnn.benchmark = True
    
    # Faster Host to GPU copies with page-locked memory
    use_pin_memory = True 

    # CUDA libraries version information
    print("CUDA Version: " + str(torch.version.cuda))
    print("cuDNN Version: " + str(torch.backends.cudnn.version()))
    print("CUDA Device Name: " + str(torch.cuda.get_device_name()))
    print("CUDA Capabilities: "+ str(torch.cuda.get_device_capability()))

elif use_colab_tpu and colab_tpu_available:
    # This needs to be installed separately
    # https://github.com/pytorch/xla/blob/master/contrib/colab/getting-started.ipynb
    import torch_xla 
    import torch_xla.core.xla_model as xm

    torch_device = xm.xla_device()

else:
    torch_device = torch.device('cpu')
    use_pin_memory = False