PyTorch device - GPU - CPU

In PyTorch, specifying the device for computations is crucial for leveraging hardware accelerators like GPUs.

1. device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
   

2. gpu_indx = 0
   device = torch.device(f"cuda:{gpu_indx}" if torch.cuda.is_available() else "cpu")
   

Key Differences:

• Default GPU Selection:
  • In the first snippet, torch.device('cuda') selects the current CUDA device, which defaults to GPU 0 unless changed elsewhere in the code.
  • In the second snippet, torch.device(f"cuda:{gpu_indx}") explicitly specifies GPU 0 (since gpu_indx is set to 0).
• Flexibility:
  • The first approach is straightforward and suitable when we intend to use the default GPU.
  • The second approach offers flexibility, allowing us to set gpu_indx to any available GPU index, which is beneficial in multi-GPU systems.

Considerations:

• Multi-GPU Systems:
  • If the system has multiple GPUs and we want to utilize a specific one, the second approach is preferable. By changing the value of gpu_indx, we can direct PyTorch to use a particular GPU.
• Consistency:
  • Ensure that the rest of the code consistently uses the device variable to move models and tensors to the correct device. For example:

    model.to(device)
    tensor = tensor.to(device)
    

In summary, both approaches aim to set the computing device for PyTorch operations. The first is concise and defaults to GPU 0 when available, while the second provides explicit control over which GPU to use, making it more suitable for scenarios involving multiple GPUs.