How to Accelerate PyTorch with CUDA and Verify GPU Usage

PyTorch is a popular open-source machine learning library that provides a flexible framework for building and training deep learning models. CUDA is a parallel computing platform and programming model developed by NVIDIA that allows developers to use GPUs to accelerate computation.

To accelerate PyTorch with CUDA, you first need to make sure that you have installed the appropriate NVIDIA drivers and CUDA toolkit on your system. Once you have done that, you can use the following steps to accelerate PyTorch with CUDA:

1. Import the necessary PyTorch libraries and modules, such as torch and torch.nn.
2. Check if CUDA is available on your system using the torch.cuda.is_available() method. If CUDA is available, set the device to use the GPU by calling the torch.cuda.set_device() method.
3. Move your PyTorch tensors and models to the GPU using the .to() method, which takes a device argument specifying the target device (CPU or GPU).

For example, the following code snippet shows how to move a PyTorch tensor to the GPU:

import torch

# create a tensor on the CPU
x = torch.tensor([1, 2, 3])

# check if CUDA is available
if torch.cuda.is_available():
    # set the device to use the GPU
    device = torch.device("cuda")
    torch.cuda.set_device(0)

    # move the tensor to the GPU
    x = x.to(device)

4. Use CUDA-enabled PyTorch operations to accelerate computation. For example, you can use the torch.cuda.amp package to perform mixed-precision training on the GPU.

By following these steps, you can accelerate PyTorch computations with CUDA, and take advantage of the parallel computing power of GPUs to speed up training and inference of deep learning models.