PyTorch provides support for GPU acceleration through CUDA. It's important to ensure that CUDA is properly configured and available in PyTorch installation to take advantage of GPU acceleration. Knowing if CUDA is available, allows making informed decisions about model deployment, resource allocation, and selecting appropriate hardware configurations for deep learning applications. This tutorial demonstrates how to check if CUDA is available in PyTorch.
Code
In the following code, we check whether CUDA is currently available or not on the current system using the torch.cuda.is_available
function. The boolean value indicating CUDA availability is printed to the console.
import torch
is_cuda = torch.cuda.is_available()
print(is_cuda)
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