How to Verify if Your Pytorch is Correctly Setup with GPU Support in WSL

If you are working with Pytorch in Windows Subsystem for Linux (WSL) and want to ensure that your Pytorch installation has GPU support, you can follow these steps to verify it.

Firstly, you need to make sure that you have installed Pytorch with GPU support. You can install Pytorch with GPU support by using the following command:

$ pip install torch torchvision

Once you have installed Pytorch with GPU support, you can check if it is correctly setup by running the following Python code:

import torch
print(torch.cuda.is_available())

If the output of the above code is True, it indicates that Pytorch is correctly setup with GPU support in your WSL environment. This means that you can now leverage the power of your GPU for training deep learning models using Pytorch.

Additionally, you can also check the CUDA version of your Pytorch installation by running the following code:

print(torch.version.cuda)

This will display the version of CUDA that is being used by Pytorch. Make sure that the CUDA version matches the version supported by your GPU for optimal performance.

By following these steps, you can easily verify if your Pytorch installation in WSL has GPU support enabled, allowing you to take advantage of the accelerated computations offered by your GPU for deep learning tasks.