How to Install WSL with CUDA support and Pytorch for Data Science and Machine Learning

If you are a data scientist or machine learning enthusiast who wants to leverage the power of CUDA support and Pytorch in the Windows Subsystem for Linux (WSL), follow these steps to set up your environment:

Step 1: Install WSL

First, you need to install WSL on your Windows system. You can follow the official Microsoft documentation for step-by-step instructions on installing WSL.

Step 2: Enable CUDA support

Once WSL is installed, you need to enable CUDA support in WSL by installing the necessary libraries and drivers. Make sure you have the latest NVIDIA graphics drivers installed on your system.

Step 3: Install Pytorch

Next, you need to install Pytorch in your WSL environment. You can do this by using the package manager of your choice, such as pip or conda. Make sure to install the CUDA version of Pytorch to take advantage of GPU acceleration.

Step 4: Verify installation

Finally, verify that CUDA support and Pytorch are correctly installed in your WSL environment by running a simple Pytorch script that utilizes GPU acceleration.

By following these steps, you can set up WSL with CUDA support and Pytorch for your data science and machine learning projects. Enjoy harnessing the power of GPU acceleration for faster model training and inference!