For developers working on machine learning (ML) and artificial intelligence (AI) projects on Windows, the Windows Subsystem for Linux (WSL) is a game-changer. WSL2, the newest version, allows you to run a full Linux distribution seamlessly alongside Windows. This unlocks a whole world of powerful AI/ML tools and libraries that were previously difficult to use on Windows.
Why Use WSL2 for ML/AI Development? :
The AI/ML ecosystem is largely built around Linux and open source tools. Many cutting-edge frameworks like TensorFlow, PyTorch, Keras and libraries like NumPy, SciPy, Pandas, etc. were created for Linux/Unix environments first. While Windows ports do exist for some of these tools, they can often lag behind the latest releases.
By using WSL2, you get the newest versions of all the AI/ML libraries and utilities without having to fight against Windows incompatibilities. Your Linux environment seamlessly integrates with Windows, allowing you to use your favorite Windows tools like Visual Studio Code while still having full access to the Linux command line.
Getting Started with WSL :
First, you’ll need to install WSL2 and a Linux distribution like Ubuntu on your Windows machine. Microsoft has detailed instructions, but the process is fairly straightforward.
Once installed, you can access your Linux distro’s terminal right from the Windows start menu. I recommend installing Visual Studio Code and some WSL extensions to get full integration between the Linux and Windows environments.
Setting up Your ML Environment :
With your Linux environment ready, you can start installing Python and key ML/AI libraries like:
- NumPy
- – SciPy
- – Pandas
- – Matplotlib
- – TensorFlow
- – Keras
- – PyTorch
- – Scikit-Learn
- – And many more
You can either install each library individually using your Linux distro’s package manager (e.g. apt on Ubuntu) or use a Python environment manager like Anaconda or virtualenv.
I’d also recommend installing CUDA and cuDNN libraries to enable GPU acceleration for deep learning computations if you have an Nvidia GPU.
From there, you’re ready to start coding your ML/AI models and running experiments while benefiting from WSL’s Linux integration with Windows.
Docker + WSL for Easy Environment Management :
A nice bonus for using WSL2 is that you can run GPU-accelerated Docker containers right on your Windows machine. You can package your entire ML/AI environment and dependencies into a reproducible Docker image to easily share or move environments between machines.
Embrace the Best of Both Worlds:
With WSL2, Windows developers no longer have to compromise when working on ML and AI projects. You get the full power of the open source Linux AI/ML software stack combined with the productivity of your Windows development environment. It’s a perfect fusion enabling you to build intelligent systems more efficiently than ever before.
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