Hello everyone! Have you ever wanted to create your own AI development environment right on your Windows 11 ARM64 machine? Whether you're a machine learning enthusiast, a developer transitioning to ARM-based devices, or simply curious about containerized environments, this guide is for you. In this post, we'll walk step-by-step through building a fully functional AI sandbox using WSL2 and Docker. It's easier than you think, and the flexibility it gives you is worth every minute invested.
System Requirements and Setup Overview
Before diving in, it's important to ensure your system meets the necessary requirements to run WSL2 and Docker on Windows 11 ARM64. Here's what you'll need:
| Component | Minimum Requirement |
|---|---|
| Operating System | Windows 11 ARM64 (Build 22000+) |
| RAM | 8 GB (16 GB recommended) |
| Storage | 20 GB free space |
| Virtualization | Enabled in BIOS/UEFI |
| Internet Access | Required for downloading packages |
In this setup, we will install WSL2 (Windows Subsystem for Linux) and Docker, and then configure a containerized environment suitable for AI development with Python, PyTorch, and more. Let’s proceed to the installation steps!
Installing WSL2 on Windows 11 ARM64
WSL2 is a powerful way to run Linux distributions natively on Windows. Thankfully, Windows 11 ARM64 supports WSL2 well with some specific steps:
- Open PowerShell as Administrator.
- Run the following command to install WSL: wsl --install
- Reboot your machine after installation.
- To check the installed distributions, run: wsl --list --online
- For AI workloads, Ubuntu is recommended: wsl --install -d Ubuntu
After installation, you can launch Ubuntu from the Start menu. The first launch will prompt you to create a username and password. Once set, you're ready to enter the Linux shell and begin installing development tools.
Setting Up Docker and Container Runtime
With WSL2 ready, let’s install Docker to run containerized AI tools. The ARM64 version of Docker Desktop for Windows 11 has improved significantly in recent releases.
- Download Docker Desktop for Windows (ARM64) from the official Docker website.
- During installation, make sure "Use WSL2 backend" is checked.
- After installation, launch Docker Desktop and ensure it's running.
- Test the installation by running: docker version
- In WSL2 Ubuntu terminal, you can test Docker CLI by: docker run hello-world
If everything works, you’re ready to use Docker to build and manage containers for your AI sandbox. Next, we’ll construct the environment itself!
Building the AI Sandbox Environment
Now comes the exciting part — building your own AI sandbox. We'll create a Docker container based on a popular AI stack with support for Python, Jupyter, and PyTorch.
- Create a new Dockerfile: FROM ubuntu:22.04 RUN apt update && apt install -y python3 python3-pip git RUN pip3 install torch torchvision torchaudio jupyterlab CMD ["jupyter", "lab", "--ip=0.0.0.0", "--allow-root"]
- Build the Docker image: docker build -t ai-sandbox .
- Run the container: docker run -p 8888:8888 ai-sandbox
Visit http://localhost:8888 in your browser to access the Jupyter Lab interface. This is your AI development playground — fully running in a container!
Testing AI Workloads and Performance
To verify the setup, let's run a quick PyTorch test inside Jupyter Lab.
import torch x = torch.rand(5, 3) print(x) print("CUDA available:", torch.cuda.is_available())While CUDA is generally not supported natively on ARM Windows yet, CPU-based training works well for development and experimentation. Try running a small model, such as a CNN on MNIST, to benchmark basic training capabilities.
| Test | CPU Time (s) |
|---|---|
| Forward pass (1000 samples) | 2.3 |
| Training epoch (1 loop) | 5.8 |
Performance will vary depending on the specific ARM chip (e.g., Snapdragon X Elite or Apple M-based SoCs in emulation). For lightweight models and prototyping, this setup is perfectly usable.
Common Issues and Troubleshooting Tips
Here are some common issues you might face during setup — and how to resolve them:
- Docker fails to start: Ensure WSL2 is enabled and Virtualization is turned on in BIOS.
- Docker command not recognized inside Ubuntu: Ensure Docker CLI is linked into the WSL environment using Docker Desktop’s integration settings.
- Network issues in container: Try restarting Docker Desktop or resetting network settings inside the container.
- Jupyter not accessible on localhost: Check port mapping and confirm container is running.
- Pytorch installation error: Make sure pip and Python versions are compatible (Python 3.8+).
Still having trouble? Feel free to check the official documentation or community forums for your specific issue.
FAQ (Frequently Asked Questions)
Is WSL2 stable on ARM64?
Yes, it is increasingly stable and well-supported as Microsoft enhances Windows 11 for ARM devices.
Can I use GPU with this setup?
Not currently. GPU support on ARM is limited; this setup is CPU-based.
Does Docker run natively on ARM64 Windows?
Yes. Docker Desktop provides native ARM64 builds for Windows 11 ARM64.
Can I run TensorFlow as well?
Yes, TensorFlow supports ARM64 builds. Use pip to install the compatible version inside your container.
What’s the benefit of using Docker in this setup?
It allows you to isolate environments, avoid conflicts, and replicate your setup easily across machines.
Is this suitable for production workloads?
This environment is best for development and testing. For production, use cloud or native ARM servers.
Final Thoughts
And that's it! You've now got a fully working AI sandbox environment on your Windows 11 ARM64 device using WSL2 and Docker. While some limitations still exist (especially around GPU support), this approach is a powerful way to experiment and build AI tools locally. Try it out and share your thoughts or questions below — we’d love to hear from you!
Related Resources and Links
Tags
WSL2, Docker, ARM64, Windows 11, AI Development, Jupyter Lab, PyTorch, TensorFlow, Linux on Windows, Containerization
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