Installing Windows Subsystem for Linux (WSL) with Docker

WSL is a Windows feature that enables users to run native Linux applications, containers and command-line tools directly on Windows 11 and later. WSL2 is the 2nd generation of WSL.

Why WSL?

• Developers working across Linux and Windows experience a disruptive workflow. They have to stop all work to switch to the other system.
• There was a need for seamless transition and better productivity.
• TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, one needs to have WSL2 to use TensorFlow. The other compromise is to use tensorflow-cpu¹.

Steps

These steps were learnt from watching Prof. Jeff Heaton's Youtube tutorial², the NVIDIA Container toolkit Installation guide³, and the CUDA on WSL User Guide⁴.

• Start with a clean slate (I did a factory reset of my Windows laptop, because I was reeling under a failed prior installation). Ensure that the hypervisor, Hyper-V, is enabled via BIOS. At this point, I had the below stack -

  • Personal Computer: Asus ROG Zephyrus G14 Laptop
  • Graphics card: NVIDIA GeForce RTX 2060Q
  • Host Operating System: Windows 11
  • Driver: NVIDIA Graphics Driver (A software that allows Windows OS and other applications use the PC's graphics hardware or GPU).
  • Hypervisor: Hyper-V. Since WSL2 is essentially a VM running the Linux kernel, it should rest on a hypervisor. The hypervisor used is Hyper-V.

• Once done, update Windows 11 OS.

• Download the latest NVIDIA graphics driver for your NVIDIA graphics card, from here. This will ensure that the driver's version is current.

• Restart the system and run Powershell as an administrator, in preparation for installing WSL2. In powershell, run the command

  wsl.exe --install
  

• Restart system again and run Powershell as an administrator. In powershell, run the command

  wsl.exe --update
  

• Restart system and run Powershell as an administrator. In powershell, run the command

  wsl -l -o  
  

• This prints several distributions of Linux on screen. Pick Ubuntu-20.04 from the list and run the command below to install the chosen version of Ubuntu.

  wsl --install -d Ubuntu-20.04
  

• From the start button on the windows taskbar, run the Ubuntu-20.04 application.

  If you get an error that the VM could not be started because the required feature could not be installed, this means that the hypervisor is not properly installed. Please enable Hyper-V from BIOS.

• Launch Ubuntu-20.04 again from start menu. Configure a UNIX user name and password when asked.

• Test if you can navigate to the C drive on Windows using

  cd /mnt/c
  

  At this point, you have a working WSL2 VM running on top of the hypervisor.

• Now, to install docker, get back to your home directory in Linux.

• Install docker by running the command below.

  You may get a message recommending using Docker Desktop for Windows instead. Ignore this. If you want to use the NVIDIA GPU, you have to use WSL anyway. The command below, is the approach of using a convenience script for installing Docker into development environments non-interactively. For other methods of installing Docker engine on Ubuntu, see here.

  curl https://get.docker.com | sh   
  

• After the installation is done, test by running

  docker
  

  From the bottom up, these are the tools that docker uses to run containers⁵ -
  1. Low-level container runtime (Lowest-level). runc is a low-level container runtime. It uses the native features of Linux to create and run containers. It follows the OCI standard, and it includes libcontainer, a Go library for creating containers.
  2. High-level container runtime. containerd sits above the low-level runtime, and adds a bunch of features, like transferring images, storage, and networking. It also fully supports the OCI spec.
  3. Docker daemon. dockerd is a daemon process (a long-running process that stays running in the background) which provides a standard API, and talks to the container runtime.
  4. Docker CLI tool (Highest level). Finally, docker-cli gives you the power to interact with the Docker daemon using "docker ..." commands. This lets you control containers without needing to understand the lower levels.
  So, in reality, when you run a container with docker, you’re actually running it through the Docker daemon, which calls containerd, which then uses runc.

• Now, to install the NVIDIA Container toolkit, run the following commands one after another -

  distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
  curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
  curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
  

  • Then run

    sudo apt-get update
    

  • Now install NVIDIA docker by running

    sudo apt-get install -y nvidia-docker2
    

  A question here is, why install further runtime libraries (notice libnvidia-container in the curl commands) when docker itself has a libcontainer? Docker containers are platform-agnostic and hardware-agnostic. This presents a problem when using specialized hardware such as NVIDIA GPUs which require kernel modules and user-level libraries to operate. As a result, Docker does not natively support NVIDIA GPUs within containers. For now, it is useful to think of this step as adding packages from the NVIDIA toolkit, that are wrappers to docker processes to enable the use of GPUs in docker containers. For more details, visit here and here

• Open a separate WSL2 window and start the docker service by running

  sudo service docker start
  

• On the original WSL2 window, run the code below to notice that nothing is running as expected

  sudo docker ps
  

• Now run a benchmark test by running the code below. You should get the benchmark result printed on screen

  sudo docker run --gpus all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark
  

• Also test this by running

  sudo docker run --rm --gpus all nvidia/cuda:11.6.2-base-ubuntu20.04 nvidia-smi
  

• Also test Jupyter notebooks by running

  sudo docker run --gpus all -p 8888:8888 tensorflow/tensorflow:latest-gpu-py3-jupyter
  

  • wait for the download to finish
  • then pick up the URL to load jupyter on browser
  • create a new notebook and run the python code below⁶ to check if GPU is available.

    import sys
    import tensorflow.keras
    import tensorflow as tf
    
    check_gpu = len(tf.config.list_physical_devices('GPU'))>0
    
    print(f"Tensor Flow Version: {tf.__version__}")
    print(f"Keras Version: {tensorflow.keras.__version__}")
    print()
    print(f"Python {sys.version}")
    print("GPU is", "available" if check_gpu else "NOT AVAILABLE")
    

Footnotes

1. Install TensorFlow ↩

2. Install WSL2 on Windows 11 with NVIDIA GPU and Docker Support ↩

3. NVIDIA Container toolkit Installation guide ↩

4. CUDA on WSL User Guide ↩

5. Docker concepts explained ↩

6. Jeff Heaton's T81-558 Jupyter notebook ↩