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Docker Container (for MONAILabel) on Windows 11
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monailabel-docker-windows.md

Steps

  1. The following steps show how to run the MONAILabel server on Windows via Docker. I have tested these steps on the following software versions:

    Name Version Terminal Command
    Windows Microsoft Windows 11 Pro for Workstations Win Command Prompt systeminfo | findstr /B /C:"OS Name" /B /C:"OS Version
    Nvidia Driver 527.56 Win Command Prompt nvidia-smi --query-gpu=driver_version --format=csv
    Nvidia CUDA Driver API 12.0 Win Command Prompt nvidia-smi
    Nvidia GPU NVIDIA GeForce RTX 2080 Ti Win Command Prompt nvidia-smi --query-gpu=driver_version --format=csv
    WSL 1.0.3.0 Win Command Prompt wsl --version
    WSL Ubuntu 2 Win Command Prompt wsl -l -v
    WSL Ubuntu Release 22.04 (Jammy) WSL Ubuntu Terminal lsb_release -a
    WSL Ubuntu Kernel 5.15.79.1 WSL Ubuntu Terminal uname -a
    WSL Ubuntu Docker 20.10.22 WSL Ubuntu Terminal docker version OR docker --version
    WSL Ubuntu Nvidia Docker 20.10.22 WSL Ubuntu Terminal nvidia-docker --version
    3D Slicer 5.2.1

  2. Ensure you have WSL (Windows Subsystem for Linux) enabled.

    • You need Windows 10 (or greater). Preferably Windows 11
    • Method 1:
      • Follow these steps
      • Install a linux distribution as shown here
      • In a Windows Command Promt or Windows PowerShell you type wsl -l -v to check the available distributions.
    • Method 2:
      • In a command prompt (run as Administrator) run wsl --install.
      • Again type wsl --install to see the list of available distros.
      • For e.g. you can type wsl --install -d Ubuntu. This will then also open up another window with the Ubuntu terminal.
      • Next time to open Ubuntu terminal in the same window, just type wsl -d Ubuntu
      • Type wsl --list to check the installed distributions.
      • You can also move Ubuntu to another directory to preserve space following these steps
    • To remove all distros
      • Type wsl --list or wsl -l -v to see the list of installed distros
      • To uninstall a distro type wsl --unregister <distro-name>
    • WSL Command list (link)

  3. Installing Docker (if you installed WSL Ubuntu)

    • Open Terminal
      • By simply searching in Windows Start menu OR
      • Open a command prompt and type wsl -d Ubuntu
    • Install docker by following these instructions
    • Start docker by typing sudo service docker start and check by sudo service docker status
    • Test docker by typing docker version OR docker run hello-world
    • You can see the downloaded image by docker images

  4. Installing the nvidia-container toolkit in WSL Ubuntu (for WSL Ubuntu docker to be able to access Windows Nvidia GPU)

    • This assumes that your host Windows already has Nvidia drivers installed (i.e. nvidia-smi should work in WIndows terminal)
    • Check if GPU is detected in WSL Ubuntu
      • Run nvidia-smi in WSL Ubuntu terminal. If that works, then simply install nvidia-container toolkit
      • If not, install CUDA toolkit in WSL Ubuntu following these instructions. You can double-check the installation by make(ing) this application.
    • Follow the installation process here for nvidia-container-toolkit.
      • Note: Ubuntu 20.04 and Ubuntu 22.04 packages are symlinked (redirected) to ubuntu18.04
    • Test the installation (assumes docker and nvidia-container toolkit installed)
      • sudo docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi
      • Interactive Mode: sudo docker run -it --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04. Then run nvidia-smi

  5. Test if deep learning software installed in WSL Ubuntu is using GPU

    • Download and install Miniconda.
    • Restart WSL Ubuntu terminal to initialize conda on terminal
    • Install pytorch: conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge and then run python -c "import torch; print (torch.cuda.get_device_name())"
    • Install tensorflow: conda install cudatoolkit==11.2.2 cudnn==8.1.0.77 and pip install tensorflow==2.10.0 and export LD_LIBRARY_PATH=/lib64:$LD_LIBRARY_PATH and export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib. Then run python -c "import tensorflow as tf; print (tf.config.list_physical_devices('GPU'))" and this should not show any loading errors.

  6. Run MONAILabel docker (to start the MONAILLabel server in WSL Ubuntu)

    • Reference video (Link)
    • Steps
      • sudo docker run --gpus all --rm -ti --ipc=host --net=host projectmonai/monailabel:latest
      • monailabel apps --download --name radiology --output apps
      • monailabel datasets --download --name Task09_Spleen --output datasets
      • monailabel start_server --app apps/radiology --studies datasets/Task09_Spleen/imagesTr --conf models deepedit
      • Open a web browser on the host Windows machine and type http://localhost:8000/.

  7. Run MONAILabel 3D Slicer Plugin

    • Follow these steps to install the 3D Slicer plugin.
    • <more steps to come>
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