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A2S Institute Docker Images

Build, test, and push images

Our stacks provide GPU-enabled Jupyter Notebook in Docker containers, which can also run on Kubernetes. The images are based on Jupyter docker-stacks jupyter/pytorch-notebook. All images are published on our ghcr.io and quay.io.

The stacks contain several machine learning packages such as TensorFlow, PyTorch, scikit-learn, and other machine learning tools. All images also include VSCode and xfce4 desktop environment.

Docker stack structure

  • gpu-base-notebook:cuda*-pytorch*: contains Jupyter related libraries and also includes different cuda and pytorch versions. It also has VSCode and xfce4 desktop environment.
  • gpu-base-notebook:cuda12-ubuntu12.04: based on nvidia/cuda:12.3.2-cudnn9-runtime-ubuntu20.04 and has Jupyter Notebook related components with Ubuntu 20.04.
    • ros:cuda12-noetic: based on gpu-base-notebook:cuda12-ubuntu20.04 and includes ros-noetic-desktop-full with the gazebo simulation, but does not have all the ML related libraries as in ml-notebook.

Avilable versions

  • quay.io/a2s-institute/gpu-base-notebook:cuda12-pytorch-2.2.2
  • quay.io/a2s-institute/ml-notebook:cuda12-pytorch-2.2.2
  • quay.io/a2s-institute/geo-notebook:cuda12-pytorch-2.2.2
  • quay.io/a2s-institute/nlp-notebook:cuda12-pytorch-2.2.2
  • quay.io/a2s-institute/qgis:cuda12-v3.36.1
  • quay.io/a2s-institute/ros:cuda12-noetic
  • quay.io/a2s-institute/gpu-base-notebook:cuda11-pytorch-2.2.2
  • quay.io/a2s-institute/ml-notebook:cuda11-pytorch-2.2.2
  • quay.io/a2s-institute/nlp-notebook:cuda11-pytorch-2.2.2
Older images
  • ghcr.io/a2s-institute/docker-stacks/gpu-notebook:cuda11.3.1-ubuntu22.04 (no vscode and xfce desktop)
  • ghcr.io/a2s-institute/docker-stacks/gpu-notebook:cuda11.8.0-ubuntu22.04 (no vscode and xfce desktop)
  • ghcr.io/a2s-institute/docker-stacks/gpu-notebook:cuda12.1.0-ubuntu22.04 (no vscode and xfce desktop)

Building and running A2S images locally

The base image contains several packages for deep learning projects with NVidia GPU support.

  • Build notebook image with gpu support

    # cuda11 and pytorch 2.2.2
    bash build_and_publish.sh --registry ghcr.io --publish "" \
        --image gpu-base-notebook --tag cuda11-pytorch-2.2.2
    
    # cuda12 and pytorch 2.2.2
    bash build_and_publish.sh --registry ghcr.io --publish "" \
        --image gpu-base-notebook --tag cuda12-pytorch-2.2.2
    
  • Run the image locally

    # with GPU
    docker run --gpus all --name ml-notebook -it --rm -d -p 8888:8888 \
           quay.io/ml-notebook:cuda12-pytorch-2.2.2
    
    # without GPU
    docker run --name ml-notebook -it --rm -d -p 8888:8888 \
           quay.io/ml-notebook:cuda12-pytorch-2.2.2
    
  • Check Jupyter Notebook token via log and open the link

    docker logs --follow ml-notebook
    
    

Monitoring

You can monitor the GPU usage using nvtop

nvtop gpu monitoring

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Ready-to-run GPU-enabled Jupyter Notebook images

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