dclong/jupyterhub-ds @DockerHub | @GitHub

JupyterHub for Data Science. This is the recommended Docker image to use if you want to do data science related work in JupyterLab/Jupyter Notebook. For deep learning leveraging GPU, please use dclong/jupyterhub-pytorch. Note: Python packages in this version are managed using pip instead of conda.

Recommended Docker Images

Prerequisite

You need to install Docker before you use this Docker image.

Usage in Linux/Unix

Please refer to the section Usage of the post My Docker Images for detailed instruction on how to use the Docker image.

The following command starts a container and mounts the current working directory and /home on the host machine to /workdir and /home_host in the container respectively.

docker run -d --init \
    --privileged \
    --cap-add SYS_ADMIN \
    --platform linux/amd64 \
    --hostname jupyterhub-ds \
    --log-opt max-size=50m \
    -p 8000:8000 \
    -p 5006:5006 \
    -e DOCKER_USER=$(id -un) \
    -e DOCKER_USER_ID=$(id -u) \
    -e DOCKER_PASSWORD=$(id -un) \
    -e DOCKER_GROUP_ID=$(id -g) \
    -e DOCKER_ADMIN_USER=$(id -un) \
    -v "$(pwd)":/workdir \
    -v "$(dirname $HOME)":/home_host \
    dclong/jupyterhub-ds /scripts/sys/init.sh

Use the image with the next tag (which is the testing/next version of dclong/jupyterhub-ds).

docker run -d --init \
    --privileged \
    --cap-add SYS_ADMIN \
    --platform linux/amd64 \
    --hostname jupyterhub-ds \
    --log-opt max-size=50m \
    -p 8000:8000 \
    -p 5006:5006 \
    -e DOCKER_USER=$(id -un) \
    -e DOCKER_USER_ID=$(id -u) \
    -e DOCKER_PASSWORD=$(id -un) \
    -e DOCKER_GROUP_ID=$(id -g) \
    -e DOCKER_ADMIN_USER=$(id -un) \
    -v "$(pwd)":/workdir \
    -v "$(dirname $HOME)":/home_host \
    dclong/jupyterhub-ds:next /scripts/sys/init.sh

The following command (only works on Linux) does the same as the above one except that it limits the use of CPU and memory.

docker run -d --init \
    --privileged \
    --cap-add SYS_ADMIN \
    --platform linux/amd64 \
    --hostname jupyterhub-ds \
    --log-opt max-size=50m \
    --memory=$(($(head -n 1 /proc/meminfo | awk '{print $2}') * 4 / 5))k \
    --cpus=$(($(nproc) - 1)) \
    -p 8000:8000 \
    -p 5006:5006 \
    -e DOCKER_USER=$(id -un) \
    -e DOCKER_USER_ID=$(id -u) \
    -e DOCKER_PASSWORD=$(id -un) \
    -e DOCKER_GROUP_ID=$(id -g) \
    -e DOCKER_ADMIN_USER=$(id -un) \
    -v "$(pwd)":/workdir \
    -v "$(dirname $HOME)":/home_host \
    dclong/jupyterhub-ds /scripts/sys/init.sh

Use the image with the next tag (which is the testing/next version of dclong/jupyterhub-ds).

docker run -d --init \
    --privileged \
    --cap-add SYS_ADMIN \
    --platform linux/amd64 \
    --hostname jupyterhub-ds \
    --log-opt max-size=50m \
    --memory=$(($(head -n 1 /proc/meminfo | awk '{print $2}') * 4 / 5))k \
    --cpus=$(($(nproc) - 1)) \
    -p 8000:8000 \
    -p 5006:5006 \
    -e DOCKER_USER=$(id -un) \
    -e DOCKER_USER_ID=$(id -u) \
    -e DOCKER_PASSWORD=$(id -un) \
    -e DOCKER_GROUP_ID=$(id -g) \
    -e DOCKER_ADMIN_USER=$(id -un) \
    -v "$(pwd)":/workdir \
    -v "$(dirname $HOME)":/home_host \
    dclong/jupyterhub-ds:next /scripts/sys/init.sh

Launch a JupyterLab Instead of JupyterHub

You can still launch a JupyterLab service using this Docker image.

docker run -d --init \
    --privileged \
    --cap-add SYS_ADMIN \
    --platform linux/amd64 \
    --hostname jupyterlab \
    --log-opt max-size=50m \
    --memory=$(($(head -n 1 /proc/meminfo | awk '{print $2}') * 4 / 5))k \
    --cpus=$(($(nproc) - 1)) \
    -p 8888:8888 \
    -e DOCKER_USER=$(id -un) \
    -e DOCKER_USER_ID=$(id -u) \
    -e DOCKER_PASSWORD=$(id -un) \
    -e DOCKER_GROUP_ID=$(id -g) \
    -e DOCKER_ADMIN_USER=$(id -un) \
    -v "$(pwd)":/workdir \
    -v "$(dirname $HOME)":/home_host \
    dclong/jupyterhub-ds /scripts/sys/init.sh /scripts/sys/launch_jlab.sh

Use the JupyterHub Server

Add a New User to the JupyterHub Server

Use Spark in JupyterLab Notebook

Log Information

Detailed Information

Known Issues

About the Author