Jupyter Docker Stacks

Jupyter Docker Stacks are a set of ready-to-run Docker images containing Jupyter applications and interactive computing tools. You can use a stack image to do any of the following (and more):

  • Start a personal Jupyter Notebook server in a local Docker container

  • Run JupyterLab servers for a team using JupyterHub

  • Write your own project Dockerfile

Quick Start

You can try a relatively recent build of the jupyter/base-notebook image on mybinder.org by simply clicking the preceding link. Otherwise, three examples below may help you get started if you have Docker installed, know which Docker image you want to use and want to launch a single Jupyter Notebook server in a container.

The other pages in this documentation describe additional uses and features in detail.

Example 1: This command pulls the jupyter/scipy-notebook image tagged 33add21fab64 from Docker Hub if it is not already present on the local host. It then starts a container running a Jupyter Notebook server and exposes the server on host port 8888. The server logs appear in the terminal. Visiting http://<hostname>:8888/?token=<token> in a browser loads the Jupyter Notebook dashboard page, where hostname is the name of the computer running docker and token is the secret token printed in the console. The container remains intact for restart after the notebook server exits.:

docker run -p 8888:8888 jupyter/scipy-notebook:33add21fab64

Example 2: This command performs the same operations as Example 1, but it exposes the server on host port 10000 instead of port 8888. Visiting http://<hostname>:10000/?token=<token> in a browser loads Jupyter Notebook server, where hostname is the name of the computer running docker and token is the secret token printed in the console.:

docker run -p 10000:8888 jupyter/scipy-notebook:33add21fab64

Example 3: This command pulls the jupyter/datascience-notebook image tagged 33add21fab64 from Docker Hub if it is not already present on the local host. It then starts an ephemeral container running a Jupyter Notebook server and exposes the server on host port 10000. The command mounts the current working directory on the host as /home/jovyan/work in the container. The server logs appear in the terminal. Visiting http://<hostname>:10000/lab?token=<token> in a browser loads JupyterLab, where hostname is the name of the computer running docker and token is the secret token printed in the console. Docker destroys the container after notebook server exit, but any files written to ~/work in the container remain intact on the host.:

docker run --rm -p 10000:8888 -e JUPYTER_ENABLE_LAB=yes -v "${PWD}":/home/jovyan/work jupyter/datascience-notebook:33add21fab64

CPU Architectures

All published containers support amd64 (x86_64). The base-notebook and minimal-notebook containers also support arm64. The ambition is to have all containers support both amd64 and arm64.

Caveats for arm64 images

  • The manifests we publish in this projects wiki as well as the image tags for the multi platform images that also support arm, are all based on the amd64 version even though details about the installed packages versions could differ between architectures. For the status about this, see #1401.

  • Only the amd64 images are actively tested currently. For the status about this, see #1402.

Table of Contents