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 Server with the JupyterLab frontend (default)
• Run JupyterLab for a team using JupyterHub
• Start a personal Jupyter Server with the Jupyter Notebook frontend in a local Docker container
• Write your own project Dockerfile

You can try a relatively recent build of the quay.io/jupyter/base-notebook image on mybinder.org. Otherwise, the 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 Application in a container.

The User Guide on ReadTheDocs describes additional uses and features in detail.

Since `2023-10-20` our images are only pushed to `Quay.io` registry.
Older images are available on Docker Hub, but they will no longer be updated.

This command pulls the jupyter/scipy-notebook image tagged 2024-01-15 from Quay.io if it is not already present on the local host. It then starts a container running a Jupyter Server with the JupyterLab frontend and exposes the container's internal port 8888 to port 10000 of the host machine:

docker run -p 10000:8888 quay.io/jupyter/scipy-notebook:2024-01-15

You can modify the port on which the container's port is exposed by changing the value of the -p option to -p 8888:8888.

Visiting http://<hostname>:10000/?token=<token> in a browser loads JupyterLab, where:

• The hostname is the name of the computer running Docker
• The token is the secret token printed in the console.

The container remains intact for restart after the Server exits.

This command pulls the jupyter/datascience-notebook image tagged 2024-01-15 from Quay.io if it is not already present on the local host. It then starts an ephemeral container running a Jupyter Server with the JupyterLab frontend and exposes the server on host port 10000.

docker run -it --rm -p 10000:8888 -v "${PWD}":/home/jovyan/work quay.io/jupyter/datascience-notebook:2024-01-15

The use of the -v flag in the command mounts the current working directory on the host (${PWD} in the example command) as /home/jovyan/work in the container. The server logs appear in the terminal.

Visiting http://<hostname>:10000/?token=<token> in a browser loads JupyterLab.

Due to the usage of the flag --rm Docker automatically cleans up the container and removes the file system when the container exits, but any changes made to the ~/work directory and its files in the container will remain intact on the host. The -it flag allocates pseudo-TTY.

By default, [jupyter's root_dir](https://jupyter-server.readthedocs.io/en/latest/other/full-config.html) is `/home/jovyan`.
So, new notebooks will be saved there, unless you change the directory in the file browser.

To change the default directory, you must specify `ServerApp.root_dir` by adding this line to the previous command: `start-notebook.py --ServerApp.root_dir=/home/jovyan/work`.

JupyterLab is the default for all the Jupyter Docker Stacks images. It is still possible to switch back to Jupyter Notebook (or to launch a different startup command). You can achieve this by passing the environment variable DOCKER_STACKS_JUPYTER_CMD=notebook (or any other valid jupyter subcommand) at container startup; more information is available in the documentation.

• Documentation on ReadTheDocs
• Issue Tracker on GitHub
• Jupyter Discourse Forum
• Jupyter Website
• Images on Quay.io

• Starting from 2022-07-05, aarch64 self-hosted runners were sponsored by @mathbunnyru. Please, consider sponsoring his work on GitHub
• Starting from 2023-10-31, aarch64 self-hosted runners are sponsored by an amazing 2i2c non-profit organization

• We publish containers for both x86_64 and aarch64 platforms
• Single-platform images have either aarch64- or x86_64- tag prefixes, for example, quay.io/jupyter/base-notebook:aarch64-python-3.11.6
• Starting from 2022-09-21, we create multi-platform images (except tensorflow-notebook)
• Starting from 2023-06-01, we create a multi-platform tensorflow-notebook image as well

This project only builds one set of images at a time. If you want to use the older Ubuntu and/or Python version, you can use the following images:

Please see the Contributor Guide on ReadTheDocs for information about how to contribute recipes, features, tests, and community-maintained stacks.

• rocker/binder - From the R focused rocker-project, lets you run both RStudio and Jupyter either standalone or in a JupyterHub
• jupyter/repo2docker - Turn git repositories into Jupyter-enabled Docker Images
• openshift/source-to-image - A tool for building artifacts from source code and injecting them into docker images
• jupyter-on-openshift/jupyter-notebooks - OpenShift compatible S2I builder for basic notebook images