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 2024-02-24 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:2024-02-24You 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
hostnameis the name of the computer running Docker - The
tokenis 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 2024-02-24 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 "${PWD}":/home/jovyan/work quay.io/jupyter/datascience-notebook:2024-02-24The use of the -v flag in the command mounts the current working directory on the host (${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 --rm flag
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 -i flag keeps the container's STDIN open, and lets you send input to the container through standard input.
The -t flag attaches a pseudo-TTY to the container.
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,aarch64self-hosted runners were sponsored by@mathbunnyru. Please, consider sponsoring his work on GitHub - Starting from
2023-10-31,aarch64self-hosted runners are sponsored by an amazing2i2c non-profit organization
- We publish containers for both
x86_64andaarch64platforms - Single-platform images have either
aarch64-orx86_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 (excepttensorflow-notebook) - Starting from
2023-06-01, we create a multi-platformtensorflow-notebookimage as well - Starting from
2024-02-24, we create CUDA enabled variant ofpytorch-notebookimage forx86_64platform
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:
| Build Date | Ubuntu | Python | Tag |
|---|---|---|---|
| 2022-10-09 | 20.04 | 3.7 | 1aac87eb7fa5 |
| 2022-10-09 | 20.04 | 3.8 | a374cab4fcb6 |
| 2022-10-09 | 20.04 | 3.9 | 5ae537728c69 |
| 2022-10-09 | 20.04 | 3.10 | f3079808ca8c |
| 2022-10-09 | 22.04 | 3.7 | b86753318aa1 |
| 2022-10-09 | 22.04 | 3.8 | 7285848c0a11 |
| 2022-10-09 | 22.04 | 3.9 | ed2908bbb62e |
| 2023-05-30 | 22.04 | 3.10 | 4d70cf8da953 |
| weekly build | 22.04 | 3.11 | latest |
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