Jupyter Docker Stacks — Docker Stacks documentation (original) (raw)
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 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
Quick Start#
You can try the quay.io/jupyter/base-notebook image on https://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.
Note
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.
Example 1#
This command pulls the jupyter/scipy-notebook
image tagged 2025-03-14
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:2025-03-14
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.
Example 2#
This command pulls the jupyter/datascience-notebook
image tagged 2025-03-14
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 "${PWD}":/home/jovyan/work quay.io/jupyter/datascience-notebook:2025-03-14
The use of the -v
flag in the command mounts the current working directory on the host (${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 --rm flagDocker 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.
Note
By default, jupyter’s root_dir 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
.
Choosing Jupyter frontend#
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.
Resources#
- Documentation on ReadTheDocs
- Issue Tracker on GitHub
- Jupyter Discourse Forum
- Jupyter Website
- Images on Quay.io
Acknowledgments#
- 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 - Starting from
2025-02-11
, we use GitHub-hostedaarch64
runners
CPU Architectures#
- We publish containers for both
x86_64
andaarch64
platforms - 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-notebook
image as well - Starting from
2024-02-24
, we create CUDA enabled variants ofpytorch-notebook
image forx86_64
platform - Starting from
2024-03-26
, we create CUDA enabled variant oftensorflow-notebook
image forx86_64
platform
Using old images#
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:
Contributing#
Please see the Contributor Guide on ReadTheDocsfor information about how to contribute recipes, features, tests, and community-maintained stacks.
Alternatives#
- 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