See the CUDA-based JupyterLab Python docker stack for GPU accelerated docker images. |
---|
Multi-arch (linux/amd64
, linux/arm64/v8
) docker images:
Images considered stable for Python versions ≥ 3.10.5.
🔬 Check out jupyterlab/python/scipy
at
https://demo.jupyter.b-data.ch.
Build chain
base → scipy
Features
- JupyterLab: A web-based interactive development environment for Jupyter
notebooks, code, and data. The images include
- code-server:
Code - OSS
in the browser. - Git: A distributed version-control system for tracking changes in source code.
- Git LFS: A Git extension for versioning large files.
- Neovim: Vim-fork focused on extensibility and usability. (3.12.5+)
- Pandoc: A universal markup converter.
- Python: An interpreted, object-oriented, high-level programming language with dynamic semantics.
- Quarto: A scientific and technical publishing system built on Pandoc.
ℹ️ scipy image - TinyTeX: A lightweight, cross-platform, portable, and easy-to-maintain
LaTeX distribution based on TeX Live.
ℹ️ scipy image - Zsh: A shell designed for interactive use, although it is also a powerful scripting language.
- code-server:
👉 See the Version Matrix for detailed information.
The following extensions are pre-installed for code-server:
- .gitignore Generator
- Black Formatter
ℹ️ devtools subtags - Docker
ℹ️ docker subtags - EditorConfig (3.11.3+)
- ESLint
ℹ️ devtools subtags - Git Graph
- GitHub Pull Requests and Issues (3.11.5+)
- GitLab Workflow
- GitLens — Git supercharged
ℹ️ Pinned to version 11.7.0 due to unsolicited AI content (3.11.4+) - Excel Viewer
- hadolint
ℹ️ docker subtags (3.11.4+) - Jupyter
- LaTeX Workshop
ℹ️ scipy image - markdownlint (3.11.6+)
- Path Intellisense
- Prettier - Code formatter
ℹ️ devtools subtags - Project Manager
- Python
- Quarto
ℹ️ scipy image - Resource Monitor (3.11.4+)
- ShellCheck
ℹ️ devtools and docker subtags (3.11.4+) - YAML
Subtags
{PYTHON_VERSION,latest}-root
(versions ≥ 3.10.5): Container runs asroot
{PYTHON_VERSION,latest}-devtools
(versions ≥ 3.10.5): Includes the requirements according to{PYTHON_VERSION,latest}-devtools-root
: The combination of both{PYTHON_VERSION,latest}-docker
(versions ≥ 3.10.11, versions ≥ 3.11.2): Includesdocker-ce-cli
docker-buildx-plugin
docker-compose-plugin
docker-scan-plugin
(amd64 only)
{PYTHON_VERSION,latest}-docker-root
: The combination of both{PYTHON_VERSION,latest}-devtools-docker
: The combination of both{PYTHON_VERSION,latest}-devtools-docker-root
: The combination of all three
This projects requires an installation of docker.
To install docker, follow the instructions for your platform:
- Install Docker Engine | Docker Documentation > Supported platforms
- Post-installation steps for Linux
latest:
cd base && docker build \
--build-arg PYTHON_VERSION=3.12.5 \
-t jupyterlab/python/base \
-f latest.Dockerfile .
version:
cd base && docker build \
-t jupyterlab/python/base:MAJOR.MINOR.PATCH \
-f MAJOR.MINOR.PATCH.Dockerfile .
For MAJOR.MINOR.PATCH
≥ 3.10.5
.
Create an empty directory using docker:
docker run --rm \
-v "${PWD}/jupyterlab-jovyan":/dummy \
alpine chown 1000:100 /dummy
It will be bind mounted as the JupyterLab user's home directory and
automatically populated.
❗ Bind mounting a subfolder of the home directory is only possible
for images with Python version ≥ 3.12.2.
self built:
docker run -it --rm \
-p 8888:8888 \
-u root \
-v "${PWD}/jupyterlab-jovyan":/home/jovyan \
-e NB_UID=$(id -u) \
-e NB_GID=$(id -g) \
-e CHOWN_HOME=yes \
-e CHOWN_HOME_OPTS='-R' \
jupyterlab/python/base[:MAJOR.MINOR.PATCH]
from the project's GitLab Container Registries:
docker run -it --rm \
-p 8888:8888 \
-u root \
-v "${PWD}/jupyterlab-jovyan":/home/jovyan \
-e NB_UID=$(id -u) \
-e NB_GID=$(id -g) \
-e CHOWN_HOME=yes \
-e CHOWN_HOME_OPTS='-R' \
IMAGE[:MAJOR[.MINOR[.PATCH]]]
IMAGE
being one of
The use of the -v
flag in the command mounts the empty directory on the host
(${PWD}/jupyterlab-jovyan
in the command) as /home/jovyan
in the container.
-e NB_UID=$(id -u) -e NB_GID=$(id -g)
instructs the startup script to switch
the user ID and the primary group ID of ${NB_USER}
to the user and group ID of
the one executing the command.
-e CHOWN_HOME=yes -e CHOWN_HOME_OPTS='-R'
instructs the startup script to
recursively change the ${NB_USER}
home directory owner and group to the
current value of ${NB_UID}
and ${NB_GID}
.
ℹ️ This is only required for the first run.
The server logs appear in the terminal.
Create an empty home directory:
mkdir "${PWD}/jupyterlab-root"
Use the following command to run the container as root
:
podman run -it --rm \
-p 8888:8888 \
-u root \
-v "${PWD}/jupyterlab-root":/home/root \
-e NB_USER=root \
-e NB_UID=0 \
-e NB_GID=0 \
-e NOTEBOOK_ARGS="--allow-root" \
IMAGE[:MAJOR[.MINOR[.PATCH]]]
Creating a home directory might not be required. Also
docker run -it --rm \
-p 8888:8888 \
-v "${PWD}/jupyterlab-jovyan":/home/jovyan \
IMAGE[:MAJOR[.MINOR[.PATCH]]]
might be sufficient.
❗ Keyring services are not available due to the difficulties of
setting them up in containers.
Therefore, provide login credentials for the following extensions as
environment variables (-e
):
Extension | Environment variable |
---|---|
GitHub Pull Requests and Issues | GITHUB_TOKEN : Personal access token with scopes repo and user .1 |
GitLab Workflow | GITLAB_WORKFLOW_INSTANCE_URL : GitLab instance URL (e.g. https://gitlab.com).GITLAB_WORKFLOW_TOKEN : Personal access token with scopes api and read_user . |
To add a JupyterLab Launcher icon for marimo:
-
Terminal: Install marimo and click
pip install marimo click
-
Terminal: Install jupyter-marimo-proxy
pip install git+https://github.com/b-data/jupyter-marimo-proxy.git@jupyterlab-docker-stack
-
Restart the container
What makes this project different:
- Multi-arch:
linux/amd64
,linux/arm64/v8
ℹ️ Runs on Apple M series using Docker Desktop. - Base image: Debian instead of
Ubuntu
ℹ️ CUDA-based images use Ubuntu. - IDE: code-server next to
JupyterLab
ℹ️ code-server =Code - OSS
in the browser. - Just Python – no Conda / Mamba
See Notes for tweaks, settings, etc.
PRs accepted. Please submit to the GitLab repository.
This project follows the Contributor Covenant Code of Conduct.
Community support: Open a new disussion here. Commercial support: Contact b-data by email.
b-data tailors the JupyterLab images to your needs, e.g.
- Integration of self-signed CA certificates to enable communication with web services on the intranet.
- Setting up the necessary environment variables so that everything works behind a corporate proxy server.
Additionally, the JupyterHub setup can be customised to allow
- authentication with AD/LDAP
- mounting CIFS/SMB file shares
and much more.
Copyright © 2022 b-data GmbH
Distributed under the terms of the MIT License, with exceptions.
Footnotes
-
Device activation may require a one-time login from the extension's sidebar. ↩