vyxan/jlpe_image repository overview

⁠JupyterLab Portable Environment

I created this Docker image to provide a consistent, fully configured JupyterLab environment across multiple machines.

⁠Features

• Python version: based on the official python:3.13.x-slim-trixie Docker image
• Package manager: uv⁠
• Notebook IDE: JupyterLab⁠
• Libraries: data manipulation, visualization, and machine learning

⁠Versioning

• The version format is {python_version}+{image_variant}.{build_date} — for example, 3.13.14+slim-trixie.20260623 means Python 3.13.14, built on the slim-trixie Debian image, released on 2026-06-23.
• Each Docker tag corresponds to a specific JLPE version.
• The repository contains a single Dockerfile and a pyproject.toml. I try to update the image whenever a new library version or a new Python patch version is released. When a new library version conflicts with existing dependencies, I resolve the issue on a case-by-case basis. For example, when migrating to pandas 3.0, shap and mlflow were excluded due to compatibility issues and may be re-added once resolved.

⁠Supported tags

For a complete list of dependencies and their versions, refer to pyproject.toml in JLPE GitHub repository⁠.

⁠Usage

Containers run as root by default.

Pull image

docker pull vyxan/jlpe_image:<tag>

Example:

docker pull vyxan/jlpe_image:3.13

Run container (without mounts):

docker run -it -p 8888:8888 vyxan/jlpe_image:uv -c "jupyter lab --allow-root --no-browser --ip=0.0.0.0 --port=8888"

After starting, open the URL shown in the terminal (e.g. http://127.0.0.1:8888/lab?token=...) in your browser.

⁠Mounting directories and starting JupyterLab

If you want to mount a project directory and JupyterLab configuration (located by default⁠ at $HOME/.jupyter), and start JupyterLab, run the container with the following command:

⁠Windows

docker run -it -p 8888:8888 `
--mount type=bind,source=C:/project_directory,target=/app/project_directory `
--mount type=bind,source=C:/Users/<username>/.jupyter,target=/root/.jupyter `
vyxan/jlpe_image:<tag> -c "jupyter lab --allow-root --no-browser --ip=0.0.0.0 --port=8888"

⁠Linux / MacOS

docker run -it -p 8888:8888 \
--mount type=bind,source=/path/to/project_directory,target=/app/project_directory \
--mount type=bind,source=/path/to/.jupyter,target=/root/.jupyter \
vyxan/jlpe_image:<tag> -c "jupyter lab --allow-root --no-browser --ip=0.0.0.0 --port=8888"

⁠Poetry-based images (older versions)

If the image was built with Poetry, add poetry run before jupyter lab. Windows example:

docker run -it -p 8888:8888 `
--mount type=bind,source=C:/project_directory,target=/app/project_directory `
--mount type=bind,source=C:/Users/<username>/.jupyter,target=/root/.jupyter `
vyxan/jlpe_image:<tag> -c "poetry run jupyter lab --allow-root --no-browser --ip=0.0.0.0 --port=8888"

⁠VS Code integration

To connect from VS Code to a running Jupyter server, add: --IdentityProvider.token="" --ServerApp.disable_check_xsrf=True

This is required because VS Code's Jupyter integration does not fully support Jupyter Server XSRF protection checks.

⁠Links

GitHub⁠

DockerHub⁠