jovykit 3.1.1

Layered Jupyter container images and project-local JovyKit environments.

JovyKit

Project-local JupyterLab containers with a venv-like CLI, layered notebook images, uv-locked dependencies, and readable Docker Compose output.

.jovy is to JovyKit what .venv is to Python.

Website · Wiki · Issues · GHCR Images

Why JovyKit

JovyKit is for notebook-heavy data science and research projects that should be easy to start, reproducible later, and still inspectable when something needs debugging.

• Create a project-local .jovy/ environment from one command.
• Track direct project packages in jovy.toml.
• Compile a deterministic .jovy/jovy.lock with uv.
• Build a generated overlay image instead of mutating container state.
• Run JupyterLab through Docker Compose without making Compose the user interface.
• Choose notebook image layers from minimal, base, extended, and full.
• Use a terminal dashboard for interactive project operations.

Quick Start

Install from a local checkout:

python -m pip install -e .

Create an environment, add packages, and run JupyterLab:

jovy init .jovy --image base --gpus auto
jovy add pandas scikit-learn plotly
jovy install
jovy run

JovyKit prints the local JupyterLab URL. The default token is jovykit.

Daily Commands

jovy                  # open the terminal dashboard
jovy status
jovy status --json
jovy add -r requirements.txt
jovy install --upgrade
jovy up
jovy logs --tail 100 --since 10m --timestamps
jovy shell -c "python --version"
jovy exec python --version
jovy down --timeout 10
jovy clean
jovy destroy --keep-image

Most commands accept --env PATH when you want to operate on a project outside the current directory tree.

What It Creates

jovy.toml
work/
.jovy/
  jovy.lock
  Containerfile
  compose.yaml
  state.json

jovy.toml is the project manifest. .jovy/ contains generated local environment files and should stay out of version control.

Image Layers

Published images use this pattern:

ghcr.io/mihneateodorstoica/jovykit-TYPE:latest
ghcr.io/mihneateodorstoica/jovykit-TYPE:nightly
ghcr.io/mihneateodorstoica/jovykit-TYPE:lts

TYPE is one of:

• minimal: Jupyter runtime plus the core scientific Python stack.
• base: everyday data science, classical machine learning, statistics, and local data access.
• extended: advanced ML, NLP, time series, distributed compute, and API tooling.
• full: heavier AI, graph, geospatial, big-data, and research tooling.

All image variants include git, OpenSSH client tools, rsync, and a prepared ~/.ssh directory for SSH-backed remotes and file sync.

Build a target locally:

docker build --target minimal -t jovykit-minimal ./image
docker build --target base -t jovykit-base ./image
docker build --target extended -t jovykit-extended ./image
docker build --target full -t jovykit-full ./image

Configuration

jovy.toml can customize runtime environment variables, extra volumes, restart policy, Jupyter command/logging, Compose Watch behavior, image build arguments, build target/platform, apt packages, and uv/pip install options.

Use the guided editor:

jovy config

or open the dashboard and run:

config

Repository Layout

jovykit/              Python CLI package
image/                Dockerfile and layered image dependency manifests
site/                 GitHub Pages promotional website
wiki/                 GitHub Wiki page source
.github/workflows/    CI, security, website, wiki, and image automation

Documentation

The website promotes the project and lives in site/. Operational documentation lives in the GitHub Wiki, with source pages in wiki/.

Testing

Run the deterministic test suite with coverage:

pytest --cov=jovykit --cov-report=term-missing --cov-fail-under=90

Docker-facing smoke tests are opt-in:

pytest -m docker --run-docker

Contributing

Contributions are welcome. See CONTRIBUTING.md for the development workflow and CODE_OF_CONDUCT.md for community expectations.

License

JovyKit is licensed under the MIT License. See LICENSE.