Opinionated stacks of ready-to-run Jupyter applications in Docker.

If you're familiar with Docker, have it configured, and know exactly what you'd like to run, this one-liner should work in most cases:

docker run -d -P jupyter/<your desired stack>

If this is your first time using Docker or any of the Jupyter projects, do the following to get started.

1. Install Docker on your host of choice.
2. Click the link for the Docker Hub repo or GitHub source for your desired stack.
3. Follow the README for that stack.

• Every folder here on GitHub has an equivalent jupyter/stack on Docker Hub.
• The latest tag in each Docker Hub repository tracks the master branch HEAD on GitHub.
• Additional 12-character tags on Docker Hub refer to git commit SHAs on GitHub.
• Stack contents (e.g., new library versions) will be updated upon request via PRs against this project.
• Users looking to remain on older builds should refer to specific git SHA tagged images in their work, not latest.
• Users who want to know the contents of a specific tagged image on Docker Hub can take its tag and use it as a git SHA to inspect the state of this repo at the time of that image build.
• For legacy reasons, there are two additional tags named 3.2 and 4.0 on Docker Hub which point to images prior to our versioning scheme switch.

For PRs that impact the definition of one or more stacks:

1. Pull a PR branch locally.
2. Try building the affected stack(s).
3. If everything builds OK locally, merge it.
4. ssh -i ~/.ssh/your-github-key [email protected]
5. Run these commands on that VM.

cd docker-stacks
# make sure we're always on clean master from github
git fetch origin
git reset --hard origin/master
# if this fails, just run it again and again (idempotent)
make release-all

When there's a security fix in the Debian base image, do the following in place of the last command:

docker pull debian:jessie
make release-all DARGS=--no-cache

This will take time as the entire set of stacks will rebuild.