docker-stacks

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

Quick Start

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>

Getting Started

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

Install Docker on your host of choice.
Open the README in one of the folders in this git repository.
Follow the README for that stack.

Visual Overview

Here's a diagram of the FROM relationships between all of the images defined in this project:

Stacks, Tags, Versioning, and Progress

Starting with git commit SHA 9bd33dcc8688:

Nearly every folder here on GitHub has an equivalent jupyter/<stack name> on Docker Hub (e.g., all-spark-notebook → jupyter/all-spark-notebook).
The latest tag in each Docker Hub repository tracks the master branch HEAD reference on GitHub.
Any 12-character image tag on Docker Hub refers to a git commit SHA here on GitHub. See the Docker build history wiki page for a table of build details.
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.
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.

Other Tips and Known Issues

tini -- start-notebook.sh is the default Docker entrypoint-plus-command in every notebook stack. If you plan to modify it in any way, be sure to check the Notebook Options section of your stack's README to understand the consequences.
Every notebook stack is compatible with JupyterHub 0.5. When running with JupyterHub, you must override the Docker run command to point to the start-singleuser.sh script, which starts a single-user instance of the Notebook server. See each stack's README for instructions on running with JupyterHub.
Check the Docker recipes wiki page attached to this project for information about extending and deploying the Docker images defined here. Add to the wiki if you have relevant information.
All stacks that derive from minimal notebook have the the conda jpeg package pinned to version 8 until jupyter#210 is resolved upstream.

Maintainer Workflow

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

Pull a PR branch locally.
Try building the affected stack(s).
If everything builds OK locally, merge it.
ssh -i ~/.ssh/your-github-key build@docker-stacks.cloudet.xyz
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 make release-all successfully pushes the last of its images to Docker Hub (currently jupyter/all-spark-notebook), Docker Hub invokes the webhook which updates the Docker build history wiki page.

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

Update the debian:jessie SHA in the most-base images (e.g., base-notebook). Submit it as a regular PR and go through the build process.

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

When there's a new stack, do the following before trying to make release-all:

Create a new repo in the jupyter org on Docker Hub named after the stack folder in the git repo.
Grant the stacks team permission to write to the repo.
Copy/paste the short and long descriptions from one of the other docker-stacks repos on Docker Hub. Modify the appropriate values.