When designing repo2docker and adding to it in the future, the developers are influenced by two primary use cases. The use cases for repo2docker which drive most design decisions are:

  1. Automated image building used by projects like BinderHub
  2. Manual image building and running the image from the command line client, jupyter-repo2docker, by users interactively on their workstations

We share our guiding design principles here. This is not an exhaustive list :)

Deterministic output

The core of repo2docker can be considered a deterministic algorithm. When given an input directory which has a particular repository checked out, it deterministically produces a Dockerfile based on the contents of the directory. So if we run repo2docker on the same directory multiple times, we get the exact same Dockerfile output.

This provides a few advantages:

  1. Reuse of cached built artifacts based on a repository's identity increases efficiency and reliability. For example, if we had already run repo2docker on a git repository at a particular commit hash, we know we can just re-use the old output, since we know it is going to be the same. This provides massive performance & architectural advantages when building additional tools (like BinderHub) on top of repo2docker.
  2. We produce Dockerfiles that have as much in common as possible across multiple repositories, enabling better use of the Docker build cache. This also provides massive performance advantages.

Reproducibility and version stability

Many ingredients go into making an image from a repository:

  1. version of the base docker image
  2. version of repo2docker itself
  3. versions of the libraries installed by the repository

repo2docker controls the first two, the user controls the third one. The current policy for the version of the base image is that we will keep pace with Ubuntu releases until we reach the next release with Long Term Support (LTS). We currently use Artful Aardvark (17.10) and the next LTS version will be Bionic Beaver (18.04).

The version of repo2docker used to build an image can influence which packages are installed by default and which features are supported during the build process. We will periodically update those packages to keep step with releases of jupyter notebook, jupyterlab, etc. For packages that are installed by default but where you want to control the version we recommend you specify them explicitly in your dependencies.

Unix principles "do one thing well"

repo2docker should do one thing, and do it well. This one thing is:

Given a repository, deterministically build a docker image from it.

There's also some convenience code (to run the built image) for users, but that's separated out cleanly. This allows easy use by other projects (like BinderHub).

There is additional (and very useful) design advice on this in the Art of Unix Programming which is a highly recommended quick read.


Although other projects, like s2i, exist to convert source to Docker images, repo2docker provides the additional functionality to support composable environments. We want to easily have an image with Python3+Julia+R-3.2 environments, rather than just one single language environment. While generally one language environment per container works well, in many scientific / datascience computing environments you need multiple languages working together to get anything done. So all buildpacks are composable, and need to be able to work well with other languages.

Pareto principle (The 80-20 Rule)

Roughly speaking, we want to support 80% of use cases, and provide an escape hatch (raw Dockerfiles) for the other 20%. We explicitly want to provide support only for the most common use cases - covering every possible use case never ends well.

An easy process for getting support for more languages here is to demonstrate their value with Dockerfiles that other people can use, and then show that this pattern is popular enough to be included inside repo2docker. Remember that 'yes' is forever (very hard to remove features!), but 'no' is only temporary!