This docker container has the latest version of openml-python downloaded and pre-installed. It can also be used by developers to run unit tests or build the docs in a fresh and/or isolated unix environment. This document contains information about:

1. Usage: how to use the image and its main modes.
2. Using local or remote code: useful when testing your own latest changes.
3. Versions: identify which image to use.
4. Development: information about the Docker image for developers.

note: each docker image is shipped with a readme, which you can read with: docker run --entrypoint=/bin/cat openml/openml-python:TAG readme.md

There are three main ways to use the image: running a pre-installed Python environment, running tests, and building documentation.

To run Python with a pre-installed OpenML-Python environment run:

docker run -it openml/openml-python

this accepts the normal Python arguments, e.g.:

docker run openml/openml-python -c "import openml; print(openml.__version__)"

if you want to run a local script, it needs to be mounted first. Mount it into the openml folder:

docker run -v PATH/TO/FILE:/openml/MY_SCRIPT.py openml/openml-python MY_SCRIPT.py

You can run the unit tests by passing test as the first argument. It also requires a local or remote repository to be specified, which is explained [below]((#using-local-or-remote-code). For this example, we specify to test the develop branch:

docker run openml/openml-python test develop

You can build the documentation by passing doc as the first argument, you should mount an output directory in which the docs will be stored. You also need to provide a remote or local repository as explained in [the section below]((#using-local-or-remote-code). In this example, we build documentation for the develop branch. On Windows:

    docker run --mount type=bind,source="E:\\files/output",destination="/output" openml/openml-python doc develop

on Linux:

    docker run --mount type=bind,source="./output",destination="/output" openml/openml-python doc develop

see [the section below]((#using-local-or-remote-code) for running against local changes or a remote branch.

Note: you can forgo mounting an output directory to test if the docs build successfully, but the result will only be available within the docker container under /openml/docs/build.

You can build docs or run tests against your local repository or a Github repository. In the examples below, change the source to match the location of your local repository.

To use a local directory, mount it in the /code directory, on Windows:

    docker run --mount type=bind,source="E:\\repositories/openml-python",destination="/code" openml/openml-python test

on Linux:

    docker run --mount type=bind,source="/Users/pietergijsbers/repositories/openml-python",destination="/code" openml/openml-python test

when building docs, you also need to mount an output directory as shown above, so add both:

docker run --mount type=bind,source="./output",destination="/output" --mount type=bind,source="/Users/pietergijsbers/repositories/openml-python",destination="/code" openml/openml-python doc

Building from a remote repository requires you to specify a branch. The branch may be specified by name directly if it exists on the original repository (https://github.com/openml/openml-python/):

docker run --mount type=bind,source=PATH_TO_OUTPUT,destination=/output openml/openml-python [test,doc] BRANCH

Where BRANCH is the name of the branch for which to generate the documentation. It is also possible to build the documentation from the branch on a fork, in this case the BRANCH should be specified as GITHUB_NAME#BRANCH (e.g. PGijsbers#my_feature_branch) and the name of the forked repository should be openml-python.

This section contains some notes about the structure of the image, intended for those who want to work on it.

The openml/openml-python image is built on a vanilla python:3 image. Additionally, it contains the following files are directories:

• /openml: contains the openml-python repository in the state with which the image was built by default. If working with a BRANCH, this repository will be set to the HEAD of BRANCH.
• /openml/venv/: contains the used virtual environment for doc and test. It has openml-python dependencies pre-installed. When invoked with doc or test, the dependencies will be updated based on the setup.py of the BRANCH or mounted /code.
• /scripts/startup.sh: the entrypoint of the image. Takes care of the automated features (e.g. doc and test).

To build the image yourself, execute docker build -f Dockerfile . from the docker directory of the openml-python repository. It will use the startup.sh as is, so any local changes will be present in the image.