This docker container has the latest development version of openml-python downloaded and pre-installed. It can be used to run the unit tests or build the docs in a fresh and/or isolated unix environment. Instructions only tested on a Windows host machine.

First pull the docker image:

docker pull openml/openml-python

docker run -it openml/openml-python [DOC,TEST] [BRANCH]

The image is designed to work with two specified directories which may be mounted (docker --mount documentation). You can mount your openml-python folder to the /code directory to run tests or build docs on your local files. You can mount an /output directory to which the container will write output (currently only used for docs). Each can be mounted by adding a --mount type=bind,source=SOURCE,destination=/DESTINATION where SOURCE is the absolute path to your code or output directory, and DESTINATION is either code or output.

E.g. mounting a code directory:

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

E.g. mounting an output directory:

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

You can mount both at the same time.

By default bash is invoked, you should also use the -i flag when starting the container so it processes input:

docker run -it openml/openml-python

There are two ways to build documentation, either directly from the HEAD of a branch on Github or from your local directory.

Building from a local directory requires you to mount it to the /code directory:

docker run --mount type=bind,source=PATH_TO_REPOSITORY,destination=/code -t openml/openml-python doc

The produced documentation will be in your repository's doc/build folder. If an /output folder is mounted, the documentation will also be copied there.

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 -t openml/openml-python 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) and the name of the forked repository should be openml-python.

There are two ways to run tests, either directly from the HEAD of a branch on Github or from your local directory. It works similar to building docs, but should specify test as mode. For example, to run tests on your local repository:

docker run --mount type=bind,source=PATH_TO_REPOSITORY,destination=/code -t openml/openml-python test

Running tests from the state of an online repository is supported similar to building documentation (i.e. specify BRANCH instead of mounting /code).

When you are mounting a directory you can check that it is mounted correctly by running the image in bash mode. Navigate to the /code and /output directories and see if the expected files are there. If e.g. there is no code in your mounted /code, you should double-check the provided path to your host directory.

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:

• /omlp: 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.
• /omlp/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 this directory. It will use the startup.sh as is, so any local changes will be present in the image.