A docker setup for developing scikit.ml, heavily inspired by https://github.com/GeographicaGS/Docker-Python_Development

This docker contains two python environments set for scikit-multilearn: 2.7 and 3.x, to use the first one run python2 and pip2, the second is available via python3 and pip3.

You can pull the latest version from Docker hub using:

$ docker pull niedakh/scikit-multilearn-dev:latest

You can start it via:

$ docker run -e "MEKA_CLASSPATH=/opt/meka/lib" -v "YOUR_CLONE_DIR:/home/python-dev/repo" --name scikit_multilearn_dev_test_docker -d niedakh/scikit-multilearn-dev:latest

To run the tests under the python 2.7 environment use:

$ docker exec -it scikit_multilearn_dev_test_docker python3 -m pytest /home/python-dev/repo

or for python 3.x use:

$ docker exec -it scikit_multilearn_dev_test_docker python2 -m pytest /home/python-dev/repo

To play around just login with:

$ docker exec -it scikit_multilearn_dev_test_docker python3 -m pytest /home/python-dev/repo

Using via docker-compose

You can also build it with docker-compose while in the directory to which you cloned the repository - if you do so, you need to clone the repository and change the volume mappings. Afterwards just run:

$ docker-compose build

You can use it in pycharm with pycharm's docker-compose interpreter.