This repository is a template for a Python-based data science project within the Alexandra Institute, and is the project structure we frequently use in our data science projects.
Install Cookiecutter:
pip3 install cookiecutter
Create a project based on the template (the -f flag ensures that you use the newest
version of the template):
cookiecutter -f gh:alexandrainst/alexandra-ml-template
A Dockerfile is included in the new repositories, which by default runs
src/scripts/main.py. You can build the Docker image and run the Docker container by
running make docker.
Run make docs to create the documentation in the docs folder, which is based on
your docstrings in your code. You can publish this documentation to Github Pages by
running make publish-docs. To add more manual documentation pages, simply add more
Markdown files to the docs directory; this will automatically be included in the
documentation.
Run make test to test your code, which also updates the "coverage badge" in the
README, showing you how much of your code base that is currently being tested.
Github CI pipelines are included in the repo, running all the tests in the tests
directory, as well as building online documentation if Github Pages has been enabled
for the repository (can be enabled on Github in the repository settings).
Code Spaces is a new feature on Github that allows you to develop on a project
completely in the cloud, without having to do any local setup at all. This repo comes
included with a configuration file for running code spaces on Github. When hosted on
alexandrainst/<project-name>, simply press the <> Code button and add a code space
to get started, which will open a VSCode window directly in your browser.