Alexandra Institute Machine Learning Repository Template

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.

Quickstart

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

Features

Docker Setup

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.

Automatic Documentation

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.

Automatic Test Coverage Calculation

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.

Continuous Integration

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

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.