Customised Cookiecutter Template

This repository builds a customised Machine Learning Engineering repository template to include the following features:

• Testing setup with pytest
• GitHubActions: Ready for Continuous Integration testing and other workflows
• Tox_ testing: Setup to easily test for multiple specified python versions (e.g. 3.8, 3.9, 3.10)
• Linting: Configured precommit hooks which can be setup locally to automatically lint codebase before pushing to repo.
• Sphinx_ docs: Documentation which will be published to ReadTheDocs
• Containers for dev: Using docker-compose, start container locally with all dependencies for development.
• Command line interface using Click or Argparse (optional)
• AWS setup (optional)

Setup

---

Install the latest Cookiecutter if you haven't installed it yet:

```bash
$ pip install cookiecutter
```

Then to initiate the run the following command, with the url of the repo

```bash
$ cookiecutter -c  https://github.com/ryankarlos/cookiecutter-mle-template.git
```

The resulting directory structure

---

Lets assume you set the project_name as Data Science, then the directory structure of your new project looks like this:

data_science
    ├── Dockerfile
    ├── LICENSE
    ├── MANIFEST.in
    ├── Makefile
    ├── README.md
    ├── _config.yml
    ├── docker-compose.yml
    ├── docs
    │   ├── Makefile
    │   ├── authors.rst
    │   ├── conf.py
    │   ├── index.rst
    │   ├── installation.rst
    │   ├── make.bat
    │   └── readme.rst
    ├── pyproject.toml
    ├── pytest.ini
    ├── requirements.txt
    ├── requirements_dev.txt
    ├── setup.cfg
    ├── setup.py
    ├── tests
    │   ├── __init__.py
    │   ├── conftest.py
    │   └── test_data_science.py
    ├── tox.ini
    └── data_science
        ├── __init__.py
        ├── cli.py
        └── data_science.py

Acknowledgements

---

This template is adapted from the and projects.