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Python ML Template

A starter kit for Python-based Machine Learning projects
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Launching a new project from scratch can be a challenge, especially with a personal commitment to high-quality work. This initial setup complexity got me thinking: "How few commands can I really get away with to kickstart a new machine learning (ML) project"?

Well, from putting together this Python ML Template project, my answer is three:

copier copy python-ml-template/ your-new-project
cd your-new-project/
make

This even automatically creates a remote repository on GitHub! Of course, this template is doing most of the heavy lifting. For more details on getting started, please Checkout this README's "Getting Started" section.

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So, what exactly did I choose to include in my essential starter kit for a Python-based Machine Learning project?

• A simple continuous integration (CI) pipeline that blocks any pull requests (PRs) with failing Python tests
• Dynamic template generation to encourage a "document as you go" approach rather than hastily throwing everything together
• A base Python environment definition so that I'm not consistently reinstalling my favorite Python packages (e.g., black) over and over
• Git and GitHub-focused templates to make version control and peer-to-peer collaboration as streamlined as possible

For a deeper dive of what's included in this project and why, please refer to the project documentation.

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This project leverages the power of the following frameworks and libraries. Refer to the acknowledgments section for the other resources that I've employed

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Want to get started with ML development as soon as possible? Then just follow the steps outlined below.

Make sure you have the following dependencies installed:

• Conda or miniconda for Python package management

• Copier for dynamic Markdown file templating

• GitHub CLI for automated project publishing and run gh auth login to setup

Getting started is simple. Here are the step-by-step instructions:

1. Clone this repository (git clone https://github.com/bellanich/python-ml-template)
2. Fill out your personal details in the relevant fields of the copier.yml file
3. Use copier to copy this template to a new directory and fill out the provided prompts (copier copy python-ml-template/ your-new-project/)
4. Execute the Makefile inside the newly created directory

   cd your-new-project/
   make

5. Activate your conda environment and you're ready to go

   # By default, your conda env will have the same name as your project
   CONDA_ENV_NAME=$(basename "$(pwd)")
   conda activate $CONDA_ENV_NAME

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Curious how my Python ML Template works? This GIF showcases how you can get started right away with ML feature development by using this template's 3 simple setup commands:

For more implementation details, please refer to the project documentation.

NOTE: This section focus is illustrating a project's business value. Therefore, in a more prototypical ML project, you would also include things like screenshots, code examples, and figures.

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If you have a suggestion that would make this project better, please fork this repository and create a pull request. You can also simply open an issue with the tag "enhancement".

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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Distributed under the MIT License. See LICENSE file for more information.

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Bella Nicholson - @bella-nicholson - [email protected]

Project Link: https://github.com/bellanich/python-ml-template

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Rather than re-inventing the wheel, I've leveraged the following great resources in my template formulation:

• The Best README Template by Othneil Drew
• Axolo's Guide to GitHub PR Templates
• Collection of GitHub Issues Templates by Steve Mao
• Collection of Git Attribute Templates by gitattributes
• Shields.io Static Badge Builder
• Guide to making custom language badges
• Terminalizer by Mohammad Anas Fares for terminal session recording
• Simple Icons's library of open source project logos
• Python's Official Logging Cookbook by Vinay Sajip
• Conventional Commit Regex by marcojahn

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