mlcookiecutter

Overview

mlcookiecutter is a command-line tool that helps you quickly set up a structured project directory for data science and machine learning projects. It creates a predefined folder structure, initializes necessary files, and provides a template for your project, making it easier to start new projects with best practices in mind.

Features

• Create a well-organized project structure
• Generate essential files such as README.md, LICENSE, and requirements.txt
• Supports various license types
• Allows specifying CODEOWNERS for collaborative projects

Installation

You can install the package directly from PyPI using pip:

pip install mlcookiecutter

Or clone this repository and install it locally:

git clone https://github.com/sarag5/mlcookiecutter.git
cd mlcookiecutter
pip install -e .

Usage To create a new project structure, use the command line interface:

mlcookiecutter --project_name <your_project_name> --license_type <license_type> --codeowners <comma_separated_owners>

Example

mlcookiecutter --project_name my_data_project --license_type mit --codeowners user1@example.com,user2@example.com

This command will create a new directory called my_data_project with the following structure:

my_data_project/
├── data/
│   ├── raw/
│   └── processed/
├── src/
│   ├── data_engineering/
│   ├── feature_engineering/
│   ├── model/
│   └── utils/
├── notebooks/
├── tests/
│   ├── data_engineering/
│   └── model/
├── models/
├── scripts/
├── deployment/
├── .github/
│   └── workflows/
├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt
├── CODEOWNERS
└── tasks.py

Available Tasks

This project uses Invoke to manage various tasks. Here are the available tasks:

run_locally: Run the project locally.
run_tests: Run the test suite.
run_lint: Run the linter (flake8).
run_build: Run the build process.
check_format: Check the code formatting.
correct_format: Correct the code formatting.

To see all available tasks, run:

invoke --list

GitHub Workflows

This project includes several GitHub workflows to ensure code quality and proper ML practices:

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Test and Lint

• Triggered on every push and pull request
• Runs the test suite using pytest
• Checks code style using flake8

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ML Model Validation

• Triggered when changes are made to the model code or data
• Runs model training and validation scripts

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Data Quality Check

• Triggered when changes are made to the data files
• Runs data quality checks to ensure data integrity

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These workflows help maintain code quality, ensure proper testing, and validate both the model and data throughout the development process.

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

Fork the repository.

1. Create your feature branch: git checkout -b feature/YourFeature
2. Commit your changes: git commit -m 'Add some feature'
3. Push to the branch: git push origin feature/YourFeature
4. Open a pull request

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Contributors

See the CODEOWNERS file for the list of contributors.