scaffml 0.2.1

Professional ML Project Structure Generator

scaffml

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🚀 Overview

scaffml is a professional ML project structure generator that allows data scientists and ML engineers to quickly scaffold production-ready machine learning projects.

It provides:

• Clean, modular project architecture
• Optional Docker support
• Templated code files for rapid development
• Preconfigured .gitignore and folder structure
• Easy integration with CI/CD, testing, and ML workflows

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📦 Features

• ✅ Generate ML projects with a single CLI command
• ✅ Optional Docker support via --docker flag
• ✅ Predefined folder hierarchy: app/, src/, data/, notebooks/, tests/
• ✅ Jinja2 templated files for customizable project boilerplate
• ✅ Safe collision detection to avoid overwriting existing projects
• ✅ Professional CLI with version info and help menus

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🛠️ Installation

Create directory:

``bash mkdir your_root_project_name cd your_root_project_name

Activate vertual Env(Recommended)
```bash
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Install via pip:

pip install scaffml

Or clone and install locally:

git clone https://github.com/epythonlab2/scaffml.git
cd scaffml
pip install .

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⚡ Usage

Check version

scaffml version

Create a new ML project

scaffml create my_ml_project

Create a new ML project with Docker support

scaffml create my_ml_project --docker

• This generates a professional folder structure with templated files.
• Empty directories are preserved with .gitkeep.

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📂 Generated Project Structure

Example project layout:

my_ml_project/
├── app/
│   ├── api/
│   │   ├── __init__.py
│   │   ├── routes/
│   │   │   └── prediction.py
│   │   ├── main.py
│   │   └── schemas.py
│   ├── pipelines/
│   ├── services/
│   └── core/
│       ├── __init__.py
│       ├── config.py
│       └── logger.py
├── data/
│   ├── raw/
│   ├── processed/
│   └── external/
├── notebooks/
├── models/
├── src/
│   ├── __init__.py
│   ├── data/
│   │   ├── __init__.py
│   │   ├── loader.py
│   │   └── preprocessing.py
│   ├── features/
│   │   ├── __init__.py
│   │   └── build_features.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── train.py
│   │   └── predict.py
│   └── utils/
│       └── __init__.py
├── tests/
│   ├── __init__.py
│   ├── test_api.py
│   └── test_data.py
├── .github/
│   └── workflows/
│       └── ci.yml
├── __init__.py
├── .gitignore
├── Dockerfile
├── requirements.txt
├── README.md
└── pyproject.toml

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📝 Contributing

We welcome contributions!

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Commit your changes (git commit -m "Add feature")
4. Push and submit a pull request

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📄 License

MIT License – see LICENSE for details.

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💡 Notes

• Designed for ML engineers who want clean, production-ready projects quickly.
• Easily extendable for additional templates and features.
• Works cross-platform (Linux/macOS/Windows) with Python 3.10+.