This repository provides a generic template for bootstrapping PyTorch projects, aimed at researchers and developers seeking a structured and reproducible environment. It streamlines project setup, reduces boilerplate code, and integrates essential tools for machine learning workflows, accelerating prototyping and ensuring experiment reproducibility.

How It Works

The template leverages Cookiecutter for guided project generation, allowing users to customize their project structure and configurations. It integrates key libraries like PyTorch Lightning, Hydra, Hugging Face Datasets, Weights and Biases, Streamlit, MkDocs, DVC, and GitHub Actions to provide a comprehensive, production-ready foundation for ML research and development.

Quick Start & Requirements

• Install Cookiecutter: pip install cookiecutter
• Generate project: cookiecutter https://github.com/grok-ai/nn-template
• Prerequisites: Python, Cookiecutter. Integrations may require additional setup (e.g., API keys for Weights and Biases).
• Documentation: https://github.com/grok-ai/nn-template

Highlighted Details

• Guided setup for project bootstrapping.
• Integrates PyTorch Lightning, Hydra, Hugging Face Datasets, Weights and Biases, Streamlit, MkDocs, DVC, and GitHub Actions.
• Enforces Python best practices and automates CI/CD.
• Aims to ensure experiment reproducibility.

Maintenance & Community

• Maintainers: Valentino Maiorca, Luca Moschella.
• No community links (Discord/Slack) or roadmap are provided in the README.

Licensing & Compatibility

• License: Not explicitly stated in the README.
• Compatibility: Designed for PyTorch projects; commercial use compatibility depends on the unstated license.

Limitations & Caveats

The README does not specify the project's license, which is crucial for determining commercial use compatibility. Community support channels and a roadmap are also not detailed, potentially impacting long-term project health and user support.