"We demand rigidly defined areas of doubt and uncertainty."

Generic template to bootstrap your PyTorch project, read more in the documentation.

If you already know cookiecutter, just generate your project with:

cookiecutter https://github.com/grok-ai/nn-template

pip install cookiecutter

More details in the documentation.

• Actually works for research!
• Guided setup to customize project bootstrapping;
• Fast prototyping of new ideas, no need to build a new code base from scratch;
• Less boilerplate with no impact on the learning curve (as long as you know the integrated tools);
• Ensure experiments reproducibility;
• Automatize via GitHub actions: testing, stylish documentation deploy, PyPi upload;
• Enforce Python best practices;
• Many more in the documentation;

Avoid writing boilerplate code to integrate:

• PyTorch Lightning, lightweight PyTorch wrapper for high-performance AI research.
• Hydra, a framework for elegantly configuring complex applications.
• Hugging Face Datasets,a library for easily accessing and sharing datasets.
• Weights and Biases, organize and analyze machine learning experiments. (educational account available)
• Streamlit, turns data scripts into shareable web apps in minutes.
• MkDocs and Material for MkDocs, a fast, simple and downright gorgeous static site generator.
• DVC, track large files, directories, or ML models. Think "Git for data".
• GitHub Actions, to run the tests, publish the documentation and to PyPI automatically.
• Python best practices for developing and publishing research projects.

• Valentino Maiorca @Flegyas
• Luca Moschella @lucmos