This is a python module that provides a set of tools for working with machine learning models. It includes utilities for neural architecture search using optuna, builders and helpers for keras/tensorflow, a monitoring system for the kernel, and several other features. The module is designed to be easy to use and flexible, allowing users to customize their machine learning workflows.
- Table of Contents
- 📚 API Documentation
- ⚙️ Installation Instructions
- 🚀 Release Flow
- 🔖 Versioning Policy
- 🤝 Contributing
- 📜 License
- 🤝 Collaborators
For comprehensive documentation, examples, and detailed usage guides, please visit our Documentation Wiki.
Install only the feature set you need:
pip install araras
pip install araras[tensorflow]
pip install araras[torch]
pip install araras[viz]
pip install araras[notebook]
pip install araras[gnn]
pip install araras[all]Notes:
- The base install is lightweight and excludes heavyweight ML backends.
- TensorFlow support is enabled via the tensorflow extra.
- PyTorch support is enabled via the torch extra.
- Visualization and notebook extras are optional and independent.
Maintainer quick path:
python -m build
twine check dist/*
git tag v1.0.0
git push origin v1.0.0Tag pushes matching v* trigger the publish workflow.
- The value in pyproject.toml project.version must match the Git tag version.
- Release order: bump version, merge to main, tag as v, push tag.
- PyPI versions are immutable and cannot be re-used.
Contributions are what make the open-source community amazing. To contribute:
- Fork the project.
- Create a feature branch (
git checkout -b feature/new-feature). - Commit your changes (
git commit -m 'Add some feature'). - Push to the branch (
git push origin feature/new-feature). - Open a Pull Request.
This project is licensed under the General Public License.
We thank the following people who contributed to this project:
 Matheus Ferreira |