Orion is a Cairo library that provides two high-level features:
- Tensor computation (like Numpy) in Cairo 1.0.
- Verifiable Machine Learning models using STARKS.
ONNX Runtime is an open-source, high-performance inference engine for machine learning models in the Open Neural Network Exchange (ONNX) format. ONNX is an interoperable format that allows deep learning models to be represented, shared, and executed across different AI frameworks and platforms.
ONNX Runtime inference can enable faster user experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with various hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
This library proposes a new ONNX runtime built with Cairo. The purpose is to provide a runtime implementation for verifiable ML model inferences using STARKs.
✨🚀 New contributors are welcome to implement new ONNX Operators in Cairo 1.0! 🌟💡
You can check our official docs here.
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⚙️ APIs includes our Getting Started guide, API Reference, and more advanced features of the core like Tensor, Operators and Optimizations.
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🧩 Algorithms is an open collection of algorithms implemented using Orion to be used by the community.
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🧠 Knowledge base is a self-serve library of tips, step-by-step tutorials, and articles that answer your questions about creating verifiable ML models in Cairo.
For a detailed list of changes, please refer to the CHANGELOG file.
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For a full list of all authors and contributors, see the contributors page.
This project is licensed under the MIT license.
See LICENSE for more information.
Thanks goes to these wonderful people (emoji key):
 Fran Algaba 💻 |
 raphaelDkhn 💻 |
 Lanre Ojetokun 💻 🐛 |
 Moody Salem 💻 🐛 |
 Roy Rotstein 💻 |
 omahs 📖 |
 Kazeem Hakeem 💻 |
This project follows the all-contributors specification. Contributions of any kind welcome!