ONNX Runtime is a cross-platform inference and training machine-learning accelerator compatible with deep learning frameworks, PyTorch and TensorFlow/Keras, as well as classical machine learning libraries such as scikit-learn, and more.

ONNX Runtime uses the portable ONNX computation graph format, backed by execution providers optimized for operating systems, drivers and hardware.

Common use cases for ONNX Runtime:

• Improve inference performance for a wide variety of ML models
• Reduce time and cost of training large models
• Train in Python but deploy into a C#/C++/Java app
• Run with optimized performance on different hardware and operating systems
• Support models created in several different frameworks

ONNX Runtime inference APIs are stable and production-ready since the 1.0 release in October 2019 and can enable faster customer experiences and lower costs.

ONNX Runtime training feature was introduced in May 2020 in preview. This feature supports acceleration of PyTorch training on multi-node NVIDIA GPUs for transformer models. Additional updates for this feature are coming soon.

Get Started

http://onnxruntime.ai/

• Install
• Inference
• Training
• Documentation
• Samples and Tutorials
• Build Instructions
• Frequently Asked Questions

Build Pipeline Status

System	CPU	GPU	EPs
Windows
Linux
Mac
Android
iOS
WebAssembly

Data/Telemetry

This project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.

Contributions and Feedback

We welcome contributions! Please see the contribution guidelines.

For feature requests or bug reports, please file a GitHub Issue.

For general discussion or questions, please use Github Discussions.

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

License

This project is licensed under the MIT License.