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.
- Install
- Inference
- Training
- Documentation
- Samples and Tutorials
- Build Instructions
- Frequently Asked Questions
| System | CPU | GPU | EPs |
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| WebAssembly |
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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.
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.
This project is licensed under the MIT License.