ONNX Runtime is a cross-platform inference and training machine-learning accelerator .
ONNX Runtime inference can enable faster customer 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 different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
General Information : onnxruntime.ai
Usage documentation and tutorials : onnxruntime.ai/docs
YouTube video tutorials : youtube.com/@ONNXRuntime
Companion sample repositories :
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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 [email protected] with any additional questions or comments.
This project is licensed under the MIT License .