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    ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

    deep-learning
    machine-learning
    ai-framework
    hardware-acceleration
    neural-networks
    onnx
    pytorch
    C++
    MIT
    21.1K stars
    4.0K forks
    21.1K GitHub watchers
    Updated 7/13/2026
    View on GitHub

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    Use Cases & Benefits

    • Provides a cross-platform, high-performance runtime for machine learning model inferencing and training acceleration.
    • Delivers optimal performance by leveraging hardware accelerators and graph optimizations across diverse frameworks and hardware.
    • Use for accelerating inference of deep learning and classical ML models from PyTorch, TensorFlow, and scikit-learn on various devices.
    • Use for speeding up multi-node NVIDIA GPU training of transformer models with minimal code changes in PyTorch scripts.
    • Use for integrating a unified runtime to deploy and optimize machine learning models in production environments across platforms.

    About onnxruntime

    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 →

    Get Started & Resources

    Releases

    The current release and past releases can be found here: https://github.com/microsoft/onnxruntime/releases.

    For details on the upcoming release, including release dates, announcements, features, and guidance on submitting feature requests, please visit the release roadmap: https://onnxruntime.ai/roadmap.

    Data/Telemetry

    Windows distributions of 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 [email protected] with any additional questions or comments.

    License

    This project is licensed under the MIT License.

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