microsoft

    microsoft/onnxruntime

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

    22.2K stars
    4.4K forks
    22.2K GitHub watchers
    Updated Nov 3, 2026
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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    5 releases in the last 90 days, the latest 5 days ago, with 402 contributors on GitHub.

    Gained 11% more stars in the last 90 days than in the 90 days before.

    Use Cases & Benefits

    • Provides a cross-platform, high-performance machine learning inference and training accelerator supporting multiple frameworks and hardware.
    • Delivers optimal performance by leveraging hardware accelerators and graph optimizations across diverse operating systems and devices.
    • Use for accelerating inference of deep learning models from PyTorch, TensorFlow, and classical ML libraries like scikit-learn.
    • Use for speeding up multi-node NVIDIA GPU training of transformer models with minimal code changes in PyTorch scripts.
    • Use for integrating efficient ML model deployment in applications requiring cross-platform compatibility and hardware acceleration.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    MIT

    Permissive: commercial use, modification and redistribution are allowed with attribution.

    Community files

    • README: present
    • Contributing guide: present
    • Code of conduct: present

    Watch-outs

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    From the README

    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 →

    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.

    Excerpt from the microsoft/onnxruntime README on GitHub (MIT license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Forks

    4.4K+301

    Open issues

    1.8K+326

    Compared with peers in Deep Learning

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth

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