onnxruntime README

The project's README from github.com/microsoft/onnxruntime. As one markdown file: README.md.

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

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.

onnxruntime (microsoft/onnxruntime)

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这是做什么用的?ONNX Runtime 是一个跨平台的高性能机器学习加速器,可用于推理和训练。它能运行 PyTorch、TensorFlow/Keras 的模型,也支持 scikit-learn、LightGBM、XGBoost 等传统机器学习库的模型。
它是集成到我应用里的库,还是独立运行的工具?它是一个运行时,需要集成到你自己的应用或训练脚本里,而不是带界面的独立工具。你把训练好的模型交给它,ONNX Runtime 会针对你的硬件做优化后执行。
我该怎么入门?文档和教程在 onnxruntime.ai/docs,YouTube 上也有视频教程。配套仓库 microsoft/onnxruntime-inference-examples 和 microsoft/onnxruntime-training-examples 里有可以直接运行的示例。
它能运行哪些框架的模型?支持 PyTorch 和 TensorFlow/Keras 的深度学习模型,也支持 scikit-learn、LightGBM、XGBoost 等类似库的传统机器学习模型。
它会向 Microsoft 发送遥测数据吗?它可能会收集使用数据并发送给 Microsoft,用于帮助改进产品和服务;具体细节见 Microsoft 的隐私声明。
它采用什么许可证——我能用在自己的产品里吗?它采用 MIT 许可证,可以免费使用,包括用在商业产品里。
是不是得有一块很强的 GPU 才值得用它?不需要特定硬件——它可以在不同的硬件、驱动和操作系统上运行,有硬件加速器时会加以利用,同时还会进行图优化和变换。
它能降低我的推理成本吗?这是它明确宣称的目标:README 中提到推理可以带来更快的客户体验和更低的成本,具体通过图优化以及利用可用的硬件加速器来实现。
有支持 Qualcomm 的 QNN 加速器的插件吗?有。QNN Plugin EP 维护在一个单独的配套仓库 onnxruntime/onnxruntime-qnn 里,与推理和训练示例仓库并列。
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