[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9588":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":36,"readmeContent":37,"aiSummary":38,"trendingCount":16,"starSnapshotCount":16,"syncStatus":39,"lastSyncTime":40,"discoverSource":41},9588,"onnxruntime","microsoft\u002Fonnxruntime","microsoft","ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator","https:\u002F\u002Fonnxruntime.ai",null,"C++",20795,3977,264,826,0,7,73,320,41,114,"MIT License",false,"main",true,[27,28,29,30,31,32,33,34,35],"ai-framework","deep-learning","hardware-acceleration","machine-learning","neural-networks","onnx","pytorch","scikit-learn","tensorflow","2026-06-12 04:00:45","\u003Cp align=\"center\">\u003Cimg width=\"50%\" src=\"docs\u002Fimages\u002FONNX_Runtime_logo_dark.png\" \u002F>\u003C\u002Fp>\n\n**ONNX Runtime is a cross-platform inference and training machine-learning accelerator**.\n\n**ONNX Runtime inference** can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow\u002FKeras 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 →](https:\u002F\u002Fwww.onnxruntime.ai\u002Fdocs\u002F#onnx-runtime-for-inferencing)\n\n**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 →](https:\u002F\u002Fwww.onnxruntime.ai\u002Fdocs\u002F#onnx-runtime-for-training)\n\n## Get Started & Resources\n\n* **General Information**: [onnxruntime.ai](https:\u002F\u002Fonnxruntime.ai)\n\n* **Usage documentation and tutorials**: [onnxruntime.ai\u002Fdocs](https:\u002F\u002Fonnxruntime.ai\u002Fdocs)\n\n* **YouTube video tutorials**: [youtube.com\u002F@ONNXRuntime](https:\u002F\u002Fwww.youtube.com\u002F@ONNXRuntime)\n\n* [**Upcoming Release Roadmap**](https:\u002F\u002Fonnxruntime.ai\u002Froadmap)\n\n* **Companion sample repositories**:\n  - ONNX Runtime Inferencing: [microsoft\u002Fonnxruntime-inference-examples](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fonnxruntime-inference-examples)\n  - ONNX Runtime Training: [microsoft\u002Fonnxruntime-training-examples](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fonnxruntime-training-examples)\n\n* **Plugin EP repositories**:\n  - ONNX Runtime QNN Plugin EP: [onnxruntime\u002Fonnxruntime-qnn](https:\u002F\u002Fgithub.com\u002Fonnxruntime\u002Fonnxruntime-qnn)\n\n## Releases\n\nThe current release and past releases can be found here: https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fonnxruntime\u002Freleases.\n\nFor details on the upcoming release, including release dates, announcements, features, and guidance on submitting feature requests, please visit the release roadmap: https:\u002F\u002Fonnxruntime.ai\u002Froadmap.\n\n## Data\u002FTelemetry\n\nWindows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the [privacy statement](docs\u002FPrivacy.md) for more details.\n\n## Contributions and Feedback\n\nWe welcome contributions! Please see the [contribution guidelines](CONTRIBUTING.md).\n\nFor feature requests or bug reports, please file a [GitHub Issue](https:\u002F\u002Fgithub.com\u002FMicrosoft\u002Fonnxruntime\u002Fissues).\n\nFor general discussion or questions, please use [GitHub Discussions](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fonnxruntime\u002Fdiscussions).\n\n## Code of Conduct\n\nThis project has adopted the [Microsoft Open Source Code of Conduct](https:\u002F\u002Fopensource.microsoft.com\u002Fcodeofconduct\u002F).\nFor more information see the [Code of Conduct FAQ](https:\u002F\u002Fopensource.microsoft.com\u002Fcodeofconduct\u002Ffaq\u002F)\nor contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.\n\n## License\n\nThis project is licensed under the [MIT License](LICENSE).\n","ONNX Runtime 是一个跨平台的机器学习推理和训练加速器。它支持来自 PyTorch、TensorFlow\u002FKeras 以及 scikit-learn 等多种深度学习框架和经典机器学习库的模型，并能在不同硬件、驱动程序和操作系统上提供优化性能，通过利用硬件加速器（如适用）以及图优化和转换来提升速度。此外，ONNX Runtime 还可以通过在现有 PyTorch 训练脚本中添加一行代码来加速多节点 NVIDIA GPU 上的变压器模型训练时间。该项目适用于需要高性能机器学习推理或希望加快模型训练过程的各种应用场景。",2,"2026-06-11 03:23:37","top_topic"]