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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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.gdn Update compliance tasks in python packaging pipeline and fix some compile warnings (#8471) 2021-07-30 17:16:37 -07:00
.github Update C/C++ API docs automation to create a PR (instead of push to publish branch) (#10093) 2022-01-07 16:16:47 -08:00
cgmanifests [TVM EP] Rename Standalone TVM (STVM) Execution Provider to TVM EP (#10260) 2022-02-15 10:21:02 +01:00
cmake Convert ConvActivationFusion transformer to a selector action transformer. (#10687) 2022-03-02 13:47:55 +10:00
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java [java] Changes OrtEnvironment so it can't be closed by users (#10670) 2022-02-28 21:03:40 -08:00
js [js] release pipeline for web and react native (#10656) 2022-03-01 21:38:33 -08:00
objectivec [Objective-C API] WIgnore clang documentation warnings from C/C++ header usage. (#9057) 2021-09-14 13:03:48 -07:00
onnxruntime Convert ConvActivationFusion transformer to a selector action transformer. (#10687) 2022-03-02 13:47:55 +10:00
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packages.config Bump winrt version (#10243) 2022-01-12 10:52:27 -08:00
README.md Fix typo 2021-08-12 15:57:15 -07:00
requirements-dev.txt Add post-install command to build PyTorch CPP extensions from within onnxruntime package (#8027) 2021-06-28 18:11:58 -07:00
requirements-doc.txt Add auto doc gen for ORTModule API during CI build (#7046) 2021-03-22 10:20:33 -07:00
requirements-training.txt Add post-install command to build PyTorch CPP extensions from within onnxruntime package (#8027) 2021-06-28 18:11:58 -07:00
requirements.txt.in Chang how numpy version is handled. (#8130) 2021-06-23 14:08:37 -07:00
setup.py [TVM EP] Integrate tests for TVM EP into public onnxruntime CI (#10505) 2022-02-24 16:24:23 +01:00
ThirdPartyNotices.txt add copyright (#9943) (#9970) 2021-12-08 14:34:53 -08:00
VERSION_NUMBER Bump master version to 1.11 (#9957) 2021-12-14 23:32:06 -08:00

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

General Information: onnxruntime.ai

Usage documention and tutorials: onnxruntime.ai/docs

Companion sample repositories:

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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 opencode@microsoft.com with any additional questions or comments.

License

This project is licensed under the MIT License.