ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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Scott McKay 0770cf3699
Remove C# SessionOptions.RegisterCustomOpsUsingFunction. (#15754)
Symbol visibility from DllImport is inconsistent across platforms resulting in the symbol not necessarily being visible to ORT native code that tries to look it up by name.

Best solution is to use DllImport to load the library and to call the registration function directly. That requires the native SessionOptions handle and OrtApiBase struct. We could either make those public, or provide a helper where the user passes in a delegate from their DllImport. Can add when needed.
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.config Update tsaoptions.json: update the email alias (#13448) 2022-10-26 15:56:16 -07:00
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.github Training Documentation (#15612) 2023-04-25 11:44:12 -07:00
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cmake [VitisAI]Update VitisAI EP to be compatible with VitisAI 3.5 (#15673) 2023-05-01 08:28:26 -07:00
csharp Remove C# SessionOptions.RegisterCustomOpsUsingFunction. (#15754) 2023-05-01 09:14:21 -07:00
dockerfiles Update cmake version in Linux build (#15707) 2023-04-27 20:02:33 -07:00
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js [js/rn] Package dependency change to manage ort-extensions for react_native app (#15641) 2023-04-29 00:07:12 -07:00
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onnxruntime Implement lite custom op API (#15590) 2023-05-01 08:45:26 -07:00
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swift/OnnxRuntimeBindingsTests Add iOS Swift Package Manager support (#15297) 2023-04-20 16:18:35 +10:00
tools [VitisAI]Update VitisAI EP to be compatible with VitisAI 3.5 (#15673) 2023-05-01 08:28:26 -07:00
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requirements.txt.in Add additional python requirements (#11522) 2022-05-20 16:16:18 -07:00
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ThirdPartyNotices.txt [js/web] WebGPU backend via JSEP (#14579) 2023-04-24 15:21:18 -07:00
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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

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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.