5f6a3158f8
### Description 1. Enable VCPKG flag in Windows CPU CI build pipelines. 2. Increased the min supported cmake version from 3.26 to 3.28. Because of it, drop the support for the old way of finding python by "find_package(PythonLibs)". Therefore, in build.py we no longer set "PYTHON_EXECUTABLE" cmake var when doing cmake configure. 3. Added "xnnpack-ep" as a feature for ORT's vcpkg config. 4. Added asset cache support for ORT's vcpkg build 5. Added VCPKG triplet files for Android build. 6. Set VCPKG triplet to "universal2-osx" if CMAKE_OSX_ARCHITECTURES was found in cmake extra defines. 7. Removed a small piece of code in build.py, which was for support CUDA version < 11.8. 8. Fixed an issue that CMAKE_OSX_ARCHITECTURES sometimes got specified twice when build.py invoked cmake. 9. Added more model tests to Android build. After this change, we will test all ONNX versions instead of just the latest one. 10. Fixed issues that are related to build.py's "--build_nuget" parameter. Also, enable the flag in most Windows CPU CI build jobs. 11. Removed a restriction in build.py that disallowed cross-compiling Windows ARM64 nuget package on Windows x86. ### Motivation and Context Adopt vcpkg. |
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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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General Information: onnxruntime.ai
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Usage documentation and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
This project is tested with BrowserStack.
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
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
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.