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* Add full iOS job in package pipeline (#9036) * Add full ios xcframework job * create zip file of the xcframework * Bump up TVM version to avoid conflict with existing one (#9159) * Bump up tvm version * Bump up onnxruntime-tvm version There are some c++17 related fixes in TVM Co-authored-by: KeDengMS <kedeng@microsoft.com> * fix bug introduced by PR9130 (#9166) * make uwp store apps link to statically-linked crt desktop builds (#9182) Co-authored-by: Sheil Kumar <sheilk@microsoft.com> * #9182 removed the `--is_store_build` option but one place where that was used was missed. (#9219) This should fix the relevant packaging pipelines. * DirectML.dll load fails when executable path contains Non-English characters (#9229) * enable unicode dml * add wide string L prefix * Add Fail Fast back Co-authored-by: Sheil Kumar <sheilk@microsoft.com> * Fix Android build break after Virtual Environment update to 20210919 (#9163) Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com> Co-authored-by: ke1337 <22626095+ke1337@users.noreply.github.com> Co-authored-by: KeDengMS <kedeng@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: Sheil Kumar <sheilk@microsoft.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> |
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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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
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.