66b3c31f76
* Fixing MORE mlas unittest failures in POWER (#8673) * Ensure ms-experimental domain Audio Ops build in mac pipeline (#8857) * Globally enable ms-experimental ops * change meaning of ms_experimental to mean *all* ms_experimental ops. Some experimental ops will still be enabled globally without this flag like audio ops. * add cmath * add cmath to signal_defs.cc * move audio back into experimental, verify on mac * remove experimental from mac builds Co-authored-by: Sheil Kumar <sheilk@microsoft.com> * Remove cpuinfo from WCOS builds (#9076) * Fix a bug for Openvino Python binding (#9130) * Fix default initialization value in C API header (#9126) * fix default initialization value in C API header * Fix conflicts * Nits * Do not generate nuget symbol packages on Linux * fix name conflict in 1.9 for Fix default initialization value in C API header * Fix nightly CI pipeline to generate ROCm 4.2 wheels and add ROCm 4.3.1 wheels (#9101) * make work for both rocm 4.2 and rocm 4.3.1 * fix rocm 4.3.1 docker image reference * fix CUDA_VERSION to ROCM_VERSION * fix ReduceConsts conflict def * add ifdef to miopen_common.h as well * trailing ws * remove OrtCUDAProviderOptions() and simply set value * revert to use custom ctor and fix tests Co-authored-by: austinpagan <fossum@us.ibm.com> Co-authored-by: Sheil Kumar <smk2007@gmail.com> Co-authored-by: Sheil Kumar <sheilk@microsoft.com> Co-authored-by: Tiago Koji Castro Shibata <ticastro@microsoft.com> Co-authored-by: Changming Sun <chasun@microsoft.com> Co-authored-by: Hariharan Seshadri <shariharan91@gmail.com> Co-authored-by: Suffian Khan <sukha@microsoft.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 | |||
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| 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.