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### Description <!-- Describe your changes. --> As title. 1. Add macos build as an optionally enabled arch for pod and changes to exsiting build_ios_framework/assemble_c_pod scripts. 2. Enable macos build arch in ios packaging pipeline (currently for variants other than Mobile) and check the output artifacts are correct. 3. Write MacOS Test Target scheme in the test app and integrate into ios packaging CI testing pipeline. Currently the changes only apply to onnxruntime-c pod. as the original request was from ORT SPM which consumes the onnxruntime-c pod only as the binary target. TODO: could look into adding macos platform to objc pod as well. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Enable macos platform support in cocoapods. and also potentially produce binary target for enabling macos platform in SPM as well. Replace https://github.com/microsoft/onnxruntime/pull/18334 --------- Co-authored-by: rachguo <rachguo@rachguos-Mac-mini.local> Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.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 & Resources
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General Information: onnxruntime.ai
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Usage documention 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 |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
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.