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* Add test for iOS package (#7816) * Add test for iOS package * Add readme * fix pep8 warning * Addressed CR comments, fixed CI failure * Address CR comments * Update readme.md * Update package name and readme, added comments to the podspec * Add podspec template for ios package, update build settings (#7907) * Add podspec template for ios package * minor formatting update * Add spec.source_files for header files * Update spec.public_header_files to spec.source_files * minor update * Add iOS packaging pipeline (#8264) Create a pipeline to produce the iOS package artifacts. * [iOS] Packaging pipeline improvements. (#8324) Updates to the iOS packaging pipeline: - Make it harder to overwrite package archives accidentally when uploading (fails if the archive already exists) - Only upload package archives for release builds - Some clean up * Add metadata_props to ORT model (#8340) * Add metadata_props to ORT model * Minor update * Update python binding, and increase the minimal pipeline size threshold * Fixed a small bug in serializing ir_version * Remove temp ort.py.fbs and add it to .gitignore * Add iOS/macOS static framework (#8357) * Add ability to generate ios static framework * Fix typos * Add pod cache clean, update some comments of previous commit * Fix CI failure with newly added cpuinfo library * Update test model (CoreML requires node has a name) * Addressed CR comments * Fix iOS packaging pipeline failure (#8433) * Fix optimizer crash (#8274) * Update iOS packaging script to default build static framework, disable bitcode (#8533) * default package build to static, disable bitcode * fix pipeline failure * Address CR comments * Add HardSigmoid to mobile packages. Used by PyTorch MobileNet v3 (#8552) * bump the version number to 1.8.2 * Change Windows GPU machine pool to onnxruntime-win-cuda11-0 * [Objective-C API] Fix ORTIsCoreMLExecutionProviderAvailable link error when used from Swift. (#8350) Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: RandySheriffH <48490400+RandySheriffH@users.noreply.github.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> Co-authored-by: Changming Sun <chasun@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
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.