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### Description See https://github.com/microsoft/onnxruntime-extensions/pull/476 and https://github.com/actions/runner-images/issues/7671 ### 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. --> ### Current issue - [ ] For default xcode 15.2, that come with the MacOS-13, We Need to update the boost container header boost/container_hash/hash.hpp version to pass the build - [x] For xcode 14.2 The Build passed but the `Run React Native Detox Android e2e Test` Failed. Possible flaky test, https://github.com/microsoft/onnxruntime/pull/21969 - [x] For xcode 14.3.1 We encountered following issue in `Build React Native Detox iOS e2e Tests` ``` ld: file not found: /Applications/Xcode_14.3.1.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/arc/libarclite_iphonesimulator.a clang: error: linker command failed with exit code 1 (use -v to see invocation) ``` Applied following code to the eof in both ios/Podfile and fixed the issue ``` post_install do |installer| installer.generated_projects.each do |project| project.targets.each do |target| target.build_configurations.each do |config| config.build_settings['IPHONEOS_DEPLOYMENT_TARGET'] = '13.0' end end end end ``` - [x] https://github.com/facebook/react-native/issues/32483 Applying changes to ios/Pofile ``` pre_install do |installer| # Custom pre-install script or commands puts "Running pre-install script..." # Recommended fix for https://github.com/facebook/react-native/issues/32483 # from https://github.com/facebook/react-native/issues/32483#issuecomment-966784501 system("sed -i '' 's/typedef uint8_t clockid_t;//' \"${SRCROOT}/Pods/RCT-Folly/folly/portability/Time.h\"") end ``` - [ ] Detox environment setting up exceeded time out of 120000ms during iso e2e test ### dependent - [x] https://github.com/microsoft/onnxruntime/pull/21159 --------- 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 & 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 |
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.