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### Description - Changes running the E2E iOS tests from running in App Center to running in BrowserStack - Steps for running locally can be found in the OneNote ### Motivation and Context - Follow-up of #22117 - App Center (the previous platform for running E2E mobile tests) is getting deprecated in 2025 ### Misc info Additional build steps were required to get the necessary testing artifacts for BrowserStack. App Center consumed an entire folder, while BrowserStack requests the following: 1. a ZIP file of all the tests 2. an IPA file of the test app #### Flow Here is a rough outline of what is happening in the pipeline: 1. The build_and_assemble_apple_pods.py script builds the relevant frameworks (currently, this means packages for iOS and Mac) 4. The test_apple_packages.py script installs the necessary cocoapods for later steps 5. XCode task to build for testing builds the iOS target for the test app 6. Now that the test app and the tests have been built, we can zip them, creating the tests .zip file 7. To create the IPA file, we need to create a .plist XML file which is generated by the generate_plist.py script. - Attempts to use the Xcode@5 task to automatically generate the plist file failed. - Also, building for testing generates some plist files -- these cannot be used to export an IPA file. 8. We run the Xcode task to build an .xcarchive file, which is required for creating an IPA file. 9. We use xcodebuild in a script step to build an IPA file with the xcarchive and plist files from the last two steps. 10. Finally, we can run the tests using the BrowserStack script. --------- Co-authored-by: Scott McKay <skottmckay@gmail.com> 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 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 |
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| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
This project is tested with BrowserStack.
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
Releases
The current release and past releases can be found here: https://github.com/microsoft/onnxruntime/releases.
For details on the upcoming release, including release dates, announcements, features, and guidance on submitting feature requests, please visit the release roadmap: https://onnxruntime.ai/roadmap.
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.