ae211999dd
### Description
<!-- Describe your changes. -->
Android emulator usage updates:
- Change approach to detecting boot has completed
- use `-delay-adb` and a simple command (`ls`) with `wait-for-device` as
the first step
- this ensures enough startup has occurred for adb to be responsive
- use secondary loop on the python side to check for sys.boot_completed
to be set
- doing the check on the python side provides more feedback and seems to
work well
- make the 'stop' logic more precise by using psutil
- add internal timeout of 20 mins for emulator startup
- waiting for the CI jobs overall timeout is way too long
- value is hardcoded for now (most CIs startup in under 10 mins) but
could be made configurable if needed
CI updates:
- add template for using the Android emulator
- update CIs to use template
- reorder React Native CI
- minimize the time the Android emulator or iOS simulator is running by
moving some build steps around
- don't run both at the same time
- unnecessary and potentially adds significant memory pressure to the
machine
- fix QNN Android emulator CI as much as possible
- now everything works apart from running onnx_test_runner with the QNN
EP
### 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. -->
Fix inconsistent detection of the emulator boot completing.
---------
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.