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### Description Windows - Fully dynamic ETW controlled logging for ORT and QNN logs The logging support is documented here - https://onnxruntime.ai/docs/performance/tune-performance/logging_tracing.html#tracing---windows - https://onnxruntime.ai/docs/performance/tune-performance/profiling-tools.html#tracelogging-etw-windows-profiling Also add support for logging ORT SessionCreation on ETW CaptureState ### Motivation and Context The previous ETW support only worked if you enabled ETW before the session started. There can commonly be long-lived AI inference processes that need to be traced & debugged. This enables logging fully on the fly. Without this support a dev would have to end up killing a process or stopping a service in order to get tracing. We had to do this for a recent issue with QNN, and it was a bit painful to get the logs and it ruined the repro. ### Testing I tested with the following cases - Leaving default ORT run - Enabling ETW prior to start and leaving running for entire session + inferences, then stopping - Starting ORT session + inf, then enabling and stopping ETW - Start ORT session /w long running Inferences - wpr -start [ort.wprp]( |
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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 |
Third-party Pipeline Status
| System | Inference | Training |
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| 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.