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### Description When I worked on PR #17173, I didn't notice that onnxruntime\core\platform\windows\debug_alloc.cc also needs to call dbghelp functions like SymInitialize. So, if we use vc runtime's stacktrace functionality, vc runtime will initialize/uninitialize the dbghelp library independently and vc runtime's stacktrace helper DLLs get unloaded before our memory leak checker starts get work. Then we call SymSetOptions, it crashes. More details: In VC runtime the C++23 stacktrace functions are implemented on top of dbgeng.dll. In C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Tools\MSVC\14.37.32822\crt\src\stl\stacktrace.cpp, you can see it has: ``` dbgeng = LoadLibraryExW(L"dbgeng.dll", nullptr, LOAD_LIBRARY_SEARCH_SYSTEM32); ``` The dbgeng.dll is a wrapper around dbghelp.dll. It calls SymInitialize and SymCleanup. dbgeng.dll gets unloaded before our memory leak check starts to run. In theory we should be able to call SymInitialize again if the previous user who called SymInitialize has also called SymCleanup. However, users can use SymRegisterCallback/SymRegisterCallback64/SymRegisterCallbackW64 to register callback functions to dbghelp.dll. These callback functions need to be alive when SymSetOptions(and some other dbghelp APIs) get called. ### Motivation and Context |
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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.