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### Description 1. Add two build jobs for enabling Address Sanitizer in CI. One for Windows CPU, One for Linux CPU. 2. Set default compiler flags/linker flags in build.py for normal Windows/Linux/MacOS build. This can help control compiler flags in a more centralized way. 3. All Windows binaries in our official packages will be built with "/PROFILE" flag. Symbols of onnxruntime.dll can be found at [Microsoft public symbol server](https://learn.microsoft.com/en-us/windows-hardware/drivers/debugger/microsoft-public-symbols). Limitations: 1. On Linux Address Sanitizer ignores RPATH settings in ELF binaries. Therefore once Address Sanitizer is enabled, before running tests we need to manually set LD_LIBRARY_PATH properly otherwise libonnxruntime.so may not be able to find custom ops and shared EPs. 4. On Linux we also need to set LD_PRELOAD before running some tests(if the main executable, like python, is not built with address sanitizer. On Windows we do not need to. 5. On Windows before running python tests we should manually copy address sanitizer DLL to the onnxruntime/capi directory, because python 3.8 and above has enabled "Safe DLL Search Mode" that wouldn't use the information provided by PATH env. 6. On Linux Address Sanitizer found a lot of memory leaks from our python binding code. Therefore right now we cannot enable Address Sanitizer when building ONNX Runtime with python binding. 7. Address Sanitizer itself uses a lot of memory address space and delays memory deallocations, which is easy to cause OOM issues in 32-bit applications. We cannot run all the tests in onnxruntime_test_all in 32-bit mode with Address Sanitizer due to this reason. However, we still can run individual tests in such a way. We just cannot run all of them in one process. ### Motivation and Context To catch memory issues. |
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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.