0d7855ea5a
### Description Re-work handling of static objects in pybind. Make sure we ref-count Environment from Sessions. The following has been done: - Make global objects function static. This ensures that the objects are constructed on demand. The first object constructed is destructed last. This is platform independent. - Make global objects ownership shared as suggested by pybind since they are not surfaced at Python level, and they cannot be referred to by dependent python objects. Verified that all python objects are GCed before globals are destroyed. This takes care of inference session dependency on environment and its default logger and this is also platform independent. - Utilize pybind atexit mechanism to clear execution providers and unload CUDA libraries (as suggested by https://github.com/microsoft/onnxruntime/pull/14903) . Since this is registered for module exit, it takes place before any other global are destroyed and clears shared objects state or even unloads the libraries. This should also work in a platform independent way. ### Motivation and Context - Global object destruction order is managed manually and that becomes source of trouble. We want to make it deterministic and platform independent. - Frequent hangs in Python layer due to the static object's destruction order. Some of the Python session objects are being garbage collected after main exits and they require ORT environment to be alive. (Use after free) |
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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
Build Pipeline Status
| System | Inference | Training |
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| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
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.