73037978f8
Maintaining one execution context on a per thread basis is suggested per TRT [doc](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#threading) to avoid synchronization issue. For previous TRT EP, we did see synchronization issues when running multithreading on some models, for example, FasterRCNN. This PR leverages per thread context implementation from CUDA EP. Followings are the modifications: - Move CUDA graph and IExecutionContext objects to per thread context. - Remove lock_gruad that previously placed for the whole compute_func() and put lock_gruad in the blocks where multiple threads may update kernel function state, access one builder, create/serialize/save engine, save profile and serialize/save timing cache. - On CentOS, don't unload TRT EP shared library and leave it around, so that destructor of thread local data is still accessible upon thread exits. Note: Tested this PR with onnxruntime_perf_test and the overhead of PerThreadContext is small. |
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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.