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* Merge CPU/GPU nuget pipeline * Include TensorRT EP libraries into existing GPU nuget package pipeline * modify to use correct YAML * Modify for test * modify for test * Add depedance * Add depedance (cont.) * modify for test * Add create TensorRT nuget package * modify for test * modify for test * Merge CPU/GPU nuget pipeline * Include TensorRT EP libraries into existing GPU nuget package pipeline * modify to use correct YAML * Modify for test * modify for test * Add depedance * Add depedance (cont.) * modify for test * Add create TensorRT nuget package * modify for test * fix merge bug * code refactor * code refactor * modify for test * modify for test * modify for test * modify for test * modify for test * modify for test * cleanup * modify for test * fix bug * modify for test * refactor * fix bug and test * Modify for test * Modify for test * Modify for test * Modify for test * Prepare for PR * Prepare for PR * code refacotr from review * Remove naming 'Microsoft.ML.OnnxRuntime.TensorRT' to avoid confusion * Add linux TensorRT libraries * Remove redundant variable in YMAL * revert file * undo revert file * Modify regular expression so that it can capture the correct file * Remove newline at end of file * small fix * Revert to CUDA11.1 on Windows * Add unit tests for nuget package on Linux Co-authored-by: Changming Sun <chasun@microsoft.com> |
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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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
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.