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* expand model tests name * skip cpu/cuda for trt when running onnxruntime_test_all * only run trt ep for c++ unit test * Update CMAKE_CUDA_ARCHITECTURES for T4 * Use new t4 agent pool * Update YAML for run T4 on Windows * revert code * Update CMAKE_CUDA_ARCHITECTURES * fix wrong value * Remove cpu/cuda directly in model tests * add only CMAKE_CUDA_ARCHITECTURES=75 * remove expanding model test name to see difference * revert code * Add fallback execution provider for unit test * Add fallback execution provider for unit test (cont) * add conditional to add fackback cuda ep * Reduction op takes much longer time for TRT 8.2, so we test smaller range of inputs * use M60 * revert code * revert code * add comments * Modify code and add comment * modify comment * update comment * add comment |
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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.