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### Description 1. Use stage to organize the pipeline and split building and testing 2. Move compilation on CPU machine 3. test stage can leverage existing artifacts 4. check wheel size, it gives warning if the size above 300M 5. docker image name wasn't change even the argument changed, which caused the docker image was always rebuilt. So update the docker image name according to the argument can save the docker build time. Pipeline duration reduced by 60% (2 hours -> 50 minutes) Compilation time reduced by 75% (1.5hours -> 20 minutes) GPU time reduced by 87% ( 8 hours to 1 hours) for debugging, the GPU time could be reduced by above 95%, because we can choose run only one test stage and skip building. ### Motivation and Context Make the pipeline efficient. Optimized https://dev.azure.com/aiinfra/Lotus/_build/results?buildId=424177&view=results Curent https://dev.azure.com/aiinfra/Lotus/_build/results?buildId=422393&view=results --------- |
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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.