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### Description <!-- Describe your changes. --> Adding static int8 quantization support for MIGraphX Execution Provider - Allows for parsing in calibration tables generated by Onnxruntime or TensorRT's toolsets - Add proper environment variables into the MIGraphX EP - Update python API to include updating execution provider flags -> was missing on python side - Hook into MIGraphX's int8 quantitation and optimization of models ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Required so that we can get onnxruntime to pass in models while leveraging the existing tooling for int8 static QDQ quantization. First step in a series of PRs which will add further static quantization on the operator level as MIGraphX releases further support. These changes drew heavily from the tensorRT EP should allow for similar functionality for GPU based (versus CPU) quantization of models before an inference is performed. --------- Co-authored-by: Ted Themistokleous <tthemist@amd.com> Co-authored-by: Ted Themistokleous <tedthemistokleous@amd.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 & 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 |
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| Windows | ||
| Linux | ||
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Third-party Pipeline Status
| System | Inference | Training |
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| 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.