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* model caching changes for 2021.4 Signed-off-by: Your Name <you@example.com> * changed the ov version check * Minor changes added Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com> * Added support for external data format Starting from OpenVINO 2021.4 version, OpenVINO-EP will support onnx models with Weights saved in external file location. Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com> * Introduced Hetero/Multi options for perf_test Enabled to use HETERO/MULTI device feature from OpenVINO-EP using the onnxruntime_perf_test tool. Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com> * cleaned up CMake code for older OV version support OV 2020.3 is now longer supported by OpenVINO-EP. This check is not required now. Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com> * Add option to disable graph partitioning Added a option to diable graph partitioning during build time for OpenVINO-EP. with this option, when the model is not fully supported on OpenVINO-EP, the model fully fall backs to default CPU EP (MLAS). Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com> * Changed the flag for diabling graph partitioning Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com> * Fixes the flake8 check error Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com> * Added changes for disable graph partition option Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com> * Fixed flake8 indentation error Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com> Co-authored-by: Your Name <you@example.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.