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### Description This PR enables execution of subgraphs in OVEP and currently, when OVEP developers install the onnxruntime-openvino package on windows from pypi, they would have to additionally download OpenVINO windows binaries and run the setupvars.bat script which sets the environment PATH to locate the OV dll's. Also this PR fixes issues of OVEP windows io buffer sample. ### Motivation and Context Fix: We want to make the user experience easy for OVEP Python developers on windows platform. This fix, introduces a function add_openvino_libs_to_path at the location tools/python/util/add_openvino_win_libs.py. The above function, can be called by OVEP python users in the application code and that takes care of setting the OpenVINO dll's to the path from the OpenVINO pypi packge (openvino) which was installed. This change also makes sure that add_openvino_libs_to_path() function is added to onnxruntime python package only when it is build for OpenVINO Execution Provider for ONNXRuntime and not for default ORT python package builds. New user experience for Python OVEP developers on windows platform: step 1: pip install onnxruntime-openvino step 2: pip install openvino step 3: <Add these 2 lines in the application code> import onnxruntime.tools.add_openvino_win_libs as utils utils.add_openvino_libs_to_path() --------- Signed-off-by: MaajidKhan <n.maajid.khan@intel.com> Co-authored-by: MaajidKhan <n.maajid.khan@intel.com> Co-authored-by: Suryaprakash Shanmugam <suryaprakash.shanmugam@intel.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 |
|---|---|---|
| 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.