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* use the lightweight compile api as default; use dnnl ep for testing * apply to tensorrt ep * fix the missing files * fix build * fix the copy issue on linux * migrate migraphx and openvino ep * fix openvino build break * fix linux build * fix unused parameter * fix coreml build * use graph view's filtered initializers * fix openvino break * fix tvm compile api * fix tvm / rknpu / vitisai ep build * add IsInitializedTensor in graph_viewer; fix nuphar build * use serializer directly as tvm ep is still static lib * fix the type mismatch * fix the type mismatch * fix merge conflict * add a comment * fix minimal build * fix the DML EP's legacy approach * save type/shape in dnnl IR * fix linux break * fix tvm failure * dnnl ep: move initializer referenced out of dnnl subgraph * Revert "add IsInitializedTensor in graph_viewer; fix nuphar build" This reverts commit 1cc3c7f08c16fee4fe3309a67209eb769d479587. * add IsInitializedTensor to graph viewer * add the legacy code for nuphar build to temporarily make nuphar build work * ignore internal test for nuphar * remove the out of date tests * keep the legacy API in EP for a while * turn serializer into a static function * update comments * fix tvm build * Update include/onnxruntime/core/framework/execution_provider.h Co-authored-by: Pranav Sharma <prs@microsoft.com> * Update include/onnxruntime/core/framework/execution_provider.h Co-authored-by: Pranav Sharma <prs@microsoft.com> * Update onnxruntime/core/framework/execution_provider.cc Co-authored-by: Pranav Sharma <prs@microsoft.com> * updatee comments; add warning message for legacy compil call * add a flag to control out of scope arg in serialization * fix trt build; improve the test * resolve merege errors * fix a typo Co-authored-by: Cheng Tang <chenta@microsoft.com> Co-authored-by: Cheng Tang <chenta@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Pranav Sharma <prs@microsoft.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.