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### Description <!-- Describe your changes. --> - Split out the code that implements the OrtKernelContext API (used by compiled nodes and custom ops) and the code that implements the custom ops API. - Exclude based on minimal build settings using helpers - the main change is to simply wrap the implementation into a lambda so it can be easily enabled/disabled - actual implementation of all functions are unchanged - Re-organize so the related implementations are together - most diffs are from this, but without the reorg it would be much harder to know which helper to use - General cleanup of lines that were too long. ### 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. --> Saves ~10KB in a minimal build. Build command used for comparison ``` ./build --android --android_api=29 --android_sdk="d:\Android" --android_abi=arm64-v8a --parallel --android_ndk_path="D:\Android\ndk\26.0.10792818\" --build_shared_lib --cmake_generator Ninja --skip_tests --minimal_build --disable_rtti --disable_ml_ops --disable_exceptions --cmake_extra_defines=onnxruntime_BUILD_UNIT_TESTS=OFF --include_ops_by_config .\no_ops.config --config MinSizeRel ``` Main: 1,218,480 bytes With changes: 1,208,320 bytes |
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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.