e5f18ba2c1
### Description Resolve #21281 and #10589 . 1. Change libonnxruntime.so's SONAME: remove the minor and patch version. By default when creating an ELF shared object, linker will set the file's internal DT_SONAME field to the specified name which is the file name plus SOVERSION . For example, the file name for our library is libonnxruntime.so. And by default SOVERSION is the lib's VERSION number, which is something like 1.19.0. So the DT_SONAME field in libonnxruntime.so is something like libonnxruntime.so.1.18.0. You can use readelf tool to examine it. ``` readelf -d libonnxruntime.so | grep SONAME 0x000000000000000e (SONAME) Library soname: [libonnxruntime.so.1.18.0] ``` When an executable is linked with a shared object which has a DT_SONAME field, then when the executable is run the dynamic linker will attempt to load the shared object specified by the DT_SONAME field rather than using the file name(which is libonnxruntime.so) given to the linker. After this change, the SONAME will be shorten to "libonnxruntime.so.1" instead. 2. Set default version strings for Windows DLLs, to resolve #10589 |
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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 |
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| 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.