934bb52cfb
Add ARM64X implementation libs, to be forwarded to by the ARM64X lib.
From Ben Niu:
For system dlls that are built outside of windows repo and ingested through vpacks or binary check-ins, we always start by trying to port them to ARM64X. However, due to immature support for ARM64X build from Visual Studio 2019, it could be quite uphill to port dlls to ARM64X.
When that happens, we have an alternative without porting the dll to ARM64X. The alternative solution is, we build an ARM64X pure forwarder from windows repo, for example, onnxruntime.dll. That forwarder does nothing but forwards all the ARM64 API calls to a native ARM64 onnxruntime_arm64.dll, and all the x64 APIs to native x64 onnxruntime_amd64.dll. Please see here for an example:
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| .github | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| nodejs | ||
| onnxruntime | ||
| orttraining | ||
| package/rpm | ||
| samples | ||
| server | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .flake8 | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| build.amd64.1411.bat | ||
| build.bat | ||
| build.sh | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| NuGet.config | ||
| ort.wprp | ||
| packages.config | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements-doc.txt | ||
| requirements.txt | ||
| setup.py | ||
| ThirdPartyNotices.txt | ||
| VERSION_NUMBER | ||
ONNX Runtime is a cross-platform inference and training machine-learning accelerator compatible with deep learning frameworks, PyTorch and TensorFlow/Keras, as well as classical machine learning libraries such as scikit-learn, and more.
ONNX Runtime uses the portable ONNX computation graph format, backed by execution providers optimized for operating systems, drivers and hardware.
Common use cases for ONNX Runtime:
- Improve inference performance for a wide variety of ML models
- Reduce time and cost of training large models
- Train in Python but deploy into a C#/C++/Java app
- Run with optimized performance on different hardware and operating systems
- Support models created in several different frameworks
ONNX Runtime inference APIs are stable and production-ready since the 1.0 release in October 2019 and can enable faster customer experiences and lower costs.
ONNX Runtime training feature was introduced in May 2020 in preview. This feature supports acceleration of PyTorch training on multi-node NVIDIA GPUs for transformer models. Additional updates for this feature are coming soon.
Get Started
- Install
- Inference
- Training
- Documentation
- Samples and Tutorials
- Build Instructions
- Frequently Asked Questions
Build Pipeline Status
| System | CPU | GPU | EPs |
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| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS |
Data/Telemetry
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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.