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### Description This pull request introduces the necessary changes to enable RISC-V 64-bit cross-compiling support for the ONNX Runtime on Linux. The RISC-V architecture has gained popularity as an open standard instruction set architecture, and this contribution aims to extend ONNX Runtime's compatibility to include RISC-V, thereby broadening the reach of ONNX models to a wider range of devices. ### Motivation and Context RISC-V is a free and open-source instruction set architecture (ISA) based on established RISC principles. It is provided under open licenses without fees. Due to its extensibility and freedom in both software and hardware, RISC-V is poised for widespread adoption in the future, especially in applications related to AI, parallel computing, and data centers. ### Example Build Command ``` ./build.sh --parallel --config Debug --rv64 --riscv_toolchain_root=/path/to/toolchain/root --skip_tests ``` ### Documentation Updates Relevant sections of the documentation will be updated to reflect the newly supported RISC-V 64-bit cross-compilation feature. https://github.com/microsoft/onnxruntime/pull/19239 --------- Signed-off-by: Phoebe Chen <phoebe.chen@sifive.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 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 |
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| 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.