3b0aaa9e0e
* add description of build ORT+TVM EP on Windows * fix cmake error related to symlink creation on Windows * add llvm config path to build flags for correct build on Windows * update TVM_EP.md for llvm_config build arg * fix warnings skipping during build on Windows * fix using string or wstring for model path to correct build on Windows (MSVC error) * fix error in custom logger for correct build on Windows * implement glob algorithm for Windows * additional build fixes * update TVM with export of VM symbols for dll * description of nasm issue and workaround * update TVM with export of Executable from VM symbols for dll * description of installation of ipp-crypto dependencies on Windows * cmake key for ipp-crypto build * fix wstring for TVMso EP * fix ipp-crypto build * cmake key onnxruntime_TVM_USE_HASH switch off not specific methods, but full hash functionality * fix absolute path to compiled lib * update TVM_EP.md, fix lint warnings * update TVM_EP.md * small fixes after review * switch on handshake functionality for Linux workflow Co-authored-by: Valery Chernov <valery.chernov@deelvin.com> Co-authored-by: KJlaccHoeUM9l <wotpricol@mail.ru> |
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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.