62cc981599
* add executor option (vm or graph) and support virtual machine methods * nullptr check for compile and run methods (see also PR#10211 from microsoft:onnxruntime) * get output shapes for VM * remove run_with_benchmark. remove run methods from python api, get it from native side * get outputs method for VM was implemented * support multiple input for VM * update python logging and exception * small fix * update tvm with patch for VM API * update nhwc transformations for TVM EP * add data alignment check and support set_input_zero_copy for GE in TVM EP * fix logger name * return back to apache/tvm with VM fixes instead of local dev branch * hide customized tvm logger while issue is not resolved. fix tvm warning related to target_host * flake8 fix Co-authored-by: Valery Chernov <valery.chernov@deelvin.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
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 |
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| Windows | |||
| Linux | |||
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| 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.