53eb948f4c
* upgrade to TensorRT 7.2.2 * extend GPU tensorrt CI timeout to 150 minutes * update docker image name * disable user interaction to avoid tensorrt container stuck when install tzdata * upgrade to libssl1.1 for ubuntu20.04 * remove libicu60 from ubuntu20.04 * add libicu66 for ubuntu20.04 * debug * llvm * llvm * disable ReverseSequenceTest.InvalidInput * disable ReverseSequenceTest.InvalidInput * fix issues * fix issues * Update linux-gpu-tensorrt-ci-pipeline.yml * disable warning 4458 for TensorRT parser * update onnx-tensorrt submodule * disable warnings for TensorRT parser * update onnx-tensorrt submodule to include latest bug fixes * update setup_env_trt * update pool for win trt ci pipeline' Co-authored-by: George Wu <jywu@microsoft.com> |
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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
Build Pipeline Status
| System | CPU | GPU | EPs |
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| Windows | |||
| Linux | |||
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| iOS |
Data/Telemetry
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.