454fcdde00
This PR includes the weight-stripped engine feature (thanks @moraxu for the #20214) which is the major feature for TRT 10 integration. Two TRT EP options are added: - `trt_weight_stripped_engine_enable`: Enable weight-stripped engine build and refit. - `trt_onnx_model_folder_path`: In the quick load case using embedded engine model / EPContext mode, the original onnx filename is in the node's attribute, and this option specifies the directory of that onnx file if needed. Normal weight-stripped engine workflow:  Weight-stripped engine and quick load workflow:  see the doc [here ](https://onnxruntime.ai/docs/execution-providers/TensorRT-ExecutionProvider.html#tensorrt-ep-caches)for more information about EPContext model. --------- Co-authored-by: yf711 <yifanl@microsoft.com> Co-authored-by: Ye Wang <52801275+wangyems@users.noreply.github.com> Co-authored-by: Michal Guzek <moraxu@users.noreply.github.com> Co-authored-by: pengwa <pengwa@microsoft.com> Co-authored-by: wejoncy <wejoncy@163.com> Co-authored-by: Yi Zhang <zhanyi@microsoft.com> Co-authored-by: Yi Zhang <your@email.com> Co-authored-by: Pranav Sharma <prs@microsoft.com> Co-authored-by: Adam Pocock <adam.pocock@oracle.com> Co-authored-by: cao lei <jslhcl@gmail.com> Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com> Co-authored-by: inisis <46103969+inisis@users.noreply.github.com> Co-authored-by: Jeff Bloomfield <38966965+jeffbloo@users.noreply.github.com> Co-authored-by: mo-ja <60505697+mo-ja@users.noreply.github.com> Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com> Co-authored-by: Sumit Agarwal <sumitagarwal330@gmail.com> Co-authored-by: Atanas Dimitrov <70822030+neNasko1@users.noreply.github.com> Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com> Co-authored-by: Yufeng Li <liyufeng1987@gmail.com> Co-authored-by: Dhruv Matani <dhruvbird@gmail.com> Co-authored-by: Dhruv Matani <dhruv.matani@grammarly.com> Co-authored-by: wangshuai09 <391746016@qq.com> Co-authored-by: Xiaoyu <85524621+xiaoyu-work@users.noreply.github.com> Co-authored-by: Xu Xing <xing.xu@intel.com> Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com> Co-authored-by: Rachel Guo <35738743+YUNQIUGUO@users.noreply.github.com> Co-authored-by: Sai Kishan Pampana <sai.kishan.pampana@intel.com> Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net> Co-authored-by: Jian Chen <cjian@microsoft.com> Co-authored-by: Shubham Bhokare <32080845+shubhambhokare1@users.noreply.github.com> Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com> Co-authored-by: Andrew Fantino <15876180+afantino951@users.noreply.github.com> Co-authored-by: Thomas Boby <thomas@boby.uk> Co-authored-by: Tianlei Wu <tlwu@microsoft.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> Co-authored-by: Michal Guzek <mguzek@nvidia.com> Co-authored-by: George Wu <jywu@microsoft.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 |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| 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.