f3402de01e
Several changes: 1. To align with other EPs' setting of EP context configs in session options, for example [QNN EP](https://github.com/microsoft/onnxruntime/pull/18877), EP context configs for TRT EP can be configured through: 1. Session Options: `ep.context_enable`, `ep.context_file_path` and `ep.context_embed_mode` 2. Provider Options: `trt_dump_ep_context_model`, `trt_ep_context_file_path` and `trt_dump_ep_context_embed_mode` 3. Above setting has 1:1 mapping and provider options has higher priority over session options. ``` Please note that there are rules for using following context model related provider options: 1. In the case of dumping the context model and loading the context model, for security reason, TRT EP doesn't allow the "ep_cache_context" node attribute of EP context node to be the absolute path or relative path that is outside of context model directory. It means engine cache needs to be in the same directory or sub-directory of context model. 2. In the case of dumping the context model, the engine cache path will be changed to the relative path of context model directory. For example: If "trt_dump_ep_context_model" is enabled and "trt_engine_cache_enable" is enabled, if "trt_ep_context_file_path" is "./context_model_dir", - if "trt_engine_cache_path" is "" -> the engine cache will be saved to "./context_model_dir" - if "trt_engine_cache_path" is "engine_dir" -> the engine cache will be saved to "./context_model_dir/engine_dir" ``` 2. User can decide the naming of the dumped "EP context" model by using `trt_ep_context_file_path`, please see GetCtxModelPath() for more details. 3. Added suggested comments from https://github.com/microsoft/onnxruntime/pull/18217 |
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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.