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### Description This will enable a user to use a TensorRT timing cache based on #10297 to accelerate build times on a device with the same compute capability. This will work across models as it simply store kernel runtimes for specific configurations. Those files are usually very small (only a few MB) which makes them very easy to ship with an application to accelerate the build time on the user end. ### Motivation and Context Especially for workstation use cases TRT build times can be a roadblock. With a few model from ONNX model zoo i evaluated speedups when a timing cache is present. `./build/onnxruntime_perf_test -e tensorrt -I -t 5 -i "trt_timing_cache_enable|true" <onnx_path>` |Model | no Cache | with Cache| | ------------- | ------------- | ------------- | |efficientnet-lite4-11 | 34.6 s | 7.7 s| |yolov4 | 108.62 s | 9.4 s| To capture this is had to modify the onnxruntime_perf_test. The time is sometimes not captured within "Session creation time cost:" which is why i introduced "First inference time cost:". --------- Co-authored-by: Chi Lo <Chi.Lo@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 documention 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
Build Pipeline Status
| System | Inference | Training |
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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.