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### Description Reduce a number of auxillary objects created to reduce GC pressure. Eliminate GCHandle type of memory pinning in most of the places. Improve string marshalling by allocating unmanaged memory that does not require pinning. Change native methods from `IntPtr` to `byte[]` (marshalling pinning is more efficient). Allocate input/output UTF-8 names in unmanaged heap for the lifetime of InferenceSession. So we do not keep converting them and pinning on every Run. Introduce a new native API that allows to allocate and convert/copy strings directly into a native tensor. The PR delivers around 50% latency improvements and less GC pauses. Inspired by: https://github.com/microsoft/onnxruntime/pull/15520 ### Motivation and Context Client experience GC pressure and performance degradation when dealing with string tensors. Co-Authored-By: @tannergooding |
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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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| Windows | ||
| Linux | ||
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| Web | ||
| Other |
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.