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### Description Following from #16578 and #16835 this migrates over `OnnxTensor.createTensor(<array>)` to first instantiate a `java.nio.Buffer` and then copy the array into that buffer in Java before creating the tensor. It also changes the `OnnxTensor.getValue()` method which returns a multidimensional array so it does the array construction and value copy in Java. This allows the removal of some unpleasant recursive C code which repeatedly calls into the JVM to traverse Java's arrays. The equivalent Java code is still unpleasant and recursive, but it's easier to reason about and memory safe. As a bonus, more `OnnxTensor`s are now backed by buffers which allow users to pin memory and reduce allocations by reusing them for same sized inputs. Some of the JNI code which parses Java arrays still exists as it's used by `OnnxMap`, removing that will be the target of a future refactor. Strings are still processed in JNI as it is easier to work with String tensors and UTF-8 arrays in C. ### Motivation and Context Minimizing the amount of JNI code makes it easier to maintain and using buffers in preference to arrays allows for fewer allocations. |
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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 |
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| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
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| 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.