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### Description <!-- Describe your changes. --> - [x] Pad operator has introduced a new input called "axes" which specifies which axis to pad. But it defaults to input_rank if axes is not provided which was the behavior before the opset upgrade. - [x] ReduceMean - [x] ReduceL2 - [x] ReduceLogSumExp - [x] ReduceSum - Reduction ops all had the axes attribute switched to an input and a new attribute called "noop_with_empty_axes" was added to define what to do when axes is not specified. - [x] Resize has had two new attributes introduced: antialias and keep_aspect_ratio_policy. From Operators.md I've gathered: "Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel." keep_aspect_ratio_policy "describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input." there are a couple enum-type options that specify different policies and what to do in each case. - NOTE: Baiju already included opset18 tests in https://github.com/microsoft/onnxruntime/pull/17772 - [x] ScatterElements/ScatterND has had a new attribute introduced called "reduction." This specifies the type of reduction to apply: none (default), add, mul, max, min. - [x] Split introduced a new attribute called "num_outputs" which specifies how many outputs to split the input tensor into. This is in contrast to the previous, default behavior of specifying a "split" input which defines the size of each resultant tensor of the output. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> |
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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.