ea12b674c0
### Description <!-- Describe your changes. --> Skip tests for opset18 models that we haven't implemented kernels for yet. Slice was checked in today so those failures should go away. Resize: #13890 (all resize failures are fixed by this PR as confirmed in output [here](https://dev.azure.com/aiinfra/530acbc4-21bc-487d-8cd8-348ff451d2ff/_apis/build/builds/264725/logs/729)) Col2Im: #12311 ScatterND and ScatterElement: #14224 Pad (should also fix CenterCropPad failures): #14219 Bitwise ops: #14197 Optional: Unknown if we're intending to support this in 1.14 Not sure about SoftPlus as that is failing due to `Could not find an implementation for Exp(1)`. ORT supports Exp from opset 6 and on, and it seems incorrect for the test model created for opset 18 to be using a version of Exp that is so old. Would have expected it to use the latest - Exp(13). @liqunfu is this something that requires a fix to the ONNX model? ### 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. --> Fix pipeline |
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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
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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.