b4e8e9b004
* Add DnnlOpManager The DnnlOpManager is able to more accurately check if a node is supported by the DNNLExecutionProvider. The DNNLExecutionProvider::GetCapability function has been updated to use the DnnlOpManager. This commit adds the ability to check if data type, attributes, and tensor dimensions of the node are supported. The IsDimensionSupported function is no longer needed since the checks it was doing have been moved into the individual implementations of the virtual class DnnlNodeCapability. Signed-off-by: George Nash <george.nash@intel.com> * Fix AveragePool entry in the DnnlOpManager Added check for ceil_mode attribute in the PoolNodeCapability check. DnnlExecutionProvider does not support ceil_mode other than the default value. Signed-off-by: George Nash <george.nash@intel.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
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.