3cd06cb38c
* Added support for ReduceMean on DNNL EP for CPU and GPU Signed-off-by: Chethan Palangotu Keshava <chethan.palangotu.keshava@intel.com> * Added fix for a resnet model failure where it was failing to create dst shape for reducemean when it was part of a subgraph with other ops Signed-off-by: Chethan Palangotu Keshava <chethan.palangotu.keshava@intel.com> * Removing the DNNL EP from these unit tests. This is in anticipation of two changes: - DNNL EP unit tests would be added in a different location later on, so addition of EP individually to these tests will not be necessary - This was causing a memory leak fail in debug build. The bug is in the EP itself and not in the code added for reducemean. The fix for this is in the i/o handling overhaul which will be added later. * Update reduction_ops_test.cc Had accidentally deleted a new line. Making sure there are no unnecessary changes in this file |
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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
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
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| WebAssembly |
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.