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35 lines
1.9 KiB
Markdown
35 lines
1.9 KiB
Markdown
# MKL-DNN Execution Provider
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Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) is an open-source performance library for deep-learning applications. The library accelerates deep-learning applications and frameworks on Intel® architecture and Intel® Processor Graphics Architecture. Intel MKL-DNN contains vectorized and threaded building blocks that you can use to implement deep neural networks (DNN) with C and C++ interfaces. For more visit MKL-DNN documentation at (https://intel.github.io/mkl-dnn/)
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Intel and Microsoft have developed MKL-DNN Execution Provider (EP) for ONNX Runtime to accelerate performance of ONNX Runtime using Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) optimized primitives
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## MKL-DNN/MKLML
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To build ONNX Runtime with MKL-DNN support, build it with `./build.sh --use_mkldnn`
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To build ONNX Runtime using MKL-DNN built with dependency on MKL small libraries, build it with `./build.sh --use_mkldnn --use_mklml`
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## Supported OS
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* Ubuntu 16.04
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* Windows 10
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* Mac OS X
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## Supported backend
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* CPU
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* More to be added soon!
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## Using the nGraph execution provider
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### C/C++
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The MKLDNNExecutionProvider execution provider needs to be registered with ONNX Runtime to enable in the inference session.
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```
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InferenceSession session_object{so};
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session_object.RegisterExecutionProvider(std::make_unique<::onnxruntime:: MKLDNNExecutionProvider >());
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status = session_object.Load(model_file_name);
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```
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The C API details are [here](../C_API.md#c-api).
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## Python
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When using the python wheel from the ONNX Runtime built with MKL-DNN execution provider, it will be automatically prioritized over the CPU execution provider. Python APIs details are [here](https://aka.ms/onnxruntime-python).
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## Performance Tuning
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For performance tuning, please see guidance on this page: [ONNX Runtime Perf Tuning](../ONNX_Runtime_Perf_Tuning.md)
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