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* Add support for sessions to share a global threadpool. * Fix build issues * Add tests, fix build issues. * Added some documentation * Fix centos issue when threadpools become nullptr due to 1 core. * Fix mac and x86 build issues * Address some PR comments * Disabled test for android, added few more tests and addressed more PR comments. * const_cast
42 lines
2.2 KiB
Markdown
42 lines
2.2 KiB
Markdown
# DNNL Execution Provider
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Intel® Math Kernel Library for Deep Neural Networks (Intel® DNNL) 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 DNNL contains vectorized and threaded building blocks that you can use to implement deep neural networks (DNN) with C and C++ interfaces. For more, please see the DNNL documentation on (https://intel.github.io/mkl-dnn/).
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Intel and Microsoft have developed DNNL Execution Provider (EP) for ONNX Runtime to accelerate performance of ONNX Runtime using Intel® Math Kernel Library for Deep Neural Networks (Intel® DNNL) optimized primitives.
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For information on how DNNL optimizes subgraphs, see [Subgraph Optimization](./MKL-DNN-Subgraphs.md)
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## Build
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For build instructions, please see the [BUILD page](../../BUILD.md#dnnl-and-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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## Using the DNNL Execution Provider
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### C/C++
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The DNNLExecutionProvider execution provider needs to be registered with ONNX Runtime to enable in the inference session.
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```
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string log_id = "Foo";
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auto logging_manager = std::make_unique<LoggingManager>
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(std::unique_ptr<ISink>{new CLogSink{}},
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static_cast<Severity>(lm_info.default_warning_level),
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false,
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LoggingManager::InstanceType::Default,
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&log_id)
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Environment::Create(std::move(logging_manager), env)
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InferenceSession session_object{so,env};
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session_object.RegisterExecutionProvider(std::make_unique<::onnxruntime:: DNNLExecutionProvider >());
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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 DNNL 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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