coder.CuDNNConfig - Parameters to configure deep learning code generation with the CUDA Deep Neural Network library - MATLAB (original) (raw)

Main Content

Parameters to configure deep learning code generation with the CUDA Deep Neural Network library

Description

The coder.CuDNNConfig object contains NVIDIA® cuDNN specific parameters that codegen uses for generating CUDA® code for deep neural networks.

To use a coder.CuDNNConfig object for code generation, assign it to theDeepLearningConfig property of a coder.gpuConfig object that you pass to codegen.

Creation

Create a cuDNN configuration object by using the coder.DeepLearningConfig function with target library set as'cudnn'.

Properties

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Enable or disable auto tuning feature. Enabling auto tuning allows the cuDNN library to find the fastest convolution algorithms. For more information, see Enable auto tuning.

Specify the precision of the inference computations in supported layers. For more information, see Data type (cuDNN).

Location of the MAT-file containing the calibration data. This option is applicable only when DataType is set to 'int8'. For more information, see Data type (cuDNN).

A read-only value that specifies the name of the target library.

Examples

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Create an entry-point function resnet_predict that uses theimagePretrainedNetwork function to load thedlnetwork object that contains the ResNet-50 network. For more information, see Code Generation for dlarray

function out = resnet_predict(in)

dlIn = dlarray(in, 'SSCB'); persistent dlnet; if isempty(dlnet) dlnet = imagePretrainedNetwork('resnet50'); end

dlOut = predict(dlnet, dlIn); out = extractdata(dlOut);

Create a coder.gpuConfig configuration object for MEX code generation.

cfg = coder.gpuConfig('mex');

Set the target language to C++.

Create a coder.CuDNNConfig deep learning configuration object and assign it to the DeepLearningConfig property of thecfg configuration object.

cfg.DeepLearningConfig = coder.DeepLearningConfig(TargetLib = 'cudnn');

Use the -config option of the codegen function to pass the cfg configuration object. The codegen function must determine the size, class, and complexity of MATLAB® function inputs. Use the -args option to specify the size of the input to the entry-point function.

codegen -args {ones(224,224,3,'single')} -config cfg resnet_predict;

The codegen command places all the generated files in thecodegen folder. The folder contains the CUDA code for the entry-point function resnet_predict.cu, header, and source files containing the C++ class definitions for the convoluted neural network (CNN), weight, and bias files.

Version History

Introduced in R2018b

See Also

Functions

Objects

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