coder.CuDNNConfig

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, create a code configuration object by using the coder.gpuConfig function, and set the DeepLearningConfig property of the object to the coder.CuDNNConfig object.

Creation

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

Properties

AutoTuning — Enable auto tuning

true (default) | false

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.

DataType — Inference computation precision

'fp32' (default) | 'int8'

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

CalibrationResultFile — Location of calibration MAT file

'' (default) | character vector | string scalar

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).

TargetLib — Target library name

'cudnn' | character vector

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

Examples

Specify Configuration Parameters for MEX Function Generation for the ResNet-50 Network

Create an entry-point function resnet_predict that uses the imagePretrainedNetwork function to load the dlnetwork 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++.

cfg.TargetLang = 'C++';

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