the utilization of cpu and gpu is low, how to increase them?

while i am using matlab to train a alexnet from the scratch on window, the utilization ratio of cpu and gpu of my computer is low, and I wonder how to increase them.

here is my code:

trainImagesetPath = 'E:\deep_learning_dataset\tiny-imagenet-200\train';
valImagesetPath = 'E:\deep_learning_dataset\tiny-imagenet-200\val';
testImagesetPath = 'E:\deep_learning_dataset\tiny-imagenet-200\test';

miniBatchSize = 960;

imdsTrain = imageDatastore(trainImagesetPath, 'IncludeSubfolders', true, ...
    'LabelSource', 'foldernames', 'FileExtensions',{'.jpg','.JPG', '.JPEG'}, 'ReadSize', miniBatchSize);


imdsValidation = imageDatastore(valImagesetPath, 'IncludeSubfolders', true, ...
    'LabelSource', 'foldernames', 'FileExtensions',{'.jpg','.JPG', '.JPEG'}, 'ReadSize', miniBatchSize);


imdsTest = imageDatastore(testImagesetPath, 'IncludeSubfolders', true);

layers = [imageInputLayer([224 224 3])
    convolution2dLayer(11, 96, 'Stride', [4, 4], 'Padding', [0 0 0 0])
    reluLayer
    batchNormalizationLayer
    maxPooling2dLayer(3, 'Stride', [2, 2], 'Padding', [0 0 0 0])
    
    groupedConvolution2dLayer(5, 128, 2, 'Stride', [1, 1], 'Padding', [2 2 2 2])
    reluLayer
    batchNormalizationLayer
    maxPooling2dLayer(3, 'Stride', [2, 2], 'Padding', [0 0 0 0])
    
    convolution2dLayer(3, 384, 'Stride', [1, 1], 'Padding', [1 1 1 1])
    reluLayer
    
    groupedConvolution2dLayer(3, 192, 2, 'Stride', [1, 1], 'Padding', [1 1 1 1])
    reluLayer
    
    groupedConvolution2dLayer(3, 128, 2, 'Stride', [1, 1], 'Padding', [1 1 1 1])
    reluLayer
    maxPooling2dLayer(3, 'Stride', [2, 2], 'Padding', [0 0 0 0])
    
    fullyConnectedLayer(4096)
    reluLayer
    dropoutLayer(0.5)
    
    fullyConnectedLayer(4096)
    reluLayer
    dropoutLayer(0.5)
    
    fullyConnectedLayer(200)
    softmaxLayer
    classificationLayer
    ];

% analyzeNetwork(layers);


inputSize = [224, 224, 3];
augimdsTrain = augmentedImageDatastore(inputSize, imdsTrain, 'ColorPreprocessing', 'gray2rgb', 'DispatchInBackground', true);
augimdsValidation = augmentedImageDatastore(inputSize, imdsValidation, 'ColorPreprocessing', 'gray2rgb', 'DispatchInBackground', true);
augimdsTest = augmentedImageDatastore(inputSize, imdsTest, 'ColorPreprocessing', 'gray2rgb');



options = trainingOptions('adam', ...
    'MiniBatchSize', miniBatchSize, ...
    'MaxEpochs',120, ...
    'InitialLearnRate',1e-4, ...
    'LearnRateSchedule', 'piecewise', ...
    'LearnRateDropFactor', 0.25, ...
    'LearnRateDropPeriod', 5, ...
    'DispatchInBackground', true, ...
    'Shuffle','every-epoch', ...
    'ValidationData', augimdsValidation, ...
    'ValidationFrequency', 20, ...
    'Verbose',true, ...
    'Plots','training-progress', ...
    'ExecutionEnvironment', 'auto');

tic

alexNetModel = trainNetwork(augimdsTrain,layers,options);
fprintf('training process time cost: ');
toc


[YPred,scores] = classify(alexNetModel,augimdsTest);

YTest = imdsTest.Labels;
accuracy = mean(YPred == YTest);

fprintf('test acc: %f\n', accuracy);

figure
confusionchart(YTest, YPred)

my computer is a lenovo laptop called r9000k 2021 with rtx3080 laptop GPU, and the utilization ratio is shown as follow:

Answer 1

[Meet]

Hi Yan,

• Make sure the "ExecutionEnvironment" is set to 'gpu' explicitly if you want to ensure the use of the GPU. Sometimes 'auto' might not choose the GPU if it detects any issues.
• Ensure you have the Parallel Computing Toolbox installed and configured. This allows MATLAB to leverage multiple CPU cores and the GPU more effectively.

You can also refer to this documentation link for more information:

https://www.mathworks.com/help/releases/R2021a/deeplearning/deep-learning-in-parallel-and-cloud.html