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i need to utilize fully of my GPUs during network training!

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Greetings,
I am currently engaged in training a hybrid neural network architecture, GoogleNet and LSTM. My training dataset comprises a voluminous set of images amounting to approximately 2.5 million samples, and the total size of these images as stored in my computer's memory is approximately 18 gigabytes. During the training process, I am encountering constraints in utilizing all available computational resources offered by my computer's GPU(the gpu load mostly under 70%). In this context, I am contemplating the application of the "imageDatastore" function as a potential solution.
Kindly find appended herewith my code for your reference, and I would highly appreciate any guidance or recommendations that you might offer.
Sincerely,
Abolfazl
reset(gpuDevice(1));
% ---------- Load Data
allImages = imageDatastore(fullfile(parentDir,dataDir),...
'IncludeSubfolders',true,...
'LabelSource','foldernames');
rng default
[imgsTrain,imgsValidation] = splitEachLabel(allImages,0.80,'randomized');
% ---------- Hybrid Google Net with LSTM Layer
net = googlenet;
lgraph = layerGraph(net);
% Modify GoogLeNet Network Parameters
newDropoutLayer = dropoutLayer(0.2,'Name','new_Dropout');
lgraph = replaceLayer(lgraph,'pool5-drop_7x7_s1',newDropoutLayer);
numClasses = numel(categories(allImages.Labels));
newConnectedLayer = fullyConnectedLayer(numClasses,...
'WeightLearnRateFactor',5,'BiasLearnRateFactor',5);
LSTM_layer = [ flattenLayer;
lstmLayer(350,OutputMode="last");
newConnectedLayer];
lgraph = replaceLayer(lgraph,'loss3-classifier',LSTM_layer);
optimizer = 'adam';
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph,'output',newClassLayer);
% ---------- Network Training
% Set Training Options and Train GoogLeNet
options = trainingOptions(optimizer,...
'MiniBatchSize',1024,...
'MaxEpochs',15,...
'InitialLearnRate',1e-4,...
'ValidationData',imgsValidation,...
'ValidationFrequency',10,...
'Verbose',1,...
'shuffle','every-epoch',...
'ExecutionEnvironment','gpu',...
'BatchNormalizationStatistics','moving',...
'Plots','training-progress');
rng default
trainedGN = trainNetwork(imgsTrain,lgraph,options);

Answers (3)

Ben
Ben on 14 Mar 2023
To use more of the GPU resource per iteration you can increase the minibatch size.
I'll note that the LSTM layer you are adding is not receiving sequence data, so it will simply run one iteration of the LSTM cell, which wouldn't be standard usage.

Abolfazl Nejatian
Abolfazl Nejatian on 14 Mar 2023
In regard to the minibatch size, it should be noted that the maximum permissible size has been selected due to memory constraints. Any attempt to further increase the minibatch size would result in a "out of memory" error being encountered.
When you say 'sequence data', could you please explain what you mean by that?
regards,
Abolfazl
  2 Comments
Ben
Ben on 14 Mar 2023
>Any attempt to further increase the minibatch size would result in a "out of memory" error
In that case there is no way to utilize more GPU resources through trainNetwork.
>When you say 'sequence data', could you please explain what you mean by that?
LSTMs are typically used on sequence data such as time series. In practical terms they will iterate the LSTM "cell" operation over the sequence data, e.g. the T dimension of a dlarray with a dimension with label T. Typically LSTM is used in this way because it has a "state" that it can use at time step N that can hold information about timesteps 1,2,...,N-1. However in your example there is no sequence data, the LSTM "cell" just operates once.
Abolfazl Nejatian
Abolfazl Nejatian on 15 Mar 2023
Thank you for your information.
GPU utilization did not improve significantly when the pure GoogleNet model was trained. Therefore, the LSTM component wasn't solely responsible for the problems.

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Joss Knight
Joss Knight on 15 Mar 2023
It's hard to be sure from the info you provide but it looks like the filesystem is your bottleneck. If you cannot load the next 1024 images before you've finished the previous iteration, your GPU will have to wait.
  5 Comments
Abolfazl Nejatian
Abolfazl Nejatian on 18 Mar 2023
I have included the profiler obtained from a smaller dataset trained for only one epoch.
Total run time: 1129.042 s
here is profiler results
https://drive.google.com/file/d/1BnrvjNgejWcxoZ5gBGc1aQKJkPOjpHcU/view?usp=share_link

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