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

7 vues (au cours des 30 derniers jours)
Abolfazl Nejatian
Abolfazl Nejatian le 13 Mar 2023
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.
% ---------- Load Data
allImages = imageDatastore(fullfile(parentDir,dataDir),...
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,...
LSTM_layer = [ flattenLayer;
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,...
rng default
trainedGN = trainNetwork(imgsTrain,lgraph,options);

Réponses (3)

Ben le 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 le 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?
  2 commentaires
Abolfazl Nejatian
Abolfazl Nejatian le 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.

Connectez-vous pour commenter.

Joss Knight
Joss Knight le 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 commentaires
Abolfazl Nejatian
Abolfazl Nejatian le 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

Connectez-vous pour commenter.


En savoir plus sur Parallel and Cloud dans Help Center et File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by