Training deep learning network on GPU is significantly slower than using CPU

I am training a CNN-LSTM regression network on matlab 2023b.
layers = […
sequenceInputLayer([1440 1 1],’Name’,’input’)
flattenLayer(‘Name’,’flatten’)

convolution1dLayer(5,2,’Padding’,’same’,’WeightsInitializer’,’he’,’Name’,’conv’,’DilationFactor’,1);
batchNormalizationLayer(‘Name’,’bn’)
reluLayer(‘Name’,’relu’)
lstmLayer(24,’OutputMode’,"last",’Name’,’bil4′,’RecurrentWeightsInitializer’,’He’,’InputWeightsInitializer’,’He’)
dropoutLayer(0.1,’Name’,’drop3′)

fullyConnectedLayer(1,’Name’,’fc’)
regressionLayer(‘Name’,’output’) ];

MaxEpochs = 200;
learningrate = 0.0061;
learningrate = 0.001;
MiniBatchSize = 64;
solver = ‘adam’;

options = trainingOptions(solver, …
‘MaxEpochs’,MaxEpochs, …
‘GradientThreshold’,1, …
‘InitialLearnRate’,learningrate, …
‘LearnRateSchedule’,"piecewise", …
‘LearnRateDropPeriod’,96, …
‘LearnRateDropFactor’,0.25, …
‘MiniBatchSize’,MiniBatchSize,…
‘Verbose’,false, …
‘Shuffle’,"every-epoch",…
‘ExecutionEnvironment’,mydevice,…
‘Plots’,’training-progress’);

[net,info] = trainNetwork(XrTrain,YrTrain,layers,options);
My GPU is an NVIDIA RTX A4500. However, when I use GPU for training (mydevice = ‘gpu’), the training speed is significantly slower than using CPU. Only 15223 iterations are completed in 5min using GPU, while 30372 iterations are completed using CPU.
The training results are acctually both good regardless of using GPU or CPU. The only difference is the time consumed. The GPU usage in Windows Task Manager is around 80% to 100% during training.
Could anyone please help with this problem? Are there special settings that need to be applied when training a DL network using GPU?I am training a CNN-LSTM regression network on matlab 2023b.
layers = […
sequenceInputLayer([1440 1 1],’Name’,’input’)
flattenLayer(‘Name’,’flatten’)

fullyConnectedLayer(1,’Name’,’fc’)
regressionLayer(‘Name’,’output’) ];

MaxEpochs = 200;
learningrate = 0.0061;
learningrate = 0.001;
MiniBatchSize = 64;
solver = ‘adam’;

[net,info] = trainNetwork(XrTrain,YrTrain,layers,options);
My GPU is an NVIDIA RTX A4500. However, when I use GPU for training (mydevice = ‘gpu’), the training speed is significantly slower than using CPU. Only 15223 iterations are completed in 5min using GPU, while 30372 iterations are completed using CPU.
The training results are acctually both good regardless of using GPU or CPU. The only difference is the time consumed. The GPU usage in Windows Task Manager is around 80% to 100% during training.
Could anyone please help with this problem? Are there special settings that need to be applied when training a DL network using GPU? I am training a CNN-LSTM regression network on matlab 2023b.
layers = […
sequenceInputLayer([1440 1 1],’Name’,’input’)
flattenLayer(‘Name’,’flatten’)

fullyConnectedLayer(1,’Name’,’fc’)
regressionLayer(‘Name’,’output’) ];

MaxEpochs = 200;
learningrate = 0.0061;
learningrate = 0.001;
MiniBatchSize = 64;
solver = ‘adam’;

[net,info] = trainNetwork(XrTrain,YrTrain,layers,options);
My GPU is an NVIDIA RTX A4500. However, when I use GPU for training (mydevice = ‘gpu’), the training speed is significantly slower than using CPU. Only 15223 iterations are completed in 5min using GPU, while 30372 iterations are completed using CPU.
The training results are acctually both good regardless of using GPU or CPU. The only difference is the time consumed. The GPU usage in Windows Task Manager is around 80% to 100% during training.
Could anyone please help with this problem? Are there special settings that need to be applied when training a DL network using GPU? gpu, deep learning, neural network MATLAB Answers — New Questions

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