GPU out of memory issue appears with trainNetwork.

Question

Mads 2023-5-3

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https://ww2.mathworks.cn/matlabcentral/answers/1957529-gpu-out-of-memory-issue-appears-with-trainnetwork

评论： Mads 2023-5-15

I have a Tesla P100 with 16 GB RAM. Yesterday, I ran the trainNetwork() with different layer achitectures and few different input data. It worked. Then I tried a larger input data set, but get the out of memory error:

Error using trainNetwork

GPU out of memory. Try reducing 'MiniBatchSize' using the trainingOptions function.

Error in A1_B1_C1a_D2 (line 152)

[net,netinfo] = trainNetwork(trainInput,trainTarget,Layers,options);

Caused by:

Error using gpuArray/hTimesTranspose

Out of memory on device. To view more detail about available memory on the GPU, use 'gpuDevice()'. If the problem persists, reset the GPU by calling 'gpuDevice(1)'.

I try to do what is suggested, but it doesn't help. I have tried many different less intensive approaches, done a reboot, and I even have returned to the scripts that used to work fine.

Now nothing works.

Any suggestions to troubleshoot hardware faults or a protective status somewhere?

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Answer 1

Matt J 2023-5-3

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https://ww2.mathworks.cn/matlabcentral/answers/1957529-gpu-out-of-memory-issue-appears-with-trainnetwork#answer_1228659

编辑：Matt J 2023-5-3

Then I tried a larger input data set, but get the out of memory error:

If you make your data larger and larger, you will eventually run out of memory. Maybe reduce the MiniBatchSize setting.

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Mads 2023-5-11

As mentioned, I managed to run something. Did two trainings with large data sets split in two to keep memory low.

Then I took the trained net and transfered it to another net. In order to train on fewer, but slightly larger cases on the outputside.

This didn't work. In my search I scaled the new data set down to a ridiculous low amount, ~300 MB, and used minibatch of 10.

Array sizes were: ...validate...

s1 =

64 64 18 100

s2 =

100 10256

and ...train...

s3 =

64 64 18 800

s4 =

800 10256

TotSize =

302342400

But the error is the same:

Error using trainNetwork (line 184)

GPU out of memory. Try reducing 'MiniBatchSize' using the trainingOptions function.

Error in A1_B1_C1d_D1 (line 97)

[net,netinfo] = trainNetwork(trainInput,trainTarget,Layers,options);

Caused by:

Error using gpuArray/hTimesTranspose

Out of memory on device. To view more detail about available memory on the GPU, use

'gpuDevice()'. If the problem persists, reset the GPU by calling 'gpuDevice(1)'.

Clearly, the describtion of the error is wrong. But what is wrong?

Mads 2023-5-12

This is the script I'm running... or want to run:

temp = load('... some previous net....mat');

% this loads my training and validation data

[trainInput,trainTarget] = LoadInputTargetFiles(Folder_C_input_DL,[1],'train');

[validateInput,validateTarget] = LoadInputTargetFiles(Folder_C_input_DL,[1],'validate');

Nt = 641;

transferLayers = temp.net.Layers(1:6);

Layers = [

transferLayers

reluLayer

fullyConnectedLayer(Nt*2*8)

reluLayer

fullyConnectedLayer(Nt*2*8)

clipLayer(1,'myclip')

regressionLayer

];

Layers(8).WeightLearnRateFactor = 10; % hints from video

Layers(8).WeightL2Factor = 1;

Layers(8).BiasLearnRateFactor = 20;

Layers(8).BiasL2Factor = 1;

options = trainingOptions(...

'sgdm', ...

'MaxEpochs',1000,...

'InitialLearnRate',0.006,...

'Momentum',0.95,...

'Shuffle','every-epoch',...

'ValidationData',{validateInput,validateTarget},...

'ValidationPatience',Inf,...

'ValidationFrequency', 500,...

'L2Regularization',1e-4,...

'Plots','training-progress',...

'CheckPointPath',Folder_D_run_DL_checkpoints,...

'ExecutionEnvironment','gpu','MiniBatchSize',10);

gpu=gpuDevice();

reset(gpu);

gpu=gpuDevice();

disp(gpu)

s1 = size(validateInput)

s2 = size(validateTarget)

s3 = size(trainInput)

s4 = size(trainTarget)

TotSize = prod(s1)+prod(s2)+prod(s3)+prod(s4); TotSize = TotSize*4 % 4 because it is type single

[net,netinfo] = trainNetwork(trainInput,trainTarget,Layers,options);

Joss Knight 2023-5-13

编辑：Joss Knight 2023-5-14

Seems fairly clearcut to me. In your first image, fc2 alone takes up 7.4GB so you're definitely going to struggle, especially for training because you need 8GB for weights, 8GB for their gradients, and probably 8 more for temporaries while you're updating the weights. You need a smaller network. Try adding more convolution layers rather than relying on a massive fully connected layer to do most of the work. Look at the Total Number of Learnables at the top of the Network Analyzer window and multiply it by 4 to get the number of bytes your network will need.

Your other network is much smaller, a 'mere' 1.4GB for the fully connected layers.

Mads 2023-5-15

Oh... right... I hadn't accounted for the total learnables and that enourmous FC. By inserting a conv layer before it, I managed to run it.

Pew...

Thanks

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