Deep Learning: Training Network with "parallel" option using only CPUs

11 vues (au cours des 30 derniers jours)
Hi,
I am trying to train a network using the follow parameters:
miniBatchSize = 10;
clear NewNetIn3D
valFrequency = floor(numel(imdsTrain)/miniBatchSize);
options = trainingOptions('sgdm', ...
'MiniBatchSize',miniBatchSize, ...
'MaxEpochs',6, ...
'InitialLearnRate',1e-5,...
'Shuffle','never',...
'ExecutionEnvironment','parallel',...
'Verbose',false, ...
'Plots','training-progress');
net = trainNetwork(imdsTrain,LabelsTrain,LayersBMC,options);
Since my graphic card is not super, I am trying to run the code using multiple CPUs, but the parallel option always go with multiple GPUs and then crashes. Is there any way to restrict the paralel pool to use only CPUs? If I define the option 'cpu' it works, but with only one core.

Réponse acceptée

Joss Knight
Joss Knight le 16 Déc 2019
Even with a weak graphics card you will usually see better performance than on multiple CPUs. However, to try it out, after you start MATLAB, type
setenv CUDA_VISIBLE_DEVICES -1
  13 commentaires
shivan artosh
shivan artosh le 8 Oct 2020
oh no problem sir
is there a way to showdown the 'parallel' command in the system, because after multiple CPU i again use single CPU but there is a little bit on my learning curve.
through the running on single CPU i found this on command window and it seems affected on my model:
IdleTimeout has been reached.
Parallel pool using the 'local' profile is shutting down.
Joss Knight
Joss Knight le 8 Oct 2020
That's nothing. You had a bunch of MATLABs running to do your parallel training, you stopped using them, so eventually they were terminated.

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