Azzera filtri
Azzera filtri

Cluster multi-gpu training Error: Current pool is not local.

1 visualizzazione (ultimi 30 giorni)
I am trying to scale up onto a multi-gpu cluster for deep learing. I can run the model on a single GPU on the cluster with no issues, however when I try to change to multiple GPU's I get this error:
Current pool is not local. Use 'delete(gcp)' to close parallel pool and run again.
My cluster submission function looks like this:
function job = submit_train_script()
cluster = parcluster();
cluster.AdditionalProperties.AdditionalSubmitArgs = '--gres=gpu:4'; % Request 4 GPU's with sbatch
cluster.AdditionalProperties.AdditionalSubmitArgs = '--mail-type=ALL'; % Send me an email if anything happens
cluster.AdditionalProperties.AdditionalSubmitArgs = '';
cluster.AdditionalProperties.AdditionalSubmitArgs = '--nodelist=Node002'; % Use node002
% Submit the job, ask for 4 CPU workers, one for each GPU
job = cluster.batch('train_fun', ...
"AutoAddClientPath",false, "CaptureDiary",true, ...
"CurrentFolder",".", "Pool",4);
With the network options below. I request 4 GPU's, four worker CPU's to match and then set the exicution enviroment to "multi-gpu". This appears to be the recommended configuration for this type of work. I cannot work out what is causing this error.
% Iteration = Number of (files*cells) / Minibatchsize
options = trainingOptions("adam", ...
ExecutionEnvironment="multi-gpu", ... % cpu,gpu multi-gpu option avaliable
GradientThreshold=1, ...
MaxEpochs=50, ... % 50
MiniBatchSize= 10, ... % 25 miniBatchSize, ... 10 for 16Gb card,
SequenceLength="longest", ...
Shuffle="never", ...
Verbose=0, ...
net = trainNetwork(ds,layers,options);
Thanks in advance,

Risposta accettata

Edric Ellis
Edric Ellis il 13 Gen 2023
I think you need to specify ExecutionEnvironment="parallel" for this situation. According to the trainingOptions reference page, "multi-gpu" is only for "multiple GPUs on one machine, using a local parallel pool based on your default cluster profile."
  2 Commenti
Christopher McCausland
Christopher McCausland il 15 Gen 2023
Hi Edric,
That seems to work. I hadn't even considered the "parallel" option as I belived that the batch submit would have made the parallel pool local with respect to the cluster. Lesson learned there, thank you!
One stange outcome is a new error, (bearing in mind this code runs without error on a single GPU). The error relates to the 'eq' fucntion which I belive is inbuilt sanity check for the == operator.
The only place the == operator is used in the entire submission is to identify any rows (within the cell variable fridges) which have lables and data I want to exclude. I can do this before I read in the data, however I was wodnering if there is anything obvious that would case this to fail in "gpu" vs "parallel"?
% Exclude lables that we don't care about
includeSet = {'N1_to_N2' 'N2_to_N1' 'N1_to_W' 'W_to_N1' 'N2_to_N3' 'N3_to_N2'};
for j = 1:length(fridges)
% Generate index for where to keep the lables
setidx(j) = sum(fridges{j,2} == includeSet);
% remove lables that are not of intrest
fridges(~setidx',:) = [];
Kind regards,
Edric Ellis
Edric Ellis il 16 Gen 2023
I can't see quite why this would change behaviour. Do you have an error stack from the failure indicating this is where the problem is coming from? I would be wary of using == to compare char-vectors (single-quote "strings"). This performs an elementwise comparison of the characters, and can fail if the vectors aren't the same length. You might be better off using strcmp.

Accedi per commentare.

Più risposte (0)


Scopri di più su Startup and Shutdown in Help Center e File Exchange

Community Treasure Hunt

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

Start Hunting!

Translated by