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

2 views (last 30 days)
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,

Accepted Answer

Edric Ellis
Edric Ellis on 13 Jan 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."
Edric Ellis
Edric Ellis on 16 Jan 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.

Sign in to comment.

More Answers (0)


Find more on Parallel Computing Fundamentals in Help Center and File Exchange

Community Treasure Hunt

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

Start Hunting!