Progress Update from Neural Network Trainer running on Parallel Server Cloud

I am performing some training of a neural network using some pretty standard code:

[net1,tr] = train(net1,X,Y1,'useParallel','yes')

When I do the training locally, the Neural Network Training window, gets constantly updated, and I can follow the progress of the Epochs and the Performance. Aborting the training if something is not working.

However, I also have Amazon AWS Setup as cloud support for the parallel toolbox.

When I activate the Parallel Pool and the training uses the cloud, I get no update at all. The Neural Network Training Window, stays at Epoch 0 of 10000, until all training is completed. Even if this is several hours. :(

Is there a way to force how often updates are reported back from the parallel pool? Or do I need to script it myself, forcing a limit of 100 Epochs per training call, and continually pass the trained network back and forwards?

Thanks in advance

[Sam Marshalik]

I just tested this out using Parallel Computing Toolbox and I seem to be able to view the Epoch progress:

This is based on the example code from the train doc page.

[SMEAC]

When I run the parallel Toolbox on my own computer, I get an update too.

But when the Parallel Pool is remote on AWS, I get no update at all.

[Sam Marshalik]

I spun up a MATLAB Parallel Server cluster using Cloud Center and ran the job there without any issues. I could see the epoch progress, just like I could with a local parallel pool:

I will note that it took a bit for the epochs to start moving, so make sure you wait a minute to ensure nothing is actually populating there.

If you are running MATLAB Parallel Server in Cloud Center like I am, I would expect you to see the progress as I can. I would suggest reaching out to Technical Support.