Background Data Dispatch with Custom Training Loop
5 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
Pascal Kutschbach
am 11 Nov. 2020
Bearbeitet: Joss Knight
am 18 Dez. 2020
I have a question regarding the training of a deep neural network with Matlab.
I have built a custom training loop for the training of a regression network on a machine with 2 GPUs.
The training loop performs fine, however it is rather slow in comparison to the automatic trainNetwork function.
The trainNetwork function does not provide the type of network progress monitor i like. The trainNetwork function also seems to error unpredictably on my machine and sometimes the network are not "finished" properly. This is why i make use of a custom training loop.
I use a parallel pool with 2 workers and the randomPatchExtraction Datastore (which is partitionable). The parallel operations
are written in an spmd block.
What would be the best way to use data dispatching in the background in a custom training loop?
I have tried to scale up the number of workers in the parallel pool. This leads to the case that some workers
cannot read data since the Datastores are only partitioned according to the number of GPUs, not the number of workers.
Which operations do i have to assign to the workers that are supposed to preload data?
Has anybody tried using a "self-written" data dispatching in a custom training loop?
Thanks in advance!
0 Kommentare
Akzeptierte Antwort
Joss Knight
am 22 Nov. 2020
4 Kommentare
Joss Knight
am 25 Nov. 2020
Great! labSend is blocking, so you can't have both workers 3 and 4 call labSend at the same time. You need to choose which one goes first.
Weitere Antworten (0)
Siehe auch
Kategorien
Mehr zu Parallel and Cloud finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!