Parallel CPU computing for recurrent Neural Networks (LSTMs)
15 views (last 30 days)
this page: https://de.mathworks.com/help/deeplearning/ug/neural-networks-with-parallel-and-gpu-computing.html
states that parallel CPU computing for LSTMs is possible using the trainNetwork function and choosing the execution environment as parallel using trainingOptions. It also states that the Parallel Computing Toolbox is necessary.
I do have the Parallel Computing Toolbox installed, writing pool = parpool gives me the number of workers as 23 (the amount of cores my CPU has)
I also added 'ExecutionEnvironment','parallel' to my trainingOptions(), however, I get the error that "Parallel training of recurrent networks is not supported. 'ExecutionEnvironment' value in trainingOptions function must be 'auto', 'gpu' or 'cpu'"
Raymond Norris on 4 Feb 2022
I'm assuming you're only running this on your local machine (with 23 cores)? And I'm assuming you don't have a GPU? If so, set ExecutionEnvironment to "cpu" (or even "auto", which defaults to gpu if it exists and cpu if a gpu doesn't exist).
Joss Knight on 7 Feb 2022
That doc page is about shallow networks (using train) rather than deep networks (using trainNetwork). Parallel training in trainNetwork for sequence networks is supported from the next release.
How are you confirming that ExecutionEnvironment 'cpu' is only using a single core? It should be using all your cores.
Parallel training for CPU is only really useful when you have a multi-node cluster of machines. Generally speaking all CPU Deep Learning code is multithreaded and makes full use of your hardware and there is no advantage to parallel training or inference - in fact it should make it slower.