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I run the dnc example and found that in 32-core machines, the CPU usage is not high. Is there any way to make the CPU work at full capacity and improve efficiency? Thank you
The text was updated successfully, but these errors were encountered:
I would recommend data-parallelism versus model parallelism here, see https://www.tensorflow.org/deploy/distributed#replicated_training. The general idea is that you want to split your input training batch across multiple groups of CPUs and have several instantiations of the DNC train on these partitions, instead of hoping the model can parallelize very well over 32 machines for a single minibatch.
I run the dnc example and found that in 32-core machines, the CPU usage is not high. Is there any way to make the CPU work at full capacity and improve efficiency? Thank you
The text was updated successfully, but these errors were encountered: