A company has recently expanded its ml engineering resources from 5 CPUs 1012 GPUs.
What challenge is likely to continue to stand in the way of accelerating deep learning (DU training?

Question

A company has recently expanded its ml engineering resources from 5 CPUs 1012 GPUs.What challenge is likely to continue to stand in the way of accelerating deep learning (DU training?

Jose Alfaro · Accepted Answer

A lack of understanding of the DL model architecture by the NL engineering team

Jose Alfaro · Answer

The complexity of adjusting model code to distribute the training process across multiple GPUs

Jose Alfaro · Answer

A lack of adequate power and cooling for the GPU-enabled servers

Jose Alfaro · Answer

The requirement that the ML team must wait for the IT team to initiate each new training process

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