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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?

A.

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

B.

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

C.

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

D.

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

A trial is running on a GPU slot within a resource pool on HPE Machine Learning Development Environment. That GPU fails. What happens next?

A.

The trial tails, and the ML engineer must restart it manually by re-running the experiment.

B.

The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.

C.

The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.

D.

The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.