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HP Exam HPE2-N69 Topic 3 Question 31 Discussion

Actual exam question for HP's HPE2-N69 exam
Question #: 31
Topic #: 3
[All HPE2-N69 Questions]

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?

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Suggested Answer: B

The complexity of adjusting model code to distribute the training process across multiple GPUs. Deep learning (DL) training requires a large amount of computing power and can be accelerated by using multiple GPUs. However, this requires adjusting the model code to distribute the training process across the GPUs, which can be a complex and time-consuming process. Thus, the complexity of adjusting the model code is likely to continue to be a challenge in accelerating DL training.


Contribute your Thoughts:

Francisca
5 months ago
I think another challenge could be the requirement for the ML team to wait for the IT team to start each new training process. That could slow down the whole operation.
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Shalon
5 months ago
You make a good point, Without proper power and cooling, the GPUs may not perform optimally.
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Pamella
5 months ago
But what about the lack of adequate power and cooling for the GPU-enabled servers? That could also be a major challenge.
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Irma
5 months ago
I agree with you, It can be tricky to optimize the code for multiple GPUs.
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Shalon
6 months ago
I think the challenge might be the complexity of adjusting model code to distribute training across multiple GPUs.
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Louvenia
7 months ago
I agree. It's important to have the right infrastructure in place for efficient deep learning training.
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Walton
7 months ago
Yeah, that makes sense. It can get really tricky when dealing with multiple GPUs.
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Marylyn
7 months ago
I think the challenge could be B) The complexity of adjusting model code to distribute the training process across multiple GPUs.
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