HP Exam HPE2-N69 Topic 5 Question 38 Discussion

Actual exam question for HP's HPE2-N69 exam

Question #: 38
Topic #: 5

[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?

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

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

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

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

Show Suggested Answer

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.

by Beckie at Jul 07, 2024, 07:30 PM

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Denny

3 months ago

Ah, the joys of scaling up ML infrastructure. Next thing you know, they'll be needing a dedicated power plant just to feed the hungry GPUs.

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Hassie

2 months ago

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

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Audry

3 months ago

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

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Emogene

3 months ago

Hold up, what about the IT team holding up the training? That's just plain old bureaucracy getting in the way of progress. Where's the 'move fast and break things' mentality, huh?

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Laquita

3 months ago

C is a close second, though. Cooling those beefy GPU servers can be a real headache. I heard one team had to install an industrial-grade AC unit just to keep their DL rig from melting down!

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Raina

3 months ago

That sounds like a nightmare! I can't imagine having to deal with all that heat and power consumption.

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Elroy

3 months ago

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

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Ailene

3 months ago

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

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Velda

3 months ago

I agree, B is the right answer. Parallelizing the training process is no easy feat, even for seasoned ML engineers.

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Tricia

2 months ago

Agreed, it's a hurdle that many ML engineering teams face when trying to accelerate deep learning training.

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Darrel

3 months ago

And adjusting the model code to distribute the training process across multiple GPUs is no easy task.

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Maricela

3 months ago

Yes, it's definitely a challenge. It requires a deep understanding of the model architecture.

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Winfred

3 months ago

I think B is the right answer. Parallelizing the training process can be quite complex.

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Marti

4 months ago

I think the lack of adequate power and cooling for the GPU-enabled servers could also be a major obstacle.

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Lyndia

4 months ago

I believe a lack of understanding of the DL model architecture could also be a significant challenge.

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Ettie

4 months ago

The complexity of adjusting model code to distribute the training process across multiple GPUs is definitely the biggest challenge. That's where the real engineering work lies.

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