HP Exam HPE2-N69 Topic 7 Question 29 Discussion

Actual exam question for HP's HPE2-N69 exam

Question #: 29
Topic #: 7

[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 Youlanda at Mar 21, 2024, 03:54 AM

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Dolores

5 months ago

I also think that a lack of understanding of the DL model architecture by the ML engineering team could hinder the acceleration of deep learning training.

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Refugia

5 months ago

That's a good point. Without proper infrastructure support, the ML team might face delays and limitations.

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Gertude

5 months ago

Another challenge could be a lack of adequate power and cooling for the GPU-enabled servers, affecting the performance of the training process.

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Sherill

6 months ago

Yeah, I agree. It can be time-consuming and require specific expertise to optimize the process.

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Kenneth

6 months ago

I think the complexity of adjusting model code to distribute the training process across multiple GPUs could be a major challenge.

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Phung

6 months ago

What challenge do you think is likely to continue to stand in the way of accelerating deep learning training?

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Thea

6 months ago

Yes, ensuring the servers have enough power and cooling is crucial for efficient deep learning training.

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Lelia

7 months ago

I feel like the lack of adequate power and cooling for the GPU-enabled servers could also pose a challenge.

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Keshia

7 months ago

I agree, distributing the training process across multiple GPUs can be quite tricky.

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Laurene

7 months ago

I think the biggest challenge will be the complexity of adjusting model code to utilize multiple GPUs.

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