Google Professional Machine Learning Engineer Exam - Topic 5 Question 80 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam

Question #: 80
Topic #: 5

[All Professional Machine Learning Engineer Questions]

You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow. Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?

ADistribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset

BCreate a custom training loop.

CUse a TPU with tf.distribute.TPUStrategy.

DIncrease the batch size.

Show Suggested Answer

Suggested Answer: D

by Chauncey at Jun 21, 2024, 09:30 AM

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Cherilyn

9 months ago

TPUs are cool, but not always necessary for speed.

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Denise

9 months ago

Definitely try distributing the dataset first.

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Nadine

9 months ago

I disagree, a custom training loop might be more effective.

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Britt

9 months ago

Surprised that 4 GPUs didn't help at all!

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Myra

9 months ago

Increasing the batch size can help speed things up!

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Anastacia

10 months ago

Using a TPU sounds interesting, but I think it might be overkill for this situation. I’d lean towards option A or D instead.

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Mary

10 months ago

I practiced a similar question, and I feel like a custom training loop could give more control, but I’m not confident that’s necessary here.

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Luz

10 months ago

I'm not entirely sure, but I think increasing the batch size could help utilize the GPUs better. That might be option D?

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Corazon

10 months ago

I remember reading that distributing the dataset can help with performance, so maybe option A is the right choice?

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Viva

11 months ago

This seems like a good opportunity to try out a custom training loop. I've heard that can really help optimize the training process, especially when working with multiple GPUs. I'm going to go with option B.

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Wynell

11 months ago

I'm a bit confused here. Distributing the dataset seems like a logical step, but the question says that didn't work. Maybe I should consider creating a custom training loop or using a TPU instead?

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Marge

11 months ago

Okay, let's think this through. If the training time didn't improve with 4 GPUs, then the issue might not be with the hardware. A custom training loop could help optimize the process, so I'm leaning towards option B.

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Raina

11 months ago

Hmm, this is a tricky one. I'm not sure why the training time didn't decrease with the 4 GPUs. Maybe I need to look into how to properly distribute the dataset.

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Joanne

11 months ago

Ah, I see now. Routers are layer 3 devices that use protocols to learn paths, not layer 2 switching. And they definitely aren't layer 4 devices. I've got my answer.

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Clorinda

11 months ago

Hmm, I'm a little unsure about this one. I'll need to think through the different IP addressing scenarios carefully.

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Halina

11 months ago

I'm a bit unsure about this one. I know enterprise analysis is important, but I'm not totally clear on the specific document that would outline the governances. I'll have to think this through carefully.

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Sharmaine

1 year ago

Ah, the age-old dilemma of training a deep learning model - GPUs, TPUs, and batch sizes, oh my! I say we just throw the whole thing in the microwave and see what happens. *chuckles*

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Rebbecca

1 year ago

Well, this is a tough one. I'm leaning towards option A - distributing the dataset with tf.distribute.Strategy.experimental_distribute_dataset. Seems like the most straightforward approach to me.

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Trinidad

1 year ago

Let's give it a try and see if it makes a difference.

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Carol

1 year ago

I agree, distributing the dataset might be the key to improving training time.

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Verona

1 year ago

I think option A is a good choice. It could help speed up the training process.

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Nada

1 year ago

Oh, I bet option D is the way to go! Increasing the batch size might just do the trick. After all, who needs GPUs when you have big batches, right? *wink*

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Curt

1 year ago

User 3: I agree, let's give it a shot and see if it makes a difference.

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Dorathy

1 year ago

User 2: Yeah, that could be a good solution to try out.

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Lourdes

1 year ago

User 1: I think increasing the batch size might help speed up the training process.

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Micheal

1 year ago

Interesting question. I think option C looks promising - using a TPU with tf.distribute.TPUStrategy could really speed up the training process.

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Leoma

1 year ago

C) Use a TPU with tf.distribute.TPUStrategy.

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Chi

1 year ago

B) Create a custom training loop.

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Dominque

1 year ago

A) Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset

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Omega

1 year ago

Hmm, I would say option B. Creating a custom training loop can help you fine-tune the distribution of the training process and potentially improve the performance.

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