Google Exam Professional Machine Learning Engineer 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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Sharmaine

1 months 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

2 months 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

14 days ago

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

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Carol

17 days ago

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

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Verona

26 days ago

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

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Nada

2 months 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

14 days ago

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

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Dorathy

17 days ago

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

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Lourdes

25 days ago

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

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Micheal

2 months 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

5 days ago

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

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Chi

10 days ago

B) Create a custom training loop.

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Dominque

26 days ago

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

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Omega

2 months 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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