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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?

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Suggested Answer: D

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

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Cherilyn
5 months ago
TPUs are cool, but not always necessary for speed.
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Denise
5 months ago
Definitely try distributing the dataset first.
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Nadine
6 months ago
I disagree, a custom training loop might be more effective.
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Britt
6 months ago
Surprised that 4 GPUs didn't help at all!
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Myra
6 months ago
Increasing the batch size can help speed things up!
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Anastacia
6 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
7 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
7 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
7 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
7 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
7 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
8 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
8 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
8 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
8 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
8 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
12 months ago
Let's give it a try and see if it makes a difference.
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Carol
12 months 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
12 months ago
User 3: I agree, let's give it a shot and see if it makes a difference.
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Dorathy
12 months 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
11 months ago
C) Use a TPU with tf.distribute.TPUStrategy.
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Chi
12 months 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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Gail
1 year ago
I think we should also consider using a TPU with tf.distribute.TPUStrategy for faster training.
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Pok
1 year ago
I agree with Ellsworth. That might help improve the training time.
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Ellsworth
1 year ago
I think we should try distributing the dataset with tf.distribute.Strategy.experimental_distribute_dataset.
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