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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?

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Suggested Answer: D

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

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Sharmaine
2 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
17 days ago
Let's give it a try and see if it makes a difference.
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Carol
20 days ago
I agree, distributing the dataset might be the key to improving training time.
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Verona
29 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
17 days ago
User 3: I agree, let's give it a shot and see if it makes a difference.
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Dorathy
20 days ago
User 2: Yeah, that could be a good solution to try out.
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Lourdes
28 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
8 days ago
C) Use a TPU with tf.distribute.TPUStrategy.
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Chi
13 days ago
B) Create a custom training loop.
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Dominque
29 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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Gail
2 months ago
I think we should also consider using a TPU with tf.distribute.TPUStrategy for faster training.
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Pok
2 months ago
I agree with Ellsworth. That might help improve the training time.
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Ellsworth
3 months ago
I think we should try distributing the dataset with tf.distribute.Strategy.experimental_distribute_dataset.
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