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Question 87 - Professional Machine Learning Engineer discussion

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While monitoring your model training's GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?

A.
Increase the CPU load
Answers
A.
Increase the CPU load
B.
Add caching to the pipeline
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B.
Add caching to the pipeline
C.
Increase the network bandwidth
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C.
Increase the network bandwidth
D.
Add parallel interleave to the pipeline
Answers
D.
Add parallel interleave to the pipeline
Suggested answer: D

Explanation:

Parallel interleave is a technique that can improve the performance of the input pipeline by reading and processing data from multiple files in parallel. This can reduce the idle time of the GPU and speed up the training process. Parallel interleave can be implemented using the tf.data.experimental.parallel_interleave () function in TensorFlow, which takes a map function that returns a dataset for each input element, and a cycle length that determines how many input elements are processed concurrently. Parallel interleave can also handle different file sizes and processing times by using a block length argument that controls how many consecutive elements are produced from each input element before switching to another input element. For more information about parallel interleave and how to use it, see the following references:

How to use parallel_interleave in TensorFlow

Better performance with the tf.data API

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asked 18/09/2024
Justin Whelan
35 questions
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