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Answer: Add parallel interleave to the pipeline
The correct answer is D: Add parallel interleave to the pipeline. When data is split into multiple files and you want to improve the execution time of your input pipeline, using parallel interleave can help. Parallel interleave allows for parallel reading and processing of input data, which reduces bottlenecks caused by synchronous data loading. This improves the overall throughput and efficiency of your model training process, especially when dealing with large datasets split across multiple files.
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During the training of your machine learning model, you notice that GPU utilization is lower than expected due to a synchronous data loading process. The training dataset is divided into multiple files for input. To improve the model training efficiency and reduce the execution time of your input pipeline, what should you do?
A
Increase the CPU load
B
Add caching to the pipeline
C
Increase the network bandwidth
D
Add parallel interleave to the pipeline
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