Optimize Tensorflow Pipeline Performance: prefetch & cache | Deep Learning Tutorial 45 (Tensorflow)

It is important to make optimal use of your hardware resources (CPU and GPU) while training a deep learning model. You can use tf.data.Dataset.prefetch(AUTOTUNE) and tf.data.Dataset.cache() methods for this purpose. They help you optimize tensorflow input pipeline performance. In this video we will go over how these two methods work and will write some code as well.
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When do we need to use .cache() and when .cache().repeat()? Some tutorials say we need the second if we have multiple epochs.

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What if there is some randomization step in between our pipeline? Should we use prefetch and cache then? Because that randomization step should ideally generate some different result for each epoch.

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You should use randomization after you cache . If you do caching after randomization step, then you will be fixing the last output of randomization step and getting the same results for each epoch.

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What is the difference between tf.data.Dataset.map(...) vs tf.data.Dataset.apply(...) ?

[@ThePietro1234]

Hi, thanks for the video, it is very clear! Only one thing I didn't understand: with .cache optimization the data remains in the CPU RAM right? Or the data remains in GPU DRAM? Because even though we talk about caching I doubt that the data remains in cache memory (trivially because cache memory is too small to fit a medium size dataset). Thanks!

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