How can I parallelize data loading with TensorFlow s tf data API

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Can you explain how to parallelize data loading with TensorFlow's tf.data API? If possible, use code.
Dec 9, 2024 in Generative AI by Ashutosh
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1 answer to this question.

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You can parallelize data loading with TensorFlow's tf.data API by using functions like map() with the num_parallel_calls argument and enabling prefetching with prefetch(). This improves data pipeline performance by overlapping data preprocessing with model training. Here is the code snippet you can refer to:

In the above code, we are using:

  • Use interleave: Load data from multiple files in parallel.
  • Parallel Processing: Use map() with num_parallel_calls=tf.data.AUTOTUNE to parallelize preprocessing.
  • Prefetching: Use prefetch(buffer_size=tf.data.AUTOTUNE) to overlap data loading with model training.

Hence, this setup ensures efficient data loading, especially for large datasets.

answered Dec 10, 2024 by html kid

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