How to improve data input pipeline performance?

Mentioning the Solution and the Important observations of @AlexisBRENON in the Answer Section, for the benefit of the Community.

Below mentioned are the Important Observations:

1. According to this GitHub issue, the TFRecordDataset interleaving is a legacy one, so interleave function is better.
2. batch before map is a good habit (vectorizing your function) and reduce the number of times the mapped function is called.
3. No need of repeat anymore. Since TF2.0, the Keras model API supports the dataset API and can use cache (see the SO post)
4. Switch from a VarLenFeature to a FixedLenSequenceFeature, removing a useless call to tf.sparse.to_dense.

Code for the Pipeline, with improved performance, in line with above observations is mentioned below:

def build_dataset(file_pattern):    tf.data.Dataset.list_files(        file_pattern    ).interleave(        TFRecordDataset,        cycle_length=tf.data.experimental.AUTOTUNE,        num_parallel_calls=tf.data.experimental.AUTOTUNE    ).shuffle(        2048    ).batch(        batch_size=64,        drop_remainder=True,    ).map(        map_func=parse_examples_batch,        num_parallel_calls=tf.data.experimental.AUTOTUNE    ).cache(    ).prefetch(        tf.data.experimental.AUTOTUNE    )def parse_examples_batch(examples):    preprocessed_sample_columns = {        "features": tf.io.FixedLenSequenceFeature((), tf.float32, allow_missing=True),        "booleanFeatures": tf.io.FixedLenFeature((), tf.string, ""),        "label": tf.io.FixedLenFeature((), tf.float32, -1)    }    samples = tf.io.parse_example(examples, preprocessed_sample_columns)    bits_to_float = tf.io.decode_raw(samples["booleanFeatures"], tf.uint8)    return (        (samples['features'], bits_to_float),        tf.expand_dims(samples["label"], 1)    )

I have a further suggestion to add:

According to the documentation of interleave(), you can as the first parameteruse a mapping function.

This means, one can write:

 dataset = tf.data.Dataset.list_files(file_pattern) dataset = dataset.interleave(lambda x:    tf.data.TFRecordDataset(x).map(parse_fn, num_parallel_calls=AUTOTUNE),    cycle_length=tf.data.experimental.AUTOTUNE,    num_parallel_calls=tf.data.experimental.AUTOTUNE    )

As I understand it, this maps a parsing function to each shard, and then interleaves the results. This then eliminates the use of dataset.map(...) later on.