parallel_interleave() maps map_func across its input to produce nested
datasets, and outputs their elements interleaved. Unlike
tf.data.Dataset.interleave, it gets elements from cycle_length nested
datasets in parallel, which increases the throughput, especially in the
presence of stragglers. Furthermore, the sloppy argument can be used to
improve performance, by relaxing the requirement that the outputs are produced
in a deterministic order, and allowing the implementation to skip over nested
datasets whose elements are not readily available when requested.
A function mapping a nested structure of tensors to a Dataset.
cycle_length
The number of input Datasets to interleave from in parallel.
block_length
The number of consecutive elements to pull from an input
Dataset before advancing to the next input Dataset.
sloppy
A boolean controlling whether determinism should be traded for
performance by allowing elements to be produced out of order. If sloppy
is None, the tf.data.Options.deterministic dataset option (True by
default) is used to decide whether to enforce a deterministic order.
buffer_output_elements
The number of elements each iterator being
interleaved should buffer (similar to the .prefetch() transformation for
each interleaved iterator).
prefetch_input_elements
The number of input elements to transform to
iterators before they are needed for interleaving.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[],[]]