tf.raw_ops.ExperimentalParallelInterleaveDataset

Creates a dataset that applies f to the outputs of input_dataset.

tf.raw_ops.ExperimentalParallelInterleaveDataset(
    input_dataset, other_arguments, cycle_length, block_length, sloppy,
    buffer_output_elements, prefetch_input_elements, f, output_types, output_shapes,
    name=None
)

The resulting dataset is similar to the InterleaveDataset, with the exception that if retrieving the next value from a dataset would cause the requester to block, it will skip that input dataset. This dataset is especially useful when loading data from a variable-latency datastores (e.g. HDFS, GCS), as it allows the training step to proceed so long as some data is available.

!! WARNING !! This dataset is not deterministic!

input_dataset A Tensor of type variant.
other_arguments A list of Tensor objects.
cycle_length A Tensor of type int64.
block_length A Tensor of type int64.
sloppy A Tensor of type bool.
buffer_output_elements A Tensor of type int64.
prefetch_input_elements A Tensor of type int64.
f A function decorated with @Defun. A function mapping elements of input_dataset, concatenated with other_arguments, to a Dataset variant that contains elements matching output_types and output_shapes.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).

A Tensor of type variant.