tf.raw_ops.LegacyParallelInterleaveDatasetV2

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

tf.raw_ops.LegacyParallelInterleaveDatasetV2(
    input_dataset,
    other_arguments,
    cycle_length,
    block_length,
    buffer_output_elements,
    prefetch_input_elements,
    f,
    output_types,
    output_shapes,
    deterministic='default',
    metadata='',
    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. 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. deterministic An optional string. Defaults to "default". metadata An optional string. Defaults to "". name A name for the operation (optional).