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!

Args:

  • 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).

Returns:

A Tensor of type variant.