Creates a dataset that shards the input dataset.
tf.raw_ops.ExperimentalAutoShardDataset( input_dataset, num_workers, index, output_types, output_shapes, auto_shard_policy=0, name=None ) Creates a dataset that shards the input dataset by num_workers, returning a sharded dataset for the index-th worker. This attempts to automatically shard a dataset by examining the Dataset graph and inserting a shard op before the inputs to a reader Dataset (e.g. CSVDataset, TFRecordDataset).
This dataset will throw a NotFound error if we cannot shard the dataset automatically.
Args | |
|---|---|
input_dataset | A Tensor of type variant. A variant tensor representing the input dataset. |
num_workers | A Tensor of type int64. A scalar representing the number of workers to distribute this dataset across. |
index | A Tensor of type int64. A scalar representing the index of the current worker out of num_workers. |
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. |
auto_shard_policy | An optional int. Defaults to 0. |
name | A name for the operation (optional). |
Returns | |
|---|---|
A Tensor of type variant. |