tf.raw_ops.ExperimentalAutoShardDataset
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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.
|
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tf.raw_ops.ExperimentalAutoShardDataset\n\n\u003cbr /\u003e\n\nCreates a dataset that shards the input dataset.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.ExperimentalAutoShardDataset`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/ExperimentalAutoShardDataset)\n\n\u003cbr /\u003e\n\n tf.raw_ops.ExperimentalAutoShardDataset(\n input_dataset,\n num_workers,\n index,\n output_types,\n output_shapes,\n auto_shard_policy=0,\n name=None\n )\n\nCreates a dataset that shards the input dataset by num_workers, returning a\nsharded dataset for the index-th worker. This attempts to automatically shard\na dataset by examining the Dataset graph and inserting a shard op before the\ninputs to a reader Dataset (e.g. CSVDataset, TFRecordDataset).\n\nThis dataset will throw a NotFound error if we cannot shard the dataset\nautomatically.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------------|--------------------------------------------------------------------------------------------------------------|\n| `input_dataset` | A `Tensor` of type `variant`. A variant tensor representing the input dataset. |\n| `num_workers` | A `Tensor` of type `int64`. A scalar representing the number of workers to distribute this dataset across. |\n| `index` | A `Tensor` of type `int64`. A scalar representing the index of the current worker out of num_workers. |\n| `output_types` | A list of `tf.DTypes` that has length `\u003e= 1`. |\n| `output_shapes` | A list of shapes (each a [`tf.TensorShape`](../../tf/TensorShape) or list of `ints`) that has length `\u003e= 1`. |\n| `auto_shard_policy` | An optional `int`. Defaults to `0`. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor` of type `variant`. ||\n\n\u003cbr /\u003e"]]
