tf.data.DatasetSpec

TensorFlow 1 version

View source on GitHub

Represents options for tf.data.Dataset.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.data.Options

tf.data.Options()

Used in the notebooks

Used in the tutorials
Distributed Input Multi-worker training with Keras

A tf.data.Options object can be, for instance, used to control which static optimizations to apply to the input pipeline graph or whether to use performance modeling to dynamically tune the parallelism of operations such as tf.data.Dataset.map or tf.data.Dataset.interleave.

The options are set for the entire dataset and are carried over to datasets created through tf.data transformations.

The options can be set either by mutating the object returned by tf.data.Dataset.options() or by constructing an Options object and using the tf.data.Dataset.with_options(options) transformation, which returns a dataset with the options set.

dataset = tf.data.Dataset.range(42)
dataset.options().experimental_deterministic = False
print(dataset.options().experimental_deterministic)
False

dataset = tf.data.Dataset.range(42)
options = tf.data.Options()
options.experimental_deterministic = False
dataset = dataset.with_options(options)
print(dataset.options().experimental_deterministic)
False

Attributes
`experimental_deterministic`	Whether the outputs need to be produced in deterministic order. If None, defaults to True.
`experimental_distribute`	The distribution strategy options associated with the dataset. See `tf.data.experimental.DistributeOptions` for more details.
`experimental_external_state_policy`	This option can be used to override the default policy for how to handle external state when serializing a dataset or checkpointing its iterator. There are three settings available - IGNORE: in which we completely ignore any state; WARN: We warn the user that some state might be thrown away; FAIL: We fail if any state is being captured.
`experimental_optimization`	The optimization options associated with the dataset. See `tf.data.experimental.OptimizationOptions` for more details.
`experimental_slack`	Whether to introduce 'slack' in the last `prefetch` of the input pipeline, if it exists. This may reduce CPU contention with accelerator host-side activity at the start of a step. The slack frequency is determined by the number of devices attached to this input pipeline. If None, defaults to False.
`experimental_stats`	The statistics options associated with the dataset. See `tf.data.experimental.StatsOptions` for more details.
`experimental_threading`	The threading options associated with the dataset. See `tf.data.experimental.ThreadingOptions` for more details.

Methods

`merge`

View source

merge(
    options
)

Merges itself with the given tf.data.Options.

If this object and the options to merge set an option differently, a warning is generated and this object's value is updated with the options object's value.

Args
`options`	a `tf.data.Options` to merge with

Returns
New `tf.data.Options` object which is the result of merging self with the input `tf.data.Options`.

`eq`

View source

__eq__(
    other
)

Return self==value.

`ne`

View source

__ne__(
    other
)

Return self!=value.

TensorFlow

tf

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tf.data / DatasetSpec

View aliases

Used in the notebooks

Attributes

Methods

merge

__eq__

__ne__

`merge`

`eq`

`ne`