tf.data.Options

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()

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 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)
options = tf.data.Options()
options.deterministic = False
dataset = dataset.with_options(options)
print(dataset.options().deterministic)
False

Note: A known limitation of the tf.data.Options implementation is that the options are not preserved across tf.function boundaries. In particular, to set options for a dataset that is iterated within a tf.function, the options need to be set within the same tf.function.

Attributes
autotune	The autotuning options associated with the dataset. See tf.data.experimental.AutotuneOptions for more details.
deterministic	Whether the outputs need to be produced in deterministic order. If None, defaults to True.
experimental_deterministic	DEPRECATED. Use deterministic instead.
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: External state is ignored without a warning; WARN: External state is ignored and a warning is logged; FAIL: External state results in an error.
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_symbolic_checkpoint	Whether to checkpoint internal input pipeline state maintaining cursors into data sources that identify last element(s) produced as output to the tf.data consumer. This is alternative to the default 'explicit' checkpointing which stores the internal input pipeline state in the checkpoint. Note that symbolic checkpointing is not supported for transformations that can reorder elements.
experimental_threading	DEPRECATED. Use threading instead.
threading	The threading options associated with the dataset. See tf.data.ThreadingOptions for more details.

Methods

merge

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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	The 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__

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__eq__(
    other
)

Return self==value.

__ne__

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__ne__(
    other
)

Return self!=value.