tf.data.Options

Represents options for tf.data.Dataset.

tf.data.Options()

Used in the notebooks

Used in the tutorials

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

autotune The autotuning options associated with the dataset. See tf.data.experimental.AutotuneOptions for more details.
dataset_name A name for the dataset, to help in debugging.
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.
experimental_warm_start Whether to start background threads of asynchronous transformations upon iterator creation, as opposed to during the first call to next(). Defaults to False. This improves the latency of the initial 'next()' calls at the expense of requiring more memory to hold prefetched elements between the time of iterator construction and usage.
framework_type The list of frameworks that are used to generate this pipeline, used for telemetry.
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.