tf.data.Options

Class Options

Defined in tensorflow/python/data/ops/dataset_ops.py.

Represents options for tf.data.Dataset.

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

__init__

__init__()

Initialize self. See help(type(self)) for accurate signature.

Properties

experimental_autotune

Whether to dynamically adjust the values of tunable parameters (e.g. degrees of parallelism). If None, defaults to True.

experimental_deterministic

Whether the outputs need to be produced in deterministic order. If None, defaults to True.

experimental_numa_aware

Whether to use NUMA-aware operations. If None, defaults to False.

experimental_optimization

The optimization options associated with the dataset. See tf.data.experimental.OptimizationOptions for more details.

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

tf.data.Options.__eq__

__eq__(other)

Return self==value.

tf.data.Options.__ne__

__ne__(other)

Return self!=value.

tf.data.Options.__setattr__

__setattr__(
    name,
    value
)

Implement setattr(self, name, value).

tf.data.Options.merge

merge(options)

Merges itself with the given tf.data.Options.

The given tf.data.Options can be merged as long as there does not exist an attribute that is set to different values in self and options.

Args:

• options: a tf.data.Options to merge with

Raises:

• ValueError: if the given tf.data.Options cannot be merged

Returns:

New tf.data.Options() object which is the result of merging self with the input tf.data.Options.