tf.data.Options     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()
 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
 
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
  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.