tf.data.experimental.OptimizationOptions

Represents options for dataset optimizations.

tf.data.experimental.OptimizationOptions()

You can set the optimization options of a dataset through the experimental_optimization property of tf.data.Options; the property is an instance of tf.data.experimental.OptimizationOptions.

options = tf.data.Options()
options.experimental_optimization.noop_elimination = True
options.experimental_optimization.apply_default_optimizations = False
dataset = dataset.with_options(options)

apply_default_optimizations Whether to apply default graph optimizations. If False, only graph optimizations that have been explicitly enabled will be applied.
autotune Whether to automatically tune performance knobs. If None, defaults to True.
autotune_buffers When autotuning is enabled (through autotune), determines whether to also autotune buffer sizes for datasets with parallelism. If None, defaults to False.
autotune_cpu_budget When autotuning is enabled (through autotune), determines the CPU budget to use. Values greater than the number of schedulable CPU cores are allowed but may result in CPU contention. If None, defaults to the number of schedulable CPU cores.
autotune_ram_budget When autotuning is enabled (through autotune), determines the RAM budget to use. Values greater than the available RAM in bytes may result in OOM. If None, defaults to half of the available RAM in bytes.
filter_fusion Whether to fuse filter transformations. If None, defaults to False.
map_and_batch_fusion Whether to fuse map and batch transformations. If None, defaults to True.
map_and_filter_fusion Whether to fuse map and filter transformations. If None, defaults to False.
map_fusion Whether to fuse map transformations. If None, defaults to False.
map_parallelization Whether to parallelize stateless map transformations. If None, defaults to True.
noop_elimination Whether to eliminate no-op transformations. If None, defaults to True.
parallel_batch Whether to parallelize copying of batch elements. This optimization is highly experimental and can cause performance degradation (e.g. when the parallelization overhead exceeds the benefits of performing the data copies in parallel). You should only enable this optimization if a) your input pipeline is bottlenecked on batching and b) you have validated that this optimization improves performance. If None, defaults to False.
shuffle_and_repeat_fusion Whether to fuse shuffle and repeat transformations. If None, defaults to True.

Methods

__eq__

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__eq__(
    other
)

Return self==value.

__ne__

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__ne__(
    other
)

Return self!=value.