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TensorFlow 1 version
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View source on GitHub
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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.map_vectorization.enabled = True
options.experimental_optimization.apply_default_optimizations = False
dataset = dataset.with_options(options)
Attributes | |
|---|---|
apply_default_optimizations
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Whether to apply default graph optimizations. If False, only graph optimizations that have been explicitly enabled will be applied. |
autotune
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Whether to automatically tune performance knobs. If None, defaults to True. |
autotune_buffers
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When autotuning is enabled (through autotune), determines whether to also autotune buffer sizes for datasets with parallelism. If None, defaults to False.
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autotune_cpu_budget
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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.
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autotune_ram_budget
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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.
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filter_fusion
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Whether to fuse filter transformations. If None, defaults to False. |
filter_with_random_uniform_fusion
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Whether to fuse filter dataset that predicts random_uniform < rate into a sampling dataset. If None, defaults to False. |
hoist_random_uniform
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Whether to hoist tf.random_uniform() ops out of map transformations. If None, defaults to False.
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map_and_batch_fusion
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Whether to fuse map and batch transformations. If None, defaults to True. |
map_and_filter_fusion
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Whether to fuse map and filter transformations. If None, defaults to False. |
map_fusion
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Whether to fuse map transformations. If None, defaults to False. |
map_parallelization
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Whether to parallelize stateless map transformations. If None, defaults to False. |
map_vectorization
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The map vectorization options associated with the dataset. See tf.data.experimental.MapVectorizationOptions for more details.
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noop_elimination
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Whether to eliminate no-op transformations. If None, defaults to True. |
parallel_batch
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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. |
reorder_data_discarding_ops
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Whether to reorder ops that will discard data to the front of unary cardinality preserving transformations, e.g. dataset.map(...).take(3) will be optimized to dataset.take(3).map(...). For now this optimization will move skip, shard and take to the front of map and prefetch. This optimization is only for performance; it will not affect the output of the dataset. If None, defaults to True.
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shuffle_and_repeat_fusion
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Whether to fuse shuffle and repeat transformations. If None, defaults to True. |
Methods
__eq__
__eq__(
other
)
Return self==value.
__ne__
__ne__(
other
)
Return self!=value.