tf.data.experimental.AutotuneAlgorithm
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Represents the type of autotuning algorithm to use.
DEFAULT: The default behavior is implementation specific and may change over
time.
HILL_CLIMB: In each optimization step, this algorithm chooses the optimial
parameter and increases its value by 1.
GRADIENT_DESCENT: In each optimization step, this algorithm updates the
parameter values in the optimal direction.
MAX_PARALLELISM: Similar to HILL_CLIMB but uses a relaxed stopping condition,
allowing the optimization to oversubscribe the CPU.
STAGE_BASED: In each optimization step, this algorithm chooses the worst
bottleneck parameter and increases its value by 1.
Class Variables |
|---|
|
DEFAULT
|
<AutotuneAlgorithm.DEFAULT: 0>
|
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GRADIENT_DESCENT
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<AutotuneAlgorithm.GRADIENT_DESCENT: 2>
|
|
HILL_CLIMB
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<AutotuneAlgorithm.HILL_CLIMB: 1>
|
|
MAX_PARALLELISM
|
<AutotuneAlgorithm.MAX_PARALLELISM: 3>
|
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STAGE_BASED
|
<AutotuneAlgorithm.STAGE_BASED: 4>
|
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Last updated 2023-10-06 UTC.
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