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tf.data.experimental.AutotuneAlgorithm

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.

DEFAULT <AutotuneAlgorithm.DEFAULT: 0>
GRADIENT_DESCENT <AutotuneAlgorithm.GRADIENT_DESCENT: 2>
HILL_CLIMB <AutotuneAlgorithm.HILL_CLIMB: 1>
MAX_PARALLELISM <AutotuneAlgorithm.MAX_PARALLELISM: 3>