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.
| Class Variables | |
|---|---|
| DEFAULT | <AutotuneAlgorithm.DEFAULT: 0> |
| GRADIENT_DESCENT | <AutotuneAlgorithm.GRADIENT_DESCENT: 2> |
| HILL_CLIMB | <AutotuneAlgorithm.HILL_CLIMB: 1> |
| MAX_PARALLELISM | <AutotuneAlgorithm.MAX_PARALLELISM: 3> |
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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/data/experimental/AutotuneAlgorithm
