sweep Package

Classes

BanditPolicy

Defines an early termination policy based on slack criteria and a frequency and delay interval for evaluation.

BayesianSamplingAlgorithm

Bayesian Sampling Algorithm.

Choice

Choice distribution configuration.

GridSamplingAlgorithm

Grid Sampling Algorithm.

LogNormal

LogNormal distribution configuration.

LogUniform

LogUniform distribution configuration.

MedianStoppingPolicy

Defines an early termination policy based on a running average of the primary metric of all runs.

Normal

Normal distribution configuration.

Objective

Optimization objective.

Optimization objective.

QLogNormal

QLogNormal distribution configuration.

QLogUniform

QLogUniform distribution configuration.

QNormal

QNormal distribution configuration.

QUniform

QUniform distribution configuration.

Randint

Randint distribution configuration.

RandomSamplingAlgorithm

Random Sampling Algorithm.

SamplingAlgorithm

Base class for sampling algorithms.

This class should not be instantiated directly. Instead, use one of its subclasses.

SweepJob

Sweep job for hyperparameter tuning.

Note

For sweep jobs, inputs, outputs, and parameters are accessible as environment variables using the prefix

AZUREML_SWEEP_. For example, if you have a parameter named "learning_rate", you can access it as

AZUREML_SWEEP_learning_rate.

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SweepJobLimits

Limits for Sweep Jobs.

TruncationSelectionPolicy

Defines an early termination policy that cancels a given percentage of runs at each evaluation interval.

Uniform

Uniform distribution configuration.