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. ] ] ]
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.