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. |
