Struct aws_sdk_sagemaker::model::hyper_parameter_tuning_job_config::Builder

pub struct Builder { /* private fields */ }

A builder for HyperParameterTuningJobConfig.

Implementations

impl Builder

pub fn strategy(self, input: HyperParameterTuningJobStrategyType) -> Self

Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works.

pub fn set_strategy(
    self,
    input: Option<HyperParameterTuningJobStrategyType>
) -> Self

Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works.

pub fn strategy_config(
    self,
    input: HyperParameterTuningJobStrategyConfig
) -> Self

The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig.

pub fn set_strategy_config(
    self,
    input: Option<HyperParameterTuningJobStrategyConfig>
) -> Self

The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig.

pub fn hyper_parameter_tuning_job_objective(
    self,
    input: HyperParameterTuningJobObjective
) -> Self

The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.

pub fn set_hyper_parameter_tuning_job_objective(
    self,
    input: Option<HyperParameterTuningJobObjective>
) -> Self

The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.

pub fn resource_limits(self, input: ResourceLimits) -> Self

The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.

pub fn set_resource_limits(self, input: Option<ResourceLimits>) -> Self

The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.

pub fn parameter_ranges(self, input: ParameterRanges) -> Self

The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.

pub fn set_parameter_ranges(self, input: Option<ParameterRanges>) -> Self

The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.

pub fn training_job_early_stopping_type(
    self,
    input: TrainingJobEarlyStoppingType
) -> Self

Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband. This parameter can take on one of the following values (the default value is OFF):

OFF

Training jobs launched by the hyperparameter tuning job do not use early stopping.

AUTO

SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.

pub fn set_training_job_early_stopping_type(
    self,
    input: Option<TrainingJobEarlyStoppingType>
) -> Self

Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband. This parameter can take on one of the following values (the default value is OFF):

OFF

Training jobs launched by the hyperparameter tuning job do not use early stopping.

AUTO

SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.

pub fn tuning_job_completion_criteria(
    self,
    input: TuningJobCompletionCriteria
) -> Self

The tuning job's completion criteria.

pub fn set_tuning_job_completion_criteria(
    self,
    input: Option<TuningJobCompletionCriteria>
) -> Self

The tuning job's completion criteria.

pub fn random_seed(self, input: i32) -> Self

A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.

pub fn set_random_seed(self, input: Option<i32>) -> Self

A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.

pub fn build(self) -> HyperParameterTuningJobConfig

Consumes the builder and constructs a HyperParameterTuningJobConfig.

Trait Implementations

impl Clone for Builder

fn clone(&self) -> Builder

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Builder

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for Builder

fn default() -> Builder

Returns the “default value” for a type.

impl PartialEq<Builder> for Builder

fn eq(&self, other: &Builder) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for Builder