Struct HyperParameterTuningJobConfig

#[non_exhaustive]
pub struct HyperParameterTuningJobConfig {
    pub strategy: Option<HyperParameterTuningJobStrategyType>,
    pub strategy_config: Option<HyperParameterTuningJobStrategyConfig>,
    pub hyper_parameter_tuning_job_objective: Option<HyperParameterTuningJobObjective>,
    pub resource_limits: Option<ResourceLimits>,
    pub parameter_ranges: Option<ParameterRanges>,
    pub training_job_early_stopping_type: Option<TrainingJobEarlyStoppingType>,
    pub tuning_job_completion_criteria: Option<TuningJobCompletionCriteria>,
    pub random_seed: Option<i32>,
}

Configures a hyperparameter tuning job.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

strategy: Option<HyperParameterTuningJobStrategyType>

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.

strategy_config: Option<HyperParameterTuningJobStrategyConfig>

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

hyper_parameter_tuning_job_objective: Option<HyperParameterTuningJobObjective>

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

resource_limits: Option<ResourceLimits>

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

parameter_ranges: Option<ParameterRanges>

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.

training_job_early_stopping_type: Option<TrainingJobEarlyStoppingType>

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.

tuning_job_completion_criteria: Option<TuningJobCompletionCriteria>

The tuning job's completion criteria.

random_seed: Option<i32>

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.

Implementations

impl HyperParameterTuningJobConfig

pub fn strategy(&self) -> Option<&HyperParameterTuningJobStrategyType>

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) -> Option<&HyperParameterTuningJobStrategyConfig>

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, ) -> Option<&HyperParameterTuningJobObjective>

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

pub fn resource_limits(&self) -> Option<&ResourceLimits>

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) -> Option<&ParameterRanges>

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, ) -> Option<&TrainingJobEarlyStoppingType>

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, ) -> Option<&TuningJobCompletionCriteria>

The tuning job's completion criteria.

pub fn random_seed(&self) -> Option<i32>

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.

impl HyperParameterTuningJobConfig

pub fn builder() -> HyperParameterTuningJobConfigBuilder

Creates a new builder-style object to manufacture HyperParameterTuningJobConfig.

Trait Implementations

impl Clone for HyperParameterTuningJobConfig

fn clone(&self) -> HyperParameterTuningJobConfig

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for HyperParameterTuningJobConfig

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

Formats the value using the given formatter.

impl PartialEq for HyperParameterTuningJobConfig

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for HyperParameterTuningJobConfig