Struct aws_sdk_sagemaker::model::HyperParameterTuningJobConfig

#[non_exhaustive]
pub struct HyperParameterTuningJobConfig {
    pub strategy: Option<HyperParameterTuningJobStrategyType>,
    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>,
}

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. To use the Bayesian search strategy, set this to Bayesian. To randomly search, set it to Random. For information about search strategies, see How Hyperparameter Tuning Works.

hyper_parameter_tuning_job_objective: Option<HyperParameterTuningJobObjective>

The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.

resource_limits: Option<ResourceLimits>

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

parameter_ranges: Option<ParameterRanges>

The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.

training_job_early_stopping_type: Option<TrainingJobEarlyStoppingType>

Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. This can be 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

Amazon 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.

Implementations

impl HyperParameterTuningJobConfig

pub fn builder() -> Builder

Creates a new builder-style object to manufacture HyperParameterTuningJobConfig

Trait Implementations

impl Clone for HyperParameterTuningJobConfig

fn clone(&self) -> HyperParameterTuningJobConfig

Returns a copy 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<HyperParameterTuningJobConfig> for HyperParameterTuningJobConfig

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

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

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

This method tests for !=.

impl StructuralPartialEq for HyperParameterTuningJobConfig