Struct aws_sdk_sagemaker::model::hyper_parameter_tuning_job_config::Builder [−][src]
#[non_exhaustive]pub struct Builder { /* fields omitted */ }
Expand description
A builder for HyperParameterTuningJobConfig
Implementations
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.
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.
pub fn hyper_parameter_tuning_job_objective(
self,
input: HyperParameterTuningJobObjective
) -> Self
pub fn hyper_parameter_tuning_job_objective(
self,
input: HyperParameterTuningJobObjective
) -> Self
The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.
pub fn set_hyper_parameter_tuning_job_objective(
self,
input: Option<HyperParameterTuningJobObjective>
) -> Self
pub fn set_hyper_parameter_tuning_job_objective(
self,
input: Option<HyperParameterTuningJobObjective>
) -> Self
The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.
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.
pub fn set_training_job_early_stopping_type(
self,
input: Option<TrainingJobEarlyStoppingType>
) -> Self
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. 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.
The tuning job's completion criteria.
pub fn set_tuning_job_completion_criteria(
self,
input: Option<TuningJobCompletionCriteria>
) -> Self
pub fn set_tuning_job_completion_criteria(
self,
input: Option<TuningJobCompletionCriteria>
) -> Self
The tuning job's completion criteria.
Consumes the builder and constructs a HyperParameterTuningJobConfig
Trait Implementations
Auto Trait Implementations
impl RefUnwindSafe for Builder
impl UnwindSafe for Builder
Blanket Implementations
Mutably borrows from an owned value. Read more
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
Attaches the current default Subscriber
to this type, returning a
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wrapper. Read more