DescribeHyperParameterTuningJobOutputBuilder

aws_sdk_sagemaker::operation::describe_hyper_parameter_tuning_job::builders

Struct DescribeHyperParameterTuningJobOutputBuilder 

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#[non_exhaustive]
pub struct DescribeHyperParameterTuningJobOutputBuilder { /* private fields */ }
Expand description

A builder for DescribeHyperParameterTuningJobOutput.

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impl DescribeHyperParameterTuningJobOutputBuilder

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pub fn hyper_parameter_tuning_job_name(self, input: impl Into<String>) -> Self

The name of the hyperparameter tuning job.

This field is required.
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pub fn set_hyper_parameter_tuning_job_name(self, input: Option<String>) -> Self

The name of the hyperparameter tuning job.

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pub fn get_hyper_parameter_tuning_job_name(&self) -> &Option<String>

The name of the hyperparameter tuning job.

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pub fn hyper_parameter_tuning_job_arn(self, input: impl Into<String>) -> Self

The Amazon Resource Name (ARN) of the tuning job.

This field is required.
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pub fn set_hyper_parameter_tuning_job_arn(self, input: Option<String>) -> Self

The Amazon Resource Name (ARN) of the tuning job.

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pub fn get_hyper_parameter_tuning_job_arn(&self) -> &Option<String>

The Amazon Resource Name (ARN) of the tuning job.

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pub fn hyper_parameter_tuning_job_config( self, input: HyperParameterTuningJobConfig, ) -> Self

The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.

This field is required.
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pub fn set_hyper_parameter_tuning_job_config( self, input: Option<HyperParameterTuningJobConfig>, ) -> Self

The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.

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pub fn get_hyper_parameter_tuning_job_config( &self, ) -> &Option<HyperParameterTuningJobConfig>

The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.

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pub fn training_job_definition( self, input: HyperParameterTrainingJobDefinition, ) -> Self

The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.

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pub fn set_training_job_definition( self, input: Option<HyperParameterTrainingJobDefinition>, ) -> Self

The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.

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pub fn get_training_job_definition( &self, ) -> &Option<HyperParameterTrainingJobDefinition>

The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.

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pub fn training_job_definitions( self, input: HyperParameterTrainingJobDefinition, ) -> Self

Appends an item to training_job_definitions.

To override the contents of this collection use set_training_job_definitions.

A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

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pub fn set_training_job_definitions( self, input: Option<Vec<HyperParameterTrainingJobDefinition>>, ) -> Self

A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

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pub fn get_training_job_definitions( &self, ) -> &Option<Vec<HyperParameterTrainingJobDefinition>>

A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

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pub fn hyper_parameter_tuning_job_status( self, input: HyperParameterTuningJobStatus, ) -> Self

The status of the tuning job.

This field is required.
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pub fn set_hyper_parameter_tuning_job_status( self, input: Option<HyperParameterTuningJobStatus>, ) -> Self

The status of the tuning job.

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pub fn get_hyper_parameter_tuning_job_status( &self, ) -> &Option<HyperParameterTuningJobStatus>

The status of the tuning job.

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pub fn creation_time(self, input: DateTime) -> Self

The date and time that the tuning job started.

This field is required.
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pub fn set_creation_time(self, input: Option<DateTime>) -> Self

The date and time that the tuning job started.

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pub fn get_creation_time(&self) -> &Option<DateTime>

The date and time that the tuning job started.

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pub fn hyper_parameter_tuning_end_time(self, input: DateTime) -> Self

The date and time that the tuning job ended.

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pub fn set_hyper_parameter_tuning_end_time( self, input: Option<DateTime>, ) -> Self

The date and time that the tuning job ended.

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pub fn get_hyper_parameter_tuning_end_time(&self) -> &Option<DateTime>

The date and time that the tuning job ended.

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pub fn last_modified_time(self, input: DateTime) -> Self

The date and time that the status of the tuning job was modified.

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pub fn set_last_modified_time(self, input: Option<DateTime>) -> Self

The date and time that the status of the tuning job was modified.

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pub fn get_last_modified_time(&self) -> &Option<DateTime>

The date and time that the status of the tuning job was modified.

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pub fn training_job_status_counters( self, input: TrainingJobStatusCounters, ) -> Self

The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.

This field is required.
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pub fn set_training_job_status_counters( self, input: Option<TrainingJobStatusCounters>, ) -> Self

The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.

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pub fn get_training_job_status_counters( &self, ) -> &Option<TrainingJobStatusCounters>

The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.

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pub fn objective_status_counters(self, input: ObjectiveStatusCounters) -> Self

The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.

This field is required.
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pub fn set_objective_status_counters( self, input: Option<ObjectiveStatusCounters>, ) -> Self

The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.

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pub fn get_objective_status_counters(&self) -> &Option<ObjectiveStatusCounters>

The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.

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pub fn best_training_job(self, input: HyperParameterTrainingJobSummary) -> Self

A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.

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pub fn set_best_training_job( self, input: Option<HyperParameterTrainingJobSummary>, ) -> Self

A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.

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pub fn get_best_training_job(&self) -> &Option<HyperParameterTrainingJobSummary>

A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.

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pub fn overall_best_training_job( self, input: HyperParameterTrainingJobSummary, ) -> Self

If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.

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pub fn set_overall_best_training_job( self, input: Option<HyperParameterTrainingJobSummary>, ) -> Self

If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.

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pub fn get_overall_best_training_job( &self, ) -> &Option<HyperParameterTrainingJobSummary>

If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.

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pub fn warm_start_config( self, input: HyperParameterTuningJobWarmStartConfig, ) -> Self

The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

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pub fn set_warm_start_config( self, input: Option<HyperParameterTuningJobWarmStartConfig>, ) -> Self

The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

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pub fn get_warm_start_config( &self, ) -> &Option<HyperParameterTuningJobWarmStartConfig>

The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

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pub fn autotune(self, input: Autotune) -> Self

A flag to indicate if autotune is enabled for the hyperparameter tuning job.

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pub fn set_autotune(self, input: Option<Autotune>) -> Self

A flag to indicate if autotune is enabled for the hyperparameter tuning job.

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pub fn get_autotune(&self) -> &Option<Autotune>

A flag to indicate if autotune is enabled for the hyperparameter tuning job.

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pub fn failure_reason(self, input: impl Into<String>) -> Self

If the tuning job failed, the reason it failed.

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pub fn set_failure_reason(self, input: Option<String>) -> Self

If the tuning job failed, the reason it failed.

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pub fn get_failure_reason(&self) -> &Option<String>

If the tuning job failed, the reason it failed.

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pub fn tuning_job_completion_details( self, input: HyperParameterTuningJobCompletionDetails, ) -> Self

Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function.

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pub fn set_tuning_job_completion_details( self, input: Option<HyperParameterTuningJobCompletionDetails>, ) -> Self

Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function.

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pub fn get_tuning_job_completion_details( &self, ) -> &Option<HyperParameterTuningJobCompletionDetails>

Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function.

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pub fn consumed_resources( self, input: HyperParameterTuningJobConsumedResources, ) -> Self

The total resources consumed by your hyperparameter tuning job.

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pub fn set_consumed_resources( self, input: Option<HyperParameterTuningJobConsumedResources>, ) -> Self

The total resources consumed by your hyperparameter tuning job.

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pub fn get_consumed_resources( &self, ) -> &Option<HyperParameterTuningJobConsumedResources>

The total resources consumed by your hyperparameter tuning job.

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pub fn build(self) -> DescribeHyperParameterTuningJobOutput

Consumes the builder and constructs a DescribeHyperParameterTuningJobOutput.

Trait Implementations§

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impl Clone for DescribeHyperParameterTuningJobOutputBuilder

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fn clone(&self) -> DescribeHyperParameterTuningJobOutputBuilder

Returns a duplicate of the value. Read more
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fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for DescribeHyperParameterTuningJobOutputBuilder

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

Formats the value using the given formatter. Read more
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impl Default for DescribeHyperParameterTuningJobOutputBuilder

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fn default() -> DescribeHyperParameterTuningJobOutputBuilder

Returns the “default value” for a type. Read more
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impl PartialEq for DescribeHyperParameterTuningJobOutputBuilder

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

Tests for self and other values to be equal, and is used by ==.
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fn ne(&self, other: &Rhs) -> bool

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

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where S: Into<Dispatch>,

Attaches the provided Subscriber to this type, returning a WithDispatch wrapper. Read more
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fn with_current_subscriber(self) -> WithDispatch<Self>

Attaches the current default Subscriber to this type, returning a WithDispatch wrapper. Read more
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impl<T> ErasedDestructor for T
where T: 'static,