Struct aws_sdk_sagemaker::model::hyper_parameter_training_job_summary::Builder

#[non_exhaustive]
pub struct Builder { /* fields omitted */ }

A builder for HyperParameterTrainingJobSummary

Implementations

impl Builder

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

The training job definition name.

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

The training job definition name.

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

The name of the training job.

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

The name of the training job.

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

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

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

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

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

The HyperParameter tuning job that launched the training job.

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

The HyperParameter tuning job that launched the training job.

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

The date and time that the training job was created.

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

The date and time that the training job was created.

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

The date and time that the training job started.

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

The date and time that the training job started.

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

Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.

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

Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.

pub fn training_job_status(self, input: TrainingJobStatus) -> Self

The status of the training job.

pub fn set_training_job_status(self, input: Option<TrainingJobStatus>) -> Self

The status of the training job.

pub fn tuned_hyper_parameters(
    self,
    k: impl Into<String>,
    v: impl Into<String>
) -> Self

Adds a key-value pair to tuned_hyper_parameters.

To override the contents of this collection use set_tuned_hyper_parameters.

A list of the hyperparameters for which you specified ranges to search.

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

A list of the hyperparameters for which you specified ranges to search.

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

The reason that the training job failed.

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

The reason that the training job failed.

pub fn final_hyper_parameter_tuning_job_objective_metric(
    self,
    input: FinalHyperParameterTuningJobObjectiveMetric
) -> Self

The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.

pub fn set_final_hyper_parameter_tuning_job_objective_metric(
    self,
    input: Option<FinalHyperParameterTuningJobObjectiveMetric>
) -> Self

The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.

pub fn objective_status(self, input: ObjectiveStatus) -> Self

The status of the objective metric for the training job:

• Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

• Pending: The training job is in progress and evaluation of its final objective metric is pending.

• Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.

pub fn set_objective_status(self, input: Option<ObjectiveStatus>) -> Self

The status of the objective metric for the training job:

• Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

• Pending: The training job is in progress and evaluation of its final objective metric is pending.

• Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.

pub fn build(self) -> HyperParameterTrainingJobSummary

Consumes the builder and constructs a HyperParameterTrainingJobSummary

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: &Builder) -> bool

This method tests for !=.

impl StructuralPartialEq for Builder