Struct aws_sdk_sagemaker::input::create_hyper_parameter_tuning_job_input::Builder

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

A builder for CreateHyperParameterTuningJobInput

Implementations

impl Builder

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

The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

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

The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

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

The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.

pub fn set_hyper_parameter_tuning_job_config(
    self,
    input: Option<HyperParameterTuningJobConfig>
) -> Self

The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.

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

The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

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

The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

pub fn training_job_definitions(
    self,
    input: impl Into<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.

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

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

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

Specifies the configuration for starting the hyperparameter 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.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

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

Specifies the configuration for starting the hyperparameter 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.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

pub fn tags(self, input: impl Into<Tag>) -> Self

Appends an item to tags.

To override the contents of this collection use set_tags.

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

pub fn build(self) -> Result<CreateHyperParameterTuningJobInput, BuildError>

Consumes the builder and constructs a CreateHyperParameterTuningJobInput

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