Struct aws_sdk_sagemaker::operation::create_hyper_parameter_tuning_job::CreateHyperParameterTuningJobInput

#[non_exhaustive]
pub struct CreateHyperParameterTuningJobInput {
    pub hyper_parameter_tuning_job_name: Option<String>,
    pub hyper_parameter_tuning_job_config: Option<HyperParameterTuningJobConfig>,
    pub training_job_definition: Option<HyperParameterTrainingJobDefinition>,
    pub training_job_definitions: Option<Vec<HyperParameterTrainingJobDefinition>>,
    pub warm_start_config: Option<HyperParameterTuningJobWarmStartConfig>,
    pub tags: Option<Vec<Tag>>,
    pub autotune: Option<Autotune>,
}

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.

hyper_parameter_tuning_job_name: Option<String>

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.

hyper_parameter_tuning_job_config: Option<HyperParameterTuningJobConfig>

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.

training_job_definition: Option<HyperParameterTrainingJobDefinition>

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.

training_job_definitions: Option<Vec<HyperParameterTrainingJobDefinition>>

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

warm_start_config: Option<HyperParameterTuningJobWarmStartConfig>

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.

tags: Option<Vec<Tag>>

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.

autotune: Option<Autotune>

Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:

• ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.

• ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.

• TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.

• RetryStrategy: The number of times to retry a training job.

• Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.

• ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

Implementations

impl CreateHyperParameterTuningJobInput

pub fn hyper_parameter_tuning_job_name(&self) -> Option<&str>

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, ) -> Option<&HyperParameterTuningJobConfig>

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, ) -> Option<&HyperParameterTrainingJobDefinition>

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) -> &[HyperParameterTrainingJobDefinition]

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

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .training_job_definitions.is_none().

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

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) -> &[Tag]

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.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .tags.is_none().

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

Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:

• ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.

• ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.

• TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.

• RetryStrategy: The number of times to retry a training job.

• Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.

• ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

impl CreateHyperParameterTuningJobInput

pub fn builder() -> CreateHyperParameterTuningJobInputBuilder

Creates a new builder-style object to manufacture CreateHyperParameterTuningJobInput.

Trait Implementations

impl Clone for CreateHyperParameterTuningJobInput

fn clone(&self) -> CreateHyperParameterTuningJobInput

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CreateHyperParameterTuningJobInput

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

Formats the value using the given formatter.

impl PartialEq for CreateHyperParameterTuningJobInput

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for CreateHyperParameterTuningJobInput