Struct aws_sdk_sagemaker::model::HyperParameterTuningJobWarmStartConfig

#[non_exhaustive]
pub struct HyperParameterTuningJobWarmStartConfig { /* private fields */ }

Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter 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, and the training job that performs the best 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.

Implementations

impl HyperParameterTuningJobWarmStartConfig

pub fn parent_hyper_parameter_tuning_jobs(
    &self
) -> Option<&[ParentHyperParameterTuningJob]>

An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point.

Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.

pub fn warm_start_type(&self) -> Option<&HyperParameterTuningJobWarmStartType>

Specifies one of the following:

IDENTICAL_DATA_AND_ALGORITHM

The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.

TRANSFER_LEARNING

The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.

impl HyperParameterTuningJobWarmStartConfig

pub fn builder() -> Builder

Creates a new builder-style object to manufacture HyperParameterTuningJobWarmStartConfig.

Trait Implementations

impl Clone for HyperParameterTuningJobWarmStartConfig

fn clone(&self) -> HyperParameterTuningJobWarmStartConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for HyperParameterTuningJobWarmStartConfig

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

Formats the value using the given formatter.

impl PartialEq<HyperParameterTuningJobWarmStartConfig> for HyperParameterTuningJobWarmStartConfig

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

This method tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for HyperParameterTuningJobWarmStartConfig