Struct aws_sdk_sagemaker::types::builders::TrainingSpecificationBuilder

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

A builder for TrainingSpecification.

Implementations

impl TrainingSpecificationBuilder

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

The Amazon ECR registry path of the Docker image that contains the training algorithm.

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

The Amazon ECR registry path of the Docker image that contains the training algorithm.

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

The Amazon ECR registry path of the Docker image that contains the training algorithm.

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

An MD5 hash of the training algorithm that identifies the Docker image used for training.

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

An MD5 hash of the training algorithm that identifies the Docker image used for training.

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

An MD5 hash of the training algorithm that identifies the Docker image used for training.

pub fn supported_hyper_parameters( self, input: HyperParameterSpecification ) -> Self

Appends an item to supported_hyper_parameters.

To override the contents of this collection use set_supported_hyper_parameters.

A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

pub fn set_supported_hyper_parameters( self, input: Option<Vec<HyperParameterSpecification>> ) -> Self

A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

pub fn get_supported_hyper_parameters( &self ) -> &Option<Vec<HyperParameterSpecification>>

A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

pub fn supported_training_instance_types( self, input: TrainingInstanceType ) -> Self

Appends an item to supported_training_instance_types.

To override the contents of this collection use set_supported_training_instance_types.

A list of the instance types that this algorithm can use for training.

pub fn set_supported_training_instance_types( self, input: Option<Vec<TrainingInstanceType>> ) -> Self

A list of the instance types that this algorithm can use for training.

pub fn get_supported_training_instance_types( &self ) -> &Option<Vec<TrainingInstanceType>>

A list of the instance types that this algorithm can use for training.

pub fn supports_distributed_training(self, input: bool) -> Self

Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.

pub fn set_supports_distributed_training(self, input: Option<bool>) -> Self

Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.

pub fn get_supports_distributed_training(&self) -> &Option<bool>

Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.

pub fn metric_definitions(self, input: MetricDefinition) -> Self

Appends an item to metric_definitions.

To override the contents of this collection use set_metric_definitions.

A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

pub fn set_metric_definitions( self, input: Option<Vec<MetricDefinition>> ) -> Self

A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

pub fn get_metric_definitions(&self) -> &Option<Vec<MetricDefinition>>

A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

pub fn training_channels(self, input: ChannelSpecification) -> Self

Appends an item to training_channels.

To override the contents of this collection use set_training_channels.

A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

pub fn set_training_channels( self, input: Option<Vec<ChannelSpecification>> ) -> Self

A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

pub fn get_training_channels(&self) -> &Option<Vec<ChannelSpecification>>

A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

pub fn supported_tuning_job_objective_metrics( self, input: HyperParameterTuningJobObjective ) -> Self

Appends an item to supported_tuning_job_objective_metrics.

To override the contents of this collection use set_supported_tuning_job_objective_metrics.

A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

pub fn set_supported_tuning_job_objective_metrics( self, input: Option<Vec<HyperParameterTuningJobObjective>> ) -> Self

A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

pub fn get_supported_tuning_job_objective_metrics( &self ) -> &Option<Vec<HyperParameterTuningJobObjective>>

A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

pub fn build(self) -> TrainingSpecification

Consumes the builder and constructs a TrainingSpecification.

Trait Implementations

impl Clone for TrainingSpecificationBuilder

fn clone(&self) -> TrainingSpecificationBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TrainingSpecificationBuilder

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

Formats the value using the given formatter.

impl Default for TrainingSpecificationBuilder

fn default() -> TrainingSpecificationBuilder

Returns the “default value” for a type.

impl PartialEq<TrainingSpecificationBuilder> for TrainingSpecificationBuilder

fn eq(&self, other: &TrainingSpecificationBuilder) -> 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 TrainingSpecificationBuilder