Struct aws_sdk_sagemaker::model::training_specification::Builder

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

A builder for TrainingSpecification

Implementations

impl Builder

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 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 supported_hyper_parameters(
    self,
    input: impl Into<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 supported_training_instance_types(
    self,
    input: impl Into<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 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 metric_definitions(self, input: impl Into<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 training_channels(self, input: impl Into<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 supported_tuning_job_objective_metrics(
    self,
    input: impl Into<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 build(self) -> TrainingSpecification

Consumes the builder and constructs a TrainingSpecification

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