Struct aws_sdk_sagemaker::types::builders::AutoMlComputeConfigBuilder

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

A builder for AutoMlComputeConfig.

Implementations

impl AutoMlComputeConfigBuilder

pub fn emr_serverless_compute_config( self, input: EmrServerlessComputeConfig, ) -> Self

The configuration for using EMR Serverless to run the AutoML job V2.

To allow your AutoML job V2 to automatically initiate a remote job on EMR Serverless when additional compute resources are needed to process large datasets, you need to provide an EmrServerlessComputeConfig object, which includes an ExecutionRoleARN attribute, to the AutoMLComputeConfig of the AutoML job V2 input request.

By seamlessly transitioning to EMR Serverless when required, the AutoML job can handle datasets that would otherwise exceed the initially provisioned resources, without any manual intervention from you.

EMR Serverless is available for the tabular and time series problem types. We recommend setting up this option for tabular datasets larger than 5 GB and time series datasets larger than 30 GB.

pub fn set_emr_serverless_compute_config( self, input: Option<EmrServerlessComputeConfig>, ) -> Self

The configuration for using EMR Serverless to run the AutoML job V2.

To allow your AutoML job V2 to automatically initiate a remote job on EMR Serverless when additional compute resources are needed to process large datasets, you need to provide an EmrServerlessComputeConfig object, which includes an ExecutionRoleARN attribute, to the AutoMLComputeConfig of the AutoML job V2 input request.

By seamlessly transitioning to EMR Serverless when required, the AutoML job can handle datasets that would otherwise exceed the initially provisioned resources, without any manual intervention from you.

EMR Serverless is available for the tabular and time series problem types. We recommend setting up this option for tabular datasets larger than 5 GB and time series datasets larger than 30 GB.

pub fn get_emr_serverless_compute_config( &self, ) -> &Option<EmrServerlessComputeConfig>

The configuration for using EMR Serverless to run the AutoML job V2.

To allow your AutoML job V2 to automatically initiate a remote job on EMR Serverless when additional compute resources are needed to process large datasets, you need to provide an EmrServerlessComputeConfig object, which includes an ExecutionRoleARN attribute, to the AutoMLComputeConfig of the AutoML job V2 input request.

By seamlessly transitioning to EMR Serverless when required, the AutoML job can handle datasets that would otherwise exceed the initially provisioned resources, without any manual intervention from you.

EMR Serverless is available for the tabular and time series problem types. We recommend setting up this option for tabular datasets larger than 5 GB and time series datasets larger than 30 GB.

pub fn build(self) -> AutoMlComputeConfig

Consumes the builder and constructs a AutoMlComputeConfig.

Trait Implementations

impl Clone for AutoMlComputeConfigBuilder

fn clone(&self) -> AutoMlComputeConfigBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AutoMlComputeConfigBuilder

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

Formats the value using the given formatter.

impl Default for AutoMlComputeConfigBuilder

fn default() -> AutoMlComputeConfigBuilder

Returns the “default value” for a type.

impl PartialEq for AutoMlComputeConfigBuilder

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