Struct aws_sdk_sagemaker::types::builders::HyperParameterTuningInstanceConfigBuilder

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

A builder for HyperParameterTuningInstanceConfig.

Implementations

impl HyperParameterTuningInstanceConfigBuilder

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

The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.

This field is required.

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

The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.

pub fn get_instance_type(&self) -> &Option<TrainingInstanceType>

The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.

pub fn instance_count(self, input: i32) -> Self

The number of instances of the type specified by InstanceType. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.

This field is required.

pub fn set_instance_count(self, input: Option<i32>) -> Self

The number of instances of the type specified by InstanceType. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.

pub fn get_instance_count(&self) -> &Option<i32>

The number of instances of the type specified by InstanceType. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.

pub fn volume_size_in_gb(self, input: i32) -> Self

The volume size in GB of the data to be processed for hyperparameter optimization (optional).

This field is required.

pub fn set_volume_size_in_gb(self, input: Option<i32>) -> Self

The volume size in GB of the data to be processed for hyperparameter optimization (optional).

pub fn get_volume_size_in_gb(&self) -> &Option<i32>

The volume size in GB of the data to be processed for hyperparameter optimization (optional).

pub fn build(self) -> HyperParameterTuningInstanceConfig

Consumes the builder and constructs a HyperParameterTuningInstanceConfig.

Trait Implementations

impl Clone for HyperParameterTuningInstanceConfigBuilder

fn clone(&self) -> HyperParameterTuningInstanceConfigBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for HyperParameterTuningInstanceConfigBuilder

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

Formats the value using the given formatter.

impl Default for HyperParameterTuningInstanceConfigBuilder

fn default() -> HyperParameterTuningInstanceConfigBuilder

Returns the “default value” for a type.

impl PartialEq for HyperParameterTuningInstanceConfigBuilder

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