Struct aws_sdk_sagemaker::types::HyperParameterTuningInstanceConfig

#[non_exhaustive]
pub struct HyperParameterTuningInstanceConfig {
    pub instance_type: Option<TrainingInstanceType>,
    pub instance_count: Option<i32>,
    pub volume_size_in_gb: Option<i32>,
}

The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

instance_type: 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.

instance_count: 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.

volume_size_in_gb: Option<i32>

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

Implementations

impl HyperParameterTuningInstanceConfig

pub fn 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) -> 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) -> Option<i32>

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

impl HyperParameterTuningInstanceConfig

pub fn builder() -> HyperParameterTuningInstanceConfigBuilder

Creates a new builder-style object to manufacture HyperParameterTuningInstanceConfig.

Trait Implementations

impl Clone for HyperParameterTuningInstanceConfig

fn clone(&self) -> HyperParameterTuningInstanceConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for HyperParameterTuningInstanceConfig

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

Formats the value using the given formatter.

impl PartialEq for HyperParameterTuningInstanceConfig

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