Struct HyperParameterTuningResourceConfig

#[non_exhaustive]
pub struct HyperParameterTuningResourceConfig {
    pub instance_type: Option<TrainingInstanceType>,
    pub instance_count: Option<i32>,
    pub volume_size_in_gb: Option<i32>,
    pub volume_kms_key_id: Option<String>,
    pub allocation_strategy: Option<HyperParameterTuningAllocationStrategy>,
    pub instance_configs: Option<Vec<HyperParameterTuningInstanceConfig>>,
}

The configuration of resources, including compute instances and storage volumes for use in training jobs launched by hyperparameter tuning jobs. HyperParameterTuningResourceConfig is similar to ResourceConfig, but has the additional InstanceConfigs and AllocationStrategy fields to allow for flexible instance management. Specify one or more instance types, count, and the allocation strategy for instance selection.

HyperParameterTuningResourceConfig supports the capabilities of ResourceConfig with the exception of KeepAlivePeriodInSeconds. Hyperparameter tuning jobs use warm pools by default, which reuse clusters between training jobs.

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 to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.

instance_count: Option<i32>

The number of compute instances of type InstanceType to use. For distributed training, select a value greater than 1.

volume_size_in_gb: Option<i32>

The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.

Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.

SageMaker supports only the General Purpose SSD (gp2) storage volume type.

volume_kms_key_id: Option<String>

A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.

KMS Key ID:

"1234abcd-12ab-34cd-56ef-1234567890ab"

Amazon Resource Name (ARN) of a KMS key:

"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId. For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.

allocation_strategy: Option<HyperParameterTuningAllocationStrategy>

The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.

instance_configs: Option<Vec<HyperParameterTuningInstanceConfig>>

A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.

If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs. Instead, use InstanceType, VolumeSizeInGB and InstanceCount. If you use InstanceConfigs, do not provide values for InstanceType, VolumeSizeInGB or InstanceCount.

Implementations

impl HyperParameterTuningResourceConfig

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

The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.

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

The number of compute instances of type InstanceType to use. For distributed training, select a value greater than 1.

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

The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.

Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.

SageMaker supports only the General Purpose SSD (gp2) storage volume type.

pub fn volume_kms_key_id(&self) -> Option<&str>

A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.

KMS Key ID:

"1234abcd-12ab-34cd-56ef-1234567890ab"

Amazon Resource Name (ARN) of a KMS key:

"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId. For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.

pub fn allocation_strategy( &self, ) -> Option<&HyperParameterTuningAllocationStrategy>

The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.

pub fn instance_configs(&self) -> &[HyperParameterTuningInstanceConfig]

A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.

If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs. Instead, use InstanceType, VolumeSizeInGB and InstanceCount. If you use InstanceConfigs, do not provide values for InstanceType, VolumeSizeInGB or InstanceCount.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .instance_configs.is_none().

impl HyperParameterTuningResourceConfig

pub fn builder() -> HyperParameterTuningResourceConfigBuilder

Creates a new builder-style object to manufacture HyperParameterTuningResourceConfig.

Trait Implementations

impl Clone for HyperParameterTuningResourceConfig

fn clone(&self) -> HyperParameterTuningResourceConfig

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for HyperParameterTuningResourceConfig

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

Formats the value using the given formatter.

impl PartialEq for HyperParameterTuningResourceConfig

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