Struct aws_sdk_sagemaker::model::ResourceConfig

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

Describes the resources, including ML compute instances and ML storage volumes, to use for model training.

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 ML compute instance type.

instance_count: i32

The number of ML compute instances to use. For distributed training, provide a value greater than 1.

volume_size_in_gb: i32

The size of the ML storage volume that you want to provision.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

You must specify sufficient ML storage for your scenario.

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

Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.

For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.

volume_kms_key_id: Option<String>

The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

For a list of instance types that support local instance storage, see Instance Store Volumes.

For more information about local instance storage encryption, see SSD Instance Store Volumes.

The VolumeKmsKeyId can be in any of the following formats:

• // 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"

Implementations

impl ResourceConfig

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

The ML compute instance type.

pub fn instance_count(&self) -> i32

The number of ML compute instances to use. For distributed training, provide a value greater than 1.

pub fn volume_size_in_gb(&self) -> i32

The size of the ML storage volume that you want to provision.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

You must specify sufficient ML storage for your scenario.

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

Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.

For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.

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

The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

For a list of instance types that support local instance storage, see Instance Store Volumes.

For more information about local instance storage encryption, see SSD Instance Store Volumes.

The VolumeKmsKeyId can be in any of the following formats:

• // 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"

impl ResourceConfig

pub fn builder() -> Builder

Creates a new builder-style object to manufacture ResourceConfig

Trait Implementations

impl Clone for ResourceConfig

fn clone(&self) -> ResourceConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for ResourceConfig

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

Formats the value using the given formatter.

impl PartialEq<ResourceConfig> for ResourceConfig

fn eq(&self, other: &ResourceConfig) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &ResourceConfig) -> bool

This method tests for !=.

impl StructuralPartialEq for ResourceConfig