Struct ResourceConfig

#[non_exhaustive]
pub struct ResourceConfig {
    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 keep_alive_period_in_seconds: Option<i32>,
    pub instance_groups: Option<Vec<InstanceGroup>>,
    pub training_plan_arn: Option<String>,
}

Describes the resources, including machine learning (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.

SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.

Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.

• US East (N. Virginia) (us-east-1)

• US West (Oregon) (us-west-2)

To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.

instance_count: Option<i32>

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

volume_size_in_gb: Option<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.

When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d, ml.g4dn, and ml.g5.

When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2.

To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.

To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.

volume_kms_key_id: Option<String>

The Amazon Web Services KMS key that 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"

keep_alive_period_in_seconds: Option<i32>

The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.

instance_groups: Option<Vec<InstanceGroup>>

The configuration of a heterogeneous cluster in JSON format.

training_plan_arn: Option<String>

The Amazon Resource Name (ARN); of the training plan to use for this resource configuration.

Implementations

impl ResourceConfig

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

The ML compute instance type.

SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.

Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.

• US East (N. Virginia) (us-east-1)

• US West (Oregon) (us-west-2)

To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.

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

When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d, ml.g4dn, and ml.g5.

When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2.

To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.

To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.

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

The Amazon Web Services KMS key that 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"

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

The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.

pub fn instance_groups(&self) -> &[InstanceGroup]

The configuration of a heterogeneous cluster in JSON format.

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_groups.is_none().

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

The Amazon Resource Name (ARN); of the training plan to use for this resource configuration.

impl ResourceConfig

pub fn builder() -> ResourceConfigBuilder

Creates a new builder-style object to manufacture ResourceConfig.

Trait Implementations

impl Clone for ResourceConfig

fn clone(&self) -> ResourceConfig

Returns a duplicate 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 for ResourceConfig

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