You are viewing documentation for version 2 of the AWS SDK for Ruby. Version 3 documentation can be found here.

Class: Aws::SageMaker::Types::ResourceConfig

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing ResourceConfig as input to an Aws::Client method, you can use a vanilla Hash:

{
  instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge
  instance_count: 1, # required
  volume_size_in_gb: 1, # required
  volume_kms_key_id: "KmsKeyId",
}

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

Returned by:

Instance Attribute Summary collapse

Instance Attribute Details

#instance_countInteger

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

Returns:

  • (Integer)

    The number of ML compute instances to use.

#instance_typeString

The ML compute instance type.

Possible values:

  • ml.m4.xlarge
  • ml.m4.2xlarge
  • ml.m4.4xlarge
  • ml.m4.10xlarge
  • ml.m4.16xlarge
  • ml.m5.large
  • ml.m5.xlarge
  • ml.m5.2xlarge
  • ml.m5.4xlarge
  • ml.m5.12xlarge
  • ml.m5.24xlarge
  • ml.c4.xlarge
  • ml.c4.2xlarge
  • ml.c4.4xlarge
  • ml.c4.8xlarge
  • ml.p2.xlarge
  • ml.p2.8xlarge
  • ml.p2.16xlarge
  • ml.p3.2xlarge
  • ml.p3.8xlarge
  • ml.p3.16xlarge
  • ml.p3dn.24xlarge
  • ml.c5.xlarge
  • ml.c5.2xlarge
  • ml.c5.4xlarge
  • ml.c5.9xlarge
  • ml.c5.18xlarge

Returns:

  • (String)

    The ML compute instance type.

#volume_kms_key_idString

The AWS 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"

Returns:

  • (String)

    The AWS 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.

#volume_size_in_gbInteger

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.

Returns:

  • (Integer)

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