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

Inherits:

Struct

• Object
• Struct
• Aws::SageMaker::Types::ResourceConfig

Includes:

Aws::Structure

Defined in:

lib/aws-sdk-sagemaker/types.rb

Overview

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

See Also:

• AWS API Documentation

Constant Summary

SENSITIVE =

[]

Instance Attribute Summary

• #instance_count ⇒ Integer

  The number of ML compute instances to use.

• #instance_groups ⇒ Array<Types::InstanceGroup>

  The configuration of a heterogeneous cluster in JSON format.

• #instance_placement_config ⇒ Types::InstancePlacementConfig

  Configuration for how training job instances are placed and allocated within UltraServers.

• #instance_type ⇒ String

  The ML compute instance type.

• #keep_alive_period_in_seconds ⇒ Integer

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

• #training_plan_arn ⇒ String

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

• #volume_kms_key_id ⇒ 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.

• #volume_size_in_gb ⇒ Integer

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

Instance Attribute Details

#instance_count ⇒ Integer

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

Returns:

• (Integer)

# File 'lib/aws-sdk-sagemaker/types.rb', line 44232

class ResourceConfig < Struct.new(
  :instance_type,
  :instance_count,
  :volume_size_in_gb,
  :volume_kms_key_id,
  :keep_alive_period_in_seconds,
  :instance_groups,
  :training_plan_arn,
  :instance_placement_config)
  SENSITIVE = []
  include Aws::Structure
end

#instance_groups ⇒ Array<Types::InstanceGroup>

The configuration of a heterogeneous cluster in JSON format.

Returns:

• (Array<Types::InstanceGroup>)

# File 'lib/aws-sdk-sagemaker/types.rb', line 44232

class ResourceConfig < Struct.new(
  :instance_type,
  :instance_count,
  :volume_size_in_gb,
  :volume_kms_key_id,
  :keep_alive_period_in_seconds,
  :instance_groups,
  :training_plan_arn,
  :instance_placement_config)
  SENSITIVE = []
  include Aws::Structure
end

#instance_placement_config ⇒ Types::InstancePlacementConfig

Configuration for how training job instances are placed and allocated within UltraServers. Only applicable for UltraServer capacity.

Returns:

• (Types::InstancePlacementConfig)

# File 'lib/aws-sdk-sagemaker/types.rb', line 44232

class ResourceConfig < Struct.new(
  :instance_type,
  :instance_count,
  :volume_size_in_gb,
  :volume_kms_key_id,
  :keep_alive_period_in_seconds,
  :instance_groups,
  :training_plan_arn,
  :instance_placement_config)
  SENSITIVE = []
  include Aws::Structure
end

#instance_type ⇒ String

The ML compute instance type.

Returns:

• (String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 44232

class ResourceConfig < Struct.new(
  :instance_type,
  :instance_count,
  :volume_size_in_gb,
  :volume_kms_key_id,
  :keep_alive_period_in_seconds,
  :instance_groups,
  :training_plan_arn,
  :instance_placement_config)
  SENSITIVE = []
  include Aws::Structure
end

#keep_alive_period_in_seconds ⇒ Integer

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

Returns:

• (Integer)

# File 'lib/aws-sdk-sagemaker/types.rb', line 44232

class ResourceConfig < Struct.new(
  :instance_type,
  :instance_count,
  :volume_size_in_gb,
  :volume_kms_key_id,
  :keep_alive_period_in_seconds,
  :instance_groups,
  :training_plan_arn,
  :instance_placement_config)
  SENSITIVE = []
  include Aws::Structure
end

#training_plan_arn ⇒ String

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

Returns:

• (String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 44232

class ResourceConfig < Struct.new(
  :instance_type,
  :instance_count,
  :volume_size_in_gb,
  :volume_kms_key_id,
  :keep_alive_period_in_seconds,
  :instance_groups,
  :training_plan_arn,
  :instance_placement_config)
  SENSITIVE = []
  include Aws::Structure
end

#volume_kms_key_id ⇒ 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.

<note markdown=“1”> 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].

</note>

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”`

[1]: docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes [2]: docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html

Returns:

• (String)

# File 'lib/aws-sdk-sagemaker/types.rb', line 44232

class ResourceConfig < Struct.new(
  :instance_type,
  :instance_count,
  :volume_size_in_gb,
  :volume_kms_key_id,
  :keep_alive_period_in_seconds,
  :instance_groups,
  :training_plan_arn,
  :instance_placement_config)
  SENSITIVE = []
  include Aws::Structure
end

#volume_size_in_gb ⇒ Integer

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].

[1]: docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html#nvme-ssd-volumes [2]: aws.amazon.com/ec2/instance-types/ [3]: docs.aws.amazon.com/sagemaker/latest/dg/model-train-storage.html

Returns:

• (Integer)

# File 'lib/aws-sdk-sagemaker/types.rb', line 44232

class ResourceConfig < Struct.new(
  :instance_type,
  :instance_count,
  :volume_size_in_gb,
  :volume_kms_key_id,
  :keep_alive_period_in_seconds,
  :instance_groups,
  :training_plan_arn,
  :instance_placement_config)
  SENSITIVE = []
  include Aws::Structure
end