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Class: Aws::SageMaker::Types::ResourceConfig
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::ResourceConfig
- Defined in:
- (unknown)
Overview
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.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.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.p4d.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.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_count ⇒ Integer
The number of ML compute instances to use.
-
#instance_type ⇒ String
The ML compute instance type.
-
#volume_kms_key_id ⇒ 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_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.
#instance_type ⇒ String
The ML compute instance type.
Possible values:
- ml.m4.xlarge
- ml.m4.2xlarge
- ml.m4.4xlarge
- ml.m4.10xlarge
- ml.m4.16xlarge
- ml.g4dn.xlarge
- ml.g4dn.2xlarge
- ml.g4dn.4xlarge
- ml.g4dn.8xlarge
- ml.g4dn.12xlarge
- ml.g4dn.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.p4d.24xlarge
- ml.c5.xlarge
- ml.c5.2xlarge
- ml.c5.4xlarge
- ml.c5.9xlarge
- ml.c5.18xlarge
- ml.c5n.xlarge
- ml.c5n.2xlarge
- ml.c5n.4xlarge
- ml.c5n.9xlarge
- ml.c5n.18xlarge
#volume_kms_key_id ⇒ 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.
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"
#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.
You must specify sufficient ML storage for your scenario.
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.