ResourceConfig
Describes the resources, including ML compute instances and ML storage volumes, to use for model training.
Contents
- InstanceCount
-
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
Type: Integer
Valid Range: Minimum value of 1.
Required: Yes
- InstanceType
-
The ML compute instance type.
Type: String
Valid 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.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.18xlargeRequired: Yes
- VolumeKmsKeyId
-
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.
Note 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
VolumeKmsKeyIdwhen 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
VolumeKmsKeyIdcan 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"
Type: String
Length Constraints: Maximum length of 2048.
Pattern:
.*Required: No
-
- VolumeSizeInGB
-
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
Fileas theTrainingInputModein the algorithm specification.You must specify sufficient ML storage for your scenario.
Note Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note 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
VolumeSizeInGBgreater 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.
Type: Integer
Valid Range: Minimum value of 1.
Required: Yes
See Also
For more information about using this API in one of the language-specific AWS SDKs, see the following:
