MonitoringClusterConfig
Configuration for the cluster used to run model monitoring jobs.
Contents
- InstanceCount
-
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
Type: Integer
Valid Range: Minimum value of 1. Maximum value of 100.
Required: Yes
- InstanceType
-
The ML compute instance type for the processing job.
Type: String
Valid Values:
ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | 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.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge
Required: Yes
- VolumeSizeInGB
-
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
Type: Integer
Valid Range: Minimum value of 1. Maximum value of 16384.
Required: Yes
- VolumeKmsKeyId
-
The Amazon Key Management Service (Amazon KMS) key that Amazon SageMaker AI uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
Type: String
Length Constraints: Maximum length of 2048.
Pattern:
^[a-zA-Z0-9:/_-]*$
Required: No
See Also
For more information about using this API in one of the language-specific Amazon SDKs, see the following: