Interface CfnModelBiasJobDefinition.ClusterConfigProperty
- All Superinterfaces:
software.amazon.jsii.JsiiSerializable
- All Known Implementing Classes:
CfnModelBiasJobDefinition.ClusterConfigProperty.Jsii$Proxy
- Enclosing class:
CfnModelBiasJobDefinition
@Stability(Stable)
public static interface CfnModelBiasJobDefinition.ClusterConfigProperty
extends software.amazon.jsii.JsiiSerializable
The configuration for the cluster resources used to run the processing job.
Example:
// The code below shows an example of how to instantiate this type. // The values are placeholders you should change. import software.amazon.awscdk.services.sagemaker.*; ClusterConfigProperty clusterConfigProperty = ClusterConfigProperty.builder() .instanceCount(123) .instanceType("instanceType") .volumeSizeInGb(123) // the properties below are optional .volumeKmsKeyId("volumeKmsKeyId") .build();
- See Also:
-
Nested Class Summary
Modifier and TypeInterfaceDescriptionstatic final class
A builder forCfnModelBiasJobDefinition.ClusterConfigProperty
static final class
An implementation forCfnModelBiasJobDefinition.ClusterConfigProperty
-
Method Summary
Modifier and TypeMethodDescriptionbuilder()
The number of ML compute instances to use in the model monitoring job.The ML compute instance type for the processing job.default String
The AWS Key Management Service ( AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.The size of the ML storage volume, in gigabytes, that you want to provision.Methods inherited from interface software.amazon.jsii.JsiiSerializable
$jsii$toJson
-
Method Details
-
getInstanceCount
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.
- See Also:
-
getInstanceType
The ML compute instance type for the processing job.- See Also:
-
getVolumeSizeInGb
The size of the ML storage volume, in gigabytes, that you want to provision.You must specify sufficient ML storage for your scenario.
- See Also:
-
getVolumeKmsKeyId
The AWS Key Management Service ( AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.- See Also:
-
builder
-