CfnInferenceExperiment L1 construct
Creates an inference experiment using the configurations specified in the request.
Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests .
Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.
While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests .
Example
ts
// The code below shows an example of how to instantiate this type.
// The values are placeholders you should change.
import { aws_sagemaker as sagemaker } from 'aws-cdk-lib';
const cfnInferenceExperiment = new sagemaker.CfnInferenceExperiment(this, 'MyCfnInferenceExperiment', {
endpointName: 'endpointName',
modelVariants: [{
infrastructureConfig: {
infrastructureType: 'infrastructureType',
realTimeInferenceConfig: {
instanceCount: 123,
instanceType: 'instanceType',
},
},
modelName: 'modelName',
variantName: 'variantName',
}],
name: 'name',
roleArn: 'roleArn',
type: 'type',
// the properties below are optional
dataStorageConfig: {
destination: 'destination',
// the properties below are optional
contentType: {
csvContentTypes: ['csvContentTypes'],
jsonContentTypes: ['jsonContentTypes'],
},
kmsKey: 'kmsKey',
},
description: 'description',
desiredState: 'desiredState',
kmsKey: 'kmsKey',
schedule: {
endTime: 'endTime',
startTime: 'startTime',
},
shadowModeConfig: {
shadowModelVariants: [{
samplingPercentage: 123,
shadowModelVariantName: 'shadowModelVariantName',
}],
sourceModelVariantName: 'sourceModelVariantName',
},
statusReason: 'statusReason',
tags: [{
key: 'key',
value: 'value',
}],
});
python
from aws_cdk import CfnTag
# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker
cfn_inference_experiment = sagemaker.CfnInferenceExperiment(self, "MyCfnInferenceExperiment",
endpoint_name="endpointName",
model_variants=[sagemaker.CfnInferenceExperiment.ModelVariantConfigProperty(
infrastructure_config=sagemaker.CfnInferenceExperiment.ModelInfrastructureConfigProperty(
infrastructure_type="infrastructureType",
real_time_inference_config=sagemaker.CfnInferenceExperiment.RealTimeInferenceConfigProperty(
instance_count=123,
instance_type="instanceType"
)
),
model_name="modelName",
variant_name="variantName"
)],
name="name",
role_arn="roleArn",
type="type",
# the properties below are optional
data_storage_config=sagemaker.CfnInferenceExperiment.DataStorageConfigProperty(
destination="destination",
# the properties below are optional
content_type=sagemaker.CfnInferenceExperiment.CaptureContentTypeHeaderProperty(
csv_content_types=["csvContentTypes"],
json_content_types=["jsonContentTypes"]
),
kms_key="kmsKey"
),
description="description",
desired_state="desiredState",
kms_key="kmsKey",
schedule=sagemaker.CfnInferenceExperiment.InferenceExperimentScheduleProperty(
end_time="endTime",
start_time="startTime"
),
shadow_mode_config=sagemaker.CfnInferenceExperiment.ShadowModeConfigProperty(
shadow_model_variants=[sagemaker.CfnInferenceExperiment.ShadowModelVariantConfigProperty(
sampling_percentage=123,
shadow_model_variant_name="shadowModelVariantName"
)],
source_model_variant_name="sourceModelVariantName"
),
status_reason="statusReason",
tags=[CfnTag(
key="key",
value="value"
)]
)
java
// 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.*;
CfnInferenceExperiment cfnInferenceExperiment = CfnInferenceExperiment.Builder.create(this, "MyCfnInferenceExperiment")
.endpointName("endpointName")
.modelVariants(List.of(ModelVariantConfigProperty.builder()
.infrastructureConfig(ModelInfrastructureConfigProperty.builder()
.infrastructureType("infrastructureType")
.realTimeInferenceConfig(RealTimeInferenceConfigProperty.builder()
.instanceCount(123)
.instanceType("instanceType")
.build())
.build())
.modelName("modelName")
.variantName("variantName")
.build()))
.name("name")
.roleArn("roleArn")
.type("type")
// the properties below are optional
.dataStorageConfig(DataStorageConfigProperty.builder()
.destination("destination")
// the properties below are optional
.contentType(CaptureContentTypeHeaderProperty.builder()
.csvContentTypes(List.of("csvContentTypes"))
.jsonContentTypes(List.of("jsonContentTypes"))
.build())
.kmsKey("kmsKey")
.build())
.description("description")
.desiredState("desiredState")
.kmsKey("kmsKey")
.schedule(InferenceExperimentScheduleProperty.builder()
.endTime("endTime")
.startTime("startTime")
.build())
.shadowModeConfig(ShadowModeConfigProperty.builder()
.shadowModelVariants(List.of(ShadowModelVariantConfigProperty.builder()
.samplingPercentage(123)
.shadowModelVariantName("shadowModelVariantName")
.build()))
.sourceModelVariantName("sourceModelVariantName")
.build())
.statusReason("statusReason")
.tags(List.of(CfnTag.builder()
.key("key")
.value("value")
.build()))
.build();
csharp
// Install: dotnet add package Amazon.CDK.Lib
// The code below shows an example of how to instantiate this type.
// The values are placeholders you should change.
using Amazon.CDK.AWS.Sagemaker;
var cfnInferenceExperiment = new CfnInferenceExperiment(this, "MyCfnInferenceExperiment", new CfnInferenceExperimentProps {
EndpointName = "endpointName",
ModelVariants = new [] { new ModelVariantConfigProperty {
InfrastructureConfig = new ModelInfrastructureConfigProperty {
InfrastructureType = "infrastructureType",
RealTimeInferenceConfig = new RealTimeInferenceConfigProperty {
InstanceCount = 123,
InstanceType = "instanceType"
}
},
ModelName = "modelName",
VariantName = "variantName"
} },
Name = "name",
RoleArn = "roleArn",
Type = "type",
// the properties below are optional
DataStorageConfig = new DataStorageConfigProperty {
Destination = "destination",
// the properties below are optional
ContentType = new CaptureContentTypeHeaderProperty {
CsvContentTypes = new [] { "csvContentTypes" },
JsonContentTypes = new [] { "jsonContentTypes" }
},
KmsKey = "kmsKey"
},
Description = "description",
DesiredState = "desiredState",
KmsKey = "kmsKey",
Schedule = new InferenceExperimentScheduleProperty {
EndTime = "endTime",
StartTime = "startTime"
},
ShadowModeConfig = new ShadowModeConfigProperty {
ShadowModelVariants = new [] { new ShadowModelVariantConfigProperty {
SamplingPercentage = 123,
ShadowModelVariantName = "shadowModelVariantName"
} },
SourceModelVariantName = "sourceModelVariantName"
},
StatusReason = "statusReason",
Tags = new [] { new CfnTag {
Key = "key",
Value = "value"
} }
});
go
// Install: go get github.com/aws/aws-cdk-go/awscdk/v2
// The code below shows an example of how to instantiate this type.
// The values are placeholders you should change.
import "github.com/aws/aws-cdk-go/awscdk"
cfnInferenceExperiment := awscdk.Aws_sagemaker.NewCfnInferenceExperiment(this, jsii.String("MyCfnInferenceExperiment"), &CfnInferenceExperimentProps{
EndpointName: jsii.String("endpointName"),
ModelVariants: []interface{}{
&ModelVariantConfigProperty{
InfrastructureConfig: &ModelInfrastructureConfigProperty{
InfrastructureType: jsii.String("infrastructureType"),
RealTimeInferenceConfig: &RealTimeInferenceConfigProperty{
InstanceCount: jsii.Number(123),
InstanceType: jsii.String("instanceType"),
},
},
ModelName: jsii.String("modelName"),
VariantName: jsii.String("variantName"),
},
},
Name: jsii.String("name"),
RoleArn: jsii.String("roleArn"),
Type: jsii.String("type"),
// the properties below are optional
DataStorageConfig: &DataStorageConfigProperty{
Destination: jsii.String("destination"),
// the properties below are optional
ContentType: &CaptureContentTypeHeaderProperty{
CsvContentTypes: []*string{
jsii.String("csvContentTypes"),
},
JsonContentTypes: []*string{
jsii.String("jsonContentTypes"),
},
},
KmsKey: jsii.String("kmsKey"),
},
Description: jsii.String("description"),
DesiredState: jsii.String("desiredState"),
KmsKey: jsii.String("kmsKey"),
Schedule: &InferenceExperimentScheduleProperty{
EndTime: jsii.String("endTime"),
StartTime: jsii.String("startTime"),
},
ShadowModeConfig: &ShadowModeConfigProperty{
ShadowModelVariants: []interface{}{
&ShadowModelVariantConfigProperty{
SamplingPercentage: jsii.Number(123),
ShadowModelVariantName: jsii.String("shadowModelVariantName"),
},
},
SourceModelVariantName: jsii.String("sourceModelVariantName"),
},
StatusReason: jsii.String("statusReason"),
Tags: []CfnTag{
&CfnTag{
Key: jsii.String("key"),
Value: jsii.String("value"),
},
},
})
Initializer
ts
new CfnInferenceExperiment(scope: Construct, id: string, props: CfnInferenceExperimentProps)
python
CfnInferenceExperiment(scope: Construct, id: str, props: CfnInferenceExperimentProps)
java
new CfnInferenceExperiment(Construct scope, String id, CfnInferenceExperimentProps props)
csharp
new CfnInferenceExperiment(Construct scope, string id, CfnInferenceExperimentProps props)
go
NewCfnInferenceExperiment(Scope Construct, Id *string, Props CfnInferenceExperimentProps)
Construct Props
dataStorageConfigoptionalIResolvable | CfnInferenceExperiment.DataStorageConfigPropertydata_storage_configoptionaltyping.Union[IResolvable, DataStorageConfigProperty]dataStorageConfigoptionalIResolvableDataStorageConfigoptionalIResolvableDataStorageConfigoptionalinterface{}#The Amazon S3 location and configuration for storing inference request and response data.
descriptionoptionalstringdescriptionoptionalstrdescriptionoptionalStringDescriptionoptionalstringDescriptionoptional*string#The description of the inference experiment.
desiredStateoptionalstringdesired_stateoptionalstrdesiredStateoptionalStringDesiredStateoptionalstringDesiredStateoptional*string#The desired state of the experiment after stopping. The possible states are the following:.
Completed: The experiment completed successfullyCancelled: The experiment was canceled
The name of the endpoint.
kmsKeyoptionalstringkms_keyoptionalstrkmsKeyoptionalStringKmsKeyoptionalstringKmsKeyoptional*string#The AWS Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption.
modelVariantsIResolvable | IResolvable | CfnInferenceExperiment.ModelVariantConfigProperty[]model_variantstyping.Union[IResolvable, typing.List[typing.Union[IResolvable, ModelVariantConfigProperty]]]modelVariantsIResolvableModelVariantsIResolvableModelVariantsinterface{}required#An array of `ModelVariantConfigSummary` objects.
There is one for each variant in the inference experiment. Each ModelVariantConfigSummary object in the array describes the infrastructure configuration for deploying the corresponding variant.
The name of the inference experiment.
The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
scheduleoptionalIResolvable | CfnInferenceExperiment.InferenceExperimentSchedulePropertyscheduleoptionaltyping.Union[IResolvable, InferenceExperimentScheduleProperty]scheduleoptionalIResolvableScheduleoptionalIResolvableScheduleoptionalinterface{}#The duration for which the inference experiment ran or will run.
The maximum duration that you can set for an inference experiment is 30 days.
shadowModeConfigoptionalIResolvable | CfnInferenceExperiment.ShadowModeConfigPropertyshadow_mode_configoptionaltyping.Union[IResolvable, ShadowModeConfigProperty]shadowModeConfigoptionalIResolvableShadowModeConfigoptionalIResolvableShadowModeConfigoptionalinterface{}#The configuration of `ShadowMode` inference experiment type, which shows the production variant that takes all the inference requests, and the shadow variant to which Amazon SageMaker replicates a percentage of the inference requests.
For the shadow variant it also shows the percentage of requests that Amazon SageMaker replicates.
statusReasonoptionalstringstatus_reasonoptionalstrstatusReasonoptionalStringStatusReasonoptionalstringStatusReasonoptional*string#The error message for the inference experiment status result.
tagsoptionalCfnTag[]tagsoptionaltyping.List[CfnTag]tagsoptionalList<CfnTag>TagsoptionalCfnTag[]Tagsoptional*[]CfnTag#An array of key-value pairs to apply to this resource.
For more information, see Tag .
The type of the inference experiment.
Properties
The ARN for your inference experiment.
attrCreationTimestringattr_creation_timestrattrCreationTimeStringAttrCreationTimestringAttrCreationTime*string#The timestamp at which the inference experiment was created.
attrEndpointMetadataIResolvableattr_endpoint_metadataIResolvableattrEndpointMetadataIResolvableAttrEndpointMetadataIResolvableAttrEndpointMetadataIResolvable#The metadata of the endpoint on which the inference experiment ran.
attrEndpointMetadataEndpointConfigNamestringattr_endpoint_metadata_endpoint_config_namestrattrEndpointMetadataEndpointConfigNameStringAttrEndpointMetadataEndpointConfigNamestringAttrEndpointMetadataEndpointConfigName*string#attrEndpointMetadataEndpointNamestringattr_endpoint_metadata_endpoint_namestrattrEndpointMetadataEndpointNameStringAttrEndpointMetadataEndpointNamestringAttrEndpointMetadataEndpointName*string#attrEndpointMetadataEndpointStatusstringattr_endpoint_metadata_endpoint_statusstrattrEndpointMetadataEndpointStatusStringAttrEndpointMetadataEndpointStatusstringAttrEndpointMetadataEndpointStatus*string#attrLastModifiedTimestringattr_last_modified_timestrattrLastModifiedTimeStringAttrLastModifiedTimestringAttrLastModifiedTime*string#The timestamp at which you last modified the inference experiment.
The status of the inference experiment. The following are the possible statuses for an inference experiment:.
notes
Creating- Amazon SageMaker is creating your experiment.Created- Amazon SageMaker has finished the creation of your experiment and will begin the experiment at the scheduled time.Updating- When you make changes to your experiment, your experiment shows as updating.Starting- Amazon SageMaker is beginning your experiment.Running- Your experiment is in progress.Stopping- Amazon SageMaker is stopping your experiment.Completed- Your experiment has completed.Cancelled- When you conclude your experiment early using the StopInferenceExperiment API, or if any operation fails with an unexpected error, it shows as cancelled.
CFN_RESOURCE_TYPE_NAMEstringcfn_resource_type_namestrCFN_RESOURCE_TYPE_NAMEStringCFN_RESOURCE_TYPE_NAMEstringCFN_RESOURCE_TYPE_NAME*stringstatic#The CloudFormation resource type name for this resource class.
cfnOptionsICfnResourceOptionscfn_optionsICfnResourceOptionscfnOptionsICfnResourceOptionsCfnOptionsICfnResourceOptionsCfnOptionsICfnResourceOptions#Options for this resource, such as condition, update policy etc.
cfnResourceTypestringcfn_resource_typestrcfnResourceTypeStringCfnResourceTypestringCfnResourceType*string#AWS resource type.
creationStackstring[]creation_stacktyping.List[str]creationStackList<String>CreationStackstring[]CreationStack*[]*string#dataStorageConfigmay be undefinedIResolvable | CfnInferenceExperiment.DataStorageConfigPropertydata_storage_configmay be undefinedtyping.Union[IResolvable, DataStorageConfigProperty]dataStorageConfigmay be undefinedIResolvableDataStorageConfigmay be undefinedIResolvableDataStorageConfigmay be undefinedinterface{}#The Amazon S3 location and configuration for storing inference request and response data.
descriptionmay be undefinedstringdescriptionmay be undefinedstrdescriptionmay be undefinedStringDescriptionmay be undefinedstringDescriptionmay be undefined*string#The description of the inference experiment.
desiredStatemay be undefinedstringdesired_statemay be undefinedstrdesiredStatemay be undefinedStringDesiredStatemay be undefinedstringDesiredStatemay be undefined*string#The desired state of the experiment after stopping.
The possible states are the following:.
The name of the endpoint.
inferenceExperimentRefInferenceExperimentReferenceinference_experiment_refInferenceExperimentReferenceinferenceExperimentRefInferenceExperimentReferenceInferenceExperimentRefInferenceExperimentReferenceInferenceExperimentRefInferenceExperimentReference#A reference to a InferenceExperiment resource.
kmsKeymay be undefinedstringkms_keymay be undefinedstrkmsKeymay be undefinedStringKmsKeymay be undefinedstringKmsKeymay be undefined*string#The AWS Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption.
The logical ID for this CloudFormation stack element.
The logical ID of the element is calculated from the path of the resource node in the construct tree.
To override this value, use overrideLogicalId(newLogicalId).
modelVariantsIResolvable | IResolvable | CfnInferenceExperiment.ModelVariantConfigProperty[]model_variantstyping.Union[IResolvable, typing.List[typing.Union[IResolvable, ModelVariantConfigProperty]]]modelVariantsIResolvableModelVariantsIResolvableModelVariantsinterface{}#An array of `ModelVariantConfigSummary` objects.
The name of the inference experiment.
Return a string that will be resolved to a CloudFormation `{ Ref }` for this element.
If, by any chance, the intrinsic reference of a resource is not a string, you could coerce it to an IResolvable through Lazy.any({ produce: resource.ref }).
The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
schedulemay be undefinedIResolvable | CfnInferenceExperiment.InferenceExperimentSchedulePropertyschedulemay be undefinedtyping.Union[IResolvable, InferenceExperimentScheduleProperty]schedulemay be undefinedIResolvableSchedulemay be undefinedIResolvableSchedulemay be undefinedinterface{}#The duration for which the inference experiment ran or will run.
shadowModeConfigmay be undefinedIResolvable | CfnInferenceExperiment.ShadowModeConfigPropertyshadow_mode_configmay be undefinedtyping.Union[IResolvable, ShadowModeConfigProperty]shadowModeConfigmay be undefinedIResolvableShadowModeConfigmay be undefinedIResolvableShadowModeConfigmay be undefinedinterface{}#The configuration of `ShadowMode` inference experiment type, which shows the production variant that takes all the inference requests, and the shadow variant to which Amazon SageMaker replicates a percentage of the inference requests.
The stack in which this element is defined.
CfnElements must be defined within a stack scope (directly or indirectly).
statusReasonmay be undefinedstringstatus_reasonmay be undefinedstrstatusReasonmay be undefinedStringStatusReasonmay be undefinedstringStatusReasonmay be undefined*string#The error message for the inference experiment status result.
Tag Manager which manages the tags for this resource.
tagsRawmay be undefinedCfnTag[]tags_rawmay be undefinedtyping.List[CfnTag]tagsRawmay be undefinedList<CfnTag>TagsRawmay be undefinedCfnTag[]TagsRawmay be undefined*[]CfnTag#An array of key-value pairs to apply to this resource.
The type of the inference experiment.
Methods
addDeletionOverride(path: string): voiddef add_deletion_override(self, path: str) -> Nonepublic void addDeletionOverride(String path)public void AddDeletionOverride(string path)func AddDeletionOverride(Path *string)#Syntactic sugar for `addOverride(path, undefined)`.
1 parameter
Parameters
pathpathpathpathPath— The path of the value to delete.
addDependency(target: CfnResource): voiddef add_dependency(self, target: CfnResource) -> Nonepublic void addDependency(CfnResource target)public void AddDependency(CfnResource target)func AddDependency(Target CfnResource)deprecated#Use `addResourceDependency` instead.
This method has been renamed to addResourceDependency to more clearly set it apart from construct.node.addDependency. See the documentation of that function for more details.
addDependsOn(target: CfnResource): voiddef add_depends_on(self, target: CfnResource) -> Nonepublic void addDependsOn(CfnResource target)public void AddDependsOn(CfnResource target)func AddDependsOn(Target CfnResource)deprecated#Use `addResourceDependency` instead.
notes
This can be used for resources across stacks (or nested stack) boundaries and the dependency will automatically be transferred to the relevant scope.
This method has been renamed to addResourceDependency, which makes it more clear that this method operates at a different level from the construct-level construct.node.addDependency() mechanism.
addMetadata(key: string, value: any): voiddef add_metadata(self, key: str, value: Any) -> Nonepublic void addMetadata(String key, Object value)public void AddMetadata(string key, object value)func AddMetadata(Key *string, Value interface{})#Add a value to the CloudFormation Resource Metadata.
addOverride(path: string, value: any): voiddef add_override(self, path: str, value: Any) -> Nonepublic void addOverride(String path, Object value)public void AddOverride(string path, object value)func AddOverride(Path *string, Value interface{})#Adds an override to the synthesized CloudFormation resource.
notes · 2 parameters
To add a property override, either use addPropertyOverride or prefix path with "Properties." (i.e. Properties.TopicName).
If the override is nested, separate each nested level using a dot (.) in the path parameter. If there is an array as part of the nesting, specify the index in the path.
To include a literal . in the property name, prefix with a \. In most programming languages you will need to write this as "\\." because the \ itself will need to be escaped.
For example,
typescript
cfnResource.addOverride('Properties.GlobalSecondaryIndexes.0.Projection.NonKeyAttributes', ['myattribute']);
cfnResource.addOverride('Properties.GlobalSecondaryIndexes.1.ProjectionType', 'INCLUDE');
would add the overrides
json
"Properties": {
"GlobalSecondaryIndexes": [
{
"Projection": {
"NonKeyAttributes": [ "myattribute" ]
...
}
...
},
{
"ProjectionType": "INCLUDE"
...
},
]
...
}
The value argument to addOverride will not be processed or translated in any way. Pass raw JSON values in here with the correct capitalization for CloudFormation. If you pass CDK classes or structs, they will be rendered with lowercased key names, and CloudFormation will reject the template.
Parameters
pathpathpathpathPath— - The path of the property, you can use dot notation to override values in complex types.valuevaluevaluevalueValue— - The value.
addPropertyDeletionOverride(propertyPath: string): voiddef add_property_deletion_override(self, property_path: str) -> Nonepublic void addPropertyDeletionOverride(String propertyPath)public void AddPropertyDeletionOverride(string propertyPath)func AddPropertyDeletionOverride(PropertyPath *string)#Adds an override that deletes the value of a property from the resource definition.
1 parameter
Parameters
propertyPathproperty_pathpropertyPathpropertyPathPropertyPath— The path to the property.
addPropertyOverride(propertyPath: string, value: any): voiddef add_property_override(self, property_path: str, value: Any) -> Nonepublic void addPropertyOverride(String propertyPath, Object value)public void AddPropertyOverride(string propertyPath, object value)func AddPropertyOverride(PropertyPath *string, Value interface{})#Adds an override to a resource property.
Syntactic sugar for addOverride("Properties.<...>", value).
2 parameters
Parameters
propertyPathproperty_pathpropertyPathpropertyPathPropertyPath— The path of the property.valuevaluevaluevalueValue— The value.
addResourceDependency(target: CfnResource, reason?: string): voiddef add_resource_dependency(self, target: CfnResource, reason: typing.Optional[str] = None) -> Nonepublic void addResourceDependency(CfnResource target, String reason)public void AddResourceDependency(CfnResource target, string? reason = null)func AddResourceDependency(Target CfnResource, Reason *string)#Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.
notes
This can be used for resources across stacks (or nested stack) boundaries and the dependency will automatically be transferred to the relevant scope.
This method only adds dependencies between L1 resources. If you are looking for a generic construct-to-construct dependency mechanism that works for all constructs including L2s, use construct.node.addDependency instead.
applyCrossStackReferenceStrength(strength: ReferenceStrength): voiddef apply_cross_stack_reference_strength(self, strength: ReferenceStrength) -> Nonepublic void applyCrossStackReferenceStrength(ReferenceStrength strength)public void ApplyCrossStackReferenceStrength(ReferenceStrength strength)func ApplyCrossStackReferenceStrength(Strength ReferenceStrength)#Sets the cross-stack reference strength for this resource.
When set, any cross-stack reference to this resource will use the specified strength instead of the global default from the consuming stack's context.
1 parameter
Parameters
strengthstrengthstrengthstrengthStrength— - The reference strength to use for this resource.
applyRemovalPolicy(policy?: RemovalPolicy, options?: RemovalPolicyOptions): voiddef apply_removal_policy(self, policy: typing.Optional[RemovalPolicy] = None, options: typing.Optional[RemovalPolicyOptions] = None) -> Nonepublic void applyRemovalPolicy(RemovalPolicy policy, RemovalPolicyOptions options)public void ApplyRemovalPolicy(RemovalPolicy? policy = null, RemovalPolicyOptions? options = null)func ApplyRemovalPolicy(Policy RemovalPolicy, Options RemovalPolicyOptions)#Sets the deletion policy of the resource based on the removal policy specified.
notes
The Removal Policy controls what happens to this resource when it stops being managed by CloudFormation, either because you've removed it from the CDK application or because you've made a change that requires the resource to be replaced.
The resource can be deleted (RemovalPolicy.DESTROY), or left in your AWS account for data recovery and cleanup later (RemovalPolicy.RETAIN). In some cases, a snapshot can be taken of the resource prior to deletion (RemovalPolicy.SNAPSHOT). A list of resources that support this policy can be found in the following link:
arnForInferenceExperiment(resource: IInferenceExperimentRef): string@staticmethod def arn_for_inference_experiment(resource: IInferenceExperimentRef) -> strpublic static String arnForInferenceExperiment(IInferenceExperimentRef resource)public static string ArnForInferenceExperiment(IInferenceExperimentRef resource)func ArnForInferenceExperiment(Resource IInferenceExperimentRef) *stringstatic#cfnPropertyName(cdkPropertyName: string): stringdef cfn_property_name(self, cdk_property_name: str) -> strpublic String cfnPropertyName(String cdkPropertyName)public string CfnPropertyName(string cdkPropertyName)func CfnPropertyName(CdkPropertyName *string) *string#fromInferenceExperimentArn(scope: Construct, id: string, arn: string): IInferenceExperimentRef@staticmethod def from_inference_experiment_arn(scope: Construct, id: str, arn: str) -> IInferenceExperimentRefpublic static IInferenceExperimentRef fromInferenceExperimentArn(Construct scope, String id, String arn)public static IInferenceExperimentRef FromInferenceExperimentArn(Construct scope, string id, string arn)func FromInferenceExperimentArn(Scope Construct, Id *string, Arn *string) IInferenceExperimentRefstatic#Creates a new IInferenceExperimentRef from an ARN.
fromInferenceExperimentName(scope: Construct, id: string, inferenceExperimentName: string): IInferenceExperimentRef@staticmethod def from_inference_experiment_name(scope: Construct, id: str, inference_experiment_name: str) -> IInferenceExperimentRefpublic static IInferenceExperimentRef fromInferenceExperimentName(Construct scope, String id, String inferenceExperimentName)public static IInferenceExperimentRef FromInferenceExperimentName(Construct scope, string id, string inferenceExperimentName)func FromInferenceExperimentName(Scope Construct, Id *string, InferenceExperimentName *string) IInferenceExperimentRefstatic#Creates a new IInferenceExperimentRef from a inferenceExperimentName.
getAtt(attributeName: string, typeHint?: ResolutionTypeHint): Referencedef get_att(self, attribute_name: str, type_hint: typing.Optional[ResolutionTypeHint] = None) -> Referencepublic Reference getAtt(String attributeName, ResolutionTypeHint typeHint)public Reference GetAtt(string attributeName, ResolutionTypeHint? typeHint = null)func GetAtt(AttributeName *string, TypeHint ResolutionTypeHint) Reference#Returns a token for an runtime attribute of this resource.
Ideally, use generated attribute accessors (e.g. resource.arn), but this can be used for future compatibility in case there is no generated attribute.
1 parameter
Parameters
attributeNameattribute_nameattributeNameattributeNameAttributeName— The name of the attribute.
getMetadata(key: string): anydef get_metadata(self, key: str) -> Anypublic Object getMetadata(String key)public object GetMetadata(string key)func GetMetadata(Key *string) interface{}#Retrieve a value value from the CloudFormation Resource Metadata.
inspect(inspector: TreeInspector): voiddef inspect(self, inspector: TreeInspector) -> Nonepublic void inspect(TreeInspector inspector)public void Inspect(TreeInspector inspector)func Inspect(Inspector TreeInspector)#Examines the CloudFormation resource and discloses attributes.
1 parameter
Parameters
inspectorinspectorinspectorinspectorInspector— tree inspector to collect and process attributes.
isCfnElement(x: any): boolean@staticmethod def is_cfn_element(x: Any) -> boolpublic static Boolean isCfnElement(Object x)public static bool IsCfnElement(object x)func IsCfnElement(X interface{}) *boolstatic#Returns `true` if a construct is a stack element (i.e. part of the synthesized cloudformation template).
Uses duck-typing instead of instanceof to allow stack elements from different versions of this library to be included in the same stack.
returns
Returns: The construct as a stack element or undefined if it is not a stack element.
isCfnInferenceExperiment(x: any): boolean@staticmethod def is_cfn_inference_experiment(x: Any) -> boolpublic static Boolean isCfnInferenceExperiment(Object x)public static bool IsCfnInferenceExperiment(object x)func IsCfnInferenceExperiment(X interface{}) *boolstatic#Checks whether the given object is a CfnInferenceExperiment.
isCfnResource(x: any): boolean@staticmethod def is_cfn_resource(x: Any) -> boolpublic static Boolean isCfnResource(Object x)public static bool IsCfnResource(object x)func IsCfnResource(X interface{}) *boolstatic#Check whether the given object is a CfnResource.
isConstruct(x: any): boolean@staticmethod def is_construct(x: Any) -> boolpublic static Boolean isConstruct(Object x)public static bool IsConstruct(object x)func IsConstruct(X interface{}) *boolstatic#Checks if `x` is a construct.
notes · 1 parameter · returns
Use this method instead of instanceof to properly detect Construct instances, even when the construct library is symlinked.
Explanation: in JavaScript, multiple copies of the constructs library on disk are seen as independent, completely different libraries. As a consequence, the class Construct in each copy of the constructs library is seen as a different class, and an instance of one class will not test as instanceof the other class. npm install will not create installations like this, but users may manually symlink construct libraries together or use a monorepo tool: in those cases, multiple copies of the constructs library can be accidentally installed, and instanceof will behave unpredictably. It is safest to avoid using instanceof, and using this type-testing method instead.
Parameters
xxxxX— Any object.
Returns: true if x is an object created from a class which extends Construct.
obtainDependencies(): Stack | CfnResource[]def obtain_dependencies(self, ) -> typing.List[typing.Union[Stack, CfnResource]]public List<Stack> obtainDependencies()public Stack[] ObtainDependencies()func ObtainDependencies() *[]interface{}#Retrieves an array of resources and stacks this resource depends on.
For resources depended on directly, returns the CfnResource object. For dependencies on other stacks, returns the Stack object. The order of the array is not guaranteed.
overrideLogicalId(newLogicalId: string): voiddef override_logical_id(self, new_logical_id: str) -> Nonepublic void overrideLogicalId(String newLogicalId)public void OverrideLogicalId(string newLogicalId)func OverrideLogicalId(NewLogicalId *string)#Overrides the auto-generated logical ID with a specific ID.
1 parameter
Parameters
newLogicalIdnew_logical_idnewLogicalIdnewLogicalIdNewLogicalId— The new logical ID to use for this stack element.
removeDependency(target: CfnResource): voiddef remove_dependency(self, target: CfnResource) -> Nonepublic void removeDependency(CfnResource target)public void RemoveDependency(CfnResource target)func RemoveDependency(Target CfnResource)deprecated#Use `removeResourceDependency` instead
This can be used for resources across stacks (including nested stacks) and the dependency will automatically be removed from the relevant scope.
removeResourceDependency(target: CfnResource): voiddef remove_resource_dependency(self, target: CfnResource) -> Nonepublic void removeResourceDependency(CfnResource target)public void RemoveResourceDependency(CfnResource target)func RemoveResourceDependency(Target CfnResource)#Indicates that this resource no longer depends on another resource.
This can be used for resources across stacks (including nested stacks) and the dependency will automatically be removed from the relevant scope.
replaceDependency(target: CfnResource, newTarget: CfnResource): voiddef replace_dependency(self, target: CfnResource, new_target: CfnResource) -> Nonepublic void replaceDependency(CfnResource target, CfnResource newTarget)public void ReplaceDependency(CfnResource target, CfnResource newTarget)func ReplaceDependency(Target CfnResource, NewTarget CfnResource)#Replaces one dependency with another.
2 parameters
Parameters
targettargettargettargetTarget— The dependency to replace.newTargetnew_targetnewTargetnewTargetNewTarget— The new dependency to add.
toString(): stringdef to_string(self, ) -> strpublic String toString()public string ToString()func ToString() *string#Returns a string representation of this construct.
returns
Returns: a string representation of this resource
with(...mixins: IMixin[]): IConstructdef with(self, mixins: IMixin) -> IConstructpublic IConstruct with(IMixin mixins)public IConstruct With(IMixin mixins)func With(Mixins IMixin) IConstruct#Applies one or more mixins to this construct.
Mixins are applied in order. The list of constructs is captured at the start of the call, so constructs added by a mixin will not be visited. Use multiple with() calls if subsequent mixins should apply to added constructs.