CfnModelExplainabilityJobDefinitionMixinProps
- class aws_cdk.mixins_preview.aws_sagemaker.mixins.CfnModelExplainabilityJobDefinitionMixinProps(*, endpoint_name=None, job_definition_name=None, job_resources=None, model_explainability_app_specification=None, model_explainability_baseline_config=None, model_explainability_job_input=None, model_explainability_job_output_config=None, network_config=None, role_arn=None, stopping_condition=None, tags=None)
Bases:
objectProperties for CfnModelExplainabilityJobDefinitionPropsMixin.
- Parameters:
endpoint_name (
Optional[str]) – The name of the endpoint used to run the monitoring job.job_definition_name (
Optional[str]) – The name of the model explainability job definition. The name must be unique within an AWS Region in the AWS account.job_resources (
Union[IResolvable,MonitoringResourcesProperty,Dict[str,Any],None]) – Identifies the resources to deploy for a monitoring job.model_explainability_app_specification (
Union[IResolvable,ModelExplainabilityAppSpecificationProperty,Dict[str,Any],None]) – Configures the model explainability job to run a specified Docker container image.model_explainability_baseline_config (
Union[IResolvable,ModelExplainabilityBaselineConfigProperty,Dict[str,Any],None]) – The baseline configuration for a model explainability job.model_explainability_job_input (
Union[IResolvable,ModelExplainabilityJobInputProperty,Dict[str,Any],None]) – Inputs for the model explainability job.model_explainability_job_output_config (
Union[IResolvable,MonitoringOutputConfigProperty,Dict[str,Any],None]) – The output configuration for monitoring jobs.network_config (
Union[IResolvable,NetworkConfigProperty,Dict[str,Any],None]) – Networking options for a model explainability job.role_arn (
Optional[str]) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.stopping_condition (
Union[IResolvable,StoppingConditionProperty,Dict[str,Any],None]) – A time limit for how long the monitoring job is allowed to run before stopping.tags (
Optional[Sequence[Union[CfnTag,Dict[str,Any]]]]) – An array of key-value pairs to apply to this resource. For more information, see Tag .
- See:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins cfn_model_explainability_job_definition_mixin_props = sagemaker_mixins.CfnModelExplainabilityJobDefinitionMixinProps( endpoint_name="endpointName", job_definition_name="jobDefinitionName", job_resources=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.MonitoringResourcesProperty( cluster_config=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ClusterConfigProperty( instance_count=123, instance_type="instanceType", volume_kms_key_id="volumeKmsKeyId", volume_size_in_gb=123 ) ), model_explainability_app_specification=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ModelExplainabilityAppSpecificationProperty( config_uri="configUri", environment={ "environment_key": "environment" }, image_uri="imageUri" ), model_explainability_baseline_config=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ModelExplainabilityBaselineConfigProperty( baselining_job_name="baseliningJobName", constraints_resource=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ConstraintsResourceProperty( s3_uri="s3Uri" ) ), model_explainability_job_input=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ModelExplainabilityJobInputProperty( batch_transform_input=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.BatchTransformInputProperty( data_captured_destination_s3_uri="dataCapturedDestinationS3Uri", dataset_format=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.DatasetFormatProperty( csv=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.CsvProperty( header=False ), json=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.JsonProperty( line=False ), parquet=False ), features_attribute="featuresAttribute", inference_attribute="inferenceAttribute", local_path="localPath", probability_attribute="probabilityAttribute", s3_data_distribution_type="s3DataDistributionType", s3_input_mode="s3InputMode" ), endpoint_input=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.EndpointInputProperty( endpoint_name="endpointName", features_attribute="featuresAttribute", inference_attribute="inferenceAttribute", local_path="localPath", probability_attribute="probabilityAttribute", s3_data_distribution_type="s3DataDistributionType", s3_input_mode="s3InputMode" ) ), model_explainability_job_output_config=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.MonitoringOutputConfigProperty( kms_key_id="kmsKeyId", monitoring_outputs=[sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.MonitoringOutputProperty( s3_output=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.S3OutputProperty( local_path="localPath", s3_upload_mode="s3UploadMode", s3_uri="s3Uri" ) )] ), network_config=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.NetworkConfigProperty( enable_inter_container_traffic_encryption=False, enable_network_isolation=False, vpc_config=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.VpcConfigProperty( security_group_ids=["securityGroupIds"], subnets=["subnets"] ) ), role_arn="roleArn", stopping_condition=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.StoppingConditionProperty( max_runtime_in_seconds=123 ), tags=[CfnTag( key="key", value="value" )] )
Attributes
- endpoint_name
The name of the endpoint used to run the monitoring job.
- job_definition_name
The name of the model explainability job definition.
The name must be unique within an AWS Region in the AWS account.
- job_resources
Identifies the resources to deploy for a monitoring job.
- model_explainability_app_specification
Configures the model explainability job to run a specified Docker container image.
- model_explainability_baseline_config
The baseline configuration for a model explainability job.
- model_explainability_job_input
Inputs for the model explainability job.
- model_explainability_job_output_config
The output configuration for monitoring jobs.
- network_config
Networking options for a model explainability job.
- role_arn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
- stopping_condition
A time limit for how long the monitoring job is allowed to run before stopping.