CfnModelQualityJobDefinitionProps
- class aws_cdk.aws_sagemaker.CfnModelQualityJobDefinitionProps(*, job_resources, model_quality_app_specification, model_quality_job_input, model_quality_job_output_config, role_arn, endpoint_name=None, job_definition_name=None, model_quality_baseline_config=None, network_config=None, stopping_condition=None, tags=None)
Bases:
object
Properties for defining a
CfnModelQualityJobDefinition
.- Parameters:
job_resources (
Union
[IResolvable
,MonitoringResourcesProperty
,Dict
[str
,Any
]]) – Identifies the resources to deploy for a monitoring job.model_quality_app_specification (
Union
[IResolvable
,ModelQualityAppSpecificationProperty
,Dict
[str
,Any
]]) – Container image configuration object for the monitoring job.model_quality_job_input (
Union
[IResolvable
,ModelQualityJobInputProperty
,Dict
[str
,Any
]]) – A list of the inputs that are monitored. Currently endpoints are supported.model_quality_job_output_config (
Union
[IResolvable
,MonitoringOutputConfigProperty
,Dict
[str
,Any
]]) – The output configuration for monitoring jobs.role_arn (
str
) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.endpoint_name (
Optional
[str
]) – The name of the endpoint used to run the monitoring job.job_definition_name (
Optional
[str
]) – The name of the monitoring job definition.model_quality_baseline_config (
Union
[IResolvable
,ModelQualityBaselineConfigProperty
,Dict
[str
,Any
],None
]) – Specifies the constraints and baselines for the monitoring job.network_config (
Union
[IResolvable
,NetworkConfigProperty
,Dict
[str
,Any
],None
]) – Specifies the network configuration for the monitoring job.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 import aws_sagemaker as sagemaker cfn_model_quality_job_definition_props = sagemaker.CfnModelQualityJobDefinitionProps( job_resources=sagemaker.CfnModelQualityJobDefinition.MonitoringResourcesProperty( cluster_config=sagemaker.CfnModelQualityJobDefinition.ClusterConfigProperty( instance_count=123, instance_type="instanceType", volume_size_in_gb=123, # the properties below are optional volume_kms_key_id="volumeKmsKeyId" ) ), model_quality_app_specification=sagemaker.CfnModelQualityJobDefinition.ModelQualityAppSpecificationProperty( image_uri="imageUri", problem_type="problemType", # the properties below are optional container_arguments=["containerArguments"], container_entrypoint=["containerEntrypoint"], environment={ "environment_key": "environment" }, post_analytics_processor_source_uri="postAnalyticsProcessorSourceUri", record_preprocessor_source_uri="recordPreprocessorSourceUri" ), model_quality_job_input=sagemaker.CfnModelQualityJobDefinition.ModelQualityJobInputProperty( ground_truth_s3_input=sagemaker.CfnModelQualityJobDefinition.MonitoringGroundTruthS3InputProperty( s3_uri="s3Uri" ), # the properties below are optional batch_transform_input=sagemaker.CfnModelQualityJobDefinition.BatchTransformInputProperty( data_captured_destination_s3_uri="dataCapturedDestinationS3Uri", dataset_format=sagemaker.CfnModelQualityJobDefinition.DatasetFormatProperty( csv=sagemaker.CfnModelQualityJobDefinition.CsvProperty( header=False ), json=sagemaker.CfnModelQualityJobDefinition.JsonProperty( line=False ), parquet=False ), local_path="localPath", # the properties below are optional end_time_offset="endTimeOffset", inference_attribute="inferenceAttribute", probability_attribute="probabilityAttribute", probability_threshold_attribute=123, s3_data_distribution_type="s3DataDistributionType", s3_input_mode="s3InputMode", start_time_offset="startTimeOffset" ), endpoint_input=sagemaker.CfnModelQualityJobDefinition.EndpointInputProperty( endpoint_name="endpointName", local_path="localPath", # the properties below are optional end_time_offset="endTimeOffset", inference_attribute="inferenceAttribute", probability_attribute="probabilityAttribute", probability_threshold_attribute=123, s3_data_distribution_type="s3DataDistributionType", s3_input_mode="s3InputMode", start_time_offset="startTimeOffset" ) ), model_quality_job_output_config=sagemaker.CfnModelQualityJobDefinition.MonitoringOutputConfigProperty( monitoring_outputs=[sagemaker.CfnModelQualityJobDefinition.MonitoringOutputProperty( s3_output=sagemaker.CfnModelQualityJobDefinition.S3OutputProperty( local_path="localPath", s3_uri="s3Uri", # the properties below are optional s3_upload_mode="s3UploadMode" ) )], # the properties below are optional kms_key_id="kmsKeyId" ), role_arn="roleArn", # the properties below are optional endpoint_name="endpointName", job_definition_name="jobDefinitionName", model_quality_baseline_config=sagemaker.CfnModelQualityJobDefinition.ModelQualityBaselineConfigProperty( baselining_job_name="baseliningJobName", constraints_resource=sagemaker.CfnModelQualityJobDefinition.ConstraintsResourceProperty( s3_uri="s3Uri" ) ), network_config=sagemaker.CfnModelQualityJobDefinition.NetworkConfigProperty( enable_inter_container_traffic_encryption=False, enable_network_isolation=False, vpc_config=sagemaker.CfnModelQualityJobDefinition.VpcConfigProperty( security_group_ids=["securityGroupIds"], subnets=["subnets"] ) ), stopping_condition=sagemaker.CfnModelQualityJobDefinition.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 monitoring job definition.
- job_resources
Identifies the resources to deploy for a monitoring job.
- model_quality_app_specification
Container image configuration object for the monitoring job.
- model_quality_baseline_config
Specifies the constraints and baselines for the monitoring job.
- model_quality_job_input
A list of the inputs that are monitored.
Currently endpoints are supported.
- model_quality_job_output_config
The output configuration for monitoring jobs.
- network_config
Specifies the network configuration for the monitoring 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.