CfnModelBiasJobDefinitionProps

class aws_cdk.aws_sagemaker.CfnModelBiasJobDefinitionProps(*, job_resources, model_bias_app_specification, model_bias_job_input, model_bias_job_output_config, role_arn, endpoint_name=None, job_definition_name=None, model_bias_baseline_config=None, network_config=None, stopping_condition=None, tags=None)

Bases: object

Properties for defining a CfnModelBiasJobDefinition.

Parameters:

job_resources (Union[IResolvable, MonitoringResourcesProperty, Dict[str, Any]]) – Identifies the resources to deploy for a monitoring job.
model_bias_app_specification (Union[IResolvable, ModelBiasAppSpecificationProperty, Dict[str, Any]]) – Configures the model bias job to run a specified Docker container image.
model_bias_job_input (Union[IResolvable, ModelBiasJobInputProperty, Dict[str, Any]]) – Inputs for the model bias job.
model_bias_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]) – AWS::SageMaker::ModelBiasJobDefinition.EndpointName.
job_definition_name (Optional[str]) – The name of the bias job definition. The name must be unique within an AWS Region in the AWS account.
model_bias_baseline_config (Union[IResolvable, ModelBiasBaselineConfigProperty, Dict[str, Any], None]) – The baseline configuration for a model bias job.
network_config (Union[IResolvable, NetworkConfigProperty, Dict[str, Any], None]) – Networking options for a model bias 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 .

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelbiasjobdefinition.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
import aws_cdk.aws_sagemaker as sagemaker

# json: Any

cfn_model_bias_job_definition_props = sagemaker.CfnModelBiasJobDefinitionProps(
    job_resources=sagemaker.CfnModelBiasJobDefinition.MonitoringResourcesProperty(
        cluster_config=sagemaker.CfnModelBiasJobDefinition.ClusterConfigProperty(
            instance_count=123,
            instance_type="instanceType",
            volume_size_in_gb=123,

            # the properties below are optional
            volume_kms_key_id="volumeKmsKeyId"
        )
    ),
    model_bias_app_specification=sagemaker.CfnModelBiasJobDefinition.ModelBiasAppSpecificationProperty(
        config_uri="configUri",
        image_uri="imageUri",

        # the properties below are optional
        environment={
            "environment_key": "environment"
        }
    ),
    model_bias_job_input=sagemaker.CfnModelBiasJobDefinition.ModelBiasJobInputProperty(
        ground_truth_s3_input=sagemaker.CfnModelBiasJobDefinition.MonitoringGroundTruthS3InputProperty(
            s3_uri="s3Uri"
        ),

        # the properties below are optional
        batch_transform_input=sagemaker.CfnModelBiasJobDefinition.BatchTransformInputProperty(
            data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
            dataset_format=sagemaker.CfnModelBiasJobDefinition.DatasetFormatProperty(
                csv=sagemaker.CfnModelBiasJobDefinition.CsvProperty(
                    header=False
                ),
                json=json,
                parquet=False
            ),
            local_path="localPath",

            # the properties below are optional
            end_time_offset="endTimeOffset",
            features_attribute="featuresAttribute",
            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.CfnModelBiasJobDefinition.EndpointInputProperty(
            endpoint_name="endpointName",
            local_path="localPath",

            # the properties below are optional
            end_time_offset="endTimeOffset",
            features_attribute="featuresAttribute",
            inference_attribute="inferenceAttribute",
            probability_attribute="probabilityAttribute",
            probability_threshold_attribute=123,
            s3_data_distribution_type="s3DataDistributionType",
            s3_input_mode="s3InputMode",
            start_time_offset="startTimeOffset"
        )
    ),
    model_bias_job_output_config=sagemaker.CfnModelBiasJobDefinition.MonitoringOutputConfigProperty(
        monitoring_outputs=[sagemaker.CfnModelBiasJobDefinition.MonitoringOutputProperty(
            s3_output=sagemaker.CfnModelBiasJobDefinition.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_bias_baseline_config=sagemaker.CfnModelBiasJobDefinition.ModelBiasBaselineConfigProperty(
        baselining_job_name="baseliningJobName",
        constraints_resource=sagemaker.CfnModelBiasJobDefinition.ConstraintsResourceProperty(
            s3_uri="s3Uri"
        )
    ),
    network_config=sagemaker.CfnModelBiasJobDefinition.NetworkConfigProperty(
        enable_inter_container_traffic_encryption=False,
        enable_network_isolation=False,
        vpc_config=sagemaker.CfnModelBiasJobDefinition.VpcConfigProperty(
            security_group_ids=["securityGroupIds"],
            subnets=["subnets"]
        )
    ),
    stopping_condition=sagemaker.CfnModelBiasJobDefinition.StoppingConditionProperty(
        max_runtime_in_seconds=123
    ),
    tags=[CfnTag(
        key="key",
        value="value"
    )]
)