Amazon SageMaker Service

2023/09/19 - Amazon SageMaker Service - 12 updated api methods

Changes  This release adds support for one-time model monitoring schedules that are executed immediately without delay, explicit data analysis windows for model monitoring schedules and exclude features attributes to remove features from model monitor analysis.

CreateDataQualityJobDefinition (updated) Link ¶
Changes (request)
{'DataQualityJobInput': {'BatchTransformInput': {'ExcludeFeaturesAttribute': 'string'},
                         'EndpointInput': {'ExcludeFeaturesAttribute': 'string'}}}

Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.

See also: AWS API Documentation

Request Syntax

client.create_data_quality_job_definition(
    JobDefinitionName='string',
    DataQualityBaselineConfig={
        'BaseliningJobName': 'string',
        'ConstraintsResource': {
            'S3Uri': 'string'
        },
        'StatisticsResource': {
            'S3Uri': 'string'
        }
    },
    DataQualityAppSpecification={
        'ImageUri': 'string',
        'ContainerEntrypoint': [
            'string',
        ],
        'ContainerArguments': [
            'string',
        ],
        'RecordPreprocessorSourceUri': 'string',
        'PostAnalyticsProcessorSourceUri': 'string',
        'Environment': {
            'string': 'string'
        }
    },
    DataQualityJobInput={
        'EndpointInput': {
            'EndpointName': 'string',
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        },
        'BatchTransformInput': {
            'DataCapturedDestinationS3Uri': 'string',
            'DatasetFormat': {
                'Csv': {
                    'Header': True|False
                },
                'Json': {
                    'Line': True|False
                },
                'Parquet': {}

            },
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        }
    },
    DataQualityJobOutputConfig={
        'MonitoringOutputs': [
            {
                'S3Output': {
                    'S3Uri': 'string',
                    'LocalPath': 'string',
                    'S3UploadMode': 'Continuous'|'EndOfJob'
                }
            },
        ],
        'KmsKeyId': 'string'
    },
    JobResources={
        'ClusterConfig': {
            'InstanceCount': 123,
            'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
            'VolumeSizeInGB': 123,
            'VolumeKmsKeyId': 'string'
        }
    },
    NetworkConfig={
        'EnableInterContainerTrafficEncryption': True|False,
        'EnableNetworkIsolation': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    RoleArn='string',
    StoppingCondition={
        'MaxRuntimeInSeconds': 123
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type JobDefinitionName

string

param JobDefinitionName

[REQUIRED]

The name for the monitoring job definition.

type DataQualityBaselineConfig

dict

param DataQualityBaselineConfig

Configures the constraints and baselines for the monitoring job.

  • BaseliningJobName (string) --

    The name of the job that performs baselining for the data quality monitoring job.

  • ConstraintsResource (dict) --

    The constraints resource for a monitoring job.

    • S3Uri (string) --

      The Amazon S3 URI for the constraints resource.

  • StatisticsResource (dict) --

    The statistics resource for a monitoring job.

    • S3Uri (string) --

      The Amazon S3 URI for the statistics resource.

type DataQualityAppSpecification

dict

param DataQualityAppSpecification

[REQUIRED]

Specifies the container that runs the monitoring job.

  • ImageUri (string) -- [REQUIRED]

    The container image that the data quality monitoring job runs.

  • ContainerEntrypoint (list) --

    The entrypoint for a container used to run a monitoring job.

    • (string) --

  • ContainerArguments (list) --

    The arguments to send to the container that the monitoring job runs.

    • (string) --

  • RecordPreprocessorSourceUri (string) --

    An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

  • PostAnalyticsProcessorSourceUri (string) --

    An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

  • Environment (dict) --

    Sets the environment variables in the container that the monitoring job runs.

    • (string) --

      • (string) --

type DataQualityJobInput

dict

param DataQualityJobInput

[REQUIRED]

A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.

  • EndpointInput (dict) --

    Input object for the endpoint

    • EndpointName (string) -- [REQUIRED]

      An endpoint in customer's account which has enabled DataCaptureConfig enabled.

    • LocalPath (string) -- [REQUIRED]

      Path to the filesystem where the endpoint data is available to the container.

    • S3InputMode (string) --

      Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

    • S3DataDistributionType (string) --

      Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

    • FeaturesAttribute (string) --

      The attributes of the input data that are the input features.

    • InferenceAttribute (string) --

      The attribute of the input data that represents the ground truth label.

    • ProbabilityAttribute (string) --

      In a classification problem, the attribute that represents the class probability.

    • ProbabilityThresholdAttribute (float) --

      The threshold for the class probability to be evaluated as a positive result.

    • StartTimeOffset (string) --

      If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • EndTimeOffset (string) --

      If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • ExcludeFeaturesAttribute (string) --

      The attributes of the input data to exclude from the analysis.

  • BatchTransformInput (dict) --

    Input object for the batch transform job.

    • DataCapturedDestinationS3Uri (string) -- [REQUIRED]

      The Amazon S3 location being used to capture the data.

    • DatasetFormat (dict) -- [REQUIRED]

      The dataset format for your batch transform job.

      • Csv (dict) --

        The CSV dataset used in the monitoring job.

        • Header (boolean) --

          Indicates if the CSV data has a header.

      • Json (dict) --

        The JSON dataset used in the monitoring job

        • Line (boolean) --

          Indicates if the file should be read as a json object per line.

      • Parquet (dict) --

        The Parquet dataset used in the monitoring job

    • LocalPath (string) -- [REQUIRED]

      Path to the filesystem where the batch transform data is available to the container.

    • S3InputMode (string) --

      Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

    • S3DataDistributionType (string) --

      Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

    • FeaturesAttribute (string) --

      The attributes of the input data that are the input features.

    • InferenceAttribute (string) --

      The attribute of the input data that represents the ground truth label.

    • ProbabilityAttribute (string) --

      In a classification problem, the attribute that represents the class probability.

    • ProbabilityThresholdAttribute (float) --

      The threshold for the class probability to be evaluated as a positive result.

    • StartTimeOffset (string) --

      If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • EndTimeOffset (string) --

      If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • ExcludeFeaturesAttribute (string) --

      The attributes of the input data to exclude from the analysis.

type DataQualityJobOutputConfig

dict

param DataQualityJobOutputConfig

[REQUIRED]

The output configuration for monitoring jobs.

  • MonitoringOutputs (list) -- [REQUIRED]

    Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

    • (dict) --

      The output object for a monitoring job.

      • S3Output (dict) -- [REQUIRED]

        The Amazon S3 storage location where the results of a monitoring job are saved.

        • S3Uri (string) -- [REQUIRED]

          A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

        • LocalPath (string) -- [REQUIRED]

          The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

        • S3UploadMode (string) --

          Whether to upload the results of the monitoring job continuously or after the job completes.

  • KmsKeyId (string) --

    The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

type JobResources

dict

param JobResources

[REQUIRED]

Identifies the resources to deploy for a monitoring job.

  • ClusterConfig (dict) -- [REQUIRED]

    The configuration for the cluster resources used to run the processing job.

    • InstanceCount (integer) -- [REQUIRED]

      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.

    • InstanceType (string) -- [REQUIRED]

      The ML compute instance type for the processing job.

    • VolumeSizeInGB (integer) -- [REQUIRED]

      The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

    • VolumeKmsKeyId (string) --

      The Amazon Web Services Key Management Service (Amazon Web Services 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.

type NetworkConfig

dict

param NetworkConfig

Specifies networking configuration for the monitoring job.

  • EnableInterContainerTrafficEncryption (boolean) --

    Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.

  • EnableNetworkIsolation (boolean) --

    Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.

  • VpcConfig (dict) --

    Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

    • SecurityGroupIds (list) -- [REQUIRED]

      The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

      • (string) --

    • Subnets (list) -- [REQUIRED]

      The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

      • (string) --

type RoleArn

string

param RoleArn

[REQUIRED]

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

type StoppingCondition

dict

param StoppingCondition

A time limit for how long the monitoring job is allowed to run before stopping.

  • MaxRuntimeInSeconds (integer) -- [REQUIRED]

    The maximum runtime allowed in seconds.

    Note

    The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

type Tags

list

param Tags

(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

rtype

dict

returns

Response Syntax

{
    'JobDefinitionArn': 'string'
}

Response Structure

  • (dict) --

    • JobDefinitionArn (string) --

      The Amazon Resource Name (ARN) of the job definition.

CreateModelBiasJobDefinition (updated) Link ¶
Changes (request)
{'ModelBiasJobInput': {'BatchTransformInput': {'ExcludeFeaturesAttribute': 'string'},
                       'EndpointInput': {'ExcludeFeaturesAttribute': 'string'}}}

Creates the definition for a model bias job.

See also: AWS API Documentation

Request Syntax

client.create_model_bias_job_definition(
    JobDefinitionName='string',
    ModelBiasBaselineConfig={
        'BaseliningJobName': 'string',
        'ConstraintsResource': {
            'S3Uri': 'string'
        }
    },
    ModelBiasAppSpecification={
        'ImageUri': 'string',
        'ConfigUri': 'string',
        'Environment': {
            'string': 'string'
        }
    },
    ModelBiasJobInput={
        'EndpointInput': {
            'EndpointName': 'string',
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        },
        'BatchTransformInput': {
            'DataCapturedDestinationS3Uri': 'string',
            'DatasetFormat': {
                'Csv': {
                    'Header': True|False
                },
                'Json': {
                    'Line': True|False
                },
                'Parquet': {}

            },
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        },
        'GroundTruthS3Input': {
            'S3Uri': 'string'
        }
    },
    ModelBiasJobOutputConfig={
        'MonitoringOutputs': [
            {
                'S3Output': {
                    'S3Uri': 'string',
                    'LocalPath': 'string',
                    'S3UploadMode': 'Continuous'|'EndOfJob'
                }
            },
        ],
        'KmsKeyId': 'string'
    },
    JobResources={
        'ClusterConfig': {
            'InstanceCount': 123,
            'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
            'VolumeSizeInGB': 123,
            'VolumeKmsKeyId': 'string'
        }
    },
    NetworkConfig={
        'EnableInterContainerTrafficEncryption': True|False,
        'EnableNetworkIsolation': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    RoleArn='string',
    StoppingCondition={
        'MaxRuntimeInSeconds': 123
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type JobDefinitionName

string

param JobDefinitionName

[REQUIRED]

The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

type ModelBiasBaselineConfig

dict

param ModelBiasBaselineConfig

The baseline configuration for a model bias job.

  • BaseliningJobName (string) --

    The name of the baseline model bias job.

  • ConstraintsResource (dict) --

    The constraints resource for a monitoring job.

    • S3Uri (string) --

      The Amazon S3 URI for the constraints resource.

type ModelBiasAppSpecification

dict

param ModelBiasAppSpecification

[REQUIRED]

Configures the model bias job to run a specified Docker container image.

  • ImageUri (string) -- [REQUIRED]

    The container image to be run by the model bias job.

  • ConfigUri (string) -- [REQUIRED]

    JSON formatted S3 file that defines bias parameters. For more information on this JSON configuration file, see Configure bias parameters.

  • Environment (dict) --

    Sets the environment variables in the Docker container.

    • (string) --

      • (string) --

type ModelBiasJobInput

dict

param ModelBiasJobInput

[REQUIRED]

Inputs for the model bias job.

  • EndpointInput (dict) --

    Input object for the endpoint

    • EndpointName (string) -- [REQUIRED]

      An endpoint in customer's account which has enabled DataCaptureConfig enabled.

    • LocalPath (string) -- [REQUIRED]

      Path to the filesystem where the endpoint data is available to the container.

    • S3InputMode (string) --

      Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

    • S3DataDistributionType (string) --

      Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

    • FeaturesAttribute (string) --

      The attributes of the input data that are the input features.

    • InferenceAttribute (string) --

      The attribute of the input data that represents the ground truth label.

    • ProbabilityAttribute (string) --

      In a classification problem, the attribute that represents the class probability.

    • ProbabilityThresholdAttribute (float) --

      The threshold for the class probability to be evaluated as a positive result.

    • StartTimeOffset (string) --

      If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • EndTimeOffset (string) --

      If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • ExcludeFeaturesAttribute (string) --

      The attributes of the input data to exclude from the analysis.

  • BatchTransformInput (dict) --

    Input object for the batch transform job.

    • DataCapturedDestinationS3Uri (string) -- [REQUIRED]

      The Amazon S3 location being used to capture the data.

    • DatasetFormat (dict) -- [REQUIRED]

      The dataset format for your batch transform job.

      • Csv (dict) --

        The CSV dataset used in the monitoring job.

        • Header (boolean) --

          Indicates if the CSV data has a header.

      • Json (dict) --

        The JSON dataset used in the monitoring job

        • Line (boolean) --

          Indicates if the file should be read as a json object per line.

      • Parquet (dict) --

        The Parquet dataset used in the monitoring job

    • LocalPath (string) -- [REQUIRED]

      Path to the filesystem where the batch transform data is available to the container.

    • S3InputMode (string) --

      Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

    • S3DataDistributionType (string) --

      Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

    • FeaturesAttribute (string) --

      The attributes of the input data that are the input features.

    • InferenceAttribute (string) --

      The attribute of the input data that represents the ground truth label.

    • ProbabilityAttribute (string) --

      In a classification problem, the attribute that represents the class probability.

    • ProbabilityThresholdAttribute (float) --

      The threshold for the class probability to be evaluated as a positive result.

    • StartTimeOffset (string) --

      If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • EndTimeOffset (string) --

      If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • ExcludeFeaturesAttribute (string) --

      The attributes of the input data to exclude from the analysis.

  • GroundTruthS3Input (dict) -- [REQUIRED]

    Location of ground truth labels to use in model bias job.

    • S3Uri (string) --

      The address of the Amazon S3 location of the ground truth labels.

type ModelBiasJobOutputConfig

dict

param ModelBiasJobOutputConfig

[REQUIRED]

The output configuration for monitoring jobs.

  • MonitoringOutputs (list) -- [REQUIRED]

    Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

    • (dict) --

      The output object for a monitoring job.

      • S3Output (dict) -- [REQUIRED]

        The Amazon S3 storage location where the results of a monitoring job are saved.

        • S3Uri (string) -- [REQUIRED]

          A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

        • LocalPath (string) -- [REQUIRED]

          The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

        • S3UploadMode (string) --

          Whether to upload the results of the monitoring job continuously or after the job completes.

  • KmsKeyId (string) --

    The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

type JobResources

dict

param JobResources

[REQUIRED]

Identifies the resources to deploy for a monitoring job.

  • ClusterConfig (dict) -- [REQUIRED]

    The configuration for the cluster resources used to run the processing job.

    • InstanceCount (integer) -- [REQUIRED]

      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.

    • InstanceType (string) -- [REQUIRED]

      The ML compute instance type for the processing job.

    • VolumeSizeInGB (integer) -- [REQUIRED]

      The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

    • VolumeKmsKeyId (string) --

      The Amazon Web Services Key Management Service (Amazon Web Services 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.

type NetworkConfig

dict

param NetworkConfig

Networking options for a model bias job.

  • EnableInterContainerTrafficEncryption (boolean) --

    Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.

  • EnableNetworkIsolation (boolean) --

    Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.

  • VpcConfig (dict) --

    Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

    • SecurityGroupIds (list) -- [REQUIRED]

      The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

      • (string) --

    • Subnets (list) -- [REQUIRED]

      The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

      • (string) --

type RoleArn

string

param RoleArn

[REQUIRED]

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

type StoppingCondition

dict

param StoppingCondition

A time limit for how long the monitoring job is allowed to run before stopping.

  • MaxRuntimeInSeconds (integer) -- [REQUIRED]

    The maximum runtime allowed in seconds.

    Note

    The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

type Tags

list

param Tags

(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

rtype

dict

returns

Response Syntax

{
    'JobDefinitionArn': 'string'
}

Response Structure

  • (dict) --

    • JobDefinitionArn (string) --

      The Amazon Resource Name (ARN) of the model bias job.

CreateModelExplainabilityJobDefinition (updated) Link ¶
Changes (request)
{'ModelExplainabilityJobInput': {'BatchTransformInput': {'ExcludeFeaturesAttribute': 'string'},
                                 'EndpointInput': {'ExcludeFeaturesAttribute': 'string'}}}

Creates the definition for a model explainability job.

See also: AWS API Documentation

Request Syntax

client.create_model_explainability_job_definition(
    JobDefinitionName='string',
    ModelExplainabilityBaselineConfig={
        'BaseliningJobName': 'string',
        'ConstraintsResource': {
            'S3Uri': 'string'
        }
    },
    ModelExplainabilityAppSpecification={
        'ImageUri': 'string',
        'ConfigUri': 'string',
        'Environment': {
            'string': 'string'
        }
    },
    ModelExplainabilityJobInput={
        'EndpointInput': {
            'EndpointName': 'string',
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        },
        'BatchTransformInput': {
            'DataCapturedDestinationS3Uri': 'string',
            'DatasetFormat': {
                'Csv': {
                    'Header': True|False
                },
                'Json': {
                    'Line': True|False
                },
                'Parquet': {}

            },
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        }
    },
    ModelExplainabilityJobOutputConfig={
        'MonitoringOutputs': [
            {
                'S3Output': {
                    'S3Uri': 'string',
                    'LocalPath': 'string',
                    'S3UploadMode': 'Continuous'|'EndOfJob'
                }
            },
        ],
        'KmsKeyId': 'string'
    },
    JobResources={
        'ClusterConfig': {
            'InstanceCount': 123,
            'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
            'VolumeSizeInGB': 123,
            'VolumeKmsKeyId': 'string'
        }
    },
    NetworkConfig={
        'EnableInterContainerTrafficEncryption': True|False,
        'EnableNetworkIsolation': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    RoleArn='string',
    StoppingCondition={
        'MaxRuntimeInSeconds': 123
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type JobDefinitionName

string

param JobDefinitionName

[REQUIRED]

The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

type ModelExplainabilityBaselineConfig

dict

param ModelExplainabilityBaselineConfig

The baseline configuration for a model explainability job.

  • BaseliningJobName (string) --

    The name of the baseline model explainability job.

  • ConstraintsResource (dict) --

    The constraints resource for a monitoring job.

    • S3Uri (string) --

      The Amazon S3 URI for the constraints resource.

type ModelExplainabilityAppSpecification

dict

param ModelExplainabilityAppSpecification

[REQUIRED]

Configures the model explainability job to run a specified Docker container image.

  • ImageUri (string) -- [REQUIRED]

    The container image to be run by the model explainability job.

  • ConfigUri (string) -- [REQUIRED]

    JSON formatted S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters.

  • Environment (dict) --

    Sets the environment variables in the Docker container.

    • (string) --

      • (string) --

type ModelExplainabilityJobInput

dict

param ModelExplainabilityJobInput

[REQUIRED]

Inputs for the model explainability job.

  • EndpointInput (dict) --

    Input object for the endpoint

    • EndpointName (string) -- [REQUIRED]

      An endpoint in customer's account which has enabled DataCaptureConfig enabled.

    • LocalPath (string) -- [REQUIRED]

      Path to the filesystem where the endpoint data is available to the container.

    • S3InputMode (string) --

      Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

    • S3DataDistributionType (string) --

      Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

    • FeaturesAttribute (string) --

      The attributes of the input data that are the input features.

    • InferenceAttribute (string) --

      The attribute of the input data that represents the ground truth label.

    • ProbabilityAttribute (string) --

      In a classification problem, the attribute that represents the class probability.

    • ProbabilityThresholdAttribute (float) --

      The threshold for the class probability to be evaluated as a positive result.

    • StartTimeOffset (string) --

      If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • EndTimeOffset (string) --

      If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • ExcludeFeaturesAttribute (string) --

      The attributes of the input data to exclude from the analysis.

  • BatchTransformInput (dict) --

    Input object for the batch transform job.

    • DataCapturedDestinationS3Uri (string) -- [REQUIRED]

      The Amazon S3 location being used to capture the data.

    • DatasetFormat (dict) -- [REQUIRED]

      The dataset format for your batch transform job.

      • Csv (dict) --

        The CSV dataset used in the monitoring job.

        • Header (boolean) --

          Indicates if the CSV data has a header.

      • Json (dict) --

        The JSON dataset used in the monitoring job

        • Line (boolean) --

          Indicates if the file should be read as a json object per line.

      • Parquet (dict) --

        The Parquet dataset used in the monitoring job

    • LocalPath (string) -- [REQUIRED]

      Path to the filesystem where the batch transform data is available to the container.

    • S3InputMode (string) --

      Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

    • S3DataDistributionType (string) --

      Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

    • FeaturesAttribute (string) --

      The attributes of the input data that are the input features.

    • InferenceAttribute (string) --

      The attribute of the input data that represents the ground truth label.

    • ProbabilityAttribute (string) --

      In a classification problem, the attribute that represents the class probability.

    • ProbabilityThresholdAttribute (float) --

      The threshold for the class probability to be evaluated as a positive result.

    • StartTimeOffset (string) --

      If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • EndTimeOffset (string) --

      If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • ExcludeFeaturesAttribute (string) --

      The attributes of the input data to exclude from the analysis.

type ModelExplainabilityJobOutputConfig

dict

param ModelExplainabilityJobOutputConfig

[REQUIRED]

The output configuration for monitoring jobs.

  • MonitoringOutputs (list) -- [REQUIRED]

    Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

    • (dict) --

      The output object for a monitoring job.

      • S3Output (dict) -- [REQUIRED]

        The Amazon S3 storage location where the results of a monitoring job are saved.

        • S3Uri (string) -- [REQUIRED]

          A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

        • LocalPath (string) -- [REQUIRED]

          The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

        • S3UploadMode (string) --

          Whether to upload the results of the monitoring job continuously or after the job completes.

  • KmsKeyId (string) --

    The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

type JobResources

dict

param JobResources

[REQUIRED]

Identifies the resources to deploy for a monitoring job.

  • ClusterConfig (dict) -- [REQUIRED]

    The configuration for the cluster resources used to run the processing job.

    • InstanceCount (integer) -- [REQUIRED]

      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.

    • InstanceType (string) -- [REQUIRED]

      The ML compute instance type for the processing job.

    • VolumeSizeInGB (integer) -- [REQUIRED]

      The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

    • VolumeKmsKeyId (string) --

      The Amazon Web Services Key Management Service (Amazon Web Services 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.

type NetworkConfig

dict

param NetworkConfig

Networking options for a model explainability job.

  • EnableInterContainerTrafficEncryption (boolean) --

    Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.

  • EnableNetworkIsolation (boolean) --

    Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.

  • VpcConfig (dict) --

    Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

    • SecurityGroupIds (list) -- [REQUIRED]

      The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

      • (string) --

    • Subnets (list) -- [REQUIRED]

      The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

      • (string) --

type RoleArn

string

param RoleArn

[REQUIRED]

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

type StoppingCondition

dict

param StoppingCondition

A time limit for how long the monitoring job is allowed to run before stopping.

  • MaxRuntimeInSeconds (integer) -- [REQUIRED]

    The maximum runtime allowed in seconds.

    Note

    The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

type Tags

list

param Tags

(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

rtype

dict

returns

Response Syntax

{
    'JobDefinitionArn': 'string'
}

Response Structure

  • (dict) --

    • JobDefinitionArn (string) --

      The Amazon Resource Name (ARN) of the model explainability job.

CreateModelQualityJobDefinition (updated) Link ¶
Changes (request)
{'ModelQualityJobInput': {'BatchTransformInput': {'ExcludeFeaturesAttribute': 'string'},
                          'EndpointInput': {'ExcludeFeaturesAttribute': 'string'}}}

Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.

See also: AWS API Documentation

Request Syntax

client.create_model_quality_job_definition(
    JobDefinitionName='string',
    ModelQualityBaselineConfig={
        'BaseliningJobName': 'string',
        'ConstraintsResource': {
            'S3Uri': 'string'
        }
    },
    ModelQualityAppSpecification={
        'ImageUri': 'string',
        'ContainerEntrypoint': [
            'string',
        ],
        'ContainerArguments': [
            'string',
        ],
        'RecordPreprocessorSourceUri': 'string',
        'PostAnalyticsProcessorSourceUri': 'string',
        'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
        'Environment': {
            'string': 'string'
        }
    },
    ModelQualityJobInput={
        'EndpointInput': {
            'EndpointName': 'string',
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        },
        'BatchTransformInput': {
            'DataCapturedDestinationS3Uri': 'string',
            'DatasetFormat': {
                'Csv': {
                    'Header': True|False
                },
                'Json': {
                    'Line': True|False
                },
                'Parquet': {}

            },
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        },
        'GroundTruthS3Input': {
            'S3Uri': 'string'
        }
    },
    ModelQualityJobOutputConfig={
        'MonitoringOutputs': [
            {
                'S3Output': {
                    'S3Uri': 'string',
                    'LocalPath': 'string',
                    'S3UploadMode': 'Continuous'|'EndOfJob'
                }
            },
        ],
        'KmsKeyId': 'string'
    },
    JobResources={
        'ClusterConfig': {
            'InstanceCount': 123,
            'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
            'VolumeSizeInGB': 123,
            'VolumeKmsKeyId': 'string'
        }
    },
    NetworkConfig={
        'EnableInterContainerTrafficEncryption': True|False,
        'EnableNetworkIsolation': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    RoleArn='string',
    StoppingCondition={
        'MaxRuntimeInSeconds': 123
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type JobDefinitionName

string

param JobDefinitionName

[REQUIRED]

The name of the monitoring job definition.

type ModelQualityBaselineConfig

dict

param ModelQualityBaselineConfig

Specifies the constraints and baselines for the monitoring job.

  • BaseliningJobName (string) --

    The name of the job that performs baselining for the monitoring job.

  • ConstraintsResource (dict) --

    The constraints resource for a monitoring job.

    • S3Uri (string) --

      The Amazon S3 URI for the constraints resource.

type ModelQualityAppSpecification

dict

param ModelQualityAppSpecification

[REQUIRED]

The container that runs the monitoring job.

  • ImageUri (string) -- [REQUIRED]

    The address of the container image that the monitoring job runs.

  • ContainerEntrypoint (list) --

    Specifies the entrypoint for a container that the monitoring job runs.

    • (string) --

  • ContainerArguments (list) --

    An array of arguments for the container used to run the monitoring job.

    • (string) --

  • RecordPreprocessorSourceUri (string) --

    An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

  • PostAnalyticsProcessorSourceUri (string) --

    An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

  • ProblemType (string) --

    The machine learning problem type of the model that the monitoring job monitors.

  • Environment (dict) --

    Sets the environment variables in the container that the monitoring job runs.

    • (string) --

      • (string) --

type ModelQualityJobInput

dict

param ModelQualityJobInput

[REQUIRED]

A list of the inputs that are monitored. Currently endpoints are supported.

  • EndpointInput (dict) --

    Input object for the endpoint

    • EndpointName (string) -- [REQUIRED]

      An endpoint in customer's account which has enabled DataCaptureConfig enabled.

    • LocalPath (string) -- [REQUIRED]

      Path to the filesystem where the endpoint data is available to the container.

    • S3InputMode (string) --

      Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

    • S3DataDistributionType (string) --

      Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

    • FeaturesAttribute (string) --

      The attributes of the input data that are the input features.

    • InferenceAttribute (string) --

      The attribute of the input data that represents the ground truth label.

    • ProbabilityAttribute (string) --

      In a classification problem, the attribute that represents the class probability.

    • ProbabilityThresholdAttribute (float) --

      The threshold for the class probability to be evaluated as a positive result.

    • StartTimeOffset (string) --

      If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • EndTimeOffset (string) --

      If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • ExcludeFeaturesAttribute (string) --

      The attributes of the input data to exclude from the analysis.

  • BatchTransformInput (dict) --

    Input object for the batch transform job.

    • DataCapturedDestinationS3Uri (string) -- [REQUIRED]

      The Amazon S3 location being used to capture the data.

    • DatasetFormat (dict) -- [REQUIRED]

      The dataset format for your batch transform job.

      • Csv (dict) --

        The CSV dataset used in the monitoring job.

        • Header (boolean) --

          Indicates if the CSV data has a header.

      • Json (dict) --

        The JSON dataset used in the monitoring job

        • Line (boolean) --

          Indicates if the file should be read as a json object per line.

      • Parquet (dict) --

        The Parquet dataset used in the monitoring job

    • LocalPath (string) -- [REQUIRED]

      Path to the filesystem where the batch transform data is available to the container.

    • S3InputMode (string) --

      Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

    • S3DataDistributionType (string) --

      Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

    • FeaturesAttribute (string) --

      The attributes of the input data that are the input features.

    • InferenceAttribute (string) --

      The attribute of the input data that represents the ground truth label.

    • ProbabilityAttribute (string) --

      In a classification problem, the attribute that represents the class probability.

    • ProbabilityThresholdAttribute (float) --

      The threshold for the class probability to be evaluated as a positive result.

    • StartTimeOffset (string) --

      If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • EndTimeOffset (string) --

      If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • ExcludeFeaturesAttribute (string) --

      The attributes of the input data to exclude from the analysis.

  • GroundTruthS3Input (dict) -- [REQUIRED]

    The ground truth label provided for the model.

    • S3Uri (string) --

      The address of the Amazon S3 location of the ground truth labels.

type ModelQualityJobOutputConfig

dict

param ModelQualityJobOutputConfig

[REQUIRED]

The output configuration for monitoring jobs.

  • MonitoringOutputs (list) -- [REQUIRED]

    Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

    • (dict) --

      The output object for a monitoring job.

      • S3Output (dict) -- [REQUIRED]

        The Amazon S3 storage location where the results of a monitoring job are saved.

        • S3Uri (string) -- [REQUIRED]

          A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

        • LocalPath (string) -- [REQUIRED]

          The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

        • S3UploadMode (string) --

          Whether to upload the results of the monitoring job continuously or after the job completes.

  • KmsKeyId (string) --

    The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

type JobResources

dict

param JobResources

[REQUIRED]

Identifies the resources to deploy for a monitoring job.

  • ClusterConfig (dict) -- [REQUIRED]

    The configuration for the cluster resources used to run the processing job.

    • InstanceCount (integer) -- [REQUIRED]

      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.

    • InstanceType (string) -- [REQUIRED]

      The ML compute instance type for the processing job.

    • VolumeSizeInGB (integer) -- [REQUIRED]

      The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

    • VolumeKmsKeyId (string) --

      The Amazon Web Services Key Management Service (Amazon Web Services 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.

type NetworkConfig

dict

param NetworkConfig

Specifies the network configuration for the monitoring job.

  • EnableInterContainerTrafficEncryption (boolean) --

    Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.

  • EnableNetworkIsolation (boolean) --

    Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.

  • VpcConfig (dict) --

    Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

    • SecurityGroupIds (list) -- [REQUIRED]

      The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

      • (string) --

    • Subnets (list) -- [REQUIRED]

      The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

      • (string) --

type RoleArn

string

param RoleArn

[REQUIRED]

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

type StoppingCondition

dict

param StoppingCondition

A time limit for how long the monitoring job is allowed to run before stopping.

  • MaxRuntimeInSeconds (integer) -- [REQUIRED]

    The maximum runtime allowed in seconds.

    Note

    The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

type Tags

list

param Tags

(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

rtype

dict

returns

Response Syntax

{
    'JobDefinitionArn': 'string'
}

Response Structure

  • (dict) --

    • JobDefinitionArn (string) --

      The Amazon Resource Name (ARN) of the model quality monitoring job.

CreateMonitoringSchedule (updated) Link ¶
Changes (request)
{'MonitoringScheduleConfig': {'MonitoringJobDefinition': {'MonitoringInputs': {'BatchTransformInput': {'ExcludeFeaturesAttribute': 'string'},
                                                                               'EndpointInput': {'ExcludeFeaturesAttribute': 'string'}}},
                              'ScheduleConfig': {'DataAnalysisEndTime': 'string',
                                                 'DataAnalysisStartTime': 'string'}}}

Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.

See also: AWS API Documentation

Request Syntax

client.create_monitoring_schedule(
    MonitoringScheduleName='string',
    MonitoringScheduleConfig={
        'ScheduleConfig': {
            'ScheduleExpression': 'string',
            'DataAnalysisStartTime': 'string',
            'DataAnalysisEndTime': 'string'
        },
        'MonitoringJobDefinition': {
            'BaselineConfig': {
                'BaseliningJobName': 'string',
                'ConstraintsResource': {
                    'S3Uri': 'string'
                },
                'StatisticsResource': {
                    'S3Uri': 'string'
                }
            },
            'MonitoringInputs': [
                {
                    'EndpointInput': {
                        'EndpointName': 'string',
                        'LocalPath': 'string',
                        'S3InputMode': 'Pipe'|'File',
                        'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                        'FeaturesAttribute': 'string',
                        'InferenceAttribute': 'string',
                        'ProbabilityAttribute': 'string',
                        'ProbabilityThresholdAttribute': 123.0,
                        'StartTimeOffset': 'string',
                        'EndTimeOffset': 'string',
                        'ExcludeFeaturesAttribute': 'string'
                    },
                    'BatchTransformInput': {
                        'DataCapturedDestinationS3Uri': 'string',
                        'DatasetFormat': {
                            'Csv': {
                                'Header': True|False
                            },
                            'Json': {
                                'Line': True|False
                            },
                            'Parquet': {}

                        },
                        'LocalPath': 'string',
                        'S3InputMode': 'Pipe'|'File',
                        'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                        'FeaturesAttribute': 'string',
                        'InferenceAttribute': 'string',
                        'ProbabilityAttribute': 'string',
                        'ProbabilityThresholdAttribute': 123.0,
                        'StartTimeOffset': 'string',
                        'EndTimeOffset': 'string',
                        'ExcludeFeaturesAttribute': 'string'
                    }
                },
            ],
            'MonitoringOutputConfig': {
                'MonitoringOutputs': [
                    {
                        'S3Output': {
                            'S3Uri': 'string',
                            'LocalPath': 'string',
                            'S3UploadMode': 'Continuous'|'EndOfJob'
                        }
                    },
                ],
                'KmsKeyId': 'string'
            },
            'MonitoringResources': {
                'ClusterConfig': {
                    'InstanceCount': 123,
                    'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
                    'VolumeSizeInGB': 123,
                    'VolumeKmsKeyId': 'string'
                }
            },
            'MonitoringAppSpecification': {
                'ImageUri': 'string',
                'ContainerEntrypoint': [
                    'string',
                ],
                'ContainerArguments': [
                    'string',
                ],
                'RecordPreprocessorSourceUri': 'string',
                'PostAnalyticsProcessorSourceUri': 'string'
            },
            'StoppingCondition': {
                'MaxRuntimeInSeconds': 123
            },
            'Environment': {
                'string': 'string'
            },
            'NetworkConfig': {
                'EnableInterContainerTrafficEncryption': True|False,
                'EnableNetworkIsolation': True|False,
                'VpcConfig': {
                    'SecurityGroupIds': [
                        'string',
                    ],
                    'Subnets': [
                        'string',
                    ]
                }
            },
            'RoleArn': 'string'
        },
        'MonitoringJobDefinitionName': 'string',
        'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type MonitoringScheduleName

string

param MonitoringScheduleName

[REQUIRED]

The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.

type MonitoringScheduleConfig

dict

param MonitoringScheduleConfig

[REQUIRED]

The configuration object that specifies the monitoring schedule and defines the monitoring job.

  • ScheduleConfig (dict) --

    Configures the monitoring schedule.

    • ScheduleExpression (string) -- [REQUIRED]

      A cron expression that describes details about the monitoring schedule.

      The supported cron expressions are:

      • If you want to set the job to start every hour, use the following: Hourly: cron(0 * ? * * *)

      • If you want to start the job daily: cron(0 [00-23] ? * * *)

      For example, the following are valid cron expressions:

      • Daily at noon UTC: cron(0 12 ? * * *)

      • Daily at midnight UTC: cron(0 0 ? * * *)

      To support running every 6, 12 hours, the following are also supported:

      cron(0 [00-23]/[01-24] ? * * *)

      For example, the following are valid cron expressions:

      • Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)

      • Every two hours starting at midnight: cron(0 0/2 ? * * *)

      Note

      • Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.

      • We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.

      You can also specify the keyword NOW to run the monitoring job immediately, one time, without recurring.

    • DataAnalysisStartTime (string) --

      Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify: "-PT5H" .

      The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the DataAnalysisEndTime parameter.

      If you set ScheduleExpression to NOW , this parameter is required.

    • DataAnalysisEndTime (string) --

      Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify: "-PT1H" .

      The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the DataAnalysisStartTime parameter.

      If you set ScheduleExpression to NOW , this parameter is required.

  • MonitoringJobDefinition (dict) --

    Defines the monitoring job.

    • BaselineConfig (dict) --

      Baseline configuration used to validate that the data conforms to the specified constraints and statistics

      • BaseliningJobName (string) --

        The name of the job that performs baselining for the monitoring job.

      • ConstraintsResource (dict) --

        The baseline constraint file in Amazon S3 that the current monitoring job should validated against.

        • S3Uri (string) --

          The Amazon S3 URI for the constraints resource.

      • StatisticsResource (dict) --

        The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.

        • S3Uri (string) --

          The Amazon S3 URI for the statistics resource.

    • MonitoringInputs (list) -- [REQUIRED]

      The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.

      • (dict) --

        The inputs for a monitoring job.

        • EndpointInput (dict) --

          The endpoint for a monitoring job.

          • EndpointName (string) -- [REQUIRED]

            An endpoint in customer's account which has enabled DataCaptureConfig enabled.

          • LocalPath (string) -- [REQUIRED]

            Path to the filesystem where the endpoint data is available to the container.

          • S3InputMode (string) --

            Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

          • S3DataDistributionType (string) --

            Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

          • FeaturesAttribute (string) --

            The attributes of the input data that are the input features.

          • InferenceAttribute (string) --

            The attribute of the input data that represents the ground truth label.

          • ProbabilityAttribute (string) --

            In a classification problem, the attribute that represents the class probability.

          • ProbabilityThresholdAttribute (float) --

            The threshold for the class probability to be evaluated as a positive result.

          • StartTimeOffset (string) --

            If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

          • EndTimeOffset (string) --

            If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

          • ExcludeFeaturesAttribute (string) --

            The attributes of the input data to exclude from the analysis.

        • BatchTransformInput (dict) --

          Input object for the batch transform job.

          • DataCapturedDestinationS3Uri (string) -- [REQUIRED]

            The Amazon S3 location being used to capture the data.

          • DatasetFormat (dict) -- [REQUIRED]

            The dataset format for your batch transform job.

            • Csv (dict) --

              The CSV dataset used in the monitoring job.

              • Header (boolean) --

                Indicates if the CSV data has a header.

            • Json (dict) --

              The JSON dataset used in the monitoring job

              • Line (boolean) --

                Indicates if the file should be read as a json object per line.

            • Parquet (dict) --

              The Parquet dataset used in the monitoring job

          • LocalPath (string) -- [REQUIRED]

            Path to the filesystem where the batch transform data is available to the container.

          • S3InputMode (string) --

            Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

          • S3DataDistributionType (string) --

            Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

          • FeaturesAttribute (string) --

            The attributes of the input data that are the input features.

          • InferenceAttribute (string) --

            The attribute of the input data that represents the ground truth label.

          • ProbabilityAttribute (string) --

            In a classification problem, the attribute that represents the class probability.

          • ProbabilityThresholdAttribute (float) --

            The threshold for the class probability to be evaluated as a positive result.

          • StartTimeOffset (string) --

            If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

          • EndTimeOffset (string) --

            If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

          • ExcludeFeaturesAttribute (string) --

            The attributes of the input data to exclude from the analysis.

    • MonitoringOutputConfig (dict) -- [REQUIRED]

      The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).

      • MonitoringOutputs (list) -- [REQUIRED]

        Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

        • (dict) --

          The output object for a monitoring job.

          • S3Output (dict) -- [REQUIRED]

            The Amazon S3 storage location where the results of a monitoring job are saved.

            • S3Uri (string) -- [REQUIRED]

              A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

            • LocalPath (string) -- [REQUIRED]

              The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

            • S3UploadMode (string) --

              Whether to upload the results of the monitoring job continuously or after the job completes.

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

    • MonitoringResources (dict) -- [REQUIRED]

      Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.

      • ClusterConfig (dict) -- [REQUIRED]

        The configuration for the cluster resources used to run the processing job.

        • InstanceCount (integer) -- [REQUIRED]

          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.

        • InstanceType (string) -- [REQUIRED]

          The ML compute instance type for the processing job.

        • VolumeSizeInGB (integer) -- [REQUIRED]

          The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

        • VolumeKmsKeyId (string) --

          The Amazon Web Services Key Management Service (Amazon Web Services 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.

    • MonitoringAppSpecification (dict) -- [REQUIRED]

      Configures the monitoring job to run a specified Docker container image.

      • ImageUri (string) -- [REQUIRED]

        The container image to be run by the monitoring job.

      • ContainerEntrypoint (list) --

        Specifies the entrypoint for a container used to run the monitoring job.

        • (string) --

      • ContainerArguments (list) --

        An array of arguments for the container used to run the monitoring job.

        • (string) --

      • RecordPreprocessorSourceUri (string) --

        An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

      • PostAnalyticsProcessorSourceUri (string) --

        An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

    • StoppingCondition (dict) --

      Specifies a time limit for how long the monitoring job is allowed to run.

      • MaxRuntimeInSeconds (integer) -- [REQUIRED]

        The maximum runtime allowed in seconds.

        Note

        The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

    • Environment (dict) --

      Sets the environment variables in the Docker container.

      • (string) --

        • (string) --

    • NetworkConfig (dict) --

      Specifies networking options for an monitoring job.

      • EnableInterContainerTrafficEncryption (boolean) --

        Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.

      • EnableNetworkIsolation (boolean) --

        Whether to allow inbound and outbound network calls to and from the containers used for the processing job.

      • VpcConfig (dict) --

        Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

        • SecurityGroupIds (list) -- [REQUIRED]

          The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

          • (string) --

        • Subnets (list) -- [REQUIRED]

          The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

          • (string) --

    • RoleArn (string) -- [REQUIRED]

      The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • MonitoringJobDefinitionName (string) --

    The name of the monitoring job definition to schedule.

  • MonitoringType (string) --

    The type of the monitoring job definition to schedule.

type Tags

list

param Tags

(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

rtype

dict

returns

Response Syntax

{
    'MonitoringScheduleArn': 'string'
}

Response Structure

  • (dict) --

    • MonitoringScheduleArn (string) --

      The Amazon Resource Name (ARN) of the monitoring schedule.

DescribeDataQualityJobDefinition (updated) Link ¶
Changes (response)
{'DataQualityJobInput': {'BatchTransformInput': {'ExcludeFeaturesAttribute': 'string'},
                         'EndpointInput': {'ExcludeFeaturesAttribute': 'string'}}}

Gets the details of a data quality monitoring job definition.

See also: AWS API Documentation

Request Syntax

client.describe_data_quality_job_definition(
    JobDefinitionName='string'
)
type JobDefinitionName

string

param JobDefinitionName

[REQUIRED]

The name of the data quality monitoring job definition to describe.

rtype

dict

returns

Response Syntax

{
    'JobDefinitionArn': 'string',
    'JobDefinitionName': 'string',
    'CreationTime': datetime(2015, 1, 1),
    'DataQualityBaselineConfig': {
        'BaseliningJobName': 'string',
        'ConstraintsResource': {
            'S3Uri': 'string'
        },
        'StatisticsResource': {
            'S3Uri': 'string'
        }
    },
    'DataQualityAppSpecification': {
        'ImageUri': 'string',
        'ContainerEntrypoint': [
            'string',
        ],
        'ContainerArguments': [
            'string',
        ],
        'RecordPreprocessorSourceUri': 'string',
        'PostAnalyticsProcessorSourceUri': 'string',
        'Environment': {
            'string': 'string'
        }
    },
    'DataQualityJobInput': {
        'EndpointInput': {
            'EndpointName': 'string',
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        },
        'BatchTransformInput': {
            'DataCapturedDestinationS3Uri': 'string',
            'DatasetFormat': {
                'Csv': {
                    'Header': True|False
                },
                'Json': {
                    'Line': True|False
                },
                'Parquet': {}
            },
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        }
    },
    'DataQualityJobOutputConfig': {
        'MonitoringOutputs': [
            {
                'S3Output': {
                    'S3Uri': 'string',
                    'LocalPath': 'string',
                    'S3UploadMode': 'Continuous'|'EndOfJob'
                }
            },
        ],
        'KmsKeyId': 'string'
    },
    'JobResources': {
        'ClusterConfig': {
            'InstanceCount': 123,
            'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
            'VolumeSizeInGB': 123,
            'VolumeKmsKeyId': 'string'
        }
    },
    'NetworkConfig': {
        'EnableInterContainerTrafficEncryption': True|False,
        'EnableNetworkIsolation': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    'RoleArn': 'string',
    'StoppingCondition': {
        'MaxRuntimeInSeconds': 123
    }
}

Response Structure

  • (dict) --

    • JobDefinitionArn (string) --

      The Amazon Resource Name (ARN) of the data quality monitoring job definition.

    • JobDefinitionName (string) --

      The name of the data quality monitoring job definition.

    • CreationTime (datetime) --

      The time that the data quality monitoring job definition was created.

    • DataQualityBaselineConfig (dict) --

      The constraints and baselines for the data quality monitoring job definition.

      • BaseliningJobName (string) --

        The name of the job that performs baselining for the data quality monitoring job.

      • ConstraintsResource (dict) --

        The constraints resource for a monitoring job.

        • S3Uri (string) --

          The Amazon S3 URI for the constraints resource.

      • StatisticsResource (dict) --

        The statistics resource for a monitoring job.

        • S3Uri (string) --

          The Amazon S3 URI for the statistics resource.

    • DataQualityAppSpecification (dict) --

      Information about the container that runs the data quality monitoring job.

      • ImageUri (string) --

        The container image that the data quality monitoring job runs.

      • ContainerEntrypoint (list) --

        The entrypoint for a container used to run a monitoring job.

        • (string) --

      • ContainerArguments (list) --

        The arguments to send to the container that the monitoring job runs.

        • (string) --

      • RecordPreprocessorSourceUri (string) --

        An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

      • PostAnalyticsProcessorSourceUri (string) --

        An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

      • Environment (dict) --

        Sets the environment variables in the container that the monitoring job runs.

        • (string) --

          • (string) --

    • DataQualityJobInput (dict) --

      The list of inputs for the data quality monitoring job. Currently endpoints are supported.

      • EndpointInput (dict) --

        Input object for the endpoint

        • EndpointName (string) --

          An endpoint in customer's account which has enabled DataCaptureConfig enabled.

        • LocalPath (string) --

          Path to the filesystem where the endpoint data is available to the container.

        • S3InputMode (string) --

          Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

        • S3DataDistributionType (string) --

          Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

        • FeaturesAttribute (string) --

          The attributes of the input data that are the input features.

        • InferenceAttribute (string) --

          The attribute of the input data that represents the ground truth label.

        • ProbabilityAttribute (string) --

          In a classification problem, the attribute that represents the class probability.

        • ProbabilityThresholdAttribute (float) --

          The threshold for the class probability to be evaluated as a positive result.

        • StartTimeOffset (string) --

          If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • EndTimeOffset (string) --

          If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • ExcludeFeaturesAttribute (string) --

          The attributes of the input data to exclude from the analysis.

      • BatchTransformInput (dict) --

        Input object for the batch transform job.

        • DataCapturedDestinationS3Uri (string) --

          The Amazon S3 location being used to capture the data.

        • DatasetFormat (dict) --

          The dataset format for your batch transform job.

          • Csv (dict) --

            The CSV dataset used in the monitoring job.

            • Header (boolean) --

              Indicates if the CSV data has a header.

          • Json (dict) --

            The JSON dataset used in the monitoring job

            • Line (boolean) --

              Indicates if the file should be read as a json object per line.

          • Parquet (dict) --

            The Parquet dataset used in the monitoring job

        • LocalPath (string) --

          Path to the filesystem where the batch transform data is available to the container.

        • S3InputMode (string) --

          Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

        • S3DataDistributionType (string) --

          Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

        • FeaturesAttribute (string) --

          The attributes of the input data that are the input features.

        • InferenceAttribute (string) --

          The attribute of the input data that represents the ground truth label.

        • ProbabilityAttribute (string) --

          In a classification problem, the attribute that represents the class probability.

        • ProbabilityThresholdAttribute (float) --

          The threshold for the class probability to be evaluated as a positive result.

        • StartTimeOffset (string) --

          If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • EndTimeOffset (string) --

          If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • ExcludeFeaturesAttribute (string) --

          The attributes of the input data to exclude from the analysis.

    • DataQualityJobOutputConfig (dict) --

      The output configuration for monitoring jobs.

      • MonitoringOutputs (list) --

        Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

        • (dict) --

          The output object for a monitoring job.

          • S3Output (dict) --

            The Amazon S3 storage location where the results of a monitoring job are saved.

            • S3Uri (string) --

              A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

            • LocalPath (string) --

              The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

            • S3UploadMode (string) --

              Whether to upload the results of the monitoring job continuously or after the job completes.

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

    • JobResources (dict) --

      Identifies the resources to deploy for a monitoring job.

      • ClusterConfig (dict) --

        The configuration for the cluster resources used to run the processing job.

        • InstanceCount (integer) --

          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.

        • InstanceType (string) --

          The ML compute instance type for the processing job.

        • VolumeSizeInGB (integer) --

          The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

        • VolumeKmsKeyId (string) --

          The Amazon Web Services Key Management Service (Amazon Web Services 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.

    • NetworkConfig (dict) --

      The networking configuration for the data quality monitoring job.

      • EnableInterContainerTrafficEncryption (boolean) --

        Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.

      • EnableNetworkIsolation (boolean) --

        Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.

      • VpcConfig (dict) --

        Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

        • SecurityGroupIds (list) --

          The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

          • (string) --

        • Subnets (list) --

          The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

          • (string) --

    • RoleArn (string) --

      The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

    • StoppingCondition (dict) --

      A time limit for how long the monitoring job is allowed to run before stopping.

      • MaxRuntimeInSeconds (integer) --

        The maximum runtime allowed in seconds.

        Note

        The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

DescribeModelBiasJobDefinition (updated) Link ¶
Changes (response)
{'ModelBiasJobInput': {'BatchTransformInput': {'ExcludeFeaturesAttribute': 'string'},
                       'EndpointInput': {'ExcludeFeaturesAttribute': 'string'}}}

Returns a description of a model bias job definition.

See also: AWS API Documentation

Request Syntax

client.describe_model_bias_job_definition(
    JobDefinitionName='string'
)
type JobDefinitionName

string

param JobDefinitionName

[REQUIRED]

The name of the model bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

rtype

dict

returns

Response Syntax

{
    'JobDefinitionArn': 'string',
    'JobDefinitionName': 'string',
    'CreationTime': datetime(2015, 1, 1),
    'ModelBiasBaselineConfig': {
        'BaseliningJobName': 'string',
        'ConstraintsResource': {
            'S3Uri': 'string'
        }
    },
    'ModelBiasAppSpecification': {
        'ImageUri': 'string',
        'ConfigUri': 'string',
        'Environment': {
            'string': 'string'
        }
    },
    'ModelBiasJobInput': {
        'EndpointInput': {
            'EndpointName': 'string',
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        },
        'BatchTransformInput': {
            'DataCapturedDestinationS3Uri': 'string',
            'DatasetFormat': {
                'Csv': {
                    'Header': True|False
                },
                'Json': {
                    'Line': True|False
                },
                'Parquet': {}
            },
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        },
        'GroundTruthS3Input': {
            'S3Uri': 'string'
        }
    },
    'ModelBiasJobOutputConfig': {
        'MonitoringOutputs': [
            {
                'S3Output': {
                    'S3Uri': 'string',
                    'LocalPath': 'string',
                    'S3UploadMode': 'Continuous'|'EndOfJob'
                }
            },
        ],
        'KmsKeyId': 'string'
    },
    'JobResources': {
        'ClusterConfig': {
            'InstanceCount': 123,
            'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
            'VolumeSizeInGB': 123,
            'VolumeKmsKeyId': 'string'
        }
    },
    'NetworkConfig': {
        'EnableInterContainerTrafficEncryption': True|False,
        'EnableNetworkIsolation': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    'RoleArn': 'string',
    'StoppingCondition': {
        'MaxRuntimeInSeconds': 123
    }
}

Response Structure

  • (dict) --

    • JobDefinitionArn (string) --

      The Amazon Resource Name (ARN) of the model bias job.

    • JobDefinitionName (string) --

      The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

    • CreationTime (datetime) --

      The time at which the model bias job was created.

    • ModelBiasBaselineConfig (dict) --

      The baseline configuration for a model bias job.

      • BaseliningJobName (string) --

        The name of the baseline model bias job.

      • ConstraintsResource (dict) --

        The constraints resource for a monitoring job.

        • S3Uri (string) --

          The Amazon S3 URI for the constraints resource.

    • ModelBiasAppSpecification (dict) --

      Configures the model bias job to run a specified Docker container image.

      • ImageUri (string) --

        The container image to be run by the model bias job.

      • ConfigUri (string) --

        JSON formatted S3 file that defines bias parameters. For more information on this JSON configuration file, see Configure bias parameters.

      • Environment (dict) --

        Sets the environment variables in the Docker container.

        • (string) --

          • (string) --

    • ModelBiasJobInput (dict) --

      Inputs for the model bias job.

      • EndpointInput (dict) --

        Input object for the endpoint

        • EndpointName (string) --

          An endpoint in customer's account which has enabled DataCaptureConfig enabled.

        • LocalPath (string) --

          Path to the filesystem where the endpoint data is available to the container.

        • S3InputMode (string) --

          Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

        • S3DataDistributionType (string) --

          Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

        • FeaturesAttribute (string) --

          The attributes of the input data that are the input features.

        • InferenceAttribute (string) --

          The attribute of the input data that represents the ground truth label.

        • ProbabilityAttribute (string) --

          In a classification problem, the attribute that represents the class probability.

        • ProbabilityThresholdAttribute (float) --

          The threshold for the class probability to be evaluated as a positive result.

        • StartTimeOffset (string) --

          If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • EndTimeOffset (string) --

          If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • ExcludeFeaturesAttribute (string) --

          The attributes of the input data to exclude from the analysis.

      • BatchTransformInput (dict) --

        Input object for the batch transform job.

        • DataCapturedDestinationS3Uri (string) --

          The Amazon S3 location being used to capture the data.

        • DatasetFormat (dict) --

          The dataset format for your batch transform job.

          • Csv (dict) --

            The CSV dataset used in the monitoring job.

            • Header (boolean) --

              Indicates if the CSV data has a header.

          • Json (dict) --

            The JSON dataset used in the monitoring job

            • Line (boolean) --

              Indicates if the file should be read as a json object per line.

          • Parquet (dict) --

            The Parquet dataset used in the monitoring job

        • LocalPath (string) --

          Path to the filesystem where the batch transform data is available to the container.

        • S3InputMode (string) --

          Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

        • S3DataDistributionType (string) --

          Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

        • FeaturesAttribute (string) --

          The attributes of the input data that are the input features.

        • InferenceAttribute (string) --

          The attribute of the input data that represents the ground truth label.

        • ProbabilityAttribute (string) --

          In a classification problem, the attribute that represents the class probability.

        • ProbabilityThresholdAttribute (float) --

          The threshold for the class probability to be evaluated as a positive result.

        • StartTimeOffset (string) --

          If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • EndTimeOffset (string) --

          If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • ExcludeFeaturesAttribute (string) --

          The attributes of the input data to exclude from the analysis.

      • GroundTruthS3Input (dict) --

        Location of ground truth labels to use in model bias job.

        • S3Uri (string) --

          The address of the Amazon S3 location of the ground truth labels.

    • ModelBiasJobOutputConfig (dict) --

      The output configuration for monitoring jobs.

      • MonitoringOutputs (list) --

        Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

        • (dict) --

          The output object for a monitoring job.

          • S3Output (dict) --

            The Amazon S3 storage location where the results of a monitoring job are saved.

            • S3Uri (string) --

              A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

            • LocalPath (string) --

              The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

            • S3UploadMode (string) --

              Whether to upload the results of the monitoring job continuously or after the job completes.

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

    • JobResources (dict) --

      Identifies the resources to deploy for a monitoring job.

      • ClusterConfig (dict) --

        The configuration for the cluster resources used to run the processing job.

        • InstanceCount (integer) --

          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.

        • InstanceType (string) --

          The ML compute instance type for the processing job.

        • VolumeSizeInGB (integer) --

          The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

        • VolumeKmsKeyId (string) --

          The Amazon Web Services Key Management Service (Amazon Web Services 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.

    • NetworkConfig (dict) --

      Networking options for a model bias job.

      • EnableInterContainerTrafficEncryption (boolean) --

        Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.

      • EnableNetworkIsolation (boolean) --

        Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.

      • VpcConfig (dict) --

        Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

        • SecurityGroupIds (list) --

          The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

          • (string) --

        • Subnets (list) --

          The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

          • (string) --

    • RoleArn (string) --

      The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.

    • StoppingCondition (dict) --

      A time limit for how long the monitoring job is allowed to run before stopping.

      • MaxRuntimeInSeconds (integer) --

        The maximum runtime allowed in seconds.

        Note

        The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

DescribeModelExplainabilityJobDefinition (updated) Link ¶
Changes (response)
{'ModelExplainabilityJobInput': {'BatchTransformInput': {'ExcludeFeaturesAttribute': 'string'},
                                 'EndpointInput': {'ExcludeFeaturesAttribute': 'string'}}}

Returns a description of a model explainability job definition.

See also: AWS API Documentation

Request Syntax

client.describe_model_explainability_job_definition(
    JobDefinitionName='string'
)
type JobDefinitionName

string

param JobDefinitionName

[REQUIRED]

The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

rtype

dict

returns

Response Syntax

{
    'JobDefinitionArn': 'string',
    'JobDefinitionName': 'string',
    'CreationTime': datetime(2015, 1, 1),
    'ModelExplainabilityBaselineConfig': {
        'BaseliningJobName': 'string',
        'ConstraintsResource': {
            'S3Uri': 'string'
        }
    },
    'ModelExplainabilityAppSpecification': {
        'ImageUri': 'string',
        'ConfigUri': 'string',
        'Environment': {
            'string': 'string'
        }
    },
    'ModelExplainabilityJobInput': {
        'EndpointInput': {
            'EndpointName': 'string',
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        },
        'BatchTransformInput': {
            'DataCapturedDestinationS3Uri': 'string',
            'DatasetFormat': {
                'Csv': {
                    'Header': True|False
                },
                'Json': {
                    'Line': True|False
                },
                'Parquet': {}
            },
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        }
    },
    'ModelExplainabilityJobOutputConfig': {
        'MonitoringOutputs': [
            {
                'S3Output': {
                    'S3Uri': 'string',
                    'LocalPath': 'string',
                    'S3UploadMode': 'Continuous'|'EndOfJob'
                }
            },
        ],
        'KmsKeyId': 'string'
    },
    'JobResources': {
        'ClusterConfig': {
            'InstanceCount': 123,
            'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
            'VolumeSizeInGB': 123,
            'VolumeKmsKeyId': 'string'
        }
    },
    'NetworkConfig': {
        'EnableInterContainerTrafficEncryption': True|False,
        'EnableNetworkIsolation': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    'RoleArn': 'string',
    'StoppingCondition': {
        'MaxRuntimeInSeconds': 123
    }
}

Response Structure

  • (dict) --

    • JobDefinitionArn (string) --

      The Amazon Resource Name (ARN) of the model explainability job.

    • JobDefinitionName (string) --

      The name of the explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

    • CreationTime (datetime) --

      The time at which the model explainability job was created.

    • ModelExplainabilityBaselineConfig (dict) --

      The baseline configuration for a model explainability job.

      • BaseliningJobName (string) --

        The name of the baseline model explainability job.

      • ConstraintsResource (dict) --

        The constraints resource for a monitoring job.

        • S3Uri (string) --

          The Amazon S3 URI for the constraints resource.

    • ModelExplainabilityAppSpecification (dict) --

      Configures the model explainability job to run a specified Docker container image.

      • ImageUri (string) --

        The container image to be run by the model explainability job.

      • ConfigUri (string) --

        JSON formatted S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters.

      • Environment (dict) --

        Sets the environment variables in the Docker container.

        • (string) --

          • (string) --

    • ModelExplainabilityJobInput (dict) --

      Inputs for the model explainability job.

      • EndpointInput (dict) --

        Input object for the endpoint

        • EndpointName (string) --

          An endpoint in customer's account which has enabled DataCaptureConfig enabled.

        • LocalPath (string) --

          Path to the filesystem where the endpoint data is available to the container.

        • S3InputMode (string) --

          Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

        • S3DataDistributionType (string) --

          Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

        • FeaturesAttribute (string) --

          The attributes of the input data that are the input features.

        • InferenceAttribute (string) --

          The attribute of the input data that represents the ground truth label.

        • ProbabilityAttribute (string) --

          In a classification problem, the attribute that represents the class probability.

        • ProbabilityThresholdAttribute (float) --

          The threshold for the class probability to be evaluated as a positive result.

        • StartTimeOffset (string) --

          If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • EndTimeOffset (string) --

          If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • ExcludeFeaturesAttribute (string) --

          The attributes of the input data to exclude from the analysis.

      • BatchTransformInput (dict) --

        Input object for the batch transform job.

        • DataCapturedDestinationS3Uri (string) --

          The Amazon S3 location being used to capture the data.

        • DatasetFormat (dict) --

          The dataset format for your batch transform job.

          • Csv (dict) --

            The CSV dataset used in the monitoring job.

            • Header (boolean) --

              Indicates if the CSV data has a header.

          • Json (dict) --

            The JSON dataset used in the monitoring job

            • Line (boolean) --

              Indicates if the file should be read as a json object per line.

          • Parquet (dict) --

            The Parquet dataset used in the monitoring job

        • LocalPath (string) --

          Path to the filesystem where the batch transform data is available to the container.

        • S3InputMode (string) --

          Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

        • S3DataDistributionType (string) --

          Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

        • FeaturesAttribute (string) --

          The attributes of the input data that are the input features.

        • InferenceAttribute (string) --

          The attribute of the input data that represents the ground truth label.

        • ProbabilityAttribute (string) --

          In a classification problem, the attribute that represents the class probability.

        • ProbabilityThresholdAttribute (float) --

          The threshold for the class probability to be evaluated as a positive result.

        • StartTimeOffset (string) --

          If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • EndTimeOffset (string) --

          If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • ExcludeFeaturesAttribute (string) --

          The attributes of the input data to exclude from the analysis.

    • ModelExplainabilityJobOutputConfig (dict) --

      The output configuration for monitoring jobs.

      • MonitoringOutputs (list) --

        Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

        • (dict) --

          The output object for a monitoring job.

          • S3Output (dict) --

            The Amazon S3 storage location where the results of a monitoring job are saved.

            • S3Uri (string) --

              A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

            • LocalPath (string) --

              The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

            • S3UploadMode (string) --

              Whether to upload the results of the monitoring job continuously or after the job completes.

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

    • JobResources (dict) --

      Identifies the resources to deploy for a monitoring job.

      • ClusterConfig (dict) --

        The configuration for the cluster resources used to run the processing job.

        • InstanceCount (integer) --

          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.

        • InstanceType (string) --

          The ML compute instance type for the processing job.

        • VolumeSizeInGB (integer) --

          The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

        • VolumeKmsKeyId (string) --

          The Amazon Web Services Key Management Service (Amazon Web Services 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.

    • NetworkConfig (dict) --

      Networking options for a model explainability job.

      • EnableInterContainerTrafficEncryption (boolean) --

        Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.

      • EnableNetworkIsolation (boolean) --

        Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.

      • VpcConfig (dict) --

        Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

        • SecurityGroupIds (list) --

          The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

          • (string) --

        • Subnets (list) --

          The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

          • (string) --

    • RoleArn (string) --

      The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.

    • StoppingCondition (dict) --

      A time limit for how long the monitoring job is allowed to run before stopping.

      • MaxRuntimeInSeconds (integer) --

        The maximum runtime allowed in seconds.

        Note

        The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

DescribeModelQualityJobDefinition (updated) Link ¶
Changes (response)
{'ModelQualityJobInput': {'BatchTransformInput': {'ExcludeFeaturesAttribute': 'string'},
                          'EndpointInput': {'ExcludeFeaturesAttribute': 'string'}}}

Returns a description of a model quality job definition.

See also: AWS API Documentation

Request Syntax

client.describe_model_quality_job_definition(
    JobDefinitionName='string'
)
type JobDefinitionName

string

param JobDefinitionName

[REQUIRED]

The name of the model quality job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

rtype

dict

returns

Response Syntax

{
    'JobDefinitionArn': 'string',
    'JobDefinitionName': 'string',
    'CreationTime': datetime(2015, 1, 1),
    'ModelQualityBaselineConfig': {
        'BaseliningJobName': 'string',
        'ConstraintsResource': {
            'S3Uri': 'string'
        }
    },
    'ModelQualityAppSpecification': {
        'ImageUri': 'string',
        'ContainerEntrypoint': [
            'string',
        ],
        'ContainerArguments': [
            'string',
        ],
        'RecordPreprocessorSourceUri': 'string',
        'PostAnalyticsProcessorSourceUri': 'string',
        'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
        'Environment': {
            'string': 'string'
        }
    },
    'ModelQualityJobInput': {
        'EndpointInput': {
            'EndpointName': 'string',
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        },
        'BatchTransformInput': {
            'DataCapturedDestinationS3Uri': 'string',
            'DatasetFormat': {
                'Csv': {
                    'Header': True|False
                },
                'Json': {
                    'Line': True|False
                },
                'Parquet': {}
            },
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string',
            'ExcludeFeaturesAttribute': 'string'
        },
        'GroundTruthS3Input': {
            'S3Uri': 'string'
        }
    },
    'ModelQualityJobOutputConfig': {
        'MonitoringOutputs': [
            {
                'S3Output': {
                    'S3Uri': 'string',
                    'LocalPath': 'string',
                    'S3UploadMode': 'Continuous'|'EndOfJob'
                }
            },
        ],
        'KmsKeyId': 'string'
    },
    'JobResources': {
        'ClusterConfig': {
            'InstanceCount': 123,
            'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
            'VolumeSizeInGB': 123,
            'VolumeKmsKeyId': 'string'
        }
    },
    'NetworkConfig': {
        'EnableInterContainerTrafficEncryption': True|False,
        'EnableNetworkIsolation': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    'RoleArn': 'string',
    'StoppingCondition': {
        'MaxRuntimeInSeconds': 123
    }
}

Response Structure

  • (dict) --

    • JobDefinitionArn (string) --

      The Amazon Resource Name (ARN) of the model quality job.

    • JobDefinitionName (string) --

      The name of the quality job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

    • CreationTime (datetime) --

      The time at which the model quality job was created.

    • ModelQualityBaselineConfig (dict) --

      The baseline configuration for a model quality job.

      • BaseliningJobName (string) --

        The name of the job that performs baselining for the monitoring job.

      • ConstraintsResource (dict) --

        The constraints resource for a monitoring job.

        • S3Uri (string) --

          The Amazon S3 URI for the constraints resource.

    • ModelQualityAppSpecification (dict) --

      Configures the model quality job to run a specified Docker container image.

      • ImageUri (string) --

        The address of the container image that the monitoring job runs.

      • ContainerEntrypoint (list) --

        Specifies the entrypoint for a container that the monitoring job runs.

        • (string) --

      • ContainerArguments (list) --

        An array of arguments for the container used to run the monitoring job.

        • (string) --

      • RecordPreprocessorSourceUri (string) --

        An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

      • PostAnalyticsProcessorSourceUri (string) --

        An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

      • ProblemType (string) --

        The machine learning problem type of the model that the monitoring job monitors.

      • Environment (dict) --

        Sets the environment variables in the container that the monitoring job runs.

        • (string) --

          • (string) --

    • ModelQualityJobInput (dict) --

      Inputs for the model quality job.

      • EndpointInput (dict) --

        Input object for the endpoint

        • EndpointName (string) --

          An endpoint in customer's account which has enabled DataCaptureConfig enabled.

        • LocalPath (string) --

          Path to the filesystem where the endpoint data is available to the container.

        • S3InputMode (string) --

          Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

        • S3DataDistributionType (string) --

          Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

        • FeaturesAttribute (string) --

          The attributes of the input data that are the input features.

        • InferenceAttribute (string) --

          The attribute of the input data that represents the ground truth label.

        • ProbabilityAttribute (string) --

          In a classification problem, the attribute that represents the class probability.

        • ProbabilityThresholdAttribute (float) --

          The threshold for the class probability to be evaluated as a positive result.

        • StartTimeOffset (string) --

          If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • EndTimeOffset (string) --

          If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • ExcludeFeaturesAttribute (string) --

          The attributes of the input data to exclude from the analysis.

      • BatchTransformInput (dict) --

        Input object for the batch transform job.

        • DataCapturedDestinationS3Uri (string) --

          The Amazon S3 location being used to capture the data.

        • DatasetFormat (dict) --

          The dataset format for your batch transform job.

          • Csv (dict) --

            The CSV dataset used in the monitoring job.

            • Header (boolean) --

              Indicates if the CSV data has a header.

          • Json (dict) --

            The JSON dataset used in the monitoring job

            • Line (boolean) --

              Indicates if the file should be read as a json object per line.

          • Parquet (dict) --

            The Parquet dataset used in the monitoring job

        • LocalPath (string) --

          Path to the filesystem where the batch transform data is available to the container.

        • S3InputMode (string) --

          Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

        • S3DataDistributionType (string) --

          Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

        • FeaturesAttribute (string) --

          The attributes of the input data that are the input features.

        • InferenceAttribute (string) --

          The attribute of the input data that represents the ground truth label.

        • ProbabilityAttribute (string) --

          In a classification problem, the attribute that represents the class probability.

        • ProbabilityThresholdAttribute (float) --

          The threshold for the class probability to be evaluated as a positive result.

        • StartTimeOffset (string) --

          If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • EndTimeOffset (string) --

          If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

        • ExcludeFeaturesAttribute (string) --

          The attributes of the input data to exclude from the analysis.

      • GroundTruthS3Input (dict) --

        The ground truth label provided for the model.

        • S3Uri (string) --

          The address of the Amazon S3 location of the ground truth labels.

    • ModelQualityJobOutputConfig (dict) --

      The output configuration for monitoring jobs.

      • MonitoringOutputs (list) --

        Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

        • (dict) --

          The output object for a monitoring job.

          • S3Output (dict) --

            The Amazon S3 storage location where the results of a monitoring job are saved.

            • S3Uri (string) --

              A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

            • LocalPath (string) --

              The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

            • S3UploadMode (string) --

              Whether to upload the results of the monitoring job continuously or after the job completes.

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

    • JobResources (dict) --

      Identifies the resources to deploy for a monitoring job.

      • ClusterConfig (dict) --

        The configuration for the cluster resources used to run the processing job.

        • InstanceCount (integer) --

          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.

        • InstanceType (string) --

          The ML compute instance type for the processing job.

        • VolumeSizeInGB (integer) --

          The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

        • VolumeKmsKeyId (string) --

          The Amazon Web Services Key Management Service (Amazon Web Services 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.

    • NetworkConfig (dict) --

      Networking options for a model quality job.

      • EnableInterContainerTrafficEncryption (boolean) --

        Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.

      • EnableNetworkIsolation (boolean) --

        Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.

      • VpcConfig (dict) --

        Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

        • SecurityGroupIds (list) --

          The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

          • (string) --

        • Subnets (list) --

          The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

          • (string) --

    • RoleArn (string) --

      The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

    • StoppingCondition (dict) --

      A time limit for how long the monitoring job is allowed to run before stopping.

      • MaxRuntimeInSeconds (integer) --

        The maximum runtime allowed in seconds.

        Note

        The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

DescribeMonitoringSchedule (updated) Link ¶
Changes (response)
{'MonitoringScheduleConfig': {'MonitoringJobDefinition': {'MonitoringInputs': {'BatchTransformInput': {'ExcludeFeaturesAttribute': 'string'},
                                                                               'EndpointInput': {'ExcludeFeaturesAttribute': 'string'}}},
                              'ScheduleConfig': {'DataAnalysisEndTime': 'string',
                                                 'DataAnalysisStartTime': 'string'}}}

Describes the schedule for a monitoring job.

See also: AWS API Documentation

Request Syntax

client.describe_monitoring_schedule(
    MonitoringScheduleName='string'
)
type MonitoringScheduleName

string

param MonitoringScheduleName

[REQUIRED]

Name of a previously created monitoring schedule.

rtype

dict

returns

Response Syntax

{
    'MonitoringScheduleArn': 'string',
    'MonitoringScheduleName': 'string',
    'MonitoringScheduleStatus': 'Pending'|'Failed'|'Scheduled'|'Stopped',
    'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability',
    'FailureReason': 'string',
    'CreationTime': datetime(2015, 1, 1),
    'LastModifiedTime': datetime(2015, 1, 1),
    'MonitoringScheduleConfig': {
        'ScheduleConfig': {
            'ScheduleExpression': 'string',
            'DataAnalysisStartTime': 'string',
            'DataAnalysisEndTime': 'string'
        },
        'MonitoringJobDefinition': {
            'BaselineConfig': {
                'BaseliningJobName': 'string',
                'ConstraintsResource': {
                    'S3Uri': 'string'
                },
                'StatisticsResource': {
                    'S3Uri': 'string'
                }
            },
            'MonitoringInputs': [
                {
                    'EndpointInput': {
                        'EndpointName': 'string',
                        'LocalPath': 'string',
                        'S3InputMode': 'Pipe'|'File',
                        'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                        'FeaturesAttribute': 'string',
                        'InferenceAttribute': 'string',
                        'ProbabilityAttribute': 'string',
                        'ProbabilityThresholdAttribute': 123.0,
                        'StartTimeOffset': 'string',
                        'EndTimeOffset': 'string',
                        'ExcludeFeaturesAttribute': 'string'
                    },
                    'BatchTransformInput': {
                        'DataCapturedDestinationS3Uri': 'string',
                        'DatasetFormat': {
                            'Csv': {
                                'Header': True|False
                            },
                            'Json': {
                                'Line': True|False
                            },
                            'Parquet': {}
                        },
                        'LocalPath': 'string',
                        'S3InputMode': 'Pipe'|'File',
                        'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                        'FeaturesAttribute': 'string',
                        'InferenceAttribute': 'string',
                        'ProbabilityAttribute': 'string',
                        'ProbabilityThresholdAttribute': 123.0,
                        'StartTimeOffset': 'string',
                        'EndTimeOffset': 'string',
                        'ExcludeFeaturesAttribute': 'string'
                    }
                },
            ],
            'MonitoringOutputConfig': {
                'MonitoringOutputs': [
                    {
                        'S3Output': {
                            'S3Uri': 'string',
                            'LocalPath': 'string',
                            'S3UploadMode': 'Continuous'|'EndOfJob'
                        }
                    },
                ],
                'KmsKeyId': 'string'
            },
            'MonitoringResources': {
                'ClusterConfig': {
                    'InstanceCount': 123,
                    'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
                    'VolumeSizeInGB': 123,
                    'VolumeKmsKeyId': 'string'
                }
            },
            'MonitoringAppSpecification': {
                'ImageUri': 'string',
                'ContainerEntrypoint': [
                    'string',
                ],
                'ContainerArguments': [
                    'string',
                ],
                'RecordPreprocessorSourceUri': 'string',
                'PostAnalyticsProcessorSourceUri': 'string'
            },
            'StoppingCondition': {
                'MaxRuntimeInSeconds': 123
            },
            'Environment': {
                'string': 'string'
            },
            'NetworkConfig': {
                'EnableInterContainerTrafficEncryption': True|False,
                'EnableNetworkIsolation': True|False,
                'VpcConfig': {
                    'SecurityGroupIds': [
                        'string',
                    ],
                    'Subnets': [
                        'string',
                    ]
                }
            },
            'RoleArn': 'string'
        },
        'MonitoringJobDefinitionName': 'string',
        'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
    },
    'EndpointName': 'string',
    'LastMonitoringExecutionSummary': {
        'MonitoringScheduleName': 'string',
        'ScheduledTime': datetime(2015, 1, 1),
        'CreationTime': datetime(2015, 1, 1),
        'LastModifiedTime': datetime(2015, 1, 1),
        'MonitoringExecutionStatus': 'Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped',
        'ProcessingJobArn': 'string',
        'EndpointName': 'string',
        'FailureReason': 'string',
        'MonitoringJobDefinitionName': 'string',
        'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
    }
}

Response Structure

  • (dict) --

    • MonitoringScheduleArn (string) --

      The Amazon Resource Name (ARN) of the monitoring schedule.

    • MonitoringScheduleName (string) --

      Name of the monitoring schedule.

    • MonitoringScheduleStatus (string) --

      The status of an monitoring job.

    • MonitoringType (string) --

      The type of the monitoring job that this schedule runs. This is one of the following values.

      • DATA_QUALITY - The schedule is for a data quality monitoring job.

      • MODEL_QUALITY - The schedule is for a model quality monitoring job.

      • MODEL_BIAS - The schedule is for a bias monitoring job.

      • MODEL_EXPLAINABILITY - The schedule is for an explainability monitoring job.

    • FailureReason (string) --

      A string, up to one KB in size, that contains the reason a monitoring job failed, if it failed.

    • CreationTime (datetime) --

      The time at which the monitoring job was created.

    • LastModifiedTime (datetime) --

      The time at which the monitoring job was last modified.

    • MonitoringScheduleConfig (dict) --

      The configuration object that specifies the monitoring schedule and defines the monitoring job.

      • ScheduleConfig (dict) --

        Configures the monitoring schedule.

        • ScheduleExpression (string) --

          A cron expression that describes details about the monitoring schedule.

          The supported cron expressions are:

          • If you want to set the job to start every hour, use the following: Hourly: cron(0 * ? * * *)

          • If you want to start the job daily: cron(0 [00-23] ? * * *)

          For example, the following are valid cron expressions:

          • Daily at noon UTC: cron(0 12 ? * * *)

          • Daily at midnight UTC: cron(0 0 ? * * *)

          To support running every 6, 12 hours, the following are also supported:

          cron(0 [00-23]/[01-24] ? * * *)

          For example, the following are valid cron expressions:

          • Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)

          • Every two hours starting at midnight: cron(0 0/2 ? * * *)

          Note

          • Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.

          • We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.

          You can also specify the keyword NOW to run the monitoring job immediately, one time, without recurring.

        • DataAnalysisStartTime (string) --

          Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify: "-PT5H" .

          The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the DataAnalysisEndTime parameter.

          If you set ScheduleExpression to NOW , this parameter is required.

        • DataAnalysisEndTime (string) --

          Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify: "-PT1H" .

          The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the DataAnalysisStartTime parameter.

          If you set ScheduleExpression to NOW , this parameter is required.

      • MonitoringJobDefinition (dict) --

        Defines the monitoring job.

        • BaselineConfig (dict) --

          Baseline configuration used to validate that the data conforms to the specified constraints and statistics

          • BaseliningJobName (string) --

            The name of the job that performs baselining for the monitoring job.

          • ConstraintsResource (dict) --

            The baseline constraint file in Amazon S3 that the current monitoring job should validated against.

            • S3Uri (string) --

              The Amazon S3 URI for the constraints resource.

          • StatisticsResource (dict) --

            The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.

            • S3Uri (string) --

              The Amazon S3 URI for the statistics resource.

        • MonitoringInputs (list) --

          The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.

          • (dict) --

            The inputs for a monitoring job.

            • EndpointInput (dict) --

              The endpoint for a monitoring job.

              • EndpointName (string) --

                An endpoint in customer's account which has enabled DataCaptureConfig enabled.

              • LocalPath (string) --

                Path to the filesystem where the endpoint data is available to the container.

              • S3InputMode (string) --

                Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

              • S3DataDistributionType (string) --

                Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

              • FeaturesAttribute (string) --

                The attributes of the input data that are the input features.

              • InferenceAttribute (string) --

                The attribute of the input data that represents the ground truth label.

              • ProbabilityAttribute (string) --

                In a classification problem, the attribute that represents the class probability.

              • ProbabilityThresholdAttribute (float) --

                The threshold for the class probability to be evaluated as a positive result.

              • StartTimeOffset (string) --

                If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

              • EndTimeOffset (string) --

                If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

              • ExcludeFeaturesAttribute (string) --

                The attributes of the input data to exclude from the analysis.

            • BatchTransformInput (dict) --

              Input object for the batch transform job.

              • DataCapturedDestinationS3Uri (string) --

                The Amazon S3 location being used to capture the data.

              • DatasetFormat (dict) --

                The dataset format for your batch transform job.

                • Csv (dict) --

                  The CSV dataset used in the monitoring job.

                  • Header (boolean) --

                    Indicates if the CSV data has a header.

                • Json (dict) --

                  The JSON dataset used in the monitoring job

                  • Line (boolean) --

                    Indicates if the file should be read as a json object per line.

                • Parquet (dict) --

                  The Parquet dataset used in the monitoring job

              • LocalPath (string) --

                Path to the filesystem where the batch transform data is available to the container.

              • S3InputMode (string) --

                Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

              • S3DataDistributionType (string) --

                Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

              • FeaturesAttribute (string) --

                The attributes of the input data that are the input features.

              • InferenceAttribute (string) --

                The attribute of the input data that represents the ground truth label.

              • ProbabilityAttribute (string) --

                In a classification problem, the attribute that represents the class probability.

              • ProbabilityThresholdAttribute (float) --

                The threshold for the class probability to be evaluated as a positive result.

              • StartTimeOffset (string) --

                If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

              • EndTimeOffset (string) --

                If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

              • ExcludeFeaturesAttribute (string) --

                The attributes of the input data to exclude from the analysis.

        • MonitoringOutputConfig (dict) --

          The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).

          • MonitoringOutputs (list) --

            Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

            • (dict) --

              The output object for a monitoring job.

              • S3Output (dict) --

                The Amazon S3 storage location where the results of a monitoring job are saved.

                • S3Uri (string) --

                  A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

                • LocalPath (string) --

                  The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

                • S3UploadMode (string) --

                  Whether to upload the results of the monitoring job continuously or after the job completes.

          • KmsKeyId (string) --

            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

        • MonitoringResources (dict) --

          Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.

          • ClusterConfig (dict) --

            The configuration for the cluster resources used to run the processing job.

            • InstanceCount (integer) --

              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.

            • InstanceType (string) --

              The ML compute instance type for the processing job.

            • VolumeSizeInGB (integer) --

              The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

            • VolumeKmsKeyId (string) --

              The Amazon Web Services Key Management Service (Amazon Web Services 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.

        • MonitoringAppSpecification (dict) --

          Configures the monitoring job to run a specified Docker container image.

          • ImageUri (string) --

            The container image to be run by the monitoring job.

          • ContainerEntrypoint (list) --

            Specifies the entrypoint for a container used to run the monitoring job.

            • (string) --

          • ContainerArguments (list) --

            An array of arguments for the container used to run the monitoring job.

            • (string) --

          • RecordPreprocessorSourceUri (string) --

            An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

          • PostAnalyticsProcessorSourceUri (string) --

            An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

        • StoppingCondition (dict) --

          Specifies a time limit for how long the monitoring job is allowed to run.

          • MaxRuntimeInSeconds (integer) --

            The maximum runtime allowed in seconds.

            Note

            The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

        • Environment (dict) --

          Sets the environment variables in the Docker container.

          • (string) --

            • (string) --

        • NetworkConfig (dict) --

          Specifies networking options for an monitoring job.

          • EnableInterContainerTrafficEncryption (boolean) --

            Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.

          • EnableNetworkIsolation (boolean) --

            Whether to allow inbound and outbound network calls to and from the containers used for the processing job.

          • VpcConfig (dict) --

            Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

            • SecurityGroupIds (list) --

              The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

              • (string) --

            • Subnets (list) --

              The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

              • (string) --

        • RoleArn (string) --

          The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

      • MonitoringJobDefinitionName (string) --

        The name of the monitoring job definition to schedule.

      • MonitoringType (string) --

        The type of the monitoring job definition to schedule.

    • EndpointName (string) --

      The name of the endpoint for the monitoring job.

    • LastMonitoringExecutionSummary (dict) --

      Describes metadata on the last execution to run, if there was one.

      • MonitoringScheduleName (string) --

        The name of the monitoring schedule.

      • ScheduledTime (datetime) --

        The time the monitoring job was scheduled.

      • CreationTime (datetime) --

        The time at which the monitoring job was created.

      • LastModifiedTime (datetime) --

        A timestamp that indicates the last time the monitoring job was modified.

      • MonitoringExecutionStatus (string) --

        The status of the monitoring job.

      • ProcessingJobArn (string) --

        The Amazon Resource Name (ARN) of the monitoring job.

      • EndpointName (string) --

        The name of the endpoint used to run the monitoring job.

      • FailureReason (string) --

        Contains the reason a monitoring job failed, if it failed.

      • MonitoringJobDefinitionName (string) --

        The name of the monitoring job.

      • MonitoringType (string) --

        The type of the monitoring job.

UpdateMonitoringSchedule (updated) Link ¶
Changes (request)
{'MonitoringScheduleConfig': {'MonitoringJobDefinition': {'MonitoringInputs': {'BatchTransformInput': {'ExcludeFeaturesAttribute': 'string'},
                                                                               'EndpointInput': {'ExcludeFeaturesAttribute': 'string'}}},
                              'ScheduleConfig': {'DataAnalysisEndTime': 'string',
                                                 'DataAnalysisStartTime': 'string'}}}

Updates a previously created schedule.

See also: AWS API Documentation

Request Syntax

client.update_monitoring_schedule(
    MonitoringScheduleName='string',
    MonitoringScheduleConfig={
        'ScheduleConfig': {
            'ScheduleExpression': 'string',
            'DataAnalysisStartTime': 'string',
            'DataAnalysisEndTime': 'string'
        },
        'MonitoringJobDefinition': {
            'BaselineConfig': {
                'BaseliningJobName': 'string',
                'ConstraintsResource': {
                    'S3Uri': 'string'
                },
                'StatisticsResource': {
                    'S3Uri': 'string'
                }
            },
            'MonitoringInputs': [
                {
                    'EndpointInput': {
                        'EndpointName': 'string',
                        'LocalPath': 'string',
                        'S3InputMode': 'Pipe'|'File',
                        'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                        'FeaturesAttribute': 'string',
                        'InferenceAttribute': 'string',
                        'ProbabilityAttribute': 'string',
                        'ProbabilityThresholdAttribute': 123.0,
                        'StartTimeOffset': 'string',
                        'EndTimeOffset': 'string',
                        'ExcludeFeaturesAttribute': 'string'
                    },
                    'BatchTransformInput': {
                        'DataCapturedDestinationS3Uri': 'string',
                        'DatasetFormat': {
                            'Csv': {
                                'Header': True|False
                            },
                            'Json': {
                                'Line': True|False
                            },
                            'Parquet': {}

                        },
                        'LocalPath': 'string',
                        'S3InputMode': 'Pipe'|'File',
                        'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                        'FeaturesAttribute': 'string',
                        'InferenceAttribute': 'string',
                        'ProbabilityAttribute': 'string',
                        'ProbabilityThresholdAttribute': 123.0,
                        'StartTimeOffset': 'string',
                        'EndTimeOffset': 'string',
                        'ExcludeFeaturesAttribute': 'string'
                    }
                },
            ],
            'MonitoringOutputConfig': {
                'MonitoringOutputs': [
                    {
                        'S3Output': {
                            'S3Uri': 'string',
                            'LocalPath': 'string',
                            'S3UploadMode': 'Continuous'|'EndOfJob'
                        }
                    },
                ],
                'KmsKeyId': 'string'
            },
            'MonitoringResources': {
                'ClusterConfig': {
                    'InstanceCount': 123,
                    'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
                    'VolumeSizeInGB': 123,
                    'VolumeKmsKeyId': 'string'
                }
            },
            'MonitoringAppSpecification': {
                'ImageUri': 'string',
                'ContainerEntrypoint': [
                    'string',
                ],
                'ContainerArguments': [
                    'string',
                ],
                'RecordPreprocessorSourceUri': 'string',
                'PostAnalyticsProcessorSourceUri': 'string'
            },
            'StoppingCondition': {
                'MaxRuntimeInSeconds': 123
            },
            'Environment': {
                'string': 'string'
            },
            'NetworkConfig': {
                'EnableInterContainerTrafficEncryption': True|False,
                'EnableNetworkIsolation': True|False,
                'VpcConfig': {
                    'SecurityGroupIds': [
                        'string',
                    ],
                    'Subnets': [
                        'string',
                    ]
                }
            },
            'RoleArn': 'string'
        },
        'MonitoringJobDefinitionName': 'string',
        'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
    }
)
type MonitoringScheduleName

string

param MonitoringScheduleName

[REQUIRED]

The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.

type MonitoringScheduleConfig

dict

param MonitoringScheduleConfig

[REQUIRED]

The configuration object that specifies the monitoring schedule and defines the monitoring job.

  • ScheduleConfig (dict) --

    Configures the monitoring schedule.

    • ScheduleExpression (string) -- [REQUIRED]

      A cron expression that describes details about the monitoring schedule.

      The supported cron expressions are:

      • If you want to set the job to start every hour, use the following: Hourly: cron(0 * ? * * *)

      • If you want to start the job daily: cron(0 [00-23] ? * * *)

      For example, the following are valid cron expressions:

      • Daily at noon UTC: cron(0 12 ? * * *)

      • Daily at midnight UTC: cron(0 0 ? * * *)

      To support running every 6, 12 hours, the following are also supported:

      cron(0 [00-23]/[01-24] ? * * *)

      For example, the following are valid cron expressions:

      • Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)

      • Every two hours starting at midnight: cron(0 0/2 ? * * *)

      Note

      • Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.

      • We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.

      You can also specify the keyword NOW to run the monitoring job immediately, one time, without recurring.

    • DataAnalysisStartTime (string) --

      Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify: "-PT5H" .

      The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the DataAnalysisEndTime parameter.

      If you set ScheduleExpression to NOW , this parameter is required.

    • DataAnalysisEndTime (string) --

      Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify: "-PT1H" .

      The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the DataAnalysisStartTime parameter.

      If you set ScheduleExpression to NOW , this parameter is required.

  • MonitoringJobDefinition (dict) --

    Defines the monitoring job.

    • BaselineConfig (dict) --

      Baseline configuration used to validate that the data conforms to the specified constraints and statistics

      • BaseliningJobName (string) --

        The name of the job that performs baselining for the monitoring job.

      • ConstraintsResource (dict) --

        The baseline constraint file in Amazon S3 that the current monitoring job should validated against.

        • S3Uri (string) --

          The Amazon S3 URI for the constraints resource.

      • StatisticsResource (dict) --

        The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.

        • S3Uri (string) --

          The Amazon S3 URI for the statistics resource.

    • MonitoringInputs (list) -- [REQUIRED]

      The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.

      • (dict) --

        The inputs for a monitoring job.

        • EndpointInput (dict) --

          The endpoint for a monitoring job.

          • EndpointName (string) -- [REQUIRED]

            An endpoint in customer's account which has enabled DataCaptureConfig enabled.

          • LocalPath (string) -- [REQUIRED]

            Path to the filesystem where the endpoint data is available to the container.

          • S3InputMode (string) --

            Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

          • S3DataDistributionType (string) --

            Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

          • FeaturesAttribute (string) --

            The attributes of the input data that are the input features.

          • InferenceAttribute (string) --

            The attribute of the input data that represents the ground truth label.

          • ProbabilityAttribute (string) --

            In a classification problem, the attribute that represents the class probability.

          • ProbabilityThresholdAttribute (float) --

            The threshold for the class probability to be evaluated as a positive result.

          • StartTimeOffset (string) --

            If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

          • EndTimeOffset (string) --

            If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

          • ExcludeFeaturesAttribute (string) --

            The attributes of the input data to exclude from the analysis.

        • BatchTransformInput (dict) --

          Input object for the batch transform job.

          • DataCapturedDestinationS3Uri (string) -- [REQUIRED]

            The Amazon S3 location being used to capture the data.

          • DatasetFormat (dict) -- [REQUIRED]

            The dataset format for your batch transform job.

            • Csv (dict) --

              The CSV dataset used in the monitoring job.

              • Header (boolean) --

                Indicates if the CSV data has a header.

            • Json (dict) --

              The JSON dataset used in the monitoring job

              • Line (boolean) --

                Indicates if the file should be read as a json object per line.

            • Parquet (dict) --

              The Parquet dataset used in the monitoring job

          • LocalPath (string) -- [REQUIRED]

            Path to the filesystem where the batch transform data is available to the container.

          • S3InputMode (string) --

            Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

          • S3DataDistributionType (string) --

            Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

          • FeaturesAttribute (string) --

            The attributes of the input data that are the input features.

          • InferenceAttribute (string) --

            The attribute of the input data that represents the ground truth label.

          • ProbabilityAttribute (string) --

            In a classification problem, the attribute that represents the class probability.

          • ProbabilityThresholdAttribute (float) --

            The threshold for the class probability to be evaluated as a positive result.

          • StartTimeOffset (string) --

            If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

          • EndTimeOffset (string) --

            If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

          • ExcludeFeaturesAttribute (string) --

            The attributes of the input data to exclude from the analysis.

    • MonitoringOutputConfig (dict) -- [REQUIRED]

      The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).

      • MonitoringOutputs (list) -- [REQUIRED]

        Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

        • (dict) --

          The output object for a monitoring job.

          • S3Output (dict) -- [REQUIRED]

            The Amazon S3 storage location where the results of a monitoring job are saved.

            • S3Uri (string) -- [REQUIRED]

              A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

            • LocalPath (string) -- [REQUIRED]

              The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

            • S3UploadMode (string) --

              Whether to upload the results of the monitoring job continuously or after the job completes.

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

    • MonitoringResources (dict) -- [REQUIRED]

      Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.

      • ClusterConfig (dict) -- [REQUIRED]

        The configuration for the cluster resources used to run the processing job.

        • InstanceCount (integer) -- [REQUIRED]

          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.

        • InstanceType (string) -- [REQUIRED]

          The ML compute instance type for the processing job.

        • VolumeSizeInGB (integer) -- [REQUIRED]

          The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

        • VolumeKmsKeyId (string) --

          The Amazon Web Services Key Management Service (Amazon Web Services 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.

    • MonitoringAppSpecification (dict) -- [REQUIRED]

      Configures the monitoring job to run a specified Docker container image.

      • ImageUri (string) -- [REQUIRED]

        The container image to be run by the monitoring job.

      • ContainerEntrypoint (list) --

        Specifies the entrypoint for a container used to run the monitoring job.

        • (string) --

      • ContainerArguments (list) --

        An array of arguments for the container used to run the monitoring job.

        • (string) --

      • RecordPreprocessorSourceUri (string) --

        An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

      • PostAnalyticsProcessorSourceUri (string) --

        An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

    • StoppingCondition (dict) --

      Specifies a time limit for how long the monitoring job is allowed to run.

      • MaxRuntimeInSeconds (integer) -- [REQUIRED]

        The maximum runtime allowed in seconds.

        Note

        The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

    • Environment (dict) --

      Sets the environment variables in the Docker container.

      • (string) --

        • (string) --

    • NetworkConfig (dict) --

      Specifies networking options for an monitoring job.

      • EnableInterContainerTrafficEncryption (boolean) --

        Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.

      • EnableNetworkIsolation (boolean) --

        Whether to allow inbound and outbound network calls to and from the containers used for the processing job.

      • VpcConfig (dict) --

        Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

        • SecurityGroupIds (list) -- [REQUIRED]

          The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

          • (string) --

        • Subnets (list) -- [REQUIRED]

          The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

          • (string) --

    • RoleArn (string) -- [REQUIRED]

      The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • MonitoringJobDefinitionName (string) --

    The name of the monitoring job definition to schedule.

  • MonitoringType (string) --

    The type of the monitoring job definition to schedule.

rtype

dict

returns

Response Syntax

{
    'MonitoringScheduleArn': 'string'
}

Response Structure

  • (dict) --

    • MonitoringScheduleArn (string) --

      The Amazon Resource Name (ARN) of the monitoring schedule.