CfnModelQualityJobDefinition

class aws_cdk.aws_sagemaker.CfnModelQualityJobDefinition(scope, id, *, job_resources, model_quality_app_specification, model_quality_job_input, model_quality_job_output_config, role_arn, endpoint_name=None, job_definition_name=None, model_quality_baseline_config=None, network_config=None, stopping_condition=None, tags=None)

Bases: CfnResource

A CloudFormation AWS::SageMaker::ModelQualityJobDefinition.

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

CloudformationResource:: AWS::SageMaker::ModelQualityJobDefinition
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelqualityjobdefinition.html
ExampleMetadata:: fixture=_generated

Example:

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

# json: Any

cfn_model_quality_job_definition = sagemaker.CfnModelQualityJobDefinition(self, "MyCfnModelQualityJobDefinition",
    job_resources=sagemaker.CfnModelQualityJobDefinition.MonitoringResourcesProperty(
        cluster_config=sagemaker.CfnModelQualityJobDefinition.ClusterConfigProperty(
            instance_count=123,
            instance_type="instanceType",
            volume_size_in_gb=123,

            # the properties below are optional
            volume_kms_key_id="volumeKmsKeyId"
        )
    ),
    model_quality_app_specification=sagemaker.CfnModelQualityJobDefinition.ModelQualityAppSpecificationProperty(
        image_uri="imageUri",
        problem_type="problemType",

        # the properties below are optional
        container_arguments=["containerArguments"],
        container_entrypoint=["containerEntrypoint"],
        environment={
            "environment_key": "environment"
        },
        post_analytics_processor_source_uri="postAnalyticsProcessorSourceUri",
        record_preprocessor_source_uri="recordPreprocessorSourceUri"
    ),
    model_quality_job_input=sagemaker.CfnModelQualityJobDefinition.ModelQualityJobInputProperty(
        ground_truth_s3_input=sagemaker.CfnModelQualityJobDefinition.MonitoringGroundTruthS3InputProperty(
            s3_uri="s3Uri"
        ),

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

            # the properties below are optional
            end_time_offset="endTimeOffset",
            inference_attribute="inferenceAttribute",
            probability_attribute="probabilityAttribute",
            probability_threshold_attribute=123,
            s3_data_distribution_type="s3DataDistributionType",
            s3_input_mode="s3InputMode",
            start_time_offset="startTimeOffset"
        ),
        endpoint_input=sagemaker.CfnModelQualityJobDefinition.EndpointInputProperty(
            endpoint_name="endpointName",
            local_path="localPath",

            # the properties below are optional
            end_time_offset="endTimeOffset",
            inference_attribute="inferenceAttribute",
            probability_attribute="probabilityAttribute",
            probability_threshold_attribute=123,
            s3_data_distribution_type="s3DataDistributionType",
            s3_input_mode="s3InputMode",
            start_time_offset="startTimeOffset"
        )
    ),
    model_quality_job_output_config=sagemaker.CfnModelQualityJobDefinition.MonitoringOutputConfigProperty(
        monitoring_outputs=[sagemaker.CfnModelQualityJobDefinition.MonitoringOutputProperty(
            s3_output=sagemaker.CfnModelQualityJobDefinition.S3OutputProperty(
                local_path="localPath",
                s3_uri="s3Uri",

                # the properties below are optional
                s3_upload_mode="s3UploadMode"
            )
        )],

        # the properties below are optional
        kms_key_id="kmsKeyId"
    ),
    role_arn="roleArn",

    # the properties below are optional
    endpoint_name="endpointName",
    job_definition_name="jobDefinitionName",
    model_quality_baseline_config=sagemaker.CfnModelQualityJobDefinition.ModelQualityBaselineConfigProperty(
        baselining_job_name="baseliningJobName",
        constraints_resource=sagemaker.CfnModelQualityJobDefinition.ConstraintsResourceProperty(
            s3_uri="s3Uri"
        )
    ),
    network_config=sagemaker.CfnModelQualityJobDefinition.NetworkConfigProperty(
        enable_inter_container_traffic_encryption=False,
        enable_network_isolation=False,
        vpc_config=sagemaker.CfnModelQualityJobDefinition.VpcConfigProperty(
            security_group_ids=["securityGroupIds"],
            subnets=["subnets"]
        )
    ),
    stopping_condition=sagemaker.CfnModelQualityJobDefinition.StoppingConditionProperty(
        max_runtime_in_seconds=123
    ),
    tags=[CfnTag(
        key="key",
        value="value"
    )]
)

Create a new AWS::SageMaker::ModelQualityJobDefinition.

Parameters:

scope (Construct) –
- scope in which this resource is defined.
id (str) –
- scoped id of the resource.
job_resources (Union[IResolvable, MonitoringResourcesProperty, Dict[str, Any]]) – Identifies the resources to deploy for a monitoring job.
model_quality_app_specification (Union[IResolvable, ModelQualityAppSpecificationProperty, Dict[str, Any]]) – Container image configuration object for the monitoring job.
model_quality_job_input (Union[IResolvable, ModelQualityJobInputProperty, Dict[str, Any]]) – A list of the inputs that are monitored. Currently endpoints are supported.
model_quality_job_output_config (Union[IResolvable, MonitoringOutputConfigProperty, Dict[str, Any]]) – The output configuration for monitoring jobs.
role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
endpoint_name (Optional[str]) – AWS::SageMaker::ModelQualityJobDefinition.EndpointName.
job_definition_name (Optional[str]) – The name of the monitoring job definition.
model_quality_baseline_config (Union[IResolvable, ModelQualityBaselineConfigProperty, Dict[str, Any], None]) – Specifies the constraints and baselines for the monitoring job.
network_config (Union[IResolvable, NetworkConfigProperty, Dict[str, Any], None]) – Specifies the network configuration for the monitoring job.
stopping_condition (Union[IResolvable, StoppingConditionProperty, Dict[str, Any], None]) – A time limit for how long the monitoring job is allowed to run before stopping.
tags (Optional[Sequence[Union[CfnTag, Dict[str, Any]]]]) – An array of key-value pairs to apply to this resource. For more information, see Tag .

Methods

add_deletion_override(path)

Syntactic sugar for addOverride(path, undefined).

Parameters:: path (str) – The path of the value to delete.
Return type:: None

add_depends_on(target)

Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

This can be used for resources across stacks (or nested stack) boundaries and the dependency will automatically be transferred to the relevant scope.

Parameters:: target (CfnResource)
Return type:: None

add_metadata(key, value)

Add a value to the CloudFormation Resource Metadata.

Parameters:

key (str)
value (Any)

See:

Return type:

None

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

add_override(path, value)

Adds an override to the synthesized CloudFormation resource.

To add a property override, either use addPropertyOverride or prefix path with “Properties.” (i.e. Properties.TopicName).

If the override is nested, separate each nested level using a dot (.) in the path parameter. If there is an array as part of the nesting, specify the index in the path.

To include a literal . in the property name, prefix with a \. In most programming languages you will need to write this as "\\." because the \ itself will need to be escaped.

For example:

cfn_resource.add_override("Properties.GlobalSecondaryIndexes.0.Projection.NonKeyAttributes", ["myattribute"])
cfn_resource.add_override("Properties.GlobalSecondaryIndexes.1.ProjectionType", "INCLUDE")

would add the overrides Example:

"Properties": {
   "GlobalSecondaryIndexes": [
     {
       "Projection": {
         "NonKeyAttributes": [ "myattribute" ]
         ...
       }
       ...
     },
     {
       "ProjectionType": "INCLUDE"
       ...
     },
   ]
   ...
}

The value argument to addOverride will not be processed or translated in any way. Pass raw JSON values in here with the correct capitalization for CloudFormation. If you pass CDK classes or structs, they will be rendered with lowercased key names, and CloudFormation will reject the template.

Parameters:

path (str) –
- The path of the property, you can use dot notation to override values in complex types. Any intermdediate keys will be created as needed.
value (Any) –
- The value. Could be primitive or complex.

Return type:

None

add_property_deletion_override(property_path)

Adds an override that deletes the value of a property from the resource definition.

Parameters:: property_path (str) – The path to the property.
Return type:: None

add_property_override(property_path, value)

Adds an override to a resource property.

Syntactic sugar for addOverride("Properties.<...>", value).

Parameters:

property_path (str) – The path of the property.
value (Any) – The value.

Return type:

None

apply_removal_policy(policy=None, *, apply_to_update_replace_policy=None, default=None)

Sets the deletion policy of the resource based on the removal policy specified.

The Removal Policy controls what happens to this resource when it stops being managed by CloudFormation, either because you’ve removed it from the CDK application or because you’ve made a change that requires the resource to be replaced.

The resource can be deleted (RemovalPolicy.DESTROY), or left in your AWS account for data recovery and cleanup later (RemovalPolicy.RETAIN).

Parameters:

policy (Optional[RemovalPolicy])
apply_to_update_replace_policy (Optional[bool]) – Apply the same deletion policy to the resource’s “UpdateReplacePolicy”. Default: true
default (Optional[RemovalPolicy]) – The default policy to apply in case the removal policy is not defined. Default: - Default value is resource specific. To determine the default value for a resoure, please consult that specific resource’s documentation.

Return type:

None

get_att(attribute_name)

Returns a token for an runtime attribute of this resource.

Ideally, use generated attribute accessors (e.g. resource.arn), but this can be used for future compatibility in case there is no generated attribute.

Parameters:: attribute_name (str) – The name of the attribute.
Return type:: Reference

get_metadata(key)

Retrieve a value value from the CloudFormation Resource Metadata.

Parameters:: key (str)
See:
Return type:: Any

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

inspect(inspector)

Examines the CloudFormation resource and discloses attributes.

Parameters:

inspector (TreeInspector) –

tree inspector to collect and process attributes.

Return type:

None

override_logical_id(new_logical_id)

Overrides the auto-generated logical ID with a specific ID.

Parameters:: new_logical_id (str) – The new logical ID to use for this stack element.
Return type:: None

to_string()

Returns a string representation of this construct.

Return type:: str
Returns:: a string representation of this resource

Attributes

CFN_RESOURCE_TYPE_NAME = 'AWS::SageMaker::ModelQualityJobDefinition'

attr_creation_time

The time when the job definition was created.

CloudformationAttribute:: CreationTime

attr_job_definition_arn

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

CloudformationAttribute:: JobDefinitionArn

cfn_options: Options for this resource, such as condition, update policy etc.

cfn_resource_type: AWS resource type.

creation_stack

return:

the stack trace of the point where this Resource was created from, sourced from the +metadata+ entry typed +aws:cdk:logicalId+, and with the bottom-most node +internal+ entries filtered.

endpoint_name

AWS::SageMaker::ModelQualityJobDefinition.EndpointName.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelqualityjobdefinition.html#cfn-sagemaker-modelqualityjobdefinition-endpointname

job_definition_name

The name of the monitoring job definition.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelqualityjobdefinition.html#cfn-sagemaker-modelqualityjobdefinition-jobdefinitionname

job_resources

Identifies the resources to deploy for a monitoring job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelqualityjobdefinition.html#cfn-sagemaker-modelqualityjobdefinition-jobresources

logical_id

The logical ID for this CloudFormation stack element.

The logical ID of the element is calculated from the path of the resource node in the construct tree.

To override this value, use overrideLogicalId(newLogicalId).

Returns:

the logical ID as a stringified token. This value will only get resolved during synthesis.

model_quality_app_specification

Container image configuration object for the monitoring job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelqualityjobdefinition.html#cfn-sagemaker-modelqualityjobdefinition-modelqualityappspecification

model_quality_baseline_config

Specifies the constraints and baselines for the monitoring job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelqualityjobdefinition.html#cfn-sagemaker-modelqualityjobdefinition-modelqualitybaselineconfig

model_quality_job_input

A list of the inputs that are monitored.

Currently endpoints are supported.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelqualityjobdefinition.html#cfn-sagemaker-modelqualityjobdefinition-modelqualityjobinput

model_quality_job_output_config

The output configuration for monitoring jobs.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelqualityjobdefinition.html#cfn-sagemaker-modelqualityjobdefinition-modelqualityjoboutputconfig

network_config

Specifies the network configuration for the monitoring job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelqualityjobdefinition.html#cfn-sagemaker-modelqualityjobdefinition-networkconfig

node: The construct tree node associated with this construct.

ref

Return a string that will be resolved to a CloudFormation { Ref } for this element.

If, by any chance, the intrinsic reference of a resource is not a string, you could coerce it to an IResolvable through Lazy.any({ produce: resource.ref }).

role_arn

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelqualityjobdefinition.html#cfn-sagemaker-modelqualityjobdefinition-rolearn

stack

The stack in which this element is defined.

CfnElements must be defined within a stack scope (directly or indirectly).

stopping_condition

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelqualityjobdefinition.html#cfn-sagemaker-modelqualityjobdefinition-stoppingcondition

tags

An array of key-value pairs to apply to this resource.

For more information, see Tag .

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelqualityjobdefinition.html#cfn-sagemaker-modelqualityjobdefinition-tags

Static Methods

classmethod is_cfn_element(x)

Returns true if a construct is a stack element (i.e. part of the synthesized cloudformation template).

Uses duck-typing instead of instanceof to allow stack elements from different versions of this library to be included in the same stack.

Parameters:: x (Any)
Return type:: bool
Returns:: The construct as a stack element or undefined if it is not a stack element.

classmethod is_cfn_resource(construct)

Check whether the given construct is a CfnResource.

Parameters:: construct (IConstruct)
Return type:: bool

classmethod is_construct(x)

Return whether the given object is a Construct.

Parameters:: x (Any)
Return type:: bool

BatchTransformInputProperty

class CfnModelQualityJobDefinition.BatchTransformInputProperty(*, data_captured_destination_s3_uri, dataset_format, local_path, end_time_offset=None, inference_attribute=None, probability_attribute=None, probability_threshold_attribute=None, s3_data_distribution_type=None, s3_input_mode=None, start_time_offset=None)

Bases: object

Parameters:

data_captured_destination_s3_uri (str) – CfnModelQualityJobDefinition.BatchTransformInputProperty.DataCapturedDestinationS3Uri.
dataset_format (Union[IResolvable, DatasetFormatProperty, Dict[str, Any]]) – CfnModelQualityJobDefinition.BatchTransformInputProperty.DatasetFormat.
local_path (str) – CfnModelQualityJobDefinition.BatchTransformInputProperty.LocalPath.
end_time_offset (Optional[str]) – CfnModelQualityJobDefinition.BatchTransformInputProperty.EndTimeOffset.
inference_attribute (Optional[str]) – CfnModelQualityJobDefinition.BatchTransformInputProperty.InferenceAttribute.
probability_attribute (Optional[str]) – CfnModelQualityJobDefinition.BatchTransformInputProperty.ProbabilityAttribute.
probability_threshold_attribute (Union[int, float, None]) – CfnModelQualityJobDefinition.BatchTransformInputProperty.ProbabilityThresholdAttribute.
s3_data_distribution_type (Optional[str]) – CfnModelQualityJobDefinition.BatchTransformInputProperty.S3DataDistributionType.
s3_input_mode (Optional[str]) – CfnModelQualityJobDefinition.BatchTransformInputProperty.S3InputMode.
start_time_offset (Optional[str]) – CfnModelQualityJobDefinition.BatchTransformInputProperty.StartTimeOffset.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-batchtransforminput.html

ExampleMetadata:

fixture=_generated

Example:

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

# json: Any

batch_transform_input_property = sagemaker.CfnModelQualityJobDefinition.BatchTransformInputProperty(
    data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
    dataset_format=sagemaker.CfnModelQualityJobDefinition.DatasetFormatProperty(
        csv=sagemaker.CfnModelQualityJobDefinition.CsvProperty(
            header=False
        ),
        json=json,
        parquet=False
    ),
    local_path="localPath",

    # the properties below are optional
    end_time_offset="endTimeOffset",
    inference_attribute="inferenceAttribute",
    probability_attribute="probabilityAttribute",
    probability_threshold_attribute=123,
    s3_data_distribution_type="s3DataDistributionType",
    s3_input_mode="s3InputMode",
    start_time_offset="startTimeOffset"
)

Attributes

data_captured_destination_s3_uri

CfnModelQualityJobDefinition.BatchTransformInputProperty.DataCapturedDestinationS3Uri.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-batchtransforminput.html#cfn-sagemaker-modelqualityjobdefinition-batchtransforminput-datacaptureddestinations3uri

dataset_format

CfnModelQualityJobDefinition.BatchTransformInputProperty.DatasetFormat.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-batchtransforminput.html#cfn-sagemaker-modelqualityjobdefinition-batchtransforminput-datasetformat

end_time_offset

CfnModelQualityJobDefinition.BatchTransformInputProperty.EndTimeOffset.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-batchtransforminput.html#cfn-sagemaker-modelqualityjobdefinition-batchtransforminput-endtimeoffset

inference_attribute

CfnModelQualityJobDefinition.BatchTransformInputProperty.InferenceAttribute.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-batchtransforminput.html#cfn-sagemaker-modelqualityjobdefinition-batchtransforminput-inferenceattribute

local_path

CfnModelQualityJobDefinition.BatchTransformInputProperty.LocalPath.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-batchtransforminput.html#cfn-sagemaker-modelqualityjobdefinition-batchtransforminput-localpath

probability_attribute

CfnModelQualityJobDefinition.BatchTransformInputProperty.ProbabilityAttribute.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-batchtransforminput.html#cfn-sagemaker-modelqualityjobdefinition-batchtransforminput-probabilityattribute

probability_threshold_attribute

CfnModelQualityJobDefinition.BatchTransformInputProperty.ProbabilityThresholdAttribute.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-batchtransforminput.html#cfn-sagemaker-modelqualityjobdefinition-batchtransforminput-probabilitythresholdattribute

s3_data_distribution_type

CfnModelQualityJobDefinition.BatchTransformInputProperty.S3DataDistributionType.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-batchtransforminput.html#cfn-sagemaker-modelqualityjobdefinition-batchtransforminput-s3datadistributiontype

s3_input_mode

CfnModelQualityJobDefinition.BatchTransformInputProperty.S3InputMode.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-batchtransforminput.html#cfn-sagemaker-modelqualityjobdefinition-batchtransforminput-s3inputmode

start_time_offset

CfnModelQualityJobDefinition.BatchTransformInputProperty.StartTimeOffset.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-batchtransforminput.html#cfn-sagemaker-modelqualityjobdefinition-batchtransforminput-starttimeoffset

ClusterConfigProperty

class CfnModelQualityJobDefinition.ClusterConfigProperty(*, instance_count, instance_type, volume_size_in_gb, volume_kms_key_id=None)

Bases: object

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

Parameters:

instance_count (Union[int, float]) – 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.
instance_type (str) – The ML compute instance type for the processing job.
volume_size_in_gb (Union[int, float]) – The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
volume_kms_key_id (Optional[str]) – The AWS Key Management Service ( AWS 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.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-clusterconfig.html

ExampleMetadata:

fixture=_generated

Example:

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

cluster_config_property = sagemaker.CfnModelQualityJobDefinition.ClusterConfigProperty(
    instance_count=123,
    instance_type="instanceType",
    volume_size_in_gb=123,

    # the properties below are optional
    volume_kms_key_id="volumeKmsKeyId"
)

Attributes

instance_count

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.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-clusterconfig.html#cfn-sagemaker-modelqualityjobdefinition-clusterconfig-instancecount

instance_type

The ML compute instance type for the processing job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-clusterconfig.html#cfn-sagemaker-modelqualityjobdefinition-clusterconfig-instancetype

volume_kms_key_id

The AWS Key Management Service ( AWS 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.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-clusterconfig.html#cfn-sagemaker-modelqualityjobdefinition-clusterconfig-volumekmskeyid

volume_size_in_gb

The size of the ML storage volume, in gigabytes, that you want to provision.

You must specify sufficient ML storage for your scenario.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-clusterconfig.html#cfn-sagemaker-modelqualityjobdefinition-clusterconfig-volumesizeingb

ConstraintsResourceProperty

class CfnModelQualityJobDefinition.ConstraintsResourceProperty(*, s3_uri=None)

Bases: object

The constraints resource for a monitoring job.

Parameters:: s3_uri (Optional[str]) – The Amazon S3 URI for the constraints resource.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-constraintsresource.html
ExampleMetadata:: fixture=_generated

Example:

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

constraints_resource_property = sagemaker.CfnModelQualityJobDefinition.ConstraintsResourceProperty(
    s3_uri="s3Uri"
)

Attributes

s3_uri

The Amazon S3 URI for the constraints resource.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-constraintsresource.html#cfn-sagemaker-modelqualityjobdefinition-constraintsresource-s3uri

CsvProperty

class CfnModelQualityJobDefinition.CsvProperty(*, header=None)

Bases: object

Parameters:: header (Union[bool, IResolvable, None]) – CfnModelQualityJobDefinition.CsvProperty.Header.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-csv.html
ExampleMetadata:: fixture=_generated

Example:

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

csv_property = sagemaker.CfnModelQualityJobDefinition.CsvProperty(
    header=False
)

Attributes

header

CfnModelQualityJobDefinition.CsvProperty.Header.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-csv.html#cfn-sagemaker-modelqualityjobdefinition-csv-header

DatasetFormatProperty

class CfnModelQualityJobDefinition.DatasetFormatProperty(*, csv=None, json=None, parquet=None)

Bases: object

Parameters:

csv (Union[IResolvable, CsvProperty, Dict[str, Any], None]) – CfnModelQualityJobDefinition.DatasetFormatProperty.Csv.
json (Optional[Any]) – CfnModelQualityJobDefinition.DatasetFormatProperty.Json.
parquet (Union[bool, IResolvable, None]) – CfnModelQualityJobDefinition.DatasetFormatProperty.Parquet.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-datasetformat.html

ExampleMetadata:

fixture=_generated

Example:

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

# json: Any

dataset_format_property = sagemaker.CfnModelQualityJobDefinition.DatasetFormatProperty(
    csv=sagemaker.CfnModelQualityJobDefinition.CsvProperty(
        header=False
    ),
    json=json,
    parquet=False
)

Attributes

csv

CfnModelQualityJobDefinition.DatasetFormatProperty.Csv.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-datasetformat.html#cfn-sagemaker-modelqualityjobdefinition-datasetformat-csv

json

CfnModelQualityJobDefinition.DatasetFormatProperty.Json.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-datasetformat.html#cfn-sagemaker-modelqualityjobdefinition-datasetformat-json

parquet

CfnModelQualityJobDefinition.DatasetFormatProperty.Parquet.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-datasetformat.html#cfn-sagemaker-modelqualityjobdefinition-datasetformat-parquet

EndpointInputProperty

class CfnModelQualityJobDefinition.EndpointInputProperty(*, endpoint_name, local_path, end_time_offset=None, inference_attribute=None, probability_attribute=None, probability_threshold_attribute=None, s3_data_distribution_type=None, s3_input_mode=None, start_time_offset=None)

Bases: object

Input object for the endpoint.

Parameters:

endpoint_name (str) – An endpoint in customer’s account which has enabled DataCaptureConfig enabled.
local_path (str) – Path to the filesystem where the endpoint data is available to the container.
end_time_offset (Optional[str]) – 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 .
inference_attribute (Optional[str]) – The attribute of the input data that represents the ground truth label.
probability_attribute (Optional[str]) – In a classification problem, the attribute that represents the class probability.
probability_threshold_attribute (Union[int, float, None]) – The threshold for the class probability to be evaluated as a positive result.
s3_data_distribution_type (Optional[str]) – Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
s3_input_mode (Optional[str]) – 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 .
start_time_offset (Optional[str]) –
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 .

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-endpointinput.html

ExampleMetadata:

fixture=_generated

Example:

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

endpoint_input_property = sagemaker.CfnModelQualityJobDefinition.EndpointInputProperty(
    endpoint_name="endpointName",
    local_path="localPath",

    # the properties below are optional
    end_time_offset="endTimeOffset",
    inference_attribute="inferenceAttribute",
    probability_attribute="probabilityAttribute",
    probability_threshold_attribute=123,
    s3_data_distribution_type="s3DataDistributionType",
    s3_input_mode="s3InputMode",
    start_time_offset="startTimeOffset"
)

Attributes

end_time_offset

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 .

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-endpointinput.html#cfn-sagemaker-modelqualityjobdefinition-endpointinput-endtimeoffset

endpoint_name

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-endpointinput.html#cfn-sagemaker-modelqualityjobdefinition-endpointinput-endpointname

inference_attribute

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-endpointinput.html#cfn-sagemaker-modelqualityjobdefinition-endpointinput-inferenceattribute

local_path

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-endpointinput.html#cfn-sagemaker-modelqualityjobdefinition-endpointinput-localpath

probability_attribute

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-endpointinput.html#cfn-sagemaker-modelqualityjobdefinition-endpointinput-probabilityattribute

probability_threshold_attribute

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-endpointinput.html#cfn-sagemaker-modelqualityjobdefinition-endpointinput-probabilitythresholdattribute

s3_data_distribution_type

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

Defaults to FullyReplicated

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-endpointinput.html#cfn-sagemaker-modelqualityjobdefinition-endpointinput-s3datadistributiontype

s3_input_mode

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 .

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-endpointinput.html#cfn-sagemaker-modelqualityjobdefinition-endpointinput-s3inputmode

start_time_offset

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 .

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-endpointinput.html#cfn-sagemaker-modelqualityjobdefinition-endpointinput-starttimeoffset

JsonProperty

class CfnModelQualityJobDefinition.JsonProperty(*, line=None)

Bases: object

Parameters:: line (Union[bool, IResolvable, None]) – CfnModelQualityJobDefinition.JsonProperty.Line.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-json.html
ExampleMetadata:: fixture=_generated

Example:

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

json_property = sagemaker.CfnModelQualityJobDefinition.JsonProperty(
    line=False
)

Attributes

line

CfnModelQualityJobDefinition.JsonProperty.Line.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-json.html#cfn-sagemaker-modelqualityjobdefinition-json-line

ModelQualityAppSpecificationProperty

class CfnModelQualityJobDefinition.ModelQualityAppSpecificationProperty(*, image_uri, problem_type, container_arguments=None, container_entrypoint=None, environment=None, post_analytics_processor_source_uri=None, record_preprocessor_source_uri=None)

Bases: object

Container image configuration object for the monitoring job.

Parameters:

image_uri (str) – The address of the container image that the monitoring job runs.
problem_type (str) – The machine learning problem type of the model that the monitoring job monitors.
container_arguments (Optional[Sequence[str]]) – An array of arguments for the container used to run the monitoring job.
container_entrypoint (Optional[Sequence[str]]) – Specifies the entrypoint for a container that the monitoring job runs.
environment (Union[IResolvable, Mapping[str, str], None]) – Sets the environment variables in the container that the monitoring job runs.
post_analytics_processor_source_uri (Optional[str]) – An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
record_preprocessor_source_uri (Optional[str]) – 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.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualityappspecification.html

ExampleMetadata:

fixture=_generated

Example:

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

model_quality_app_specification_property = sagemaker.CfnModelQualityJobDefinition.ModelQualityAppSpecificationProperty(
    image_uri="imageUri",
    problem_type="problemType",

    # the properties below are optional
    container_arguments=["containerArguments"],
    container_entrypoint=["containerEntrypoint"],
    environment={
        "environment_key": "environment"
    },
    post_analytics_processor_source_uri="postAnalyticsProcessorSourceUri",
    record_preprocessor_source_uri="recordPreprocessorSourceUri"
)

Attributes

container_arguments

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualityappspecification.html#cfn-sagemaker-modelqualityjobdefinition-modelqualityappspecification-containerarguments

container_entrypoint

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualityappspecification.html#cfn-sagemaker-modelqualityjobdefinition-modelqualityappspecification-containerentrypoint

environment

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualityappspecification.html#cfn-sagemaker-modelqualityjobdefinition-modelqualityappspecification-environment

image_uri

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualityappspecification.html#cfn-sagemaker-modelqualityjobdefinition-modelqualityappspecification-imageuri

post_analytics_processor_source_uri

An Amazon S3 URI to a script that is called after analysis has been performed.

Applicable only for the built-in (first party) containers.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualityappspecification.html#cfn-sagemaker-modelqualityjobdefinition-modelqualityappspecification-postanalyticsprocessorsourceuri

problem_type

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualityappspecification.html#cfn-sagemaker-modelqualityjobdefinition-modelqualityappspecification-problemtype

record_preprocessor_source_uri

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.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualityappspecification.html#cfn-sagemaker-modelqualityjobdefinition-modelqualityappspecification-recordpreprocessorsourceuri

ModelQualityBaselineConfigProperty

class CfnModelQualityJobDefinition.ModelQualityBaselineConfigProperty(*, baselining_job_name=None, constraints_resource=None)

Bases: object

Configuration for monitoring constraints and monitoring statistics.

These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.

Parameters:

baselining_job_name (Optional[str]) – The name of the job that performs baselining for the monitoring job.
constraints_resource (Union[IResolvable, ConstraintsResourceProperty, Dict[str, Any], None]) – The constraints resource for a monitoring job.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualitybaselineconfig.html

ExampleMetadata:

fixture=_generated

Example:

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

model_quality_baseline_config_property = sagemaker.CfnModelQualityJobDefinition.ModelQualityBaselineConfigProperty(
    baselining_job_name="baseliningJobName",
    constraints_resource=sagemaker.CfnModelQualityJobDefinition.ConstraintsResourceProperty(
        s3_uri="s3Uri"
    )
)

Attributes

baselining_job_name

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualitybaselineconfig.html#cfn-sagemaker-modelqualityjobdefinition-modelqualitybaselineconfig-baseliningjobname

constraints_resource

The constraints resource for a monitoring job.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualitybaselineconfig.html#cfn-sagemaker-modelqualityjobdefinition-modelqualitybaselineconfig-constraintsresource

ModelQualityJobInputProperty

class CfnModelQualityJobDefinition.ModelQualityJobInputProperty(*, ground_truth_s3_input, batch_transform_input=None, endpoint_input=None)

Bases: object

The input for the model quality monitoring job.

Currently endponts are supported for input for model quality monitoring jobs.

Parameters:

ground_truth_s3_input (Union[IResolvable, MonitoringGroundTruthS3InputProperty, Dict[str, Any]]) – The ground truth label provided for the model.
batch_transform_input (Union[IResolvable, BatchTransformInputProperty, Dict[str, Any], None]) – CfnModelQualityJobDefinition.ModelQualityJobInputProperty.BatchTransformInput.
endpoint_input (Union[IResolvable, EndpointInputProperty, Dict[str, Any], None]) – Input object for the endpoint.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualityjobinput.html

ExampleMetadata:

fixture=_generated

Example:

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

# json: Any

model_quality_job_input_property = sagemaker.CfnModelQualityJobDefinition.ModelQualityJobInputProperty(
    ground_truth_s3_input=sagemaker.CfnModelQualityJobDefinition.MonitoringGroundTruthS3InputProperty(
        s3_uri="s3Uri"
    ),

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

        # the properties below are optional
        end_time_offset="endTimeOffset",
        inference_attribute="inferenceAttribute",
        probability_attribute="probabilityAttribute",
        probability_threshold_attribute=123,
        s3_data_distribution_type="s3DataDistributionType",
        s3_input_mode="s3InputMode",
        start_time_offset="startTimeOffset"
    ),
    endpoint_input=sagemaker.CfnModelQualityJobDefinition.EndpointInputProperty(
        endpoint_name="endpointName",
        local_path="localPath",

        # the properties below are optional
        end_time_offset="endTimeOffset",
        inference_attribute="inferenceAttribute",
        probability_attribute="probabilityAttribute",
        probability_threshold_attribute=123,
        s3_data_distribution_type="s3DataDistributionType",
        s3_input_mode="s3InputMode",
        start_time_offset="startTimeOffset"
    )
)

Attributes

batch_transform_input

CfnModelQualityJobDefinition.ModelQualityJobInputProperty.BatchTransformInput.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualityjobinput.html#cfn-sagemaker-modelqualityjobdefinition-modelqualityjobinput-batchtransforminput

endpoint_input

Input object for the endpoint.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualityjobinput.html#cfn-sagemaker-modelqualityjobdefinition-modelqualityjobinput-endpointinput

ground_truth_s3_input

The ground truth label provided for the model.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-modelqualityjobinput.html#cfn-sagemaker-modelqualityjobdefinition-modelqualityjobinput-groundtruths3input

MonitoringGroundTruthS3InputProperty

class CfnModelQualityJobDefinition.MonitoringGroundTruthS3InputProperty(*, s3_uri)

Bases: object

The ground truth labels for the dataset used for the monitoring job.

Parameters:: s3_uri (str) – The address of the Amazon S3 location of the ground truth labels.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-monitoringgroundtruths3input.html
ExampleMetadata:: fixture=_generated

Example:

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

monitoring_ground_truth_s3_input_property = sagemaker.CfnModelQualityJobDefinition.MonitoringGroundTruthS3InputProperty(
    s3_uri="s3Uri"
)

Attributes

s3_uri

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-monitoringgroundtruths3input.html#cfn-sagemaker-modelqualityjobdefinition-monitoringgroundtruths3input-s3uri

MonitoringOutputConfigProperty

class CfnModelQualityJobDefinition.MonitoringOutputConfigProperty(*, monitoring_outputs, kms_key_id=None)

Bases: object

The output configuration for monitoring jobs.

Parameters:

monitoring_outputs (Union[IResolvable, Sequence[Union[IResolvable, MonitoringOutputProperty, Dict[str, Any]]]]) – Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
kms_key_id (Optional[str]) – The AWS Key Management Service ( AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-monitoringoutputconfig.html

ExampleMetadata:

fixture=_generated

Example:

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

monitoring_output_config_property = sagemaker.CfnModelQualityJobDefinition.MonitoringOutputConfigProperty(
    monitoring_outputs=[sagemaker.CfnModelQualityJobDefinition.MonitoringOutputProperty(
        s3_output=sagemaker.CfnModelQualityJobDefinition.S3OutputProperty(
            local_path="localPath",
            s3_uri="s3Uri",

            # the properties below are optional
            s3_upload_mode="s3UploadMode"
        )
    )],

    # the properties below are optional
    kms_key_id="kmsKeyId"
)

Attributes

kms_key_id

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-monitoringoutputconfig.html#cfn-sagemaker-modelqualityjobdefinition-monitoringoutputconfig-kmskeyid

monitoring_outputs

Monitoring outputs for monitoring jobs.

This is where the output of the periodic monitoring jobs is uploaded.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-monitoringoutputconfig.html#cfn-sagemaker-modelqualityjobdefinition-monitoringoutputconfig-monitoringoutputs

MonitoringOutputProperty

class CfnModelQualityJobDefinition.MonitoringOutputProperty(*, s3_output)

Bases: object

The output object for a monitoring job.

Parameters:: s3_output (Union[IResolvable, S3OutputProperty, Dict[str, Any]]) – The Amazon S3 storage location where the results of a monitoring job are saved.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-monitoringoutput.html
ExampleMetadata:: fixture=_generated

Example:

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

monitoring_output_property = sagemaker.CfnModelQualityJobDefinition.MonitoringOutputProperty(
    s3_output=sagemaker.CfnModelQualityJobDefinition.S3OutputProperty(
        local_path="localPath",
        s3_uri="s3Uri",

        # the properties below are optional
        s3_upload_mode="s3UploadMode"
    )
)

Attributes

s3_output

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-monitoringoutput.html#cfn-sagemaker-modelqualityjobdefinition-monitoringoutput-s3output

MonitoringResourcesProperty

class CfnModelQualityJobDefinition.MonitoringResourcesProperty(*, cluster_config)

Bases: object

Identifies the resources to deploy for a monitoring job.

Parameters:: cluster_config (Union[IResolvable, ClusterConfigProperty, Dict[str, Any]]) – The configuration for the cluster resources used to run the processing job.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-monitoringresources.html
ExampleMetadata:: fixture=_generated

Example:

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

monitoring_resources_property = sagemaker.CfnModelQualityJobDefinition.MonitoringResourcesProperty(
    cluster_config=sagemaker.CfnModelQualityJobDefinition.ClusterConfigProperty(
        instance_count=123,
        instance_type="instanceType",
        volume_size_in_gb=123,

        # the properties below are optional
        volume_kms_key_id="volumeKmsKeyId"
    )
)

Attributes

cluster_config

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-monitoringresources.html#cfn-sagemaker-modelqualityjobdefinition-monitoringresources-clusterconfig

NetworkConfigProperty

class CfnModelQualityJobDefinition.NetworkConfigProperty(*, enable_inter_container_traffic_encryption=None, enable_network_isolation=None, vpc_config=None)

Bases: object

Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.

Parameters:

enable_inter_container_traffic_encryption (Union[bool, IResolvable, None]) – 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.
enable_network_isolation (Union[bool, IResolvable, None]) – Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
vpc_config (Union[IResolvable, VpcConfigProperty, Dict[str, Any], None]) – 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.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-networkconfig.html

ExampleMetadata:

fixture=_generated

Example:

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

network_config_property = sagemaker.CfnModelQualityJobDefinition.NetworkConfigProperty(
    enable_inter_container_traffic_encryption=False,
    enable_network_isolation=False,
    vpc_config=sagemaker.CfnModelQualityJobDefinition.VpcConfigProperty(
        security_group_ids=["securityGroupIds"],
        subnets=["subnets"]
    )
)

Attributes

enable_inter_container_traffic_encryption

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.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-networkconfig.html#cfn-sagemaker-modelqualityjobdefinition-networkconfig-enableintercontainertrafficencryption

enable_network_isolation

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-networkconfig.html#cfn-sagemaker-modelqualityjobdefinition-networkconfig-enablenetworkisolation

vpc_config

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.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-networkconfig.html#cfn-sagemaker-modelqualityjobdefinition-networkconfig-vpcconfig

S3OutputProperty

class CfnModelQualityJobDefinition.S3OutputProperty(*, local_path, s3_uri, s3_upload_mode=None)

Bases: object

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

Parameters:

local_path (str) – 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.
s3_uri (str) – A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
s3_upload_mode (Optional[str]) – Whether to upload the results of the monitoring job continuously or after the job completes.

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-s3output.html

ExampleMetadata:

fixture=_generated

Example:

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

s3_output_property = sagemaker.CfnModelQualityJobDefinition.S3OutputProperty(
    local_path="localPath",
    s3_uri="s3Uri",

    # the properties below are optional
    s3_upload_mode="s3UploadMode"
)

Attributes

local_path

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.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-s3output.html#cfn-sagemaker-modelqualityjobdefinition-s3output-localpath

s3_upload_mode

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-s3output.html#cfn-sagemaker-modelqualityjobdefinition-s3output-s3uploadmode

s3_uri

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

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-s3output.html#cfn-sagemaker-modelqualityjobdefinition-s3output-s3uri

StoppingConditionProperty

class CfnModelQualityJobDefinition.StoppingConditionProperty(*, max_runtime_in_seconds)

Bases: object

Specifies a limit to how long a model training job or model compilation job can run.

It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training or compilation job. Use this API to cap model training costs.

To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel . .. epigraph:

The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.

Parameters:: max_runtime_in_seconds (Union[int, float]) – The maximum length of time, in seconds, that a training or compilation job can run before it is stopped. For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model. For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days. The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-stoppingcondition.html
ExampleMetadata:: fixture=_generated

Example:

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

stopping_condition_property = sagemaker.CfnModelQualityJobDefinition.StoppingConditionProperty(
    max_runtime_in_seconds=123
)

Attributes

max_runtime_in_seconds

The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-stoppingcondition.html#cfn-sagemaker-modelqualityjobdefinition-stoppingcondition-maxruntimeinseconds

VpcConfigProperty

class CfnModelQualityJobDefinition.VpcConfigProperty(*, security_group_ids, subnets)

Bases: object

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 .

Parameters:

security_group_ids (Sequence[str]) – The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
subnets (Sequence[str]) – 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 .

Link:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-vpcconfig.html

ExampleMetadata:

fixture=_generated

Example:

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

vpc_config_property = sagemaker.CfnModelQualityJobDefinition.VpcConfigProperty(
    security_group_ids=["securityGroupIds"],
    subnets=["subnets"]
)

Attributes

security_group_ids

The VPC security group IDs, in the form sg-xxxxxxxx.

Specify the security groups for the VPC that is specified in the Subnets field.

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-vpcconfig.html#cfn-sagemaker-modelqualityjobdefinition-vpcconfig-securitygroupids

subnets

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 .

Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelqualityjobdefinition-vpcconfig.html#cfn-sagemaker-modelqualityjobdefinition-vpcconfig-subnets