CfnModelExplainabilityJobDefinitionPropsMixin

class aws_cdk.mixins_preview.aws_sagemaker.mixins.CfnModelExplainabilityJobDefinitionPropsMixin(props, *, strategy=None)

Bases: Mixin

Creates the definition for a model explainability job.

See:

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

CloudformationResource:

AWS::SageMaker::ModelExplainabilityJobDefinition

Mixin:

true

ExampleMetadata:

fixture=_generated

Example:

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

cfn_model_explainability_job_definition_props_mixin = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin(sagemaker_mixins.CfnModelExplainabilityJobDefinitionMixinProps(
    endpoint_name="endpointName",
    job_definition_name="jobDefinitionName",
    job_resources=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.MonitoringResourcesProperty(
        cluster_config=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ClusterConfigProperty(
            instance_count=123,
            instance_type="instanceType",
            volume_kms_key_id="volumeKmsKeyId",
            volume_size_in_gb=123
        )
    ),
    model_explainability_app_specification=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ModelExplainabilityAppSpecificationProperty(
        config_uri="configUri",
        environment={
            "environment_key": "environment"
        },
        image_uri="imageUri"
    ),
    model_explainability_baseline_config=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ModelExplainabilityBaselineConfigProperty(
        baselining_job_name="baseliningJobName",
        constraints_resource=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ConstraintsResourceProperty(
            s3_uri="s3Uri"
        )
    ),
    model_explainability_job_input=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ModelExplainabilityJobInputProperty(
        batch_transform_input=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.BatchTransformInputProperty(
            data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
            dataset_format=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.DatasetFormatProperty(
                csv=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.CsvProperty(
                    header=False
                ),
                json=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.JsonProperty(
                    line=False
                ),
                parquet=False
            ),
            features_attribute="featuresAttribute",
            inference_attribute="inferenceAttribute",
            local_path="localPath",
            probability_attribute="probabilityAttribute",
            s3_data_distribution_type="s3DataDistributionType",
            s3_input_mode="s3InputMode"
        ),
        endpoint_input=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.EndpointInputProperty(
            endpoint_name="endpointName",
            features_attribute="featuresAttribute",
            inference_attribute="inferenceAttribute",
            local_path="localPath",
            probability_attribute="probabilityAttribute",
            s3_data_distribution_type="s3DataDistributionType",
            s3_input_mode="s3InputMode"
        )
    ),
    model_explainability_job_output_config=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.MonitoringOutputConfigProperty(
        kms_key_id="kmsKeyId",
        monitoring_outputs=[sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.MonitoringOutputProperty(
            s3_output=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.S3OutputProperty(
                local_path="localPath",
                s3_upload_mode="s3UploadMode",
                s3_uri="s3Uri"
            )
        )]
    ),
    network_config=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.NetworkConfigProperty(
        enable_inter_container_traffic_encryption=False,
        enable_network_isolation=False,
        vpc_config=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.VpcConfigProperty(
            security_group_ids=["securityGroupIds"],
            subnets=["subnets"]
        )
    ),
    role_arn="roleArn",
    stopping_condition=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.StoppingConditionProperty(
        max_runtime_in_seconds=123
    ),
    tags=[CfnTag(
        key="key",
        value="value"
    )]
),
    strategy=mixins.PropertyMergeStrategy.OVERRIDE
)

Create a mixin to apply properties to AWS::SageMaker::ModelExplainabilityJobDefinition.

Parameters:

• props (Union[CfnModelExplainabilityJobDefinitionMixinProps, Dict[str, Any]]) – L1 properties to apply.

• strategy (Optional[PropertyMergeStrategy]) – (experimental) Strategy for merging nested properties. Default: - PropertyMergeStrategy.MERGE

Methods

apply_to(construct)

Apply the mixin properties to the construct.

Parameters:

construct (IConstruct)

Return type:

IConstruct

supports(construct)

Check if this mixin supports the given construct.

Parameters:

construct (IConstruct)

Return type:

bool

Attributes

CFN_PROPERTY_KEYS = ['endpointName', 'jobDefinitionName', 'jobResources', 'modelExplainabilityAppSpecification', 'modelExplainabilityBaselineConfig', 'modelExplainabilityJobInput', 'modelExplainabilityJobOutputConfig', 'networkConfig', 'roleArn', 'stoppingCondition', 'tags']

Static Methods

classmethod is_mixin(x)

(experimental) Checks if x is a Mixin.

Parameters:

x (Any) – Any object.

Return type:

bool

Returns:

true if x is an object created from a class which extends Mixin.

Stability:

experimental

BatchTransformInputProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.BatchTransformInputProperty(*, data_captured_destination_s3_uri=None, dataset_format=None, features_attribute=None, inference_attribute=None, local_path=None, probability_attribute=None, s3_data_distribution_type=None, s3_input_mode=None)

Bases: object

Input object for the batch transform job.

Parameters:

• data_captured_destination_s3_uri (Optional[str]) – The Amazon S3 location being used to capture the data.

• dataset_format (Union[IResolvable, DatasetFormatProperty, Dict[str, Any], None]) – The dataset format for your batch transform job.

• features_attribute (Optional[str]) – The attributes of the input data that are the input features.

• inference_attribute (Optional[str]) – The attribute of the input data that represents the ground truth label.

• local_path (Optional[str]) – Path to the filesystem where the batch transform data is available to the container.

• probability_attribute (Optional[str]) – In a classification problem, the attribute that represents the class probability.

• 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 .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

batch_transform_input_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.BatchTransformInputProperty(
    data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
    dataset_format=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.DatasetFormatProperty(
        csv=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.CsvProperty(
            header=False
        ),
        json=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.JsonProperty(
            line=False
        ),
        parquet=False
    ),
    features_attribute="featuresAttribute",
    inference_attribute="inferenceAttribute",
    local_path="localPath",
    probability_attribute="probabilityAttribute",
    s3_data_distribution_type="s3DataDistributionType",
    s3_input_mode="s3InputMode"
)

Attributes

data_captured_destination_s3_uri

The Amazon S3 location being used to capture the data.

See:

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

dataset_format

The dataset format for your batch transform job.

See:

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

features_attribute

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

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-batchtransforminput.html#cfn-sagemaker-modelexplainabilityjobdefinition-batchtransforminput-featuresattribute

inference_attribute

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

See:

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

local_path

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

See:

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

probability_attribute

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

See:

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

s3_data_distribution_type

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

Defaults to FullyReplicated

See:

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

See:

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

ClusterConfigProperty

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

Bases: object

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

Parameters:

• instance_count (Union[int, float, None]) – 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 (Optional[str]) – The ML compute instance type for the processing job.

• 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.

• volume_size_in_gb (Union[int, float, None]) – The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

cluster_config_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ClusterConfigProperty(
    instance_count=123,
    instance_type="instanceType",
    volume_kms_key_id="volumeKmsKeyId",
    volume_size_in_gb=123
)

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.

See:

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

instance_type

The ML compute instance type for the processing job.

See:

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

See:

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

See:

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

ConstraintsResourceProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.ConstraintsResourceProperty(*, s3_uri=None)

Bases: object

Input object for the endpoint.

Parameters:

s3_uri (Optional[str]) – The Amazon S3 URI for the constraints resource.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

constraints_resource_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ConstraintsResourceProperty(
    s3_uri="s3Uri"
)

Attributes

s3_uri

The Amazon S3 URI for the constraints resource.

See:

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

CsvProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.CsvProperty(*, header=None)

Bases: object

The CSV format.

Parameters:

header (Union[bool, IResolvable, None]) – A boolean flag indicating if given CSV has header.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

csv_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.CsvProperty(
    header=False
)

Attributes

header

A boolean flag indicating if given CSV has header.

See:

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

DatasetFormatProperty

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

Bases: object

The dataset format of the data to monitor.

Parameters:

• csv (Union[IResolvable, CsvProperty, Dict[str, Any], None]) – The CSV format.

• json (Union[IResolvable, JsonProperty, Dict[str, Any], None]) – The Json format.

• parquet (Union[bool, IResolvable, None]) – A flag indicating if the dataset format is Parquet.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

dataset_format_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.DatasetFormatProperty(
    csv=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.CsvProperty(
        header=False
    ),
    json=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.JsonProperty(
        line=False
    ),
    parquet=False
)

Attributes

csv

The CSV format.

See:

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

json

The Json format.

See:

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

parquet

A flag indicating if the dataset format is Parquet.

See:

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

EndpointInputProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.EndpointInputProperty(*, endpoint_name=None, features_attribute=None, inference_attribute=None, local_path=None, probability_attribute=None, s3_data_distribution_type=None, s3_input_mode=None)

Bases: object

Input object for the endpoint.

Parameters:

• endpoint_name (Optional[str]) – An endpoint in customer’s account which has enabled DataCaptureConfig enabled.

• features_attribute (Optional[str]) – The attributes of the input data that are the input features.

• inference_attribute (Optional[str]) – The attribute of the input data that represents the ground truth label.

• local_path (Optional[str]) – Path to the filesystem where the endpoint data is available to the container.

• probability_attribute (Optional[str]) – In a classification problem, the attribute that represents the class probability.

• s3_data_distribution_type (Optional[str]) – Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon 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 .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

endpoint_input_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.EndpointInputProperty(
    endpoint_name="endpointName",
    features_attribute="featuresAttribute",
    inference_attribute="inferenceAttribute",
    local_path="localPath",
    probability_attribute="probabilityAttribute",
    s3_data_distribution_type="s3DataDistributionType",
    s3_input_mode="s3InputMode"
)

Attributes

endpoint_name

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

See:

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

features_attribute

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

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-endpointinput.html#cfn-sagemaker-modelexplainabilityjobdefinition-endpointinput-featuresattribute

inference_attribute

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

See:

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

local_path

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

See:

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

probability_attribute

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

See:

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

s3_data_distribution_type

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

Defaults to FullyReplicated

See:

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

See:

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

JsonProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.JsonProperty(*, line=None)

Bases: object

The Json format.

Parameters:

line (Union[bool, IResolvable, None]) – A boolean flag indicating if it is JSON line format.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

json_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.JsonProperty(
    line=False
)

Attributes

line

A boolean flag indicating if it is JSON line format.

See:

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

ModelExplainabilityAppSpecificationProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.ModelExplainabilityAppSpecificationProperty(*, config_uri=None, environment=None, image_uri=None)

Bases: object

Docker container image configuration object for the model explainability job.

Parameters:

• config_uri (Optional[str]) – JSON formatted Amazon S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters .

• environment (Union[Mapping[str, str], IResolvable, None]) – Sets the environment variables in the Docker container.

• image_uri (Optional[str]) – The container image to be run by the model explainability job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-modelexplainabilityappspecification.html

ExampleMetadata:

fixture=_generated

Example:

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

model_explainability_app_specification_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ModelExplainabilityAppSpecificationProperty(
    config_uri="configUri",
    environment={
        "environment_key": "environment"
    },
    image_uri="imageUri"
)

Attributes

config_uri

JSON formatted Amazon S3 file that defines explainability parameters.

For more information on this JSON configuration file, see Configure model explainability parameters .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-modelexplainabilityappspecification.html#cfn-sagemaker-modelexplainabilityjobdefinition-modelexplainabilityappspecification-configuri

environment

Sets the environment variables in the Docker container.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-modelexplainabilityappspecification.html#cfn-sagemaker-modelexplainabilityjobdefinition-modelexplainabilityappspecification-environment

image_uri

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

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-modelexplainabilityappspecification.html#cfn-sagemaker-modelexplainabilityjobdefinition-modelexplainabilityappspecification-imageuri

ModelExplainabilityBaselineConfigProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.ModelExplainabilityBaselineConfigProperty(*, baselining_job_name=None, constraints_resource=None)

Bases: object

The configuration for a baseline model explainability job.

Parameters:

• baselining_job_name (Optional[str]) – The name of the baseline model explainability job.

• constraints_resource (Union[IResolvable, ConstraintsResourceProperty, Dict[str, Any], None]) – The constraints resource for a model explainability job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-modelexplainabilitybaselineconfig.html

ExampleMetadata:

fixture=_generated

Example:

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

model_explainability_baseline_config_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ModelExplainabilityBaselineConfigProperty(
    baselining_job_name="baseliningJobName",
    constraints_resource=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ConstraintsResourceProperty(
        s3_uri="s3Uri"
    )
)

Attributes

baselining_job_name

The name of the baseline model explainability job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-modelexplainabilitybaselineconfig.html#cfn-sagemaker-modelexplainabilityjobdefinition-modelexplainabilitybaselineconfig-baseliningjobname

constraints_resource

The constraints resource for a model explainability job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-modelexplainabilitybaselineconfig.html#cfn-sagemaker-modelexplainabilityjobdefinition-modelexplainabilitybaselineconfig-constraintsresource

ModelExplainabilityJobInputProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.ModelExplainabilityJobInputProperty(*, batch_transform_input=None, endpoint_input=None)

Bases: object

Inputs for the model explainability job.

Parameters:

• batch_transform_input (Union[IResolvable, BatchTransformInputProperty, Dict[str, Any], None]) – Input object for the batch transform job.

• endpoint_input (Union[IResolvable, EndpointInputProperty, Dict[str, Any], None]) – Input object for the endpoint.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-modelexplainabilityjobinput.html

ExampleMetadata:

fixture=_generated

Example:

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

model_explainability_job_input_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ModelExplainabilityJobInputProperty(
    batch_transform_input=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.BatchTransformInputProperty(
        data_captured_destination_s3_uri="dataCapturedDestinationS3Uri",
        dataset_format=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.DatasetFormatProperty(
            csv=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.CsvProperty(
                header=False
            ),
            json=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.JsonProperty(
                line=False
            ),
            parquet=False
        ),
        features_attribute="featuresAttribute",
        inference_attribute="inferenceAttribute",
        local_path="localPath",
        probability_attribute="probabilityAttribute",
        s3_data_distribution_type="s3DataDistributionType",
        s3_input_mode="s3InputMode"
    ),
    endpoint_input=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.EndpointInputProperty(
        endpoint_name="endpointName",
        features_attribute="featuresAttribute",
        inference_attribute="inferenceAttribute",
        local_path="localPath",
        probability_attribute="probabilityAttribute",
        s3_data_distribution_type="s3DataDistributionType",
        s3_input_mode="s3InputMode"
    )
)

Attributes

batch_transform_input

Input object for the batch transform job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-modelexplainabilityjobinput.html#cfn-sagemaker-modelexplainabilityjobdefinition-modelexplainabilityjobinput-batchtransforminput

endpoint_input

Input object for the endpoint.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-modelexplainabilityjobinput.html#cfn-sagemaker-modelexplainabilityjobdefinition-modelexplainabilityjobinput-endpointinput

MonitoringOutputConfigProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.MonitoringOutputConfigProperty(*, kms_key_id=None, monitoring_outputs=None)

Bases: object

The output configuration for monitoring jobs.

Parameters:

• kms_key_id (Optional[str]) – The AWS Key Management Service ( AWS ) key that Amazon SageMaker AI uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

• monitoring_outputs (Union[IResolvable, Sequence[Union[IResolvable, MonitoringOutputProperty, Dict[str, Any]]], None]) – Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

monitoring_output_config_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.MonitoringOutputConfigProperty(
    kms_key_id="kmsKeyId",
    monitoring_outputs=[sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.MonitoringOutputProperty(
        s3_output=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.S3OutputProperty(
            local_path="localPath",
            s3_upload_mode="s3UploadMode",
            s3_uri="s3Uri"
        )
    )]
)

Attributes

kms_key_id

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

See:

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

monitoring_outputs

Monitoring outputs for monitoring jobs.

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

See:

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

MonitoringOutputProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.MonitoringOutputProperty(*, s3_output=None)

Bases: object

The output object for a monitoring job.

Parameters:

s3_output (Union[IResolvable, S3OutputProperty, Dict[str, Any], None]) – The Amazon S3 storage location where the results of a monitoring job are saved.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

monitoring_output_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.MonitoringOutputProperty(
    s3_output=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.S3OutputProperty(
        local_path="localPath",
        s3_upload_mode="s3UploadMode",
        s3_uri="s3Uri"
    )
)

Attributes

s3_output

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

See:

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

MonitoringResourcesProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.MonitoringResourcesProperty(*, cluster_config=None)

Bases: object

Identifies the resources to deploy for a monitoring job.

Parameters:

cluster_config (Union[IResolvable, ClusterConfigProperty, Dict[str, Any], None]) – The configuration for the cluster resources used to run the processing job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

monitoring_resources_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.MonitoringResourcesProperty(
    cluster_config=sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.ClusterConfigProperty(
        instance_count=123,
        instance_type="instanceType",
        volume_kms_key_id="volumeKmsKeyId",
        volume_size_in_gb=123
    )
)

Attributes

cluster_config

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

See:

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

NetworkConfigProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.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.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

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

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.

See:

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

enable_network_isolation

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

See:

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

See:

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

S3OutputProperty

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

Bases: object

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

Parameters:

• local_path (Optional[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_upload_mode (Optional[str]) – Whether to upload the results of the monitoring job continuously or after the job completes.

• s3_uri (Optional[str]) – A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

s3_output_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.S3OutputProperty(
    local_path="localPath",
    s3_upload_mode="s3UploadMode",
    s3_uri="s3Uri"
)

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.

See:

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

s3_upload_mode

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

See:

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

s3_uri

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

See:

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

StoppingConditionProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.StoppingConditionProperty(*, max_runtime_in_seconds=None)

Bases: object

Specifies a limit to how long a job can run.

When the job reaches the time limit, SageMaker ends the job. Use this API to cap 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, None]) – 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.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

stopping_condition_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.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.

See:

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

VpcConfigProperty

class CfnModelExplainabilityJobDefinitionPropsMixin.VpcConfigProperty(*, security_group_ids=None, subnets=None)

Bases: object

Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to.

You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .

Parameters:

• security_group_ids (Optional[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 (Optional[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 .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelexplainabilityjobdefinition-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.
from aws_cdk.mixins_preview.aws_sagemaker import mixins as sagemaker_mixins

vpc_config_property = sagemaker_mixins.CfnModelExplainabilityJobDefinitionPropsMixin.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.

See:

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

See:

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