CfnModelCardPropsMixin

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

Bases: Mixin

Creates an Amazon SageMaker Model Card.

For information about how to use model cards, see Amazon SageMaker Model Card .

See:

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

CloudformationResource:

AWS::SageMaker::ModelCard

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

# value: Any

cfn_model_card_props_mixin = sagemaker_mixins.CfnModelCardPropsMixin(sagemaker_mixins.CfnModelCardMixinProps(
    content=sagemaker_mixins.CfnModelCardPropsMixin.ContentProperty(
        additional_information=sagemaker_mixins.CfnModelCardPropsMixin.AdditionalInformationProperty(
            caveats_and_recommendations="caveatsAndRecommendations",
            custom_details={
                "custom_details_key": "customDetails"
            },
            ethical_considerations="ethicalConsiderations"
        ),
        business_details=sagemaker_mixins.CfnModelCardPropsMixin.BusinessDetailsProperty(
            business_problem="businessProblem",
            business_stakeholders="businessStakeholders",
            line_of_business="lineOfBusiness"
        ),
        evaluation_details=[sagemaker_mixins.CfnModelCardPropsMixin.EvaluationDetailProperty(
            datasets=["datasets"],
            evaluation_job_arn="evaluationJobArn",
            evaluation_observation="evaluationObservation",
            metadata={
                "metadata_key": "metadata"
            },
            metric_groups=[sagemaker_mixins.CfnModelCardPropsMixin.MetricGroupProperty(
                metric_data=[sagemaker_mixins.CfnModelCardPropsMixin.MetricDataItemsProperty(
                    name="name",
                    notes="notes",
                    type="type",
                    value=value,
                    x_axis_name=["xAxisName"],
                    y_axis_name=["yAxisName"]
                )],
                name="name"
            )],
            name="name"
        )],
        intended_uses=sagemaker_mixins.CfnModelCardPropsMixin.IntendedUsesProperty(
            explanations_for_risk_rating="explanationsForRiskRating",
            factors_affecting_model_efficiency="factorsAffectingModelEfficiency",
            intended_uses="intendedUses",
            purpose_of_model="purposeOfModel",
            risk_rating="riskRating"
        ),
        model_overview=sagemaker_mixins.CfnModelCardPropsMixin.ModelOverviewProperty(
            algorithm_type="algorithmType",
            inference_environment=sagemaker_mixins.CfnModelCardPropsMixin.InferenceEnvironmentProperty(
                container_image=["containerImage"]
            ),
            model_artifact=["modelArtifact"],
            model_creator="modelCreator",
            model_description="modelDescription",
            model_id="modelId",
            model_name="modelName",
            model_owner="modelOwner",
            model_version=123,
            problem_type="problemType"
        ),
        model_package_details=sagemaker_mixins.CfnModelCardPropsMixin.ModelPackageDetailsProperty(
            approval_description="approvalDescription",
            created_by=sagemaker_mixins.CfnModelCardPropsMixin.ModelPackageCreatorProperty(
                user_profile_name="userProfileName"
            ),
            domain="domain",
            inference_specification=sagemaker_mixins.CfnModelCardPropsMixin.InferenceSpecificationProperty(
                containers=[sagemaker_mixins.CfnModelCardPropsMixin.ContainerProperty(
                    image="image",
                    model_data_url="modelDataUrl",
                    nearest_model_name="nearestModelName"
                )]
            ),
            model_approval_status="modelApprovalStatus",
            model_package_arn="modelPackageArn",
            model_package_description="modelPackageDescription",
            model_package_group_name="modelPackageGroupName",
            model_package_name="modelPackageName",
            model_package_status="modelPackageStatus",
            model_package_version=123,
            source_algorithms=[sagemaker_mixins.CfnModelCardPropsMixin.SourceAlgorithmProperty(
                algorithm_name="algorithmName",
                model_data_url="modelDataUrl"
            )],
            task="task"
        ),
        training_details=sagemaker_mixins.CfnModelCardPropsMixin.TrainingDetailsProperty(
            objective_function=sagemaker_mixins.CfnModelCardPropsMixin.ObjectiveFunctionProperty(
                function=sagemaker_mixins.CfnModelCardPropsMixin.FunctionProperty(
                    condition="condition",
                    facet="facet",
                    function="function"
                ),
                notes="notes"
            ),
            training_job_details=sagemaker_mixins.CfnModelCardPropsMixin.TrainingJobDetailsProperty(
                hyper_parameters=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingHyperParameterProperty(
                    name="name",
                    value="value"
                )],
                training_arn="trainingArn",
                training_datasets=["trainingDatasets"],
                training_environment=sagemaker_mixins.CfnModelCardPropsMixin.TrainingEnvironmentProperty(
                    container_image=["containerImage"]
                ),
                training_metrics=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingMetricProperty(
                    name="name",
                    notes="notes",
                    value=123
                )],
                user_provided_hyper_parameters=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingHyperParameterProperty(
                    name="name",
                    value="value"
                )],
                user_provided_training_metrics=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingMetricProperty(
                    name="name",
                    notes="notes",
                    value=123
                )]
            ),
            training_observations="trainingObservations"
        )
    ),
    created_by=sagemaker_mixins.CfnModelCardPropsMixin.UserContextProperty(
        domain_id="domainId",
        user_profile_arn="userProfileArn",
        user_profile_name="userProfileName"
    ),
    last_modified_by=sagemaker_mixins.CfnModelCardPropsMixin.UserContextProperty(
        domain_id="domainId",
        user_profile_arn="userProfileArn",
        user_profile_name="userProfileName"
    ),
    model_card_name="modelCardName",
    model_card_status="modelCardStatus",
    security_config=sagemaker_mixins.CfnModelCardPropsMixin.SecurityConfigProperty(
        kms_key_id="kmsKeyId"
    ),
    tags=[CfnTag(
        key="key",
        value="value"
    )]
),
    strategy=mixins.PropertyMergeStrategy.OVERRIDE
)

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

Parameters:

  • props (Union[CfnModelCardMixinProps, 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 = ['content', 'createdBy', 'lastModifiedBy', 'modelCardName', 'modelCardStatus', 'securityConfig', '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

AdditionalInformationProperty

class CfnModelCardPropsMixin.AdditionalInformationProperty(*, caveats_and_recommendations=None, custom_details=None, ethical_considerations=None)

Bases: object

Additional information about the model.

Parameters:

  • caveats_and_recommendations (Optional[str]) – Caveats and recommendations for those who might use this model in their applications.

  • custom_details (Union[Mapping[str, str], IResolvable, None]) – Any additional information to document about the model.

  • ethical_considerations (Optional[str]) – Any ethical considerations documented by the model card author.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-additionalinformation.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

additional_information_property = sagemaker_mixins.CfnModelCardPropsMixin.AdditionalInformationProperty(
    caveats_and_recommendations="caveatsAndRecommendations",
    custom_details={
        "custom_details_key": "customDetails"
    },
    ethical_considerations="ethicalConsiderations"
)

Attributes

caveats_and_recommendations

Caveats and recommendations for those who might use this model in their applications.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-additionalinformation.html#cfn-sagemaker-modelcard-additionalinformation-caveatsandrecommendations

custom_details

Any additional information to document about the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-additionalinformation.html#cfn-sagemaker-modelcard-additionalinformation-customdetails

ethical_considerations

Any ethical considerations documented by the model card author.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-additionalinformation.html#cfn-sagemaker-modelcard-additionalinformation-ethicalconsiderations

BusinessDetailsProperty

class CfnModelCardPropsMixin.BusinessDetailsProperty(*, business_problem=None, business_stakeholders=None, line_of_business=None)

Bases: object

Information about how the model supports business goals.

Parameters:

  • business_problem (Optional[str]) – The specific business problem that the model is trying to solve.

  • business_stakeholders (Optional[str]) – The relevant stakeholders for the model.

  • line_of_business (Optional[str]) – The broader business need that the model is serving.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-businessdetails.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

business_details_property = sagemaker_mixins.CfnModelCardPropsMixin.BusinessDetailsProperty(
    business_problem="businessProblem",
    business_stakeholders="businessStakeholders",
    line_of_business="lineOfBusiness"
)

Attributes

business_problem

The specific business problem that the model is trying to solve.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-businessdetails.html#cfn-sagemaker-modelcard-businessdetails-businessproblem

business_stakeholders

The relevant stakeholders for the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-businessdetails.html#cfn-sagemaker-modelcard-businessdetails-businessstakeholders

line_of_business

The broader business need that the model is serving.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-businessdetails.html#cfn-sagemaker-modelcard-businessdetails-lineofbusiness

ContainerProperty

class CfnModelCardPropsMixin.ContainerProperty(*, image=None, model_data_url=None, nearest_model_name=None)

Bases: object

Parameters:

  • image (Optional[str]) – Inference environment path. The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

  • model_data_url (Optional[str]) – The Amazon S3 path where the model artifacts, which result from model training, are stored.

  • nearest_model_name (Optional[str]) – The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-container.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

container_property = sagemaker_mixins.CfnModelCardPropsMixin.ContainerProperty(
    image="image",
    model_data_url="modelDataUrl",
    nearest_model_name="nearestModelName"
)

Attributes

image

Inference environment path.

The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-container.html#cfn-sagemaker-modelcard-container-image

model_data_url

The Amazon S3 path where the model artifacts, which result from model training, are stored.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-container.html#cfn-sagemaker-modelcard-container-modeldataurl

nearest_model_name

The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-container.html#cfn-sagemaker-modelcard-container-nearestmodelname

ContentProperty

class CfnModelCardPropsMixin.ContentProperty(*, additional_information=None, business_details=None, evaluation_details=None, intended_uses=None, model_overview=None, model_package_details=None, training_details=None)

Bases: object

The content of the model card.

It follows the model card json schema .

Parameters:
See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-content.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

# value: Any

content_property = sagemaker_mixins.CfnModelCardPropsMixin.ContentProperty(
    additional_information=sagemaker_mixins.CfnModelCardPropsMixin.AdditionalInformationProperty(
        caveats_and_recommendations="caveatsAndRecommendations",
        custom_details={
            "custom_details_key": "customDetails"
        },
        ethical_considerations="ethicalConsiderations"
    ),
    business_details=sagemaker_mixins.CfnModelCardPropsMixin.BusinessDetailsProperty(
        business_problem="businessProblem",
        business_stakeholders="businessStakeholders",
        line_of_business="lineOfBusiness"
    ),
    evaluation_details=[sagemaker_mixins.CfnModelCardPropsMixin.EvaluationDetailProperty(
        datasets=["datasets"],
        evaluation_job_arn="evaluationJobArn",
        evaluation_observation="evaluationObservation",
        metadata={
            "metadata_key": "metadata"
        },
        metric_groups=[sagemaker_mixins.CfnModelCardPropsMixin.MetricGroupProperty(
            metric_data=[sagemaker_mixins.CfnModelCardPropsMixin.MetricDataItemsProperty(
                name="name",
                notes="notes",
                type="type",
                value=value,
                x_axis_name=["xAxisName"],
                y_axis_name=["yAxisName"]
            )],
            name="name"
        )],
        name="name"
    )],
    intended_uses=sagemaker_mixins.CfnModelCardPropsMixin.IntendedUsesProperty(
        explanations_for_risk_rating="explanationsForRiskRating",
        factors_affecting_model_efficiency="factorsAffectingModelEfficiency",
        intended_uses="intendedUses",
        purpose_of_model="purposeOfModel",
        risk_rating="riskRating"
    ),
    model_overview=sagemaker_mixins.CfnModelCardPropsMixin.ModelOverviewProperty(
        algorithm_type="algorithmType",
        inference_environment=sagemaker_mixins.CfnModelCardPropsMixin.InferenceEnvironmentProperty(
            container_image=["containerImage"]
        ),
        model_artifact=["modelArtifact"],
        model_creator="modelCreator",
        model_description="modelDescription",
        model_id="modelId",
        model_name="modelName",
        model_owner="modelOwner",
        model_version=123,
        problem_type="problemType"
    ),
    model_package_details=sagemaker_mixins.CfnModelCardPropsMixin.ModelPackageDetailsProperty(
        approval_description="approvalDescription",
        created_by=sagemaker_mixins.CfnModelCardPropsMixin.ModelPackageCreatorProperty(
            user_profile_name="userProfileName"
        ),
        domain="domain",
        inference_specification=sagemaker_mixins.CfnModelCardPropsMixin.InferenceSpecificationProperty(
            containers=[sagemaker_mixins.CfnModelCardPropsMixin.ContainerProperty(
                image="image",
                model_data_url="modelDataUrl",
                nearest_model_name="nearestModelName"
            )]
        ),
        model_approval_status="modelApprovalStatus",
        model_package_arn="modelPackageArn",
        model_package_description="modelPackageDescription",
        model_package_group_name="modelPackageGroupName",
        model_package_name="modelPackageName",
        model_package_status="modelPackageStatus",
        model_package_version=123,
        source_algorithms=[sagemaker_mixins.CfnModelCardPropsMixin.SourceAlgorithmProperty(
            algorithm_name="algorithmName",
            model_data_url="modelDataUrl"
        )],
        task="task"
    ),
    training_details=sagemaker_mixins.CfnModelCardPropsMixin.TrainingDetailsProperty(
        objective_function=sagemaker_mixins.CfnModelCardPropsMixin.ObjectiveFunctionProperty(
            function=sagemaker_mixins.CfnModelCardPropsMixin.FunctionProperty(
                condition="condition",
                facet="facet",
                function="function"
            ),
            notes="notes"
        ),
        training_job_details=sagemaker_mixins.CfnModelCardPropsMixin.TrainingJobDetailsProperty(
            hyper_parameters=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingHyperParameterProperty(
                name="name",
                value="value"
            )],
            training_arn="trainingArn",
            training_datasets=["trainingDatasets"],
            training_environment=sagemaker_mixins.CfnModelCardPropsMixin.TrainingEnvironmentProperty(
                container_image=["containerImage"]
            ),
            training_metrics=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingMetricProperty(
                name="name",
                notes="notes",
                value=123
            )],
            user_provided_hyper_parameters=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingHyperParameterProperty(
                name="name",
                value="value"
            )],
            user_provided_training_metrics=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingMetricProperty(
                name="name",
                notes="notes",
                value=123
            )]
        ),
        training_observations="trainingObservations"
    )
)

Attributes

additional_information

Additional information about the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-content.html#cfn-sagemaker-modelcard-content-additionalinformation

business_details

Information about how the model supports business goals.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-content.html#cfn-sagemaker-modelcard-content-businessdetails

evaluation_details

An overview about the model’s evaluation.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-content.html#cfn-sagemaker-modelcard-content-evaluationdetails

intended_uses

The intended usage of the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-content.html#cfn-sagemaker-modelcard-content-intendeduses

model_overview

An overview about the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-content.html#cfn-sagemaker-modelcard-content-modeloverview

model_package_details

Metadata information related to model package version.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-content.html#cfn-sagemaker-modelcard-content-modelpackagedetails

training_details

An overview about model training.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-content.html#cfn-sagemaker-modelcard-content-trainingdetails

EvaluationDetailProperty

class CfnModelCardPropsMixin.EvaluationDetailProperty(*, datasets=None, evaluation_job_arn=None, evaluation_observation=None, metadata=None, metric_groups=None, name=None)

Bases: object

The evaluation details of the model.

Parameters:

  • datasets (Optional[Sequence[str]]) – The location of the datasets used to evaluate the model.

  • evaluation_job_arn (Optional[str]) – The Amazon Resource Name (ARN) of the evaluation job.

  • evaluation_observation (Optional[str]) – Any observations made during the model evaluation.

  • metadata (Union[Mapping[str, str], IResolvable, None]) – Additional attributes associated with the evaluation results.

  • metric_groups (Union[IResolvable, Sequence[Union[IResolvable, MetricGroupProperty, Dict[str, Any]]], None]) – An evaluation Metric Group object.

  • name (Optional[str]) – The evaluation job name.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-evaluationdetail.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

# value: Any

evaluation_detail_property = sagemaker_mixins.CfnModelCardPropsMixin.EvaluationDetailProperty(
    datasets=["datasets"],
    evaluation_job_arn="evaluationJobArn",
    evaluation_observation="evaluationObservation",
    metadata={
        "metadata_key": "metadata"
    },
    metric_groups=[sagemaker_mixins.CfnModelCardPropsMixin.MetricGroupProperty(
        metric_data=[sagemaker_mixins.CfnModelCardPropsMixin.MetricDataItemsProperty(
            name="name",
            notes="notes",
            type="type",
            value=value,
            x_axis_name=["xAxisName"],
            y_axis_name=["yAxisName"]
        )],
        name="name"
    )],
    name="name"
)

Attributes

datasets

The location of the datasets used to evaluate the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-evaluationdetail.html#cfn-sagemaker-modelcard-evaluationdetail-datasets

evaluation_job_arn

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

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-evaluationdetail.html#cfn-sagemaker-modelcard-evaluationdetail-evaluationjobarn

evaluation_observation

Any observations made during the model evaluation.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-evaluationdetail.html#cfn-sagemaker-modelcard-evaluationdetail-evaluationobservation

metadata

Additional attributes associated with the evaluation results.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-evaluationdetail.html#cfn-sagemaker-modelcard-evaluationdetail-metadata

metric_groups

An evaluation Metric Group object.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-evaluationdetail.html#cfn-sagemaker-modelcard-evaluationdetail-metricgroups

name

The evaluation job name.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-evaluationdetail.html#cfn-sagemaker-modelcard-evaluationdetail-name

FunctionProperty

class CfnModelCardPropsMixin.FunctionProperty(*, condition=None, facet=None, function=None)

Bases: object

Function details.

Parameters:

  • condition (Optional[str]) – An optional description of any conditions of your objective function metric.

  • facet (Optional[str]) – The metric of the model’s objective function. For example, loss or rmse . The following list shows examples of the values that you can specify for the metric: - ACCURACY - AUC - LOSS - MAE - RMSE

  • function (Optional[str]) – The optimization direction of the model’s objective function. You must specify one of the following values:. - Maximize - Minimize

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-function.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

function_property = sagemaker_mixins.CfnModelCardPropsMixin.FunctionProperty(
    condition="condition",
    facet="facet",
    function="function"
)

Attributes

condition

An optional description of any conditions of your objective function metric.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-function.html#cfn-sagemaker-modelcard-function-condition

facet

The metric of the model’s objective function.

For example, loss or rmse . The following list shows examples of the values that you can specify for the metric:

  • ACCURACY

  • AUC

  • LOSS

  • MAE

  • RMSE

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-function.html#cfn-sagemaker-modelcard-function-facet

function

.

  • Maximize

  • Minimize

See:

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

Type:

The optimization direction of the model’s objective function. You must specify one of the following values

InferenceEnvironmentProperty

class CfnModelCardPropsMixin.InferenceEnvironmentProperty(*, container_image=None)

Bases: object

An overview of a model’s inference environment.

Parameters:

container_image (Optional[Sequence[str]]) – The container used to run the inference environment.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-inferenceenvironment.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

inference_environment_property = sagemaker_mixins.CfnModelCardPropsMixin.InferenceEnvironmentProperty(
    container_image=["containerImage"]
)

Attributes

container_image

The container used to run the inference environment.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-inferenceenvironment.html#cfn-sagemaker-modelcard-inferenceenvironment-containerimage

InferenceSpecificationProperty

class CfnModelCardPropsMixin.InferenceSpecificationProperty(*, containers=None)

Bases: object

Defines how to perform inference generation after a training job is run.

Parameters:

containers (Union[IResolvable, Sequence[Union[IResolvable, ContainerProperty, Dict[str, Any]]], None]) – The Amazon ECR registry path of the Docker image that contains the inference code.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-inferencespecification.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

inference_specification_property = sagemaker_mixins.CfnModelCardPropsMixin.InferenceSpecificationProperty(
    containers=[sagemaker_mixins.CfnModelCardPropsMixin.ContainerProperty(
        image="image",
        model_data_url="modelDataUrl",
        nearest_model_name="nearestModelName"
    )]
)

Attributes

containers

The Amazon ECR registry path of the Docker image that contains the inference code.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-inferencespecification.html#cfn-sagemaker-modelcard-inferencespecification-containers

IntendedUsesProperty

class CfnModelCardPropsMixin.IntendedUsesProperty(*, explanations_for_risk_rating=None, factors_affecting_model_efficiency=None, intended_uses=None, purpose_of_model=None, risk_rating=None)

Bases: object

The intended uses of a model.

Parameters:

  • explanations_for_risk_rating (Optional[str]) – An explanation of why your organization categorizes the model with its risk rating.

  • factors_affecting_model_efficiency (Optional[str]) – Factors affecting model efficacy.

  • intended_uses (Optional[str]) – The intended use cases for the model.

  • purpose_of_model (Optional[str]) – The general purpose of the model.

  • risk_rating (Optional[str]) – Your organization’s risk rating. You can specify one the following values as the risk rating:. - High - Medium - Low - Unknown

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-intendeduses.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

intended_uses_property = sagemaker_mixins.CfnModelCardPropsMixin.IntendedUsesProperty(
    explanations_for_risk_rating="explanationsForRiskRating",
    factors_affecting_model_efficiency="factorsAffectingModelEfficiency",
    intended_uses="intendedUses",
    purpose_of_model="purposeOfModel",
    risk_rating="riskRating"
)

Attributes

explanations_for_risk_rating

An explanation of why your organization categorizes the model with its risk rating.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-intendeduses.html#cfn-sagemaker-modelcard-intendeduses-explanationsforriskrating

factors_affecting_model_efficiency

Factors affecting model efficacy.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-intendeduses.html#cfn-sagemaker-modelcard-intendeduses-factorsaffectingmodelefficiency

intended_uses

The intended use cases for the model.

See:

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

purpose_of_model

The general purpose of the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-intendeduses.html#cfn-sagemaker-modelcard-intendeduses-purposeofmodel

risk_rating

.

  • High

  • Medium

  • Low

  • Unknown

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-intendeduses.html#cfn-sagemaker-modelcard-intendeduses-riskrating

Type:

Your organization’s risk rating. You can specify one the following values as the risk rating

MetricDataItemsProperty

class CfnModelCardPropsMixin.MetricDataItemsProperty(*, name=None, notes=None, type=None, value=None, x_axis_name=None, y_axis_name=None)

Bases: object

Metric data.

The type determines the data types that you specify for value , XAxisName and YAxisName . For information about specifying values for metrics, see model card JSON schema .

Parameters:

  • name (Optional[str])

  • notes (Optional[str])

  • type (Optional[str])

  • value (Any)

  • x_axis_name (Optional[Sequence[str]])

  • y_axis_name (Optional[Sequence[str]])

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-metricdataitems.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

# value: Any

metric_data_items_property = sagemaker_mixins.CfnModelCardPropsMixin.MetricDataItemsProperty(
    name="name",
    notes="notes",
    type="type",
    value=value,
    x_axis_name=["xAxisName"],
    y_axis_name=["yAxisName"]
)

Attributes

name

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-metricdataitems.html#cfn-sagemaker-modelcard-metricdataitems-name

Type:

see

notes

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-metricdataitems.html#cfn-sagemaker-modelcard-metricdataitems-notes

Type:

see

type

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-metricdataitems.html#cfn-sagemaker-modelcard-metricdataitems-type

Type:

see

value

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-metricdataitems.html#cfn-sagemaker-modelcard-metricdataitems-value

Type:

see

x_axis_name

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-metricdataitems.html#cfn-sagemaker-modelcard-metricdataitems-xaxisname

Type:

see

y_axis_name

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-metricdataitems.html#cfn-sagemaker-modelcard-metricdataitems-yaxisname

Type:

see

MetricGroupProperty

class CfnModelCardPropsMixin.MetricGroupProperty(*, metric_data=None, name=None)

Bases: object

A group of metric data that you use to initialize a metric group object.

Parameters:

  • metric_data (Union[IResolvable, Sequence[Union[IResolvable, MetricDataItemsProperty, Dict[str, Any]]], None]) –

    A list of metric objects. The MetricDataItems list can have one of the following values:. - bar_chart_metric - matrix_metric - simple_metric - linear_graph_metric For more information about the metric schema, see the definition section of the model card JSON schema .

  • name (Optional[str]) – The metric group name.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-metricgroup.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

# value: Any

metric_group_property = sagemaker_mixins.CfnModelCardPropsMixin.MetricGroupProperty(
    metric_data=[sagemaker_mixins.CfnModelCardPropsMixin.MetricDataItemsProperty(
        name="name",
        notes="notes",
        type="type",
        value=value,
        x_axis_name=["xAxisName"],
        y_axis_name=["yAxisName"]
    )],
    name="name"
)

Attributes

metric_data

.

  • bar_chart_metric

  • matrix_metric

  • simple_metric

  • linear_graph_metric

For more information about the metric schema, see the definition section of the model card JSON schema .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-metricgroup.html#cfn-sagemaker-modelcard-metricgroup-metricdata

Type:

A list of metric objects. The MetricDataItems list can have one of the following values

name

The metric group name.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-metricgroup.html#cfn-sagemaker-modelcard-metricgroup-name

ModelOverviewProperty

class CfnModelCardPropsMixin.ModelOverviewProperty(*, algorithm_type=None, inference_environment=None, model_artifact=None, model_creator=None, model_description=None, model_id=None, model_name=None, model_owner=None, model_version=None, problem_type=None)

Bases: object

An overview about the model.

Parameters:

  • algorithm_type (Optional[str]) – The algorithm used to solve the problem.

  • inference_environment (Union[IResolvable, InferenceEnvironmentProperty, Dict[str, Any], None]) – An overview about model inference.

  • model_artifact (Optional[Sequence[str]]) – The location of the model artifact.

  • model_creator (Optional[str]) – The creator of the model.

  • model_description (Optional[str]) – A description of the model.

  • model_id (Optional[str]) – The SageMaker AI Model ARN or non- SageMaker AI Model ID.

  • model_name (Optional[str]) – The name of the model.

  • model_owner (Optional[str]) – The owner of the model.

  • model_version (Union[int, float, None]) – The version of the model.

  • problem_type (Optional[str]) – The problem being solved with the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modeloverview.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_overview_property = sagemaker_mixins.CfnModelCardPropsMixin.ModelOverviewProperty(
    algorithm_type="algorithmType",
    inference_environment=sagemaker_mixins.CfnModelCardPropsMixin.InferenceEnvironmentProperty(
        container_image=["containerImage"]
    ),
    model_artifact=["modelArtifact"],
    model_creator="modelCreator",
    model_description="modelDescription",
    model_id="modelId",
    model_name="modelName",
    model_owner="modelOwner",
    model_version=123,
    problem_type="problemType"
)

Attributes

algorithm_type

The algorithm used to solve the problem.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modeloverview.html#cfn-sagemaker-modelcard-modeloverview-algorithmtype

inference_environment

An overview about model inference.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modeloverview.html#cfn-sagemaker-modelcard-modeloverview-inferenceenvironment

model_artifact

The location of the model artifact.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modeloverview.html#cfn-sagemaker-modelcard-modeloverview-modelartifact

model_creator

The creator of the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modeloverview.html#cfn-sagemaker-modelcard-modeloverview-modelcreator

model_description

A description of the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modeloverview.html#cfn-sagemaker-modelcard-modeloverview-modeldescription

model_id

The SageMaker AI Model ARN or non- SageMaker AI Model ID.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modeloverview.html#cfn-sagemaker-modelcard-modeloverview-modelid

model_name

The name of the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modeloverview.html#cfn-sagemaker-modelcard-modeloverview-modelname

model_owner

The owner of the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modeloverview.html#cfn-sagemaker-modelcard-modeloverview-modelowner

model_version

The version of the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modeloverview.html#cfn-sagemaker-modelcard-modeloverview-modelversion

problem_type

The problem being solved with the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modeloverview.html#cfn-sagemaker-modelcard-modeloverview-problemtype

ModelPackageCreatorProperty

class CfnModelCardPropsMixin.ModelPackageCreatorProperty(*, user_profile_name=None)

Bases: object

Parameters:

user_profile_name (Optional[str]) – The name of the user’s profile in Studio.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagecreator.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_package_creator_property = sagemaker_mixins.CfnModelCardPropsMixin.ModelPackageCreatorProperty(
    user_profile_name="userProfileName"
)

Attributes

user_profile_name

The name of the user’s profile in Studio.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagecreator.html#cfn-sagemaker-modelcard-modelpackagecreator-userprofilename

ModelPackageDetailsProperty

class CfnModelCardPropsMixin.ModelPackageDetailsProperty(*, approval_description=None, created_by=None, domain=None, inference_specification=None, model_approval_status=None, model_package_arn=None, model_package_description=None, model_package_group_name=None, model_package_name=None, model_package_status=None, model_package_version=None, source_algorithms=None, task=None)

Bases: object

Metadata information related to model package version.

Parameters:

  • approval_description (Optional[str]) – A description provided for the model approval.

  • created_by (Union[IResolvable, ModelPackageCreatorProperty, Dict[str, Any], None])

  • domain (Optional[str]) – The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing.

  • inference_specification (Union[IResolvable, InferenceSpecificationProperty, Dict[str, Any], None])

  • model_approval_status (Optional[str]) – Current approval status of model package.

  • model_package_arn (Optional[str]) – The Amazon Resource Name (ARN) of the model package.

  • model_package_description (Optional[str]) – A brief summary of the model package.

  • model_package_group_name (Optional[str]) – If the model is a versioned model, the name of the model group that the versioned model belongs to.

  • model_package_name (Optional[str]) – Name of the model package.

  • model_package_status (Optional[str]) – Current status of model package.

  • model_package_version (Union[int, float, None]) – Version of the model package.

  • source_algorithms (Union[IResolvable, Sequence[Union[IResolvable, SourceAlgorithmProperty, Dict[str, Any]]], None])

  • task (Optional[str]) – The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.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_package_details_property = sagemaker_mixins.CfnModelCardPropsMixin.ModelPackageDetailsProperty(
    approval_description="approvalDescription",
    created_by=sagemaker_mixins.CfnModelCardPropsMixin.ModelPackageCreatorProperty(
        user_profile_name="userProfileName"
    ),
    domain="domain",
    inference_specification=sagemaker_mixins.CfnModelCardPropsMixin.InferenceSpecificationProperty(
        containers=[sagemaker_mixins.CfnModelCardPropsMixin.ContainerProperty(
            image="image",
            model_data_url="modelDataUrl",
            nearest_model_name="nearestModelName"
        )]
    ),
    model_approval_status="modelApprovalStatus",
    model_package_arn="modelPackageArn",
    model_package_description="modelPackageDescription",
    model_package_group_name="modelPackageGroupName",
    model_package_name="modelPackageName",
    model_package_status="modelPackageStatus",
    model_package_version=123,
    source_algorithms=[sagemaker_mixins.CfnModelCardPropsMixin.SourceAlgorithmProperty(
        algorithm_name="algorithmName",
        model_data_url="modelDataUrl"
    )],
    task="task"
)

Attributes

approval_description

A description provided for the model approval.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.html#cfn-sagemaker-modelcard-modelpackagedetails-approvaldescription

created_by

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.html#cfn-sagemaker-modelcard-modelpackagedetails-createdby

Type:

see

domain

The machine learning domain of the model package you specified.

Common machine learning domains include computer vision and natural language processing.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.html#cfn-sagemaker-modelcard-modelpackagedetails-domain

inference_specification

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.html#cfn-sagemaker-modelcard-modelpackagedetails-inferencespecification

Type:

see

model_approval_status

Current approval status of model package.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.html#cfn-sagemaker-modelcard-modelpackagedetails-modelapprovalstatus

model_package_arn

The Amazon Resource Name (ARN) of the model package.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.html#cfn-sagemaker-modelcard-modelpackagedetails-modelpackagearn

model_package_description

A brief summary of the model package.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.html#cfn-sagemaker-modelcard-modelpackagedetails-modelpackagedescription

model_package_group_name

If the model is a versioned model, the name of the model group that the versioned model belongs to.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.html#cfn-sagemaker-modelcard-modelpackagedetails-modelpackagegroupname

model_package_name

Name of the model package.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.html#cfn-sagemaker-modelcard-modelpackagedetails-modelpackagename

model_package_status

Current status of model package.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.html#cfn-sagemaker-modelcard-modelpackagedetails-modelpackagestatus

model_package_version

Version of the model package.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.html#cfn-sagemaker-modelcard-modelpackagedetails-modelpackageversion

source_algorithms

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.html#cfn-sagemaker-modelcard-modelpackagedetails-sourcealgorithms

Type:

see

task

The machine learning task you specified that your model package accomplishes.

Common machine learning tasks include object detection and image classification.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-modelpackagedetails.html#cfn-sagemaker-modelcard-modelpackagedetails-task

ObjectiveFunctionProperty

class CfnModelCardPropsMixin.ObjectiveFunctionProperty(*, function=None, notes=None)

Bases: object

The function that is optimized during model training.

Parameters:

  • function (Union[IResolvable, FunctionProperty, Dict[str, Any], None]) – A function object that details optimization direction, metric, and additional descriptions.

  • notes (Optional[str]) – Notes about the object function, including other considerations for possible objective functions.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-objectivefunction.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

objective_function_property = sagemaker_mixins.CfnModelCardPropsMixin.ObjectiveFunctionProperty(
    function=sagemaker_mixins.CfnModelCardPropsMixin.FunctionProperty(
        condition="condition",
        facet="facet",
        function="function"
    ),
    notes="notes"
)

Attributes

function

A function object that details optimization direction, metric, and additional descriptions.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-objectivefunction.html#cfn-sagemaker-modelcard-objectivefunction-function

notes

Notes about the object function, including other considerations for possible objective functions.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-objectivefunction.html#cfn-sagemaker-modelcard-objectivefunction-notes

SecurityConfigProperty

class CfnModelCardPropsMixin.SecurityConfigProperty(*, kms_key_id=None)

Bases: object

The security configuration used to protect model card data.

Parameters:

kms_key_id (Optional[str]) – A AWS Key Management Service key ID used to encrypt a model card.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-securityconfig.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

security_config_property = sagemaker_mixins.CfnModelCardPropsMixin.SecurityConfigProperty(
    kms_key_id="kmsKeyId"
)

Attributes

kms_key_id

A AWS Key Management Service key ID used to encrypt a model card.

See:

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

SourceAlgorithmProperty

class CfnModelCardPropsMixin.SourceAlgorithmProperty(*, algorithm_name=None, model_data_url=None)

Bases: object

Specifies an algorithm that was used to create the model package.

The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.

Parameters:

  • algorithm_name (Optional[str]) – The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.

  • model_data_url (Optional[str]) – The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive ( .tar.gz suffix). .. epigraph:: The model artifacts must be in an S3 bucket that is in the same AWS region as the algorithm.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-sourcealgorithm.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

source_algorithm_property = sagemaker_mixins.CfnModelCardPropsMixin.SourceAlgorithmProperty(
    algorithm_name="algorithmName",
    model_data_url="modelDataUrl"
)

Attributes

algorithm_name

The name of an algorithm that was used to create the model package.

The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-sourcealgorithm.html#cfn-sagemaker-modelcard-sourcealgorithm-algorithmname

model_data_url

The Amazon S3 path where the model artifacts, which result from model training, are stored.

This path must point to a single gzip compressed tar archive ( .tar.gz suffix). .. epigraph:

The model artifacts must be in an S3 bucket that is in the same AWS region as the algorithm.
See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-sourcealgorithm.html#cfn-sagemaker-modelcard-sourcealgorithm-modeldataurl

TrainingDetailsProperty

class CfnModelCardPropsMixin.TrainingDetailsProperty(*, objective_function=None, training_job_details=None, training_observations=None)

Bases: object

The training details of the model.

Parameters:
See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingdetails.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

training_details_property = sagemaker_mixins.CfnModelCardPropsMixin.TrainingDetailsProperty(
    objective_function=sagemaker_mixins.CfnModelCardPropsMixin.ObjectiveFunctionProperty(
        function=sagemaker_mixins.CfnModelCardPropsMixin.FunctionProperty(
            condition="condition",
            facet="facet",
            function="function"
        ),
        notes="notes"
    ),
    training_job_details=sagemaker_mixins.CfnModelCardPropsMixin.TrainingJobDetailsProperty(
        hyper_parameters=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingHyperParameterProperty(
            name="name",
            value="value"
        )],
        training_arn="trainingArn",
        training_datasets=["trainingDatasets"],
        training_environment=sagemaker_mixins.CfnModelCardPropsMixin.TrainingEnvironmentProperty(
            container_image=["containerImage"]
        ),
        training_metrics=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingMetricProperty(
            name="name",
            notes="notes",
            value=123
        )],
        user_provided_hyper_parameters=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingHyperParameterProperty(
            name="name",
            value="value"
        )],
        user_provided_training_metrics=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingMetricProperty(
            name="name",
            notes="notes",
            value=123
        )]
    ),
    training_observations="trainingObservations"
)

Attributes

objective_function

The function that is optimized during model training.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingdetails.html#cfn-sagemaker-modelcard-trainingdetails-objectivefunction

training_job_details

Details about any associated training jobs.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingdetails.html#cfn-sagemaker-modelcard-trainingdetails-trainingjobdetails

training_observations

Any observations about training.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingdetails.html#cfn-sagemaker-modelcard-trainingdetails-trainingobservations

TrainingEnvironmentProperty

class CfnModelCardPropsMixin.TrainingEnvironmentProperty(*, container_image=None)

Bases: object

SageMaker AI training image.

Parameters:

container_image (Optional[Sequence[str]]) – SageMaker AI inference image URI.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingenvironment.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

training_environment_property = sagemaker_mixins.CfnModelCardPropsMixin.TrainingEnvironmentProperty(
    container_image=["containerImage"]
)

Attributes

container_image

SageMaker AI inference image URI.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingenvironment.html#cfn-sagemaker-modelcard-trainingenvironment-containerimage

TrainingHyperParameterProperty

class CfnModelCardPropsMixin.TrainingHyperParameterProperty(*, name=None, value=None)

Bases: object

A hyper parameter that was configured in training the model.

Parameters:

  • name (Optional[str]) – The name of the hyper parameter.

  • value (Optional[str]) – The value specified for the hyper parameter.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-traininghyperparameter.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

training_hyper_parameter_property = sagemaker_mixins.CfnModelCardPropsMixin.TrainingHyperParameterProperty(
    name="name",
    value="value"
)

Attributes

name

The name of the hyper parameter.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-traininghyperparameter.html#cfn-sagemaker-modelcard-traininghyperparameter-name

value

The value specified for the hyper parameter.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-traininghyperparameter.html#cfn-sagemaker-modelcard-traininghyperparameter-value

TrainingJobDetailsProperty

class CfnModelCardPropsMixin.TrainingJobDetailsProperty(*, hyper_parameters=None, training_arn=None, training_datasets=None, training_environment=None, training_metrics=None, user_provided_hyper_parameters=None, user_provided_training_metrics=None)

Bases: object

The overview of a training job.

Parameters:
See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingjobdetails.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

training_job_details_property = sagemaker_mixins.CfnModelCardPropsMixin.TrainingJobDetailsProperty(
    hyper_parameters=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingHyperParameterProperty(
        name="name",
        value="value"
    )],
    training_arn="trainingArn",
    training_datasets=["trainingDatasets"],
    training_environment=sagemaker_mixins.CfnModelCardPropsMixin.TrainingEnvironmentProperty(
        container_image=["containerImage"]
    ),
    training_metrics=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingMetricProperty(
        name="name",
        notes="notes",
        value=123
    )],
    user_provided_hyper_parameters=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingHyperParameterProperty(
        name="name",
        value="value"
    )],
    user_provided_training_metrics=[sagemaker_mixins.CfnModelCardPropsMixin.TrainingMetricProperty(
        name="name",
        notes="notes",
        value=123
    )]
)

Attributes

hyper_parameters

The hyper parameters used in the training job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingjobdetails.html#cfn-sagemaker-modelcard-trainingjobdetails-hyperparameters

training_arn

The SageMaker AI training job Amazon Resource Name (ARN).

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingjobdetails.html#cfn-sagemaker-modelcard-trainingjobdetails-trainingarn

training_datasets

The location of the datasets used to train the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingjobdetails.html#cfn-sagemaker-modelcard-trainingjobdetails-trainingdatasets

training_environment

The SageMaker AI training job image URI.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingjobdetails.html#cfn-sagemaker-modelcard-trainingjobdetails-trainingenvironment

training_metrics

The SageMaker AI training job results.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingjobdetails.html#cfn-sagemaker-modelcard-trainingjobdetails-trainingmetrics

user_provided_hyper_parameters

Additional hyper parameters that you’ve specified when training the model.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingjobdetails.html#cfn-sagemaker-modelcard-trainingjobdetails-userprovidedhyperparameters

user_provided_training_metrics

Custom training job results.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingjobdetails.html#cfn-sagemaker-modelcard-trainingjobdetails-userprovidedtrainingmetrics

TrainingMetricProperty

class CfnModelCardPropsMixin.TrainingMetricProperty(*, name=None, notes=None, value=None)

Bases: object

A result from a SageMaker AI training job.

Parameters:

  • name (Optional[str]) – The name of the result from the SageMaker AI training job.

  • notes (Optional[str]) – Any additional notes describing the result of the training job.

  • value (Union[int, float, None]) – The value of a result from the SageMaker AI training job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingmetric.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

training_metric_property = sagemaker_mixins.CfnModelCardPropsMixin.TrainingMetricProperty(
    name="name",
    notes="notes",
    value=123
)

Attributes

name

The name of the result from the SageMaker AI training job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingmetric.html#cfn-sagemaker-modelcard-trainingmetric-name

notes

Any additional notes describing the result of the training job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingmetric.html#cfn-sagemaker-modelcard-trainingmetric-notes

value

The value of a result from the SageMaker AI training job.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-trainingmetric.html#cfn-sagemaker-modelcard-trainingmetric-value

UserContextProperty

class CfnModelCardPropsMixin.UserContextProperty(*, domain_id=None, user_profile_arn=None, user_profile_name=None)

Bases: object

Information about the user who created or modified a SageMaker resource.

Parameters:

  • domain_id (Optional[str]) – The domain associated with the user. Default: - “UnsetValue”

  • user_profile_arn (Optional[str]) – The Amazon Resource Name (ARN) of the user’s profile. Default: - “UnsetValue”

  • user_profile_name (Optional[str]) – The name of the user’s profile. Default: - “UnsetValue”

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-usercontext.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

user_context_property = sagemaker_mixins.CfnModelCardPropsMixin.UserContextProperty(
    domain_id="domainId",
    user_profile_arn="userProfileArn",
    user_profile_name="userProfileName"
)

Attributes

domain_id

The domain associated with the user.

Default:

  • “UnsetValue”

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-usercontext.html#cfn-sagemaker-modelcard-usercontext-domainid

user_profile_arn

The Amazon Resource Name (ARN) of the user’s profile.

Default:

  • “UnsetValue”

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-usercontext.html#cfn-sagemaker-modelcard-usercontext-userprofilearn

user_profile_name

The name of the user’s profile.

Default:

  • “UnsetValue”

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sagemaker-modelcard-usercontext.html#cfn-sagemaker-modelcard-usercontext-userprofilename