CfnModelCard
- class aws_cdk.aws_sagemaker.CfnModelCard(scope, id, *, content, model_card_name, model_card_status, created_by=None, last_modified_by=None, security_config=None, tags=None)
Bases:
CfnResource
A CloudFormation
AWS::SageMaker::ModelCard
.Creates an Amazon SageMaker Model Card.
For information about how to use model cards, see Amazon SageMaker Model Card .
- CloudformationResource:
AWS::SageMaker::ModelCard
- Link:
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-modelcard.html
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker # value: Any cfn_model_card = sagemaker.CfnModelCard(self, "MyCfnModelCard", content=sagemaker.CfnModelCard.ContentProperty( additional_information=sagemaker.CfnModelCard.AdditionalInformationProperty( caveats_and_recommendations="caveatsAndRecommendations", custom_details={ "custom_details_key": "customDetails" }, ethical_considerations="ethicalConsiderations" ), business_details=sagemaker.CfnModelCard.BusinessDetailsProperty( business_problem="businessProblem", business_stakeholders="businessStakeholders", line_of_business="lineOfBusiness" ), evaluation_details=[sagemaker.CfnModelCard.EvaluationDetailProperty( name="name", # the properties below are optional datasets=["datasets"], evaluation_job_arn="evaluationJobArn", evaluation_observation="evaluationObservation", metadata={ "metadata_key": "metadata" }, metric_groups=[sagemaker.CfnModelCard.MetricGroupProperty( metric_data=[sagemaker.CfnModelCard.MetricDataItemsProperty( name="name", type="type", value=value, # the properties below are optional notes="notes", x_axis_name=["xAxisName"], y_axis_name=["yAxisName"] )], name="name" )] )], intended_uses=sagemaker.CfnModelCard.IntendedUsesProperty( explanations_for_risk_rating="explanationsForRiskRating", factors_affecting_model_efficiency="factorsAffectingModelEfficiency", intended_uses="intendedUses", purpose_of_model="purposeOfModel", risk_rating="riskRating" ), model_overview=sagemaker.CfnModelCard.ModelOverviewProperty( algorithm_type="algorithmType", inference_environment=sagemaker.CfnModelCard.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.CfnModelCard.ModelPackageDetailsProperty( approval_description="approvalDescription", created_by=sagemaker.CfnModelCard.ModelPackageCreatorProperty( user_profile_name="userProfileName" ), domain="domain", inference_specification=sagemaker.CfnModelCard.InferenceSpecificationProperty( containers=[sagemaker.CfnModelCard.ContainerProperty( image="image", # the properties below are optional 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.CfnModelCard.SourceAlgorithmProperty( algorithm_name="algorithmName", # the properties below are optional model_data_url="modelDataUrl" )], task="task" ), training_details=sagemaker.CfnModelCard.TrainingDetailsProperty( objective_function=sagemaker.CfnModelCard.ObjectiveFunctionProperty( function=sagemaker.CfnModelCard.FunctionProperty( condition="condition", facet="facet", function="function" ), notes="notes" ), training_job_details=sagemaker.CfnModelCard.TrainingJobDetailsProperty( hyper_parameters=[sagemaker.CfnModelCard.TrainingHyperParameterProperty( name="name", value="value" )], training_arn="trainingArn", training_datasets=["trainingDatasets"], training_environment=sagemaker.CfnModelCard.TrainingEnvironmentProperty( container_image=["containerImage"] ), training_metrics=[sagemaker.CfnModelCard.TrainingMetricProperty( name="name", value=123, # the properties below are optional notes="notes" )], user_provided_hyper_parameters=[sagemaker.CfnModelCard.TrainingHyperParameterProperty( name="name", value="value" )], user_provided_training_metrics=[sagemaker.CfnModelCard.TrainingMetricProperty( name="name", value=123, # the properties below are optional notes="notes" )] ), training_observations="trainingObservations" ) ), model_card_name="modelCardName", model_card_status="modelCardStatus", # the properties below are optional created_by=sagemaker.CfnModelCard.UserContextProperty( domain_id="domainId", user_profile_arn="userProfileArn", user_profile_name="userProfileName" ), last_modified_by=sagemaker.CfnModelCard.UserContextProperty( domain_id="domainId", user_profile_arn="userProfileArn", user_profile_name="userProfileName" ), security_config=sagemaker.CfnModelCard.SecurityConfigProperty( kms_key_id="kmsKeyId" ), tags=[CfnTag( key="key", value="value" )] )
Create a new
AWS::SageMaker::ModelCard
.- Parameters:
scope (
Construct
) –scope in which this resource is defined.
id (
str
) –scoped id of the resource.
content (
Union
[IResolvable
,ContentProperty
,Dict
[str
,Any
]]) – The content of the model card. Content uses the model card JSON schema .model_card_name (
str
) – The unique name of the model card.model_card_status (
str
) – The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval. -Draft
: The model card is a work in progress. -PendingReview
: The model card is pending review. -Approved
: The model card is approved. -Archived
: The model card is archived. No more updates should be made to the model card, but it can still be exported.created_by (
Union
[IResolvable
,UserContextProperty
,Dict
[str
,Any
],None
]) – Information about the user who created or modified one or more of the following:. - Experiment - Trial - Trial component - Lineage group - Project - Model Cardlast_modified_by (
Union
[IResolvable
,UserContextProperty
,Dict
[str
,Any
],None
]) –AWS::SageMaker::ModelCard.LastModifiedBy
.security_config (
Union
[IResolvable
,SecurityConfigProperty
,Dict
[str
,Any
],None
]) – The security configuration used to protect model card data.tags (
Optional
[Sequence
[Union
[CfnTag
,Dict
[str
,Any
]]]]) – Key-value pairs used to manage metadata for the model card.
Methods
- add_deletion_override(path)
Syntactic sugar for
addOverride(path, undefined)
.- Parameters:
path (
str
) – The path of the value to delete.- Return type:
None
- add_depends_on(target)
Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.
This can be used for resources across stacks (or nested stack) boundaries and the dependency will automatically be transferred to the relevant scope.
- Parameters:
target (
CfnResource
) –- Return type:
None
- add_metadata(key, value)
Add a value to the CloudFormation Resource Metadata.
- Parameters:
key (
str
) –value (
Any
) –
- See:
- Return type:
None
Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.
- add_override(path, value)
Adds an override to the synthesized CloudFormation resource.
To add a property override, either use
addPropertyOverride
or prefixpath
with “Properties.” (i.e.Properties.TopicName
).If the override is nested, separate each nested level using a dot (.) in the path parameter. If there is an array as part of the nesting, specify the index in the path.
To include a literal
.
in the property name, prefix with a\
. In most programming languages you will need to write this as"\\."
because the\
itself will need to be escaped.For example:
cfn_resource.add_override("Properties.GlobalSecondaryIndexes.0.Projection.NonKeyAttributes", ["myattribute"]) cfn_resource.add_override("Properties.GlobalSecondaryIndexes.1.ProjectionType", "INCLUDE")
would add the overrides Example:
"Properties": { "GlobalSecondaryIndexes": [ { "Projection": { "NonKeyAttributes": [ "myattribute" ] ... } ... }, { "ProjectionType": "INCLUDE" ... }, ] ... }
The
value
argument toaddOverride
will not be processed or translated in any way. Pass raw JSON values in here with the correct capitalization for CloudFormation. If you pass CDK classes or structs, they will be rendered with lowercased key names, and CloudFormation will reject the template.- Parameters:
path (
str
) –The path of the property, you can use dot notation to override values in complex types. Any intermdediate keys will be created as needed.
value (
Any
) –The value. Could be primitive or complex.
- Return type:
None
- add_property_deletion_override(property_path)
Adds an override that deletes the value of a property from the resource definition.
- Parameters:
property_path (
str
) – The path to the property.- Return type:
None
- add_property_override(property_path, value)
Adds an override to a resource property.
Syntactic sugar for
addOverride("Properties.<...>", value)
.- Parameters:
property_path (
str
) – The path of the property.value (
Any
) – The value.
- Return type:
None
- apply_removal_policy(policy=None, *, apply_to_update_replace_policy=None, default=None)
Sets the deletion policy of the resource based on the removal policy specified.
The Removal Policy controls what happens to this resource when it stops being managed by CloudFormation, either because you’ve removed it from the CDK application or because you’ve made a change that requires the resource to be replaced.
The resource can be deleted (
RemovalPolicy.DESTROY
), or left in your AWS account for data recovery and cleanup later (RemovalPolicy.RETAIN
).- Parameters:
policy (
Optional
[RemovalPolicy
]) –apply_to_update_replace_policy (
Optional
[bool
]) – Apply the same deletion policy to the resource’s “UpdateReplacePolicy”. Default: truedefault (
Optional
[RemovalPolicy
]) – The default policy to apply in case the removal policy is not defined. Default: - Default value is resource specific. To determine the default value for a resoure, please consult that specific resource’s documentation.
- Return type:
None
- get_att(attribute_name)
Returns a token for an runtime attribute of this resource.
Ideally, use generated attribute accessors (e.g.
resource.arn
), but this can be used for future compatibility in case there is no generated attribute.- Parameters:
attribute_name (
str
) – The name of the attribute.- Return type:
- get_metadata(key)
Retrieve a value value from the CloudFormation Resource Metadata.
- Parameters:
key (
str
) –- See:
- Return type:
Any
Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.
- inspect(inspector)
Examines the CloudFormation resource and discloses attributes.
- Parameters:
inspector (
TreeInspector
) –tree inspector to collect and process attributes.
- Return type:
None
- override_logical_id(new_logical_id)
Overrides the auto-generated logical ID with a specific ID.
- Parameters:
new_logical_id (
str
) – The new logical ID to use for this stack element.- Return type:
None
- to_string()
Returns a string representation of this construct.
- Return type:
str
- Returns:
a string representation of this resource
Attributes
- CFN_RESOURCE_TYPE_NAME = 'AWS::SageMaker::ModelCard'
- attr_created_by_domain_id
CreatedBy.DomainId
- Type:
cloudformationAttribute
- attr_created_by_user_profile_arn
CreatedBy.UserProfileArn
- Type:
cloudformationAttribute
- attr_created_by_user_profile_name
CreatedBy.UserProfileName
- Type:
cloudformationAttribute
- attr_creation_time
CreationTime
- Type:
cloudformationAttribute
- attr_last_modified_by_domain_id
LastModifiedBy.DomainId
- Type:
cloudformationAttribute
- attr_last_modified_by_user_profile_arn
LastModifiedBy.UserProfileArn
- Type:
cloudformationAttribute
- attr_last_modified_by_user_profile_name
LastModifiedBy.UserProfileName
- Type:
cloudformationAttribute
- attr_last_modified_time
LastModifiedTime
- Type:
cloudformationAttribute
- attr_model_card_arn
The Amazon Resource Number (ARN) of the model card.
For example,
arn:aws:sagemaker:us-west-2:012345678901:modelcard/examplemodelcard
.- CloudformationAttribute:
ModelCardArn
- attr_model_card_processing_status
ModelCardProcessingStatus
- Type:
cloudformationAttribute
- attr_model_card_version
ModelCardVersion
- Type:
cloudformationAttribute
- cfn_options
Options for this resource, such as condition, update policy etc.
- cfn_resource_type
AWS resource type.
- content
The content of the model card.
Content uses the model card JSON schema .
- created_by
.
Experiment
Trial
Trial component
Lineage group
Project
Model Card
- Link:
- Type:
Information about the user who created or modified one or more of the following
- creation_stack
return:
the stack trace of the point where this Resource was created from, sourced from the +metadata+ entry typed +aws:cdk:logicalId+, and with the bottom-most node +internal+ entries filtered.
- last_modified_by
AWS::SageMaker::ModelCard.LastModifiedBy
.
- logical_id
The logical ID for this CloudFormation stack element.
The logical ID of the element is calculated from the path of the resource node in the construct tree.
To override this value, use
overrideLogicalId(newLogicalId)
.- Returns:
the logical ID as a stringified token. This value will only get resolved during synthesis.
- model_card_name
The unique name of the model card.
- model_card_status
The approval status of the model card within your organization.
Different organizations might have different criteria for model card review and approval.
Draft
: The model card is a work in progress.PendingReview
: The model card is pending review.Approved
: The model card is approved.Archived
: The model card is archived. No more updates should be made to the model card, but it can still be exported.
- node
The construct tree node associated with this construct.
- ref
Return a string that will be resolved to a CloudFormation
{ Ref }
for this element.If, by any chance, the intrinsic reference of a resource is not a string, you could coerce it to an IResolvable through
Lazy.any({ produce: resource.ref })
.
- security_config
The security configuration used to protect model card data.
- stack
The stack in which this element is defined.
CfnElements must be defined within a stack scope (directly or indirectly).
- tags
Key-value pairs used to manage metadata for the model card.
Static Methods
- classmethod is_cfn_element(x)
Returns
true
if a construct is a stack element (i.e. part of the synthesized cloudformation template).Uses duck-typing instead of
instanceof
to allow stack elements from different versions of this library to be included in the same stack.- Parameters:
x (
Any
) –- Return type:
bool
- Returns:
The construct as a stack element or undefined if it is not a stack element.
- classmethod is_cfn_resource(construct)
Check whether the given construct is a CfnResource.
- Parameters:
construct (
IConstruct
) –- Return type:
bool
- classmethod is_construct(x)
Return whether the given object is a Construct.
- Parameters:
x (
Any
) –- Return type:
bool
AdditionalInformationProperty
- class CfnModelCard.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
[IResolvable
,Mapping
[str
,str
],None
]) – Any additional information to document about the model.ethical_considerations (
Optional
[str
]) – Any ethical considerations documented by the model card author.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker additional_information_property = sagemaker.CfnModelCard.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.
- custom_details
Any additional information to document about the model.
- ethical_considerations
Any ethical considerations documented by the model card author.
BusinessDetailsProperty
- class CfnModelCard.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.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker business_details_property = sagemaker.CfnModelCard.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.
- business_stakeholders
The relevant stakeholders for the model.
- line_of_business
The broader business need that the model is serving.
ContainerProperty
- class CfnModelCard.ContainerProperty(*, image, model_data_url=None, nearest_model_name=None)
Bases:
object
- Parameters:
image (
str
) –CfnModelCard.ContainerProperty.Image
.model_data_url (
Optional
[str
]) –CfnModelCard.ContainerProperty.ModelDataUrl
.nearest_model_name (
Optional
[str
]) –CfnModelCard.ContainerProperty.NearestModelName
.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker container_property = sagemaker.CfnModelCard.ContainerProperty( image="image", # the properties below are optional model_data_url="modelDataUrl", nearest_model_name="nearestModelName" )
Attributes
- image
CfnModelCard.ContainerProperty.Image
.
- model_data_url
CfnModelCard.ContainerProperty.ModelDataUrl
.
- nearest_model_name
CfnModelCard.ContainerProperty.NearestModelName
.
ContentProperty
- class CfnModelCard.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:
additional_information (
Union
[IResolvable
,AdditionalInformationProperty
,Dict
[str
,Any
],None
]) – Additional information about the model.business_details (
Union
[IResolvable
,BusinessDetailsProperty
,Dict
[str
,Any
],None
]) – Information about how the model supports business goals.evaluation_details (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,EvaluationDetailProperty
,Dict
[str
,Any
]]],None
]) – An overview about the model’s evaluation.intended_uses (
Union
[IResolvable
,IntendedUsesProperty
,Dict
[str
,Any
],None
]) – The intended usage of the model.model_overview (
Union
[IResolvable
,ModelOverviewProperty
,Dict
[str
,Any
],None
]) – An overview about the model.model_package_details (
Union
[IResolvable
,ModelPackageDetailsProperty
,Dict
[str
,Any
],None
]) –CfnModelCard.ContentProperty.ModelPackageDetails
.training_details (
Union
[IResolvable
,TrainingDetailsProperty
,Dict
[str
,Any
],None
]) – An overview about model training.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker # value: Any content_property = sagemaker.CfnModelCard.ContentProperty( additional_information=sagemaker.CfnModelCard.AdditionalInformationProperty( caveats_and_recommendations="caveatsAndRecommendations", custom_details={ "custom_details_key": "customDetails" }, ethical_considerations="ethicalConsiderations" ), business_details=sagemaker.CfnModelCard.BusinessDetailsProperty( business_problem="businessProblem", business_stakeholders="businessStakeholders", line_of_business="lineOfBusiness" ), evaluation_details=[sagemaker.CfnModelCard.EvaluationDetailProperty( name="name", # the properties below are optional datasets=["datasets"], evaluation_job_arn="evaluationJobArn", evaluation_observation="evaluationObservation", metadata={ "metadata_key": "metadata" }, metric_groups=[sagemaker.CfnModelCard.MetricGroupProperty( metric_data=[sagemaker.CfnModelCard.MetricDataItemsProperty( name="name", type="type", value=value, # the properties below are optional notes="notes", x_axis_name=["xAxisName"], y_axis_name=["yAxisName"] )], name="name" )] )], intended_uses=sagemaker.CfnModelCard.IntendedUsesProperty( explanations_for_risk_rating="explanationsForRiskRating", factors_affecting_model_efficiency="factorsAffectingModelEfficiency", intended_uses="intendedUses", purpose_of_model="purposeOfModel", risk_rating="riskRating" ), model_overview=sagemaker.CfnModelCard.ModelOverviewProperty( algorithm_type="algorithmType", inference_environment=sagemaker.CfnModelCard.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.CfnModelCard.ModelPackageDetailsProperty( approval_description="approvalDescription", created_by=sagemaker.CfnModelCard.ModelPackageCreatorProperty( user_profile_name="userProfileName" ), domain="domain", inference_specification=sagemaker.CfnModelCard.InferenceSpecificationProperty( containers=[sagemaker.CfnModelCard.ContainerProperty( image="image", # the properties below are optional 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.CfnModelCard.SourceAlgorithmProperty( algorithm_name="algorithmName", # the properties below are optional model_data_url="modelDataUrl" )], task="task" ), training_details=sagemaker.CfnModelCard.TrainingDetailsProperty( objective_function=sagemaker.CfnModelCard.ObjectiveFunctionProperty( function=sagemaker.CfnModelCard.FunctionProperty( condition="condition", facet="facet", function="function" ), notes="notes" ), training_job_details=sagemaker.CfnModelCard.TrainingJobDetailsProperty( hyper_parameters=[sagemaker.CfnModelCard.TrainingHyperParameterProperty( name="name", value="value" )], training_arn="trainingArn", training_datasets=["trainingDatasets"], training_environment=sagemaker.CfnModelCard.TrainingEnvironmentProperty( container_image=["containerImage"] ), training_metrics=[sagemaker.CfnModelCard.TrainingMetricProperty( name="name", value=123, # the properties below are optional notes="notes" )], user_provided_hyper_parameters=[sagemaker.CfnModelCard.TrainingHyperParameterProperty( name="name", value="value" )], user_provided_training_metrics=[sagemaker.CfnModelCard.TrainingMetricProperty( name="name", value=123, # the properties below are optional notes="notes" )] ), training_observations="trainingObservations" ) )
Attributes
- additional_information
Additional information about the model.
- business_details
Information about how the model supports business goals.
- evaluation_details
An overview about the model’s evaluation.
- intended_uses
The intended usage of the model.
- model_overview
An overview about the model.
- model_package_details
CfnModelCard.ContentProperty.ModelPackageDetails
.
- training_details
An overview about model training.
EvaluationDetailProperty
- class CfnModelCard.EvaluationDetailProperty(*, name, datasets=None, evaluation_job_arn=None, evaluation_observation=None, metadata=None, metric_groups=None)
Bases:
object
The evaluation details of the model.
- Parameters:
name (
str
) – The evaluation job name.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
[IResolvable
,Mapping
[str
,str
],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.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker # value: Any evaluation_detail_property = sagemaker.CfnModelCard.EvaluationDetailProperty( name="name", # the properties below are optional datasets=["datasets"], evaluation_job_arn="evaluationJobArn", evaluation_observation="evaluationObservation", metadata={ "metadata_key": "metadata" }, metric_groups=[sagemaker.CfnModelCard.MetricGroupProperty( metric_data=[sagemaker.CfnModelCard.MetricDataItemsProperty( name="name", type="type", value=value, # the properties below are optional notes="notes", x_axis_name=["xAxisName"], y_axis_name=["yAxisName"] )], name="name" )] )
Attributes
- datasets
The location of the datasets used to evaluate the model.
- evaluation_job_arn
The Amazon Resource Name (ARN) of the evaluation job.
- evaluation_observation
Any observations made during the model evaluation.
- metadata
Additional attributes associated with the evaluation results.
- metric_groups
An evaluation Metric Group object.
FunctionProperty
- class CfnModelCard.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
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker function_property = sagemaker.CfnModelCard.FunctionProperty( condition="condition", facet="facet", function="function" )
Attributes
- condition
An optional description of any conditions of your objective function metric.
- 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
- function
.
Maximize
Minimize
- Link:
- Type:
The optimization direction of the model’s objective function. You must specify one of the following values
InferenceEnvironmentProperty
- class CfnModelCard.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.- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker inference_environment_property = sagemaker.CfnModelCard.InferenceEnvironmentProperty( container_image=["containerImage"] )
Attributes
- container_image
The container used to run the inference environment.
InferenceSpecificationProperty
- class CfnModelCard.InferenceSpecificationProperty(*, containers)
Bases:
object
- Parameters:
containers (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,ContainerProperty
,Dict
[str
,Any
]]]]) –CfnModelCard.InferenceSpecificationProperty.Containers
.- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker inference_specification_property = sagemaker.CfnModelCard.InferenceSpecificationProperty( containers=[sagemaker.CfnModelCard.ContainerProperty( image="image", # the properties below are optional model_data_url="modelDataUrl", nearest_model_name="nearestModelName" )] )
Attributes
- containers
CfnModelCard.InferenceSpecificationProperty.Containers
.
IntendedUsesProperty
- class CfnModelCard.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
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker intended_uses_property = sagemaker.CfnModelCard.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.
- factors_affecting_model_efficiency
Factors affecting model efficacy.
- intended_uses
The intended use cases for the model.
- purpose_of_model
The general purpose of the model.
- risk_rating
.
High
Medium
Low
Unknown
- Link:
- Type:
Your organization’s risk rating. You can specify one the following values as the risk rating
MetricDataItemsProperty
- class CfnModelCard.MetricDataItemsProperty(*, name, type, value, notes=None, x_axis_name=None, y_axis_name=None)
Bases:
object
Metric data.
The
type
determines the data types that you specify forvalue
,XAxisName
andYAxisName
. For information about specifying values for metrics, see model card JSON schema .- Parameters:
name (
str
) – The names of the metrics.type (
str
) – You must specify one of the following data types:. - Bar Chartbar_char
- Booleanboolean
- Linear Graphlinear_graph
- Matrixmatrix
- Numbernumber
- Stringstring
value (
Any
) – The datatype of the metric. The metric’s value must be compatible with the metric’s type .notes (
Optional
[str
]) – Any notes to add to the metric.x_axis_name (
Optional
[Sequence
[str
]]) – The name of the x axis.y_axis_name (
Optional
[Sequence
[str
]]) – The name of the y axis.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker # value: Any metric_data_items_property = sagemaker.CfnModelCard.MetricDataItemsProperty( name="name", type="type", value=value, # the properties below are optional notes="notes", x_axis_name=["xAxisName"], y_axis_name=["yAxisName"] )
Attributes
- name
The names of the metrics.
- notes
Any notes to add to the metric.
- type
.
Bar Chart
bar_char
Boolean
boolean
Linear Graph
linear_graph
Matrix
matrix
Number
number
String
string
- Link:
- Type:
You must specify one of the following data types
- value
The datatype of the metric.
The metric’s value must be compatible with the metric’s type .
- x_axis_name
The name of the x axis.
- y_axis_name
The name of the y axis.
MetricGroupProperty
- class CfnModelCard.MetricGroupProperty(*, metric_data, name)
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
]]]]) –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 (
str
) – The metric group name.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker # value: Any metric_group_property = sagemaker.CfnModelCard.MetricGroupProperty( metric_data=[sagemaker.CfnModelCard.MetricDataItemsProperty( name="name", type="type", value=value, # the properties below are optional notes="notes", 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 .
- Link:
- Type:
A list of metric objects. The
MetricDataItems
list can have one of the following values
ModelOverviewProperty
- class CfnModelCard.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 Model ARN or non- SageMaker 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.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker model_overview_property = sagemaker.CfnModelCard.ModelOverviewProperty( algorithm_type="algorithmType", inference_environment=sagemaker.CfnModelCard.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.
- inference_environment
An overview about model inference.
- model_artifact
The location of the model artifact.
- model_creator
The creator of the model.
- model_description
A description of the model.
- model_id
The SageMaker Model ARN or non- SageMaker Model ID.
- model_name
The name of the model.
- model_owner
The owner of the model.
- model_version
The version of the model.
- problem_type
The problem being solved with the model.
ModelPackageCreatorProperty
- class CfnModelCard.ModelPackageCreatorProperty(*, user_profile_name=None)
Bases:
object
- Parameters:
user_profile_name (
Optional
[str
]) –CfnModelCard.ModelPackageCreatorProperty.UserProfileName
.- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker model_package_creator_property = sagemaker.CfnModelCard.ModelPackageCreatorProperty( user_profile_name="userProfileName" )
Attributes
- user_profile_name
CfnModelCard.ModelPackageCreatorProperty.UserProfileName
.
ModelPackageDetailsProperty
- class CfnModelCard.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
- Parameters:
approval_description (
Optional
[str
]) –CfnModelCard.ModelPackageDetailsProperty.ApprovalDescription
.created_by (
Union
[IResolvable
,ModelPackageCreatorProperty
,Dict
[str
,Any
],None
]) –CfnModelCard.ModelPackageDetailsProperty.CreatedBy
.domain (
Optional
[str
]) –CfnModelCard.ModelPackageDetailsProperty.Domain
.inference_specification (
Union
[IResolvable
,InferenceSpecificationProperty
,Dict
[str
,Any
],None
]) –CfnModelCard.ModelPackageDetailsProperty.InferenceSpecification
.model_approval_status (
Optional
[str
]) –CfnModelCard.ModelPackageDetailsProperty.ModelApprovalStatus
.model_package_arn (
Optional
[str
]) –CfnModelCard.ModelPackageDetailsProperty.ModelPackageArn
.model_package_description (
Optional
[str
]) –CfnModelCard.ModelPackageDetailsProperty.ModelPackageDescription
.model_package_group_name (
Optional
[str
]) –CfnModelCard.ModelPackageDetailsProperty.ModelPackageGroupName
.model_package_name (
Optional
[str
]) –CfnModelCard.ModelPackageDetailsProperty.ModelPackageName
.model_package_status (
Optional
[str
]) –CfnModelCard.ModelPackageDetailsProperty.ModelPackageStatus
.model_package_version (
Union
[int
,float
,None
]) –CfnModelCard.ModelPackageDetailsProperty.ModelPackageVersion
.source_algorithms (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,SourceAlgorithmProperty
,Dict
[str
,Any
]]],None
]) –CfnModelCard.ModelPackageDetailsProperty.SourceAlgorithms
.task (
Optional
[str
]) –CfnModelCard.ModelPackageDetailsProperty.Task
.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker model_package_details_property = sagemaker.CfnModelCard.ModelPackageDetailsProperty( approval_description="approvalDescription", created_by=sagemaker.CfnModelCard.ModelPackageCreatorProperty( user_profile_name="userProfileName" ), domain="domain", inference_specification=sagemaker.CfnModelCard.InferenceSpecificationProperty( containers=[sagemaker.CfnModelCard.ContainerProperty( image="image", # the properties below are optional 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.CfnModelCard.SourceAlgorithmProperty( algorithm_name="algorithmName", # the properties below are optional model_data_url="modelDataUrl" )], task="task" )
Attributes
- approval_description
CfnModelCard.ModelPackageDetailsProperty.ApprovalDescription
.
- created_by
CfnModelCard.ModelPackageDetailsProperty.CreatedBy
.
- domain
CfnModelCard.ModelPackageDetailsProperty.Domain
.
- inference_specification
CfnModelCard.ModelPackageDetailsProperty.InferenceSpecification
.
- model_approval_status
CfnModelCard.ModelPackageDetailsProperty.ModelApprovalStatus
.
- model_package_arn
CfnModelCard.ModelPackageDetailsProperty.ModelPackageArn
.
- model_package_description
CfnModelCard.ModelPackageDetailsProperty.ModelPackageDescription
.
- model_package_group_name
CfnModelCard.ModelPackageDetailsProperty.ModelPackageGroupName
.
- model_package_name
CfnModelCard.ModelPackageDetailsProperty.ModelPackageName
.
- model_package_status
CfnModelCard.ModelPackageDetailsProperty.ModelPackageStatus
.
- model_package_version
CfnModelCard.ModelPackageDetailsProperty.ModelPackageVersion
.
- source_algorithms
CfnModelCard.ModelPackageDetailsProperty.SourceAlgorithms
.
- task
CfnModelCard.ModelPackageDetailsProperty.Task
.
ObjectiveFunctionProperty
- class CfnModelCard.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.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker objective_function_property = sagemaker.CfnModelCard.ObjectiveFunctionProperty( function=sagemaker.CfnModelCard.FunctionProperty( condition="condition", facet="facet", function="function" ), notes="notes" )
Attributes
- function
A function object that details optimization direction, metric, and additional descriptions.
- notes
Notes about the object function, including other considerations for possible objective functions.
SecurityConfigProperty
- class CfnModelCard.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.- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker security_config_property = sagemaker.CfnModelCard.SecurityConfigProperty( kms_key_id="kmsKeyId" )
Attributes
SourceAlgorithmProperty
- class CfnModelCard.SourceAlgorithmProperty(*, algorithm_name, model_data_url=None)
Bases:
object
- Parameters:
algorithm_name (
str
) –CfnModelCard.SourceAlgorithmProperty.AlgorithmName
.model_data_url (
Optional
[str
]) –CfnModelCard.SourceAlgorithmProperty.ModelDataUrl
.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker source_algorithm_property = sagemaker.CfnModelCard.SourceAlgorithmProperty( algorithm_name="algorithmName", # the properties below are optional model_data_url="modelDataUrl" )
Attributes
- algorithm_name
CfnModelCard.SourceAlgorithmProperty.AlgorithmName
.
- model_data_url
CfnModelCard.SourceAlgorithmProperty.ModelDataUrl
.
TrainingDetailsProperty
- class CfnModelCard.TrainingDetailsProperty(*, objective_function=None, training_job_details=None, training_observations=None)
Bases:
object
The training details of the model.
- Parameters:
objective_function (
Union
[IResolvable
,ObjectiveFunctionProperty
,Dict
[str
,Any
],None
]) – The function that is optimized during model training.training_job_details (
Union
[IResolvable
,TrainingJobDetailsProperty
,Dict
[str
,Any
],None
]) – Details about any associated training jobs.training_observations (
Optional
[str
]) – Any observations about training.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker training_details_property = sagemaker.CfnModelCard.TrainingDetailsProperty( objective_function=sagemaker.CfnModelCard.ObjectiveFunctionProperty( function=sagemaker.CfnModelCard.FunctionProperty( condition="condition", facet="facet", function="function" ), notes="notes" ), training_job_details=sagemaker.CfnModelCard.TrainingJobDetailsProperty( hyper_parameters=[sagemaker.CfnModelCard.TrainingHyperParameterProperty( name="name", value="value" )], training_arn="trainingArn", training_datasets=["trainingDatasets"], training_environment=sagemaker.CfnModelCard.TrainingEnvironmentProperty( container_image=["containerImage"] ), training_metrics=[sagemaker.CfnModelCard.TrainingMetricProperty( name="name", value=123, # the properties below are optional notes="notes" )], user_provided_hyper_parameters=[sagemaker.CfnModelCard.TrainingHyperParameterProperty( name="name", value="value" )], user_provided_training_metrics=[sagemaker.CfnModelCard.TrainingMetricProperty( name="name", value=123, # the properties below are optional notes="notes" )] ), training_observations="trainingObservations" )
Attributes
- objective_function
The function that is optimized during model training.
- training_job_details
Details about any associated training jobs.
- training_observations
Any observations about training.
TrainingEnvironmentProperty
- class CfnModelCard.TrainingEnvironmentProperty(*, container_image=None)
Bases:
object
SageMaker training image.
- Parameters:
container_image (
Optional
[Sequence
[str
]]) – SageMaker inference image URI.- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker training_environment_property = sagemaker.CfnModelCard.TrainingEnvironmentProperty( container_image=["containerImage"] )
Attributes
- container_image
SageMaker inference image URI.
TrainingHyperParameterProperty
- class CfnModelCard.TrainingHyperParameterProperty(*, name, value)
Bases:
object
A hyper parameter that was configured in training the model.
- Parameters:
name (
str
) – The name of the hyper parameter.value (
str
) – The value specified for the hyper parameter.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker training_hyper_parameter_property = sagemaker.CfnModelCard.TrainingHyperParameterProperty( name="name", value="value" )
Attributes
- name
The name of the hyper parameter.
- value
The value specified for the hyper parameter.
TrainingJobDetailsProperty
- class CfnModelCard.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:
hyper_parameters (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,TrainingHyperParameterProperty
,Dict
[str
,Any
]]],None
]) – The hyper parameters used in the training job.training_arn (
Optional
[str
]) – The SageMaker training job Amazon Resource Name (ARN).training_datasets (
Optional
[Sequence
[str
]]) – The location of the datasets used to train the model.training_environment (
Union
[IResolvable
,TrainingEnvironmentProperty
,Dict
[str
,Any
],None
]) – The SageMaker training job image URI.training_metrics (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,TrainingMetricProperty
,Dict
[str
,Any
]]],None
]) – The SageMaker training job results.user_provided_hyper_parameters (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,TrainingHyperParameterProperty
,Dict
[str
,Any
]]],None
]) – Additional hyper parameters that you’ve specified when training the model.user_provided_training_metrics (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,TrainingMetricProperty
,Dict
[str
,Any
]]],None
]) – Custom training job results.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker training_job_details_property = sagemaker.CfnModelCard.TrainingJobDetailsProperty( hyper_parameters=[sagemaker.CfnModelCard.TrainingHyperParameterProperty( name="name", value="value" )], training_arn="trainingArn", training_datasets=["trainingDatasets"], training_environment=sagemaker.CfnModelCard.TrainingEnvironmentProperty( container_image=["containerImage"] ), training_metrics=[sagemaker.CfnModelCard.TrainingMetricProperty( name="name", value=123, # the properties below are optional notes="notes" )], user_provided_hyper_parameters=[sagemaker.CfnModelCard.TrainingHyperParameterProperty( name="name", value="value" )], user_provided_training_metrics=[sagemaker.CfnModelCard.TrainingMetricProperty( name="name", value=123, # the properties below are optional notes="notes" )] )
Attributes
- hyper_parameters
The hyper parameters used in the training job.
- training_arn
The SageMaker training job Amazon Resource Name (ARN).
- training_datasets
The location of the datasets used to train the model.
- training_environment
The SageMaker training job image URI.
- training_metrics
The SageMaker training job results.
- user_provided_hyper_parameters
Additional hyper parameters that you’ve specified when training the model.
- user_provided_training_metrics
Custom training job results.
TrainingMetricProperty
- class CfnModelCard.TrainingMetricProperty(*, name, value, notes=None)
Bases:
object
A result from a SageMaker training job.
- Parameters:
name (
str
) – The name of the result from the SageMaker training job.value (
Union
[int
,float
]) – The value of a result from the SageMaker training job.notes (
Optional
[str
]) – Any additional notes describing the result of the training job.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker training_metric_property = sagemaker.CfnModelCard.TrainingMetricProperty( name="name", value=123, # the properties below are optional notes="notes" )
Attributes
- name
The name of the result from the SageMaker training job.
- notes
Any additional notes describing the result of the training job.
- value
The value of a result from the SageMaker training job.
UserContextProperty
- class CfnModelCard.UserContextProperty(*, domain_id=None, user_profile_arn=None, user_profile_name=None)
Bases:
object
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
- Parameters:
domain_id (
Optional
[str
]) – The domain associated with the user.user_profile_arn (
Optional
[str
]) – The Amazon Resource Name (ARN) of the user’s profile.user_profile_name (
Optional
[str
]) – The name of the user’s profile.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_sagemaker as sagemaker user_context_property = sagemaker.CfnModelCard.UserContextProperty( domain_id="domainId", user_profile_arn="userProfileArn", user_profile_name="userProfileName" )
Attributes
- domain_id
The domain associated with the user.
- user_profile_arn
The Amazon Resource Name (ARN) of the user’s profile.
- user_profile_name
The name of the user’s profile.