add_association ¶

def add_association(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    source_arn: Optional[
        "capo_sagemaker.types.association_entity_arn.AssociationEntityArn"
    ] = None,
    destination_arn: Optional[
        "capo_sagemaker.types.association_entity_arn.AssociationEntityArn"
    ] = None,
    association_type: Optional[
        "capo_sagemaker.types.association_edge_type.AssociationEdgeType"
    ] = None,
) -> "capo_sagemaker.types.add_association_response.AddAssociationResponse":
    """<p>Creates an <i>association</i> between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html">Amazon SageMaker ML Lineage Tracking</a>.</p>

    Args:
        source_arn: <p>The ARN of the source.</p>
        destination_arn: <p>The Amazon Resource Name (ARN) of the destination.</p>
        association_type: <p>The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use.</p> <ul> <li> <p>ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job.</p> </li> <li> <p>AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment.</p> </li> <li> <p>DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs.</p> </li> <li> <p>Produced - The source generated the destination. For example, a training job produced a model artifact.</p> </li> </ul>

    Raises:
        capo_sagemaker.errors.resource_limit_exceeded.ResourceLimitExceeded: <p> You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. </p>
        capo_sagemaker.errors.resource_not_found.ResourceNotFound: <p>Resource being access is not found.</p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.add_association_request.AddAssociationRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.add_association_response.AddAssociationResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.add_association

        output, http_response = (
            capo_sagemaker._operations.sage_maker.add_association.add_association(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("AddAssociation")
    )
    input_: capo_sagemaker.types.add_association_request.AddAssociationRequest = {}
    if source_arn is not None:
        input_["source_arn"] = source_arn
    if destination_arn is not None:
        input_["destination_arn"] = destination_arn
    if association_type is not None:
        input_["association_type"] = association_type

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output