update_model_card ¶

def update_model_card(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    model_card_name: Optional[
        "capo_sagemaker.types.model_card_name_or_arn.ModelCardNameOrArn"
    ] = None,
    content: Optional[
        "capo_sagemaker.types.model_card_content.ModelCardContent"
    ] = None,
    model_card_status: Optional[
        "capo_sagemaker.types.model_card_status.ModelCardStatus"
    ] = None,
) -> "capo_sagemaker.types.update_model_card_response.UpdateModelCardResponse":
    """<p>Update an Amazon SageMaker Model Card.</p> <important> <p>You cannot update both model card content and model card status in a single call.</p> </important>

    Args:
        model_card_name: <p>The name or Amazon Resource Name (ARN) of the model card to update.</p>
        content: <p>The updated model card content. Content must be in <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html#model-cards-json-schema">model card JSON schema</a> and provided as a string.</p> <p>When updating model card content, be sure to include the full content and not just updated content.</p>
        model_card_status: <p>The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.</p> <ul> <li> <p> <code>Draft</code>: The model card is a work in progress.</p> </li> <li> <p> <code>PendingReview</code>: The model card is pending review.</p> </li> <li> <p> <code>Approved</code>: The model card is approved.</p> </li> <li> <p> <code>Archived</code>: The model card is archived. No more updates should be made to the model card, but it can still be exported.</p> </li> </ul>

    Raises:
        capo_sagemaker.errors.conflict_exception.ConflictException: <p>There was a conflict when you attempted to modify a SageMaker entity such as an <code>Experiment</code> or <code>Artifact</code>.</p>
        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.update_model_card_request.UpdateModelCardRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.update_model_card_response.UpdateModelCardResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.update_model_card

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

    def _redact_input(
        input_: "capo_sagemaker.types.update_model_card_request.UpdateModelCardRequest",
    ) -> dict[str, Any]:
        import capo_sagemaker.types.update_model_card_request

        return capo_sagemaker.types.update_model_card_request.redact_sensitive(
            input_
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("UpdateModelCard", redact_input=_redact_input),
    )
    input_: capo_sagemaker.types.update_model_card_request.UpdateModelCardRequest = {}
    if model_card_name is not None:
        input_["model_card_name"] = model_card_name
    if content is not None:
        input_["content"] = content
    if model_card_status is not None:
        input_["model_card_status"] = model_card_status

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