def from_model_name()

in src/sagemaker/model_card/model_card.py [0:0]

• 61 lines of code
• 10 McCabe index (conditional complexity)

    def from_model_name(cls, model_name: str, sagemaker_session: Session = None, **kwargs):
        """Initialize a model overview object from auto-discovered data.

        Args:
            model_name (str): The unique name of the model.
            sagemaker_session (Session, optional): A SageMaker Session
                object, used for SageMaker interactions (default: None). If not
                specified, a SageMaker Session is created using the default AWS configuration
                chain.
            **kwargs: Other arguments in ModelOverview, i.e. model_description,
                problem_type, algorithm_type, model_creator, model_owner, model_version
        Raises:
            ValueError: A model with this name does not exist.
            ValueError: A model card already exists for this model.
        """

        def call_describe_model():
            """Load existing model."""
            try:
                model_response = sagemaker_session.sagemaker_client.describe_model(
                    ModelName=model_name
                )
            except ClientError as e:
                if e.response["Error"]["Message"].startswith(  # pylint: disable=r1720
                    "Could not find model"
                ):
                    raise ValueError(
                        (
                            f"Model details for model {model_name} could not be found. "
                            "Make sure the model name is valid."
                        )
                    )
                else:
                    raise
            return model_response

        def search_model_associated_model_cards(model_id: str):
            """Check if a model card already exists for this model.

            Args:
                model_id (str): A SageMaker model ID.
            """
            response = sagemaker_session.sagemaker_client.search(
                Resource="ModelCard",
                SearchExpression={
                    "Filters": [
                        {
                            "Name": "ModelId",
                            "Operator": "Equals",
                            "Value": model_id,
                        }
                    ]
                },
            )
            return [c["ModelCard"]["ModelCardName"] for c in response["Results"]]

        if not sagemaker_session:
            sagemaker_session = Session()  # pylint: disable=W0106

        model_response = call_describe_model()

        associated_model_cards = search_model_associated_model_cards(model_response["ModelArn"])
        if associated_model_cards:
            raise ValueError(
                f"The model has been associated with {associated_model_cards} model cards."
            )

        if "Containers" in model_response:  # inference pipeline model
            artifacts = [c["ModelDataUrl"] for c in model_response["Containers"]]
        elif (
            "PrimaryContainer" in model_response
            and "ModelDataUrl" in model_response["PrimaryContainer"]
        ):
            artifacts = [model_response["PrimaryContainer"]["ModelDataUrl"]]
        else:
            artifacts = []

        kwargs.update(
            {
                "model_name": model_name,
                "model_id": model_response["ModelArn"],
                "inference_environment": Environment(
                    container_image=[model_response["PrimaryContainer"]["Image"]]
                ),
                "model_artifact": artifacts,
            }
        )

        return cls(**kwargs)