src/sagemaker/automl/automl.py [516:640]:
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                vpc_config=vpc_config,
                sagemaker_session=sagemaker_session or self.sagemaker_session,
                enable_network_isolation=enable_network_isolation,
                model_kms_key=model_kms_key,
            )
            models.append(model)

        pipeline = PipelineModel(
            models=models,
            role=self.role,
            predictor_cls=predictor_cls,
            name=name,
            vpc_config=vpc_config,
            enable_network_isolation=enable_network_isolation,
            sagemaker_session=sagemaker_session or self.sagemaker_session,
        )
        return pipeline

    def deploy(
        self,
        initial_instance_count,
        instance_type,
        serializer=None,
        deserializer=None,
        candidate=None,
        sagemaker_session=None,
        name=None,
        endpoint_name=None,
        tags=None,
        wait=True,
        vpc_config=None,
        enable_network_isolation=False,
        model_kms_key=None,
        predictor_cls=None,
        inference_response_keys=None,
        volume_size=None,
        model_data_download_timeout=None,
        container_startup_health_check_timeout=None,
    ):
        """Deploy a candidate to a SageMaker Inference Pipeline.

        Args:
            initial_instance_count (int): The initial number of instances to run
                in the ``Endpoint`` created from this ``Model``.
            instance_type (str): The EC2 instance type to deploy this Model to.
                For example, 'ml.p2.xlarge'.
            serializer (:class:`~sagemaker.serializers.BaseSerializer`): A
                serializer object, used to encode data for an inference endpoint
                (default: None). If ``serializer`` is not None, then
                ``serializer`` will override the default serializer. The
                default serializer is set by the ``predictor_cls``.
            deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A
                deserializer object, used to decode data from an inference
                endpoint (default: None). If ``deserializer`` is not None, then
                ``deserializer`` will override the default deserializer. The
                default deserializer is set by the ``predictor_cls``.
            candidate (CandidateEstimator or dict): a CandidateEstimator used for deploying
                to a SageMaker Inference Pipeline. If None, the best candidate will
                be used. If the candidate input is a dict, a CandidateEstimator will be
                created from it.
            sagemaker_session (sagemaker.session.Session): A SageMaker Session
                object, used for SageMaker interactions (default: None). If not
                specified, the one originally associated with the ``AutoML`` instance is used.
            name (str): The pipeline model name. If None, a default model name will
                be selected on each ``deploy``.
            endpoint_name (str): The name of the endpoint to create (default:
                None). If not specified, a unique endpoint name will be created.
            tags (Optional[Tags]): The list of tags to attach to this
                specific endpoint.
            wait (bool): Whether the call should wait until the deployment of
                model completes (default: True).
            vpc_config (dict): Specifies a VPC that your training jobs and hosted models have
                access to. Contents include "SecurityGroupIds" and "Subnets".
            enable_network_isolation (bool): Isolates the training container. No inbound or
                outbound network calls can be made, except for calls between peers within a
                training cluster for distributed training. Default: False
            model_kms_key (str): KMS key ARN used to encrypt the repacked
                model archive file if the model is repacked
            predictor_cls (Callable[[string, sagemaker.session.Session], Any]): A
                function to call to create a predictor (default: None). If
                specified, ``deploy()``  returns the result of invoking this
                function on the created endpoint name.
            inference_response_keys (list): List of keys for response content. The order of the
                keys will dictate the content order in the response.
            volume_size (int): The size, in GB, of the ML storage volume attached to individual
                inference instance associated with the production variant. Currenly only Amazon EBS
                gp2 storage volumes are supported.
            model_data_download_timeout (int): The timeout value, in seconds, to download and
                extract model data from Amazon S3 to the individual inference instance associated
                with this production variant.
            container_startup_health_check_timeout (int): The timeout value, in seconds, for your
                inference container to pass health check by SageMaker Hosting. For more information
                about health check see:
                https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-algo-ping-requests

        Returns:
            Optional[Callable[[string, sagemaker.session.Session], Any]]:
                If ``predictor_cls`` is specified, the invocation of ``self.predictor_cls`` on
                the created endpoint name. Otherwise, ``None``.
        """
        sagemaker_session = sagemaker_session or self.sagemaker_session
        model = self.create_model(
            name=name,
            sagemaker_session=sagemaker_session,
            candidate=candidate,
            inference_response_keys=inference_response_keys,
            vpc_config=vpc_config,
            enable_network_isolation=enable_network_isolation,
            model_kms_key=model_kms_key,
            predictor_cls=predictor_cls,
        )

        return model.deploy(
            initial_instance_count=initial_instance_count,
            instance_type=instance_type,
            serializer=serializer,
            deserializer=deserializer,
            endpoint_name=endpoint_name,
            kms_key=model_kms_key,
            tags=format_tags(tags),
            wait=wait,
            volume_size=volume_size,
            model_data_download_timeout=model_data_download_timeout,
            container_startup_health_check_timeout=container_startup_health_check_timeout,
        )
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src/sagemaker/automl/automlv2.py [1055:1179]:
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                vpc_config=vpc_config,
                sagemaker_session=sagemaker_session or self.sagemaker_session,
                enable_network_isolation=enable_network_isolation,
                model_kms_key=model_kms_key,
            )
            models.append(model)

        pipeline = PipelineModel(
            models=models,
            role=self.role,
            predictor_cls=predictor_cls,
            name=name,
            vpc_config=vpc_config,
            enable_network_isolation=enable_network_isolation,
            sagemaker_session=sagemaker_session or self.sagemaker_session,
        )
        return pipeline

    def deploy(
        self,
        initial_instance_count,
        instance_type,
        serializer=None,
        deserializer=None,
        candidate=None,
        sagemaker_session=None,
        name=None,
        endpoint_name=None,
        tags=None,
        wait=True,
        vpc_config=None,
        enable_network_isolation=False,
        model_kms_key=None,
        predictor_cls=None,
        inference_response_keys=None,
        volume_size=None,
        model_data_download_timeout=None,
        container_startup_health_check_timeout=None,
    ):
        """Deploy a candidate to a SageMaker Inference Pipeline.

        Args:
            initial_instance_count (int): The initial number of instances to run
                in the ``Endpoint`` created from this ``Model``.
            instance_type (str): The EC2 instance type to deploy this Model to.
                For example, 'ml.p2.xlarge'.
            serializer (:class:`~sagemaker.serializers.BaseSerializer`): A
                serializer object, used to encode data for an inference endpoint
                (default: None). If ``serializer`` is not None, then
                ``serializer`` will override the default serializer. The
                default serializer is set by the ``predictor_cls``.
            deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A
                deserializer object, used to decode data from an inference
                endpoint (default: None). If ``deserializer`` is not None, then
                ``deserializer`` will override the default deserializer. The
                default deserializer is set by the ``predictor_cls``.
            candidate (CandidateEstimator or dict): a CandidateEstimator used for deploying
                to a SageMaker Inference Pipeline. If None, the best candidate will
                be used. If the candidate input is a dict, a CandidateEstimator will be
                created from it.
            sagemaker_session (sagemaker.session.Session): A SageMaker Session
                object, used for SageMaker interactions (default: None). If not
                specified, the one originally associated with the ``AutoML`` instance is used.
            name (str): The pipeline model name. If None, a default model name will
                be selected on each ``deploy``.
            endpoint_name (str): The name of the endpoint to create (default:
                None). If not specified, a unique endpoint name will be created.
            tags (Optional[Tags]): The list of tags to attach to this
                specific endpoint.
            wait (bool): Whether the call should wait until the deployment of
                model completes (default: True).
            vpc_config (dict): Specifies a VPC that your training jobs and hosted models have
                access to. Contents include "SecurityGroupIds" and "Subnets".
            enable_network_isolation (bool): Isolates the training container. No inbound or
                outbound network calls can be made, except for calls between peers within a
                training cluster for distributed training. Default: False
            model_kms_key (str): KMS key ARN used to encrypt the repacked
                model archive file if the model is repacked
            predictor_cls (Callable[[string, sagemaker.session.Session], Any]): A
                function to call to create a predictor (default: None). If
                specified, ``deploy()``  returns the result of invoking this
                function on the created endpoint name.
            inference_response_keys (list): List of keys for response content. The order of the
                keys will dictate the content order in the response.
            volume_size (int): The size, in GB, of the ML storage volume attached to individual
                inference instance associated with the production variant. Currenly only Amazon EBS
                gp2 storage volumes are supported.
            model_data_download_timeout (int): The timeout value, in seconds, to download and
                extract model data from Amazon S3 to the individual inference instance associated
                with this production variant.
            container_startup_health_check_timeout (int): The timeout value, in seconds, for your
                inference container to pass health check by SageMaker Hosting. For more information
                about health check see:
                https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-algo-ping-requests

        Returns:
            Optional[Callable[[string, sagemaker.session.Session], Any]]:
                If ``predictor_cls`` is specified, the invocation of ``self.predictor_cls`` on
                the created endpoint name. Otherwise, ``None``.
        """
        sagemaker_session = sagemaker_session or self.sagemaker_session
        model = self.create_model(
            name=name,
            sagemaker_session=sagemaker_session,
            candidate=candidate,
            inference_response_keys=inference_response_keys,
            vpc_config=vpc_config,
            enable_network_isolation=enable_network_isolation,
            model_kms_key=model_kms_key,
            predictor_cls=predictor_cls,
        )

        return model.deploy(
            initial_instance_count=initial_instance_count,
            instance_type=instance_type,
            serializer=serializer,
            deserializer=deserializer,
            endpoint_name=endpoint_name,
            kms_key=model_kms_key,
            tags=format_tags(tags),
            wait=wait,
            volume_size=volume_size,
            model_data_download_timeout=model_data_download_timeout,
            container_startup_health_check_timeout=container_startup_health_check_timeout,
        )
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