class_docstr: Make async prediction requests to an Amazon SageMaker endpoint.
class_docstr_tok: ["Make","async","prediction","requests","to","an","Amazon","SageMaker","endpoint","."]
docstr: Update the existing endpoint with the provided attributes.
  
  This creates a new EndpointConfig in the process. If ``initial_instance_count``,
  ``instance_type``, ``accelerator_type``, or ``model_name`` is specified, then a new
  ProductionVariant configuration is created; values from the existing configuration
  are not preserved if any of those parameters are specified.
  
  Args:
      initial_instance_count (int): The initial number of instances to run in the endpoint.
          This is required if ``instance_type``, ``accelerator_type``, or ``model_name`` is
          specified. Otherwise, the values from the existing endpoint configuration's
          ProductionVariants are used.
      instance_type (str): The EC2 instance type to deploy the endpoint to.
          This is required if ``initial_instance_count`` or ``accelerator_type`` is specified.
          Otherwise, the values from the existing endpoint configuration's
          ``ProductionVariants`` are used.
      accelerator_type (str): The type of Elastic Inference accelerator to attach to
          the endpoint, e.g. "ml.eia1.medium". If not specified, and
          ``initial_instance_count``, ``instance_type``, and ``model_name`` are also ``None``,
          the values from the existing endpoint configuration's ``ProductionVariants`` are
          used. Otherwise, no Elastic Inference accelerator is attached to the endpoint.
      model_name (str): The name of the model to be associated with the endpoint.
          This is required if ``initial_instance_count``, ``instance_type``, or
          ``accelerator_type`` is specified and if there is more than one model associated
          with the endpoint. Otherwise, the existing model for the endpoint is used.
      tags (list[dict[str, str]]): The list of tags to add to the endpoint
          config. If not specified, the tags of the existing endpoint configuration are used.
          If any of the existing tags are reserved AWS ones (i.e. begin with "aws"),
          they are not carried over to the new endpoint configuration.
      kms_key (str): The KMS key that is used to encrypt the data on the storage volume
          attached to the instance hosting the endpoint If not specified,
          the KMS key of the existing endpoint configuration is used.
      data_capture_config_dict (dict): The endpoint data capture configuration
          for use with Amazon SageMaker Model Monitoring. If not specified,
          the data capture configuration of the existing endpoint configuration is used.
      wait (bool): Wait for updating to finish
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code: def update_endpoint(
      self,
      initial_instance_count=None,
      instance_type=None,
      accelerator_type=None,
      model_name=None,
      tags=None,
      kms_key=None,
      data_capture_config_dict=None,
      wait=True,
  ):
      """Update the existing endpoint with the provided attributes.
  
      This creates a new EndpointConfig in the process. If ``initial_instance_count``,
      ``instance_type``, ``accelerator_type``, or ``model_name`` is specified, then a new
      ProductionVariant configuration is created; values from the existing configuration
      are not preserved if any of those parameters are specified.
  
      Args:
          initial_instance_count (int): The initial number of instances to run in the endpoint.
              This is required if ``instance_type``, ``accelerator_type``, or ``model_name`` is
              specified. Otherwise, the values from the existing endpoint configuration's
              ProductionVariants are used.
          instance_type (str): The EC2 instance type to deploy the endpoint to.
              This is required if ``initial_instance_count`` or ``accelerator_type`` is specified.
              Otherwise, the values from the existing endpoint configuration's
              ``ProductionVariants`` are used.
          accelerator_type (str): The type of Elastic Inference accelerator to attach to
              the endpoint, e.g. "ml.eia1.medium". If not specified, and
              ``initial_instance_count``, ``instance_type``, and ``model_name`` are also ``None``,
              the values from the existing endpoint configuration's ``ProductionVariants`` are
              used. Otherwise, no Elastic Inference accelerator is attached to the endpoint.
          model_name (str): The name of the model to be associated with the endpoint.
              This is required if ``initial_instance_count``, ``instance_type``, or
              ``accelerator_type`` is specified and if there is more than one model associated
              with the endpoint. Otherwise, the existing model for the endpoint is used.
          tags (list[dict[str, str]]): The list of tags to add to the endpoint
              config. If not specified, the tags of the existing endpoint configuration are used.
              If any of the existing tags are reserved AWS ones (i.e. begin with "aws"),
              they are not carried over to the new endpoint configuration.
          kms_key (str): The KMS key that is used to encrypt the data on the storage volume
              attached to the instance hosting the endpoint If not specified,
              the KMS key of the existing endpoint configuration is used.
          data_capture_config_dict (dict): The endpoint data capture configuration
              for use with Amazon SageMaker Model Monitoring. If not specified,
              the data capture configuration of the existing endpoint configuration is used.
          wait (bool): Wait for updating to finish
      """
  
      self.predictor.update_endpoint(
          initial_instance_count=initial_instance_count,
          instance_type=instance_type,
          accelerator_type=accelerator_type,
          model_name=model_name,
          tags=format_tags(tags),
          kms_key=kms_key,
          data_capture_config_dict=data_capture_config_dict,
          wait=wait,
      )
  
imports: from __future__ import absolute_import
  import threading
  import time
  import uuid
  from botocore.exceptions import WaiterError
  from sagemaker import s3
  from sagemaker.exceptions import PollingTimeoutError, AsyncInferenceModelError
  from sagemaker.async_inference import WaiterConfig, AsyncInferenceResponse
  from sagemaker.s3 import parse_s3_url
  from sagemaker.session import Session
  from sagemaker.utils import name_from_base, sagemaker_timestamp, format_tags
label_desc: Use image node_id 10 for calling the AsyncPredictor obj's underlying member method code with example usage: obj.update_endpoint(initial_instance_count, instance_type, accelerator_type, model_name, tags, kms_key, data_capture_config_dict, wait) without return types