SageMakerEndpointOperator

Amazon

When you create a serverless endpoint, SageMaker provisions and manages the compute resources for you. Then, you can make inference requests to the endpoint and receive model predictions in response. SageMaker scales the compute resources up and down as needed to handle your request traffic.

View on GitHub

Last Updated: Feb. 27, 2023

Access Instructions

Install the Amazon provider package into your Airflow environment.

Import the module into your DAG file and instantiate it with your desired params.

Parameters

Documentation

Connections

Parameters

configRequiredThe configuration necessary to create an endpoint. If you need to create a SageMaker endpoint based on an existed SageMaker model and an existed SageMaker endpoint config: config = endpoint_configuration; If you need to create all of SageMaker model, SageMaker endpoint-config and SageMaker endpoint: config = { 'Model': model_configuration, 'EndpointConfig': endpoint_config_configuration, 'Endpoint': endpoint_configuration } For details of the configuration parameter of model_configuration see SageMaker.Client.create_model() For details of the configuration parameter of endpoint_config_configuration see SageMaker.Client.create_endpoint_config() For details of the configuration parameter of endpoint_configuration see SageMaker.Client.create_endpoint()

wait_for_completionWhether the operator should wait until the endpoint creation finishes.

check_intervalIf wait is set to True, this is the time interval, in seconds, that this operation waits before polling the status of the endpoint creation.

max_ingestion_timeIf wait is set to True, this operation fails if the endpoint creation doesn’t finish within max_ingestion_time seconds. If you set this parameter to None it never times out.

operationWhether to create an endpoint or update an endpoint. Must be either ‘create or ‘update’.

aws_conn_idThe AWS connection ID to use.

Dict

Documentation

Requires an Endpoint Config.