Description

Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the create_endpoint_config API.

Use this API to deploy models using Amazon SageMaker hosting services.

For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).

You must not delete an EndpointConfig that is in use by an endpoint that is live or while the update_endpoint or create_endpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.

The endpoint name must be unique within an AWS Region in your AWS account.

When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.

When you call create_endpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call describe_endpoint_config before calling create_endpoint to minimize the potential impact of a DynamoDB eventually consistent read.

When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the describe_endpoint API.

If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.

To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the create_endpoint and create_endpoint_config API operations, add the following policies to the role.

• Option 1: For a full Amazon SageMaker access, search and attach the AmazonSageMakerFullAccess policy.

• Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:

  "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]

  "Resource": [

  "arn:aws:sagemaker:region:account-id:endpoint/endpointName"

  "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"

  ]

  For more information, see Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference.

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Usage

Arguments

Value

A list with the following syntax:

Request syntax