Create an Endpoint Configuration

Once you have a model, create an endpoint configuration with CreateEndpointConfig. Amazon SageMaker AI hosting services uses this configuration to deploy models. In the configuration, you identify one or more models, created using with CreateModel, to deploy the resources that you want Amazon SageMaker AI to provision. Specify the AsyncInferenceConfig object and provide an output Amazon S3 location for OutputConfig. You can optionally specify Amazon SNS topics on which to send notifications about prediction results. For more information about Amazon SNS topics, see Configuring Amazon SNS.

To improve endpoint availability and reduce insufficient capacity errors, you can configure instance pools in your production variant. Instance pools let you specify an ordered list of instance types so that SageMaker AI automatically falls back to lower-priority types when your preferred instance type is unavailable. For more information, see Deploy to multiple instance types with instance pools.

The following example shows how to create an endpoint configuration using AWS SDK for Python (Boto3):

import datetime
from time import gmtime, strftime

# Create an endpoint config name. Here we create one based on the date  
# so it we can search endpoints based on creation time.
endpoint_config_name = f"XGBoostEndpointConfig-{strftime('%Y-%m-%d-%H-%M-%S', gmtime())}"

# The name of the model that you want to host. This is the name that you specified when creating the model.
model_name='<The_name_of_your_model>'

create_endpoint_config_response = sagemaker_client.create_endpoint_config(
    EndpointConfigName=endpoint_config_name, # You will specify this name in a CreateEndpoint request.
    # List of ProductionVariant objects, one for each model that you want to host at this endpoint.
    ProductionVariants=[
        {
            "VariantName": "variant1", # The name of the production variant.
            "ModelName": model_name, 
            "InstanceType": "ml.m5.xlarge", # Specify the compute instance type.
            "InitialInstanceCount": 1 # Number of instances to launch initially.
        }
    ],
    AsyncInferenceConfig={
        "OutputConfig": {
            # Location to upload response outputs when no location is provided in the request.
            "S3OutputPath": f"s3://{s3_bucket}/{bucket_prefix}/output"
            # (Optional) specify Amazon SNS topics
            "NotificationConfig": {
                "SuccessTopic": "arn:aws:sns:aws-region:account-id:topic-name",
                "ErrorTopic": "arn:aws:sns:aws-region:account-id:topic-name",
            }
        },
        "ClientConfig": {
            # (Optional) Specify the max number of inflight invocations per instance
            # If no value is provided, Amazon SageMaker will choose an optimal value for you
            "MaxConcurrentInvocationsPerInstance": 4
        }
    }
)

print(f"Created EndpointConfig: {create_endpoint_config_response['EndpointConfigArn']}")

In the aforementioned example, you specify the following keys for OutputConfig for the AsyncInferenceConfig field:

You can also specify the following optional argument for ClientConfig in the AsyncInferenceConfig field: