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endpoint_configs

Creates, updates, deletes, gets or lists an endpoint_configs resource.

Overview​

Nameendpoint_configs
TypeResource
Idaws.sagemaker.endpoint_configs

Fields​

The following fields are returned by SELECT queries:

NameDatatypeDescription
async_inference_configobjectReturns the description of an endpoint configuration created using the CreateEndpointConfig API.
creation_timestring (date-time)A timestamp that shows when the endpoint configuration was created.
data_capture_configobjectConfiguration to control how SageMaker AI captures inference data.
enable_network_isolationbooleanIndicates whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.
endpoint_config_arnstringThe Amazon Resource Name (ARN) of the endpoint configuration. (pattern: <code>arn:aws[a-z-]:sagemaker:[a-z0-9-]:[0-9]{12}:endpoint-config/.*</code>)
endpoint_config_namestringName of the SageMaker endpoint configuration. (pattern: <code>[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}</code>)
execution_role_arnstringThe Amazon Resource Name (ARN) of the IAM role that you assigned to the endpoint configuration. (pattern: <code>arn:aws[a-z-]*:iam::\d{12}:role/?[a-zA-Z_0-9+=,.@-_/]+</code>)
explainer_configobjectThe configuration parameters for an explainer.
kms_key_idstringAmazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance. (pattern: <code>[a-zA-Z0-9:/_-]*</code>)
metrics_configobjectThe configuration parameters for utilization metrics.
production_variantsarrayAn array of ProductionVariant objects, one for each model that you want to host at this endpoint.
shadow_production_variantsarrayAn array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants.
vpc_configobjectSpecifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.

Methods​

The following methods are available for this resource:

NameAccessible byRequired ParamsOptional ParamsDescription
describe_endpoint_configselectregionReturns the description of an endpoint configuration created using the CreateEndpointConfig API.
list_endpoint_configsselectregionLists endpoint configurations.
create_endpoint_configinsertregion, EndpointConfigName, ProductionVariantsCreates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API. Use this API if you want to use SageMaker hosting services to deploy models into production. In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy. If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B. When you call CreateEndpoint, 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 DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
delete_endpoint_configdeleteregionDeletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration. You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.

Parameters​

Parameters can be passed in the WHERE clause of a query. Check the Methods section to see which parameters are required or optional for each operation.

NameDatatypeDescription
regionstringAWS region (default: us-east-1)

SELECT examples​

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

SELECT
async_inference_config,
creation_time,
data_capture_config,
enable_network_isolation,
endpoint_config_arn,
endpoint_config_name,
execution_role_arn,
explainer_config,
kms_key_id,
metrics_config,
production_variants,
shadow_production_variants,
vpc_config
FROM aws.sagemaker.endpoint_configs
WHERE region = '{{ region }}' -- required
;

INSERT examples​

Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API. Use this API if you want to use SageMaker hosting services to deploy models into production. In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy. If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B. When you call CreateEndpoint, 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 DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

INSERT INTO aws.sagemaker.endpoint_configs (
EndpointConfigName,
ProductionVariants,
DataCaptureConfig,
Tags,
KmsKeyId,
AsyncInferenceConfig,
ExplainerConfig,
ShadowProductionVariants,
ExecutionRoleArn,
VpcConfig,
EnableNetworkIsolation,
MetricsConfig,
region
)
SELECT 
'{{ EndpointConfigName }}' /* required */,
'{{ ProductionVariants }}' /* required */,
'{{ DataCaptureConfig }}',
'{{ Tags }}',
'{{ KmsKeyId }}',
'{{ AsyncInferenceConfig }}',
'{{ ExplainerConfig }}',
'{{ ShadowProductionVariants }}',
'{{ ExecutionRoleArn }}',
'{{ VpcConfig }}',
{{ EnableNetworkIsolation }},
'{{ MetricsConfig }}',
'{{ region }}'
RETURNING
endpoint_config_arn
;

DELETE examples​

Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration. You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.

DELETE FROM aws.sagemaker.endpoint_configs
WHERE region = '{{ region }}' --required
;
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