Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
Use this API to deploy models using SageMaker hosting services.
You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint 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 Amazon Web Services Region in your Amazon Web Services account.
When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
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.
When SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services 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 CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.
-
Option 1: For a full 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 SageMaker API Permissions: Actions, Permissions, and Resources Reference.
Parameters:
-
endpoint_name
(Optional[EndpointName], default:
None
)
–
The name of the endpoint.The name must be unique within an Amazon Web Services Region in your Amazon Web Services account. The name is case-insensitive in CreateEndpoint, but the case is preserved and must be matched in InvokeEndpoint.
-
endpoint_config_name
(Optional[EndpointConfigName], default:
None
)
–
-
tags
(Optional[TagList], default:
None
)
–
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Raises:
-
ResourceLimitExceeded
–
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
UnknownServiceError
–
The service returned an error code this client does not model.
Source code in docs_src/capo_sagemaker/_services/sage_maker.py
| def create_endpoint(
self,
*,
config_overrides: Optional[SageMakerClientConfig] = None,
endpoint_name: Optional[
"capo_sagemaker.types.endpoint_name.EndpointName"
] = None,
endpoint_config_name: Optional[
"capo_sagemaker.types.endpoint_config_name.EndpointConfigName"
] = None,
deployment_config: Optional[
"capo_sagemaker.types.deployment_config.DeploymentConfig"
] = None,
tags: Optional["capo_sagemaker.types.tag_list.TagList"] = None,
) -> "capo_sagemaker.types.create_endpoint_output.CreateEndpointOutput":
"""<p>Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html">CreateEndpointConfig</a> API. </p> <p> Use this API to deploy models using SageMaker hosting services. </p> <note> <p> You must not delete an <code>EndpointConfig</code> that is in use by an endpoint that is live or while the <code>UpdateEndpoint</code> or <code>CreateEndpoint</code> operations are being performed on the endpoint. To update an endpoint, you must create a new <code>EndpointConfig</code>.</p> </note> <p>The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account. </p> <p>When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them. </p> <note> <p>When you call <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html">CreateEndpoint</a>, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting <a href="https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html"> <code>Eventually Consistent Reads</code> </a>, 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 <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeEndpointConfig.html">DescribeEndpointConfig</a> before calling <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html">CreateEndpoint</a> to minimize the potential impact of a DynamoDB eventually consistent read.</p> </note> <p>When SageMaker receives the request, it sets the endpoint status to <code>Creating</code>. After it creates the endpoint, it sets the status to <code>InService</code>. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeEndpoint.html">DescribeEndpoint</a> API.</p> <p>If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see <a href="https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html">Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region</a> in the <i>Amazon Web Services Identity and Access Management User Guide</i>.</p> <note> <p> To add the IAM role policies for using this API operation, go to the <a href="https://console.aws.amazon.com/iam/">IAM console</a>, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html">CreateEndpoint</a> and <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html">CreateEndpointConfig</a> API operations, add the following policies to the role. </p> <ul> <li> <p>Option 1: For a full SageMaker access, search and attach the <code>AmazonSageMakerFullAccess</code> policy.</p> </li> <li> <p>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: </p> <p> <code>"Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]</code> </p> <p> <code>"Resource": [</code> </p> <p> <code>"arn:aws:sagemaker:region:account-id:endpoint/endpointName"</code> </p> <p> <code>"arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"</code> </p> <p> <code>]</code> </p> <p>For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/api-permissions-reference.html">SageMaker API Permissions: Actions, Permissions, and Resources Reference</a>.</p> </li> </ul> </note>
Args:
endpoint_name: <p>The name of the endpoint.The name must be unique within an Amazon Web Services Region in your Amazon Web Services account. The name is case-insensitive in <code>CreateEndpoint</code>, but the case is preserved and must be matched in <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html">InvokeEndpoint</a>.</p>
endpoint_config_name: <p>The name of an endpoint configuration. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html">CreateEndpointConfig</a>. </p>
tags: <p>An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services Resources</a>.</p>
Raises:
capo_sagemaker.errors.resource_limit_exceeded.ResourceLimitExceeded: <p> You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. </p>
capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
"""
def _handler(
req: "OperationRequest[capo_sagemaker.types.create_endpoint_input.CreateEndpointInput]",
) -> OperationResponse[
"capo_sagemaker.types.create_endpoint_output.CreateEndpointOutput"
]:
import capo_sagemaker._operations.sage_maker.create_endpoint
output, http_response = (
capo_sagemaker._operations.sage_maker.create_endpoint.create_endpoint(
req.options, req.input
)
)
return OperationResponse(output=output, response=http_response)
interceptors_, options_ = self.operation_options(
config_overrides, OperationLogging("CreateEndpoint")
)
input_: capo_sagemaker.types.create_endpoint_input.CreateEndpointInput = {}
if endpoint_name is not None:
input_["endpoint_name"] = endpoint_name
if endpoint_config_name is not None:
input_["endpoint_config_name"] = endpoint_config_name
if deployment_config is not None:
input_["deployment_config"] = deployment_config
if tags is not None:
input_["tags"] = tags
response = execute_pipeline(
OperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
response.response.close()
return response.output
|