DeleteMLEndpoint
Cancels the creation of a Neptune ML inference endpoint. See Managing inference endpoints using the endpoints command.
When invoking this operation in a Neptune cluster that has IAM authentication enabled, the IAM user or role making the request must have a policy attached that allows the neptune-db:DeleteMLEndpoint IAM action in that cluster.
Request Syntax
DELETE /ml/endpoints/id
?clean=clean
&neptuneIamRoleArn=neptuneIamRoleArn
HTTP/1.1
URI Request Parameters
The request uses the following URI parameters.
- clean
-
If this flag is set to
TRUE
, all Neptune ML S3 artifacts should be deleted when the job is stopped. The default isFALSE
. - id
-
The unique identifier of the inference endpoint.
Required: Yes
- neptuneIamRoleArn
-
The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will be thrown.
Request Body
The request does not have a request body.
Response Syntax
HTTP/1.1 200
Content-type: application/json
{
"status": "string"
}
Response Elements
If the action is successful, the service sends back an HTTP 200 response.
The following data is returned in JSON format by the service.
- status
-
The status of the cancellation.
Type: String
Errors
For information about the errors that are common to all actions, see Common Errors.
- BadRequestException
-
Raised when a request is submitted that cannot be processed.
HTTP Status Code: 400
- ClientTimeoutException
-
Raised when a request timed out in the client.
HTTP Status Code: 408
- ConstraintViolationException
-
Raised when a value in a request field did not satisfy required constraints.
HTTP Status Code: 400
- IllegalArgumentException
-
Raised when an argument in a request is not supported.
HTTP Status Code: 400
- InvalidArgumentException
-
Raised when an argument in a request has an invalid value.
HTTP Status Code: 400
- InvalidParameterException
-
Raised when a parameter value is not valid.
HTTP Status Code: 400
- MissingParameterException
-
Raised when a required parameter is missing.
HTTP Status Code: 400
- MLResourceNotFoundException
-
Raised when a specified machine-learning resource could not be found.
HTTP Status Code: 404
- PreconditionsFailedException
-
Raised when a precondition for processing a request is not satisfied.
HTTP Status Code: 400
- TooManyRequestsException
-
Raised when the number of requests being processed exceeds the limit.
HTTP Status Code: 429
- UnsupportedOperationException
-
Raised when a request attempts to initiate an operation that is not supported.
HTTP Status Code: 400
See Also
For more information about using this API in one of the language-specific Amazon SDKs, see the following: