AWS CLI Command Reference

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[ aws . neptunedata ]

create-ml-endpoint

Description

Creates a new Neptune ML inference endpoint that lets you query one specific model that the model-training process constructed. 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:CreateMLEndpoint IAM action in that cluster.

See also: AWS API Documentation

Synopsis

  create-ml-endpoint
[--id <value>]
[--ml-model-training-job-id <value>]
[--ml-model-transform-job-id <value>]
[--update | --no-update]
[--neptune-iam-role-arn <value>]
[--model-name <value>]
[--instance-type <value>]
[--instance-count <value>]
[--volume-encryption-kms-key <value>]
[--cli-input-json <value>]
[--generate-cli-skeleton <value>]
[--debug]
[--endpoint-url <value>]
[--no-verify-ssl]
[--no-paginate]
[--output <value>]
[--query <value>]
[--profile <value>]
[--region <value>]
[--version <value>]
[--color <value>]
[--no-sign-request]
[--ca-bundle <value>]
[--cli-read-timeout <value>]
[--cli-connect-timeout <value>]

Options

--id (string)

A unique identifier for the new inference endpoint. The default is an autogenerated timestamped name.

--ml-model-training-job-id (string)

The job Id of the completed model-training job that has created the model that the inference endpoint will point to. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId .

--ml-model-transform-job-id (string)

The job Id of the completed model-transform job. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId .

--update | --no-update (boolean)

If set to true , update indicates that this is an update request. The default is false . You must supply either the mlModelTrainingJobId or the mlModelTransformJobId .

--neptune-iam-role-arn (string)

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.

--model-name (string)

Model type for training. By default the Neptune ML model is automatically based on the modelType used in data processing, but you can specify a different model type here. The default is rgcn for heterogeneous graphs and kge for knowledge graphs. The only valid value for heterogeneous graphs is rgcn . Valid values for knowledge graphs are: kge , transe , distmult , and rotate .

--instance-type (string)

The type of Neptune ML instance to use for online servicing. The default is ml.m5.xlarge . Choosing the ML instance for an inference endpoint depends on the task type, the graph size, and your budget.

--instance-count (integer)

The minimum number of Amazon EC2 instances to deploy to an endpoint for prediction. The default is 1

--volume-encryption-kms-key (string)

The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.

--cli-input-json (string) Performs service operation based on the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, the CLI values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

Global Options

--debug (boolean)

Turn on debug logging.

--endpoint-url (string)

Override command's default URL with the given URL.

--no-verify-ssl (boolean)

By default, the AWS CLI uses SSL when communicating with AWS services. For each SSL connection, the AWS CLI will verify SSL certificates. This option overrides the default behavior of verifying SSL certificates.

--no-paginate (boolean)

Disable automatic pagination. If automatic pagination is disabled, the AWS CLI will only make one call, for the first page of results.

--output (string)

The formatting style for command output.

  • json
  • text
  • table

--query (string)

A JMESPath query to use in filtering the response data.

--profile (string)

Use a specific profile from your credential file.

--region (string)

The region to use. Overrides config/env settings.

--version (string)

Display the version of this tool.

--color (string)

Turn on/off color output.

  • on
  • off
  • auto

--no-sign-request (boolean)

Do not sign requests. Credentials will not be loaded if this argument is provided.

--ca-bundle (string)

The CA certificate bundle to use when verifying SSL certificates. Overrides config/env settings.

--cli-read-timeout (int)

The maximum socket read time in seconds. If the value is set to 0, the socket read will be blocking and not timeout. The default value is 60 seconds.

--cli-connect-timeout (int)

The maximum socket connect time in seconds. If the value is set to 0, the socket connect will be blocking and not timeout. The default value is 60 seconds.

Output

id -> (string)

The unique ID of the new inference endpoint.

arn -> (string)

The ARN for the new inference endpoint.

creationTimeInMillis -> (long)

The endpoint creation time, in milliseconds.