Managing inference endpoints using the endpoints command - Amazon Neptune
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Managing inference endpoints using the endpoints command

You use the Neptune ML endpoints command to create an inference endpoint, check its status, delete it, or list existing inference endpoints.

Creating an inference endpoint using the Neptune ML endpoints command

A Neptune ML endpoints command for creating an inference endpoint from a model created by a training job looks like this:

curl \ -X POST https://(your Neptune endpoint)/ml/endpoints -H 'Content-Type: application/json' \ -d '{ "id" : "(a unique ID for the new endpoint)", "mlModelTrainingJobId": "(the model-training job-id of a completed job)" }'

A Neptune ML endpoints command for updating an existing inference endpoint from a model created by a training job looks like this:

curl \ -X POST https://(your Neptune endpoint)/ml/endpoints -H 'Content-Type: application/json' \ -d '{ "id" : "(a unique ID for the new endpoint)", "update" : "true", "mlModelTrainingJobId": "(the model-training job-id of a completed job)" }'

A Neptune ML endpoints command for creating an inference endpoint from a model created by a model-transform job looks like this:

curl \ -X POST https://(your Neptune endpoint)/ml/endpoints -H 'Content-Type: application/json' \ -d '{ "id" : "(a unique ID for the new endpoint)", "mlModelTransformJobId": "(the model-training job-id of a completed job)" }'

A Neptune ML endpoints command for updating an existing inference endpoint from a model created by a model-transform job looks like this:

curl \ -X POST https://(your Neptune endpoint)/ml/endpoints -H 'Content-Type: application/json' \ -d '{ "id" : "(a unique ID for the new endpoint)", "update" : "true", "mlModelTransformJobId": "(the model-training job-id of a completed job)" }'

Parameters for endpoints inference endpoint creation

  • id   –   (Optional) A unique identifier for the new inference endpoint.

    Type: string. Default: An autogenerated timestamped name.

  • mlModelTrainingJobId   –   The job Id of the completed model-training job that has created the model that the inference endpoint will point to.

    Type: string.

    Note: You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

  • mlModelTransformJobId   –   The job Id of the completed model-transform job.

    Type: string.

    Note: You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

  • update   –   (Optional) If present, this parameter indicates that this is an update request.

    Type: Boolean. Default: false

    Note: You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

  • neptuneIamRoleArn   –   (Optional) The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources.

    Type: string. Note: This must be listed in your DB cluster parameter group or an error will be thrown.

  • modelName   –   (Optional) Model type for training. By default the ML model is automatically based on the modelType used in data processing, but you can specify a different model type here.

    Type: string. Default: rgcn for heterogeneous graphs and kge for knowledge graphs. Valid values: For heterogeneous graphs: rgcn. For knowledge graphs: kge, transe, distmult, or rotate.

  • instanceType   –   (Optional) The type of ML instance used for online servicing.

    Type: string. Default: ml.m5.xlarge.

    Note: Choosing the ML instance for an inference endpoint depends on the task type, the graph size, and your budget. See Selecting an instance for an inference endpoint.

  • instanceCount   –   (Optional) The minimum number of Amazon EC2 instances to deploy to an endpoint for prediction.

    Type: integer. Default: 1.

  • volumeEncryptionKMSKey   –   (Optional) The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the endpoints.

    Type: string. Default: none.

Getting the status of an inference endpoint using the Neptune ML endpoints command

A sample Neptune ML endpoints command for the status of an instance endpoint looks like this:

curl -s \ "https://(your Neptune endpoint)/ml/endpoints/(the inference endpoint ID)" \ | python -m json.tool

Parameters for endpoints instance-endpoint status

  • id   –   (Required) The unique identifier of the inference endpoint.

    Type: string.

  • neptuneIamRoleArn   –   (Optional) The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources.

    Type: string. Note: This must be listed in your DB cluster parameter group or an error will be thrown.

Deleting an instance endpoint using the Neptune ML endpoints command

A sample Neptune ML endpoints command for deleting an instance endpoint looks like this:

curl -s \ -X DELETE "https://(your Neptune endpoint)/ml/endpoints/(the inference endpoint ID)"

Or this:

curl -s \ -X DELETE "https://(your Neptune endpoint)/ml/endpoints/(the inference endpoint ID)?clean=true"

Parameters for endpoints deleting an inference endpoint

  • id   –   (Required) The unique identifier of the inference endpoint.

    Type: string.

  • neptuneIamRoleArn   –   (Optional) The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources.

    Type: string. Note: This must be listed in your DB cluster parameter group or an error will be thrown.

  • clean   –   (Optional) Indicates that all artifacts related to this endpoint should also be deleted.

    Type: Boolean. Default: FALSE.

Listing inference endpoints using the Neptune ML endpoints command

A Neptune ML endpoints command for listing inference endpoints looks like this:

curl -s "https://(your Neptune endpoint)/ml/endpoints" \ | python -m json.tool

Or this:

curl -s "https://(your Neptune endpoint)/ml/endpoints?maxItems=3" \ | python -m json.tool

Parameters for dataprocessing list inference endpoints

  • maxItems   –   (Optional) The maximum number of items to return.

    Type: integer. Default: 10. Maximum allowed value: 1024.

  • neptuneIamRoleArn   –   (Optional) The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources.

    Type: string. Note: This must be listed in your DB cluster parameter group or an error will be thrown.