neptunedata_create_ml_endpoint: Creates a new Neptune ML inference endpoint that lets you...

In paws.database: 'Amazon Web Services' Database Services

Creates a new Neptune ML inference endpoint that lets you query one specific model that the model-training process constructed

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.

See https://www.paws-r-sdk.com/docs/neptunedata_create_ml_endpoint/ for full documentation.

Usage

neptunedata_create_ml_endpoint(
  id = NULL,
  mlModelTrainingJobId = NULL,
  mlModelTransformJobId = NULL,
  update = NULL,
  neptuneIamRoleArn = NULL,
  modelName = NULL,
  instanceType = NULL,
  instanceCount = NULL,
  volumeEncryptionKMSKey = NULL
)

Arguments

id	A unique identifier for the new inference endpoint. The default is 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. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.
mlModelTransformJobId	The job Id of the completed model-transform job. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.
update	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.
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.
modelName	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.
instanceType	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.
instanceCount	The minimum number of Amazon EC2 instances to deploy to an endpoint for prediction. The default is 1
volumeEncryptionKMSKey	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.

paws.database documentation built on Sept. 12, 2024, 6:37 a.m.