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Start Ml Model Transform Job

neptunedata_start_ml_model_transform_job R Documentation

Creates a new model transform job

Description

Creates a new model transform job. See Use a trained model to generate new model artifacts.

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:StartMLModelTransformJob IAM action in that cluster.

Usage

neptunedata_start_ml_model_transform_job(id, dataProcessingJobId,
  mlModelTrainingJobId, trainingJobName, modelTransformOutputS3Location,
  sagemakerIamRoleArn, neptuneIamRoleArn, customModelTransformParameters,
  baseProcessingInstanceType, baseProcessingInstanceVolumeSizeInGB,
  subnets, securityGroupIds, volumeEncryptionKMSKey,
  s3OutputEncryptionKMSKey)

Arguments

id

A unique identifier for the new job. The default is an autogenerated UUID.

dataProcessingJobId

The job ID of a completed data-processing job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

mlModelTrainingJobId

The job ID of a completed model-training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

trainingJobName

The name of a completed SageMaker training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

modelTransformOutputS3Location

[required] The location in Amazon S3 where the model artifacts are to be stored.

sagemakerIamRoleArn

The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur.

neptuneIamRoleArn

The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

customModelTransformParameters

Configuration information for a model transform using a custom model. The customModelTransformParameters object contains the following fields, which must have values compatible with the saved model parameters from the training job:

baseProcessingInstanceType

The type of ML instance used in preparing and managing training of ML models. This is an ML compute instance chosen based on memory requirements for processing the training data and model.

baseProcessingInstanceVolumeSizeInGB

The disk volume size of the training instance in gigabytes. The default is 0. Both input data and the output model are stored on disk, so the volume size must be large enough to hold both data sets. If not specified or 0, Neptune ML selects a disk volume size based on the recommendation generated in the data processing step.

subnets

The IDs of the subnets in the Neptune VPC. The default is None.

securityGroupIds

The VPC security group IDs. The default is None.

volumeEncryptionKMSKey

The Amazon Key Management Service (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.

s3OutputEncryptionKMSKey

The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.

Value

A list with the following syntax: 

list(
  id = "string",
  arn = "string",
  creationTimeInMillis = 123
)

Request syntax

svc$start_ml_model_transform_job(
  id = "string",
  dataProcessingJobId = "string",
  mlModelTrainingJobId = "string",
  trainingJobName = "string",
  modelTransformOutputS3Location = "string",
  sagemakerIamRoleArn = "string",
  neptuneIamRoleArn = "string",
  customModelTransformParameters = list(
    sourceS3DirectoryPath = "string",
    transformEntryPointScript = "string"
  ),
  baseProcessingInstanceType = "string",
  baseProcessingInstanceVolumeSizeInGB = 123,
  subnets = list(
    "string"
  ),
  securityGroupIds = list(
    "string"
  ),
  volumeEncryptionKMSKey = "string",
  s3OutputEncryptionKMSKey = "string"
)