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Start Ml Model Training Job

neptunedata_start_ml_model_training_job R Documentation

Creates a new Neptune ML model training job

Description

Creates a new Neptune ML model training job. See Model training using the modeltraining 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:StartMLModelTrainingJob IAM action in that cluster.

Usage

neptunedata_start_ml_model_training_job(id, previousModelTrainingJobId,
  dataProcessingJobId, trainModelS3Location, sagemakerIamRoleArn,
  neptuneIamRoleArn, baseProcessingInstanceType, trainingInstanceType,
  trainingInstanceVolumeSizeInGB, trainingTimeOutInSeconds,
  maxHPONumberOfTrainingJobs, maxHPOParallelTrainingJobs, subnets,
  securityGroupIds, volumeEncryptionKMSKey, s3OutputEncryptionKMSKey,
  enableManagedSpotTraining, customModelTrainingParameters)

Arguments

id

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

previousModelTrainingJobId

The job ID of a completed model-training job that you want to update incrementally based on updated data.

dataProcessingJobId

[required] The job ID of the completed data-processing job that has created the data that the training will work with.

trainModelS3Location

[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.

baseProcessingInstanceType

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

trainingInstanceType

The type of ML instance used for model training. All Neptune ML models support CPU, GPU, and multiGPU training. The default is ml.p3.2xlarge. Choosing the right instance type for training depends on the task type, graph size, and your budget.

trainingInstanceVolumeSizeInGB

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

trainingTimeOutInSeconds

Timeout in seconds for the training job. The default is 86,400 (1 day).

maxHPONumberOfTrainingJobs

Maximum total number of training jobs to start for the hyperparameter tuning job. The default is 2. Neptune ML automatically tunes the hyperparameters of the machine learning model. To obtain a model that performs well, use at least 10 jobs (in other words, set maxHPONumberOfTrainingJobs to 10). In general, the more tuning runs, the better the results.

maxHPOParallelTrainingJobs

Maximum number of parallel training jobs to start for the hyperparameter tuning job. The default is 2. The number of parallel jobs you can run is limited by the available resources on your training instance.

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.

enableManagedSpotTraining

Optimizes the cost of training machine-learning models by using Amazon Elastic Compute Cloud spot instances. The default is False.

customModelTrainingParameters

The configuration for custom model training. This is a JSON object.

Value

A list with the following syntax: 

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

Request syntax

svc$start_ml_model_training_job(
  id = "string",
  previousModelTrainingJobId = "string",
  dataProcessingJobId = "string",
  trainModelS3Location = "string",
  sagemakerIamRoleArn = "string",
  neptuneIamRoleArn = "string",
  baseProcessingInstanceType = "string",
  trainingInstanceType = "string",
  trainingInstanceVolumeSizeInGB = 123,
  trainingTimeOutInSeconds = 123,
  maxHPONumberOfTrainingJobs = 123,
  maxHPOParallelTrainingJobs = 123,
  subnets = list(
    "string"
  ),
  securityGroupIds = list(
    "string"
  ),
  volumeEncryptionKMSKey = "string",
  s3OutputEncryptionKMSKey = "string",
  enableManagedSpotTraining = TRUE|FALSE,
  customModelTrainingParameters = list(
    sourceS3DirectoryPath = "string",
    trainingEntryPointScript = "string",
    transformEntryPointScript = "string"
  )
)