Builder
Properties
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.
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.
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:
The job ID of a completed data-processing job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.
The job ID of a completed model-training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.
The location in Amazon S3 where the model artifacts are to be stored.
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.
The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.
The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur.
The VPC security group IDs. The default is None.
The name of a completed SageMaker training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.
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.
Functions
construct an aws.sdk.kotlin.services.neptunedata.model.CustomModelTransformParameters inside the given block