Builder
Properties
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.
The configuration for custom model training. This is a JSON object.
The job ID of the completed data-processing job that has created the data that the training will work with.
Optimizes the cost of training machine-learning models by using Amazon Elastic Compute Cloud spot instances. The default is False.
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.
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.
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 job ID of a completed model-training job that you want to update incrementally based on updated data.
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 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.
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.
Timeout in seconds for the training job. The default is 86,400 (1 day).
The location in Amazon S3 where the model artifacts are to be stored.
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.CustomModelTrainingParameters inside the given block