aws neptunedata start-ml-model-training-job

Creates a new Neptune ML model training job. AWS CLI reference

18 options Its page in the AWS CLI reference

Options

18 in the AWS CLI reference

--base-processing-instance-typeThe type of ML instance used in preparing and managing training of ML models.

--custom-model-training-parametersThe configuration for custom model training.

--data-processing-job-idThe job ID of the completed data-processing job that has created the data that the training will work with.

--enable-managed-spot-trainingOptimizes the cost of training machine-learning models by using Amazon Elastic Compute Cloud spot instances.

--idA unique identifier for the new job.

--max-hpo-number-of-training-jobsMaximum total number of training jobs to start for the hyperparameter tuning job.

--max-hpo-parallel-training-jobsMaximum number of parallel training jobs to start for the hyperparameter tuning job.

--neptune-iam-role-arnThe ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources.

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

--s3-output-encryption-kms-keyThe Amazon Key Management Service (KMS) key that SageMaker uses to encrypt the output of the processing job.

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

--security-group-idsThe VPC security group IDs.

--subnetsThe IDs of the subnets in the Neptune VPC.

--train-model-s3-locationThe location in Amazon S3 where the model artifacts are to be stored.

--training-instance-typeThe type of ML instance used for model training.

--training-instance-volume-size-in-gbThe disk volume size of the training instance.

--training-time-out-in-secondsTimeout in seconds for the training job.

--volume-encryption-kms-keyThe 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.