CmdID / aws / aws neptunedata

aws neptunedata start-ml-data-processing-job

Creates a new Neptune ML data processing job for processing the graph data exported from Neptune for training. AWS CLI reference

15 options Its page in the AWS CLI reference

Options

15 in the AWS CLI reference

--config-file-nameA data specification file that describes how to load the exported graph data for training.

--idA unique identifier for the new job.

--input-data-s3-locationThe URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.

--model-typeOne of the two model types that Neptune ML currently supports: heterogeneous graph models (heterogeneous), and knowledge graph (kge).

--neptune-iam-role-arnThe Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.

--previous-data-processing-job-idThe job ID of a completed data processing job run on an earlier version of the data.

--processed-data-s3-locationThe URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.

--processing-instance-typeThe type of ML instance used during data processing.

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

--processing-time-out-in-secondsTimeout in seconds for the data processing job.

--s3-output-encryption-kms-keyThe Amazon Key Management Service (Amazon 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.

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

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

--volume-encryption-kms-keyThe Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job.