NeptuneData / Client / start_ml_data_processing_job

start_ml_data_processing_job#

NeptuneData.Client.start_ml_data_processing_job(**kwargs)#

Creates a new Neptune ML data processing job for processing the graph data exported from Neptune for training. See The dataprocessing command.

See also: AWS API Documentation

Request Syntax

response = client.start_ml_data_processing_job(
    id='string',
    previousDataProcessingJobId='string',
    inputDataS3Location='string',
    processedDataS3Location='string',
    sagemakerIamRoleArn='string',
    neptuneIamRoleArn='string',
    processingInstanceType='string',
    processingInstanceVolumeSizeInGB=123,
    processingTimeOutInSeconds=123,
    modelType='string',
    configFileName='string',
    subnets=[
        'string',
    ],
    securityGroupIds=[
        'string',
    ],
    volumeEncryptionKMSKey='string',
    s3OutputEncryptionKMSKey='string'
)
Parameters:

  • id (string) – A unique identifier for the new job. The default is an autogenerated UUID.

  • previousDataProcessingJobId (string) – The job ID of a completed data processing job run on an earlier version of the data.

  • inputDataS3Location (string) –

    [REQUIRED]

    The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.

  • processedDataS3Location (string) –

    [REQUIRED]

    The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.

  • sagemakerIamRoleArn (string) – 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 (string) – The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. This must be listed in your DB cluster parameter group or an error will occur.

  • processingInstanceType (string) – The type of ML instance used during data processing. Its memory should be large enough to hold the processed dataset. The default is the smallest ml.r5 type whose memory is ten times larger than the size of the exported graph data on disk.

  • processingInstanceVolumeSizeInGB (integer) – The disk volume size of the processing instance. Both input data and processed data 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 chooses the volume size automatically based on the data size.

  • processingTimeOutInSeconds (integer) – Timeout in seconds for the data processing job. The default is 86,400 (1 day).

  • modelType (string) – One of the two model types that Neptune ML currently supports: heterogeneous graph models ( heterogeneous), and knowledge graph ( kge). The default is none. If not specified, Neptune ML chooses the model type automatically based on the data.

  • configFileName (string) – A data specification file that describes how to load the exported graph data for training. The file is automatically generated by the Neptune export toolkit. The default is training-data-configuration.json.

  • subnets (list) –

    The IDs of the subnets in the Neptune VPC. The default is None.

    • (string) –

  • securityGroupIds (list) –

    The VPC security group IDs. The default is None.

    • (string) –

  • volumeEncryptionKMSKey (string) – The 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. The default is None.

  • s3OutputEncryptionKMSKey (string) – The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.

Return type:

dict

Returns:

Response Syntax

{
    'id': 'string',
    'arn': 'string',
    'creationTimeInMillis': 123
}

Response Structure

  • (dict) –

    • id (string) –

      The unique ID of the new data processing job.

    • arn (string) –

      The ARN of the data processing job.

    • creationTimeInMillis (integer) –

      The time it took to create the new processing job, in milliseconds.

Exceptions

  • NeptuneData.Client.exceptions.UnsupportedOperationException

  • NeptuneData.Client.exceptions.BadRequestException

  • NeptuneData.Client.exceptions.MLResourceNotFoundException

  • NeptuneData.Client.exceptions.InvalidParameterException

  • NeptuneData.Client.exceptions.ClientTimeoutException

  • NeptuneData.Client.exceptions.PreconditionsFailedException

  • NeptuneData.Client.exceptions.ConstraintViolationException

  • NeptuneData.Client.exceptions.InvalidArgumentException

  • NeptuneData.Client.exceptions.MissingParameterException

  • NeptuneData.Client.exceptions.IllegalArgumentException

  • NeptuneData.Client.exceptions.TooManyRequestsException