Model transform using the modeltransform command - Amazon Neptune
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Model transform using the modeltransform command

You use the Neptune ML modeltransform command to create a model transform job, check its status, stop it, or list all active model-transform jobs.

Creating a model-transform job using the Neptune ML modeltransform command

A Neptune ML modeltransform command for creating an incremental transform job, without model retraining, looks like this:

curl \ -X POST https://(your Neptune endpoint)/ml/modeltransform -H 'Content-Type: application/json' \ -d '{ "id" : "(a unique model-transform job ID)", "dataProcessingJobId" : "(the job-id of a completed data-processing job)", "mlModelTrainingJobId" : "(the job-id of a completed model-training job)", "modelTransformOutputS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-transform" }'

A Neptune ML modeltransform command for creating a job from a completed SageMaker training job looks like this:

curl \ -X POST https://(your Neptune endpoint)/ml/modeltransform -H 'Content-Type: application/json' \ -d '{ "id" : "(a unique model-transform job ID)", "trainingJobName" : "(name of a completed SageMaker training job)", "modelTransformOutputS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-transform", "baseProcessingInstanceType" : "" }'

A Neptune ML modeltransform command for creating a job that uses a custom model implementation looks like:

curl \ -X POST https://(your Neptune endpoint)/ml/modeltransform -H 'Content-Type: application/json' \ -d '{ "id" : "(a unique model-training job ID)", "trainingJobName" : "(name of a completed SageMaker training job)", "modelTransformOutputS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-transform/" "customModelTransformParameters" : { "sourceS3DirectoryPath": "s3://(your Amazon S3 bucket)/(path to your Python module)", "transformEntryPointScript": "(your transform script entry-point name in the Python module)" } }'

Parameters for modeltransform job creation

  • id   –   (Optional) A unique identifier for the new job.

    Type: string. Default: An autogenerated UUID.

  • dataProcessingJobId   –   The job Id of a completed data-processing job.

    Type: string.

    Note: You must include either both dataProcessingJobId and mlModelTrainingJobId, or trainingJobName.

  • mlModelTrainingJobId   –   The job Id of a completed model-training job.

    Type: string.

    Note: You must include either both dataProcessingJobId and mlModelTrainingJobId, or trainingJobName.

  • trainingJobName   –   The name of a completed SageMaker training job.

    Type: string.

    Note: You must include either both the dataProcessingJobId and the mlModelTrainingJobId parameters, or the trainingJobName parameter.

  • sagemakerIamRoleArn   –   (Optional) The ARN of an IAM role for SageMaker execution.

    Type: string. Note: This must be listed in your DB cluster parameter group or an error will occur.

  • neptuneIamRoleArn   –   (Optional) The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources.

    Type: string. Note: This must be listed in your DB cluster parameter group or an error will occur.

  • customModelTransformParameters   –   (Optional) 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:

    • sourceS3DirectoryPath   –   (Required) The path to the Amazon S3 location where the Python module implementing your model is located. This must point to a valid existing Amazon S3 location that contains, at a minimum, a training script, a transform script, and a model-hpo-configuration.json file.

    • transformEntryPointScript   –   (Optional) The name of the entry point in your module of a script that should be run after the best model from the hyperparameter search has been identified, to compute the model artifacts necessary for model deployment. It should be able to run with no command-line arguments.

      Default: transform.py.

  • baseProcessingInstanceType   –   (Optional) The type of ML instance used in preparing and managing training of ML models.

    Type: string. Note: This is a CPU instance chosen based on memory requirements for processing the transform data and model. See Selecting an instance for model training and model transform.

  • baseProcessingInstanceVolumeSizeInGB   –   (Optional) 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.

    Type: integer. Default: 0.

    Note: If not specified or 0, Neptune ML selects a disk volume size based on the recommendation generated in the data processing step. See Selecting an instance for model training and model transform.

  • subnets   –   (Optional) The IDs of the subnets in the Neptune VPC.

    Type: list of strings. Default: none.

  • securityGroupIds   –   (Optional) The VPC security group IDs.

    Type: list of strings. Default: none.

  • volumeEncryptionKMSKey   –   (Optional) 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 transform job.

    Type: string. Default: none.

  • s3OutputEncryptionKMSKey   –   (Optional) The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job.

    Type: string. Default: none.

Getting the status of a model-transform job using the Neptune ML modeltransform command

A sample Neptune ML modeltransform command for the status of a job looks like this:

curl -s \ "https://(your Neptune endpoint)/ml/modeltransform/(the job ID)" \ | python -m json.tool

Parameters for modeltransform job status

  • id   –   (Required) The unique identifier of the model-transform job.

    Type: string.

  • neptuneIamRoleArn   –   (Optional) The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources.

    Type: string. Note: This must be listed in your DB cluster parameter group or an error will occur.

Stopping a model-transform job using the Neptune ML modeltransform command

A sample Neptune ML modeltransform command for stopping a job looks like this:

curl -s \ -X DELETE "https://(your Neptune endpoint)/ml/modeltransform/(the job ID)"

Or this:

curl -s \ -X DELETE "https://(your Neptune endpoint)/ml/modeltransform/(the job ID)?clean=true"

Parameters for modeltransform stop job

  • id   –   (Required) The unique identifier of the model-transform job.

    Type: string.

  • neptuneIamRoleArn   –   (Optional) The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources.

    Type: string. Note: This must be listed in your DB cluster parameter group or an error will occur.

  • clean   –   (Optional) This flag specifies that all Amazon S3 artifacts should be deleted when the job is stopped.

    Type: Boolean. Default: FALSE.

Listing active model-transform jobs using the Neptune ML modeltransform command

A sample Neptune ML modeltransform command for listing active jobs looks like this:

curl -s "https://(your Neptune endpoint)/ml/modeltransform" | python -m json.tool

Or this:

curl -s "https://(your Neptune endpoint)/ml/modeltransform?maxItems=3" | python -m json.tool

Parameters for modeltransform list jobs

  • maxItems   –   (Optional) The maximum number of items to return.

    Type: integer. Default: 10. Maximum allowed value: 1024.

  • neptuneIamRoleArn   –   (Optional) The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources.

    Type: string. Note: This must be listed in your DB cluster parameter group or an error will occur.