AWS neptune documentation change
Summary
Expanded documentation for Neptune ML model transform command with multiple implementation examples (AWS CLI, SDK, awscurl, curl) for both incremental model inference and SageMaker training job scenarios, including corrected parameter names and values.
Security assessment
This change provides comprehensive examples for starting ML model transform jobs using various methods. It includes standard AWS credential configuration notes and corrects placeholder values (changing 'model-training job ID' to 'model-transform job ID'). These are routine documentation improvements and feature enhancements without evidence of addressing specific security vulnerabilities or adding new security features.
Diff
diff --git a/neptune/latest/userguide/machine-learning-model-transform.md b/neptune/latest/userguide/machine-learning-model-transform.md index 68311425c..9a1a660bf 100644 --- a//neptune/latest/userguide/machine-learning-model-transform.md +++ b//neptune/latest/userguide/machine-learning-model-transform.md @@ -13 +13,59 @@ Using the Neptune ML model transform command, you can compute model artifacts li -In the [incremental model inference workflow](./machine-learning-overview-evolving-data-incremental.html#machine-learning-overview-incremental), after you have processed the updated graph data that you exported from Neptune you can start a model transform job using a curl (or awscurl) command like the following: +In the [incremental model inference workflow](./machine-learning-overview-evolving-data-incremental.html#machine-learning-overview-incremental), after you have processed the updated graph data that you exported from Neptune you can start a model transform job using a command like the following: + +AWS CLI + + + + aws neptunedata start-ml-model-transform-job \ + --endpoint-url https://your-neptune-endpoint:port \ + --id "(a unique model-transform job ID)" \ + --data-processing-job-id "(the data-processing job-id of a completed job)" \ + --ml-model-training-job-id "(the ML model training job-id)" \ + --model-transform-output-s3-location "s3://(your S3 bucket)/neptune-model-transform/" + +For more information, see [start-ml-model-transform-job](https://docs.aws.amazon.com/cli/latest/reference/neptunedata/start-ml-model-transform-job.html) in the AWS CLI Command Reference. + +SDK + + + + import boto3 + from botocore.config import Config + + client = boto3.client( + 'neptunedata', + endpoint_url='https://your-neptune-endpoint:port', + config=Config(read_timeout=None, retries={'total_max_attempts': 1}) + ) + + response = client.start_ml_model_transform_job( + id='(a unique model-transform job ID)', + dataProcessingJobId='(the data-processing job-id of a completed job)', + mlModelTrainingJobId='(the ML model training job-id)', + modelTransformOutputS3Location='s3://(your S3 bucket)/neptune-model-transform/' + ) + + print(response) + +awscurl + + + + awscurl https://your-neptune-endpoint:port/ml/modeltransform \ + --region us-east-1 \ + --service neptune-db \ + -X POST \ + -H 'Content-Type: application/json' \ + -d '{ + "id" : "(a unique model-transform job ID)", + "dataProcessingJobId" : "(the data-processing job-id of a completed job)", + "mlModelTrainingJobId": "(the ML model training job-id)", + "modelTransformOutputS3Location" : "s3://(your S3 bucket)/neptune-model-transform/" + }' + +###### Note + +This example assumes that your AWS credentials are configured in your environment. Replace `us-east-1` with the Region of your Neptune cluster. + +curl + @@ -17 +75 @@ In the [incremental model inference workflow](./machine-learning-overview-evolvi - -X POST https://(your Neptune endpoint)/ml/modeltransform + -X POST https://your-neptune-endpoint:port/ml/modeltransform \ @@ -20 +78 @@ In the [incremental model inference workflow](./machine-learning-overview-evolvi - "id" : "(a unique model-training job ID)", + "id" : "(a unique model-transform job ID)", @@ -23 +81 @@ In the [incremental model inference workflow](./machine-learning-overview-evolvi - "modelTransformOutputS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-transform/" + "modelTransformOutputS3Location" : "s3://(your S3 bucket)/neptune-model-transform/" @@ -33,0 +92,55 @@ For example: +AWS CLI + + + + aws neptunedata start-ml-model-transform-job \ + --endpoint-url https://your-neptune-endpoint:port \ + --id "(a unique model-transform job ID)" \ + --training-job-name "(name of a completed SageMaker training job)" \ + --model-transform-output-s3-location "s3://(your S3 bucket)/neptune-model-transform/" + +For more information, see [start-ml-model-transform-job](https://docs.aws.amazon.com/cli/latest/reference/neptunedata/start-ml-model-transform-job.html) in the AWS CLI Command Reference. + +SDK + + + + import boto3 + from botocore.config import Config + + client = boto3.client( + 'neptunedata', + endpoint_url='https://your-neptune-endpoint:port', + config=Config(read_timeout=None, retries={'total_max_attempts': 1}) + ) + + response = client.start_ml_model_transform_job( + id='(a unique model-transform job ID)', + trainingJobName='(name of a completed SageMaker training job)', + modelTransformOutputS3Location='s3://(your S3 bucket)/neptune-model-transform/' + ) + + print(response) + +awscurl + + + + awscurl https://your-neptune-endpoint:port/ml/modeltransform \ + --region us-east-1 \ + --service neptune-db \ + -X POST \ + -H 'Content-Type: application/json' \ + -d '{ + "id" : "(a unique model-transform job ID)", + "trainingJobName" : "(name of a completed SageMaker training job)", + "modelTransformOutputS3Location" : "s3://(your S3 bucket)/neptune-model-transform/" + }' + +###### Note + +This example assumes that your AWS credentials are configured in your environment. Replace `us-east-1` with the Region of your Neptune cluster. + +curl + + @@ -36 +149 @@ For example: - -X POST https://(your Neptune endpoint)/ml/modeltransform + -X POST https://your-neptune-endpoint:port/ml/modeltransform \ @@ -39,3 +152,3 @@ For example: - "id" : "(a unique model-training job ID)", - "trainingJobName" : "(name a completed SageMaker AI training job)", - "modelTransformOutputS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-transform/" + "id" : "(a unique model-transform job ID)", + "trainingJobName" : "(name of a completed SageMaker training job)", + "modelTransformOutputS3Location" : "s3://(your S3 bucket)/neptune-model-transform/"