AWS neptune documentation change
Summary
Added comprehensive examples for AWS CLI, SDK, awscurl, and curl commands for starting, getting status, canceling, and listing Neptune ML model transform jobs. Updated existing curl examples with consistent endpoint formatting and parameter names.
Security assessment
The changes are routine documentation updates that add multiple implementation examples for existing API operations. There is no mention of security vulnerabilities, patches, or security incidents. The updates focus on improving developer experience by providing consistent examples across different tools (CLI, SDK, curl). No security features or configurations are introduced.
Diff
diff --git a/neptune/latest/userguide/machine-learning-api-modeltransform.md b/neptune/latest/userguide/machine-learning-api-modeltransform.md index 46989d104..8792e21d7 100644 --- a//neptune/latest/userguide/machine-learning-api-modeltransform.md +++ b//neptune/latest/userguide/machine-learning-api-modeltransform.md @@ -14,0 +15,58 @@ A Neptune ML `modeltransform` command for creating an incremental transform job, +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 job-id of a completed data-processing job)" \ + --ml-model-training-job-id "(the job-id of a completed model-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)', + dataProcessingJobId='(the job-id of a completed data-processing job)', + mlModelTrainingJobId='(the job-id of a completed model-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)", + "dataProcessingJobId" : "(the job-id of a completed data-processing job)", + "mlModelTrainingJobId" : "(the job-id of a completed model-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 + + @@ -17 +75 @@ A Neptune ML `modeltransform` command for creating an incremental transform job, - -X POST https://(your Neptune endpoint)/ml/modeltransform + -X POST https://your-neptune-endpoint:port/ml/modeltransform \ @@ -23 +81 @@ A Neptune ML `modeltransform` command for creating an incremental transform job, - "modelTransformOutputS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-transform" + "modelTransformOutputS3Location" : "s3://(your S3 bucket)/neptune-model-transform" @@ -27,0 +86,55 @@ A Neptune ML `modeltransform` command for creating a job from a completed SageMa +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 + + @@ -30 +143 @@ A Neptune ML `modeltransform` command for creating a job from a completed SageMa - -X POST https://(your Neptune endpoint)/ml/modeltransform + -X POST https://your-neptune-endpoint:port/ml/modeltransform \ @@ -34,3 +147,2 @@ A Neptune ML `modeltransform` command for creating a job from a completed SageMa - "trainingJobName" : "(name of a completed SageMaker AI training job)", - "modelTransformOutputS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-transform", - "baseProcessingInstanceType" : "" + "trainingJobName" : "(name of a completed SageMaker training job)", + "modelTransformOutputS3Location" : "s3://(your S3 bucket)/neptune-model-transform" @@ -40,0 +153,67 @@ A Neptune ML `modeltransform` command for creating a job that uses a custom mode +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 Amazon S3 bucket)/neptune-model-transform/" \ + --custom-model-transform-parameters '{ + "sourceS3DirectoryPath": "s3://(your Amazon S3 bucket)/(path to your Python module)", + "transformEntryPointScript": "(your transform script entry-point name in the Python module)" + }' + +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 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)' + } + ) + + 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 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)" + } + }' + +###### 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