AWS neptune documentation change
Summary
Expanded documentation for starting ML data processing jobs in Neptune, adding detailed examples using AWS CLI, Python SDK (boto3), awscurl, and curl commands with proper parameter formatting and endpoint URLs.
Security assessment
This change is purely a documentation enhancement that adds multiple implementation examples for the same functionality. There is no mention of security vulnerabilities, patches, or security incidents. The changes improve clarity by showing different ways to call the same API endpoint but don't introduce or document any security features. The note about AWS credentials being configured in the environment is standard practice, not a security fix.
Diff
diff --git a/neptune/latest/userguide/machine-learning-on-graphs-processing.md b/neptune/latest/userguide/machine-learning-on-graphs-processing.md index 89da3e750..783f00e44 100644 --- a//neptune/latest/userguide/machine-learning-on-graphs-processing.md +++ b//neptune/latest/userguide/machine-learning-on-graphs-processing.md @@ -22 +22,59 @@ The data-processing step takes the Neptune graph data created by the export proc -After you have exported the data from Neptune that you want to use for model training, you can start a data-processing job using a `curl` (or `awscurl`) command like the following: +After you have exported the data from Neptune that you want to use for model training, you can start a data-processing job using a command like the following: + +AWS CLI + + + + aws neptunedata start-ml-data-processing-job \ + --endpoint-url https://your-neptune-endpoint:port \ + --input-data-s3-location "s3://(S3 bucket name)/(path to your input folder)" \ + --id "(a job ID for the new job)" \ + --processed-data-s3-location "s3://(S3 bucket name)/(path to your output folder)" \ + --config-file-name "training-job-configuration.json" + +For more information, see [start-ml-data-processing-job](https://docs.aws.amazon.com/cli/latest/reference/neptunedata/start-ml-data-processing-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_data_processing_job( + inputDataS3Location='s3://(S3 bucket name)/(path to your input folder)', + id='(a job ID for the new job)', + processedDataS3Location='s3://(S3 bucket name)/(path to your output folder)', + configFileName='training-job-configuration.json' + ) + + print(response) + +awscurl + + + + awscurl https://your-neptune-endpoint:port/ml/dataprocessing \ + --region us-east-1 \ + --service neptune-db \ + -X POST \ + -H 'Content-Type: application/json' \ + -d '{ + "inputDataS3Location" : "s3://(S3 bucket name)/(path to your input folder)", + "id" : "(a job ID for the new job)", + "processedDataS3Location" : "s3://(S3 bucket name)/(path to your output folder)", + "configFileName" : "training-job-configuration.json" + }' + +###### 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 + @@ -26 +84 @@ After you have exported the data from Neptune that you want to use for model tra - -X POST https://(your Neptune endpoint)/ml/dataprocessing \ + -X POST https://your-neptune-endpoint:port/ml/dataprocessing \ @@ -29 +87 @@ After you have exported the data from Neptune that you want to use for model tra - "inputDataS3Location" : "s3://(Amazon S3 bucket name)/(path to your input folder)", + "inputDataS3Location" : "s3://(S3 bucket name)/(path to your input folder)", @@ -41 +99,59 @@ You can also supply a `previousDataProcessingJobId` to the API to ensure that th -You do this by using a `curl` (or `awscurl`) command like this: +You do this by using a command like this: + +AWS CLI + + + + aws neptunedata start-ml-data-processing-job \ + --endpoint-url https://your-neptune-endpoint:port \ + --input-data-s3-location "s3://(Amazon S3 bucket name)/(path to your input folder)" \ + --id "(a job ID for the new job)" \ + --processed-data-s3-location "s3://(Amazon S3 bucket name)/(path to your output folder)" \ + --previous-data-processing-job-id "(the job ID of the previous data-processing job)" + +For more information, see [start-ml-data-processing-job](https://docs.aws.amazon.com/cli/latest/reference/neptunedata/start-ml-data-processing-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_data_processing_job( + inputDataS3Location='s3://(Amazon S3 bucket name)/(path to your input folder)', + id='(a job ID for the new job)', + processedDataS3Location='s3://(Amazon S3 bucket name)/(path to your output folder)', + previousDataProcessingJobId='(the job ID of the previous data-processing job)' + ) + + print(response) + +awscurl + + + + awscurl https://your-neptune-endpoint:port/ml/dataprocessing \ + --region us-east-1 \ + --service neptune-db \ + -X POST \ + -H 'Content-Type: application/json' \ + -d '{ + "inputDataS3Location" : "s3://(Amazon S3 bucket name)/(path to your input folder)", + "id" : "(a job ID for the new job)", + "processedDataS3Location" : "s3://(Amazon S3 bucket name)/(path to your output folder)", + "previousDataProcessingJobId" : "(the job ID of the previous data-processing job)" + }' + +###### 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 + @@ -45 +161 @@ You do this by using a `curl` (or `awscurl`) command like this: - -X POST https://(your Neptune endpoint)/ml/dataprocessing \ + -X POST https://your-neptune-endpoint:port/ml/dataprocessing \ @@ -47 +163,2 @@ You do this by using a `curl` (or `awscurl`) command like this: - -d '{ "inputDataS3Location" : "s3://(Amazon S3 bucket name)/(path to your input folder)", + -d '{ + "inputDataS3Location" : "s3://(Amazon S3 bucket name)/(path to your input folder)", @@ -50 +167,2 @@ You do this by using a `curl` (or `awscurl`) command like this: - "previousDataProcessingJobId", "(the job ID of the previous data-processing job)"}' + "previousDataProcessingJobId" : "(the job ID of the previous data-processing job)" + }'