AWS neptune documentation change
Summary
Added comprehensive examples for Neptune ML model training API operations using AWS CLI, SDK, awscurl, and curl for creating new jobs, update jobs, custom jobs, getting status, stopping jobs, and listing jobs. Updated endpoint URLs to use consistent format with port numbers.
Security assessment
The changes are purely documentation improvements adding multiple code examples for different API clients. There is no evidence of security vulnerability fixes, patches, or security incident responses. The changes improve usability by providing clearer examples but don't address security issues or add security-specific documentation. The endpoint URL format changes are for consistency and don't indicate security remediation.
Diff
diff --git a/neptune/latest/userguide/machine-learning-api-modeltraining.md b/neptune/latest/userguide/machine-learning-api-modeltraining.md index 790068ef0..806672a8a 100644 --- a//neptune/latest/userguide/machine-learning-api-modeltraining.md +++ b//neptune/latest/userguide/machine-learning-api-modeltraining.md @@ -14,0 +15,55 @@ A Neptune ML `modeltraining` command for creating a completely new job looks lik +AWS CLI + + + + aws neptunedata start-ml-model-training-job \ + --endpoint-url https://your-neptune-endpoint:port \ + --id "(a unique model-training job ID)" \ + --data-processing-job-id "(the data-processing job-id of a completed job)" \ + --train-model-s3-location "s3://(your S3 bucket)/neptune-model-graph-autotrainer" + +For more information, see [start-ml-model-training-job](https://docs.aws.amazon.com/cli/latest/reference/neptunedata/start-ml-model-training-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_training_job( + id='(a unique model-training job ID)', + dataProcessingJobId='(the data-processing job-id of a completed job)', + trainModelS3Location='s3://(your S3 bucket)/neptune-model-graph-autotrainer' + ) + + print(response) + +awscurl + + + + awscurl https://your-neptune-endpoint:port/ml/modeltraining \ + --region us-east-1 \ + --service neptune-db \ + -X POST \ + -H 'Content-Type: application/json' \ + -d '{ + "id" : "(a unique model-training job ID)", + "dataProcessingJobId" : "(the data-processing job-id of a completed job)", + "trainModelS3Location" : "s3://(your S3 bucket)/neptune-model-graph-autotrainer" + }' + +###### 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 +72 @@ A Neptune ML `modeltraining` command for creating a completely new job looks lik - -X POST https://(your Neptune endpoint)/ml/modeltraining + -X POST https://your-neptune-endpoint:port/ml/modeltraining \ @@ -22 +77 @@ A Neptune ML `modeltraining` command for creating a completely new job looks lik - "trainModelS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-graph-autotrainer" + "trainModelS3Location" : "s3://(your S3 bucket)/neptune-model-graph-autotrainer" @@ -26,0 +82,58 @@ A Neptune ML `modeltraining` command for creating an update job for incremental +AWS CLI + + + + aws neptunedata start-ml-model-training-job \ + --endpoint-url https://your-neptune-endpoint:port \ + --id "(a unique model-training job ID)" \ + --data-processing-job-id "(the data-processing job-id of a completed job)" \ + --train-model-s3-location "s3://(your S3 bucket)/neptune-model-graph-autotrainer" \ + --previous-model-training-job-id "(the job ID of a completed model-training job to update)" + +For more information, see [start-ml-model-training-job](https://docs.aws.amazon.com/cli/latest/reference/neptunedata/start-ml-model-training-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_training_job( + id='(a unique model-training job ID)', + dataProcessingJobId='(the data-processing job-id of a completed job)', + trainModelS3Location='s3://(your S3 bucket)/neptune-model-graph-autotrainer', + previousModelTrainingJobId='(the job ID of a completed model-training job to update)' + ) + + print(response) + +awscurl + + + + awscurl https://your-neptune-endpoint:port/ml/modeltraining \ + --region us-east-1 \ + --service neptune-db \ + -X POST \ + -H 'Content-Type: application/json' \ + -d '{ + "id" : "(a unique model-training job ID)", + "dataProcessingJobId" : "(the data-processing job-id of a completed job)", + "trainModelS3Location" : "s3://(your S3 bucket)/neptune-model-graph-autotrainer", + "previousModelTrainingJobId" : "(the job ID of a completed model-training job to update)" + }' + +###### 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 + + @@ -29 +142 @@ A Neptune ML `modeltraining` command for creating an update job for incremental - -X POST https://(your Neptune endpoint)/ml/modeltraining + -X POST https://your-neptune-endpoint:port/ml/modeltraining \ @@ -34,2 +147,2 @@ A Neptune ML `modeltraining` command for creating an update job for incremental - "trainModelS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-graph-autotrainer" - "previousModelTrainingJobId" : "(the job ID of a completed model-training job to update)", + "trainModelS3Location" : "s3://(your S3 bucket)/neptune-model-graph-autotrainer", + "previousModelTrainingJobId" : "(the job ID of a completed model-training job to update)" @@ -39,0 +153,73 @@ A Neptune ML `modeltraining` command for creating a new job with user provided c +AWS CLI + + + + aws neptunedata start-ml-model-training-job \ + --endpoint-url https://your-neptune-endpoint:port \ + --id "(a unique model-training job ID)" \ + --data-processing-job-id "(the data-processing job-id of a completed job)" \ + --train-model-s3-location "s3://(your Amazon S3 bucket)/neptune-model-graph-autotrainer" \ + --model-name "custom" \ + --custom-model-training-parameters '{ + "sourceS3DirectoryPath": "s3://(your Amazon S3 bucket)/(path to your Python module)", + "trainingEntryPointScript": "(your training script entry-point name in the Python module)", + "transformEntryPointScript": "(your transform script entry-point name in the Python module)" + }' + +For more information, see [start-ml-model-training-job](https://docs.aws.amazon.com/cli/latest/reference/neptunedata/start-ml-model-training-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_training_job( + id='(a unique model-training job ID)', + dataProcessingJobId='(the data-processing job-id of a completed job)', + trainModelS3Location='s3://(your Amazon S3 bucket)/neptune-model-graph-autotrainer', + modelName='custom', + customModelTrainingParameters={ + 'sourceS3DirectoryPath': 's3://(your Amazon S3 bucket)/(path to your Python module)', + 'trainingEntryPointScript': '(your training script entry-point name in the Python module)', + 'transformEntryPointScript': '(your transform script entry-point name in the Python module)' + } + ) + + print(response) + +awscurl + + + + awscurl https://your-neptune-endpoint:port/ml/modeltraining \ + --region us-east-1 \ + --service neptune-db \ + -X POST \ + -H 'Content-Type: application/json' \ + -d '{ + "id" : "(a unique model-training job ID)", + "dataProcessingJobId" : "(the data-processing job-id of a completed job)", + "trainModelS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-graph-autotrainer", + "modelName": "custom", + "customModelTrainingParameters" : { + "sourceS3DirectoryPath": "s3://(your Amazon S3 bucket)/(path to your Python module)", + "trainingEntryPointScript": "(your training script entry-point name in the Python module)", + "transformEntryPointScript": "(your transform script entry-point name in the Python module)" + } + }' +