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Training a model using Neptune ML - Amazon Neptune

Training a model using Neptune ML

After you have processed the data that you exported from Neptune for model training, you can start a model-training job using a command like the following:

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 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
curl \
  -X POST https://your-neptune-endpoint:port/ml/modeltraining \
  -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"
      }'

The details of how to use this command are explained in The modeltraining command, along with information about how to get the status of a running job, how to stop a running job, and how to list all running jobs.

You can also supply a previousModelTrainingJobId to use information from a completed Neptune ML model training job to accelerate the hyperparameter search in a new training job. This is useful during model retraining on new graph data, as well as incremental training on the same graph data. Use a command like this one:

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 model-training job-id of a completed job)"

For more information, see start-ml-model-training-job 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 model-training job-id of a completed job)'
)

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 model-training job-id of a completed 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
curl \
  -X POST https://your-neptune-endpoint:port/ml/modeltraining \
  -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 model-training job-id of a completed job)"
      }'

You can train your own model implementation on the Neptune ML training infrastructure by supplying a customModelTrainingParameters object, like this:

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 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)"
        }
      }'
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
curl \
  -X POST https://your-neptune-endpoint:port/ml/modeltraining \
  -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)"
        }
      }'

See The modeltraining command for more information, such as about how to get the status of a running job, how to stop a running job, and how to list all running jobs. See Custom models in Neptune ML for information about how to implement and use a custom model.

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