Creating an inference endpoint to query - Amazon Neptune
Managing inference endpoints

Creating an inference endpoint to query

An inference endpoint lets you query one specific model that the model-training process constructed. The endpoint attaches to the best-performing model of a given type that the training process was able to generate. The endpoint is then able to accept Gremlin queries from Neptune and return that model's predictions for inputs in the queries. After you have created an inference endpoint, it stays active until you delete it.

Managing inference endpoints for Neptune ML

After you have completed model training on data that you exported from Neptune, you can create an inference endpoint using a command like the following:

AWS CLI
aws neptunedata create-ml-endpoint \
  --endpoint-url https://your-neptune-endpoint:port \
  --id "(a unique ID for the new endpoint)" \
  --ml-model-training-job-id "(the model-training job-id of a completed job)"

For more information, see create-ml-endpoint 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.create_ml_endpoint(
    id='(a unique ID for the new endpoint)',
    mlModelTrainingJobId='(the model-training job-id of a completed job)'
)

print(response)
awscurl
awscurl https://your-neptune-endpoint:port/ml/endpoints \
  --region us-east-1 \
  --service neptune-db \
  -X POST \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "(a unique ID for the new endpoint)",
        "mlModelTrainingJobId": "(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/endpoints \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "(a unique ID for the new endpoint)",
        "mlModelTrainingJobId": "(the model-training job-id of a completed job)"
      }'

You can also create an inference endpoint from a model created by a completed model transform job, in much the same way:

AWS CLI
aws neptunedata create-ml-endpoint \
  --endpoint-url https://your-neptune-endpoint:port \
  --id "(a unique ID for the new endpoint)" \
  --ml-model-transform-job-id "(the model-transform job-id of a completed job)"

For more information, see create-ml-endpoint 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.create_ml_endpoint(
    id='(a unique ID for the new endpoint)',
    mlModelTransformJobId='(the model-transform job-id of a completed job)'
)

print(response)
awscurl
awscurl https://your-neptune-endpoint:port/ml/endpoints \
  --region us-east-1 \
  --service neptune-db \
  -X POST \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "(a unique ID for the new endpoint)",
        "mlModelTransformJobId": "(the model-transform 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/endpoints \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "(a unique ID for the new endpoint)",
        "mlModelTransformJobId": "(the model-transform job-id of a completed job)"
      }'

The details of how to use these commands are explained in The endpoints command, along with information about how to get the status of an endpoint, how to delete an endpoint, and how to list all inference endpoints.