Using the Amazon CLI to set up Neptune ML on a DB cluster - Amazon Neptune
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Using the Amazon CLI to set up Neptune ML on a DB cluster

In addition to the Amazon CloudFormation quick-start template and the Amazon Web Services Management Console, you can also set up Neptune ML using the Amazon CLI.

1. Create a DB cluster parameter group for your new Neptune ML cluster

The following Amazon CLI commands create a new DB cluster parameter group and set it up to work with Neptune ML:

To create and configure a DB cluster parameter group for Neptune ML

  1. Create a new DB cluster parameter group:

    aws neptune create-db-cluster-parameter-group \ --db-cluster-parameter-group-name (name of the new DB cluster parameter group) \ --db-parameter-group-family neptune1 --description "(description of your machine learning project)" \ --region (Amazon region, such as us-east-1)
  2. Create a neptune_ml_iam_role DB cluster parameter set to the ARN of the SageMakerExcecutionIAMRole for your DB cluster to use while calling SageMaker for creating jobs and getting prediction from hosted ML models:

    aws neptune modify-db-cluster-parameter-group \ --db-cluster-parameter-group-name (name of the new DB cluster parameter group) \ --parameters "ParameterName=neptune_ml_iam_role, \ ParameterValue=ARN of the SageMakerExcecutionIAMRole, \ Description=NeptuneMLRole, \ ApplyMethod=pending-reboot" \ --region (Amazon region, such as us-east-1)

    Setting this parameter allows Neptune to access SageMaker without you having to pass in the role with every call.

    For information about how to create the SageMakerExcecutionIAMRole, see Create a custom NeptuneSageMakerIAMRole role.

  3. Finally, use describe-db-cluster-parameters to check that all the parameters in the new DB cluster parameter group are set as you want them to be:

    aws neptune describe-db-cluster-parameters \ --db-cluster-parameter-group-name (name of the new DB cluster parameter group) \ --region (Amazon region, such as us-east-1)

Attach the new DB cluster parameter group to the DB cluster you will use with Neptune ML

Now you can attach the new DB cluster parameter group that you just created to an existing DB cluster by using the following command:

aws neptune modify-db-cluster \ --db-cluster-identifier (the name of your existing DB cluster) \ --apply-immediately --db-cluster-parameter-group-name (name of your new DB cluster parameter group) \ --region (Amazon region, such as us-east-1)

To make all the parameters effective, you can then reboot the DB cluster:

aws neptune reboot-db-instance --db-instance-identifier (name of the primary instance of your DB cluster) \ --profile (name of your Amazon profile to use) \ --region (Amazon region, such as us-east-1)

Or, if you're creating a new DB cluster to use with Neptune ML, you can use the following command to create the cluster with the new parameter group attached, and then create a new primary (writer) instance:

cluster-name=(the name of the new DB cluster) aws neptune create-db-cluster --db-cluster-identifier ${cluster-name} --engine graphdb \ --engine-version 1.0.4.1 \ --db-cluster-parameter-group-name (name of your new DB cluster parameter group) \ --db-subnet-group-name (name of the subnet to use) \ --region (Amazon region, such as us-east-1) aws neptune create-db-instance --db-cluster-identifier ${cluster-name} --db-instance-identifier ${cluster-name}-i \ --db-instance-class (the instance class to use, such as db.r5.xlarge) --engine graphdb \ --region (Amazon region, such as us-east-1)

Attach the NeptuneSageMakerIAMRole to your DB cluster so that it can access SageMaker and Amazon S3 resources

Finally, follow the instructions in Create a custom NeptuneSageMakerIAMRole role to create an IAM role that will allow your DB cluster to communicate with SageMaker and Amazon S3. Then, use the following command to attach the NeptuneSageMakerIAMRole role you created to your DB cluster:

aws neptune add-role-to-db-cluster --db-cluster-identifier ${cluster-name} --role-arn arn:aws:iam::(the ARN number of the role's ARN):role/NeptuneMLRole \ --region (Amazon region, such as us-east-1)

Create two endpoints for SageMaker in your Neptune VPC

Neptune ML needs two SageMaker endpoints in your Neptune DB cluster's VPC:

  • com.amazonaws.(Amazon region, like us-east-1).sagemaker.runtime

  • com.amazonaws.(Amazon region, like us-east-1).sagemaker.api

If you haven't used the quick-start Amazon CloudFormation template, which creates these automatically for you, you can use the following Amazon CLI commands to create them:

This one creates the sagemaker.runtime endpoint:

create-vpc-endpoint --vpc-id (the ID of your Neptune DB cluster's VPC) --service-name com.amazonaws.(Amazon region, like us-east-1).sagemaker.runtime --subnet-ids (the subnet ID or IDs that you want to use) --security-group-ids (the security group for the endpoint network interface, or omit to use the default) --private-dns-enabled

And this one creates the sagemaker.api endpoint:

aws create-vpc-endpoint --vpc-id (the ID of your Neptune DB cluster's VPC) --service-name com.amazonaws.(Amazon region, like us-east-1).sagemaker.api --subnet-ids (the subnet ID or IDs that you want to use) --security-group-ids (the security group for the endpoint network interface, or omit to use the default) --private-dns-enabled

You can also use the VPC console to create these endpoints. See Secure prediction calls in Amazon SageMaker with Amazon PrivateLink and Securing all Amazon SageMaker API calls with Amazon PrivateLink.

Create a SageMaker inference endpoint parameter in your DB cluster parameter group

To avoid having to specify the SageMaker inference endpoint of the model that you're using in every query you make to it, create a DB cluster parameter named neptune_ml_endpoint in the DB cluster parameter group for Neptune ML. Set the parameter to the id of the instance endpoint in question.

You can use the following Amazon CLI command to do that:

aws neptune modify-db-cluster-parameter-group \ --db-cluster-parameter-group-name neptune-ml-demo \ --parameters "ParameterName=neptune_ml_endpoint, \ ParameterValue=(the name of the SageMaker inference endpoint you want to query), \ Description=NeptuneMLEndpoint, \ ApplyMethod=pending-reboot" \ --region (Amazon region, such as us-east-1)