1. Controlling Access to SageMaker Endpoints for Deployed Models


    To control access to AWS SageMaker Endpoints for deployed models, you need to take several steps:

    1. Configure a SageMaker model: The model is the artifact you create after training your algorithm to make predictions.
    2. Create a SageMaker Endpoint Configuration: The endpoint configuration is a blueprint for your endpoint. It defines the resources to deploy for hosting your model.
    3. Deploy the SageMaker Endpoint: Once you have your model and endpoint configuration ready, you deploy the endpoint. This is what makes the model available for use.

    In AWS SageMaker, the endpoint you create is automatically scalable and can be secured in multiple ways, including:

    • Using AWS Identity and Access Management (IAM) roles and policies to control access at the API level.
    • Using VPC configurations to restrict access to the endpoint within a virtual private cloud.

    In Pulumi, this can be translated into creating resources like aws.sagemaker.Model, aws.sagemaker.EndpointConfig, and aws.sagemaker.Endpoint. These Pulumi resources will reside in the pulumi_aws Python package.

    Below is a Pulumi Python program that shows how to define these resources to control access to SageMaker Endpoints:

    import pulumi import pulumi_aws as aws # This is the AWS SageMaker role you'll need to allow SageMaker to run tasks on your behalf sagemaker_role = aws.iam.Role("SageMakerRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"} }] }""" ) # Attach policies to the role - The policies attached would depend on what your model needs. # For example, sagemaker full access and access to necessary S3 buckets. aws.iam.RolePolicyAttachment("SageMakerFullAccess", role=sagemaker_role.name, policy_arn=aws.iam.ManagedPolicy.AMAZON_SAGEMAKER_FULL_ACCESS ) # The SageMaker model which points to the location of the trained model data. sagemaker_model = aws.sagemaker.Model("MyModel", execution_role_arn=sagemaker_role.arn, primary_container={ # Here you'd specify the container image registry and location of your model data. # For instance, using a pre-built SageMaker Docker image for your specific deep learning # framework and the S3 location of your trained model: "image": "<ECR-REGISTRY-URL>/sagemaker-tensorflow:latest", # example image "model_data_url": "s3://my-model-bucket/my-model-path/model.tar.gz" } ) # The endpoint config - instance type, count, and possibly other configurations. sagemaker_endpoint_config = aws.sagemaker.EndpointConfig("MyEndpointConfig", production_variants=[{ "modelName": sagemaker_model.name, "variantName": "AllTraffic", # This could be 'DirectTraffic' for more control "initialInstanceCount": 1, "instanceType": "ml.t2.medium", # Choose the appropriate instance type }] ) # Deploying the SageMaker endpoint sagemaker_endpoint = aws.sagemaker.Endpoint("MyEndpoint", endpoint_config_name=sagemaker_endpoint_config.name ) # Export the endpoint name for easier access/reference pulumi.export("sagemaker_endpoint_name", sagemaker_endpoint.name)

    This program outlines a simple scenario for controlling a SageMaker Endpoint:

    • The IAM role created provides the necessary permissions for SageMaker to operate.
    • The SageMaker model is where you specify the machine learning model details.
    • The Endpoint Config allows you to control things like the number of instances and the type of instance.
    • Finally, the Endpoint is the resource that deploys the model for use.

    This program should give you a good starting point. You might need to secure the endpoint further depending on your scenario. You can enforce networking restrictions using a VPC (not shown here) or set up additional IAM policies to control who or what can invoke your endpoint.