1. Managed Endpoints for Secure SageMaker Access


    To create managed endpoints for secure access to AWS SageMaker, we'll need to use a combination of AWS services including SageMaker, networking infrastructure like VPCs and security groups, as well as IAM roles for access control. We'll define them in a Pulumi program using the Python language with the pulumi_aws package.

    Here's a step-by-step guide followed by the program to set up managed endpoints to allow secure access to SageMaker with Pulumi:

    Step 1: Import necessary Pulumi packages

    We'll need pulumi to run the code and pulumi_aws for accessing AWS resources.

    Step 2: Define the VPC resources

    We'll start by creating a Virtual Private Cloud (VPC) and subnets that will host our SageMaker runtime. These resources will provide network isolation and define the IP range that our resources can use.

    Step 3: Create Security Group

    Security groups act as a virtual firewall to control the traffic to and from our SageMaker endpoint.

    Step 4: Create the IAM Role

    An IAM role is needed to give SageMaker access to other AWS services.

    Step 5: Create an SageMaker Notebook Instance

    Managed endpoint in SageMaker terminology could refer to the endpoints used to host machine learning models for real-time inference. However, for an accessible and managed environment for data scientists, we use a SageMaker Notebook instance, which serves as a managed environment for developing machine learning models.

    Step 6: Endpoint Configuration and Deployment

    Deploy SageMaker endpoint configuration with a model to serve the inference requests. The endpoint configuration specifies the resources needed for model deployment.

    Step 7: Export the endpoint

    We will export the URL of the managed endpoint, so you know where to send inference requests once the model is deployed.

    Here's what the Pulumi program would look like:

    import pulumi import pulumi_aws as aws # Step 2: Define the VPC resources vpc = aws.ec2.Vpc("sagemaker_vpc", cidr_block="") public_subnet = aws.ec2.Subnet("sagemaker_public_subnet", vpc_id=vpc.id, cidr_block="", map_public_ip_on_launch=True, availability_zone="us-west-2a") # Step 3: Create Security Group sg = aws.ec2.SecurityGroup("sagemaker_sg", vpc_id=vpc.id, description="Allow SageMaker Inbound Traffic") # Allow HTTP and HTTPS ingress within the VPC http_ingress = aws.ec2.SecurityGroupRule("http_ingress", type="ingress", from_port=80, to_port=80, protocol="tcp", cidr_blocks=[vpc.cidr_block], security_group_id=sg.id) https_ingress = aws.ec2.SecurityGroupRule("https_ingress", type="ingress", from_port=443, to_port=443, protocol="tcp", cidr_blocks=[vpc.cidr_block], security_group_id=sg.id) # Step 4: Create the IAM Role sagemaker_role = aws.iam.Role("sagemaker_role", assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "sagemaker.amazonaws.com" }, }], })) # Attach a policy to the role sagemaker_policy_attachment = aws.iam.RolePolicyAttachment("sagemaker_policy_attachment", role=sagemaker_role.name, policy_arn="arn:aws:iam::aws:policy/AmazonSageMakerFullAccess") # Step 5: Create a SageMaker Notebook Instance notebook_instance = aws.sagemaker.NotebookInstance("sagemaker_notebook", instance_type="ml.t2.medium", role_arn=sagemaker_role.arn, security_groups=[sg.id], subnet_id=public_subnet.id) # Step 6: Endpoint Configuration and Deployment (Omitted in this example for simplicity) # Step 7: Export the endpoint pulumi.export("notebook_instance_url", notebook_instance.url)

    In this program, we have created a VPC and associated resources, a security group to manage access, and an IAM role with the required permissions for SageMaker. We then launched a managed SageMaker Notebook Instance where you can develop machine learning models securely within the VPC.

    Please replace "ml.t2.medium" with the SageMaker instance type you wish to use and configure the security group rules (http_ingress and https_ingress) to fit your network requirements (this example opens HTTP/HTTPS traffic within the VPC).

    Keep in mind, this example does not include the creation of SageMaker endpoint for model deployment. Typically, you would create a model, an endpoint configuration, and finally deploy the endpoint which can be invoked for real-time inference.

    This should serve as a solid starting point to setting up endpoints for secure SageMaker access in AWS with Pulumi.