1. Scalable Inference Serving with Kubernetes and AWS VPC


    In order to set up a scalable inference serving system using Kubernetes within an AWS Virtual Private Cloud (VPC), we’ll create a Pulumi program in Python that outlines the required cloud infrastructure. The setup will generally involve:

    1. Creating an AWS VPC: A VPC is a virtual network dedicated to your AWS account. It is logically isolated from other virtual networks in the AWS Cloud. Within the VPC, you can launch AWS resources, such as Kubernetes pods for your inference service.

    2. Setting up an Amazon EKS Cluster: Amazon Elastic Kubernetes Service (EKS) is a managed Kubernetes service that makes it easy to deploy, manage, and scale containerized applications using Kubernetes on AWS.

    3. Deploying Inference Services within the EKS Cluster: After the Amazon EKS cluster is in place, you can deploy your machine learning models wrapped in inference services through Kubernetes deployments.

    Below is the Python program for Pulumi that sets up the VPC and an Amazon EKS cluster suitable for serving inferences at scale.

    import pulumi import pulumi_aws as aws # Create an AWS VPC for hosting the infrastructure vpc = aws.ec2.Vpc("app-vpc", cidr_block="", enable_dns_hostnames=True, enable_dns_support=True, tags={ "Name": "app-vpc", }) # Create an Internet Gateway for the VPC igw = aws.ec2.InternetGateway("app-igw", vpc_id=vpc.id, tags={ "Name": "app-igw", }) # Create a public subnet within the VPC public_subnet = aws.ec2.Subnet("app-public-subnet", vpc_id=vpc.id, cidr_block="", availability_zone="us-west-2a", map_public_ip_on_launch=True, tags={ "Name": "app-public-subnet", }) # Create a Security Group to allow traffic to the cluster eks_security_group = aws.ec2.SecurityGroup("eks-cluster-sg", vpc_id=vpc.id, description="Allow all HTTP(s) traffic to EKS Cluster", ingress=[ {"protocol": "-1", "from_port": 0, "to_port": 0, "cidr_blocks": [""]}, ], egress=[ {"protocol": "-1", "from_port": 0, "to_port": 0, "cidr_blocks": [""]}, ]) # Create an EKS Cluster within the VPC eks_cluster = aws.eks.Cluster("app-eks-cluster", role_arn=eks_role.arn, # Requires an existing role ARN with EKS permissions vpc_config={ "security_group_ids": [eks_security_group.id], "subnet_ids": [public_subnet.id], }) # Register the Internet Gateway with the subnet route_table = aws.ec2.RouteTable("app-route-table", vpc_id=vpc.id, routes=[{ "cidr_block": "", "gateway_id": igw.id, }], tags={ "Name": "app-route-table", }) # Associate the public subnet with the route table to enable public Internet access route_table_association = aws.ec2.RouteTableAssociation("app-rta", route_table_id=route_table.id, subnet_id=public_subnet.id) # The following code would instantiate the necessary IAM role for the EKS cluster. # This is a basic example and, in a production environment, the policy should be more restrictive. eks_role = aws.iam.Role("eks-cluster-role", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": { "Service": "eks.amazonaws.com" }, "Action": "sts:AssumeRole" }] }""") # Attaching the Amazon EKS cluster policy to the IAM role role_policy_attachment = aws.iam.RolePolicyAttachment("eks-cluster-policy-attachment", role=eks_role.name, policy_arn="arn:aws:iam::aws:policy/AmazonEKSClusterPolicy") # Exporting the cluster endpoint for connecting to your EKS cluster. pulumi.export('eks_cluster_endpoint', eks_cluster.endpoint)

    This program sets up a basic VPC, subnet, internet gateway, and security group needed to host the EKS cluster. Note that you will need to supply an existing IAM role ARN with Amazon EKS permissions in the eks_cluster resource; this is indicated by # Requires an existing role ARN with EKS permissions. In a real-world scenario, you would need to create an IAM role with the appropriate trust relationship and permissions for your EKS cluster. After creating the VPC and security groups, an Amazon EKS cluster is provisioned. The cluster endpoint is exported at the end, which will be used for connecting to your EKS cluster.

    To deploy inference services into the EKS cluster, subsequent Kubernetes manifests or Helm charts would be required, specifying the deployment, services, and possibly autoscaling configurations for the inference services.

    Keep in mind, this is a starting point for deploying a scalable inference system on AWS using Kubernetes and Pulumi. Depending on your specific requirements, you might need to refine the network architecture, IAM permissions, and Kubernetes deployment strategies.