1. Distributed AI Inference using Kubernetes Clusters


    To set up a distributed AI inference using Kubernetes clusters, you would typically start by provisioning Kubernetes clusters where you can deploy your AI models for inference. You might choose a managed Kubernetes service for easier setup and management.

    With Pulumi, you can define your infrastructure as code using programming languages like Python. In this setup, I'll guide you through creating a Kubernetes cluster on AWS using Amazon Elastic Kubernetes Service (EKS), which allows you to run Kubernetes without needing to install, operate, and maintain your own Kubernetes control plane or nodes.

    To get started, you'll need to have Pulumi installed and configured to work with your AWS account. You'll write a Python program using the Pulumi SDK to define the infrastructure.

    Here's a step-by-step explanation of the code you'll write:

    1. Importing the Required Modules: You'll import several Pulumi libraries that are necessary to create an EKS cluster. This includes the Pulumi AWS SDK and the Pulumi EKS module, which provides a higher-level abstraction over the AWS resources needed to create an EKS cluster.

    2. Creating an EKS Cluster: With Pulumi's EKS library, you'll instantiate an EKS cluster by specifying the required parameters such as the desired node count, the instance type for the worker nodes, and the Kubernetes version. The EKS module takes care of creating all the necessary resources like the VPC, IAM roles, security groups, etc.

    3. Exporting the kubeconfig: After the EKS cluster is provisioned, you will export the kubeconfig. This is needed to interact with your Kubernetes cluster using kubectl or other Kubernetes tools.

    Now, let's write the Pulumi Python program to create an EKS cluster:

    import pulumi import pulumi_aws as aws import pulumi_eks as eks # Create an AWS resource (VPC) vpc = aws.ec2.Vpc("vpc", cidr_block="") # Create an EKS cluster with the default configuration cluster = eks.Cluster("cluster", vpc_id=vpc.id, instance_type="t2.medium", desired_capacity=2, min_size=1, max_size=3, deploy_dashboard=False) # Export the kubeconfig to connect to the cluster kubeconfig = cluster.kubeconfig.apply(lambda kc: kc.to_json()) pulumi.export('kubeconfig', kubeconfig) # After this program runs successfully, the output will include a kubeconfig that you can use with kubectl.

    This program starts by importing the modules you need from the Pulumi library. It creates a new VPC for the cluster to ensure that you're working within a clean network space. Then, it creates a new EKS cluster within that VPC. The cluster's worker nodes are configured to use t2.medium instances, and the autoscaling group is set up to allow between one and three instances. The deploy_dashboard is set to False because the Kubernetes dashboard isn't typically advisable for security reasons in production clusters. Finally, the program exports the cluster's kubeconfig, which you'll use to connect to the cluster with kubectl.

    After the cluster is created, you can deploy your AI inference services as Docker containers within the cluster. You'll define Kubernetes deployment and service manifests for this, which can also be managed as code using Pulumi.

    The provided program is a starting point. Depending on your requirements, you might want to customize your VPC, node instance types, number of nodes, and enable various services such as logging, monitoring, and more. You can also integrate other AWS services that you plan to use alongside EKS, like S3 for storage or CloudWatch for logs and metrics.