1. AI Model Serving and Inference Scaling with AWX on Kubernetes


    If you're looking to serve AI models and manage inference scaling using AWX (Ansible for Kubernetes) on a Kubernetes cluster, you can use Pulumi to deploy your Kubernetes infrastructure, including the setup for AWX. Before diving into the Pulumi code, let's outline the steps typically involved in this process:

    1. Set up a Kubernetes Cluster: You need a cluster where you can schedule the AI model-serving workloads. This can be done on any cloud provider like AWS, Azure, or GCP, or on-premise with a tool like kubeadm or Minikube for local development.

    2. Install AWX: AWX is the open-source version of Ansible Tower that provides a web-based user interface, REST API, and task engine for Ansible. It's commonly run inside a Kubernetes cluster where it can manage the deployment and scaling of applications.

    3. Deploy Your AI Models: You would typically containerize your AI models as Docker images and then create Kubernetes deployments to run these images.

    4. Set up Autoscaling: For inference scaling, you'll want to set up horizontal pod autoscaling in Kubernetes based on metrics like CPU utilization or custom metrics.

    5. Create a CI/CD Pipeline: To automate the deployment and scaling process, you could create a CI/CD pipeline that integrates with AWX. This way, you can automatically roll out new versions of the AI models or modify the scaling parameters.

    Pulumi can be used to define the infrastructure code needed to set up parts 1, 3, and 4 of this process. For part 2, while Pulumi can be used to deploy AWX on the Kubernetes cluster, the setup of AWX itself might involve additional steps that are beyond the scope of Pulumi, such as configuring AWX after installation.

    Below is a Pulumi Python program for setting up a basic Kubernetes cluster with AWS Elastic Kubernetes Service (EKS). This example does not cover the full infrastructure needed for AI model serving and scaling but provides a starting point:

    import pulumi import pulumi_aws as aws import pulumi_eks as eks # Create an EKS cluster to serve as the foundation for our AI workloads. cluster = eks.Cluster( "ai-model-serving-cluster", # Specify the desired number of cluster nodes; this can be adjusted as needed. desired_capacity=2, min_size=1, max_size=3, # Select the instance type based on the needs of the AI workloads. instance_type="t3.medium", ) # Exposing the EKS cluster's kubeconfig. pulumi.export('kubeconfig', cluster.kubeconfig) # Note: Deploying AI models and setting up inference scaling with AWX is not included in this code snippet. # After the creation of the EKS cluster, you would typically: # 1. Set up AWX in the cluster (this step requires further actions post-Pulumi deployment). # 2. Define Kubernetes Deployments for your AI models in Docker containers. # 3. Create Kubernetes HorizontalPodAutoscaler resources to scale your AI model deployments # based on metrics like CPU or custom metrics provided by your workload.

    This Python program creates an AWS EKS cluster that you can later use to deploy your AI models via Kubernetes deployments and use AWX for automation.

    Please note that serving AI models and inference scaling will require you to have Docker images of your AI models and further Kubernetes resources like deployments, services, ingresses, and horizontal pod autoscalers for scaling. Once the cluster is ready, you can deploy these additional resources to the cluster using Pulumi or kubectl.

    For set up and management of AWX on Kubernetes, you will need to follow the official AWX operator documentation, which provides details on how to deploy AWX in a Kubernetes cluster.

    Remember to tailor the instance types and scaling options based on the requirements of your AI model workloads, as CPU and memory needs may vary significantly depending on the complexity and size of your models.