Scalable AI Feature Store with Milvus on Kubernetes

Question

Pulumi · Accepted Answer

Creating a scalable AI feature store typically involves creating a vector database that allows you to store, index, and search through high-dimensional feature vectors efficiently. Milvus is an open-source vector database that is built for scalable similarity search and AI applications. To deploy Milvus on Kubernetes, you'll need to set up a Kubernetes cluster and deploy the necessary resources for Milvus to run.

Below is an example of a Pulumi program that deploys Milvus on a Kubernetes cluster using Pulumi with the Kubernetes SDK. In this Python program, we'll set up the necessary configurations for the Kubernetes cluster and deploy Milvus using a Helm chart.

We'll take the following steps:

Import the necessary Pulumi modules for Kubernetes support.
Create a Kubernetes cluster using a chosen provider (AWS EKS, Azure AKS, Google GKE, or any other supported Kubernetes Cluster).
Use the Helm chart for Milvus to deploy it on the Kubernetes cluster.

Let's begin by configuring a Kubernetes cluster. In this example, we'll assume you're using AWS EKS as the provider for simplicity, but the general steps should be adaptable to other cloud providers with changes to the specific cluster creation steps.

Here's a Pulumi program in Python that creates the AI Feature Store with Milvus on Kubernetes:

import pulumi
import pulumi_aws as aws
import pulumi_kubernetes as kubernetes
from pulumi_kubernetes.helm.v3 import Chart, ChartOpts

# Configure the AWS provider using the default configuration options
aws_provider = aws.Provider("aws", region="us-west-2")

# Create an EKS cluster to host our Milvus deployment.
# Note: Configuration here is minimal for demonstration purposes.
# You'll typically adjust this according to your particular cluster requirements.
eks_cluster = aws.eks.Cluster("milvus-eks-cluster",
    role_arn="<Your-EKS-Cluster-Role-ARN>",
    vpc_config=aws.eks.ClusterVpcConfigArgs(
        public_access_cidrs=["0.0.0.0/0"]
    ))

# Declare the Kubernetes provider instance using the cluster details from EKS
k8s_provider = kubernetes.Provider("k8s-provider",
    kubeconfig=eks_cluster.kubeconfig)

# Deploy Milvus using Helm Chart
# The Helm chart for Milvus is available in the official helm chart repository.
# Here we install Milvus with some custom values to make it more scalable and suitable for production use.
milvus_chart = Chart(
    "milvus",
    ChartOpts(
        chart="milvus",
        version="2.1.2",  # specify the exact chart version you need
        fetch_opts=kubernetes.helm.v3.FetchOpts(
            repo="https://milvus-io.github.io/milvus-helm/"
        ),
        values={
            "cluster": {
                "enabled": True  # Turn on clustering for high availability and scalability
            },
            "persistence": {
                "storageClass": "standard",  # Modify to match the appropriate storage class
                "enabled": True,
                "size": "50Gi",  # Example size, adjust as needed
            },
            # Include any additional customizations required for your deployment
        },
    ),
    opts=pulumi.ResourceOptions(provider=k8s_provider)
)

# Export the cluster name and kubeconfig to be easily accessed if needed
pulumi.export("cluster_name", eks_cluster.name)
pulumi.export("kubeconfig", eks_cluster.kubeconfig)

In the example above:

We set up an AWS EKS cluster and configure it to be publicly accessible.
We define the Kubernetes provider to interact with the EKS cluster using the generated kubeconfig.
We use the Helm chart for Milvus to deploy it in our EKS cluster. The Milvus Helm chart is fetched from its official Helm chart repository.
We export the cluster name and kubeconfig for potential use elsewhere (such as to configure access to the Kubernetes dashboard).

Remember to replace <Your-EKS-Cluster-Role-ARN> with the appropriate AWS IAM role ARN that EKS can assume for managing Kubernetes resources. Moreover, adjust the values under the milvus_chart as per the documentation and your requirements for storage classes, persistence options, resource requests, and limits for various Milvus components.

Before running this Pulumi program, ensure that the respective provider's CLI tool is installed and configured on your system (in this case, aws and kubectl). The program assumes that you have already set up your cloud credentials appropriately. To run the program, use the Pulumi CLI to create a new Pulumi stack and run pulumi up. The CLI will guide you through the process of deploying the resources specified by the program.

Keep in mind that this is a simplified example to get you started. Based on your specific needs, you may need to customize the Kubernetes resources and the Milvus Helm chart configuration further. Always refer to the official documentation for the cloud provider and Milvus Helm chart for more detailed instructions and best practices.