Multi-tenant AI Platform Hosting with Amazon EKS

Question

Pulumi · Accepted Answer

To build a multi-tenant AI platform on AWS using Amazon Elastic Kubernetes Service (EKS), we'll need to set up a few components:

1. **Amazon EKS Cluster**: This is the core Kubernetes managed service that runs the Kubernetes control plane across multiple AWS availability zones to ensure high availability.
2. **Amazon Elastic Container Registry (ECR)**: To store and manage our Docker container images.
3. **Worker Nodes**: Typically, you would provision EC2 instances as worker nodes in your EKS cluster. However, for simplicity, I will use the managed node groups provided by EKS, which abstract away some of the complexities.
4. **AWS IAM Role**: IAM Roles are required for EKS and worker nodes to interact with other AWS services securely.
5. **VPC, Subnets, and Security Groups**: These resources are needed to isolate your cluster and define rules for traffic flow.
6. **App Mesh**: This is an optional service mesh that can be used to monitor and control communications between the micro-services in your AI Platform.

For this explanation, I'll walk you through the creation of the EKS Cluster, ECR Repository, and managed node groups. Due to the complexity of a full multi-tenant AI platform setup, I will also assume you've got the container images for running your AI workloads. At the end of this program, you will have a working EKS cluster ready to deploy these images.

First, let's write the Python program using Pulumi:

```python
import pulumi
import pulumi_aws as aws
import pulumi_eks as eks

# Specify the desired EKS cluster version
eks_cluster_version = '1.21'

# Create an AWS IAM role that EKS will use to manage resources on your behalf
eks_role = aws.iam.Role('eksRole',
    assume_role_policy=aws.iam.get_assume_role_policy_document(
        service='eks.amazonaws.com'
    ).json
)

# Attach the necessary AWS managed policies to the IAM role
# These policies allow EKS to manage clusters on your behalf
aws_managed_policy_arns = [
    "arn:aws:iam::aws:policy/AmazonEKSClusterPolicy",
    "arn:aws:iam::aws:policy/AmazonEKSServicePolicy",
]

for policy_arn in aws_managed_policy_arns:
    aws.iam.RolePolicyAttachment(policy_arn.split('/')[-1],
        role=eks_role.name,
        policy_arn=policy_arn
    )

# Create the EKS Cluster
eks_cluster = eks.Cluster('eksCluster',
    role_arn=eks_role.arn,
    version=eks_cluster_version,
    # Enabling default addons can be done using the `createOidcProvider` and other related args.
)

# Create an ECR repository to store your container images
ecr_repository = aws.ecr.Repository('aiPlatformEcrRepo')

# Outputs to use in the next steps of deployment
pulumi.export('eks_cluster_name', eks_cluster.eks_cluster.name)
pulumi.export('ecr_repository_url', ecr_repository.repository_url)
```

Let's break down what the program does:

1. **Define EKS Cluster Version**: This specifies the version of Kubernetes you want EKS to manage. It's important to use a version supported by EKS and compatible with your workloads.
2. **Create an IAM Role**: AWS requires that EKS have an IAM role to interact with other AWS services on your behalf.
3. **Attach IAM Policies**: We attach policies that provide the necessary permissions for the EKS control plane to operate. EKS needs certain permissions to manage resources.
4. **Create an EKS Cluster**: Using the `pulumi_eks` module, we create an EKS cluster with the defined role and version.
5. **Create an ECR Repository**: The repository is where you'll push the Docker images for your AI workloads. ECR is a managed Docker container registry service that simplifies storing, managing, and deploying your container images.
6. **Export Outputs**: Finally, we export the cluster name and ECR repository URL, which you will use to configure your CI/CD system to deploy container images to ECR and workloads on EKS.

To deploy this, the user must have the Pulumi CLI installed, an AWS account, and their AWS credentials configured.

After deployment, the next steps would include setting up Kubernetes configurations (like namespaces and policies for multi-tenancy), deploying your AI applications, and configuring them to use services like RDS (for databases) or S3 (for storage). You may also want to add monitoring and logging by integrating with services like CloudWatch or Prometheus and Grafana.