Scalable AI Microservices with Stateless EKS Fargate Pods

Question

Pulumi · Accepted Answer

When creating scalable AI microservices using AWS, a good approach is to use Amazon Elastic Kubernetes Service (EKS) with AWS Fargate. EKS provides a managed Kubernetes service where you don't have to worry about the underlying infrastructure, and Fargate allows you to run containers without managing servers or clusters.

With this arrangement, you can focus on building your AI microservices, while AWS takes care of the scaling and infrastructure management. AWS Fargate is especially suitable for stateless applications, as it can quickly scale up or down based on demand.

To get started with deploying stateless AI microservices on EKS using Fargate pods, you need to:

1. Create an EKS cluster.
2. Define a Fargate profile for your cluster.
3. Deploy your containerized applications to the cluster.

Below is a Pulumi program written in Python, which sets up these resources. This program assumes that you have already set up your AWS credentials for Pulumi to use.

Here's the step-by-step Pulumi program to achieve the goal:

```python
import pulumi
import pulumi_aws as aws
import pulumi_awsx as awsx

# Create an EKS cluster.
eks_cluster = awsx.eks.Cluster("ai-microservices-cluster",
    # You can specify additional options here to configure your cluster.
)

# Define a Fargate profile for your EKS cluster.
# It specifies which pods should run on Fargate.
fargate_profile = aws.eks.FargateProfile("ai-microservices-fargate-profile",
    cluster_name=eks_cluster.eks_cluster.name,
    pod_execution_role_arn=eks_cluster.instance_roles[0].arn,  # This role provides AWS permissions to the Fargate pods.
    selectors=[aws.eks.FargateProfileSelectorArgs(
        namespace="default",
        labels={
            "app": "my-ai-app",  # Label that your microservice Deployment/controller will use.
        }
    )],
    # You can specify additional settings here such as subnets.
)

# Deploy your containerized application.
# Here you would specify the image URL for your AI microservice, as well as any required configurations.
app_service = awsx.ecs.FargateService("ai-microservices-app-service",
    cluster=eks_cluster.ecs_cluster.id,
    task_definition_args=awsx.ecs.FargateServiceTaskDefinitionArgs(
        # Define the containers included in the task definition.
        containers={
            "app": awsx.ecs.TaskDefinitionContainerDefinitionArgs(
                image="my-ai-app-image",  # Replace this with your actual Docker image URI.
                memory=512,
                essential=True,
                port_mappings=[awsx.ecs.TaskDefinitionPortMappingArgs(
                    container_port=80,
                    # If needed, you can specify host_port, or leave it out to let ECS choose.
                )],
                # Additional configuration here like environment variables, secrets, etc.
            ),
        },
        # You can further customize CPU, memory, IAM roles, log configuration, etc.
    ),
    desired_count=1,  # Start with 1 task (can scale automatically based on your Fargate setup).
    # Additional networking and service configurations can be defined here if needed.
)

# Export the URL of the load balancer if you have set up one.
# This would typically be done if you want the service to be publicly accessible.
# pulumi.export("app_url", app_service.load_balancers[0].external.load_balancer.dns_name)

# Here, we're presuming that the Fargate service is set to private,
# and you would access your services within the cluster or via a private endpoint.
```

What this code is doing:

- It creates an EKS cluster that will act as the orchestration service for your microservices.
- It sets up a Fargate Profile on that cluster which will specify which pods should run as Fargate tasks.
- It deploys a task definition for your microservice. You would replace `"my-ai-app-image"` with the Docker image URI for your own AI microservice.
- It specifies the desired count for your microservices.

Please ensure that you replace placeholders like `"my-ai-app-image"` with your actual Docker image URI.

As a next step, you would write Kubernetes manifests for your services and deployments and apply those to your cluster. With Pulumi, you can also use the `pulumi_kubernetes` library to manage Kubernetes resources in the same program.

Remember that Pulumi programs are executed with the `pulumi up` command, which will prompt you to confirm the creation of the specified resources. Make sure to review the planned changes before confirming to apply them.