Deploying AI Model Serving Endpoints on EKS

Question

Pulumi · Accepted Answer

To deploy AI Model Serving Endpoints on Amazon EKS (Elastic Kubernetes Service), we will deploy an EKS Cluster, then configure Kubernetes resources within that cluster to serve the AI model. Here's the general process we'll follow:

1. **Create an EKS Cluster**: We'll create an EKS cluster that will form the foundation of our compute infrastructure where our AI model will be served.
2. **Configure Compute Resources**: We'll define the desired compute resources like CPU, memory, and, if required, GPU for serving the AI model.
3. **Deploy Model Serving Application**: We'll create Kubernetes deployments that will run our model serving application containers. This can be a custom model server or frameworks like TensorFlow Serving, NVIDIA Triton, etc.
4. **Expose Endpoints**: We'll expose the application to the outside world using Kubernetes services or ingress for clients to send inference requests to our AI model.

We will use Pulumi's EKS package to do this, as this abstracts away much of the complexity and lets us define the cluster and its compute resources in a declarative way.

Below is a Pulumi program in Python that sets up an EKS cluster and deploys a placeholder for your AI model serving endpoints. After the cluster is set up, you would use Kubernetes manifests or Helm charts to deploy and manage your AI models within the EKS cluster.

```python
import pulumi
import pulumi_eks as eks

# Create an EKS cluster with default settings.
# 'eks.Cluster()' creates all the necessary resources for an EKS cluster.
# This includes the EKS cluster itself, along with at least one node group.
cluster = eks.Cluster("ai-model-serving-cluster")

# Once the cluster is created, we can obtain the kubeconfig file which is needed to interact with the cluster.
# This kubeconfig will allow us to deploy our model-serving application in the next steps.
kubeconfig = pulumi.Output.secret(cluster.kubeconfig)

# Export the cluster's kubeconfig and endpoint.
pulumi.export("kubeconfig", kubeconfig)
pulumi.export("clusterEndpoint", cluster.core.endpoint)

# For production use, you may need to customize your cluster and node group configurations.
# The 'eks.Cluster()' function in Pulumi has a wide range of options:
# https://www.pulumi.com/registry/packages/eks/api-docs/cluster/

# You can use the kubeconfig to connect to the cluster and then use kubectl to deploy the actual model serving applications.
```

The `eks.Cluster` resource will provision a new EKS cluster and create an associated node group that hosts the compute resources (EC2 instances). This is a minimal starting point. Depending on your model requirements, you would adjust the cluster configuration for things like GPU support, high availability, or specific Kubernetes addons.

To actually serve your AI models, you would need to containerize your AI model into a Docker image, push that image to a container registry like Amazon ECR (Elastic Container Registry), and deploy the serving software onto this EKS cluster using the Kubernetes manifests or Helm charts, which you need to define separately.

The endpoint to interact with the AI model would typically be exposed using a Kubernetes `Service` or `Ingress`, ensuring that the endpoint can be accessed from outside the Kubernetes cluster.

Please replace the placeholder deployment steps with your own model serving code and configurations. You can use the Pulumi `kubeconfig` to manage the Kubernetes resources using tools like `kubectl` or integrate with a CI/CD pipeline.

Keep in mind that managing Kubernetes resources is a complex task, and deploying AI models in production requires careful planning regarding versioning, scaling, and security. Make sure to review guidelines for production readiness before deploying your AI models.