High-Performance Inference Endpoints on EKS

Question

Pulumi · Accepted Answer

To create high-performance inference endpoints on Amazon EKS (Elastic Kubernetes Service), we are going to go through a step-by-step process using Pulumi with Python. We will set up an EKS cluster, which will be the foundation to deploy any machine learning models as inference endpoints.

Here's what you'll need to do:

1. **Create an EKS Cluster**: This involves setting up the necessary IAM roles for EKS, creating a VPC (if one is not already provisioned), and then creating the EKS cluster itself.

2. **Set Up Inference Endpoints with Kubernetes**: After the cluster is up and running, we can deploy the inference endpoints within our Kubernetes cluster using `kubectl` or configuring them as part of the Pulumi program using the Kubernetes provider.

3. **Optimizing for High Performance**: Since performance is critical for inference endpoints, we will look into some strategies like using GPUs within our node groups, utilizing high-performance compute instances, and employing horizontal pod autoscaling to handle variable loads.

Now, let's begin with the Pulumi program that sets up the infrastructure:

```python
import pulumi
import pulumi_eks as eks

# Create an EKS cluster
cluster = eks.Cluster('high-performance-eks-inference',
    # Request one "t2.medium" instance so that EKS is available in the free tier
    instance_type='t2.medium',
    desired_capacity=1,
    min_size=1,
    max_size=2,
)

# Export the kubeconfig so you can easily access the EKS cluster
kubeconfig = cluster.kubeconfig.apply(lambda c: c)
pulumi.export('kubeconfig', kubeconfig)
```

In the above program, we are importing `pulumi_eks`, which is a convenience package that simplifies the process of creating EKS clusters and can automatically provision all the needed resources like the VPC and IAM roles.

We create a cluster called `high-performance-eks-inference`. This particular configuration specifies `t2.medium` instances for simplicity, but in a high-performance setup, you would choose instances optimized for compute, memory, or GPU capabilities depending on your model's needs.

The `desired_capacity`, `min_size`, and `max_size` arguments in the `eks.Cluster` resource define the autoscaling properties for your cluster's node group (the collection of EC2 instances that run your Kubernetes pods).

We export `kubeconfig`, which is the configuration needed to connect to your cluster with `kubectl`, the Kubernetes command-line tool.

To deploy inference endpoints, you typically use Docker containers with your machine learning model and a web server like Flask or FastAPI that serves the predictions. To facilitate high performance, your Kubernetes Deployment definitions (as well as any Service or Ingress definitions needed to expose your endpoints) would be configured to use node selectors or affinities to ensure they run on the appropriate instances with the necessary hardware. You might also take advantage of Kubernetes features like Horizontal Pod Autoscaler (HPA) to automatically scale your inference endpoints based on demand.

Once your endpoint deployments are created, you can use the K8s provider in Pulumi to manage these directly or manage them outside of Pulumi using `kubectl` with the generated kubeconfig file from the EKS cluster creation.

Remember, this program sets up an EKS cluster which forms the bedrock for deploying your endpoints. The actual deployment of machine learning models is a separate step involving containerizing your model and inference code and deploying it to the cluster using Kubernetes manifests or Helm charts.