Scalable ML Model Serving with AWS EKS

Question

Pulumi · Accepted Answer

In order to serve a Machine Learning (ML) model scalably using Amazon Web Services (AWS), one of the best approaches is to deploy the model on an Elastic Kubernetes Service (EKS) cluster. AWS EKS is a managed service that lets you run Kubernetes on AWS without needing to install, operate, and maintain your own Kubernetes control plane or nodes. It is highly scalable and secure, which makes it suitable for ML workloads.

Below is a breakdown of the process we will follow to deploy a scalable ML model on AWS EKS using Pulumi:

1. **EKS Cluster Creation**: We will create an EKS cluster that will be the foundation for running our Kubernetes services. This will include the control plane and worker nodes responsible for running our applications.

2. **Node Group Configuration**: To ensure our cluster can scale, we'll configure node groups. Node groups are a set of workers that have the same configuration which can scale up and down based on demand.

3. **ECR Repository**: Although not explicitly mentioned in the query, for an ML model, you would typically need a place to store your container images. AWS Elastic Container Registry (ECR) is a fully-managed Docker container registry provided by AWS that makes it easy for developers to store, manage, and deploy Docker container images.

Here is the Pulumi code to create the EKS cluster and the necessary components for serving an ML model:

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

# Create an EKS cluster with the desired properties
cluster = eks.Cluster('ml-model-cluster',
    desired_capacity=2,
    min_size=1,
    max_size=4,
    instance_type='m5.large',  # m5.large is a good general-purpose instance with balance of compute, memory, and networking resources.
    # Allocate GPUs if your ML model benefits from it
    node_group_options=eks.NodeGroupOptionsArgs(
        gpu=True,
        instance_type='p2.xlarge'  # GPU instances are good for machine learning workloads.
    ))

# Creating an AWS ECR repository to store our ML model's container images.
# It's essential to use an AWS ECR repository if we are planning to use AWS services like EKS to manage our ML model.
repo = aws.ecr.Repository('ml-model-repo',
    image_scanning_configuration=aws.ecr.RepositoryImageScanningConfigurationArgs(
        scan_on_push=True
    ))

# Exporting the repository URL to be used in our CI/CD system for image push
pulumi.export('repository_url', repo.repository_url)

# Exporting the cluster name and kubeconfig to use in kubectl or other CI/CD systems
pulumi.export('cluster_name', cluster.eks_cluster.name)
pulumi.export('kubeconfig', cluster.kubeconfig)
```

This Pulumi program performs the following actions:

- **eks.Cluster**: Creates an EKS cluster to serve as the hosting environment for the ML model. The `desired_capacity`, `min_size`, and `max_size` parameters are set to determine the initial number and range of instances within our node group. The instance type is set to `m5.large` for a balanced general-purpose machine, and a separate GPU-enabled instance type `p2.xlarge` is specified for ML workloads that would benefit from the acceleration.
  
  [More about eks.Cluster](https://www.pulumi.com/registry/packages/eks/api-docs/cluster/)

- **aws.ecr.Repository**: This block defines the container registry where we will store our ML model's images. Enabling `scan_on_push` ensures that any image pushed to the repository will be scanned for vulnerabilities, which is a best practice for container security.
  
  [More about aws.ecr.Repository](https://www.pulumi.com/registry/packages/aws/api-docs/ecr/repository/)

- **pulumi.export**: This statement is used to output certain values like the EKS cluster name, the kubeconfig, and the ECR repository URL that will be needed to manage the cluster or push images post-deployment.

To run this Pulumi program, save the file with a `.py` extension, ensure you have the AWS CLI configured with the required permissions, and then run `pulumi up`. Pulumi will handle the provisioning and configuration of the resources defined above.

Keep in mind that ML serving often requires additional considerations like setting up a serving layer (like TensorFlow Serving or TorchServe), handling ingress traffic with load balancers, setting auto-scaling policies based on request load, and managing ML pipelines. These aspects go beyond infrastructure provisioning and into the application deployment domain, which would influence the Kubernetes manifests you apply to the cluster.