Scalable ML Model Serving with Amazon EKS

Question

Pulumi · Accepted Answer

To serve a scalable Machine Learning (ML) model using Amazon Elastic Kubernetes Service (EKS), you'll need a few components:

1. An Amazon EKS cluster to run your Kubernetes pods.
2. An Amazon Elastic Container Registry (ECR) to store your container images.
3. Optionally, AWS App Mesh to manage network traffic for your microservices.
4. An ML model containerized in a Docker image and pushed to ECR.

In the Pulumi program below, I'm going to create an EKS cluster and an ECR repository. You'll need to have Docker installed on your machine for building and pushing the container image to the registry.

Here is a journey through the code:

- Import the necessary Pulumi SDKs.
- Create an ECR repository where your ML model's Docker image will be stored.
- Create an EKS cluster which will be used to deploy and manage the ML model services.
- (Optional) Configure AWS App Mesh for service mesh capabilities which can be beneficial for complex microservice architectures.

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

# Create an Amazon ECR Repository to store your ML model images.
ml_model_repository = aws.ecr.Repository("mlModelRepository")

# Output the ECR repository URL which will be used for pushing the Docker image.
pulumi.export("repository_url", ml_model_repository.repository_url)

# Define the EKS cluster where the ML model services will be deployed.
# The default node group configuration is specified here. Adjust the `min_size` and `max_size`
# to set the scaling parameters for the cluster to your requirements.
ml_eks_cluster = eks.Cluster("mlEksCluster",
  skip_default_node_group=True,
  deploy_dashboard=False
)

# Create a node group for the EKS cluster with specific instance types and scaling configuration.
# Taints and labels are crucial for segregating workloads and can be adjusted to suit the needs of the ML model serving.
ml_node_group = eks.NodeGroup("mlNodeGroup",
    cluster=ml_eks_cluster.core.cluster.name,
    node_group_args=eks.NodeGroupArgs(
        desired_capacity=2,
        min_size=1,
        max_size=4,
        instance_type="m5.large",  # Example instance type, choose based on your model's resource requirements.
        labels={
            "workload-type": "ml-model-serving"
        },
        taints={
            "dedicated": eks.TaintArgs(
                value="mlModelServing",
                effect="NO_SCHEDULE"
            )
        }
    )
)

# Output the kubeconfig to access the EKS cluster.
# IMPORTANT: For security reasons, you should not expose the kubeconfig in production environments.
kubeconfig = pulumi.Output.all(ml_eks_cluster.kubeconfig).apply(lambda kc: kc[0])
pulumi.export("kubeconfig", kubeconfig)

# If needed, the following could be the App Mesh configuration steps.
# This has been omitted for brevity as it is an optional component and dependent on the use-case.
```

### Important Points:

- Update the node group instance types (`instance_type`) and scaling configurations (`desired_capacity`, `min_size`, and `max_size`) based on the computational needs of your ML model.
- Add your container images to the ECR repo created. These images will hold your ML models that have been containerized.
- Update your EKS cluster deployment with additional configurations such as VPC and subnets if needed.
- Ensure you install `pulumi`, `pulumi_aws`, and `pulumi_eks` Python packages for this Pulumi program to run.
- In the provided Pulumi program, the kubeconfig output is for accessing the EKS cluster, but in a production scenario, you should handle this securely.

Once the infrastructure is set up using Pulumi, you will deploy your ML models using Kubernetes manifests or Helm charts that pull the model's image from the created ECR repository and run on the provisioned EKS cluster. You can scale the ML workloads by configuring the autoscaling features of the EKS node group based on the ML model serving demand.