Scalable BERT Model Deployments on AWS EKS

Question

Pulumi · Accepted Answer

Deploying a scalable BERT (Bidirectional Encoder Representations from Transformers) model on AWS EKS (Elastic Kubernetes Service) involves creating and configuring an EKS cluster, potentially with GPU support for machine learning tasks. This setup would allow you to leverage Kubernetes to manage the deployment, scaling, and operations of your BERT model containers.

Here’s a step-by-step guide to create the necessary infrastructure using Pulumi with Python:

1. **Setting up the EKS Cluster:** You’ll need to create an EKS cluster, which is a managed Kubernetes service. You have the option to enable GPU support on your worker nodes, which is beneficial for machine learning workloads. We'll use the `eks.Cluster` resource for creating our EKS cluster.
2. **Configuring Node Groups:** Define the instance types and desired capacity for your node groups. For machine learning workloads, you'd prefer instances that are optimized for compute-intensive tasks and perhaps with GPU support, like the `p2` or `p3` instance types.
3. **Deploying the BERT Model:** Once the cluster is set up, you would containerize your BERT model and deploy it to the EKS cluster. This step typically involves Docker and Kubernetes manifests but is not directly handled by Pulumi.
4. **Autoscaling:** To handle varying loads, you might want to set up Kubernetes autoscaler, which will automatically adjust the number of pods running based on the demand.

Let's write the Pulumi program to set up the EKS cluster:

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

# Create an AWS EKS Cluster with GPU support enabled
cluster = eks.Cluster("bert-eks-cluster",
    desired_capacity=2,
    min_size=1,
    max_size=3,
    instance_type="p3.2xlarge", # This is a GPU-optimized instance type for machine learning workloads.
    # Enable GPU support
    node_group_options=eks.ClusterNodeGroupOptionsArgs(
        gpu=True,
    )
)

# Export the cluster's kubeconfig and the cluster name
pulumi.export('kubeconfig', cluster.kubeconfig)
pulumi.export('cluster_name', cluster.core.cluster.name)
```

In this program:

- We import the required Pulumi modules for AWS and EKS.
- We create an EKS cluster with the `eks.Cluster` resource with a minimum size of 1, desired capacity of 2, and a maximum size of 3 worker nodes.
- We are opting for the `p3.2xlarge` instances, which are well-suited for GPU-accelerated machine learning tasks. This is specified in the `instance_type` field.
- We enable GPU support on the node group through the `node_group_options`, setting `gpu` to `True`.
- After the cluster is created, we export the `kubeconfig` which is necessary to interact with the Kubernetes cluster using kubectl and other tools. We also export the cluster name for reference.

Please note that while we have defined the infrastructure to host the BERT model on EKS, the actual application deployment (containerizing the BERT model, writing Kubernetes manifests, and deploying them to the cluster) involves steps beyond the scope of infrastructure definition and would be done after this infrastructure is provisioned.

Also, review [Amazon EKS pricing](https://aws.amazon.com/eks/pricing/) and instance pricing for `p3` instances or your selected instance type. Make sure you understand the cost implications of running GPU instances before provisioning them.

You can run this Pulumi program by saving it to a Python file (e.g., `eks_bert.py`) and executing it with the Pulumi CLI. Make sure to have your AWS credentials configured and Pulumi installed on your machine. After you’ve set up your EKS cluster, you can start the processes of containerizing your BERT model and deploying it using Kubernetes tooling.