Hosting Large Language Models with EKS GPU Instances

Question

Pulumi · Accepted Answer

To host Large Language Models using AWS EKS with GPU instances, we need to set up an EKS (Elastic Kubernetes Service) cluster and configure it with GPU-enabled EC2 instances. AWS provides EC2 instances that cater to applications requiring GPU compute power, like the `p2` or `p3` instance types, which are suitable for machine learning workloads.

Here's what we'll do in this Pulumi program:

1. Set up an EKS cluster using AWS's managed Kubernetes service.
2. Configure a node group with GPU-enabled instance types.
3. Ensure that the required IAM roles and policies are in place for the EKS cluster to manage resources on your behalf.
4. Apply the necessary Kubernetes resources to host the language model, such as Deployments and Services.

Before you get started with the code, ensure that you have the Pulumi AWS SDK installed in your Python environment and that your AWS credentials are configured for use with Pulumi.

Here's how you can host Large Language Models with AWS EKS using GPU instances:

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

# First, we'll create a new VPC for our EKS cluster to avoid clashes with the default VPC
# We set up the VPC with public and private subnets, an internet gateway, route tables, and NAT gateways.
vpc = aws.ec2.Vpc("eks-vpc", cidr_block="10.100.0.0/16", enable_dns_hostnames=True)

public_subnet = aws.ec2.Subnet("eks-public-subnet",
    vpc_id=vpc.id,
    cidr_block="10.100.10.0/24",
    map_public_ip_on_launch=True,
    availability_zone="us-west-2a")

private_subnet = aws.ec2.Subnet("eks-private-subnet",
    vpc_id=vpc.id,
    cidr_block="10.100.20.0/24",
    availability_zone="us-west-2a")

ig = aws.ec2.InternetGateway("eks-ig", vpc_id=vpc.id)

public_route_table = aws.ec2.RouteTable("eks-public-rt",
    vpc_id=vpc.id,
    routes=[
        aws.ec2.RouteTableRouteArgs(
            cidr_block="0.0.0.0/0",
            gateway_id=ig.id,
        ),
    ])

public_route_table_assoc = aws.ec2.RouteTableAssociation("eks-public-rta",
    subnet_id=public_subnet.id,
    route_table_id=public_route_table.id)

# Create an EKS cluster in the VPC.
eks_cluster = eks.Cluster("eks-cluster",
    vpc_id=vpc.id,
    private_subnet_ids=[private_subnet.id],
    instance_type="p2.xlarge",  # Specify a GPU instance type
    desired_capacity=2,          # Specify the number of instances
    min_size=1,
    max_size=3,
    deploy_dashboard=False)      # It's recommended to not deploy the Kubernetes dashboard for security

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

# Note: You might want to load the kubeconfig into your Kubernetes tooling like kubectl or a relevant IDE.
```

This program will create a new EKS cluster with a default node group consisting of `p2.xlarge` instances, which are equipped with GPUs. We've configured the cluster to start with 2 instances and allowed it to scale up to 3 if necessary. We've also omitted the Kubernetes dashboard as it can become a security concern if not properly secured.

After setting up the EKS cluster, you would generally proceed to deploy your Large Language Model as a Kubernetes Deployment, ensuring that you have the necessary Docker image for your model. The specifics of the deployment would depend on your model's architecture and requirements. This might involve setting up appropriate Kubernetes Services, persistent storage, and so on.