Auto-Scaling GPU Nodes for High-Performance AI on EKS

Question

Pulumi · Accepted Answer

To create of a high-performance computing cluster capable of auto-scaling on AWS Elastic Kubernetes Service (EKS) with GPU support, you will need to set up several components:

1. **EKS Cluster**: The foundational Kubernetes managed cluster provided by AWS.
2. **Node Group**: A group of worker nodes within the EKS cluster. For high-performance AI tasks, you will use GPU-enabled instance types.
3. **Auto-Scaling**: Configuration that allows the node group to automatically scale in response to workload demands.

We'll use Pulumi's `aws` and `eks` packages because they encapsulate common tasks in easy-to-use components. These components handle the underlying details, allowing us to define our infrastructure with less code and complexity.

The following Pulumi program in Python sets up an EKS cluster with an auto-scaling node group that has GPU support:

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

# Create an EKS Cluster.
cluster = eks.Cluster("gpu-cluster",
                      desired_capacity=2,
                      min_size=1,
                      max_size=4,
                      instance_type="p2.xlarge",  # This is a GPU-enabled instance type.
                      node_root_volume_size=10)

# Prints out the kubeconfig and cluster details after deployment.
pulumi.export("kubeconfig", cluster.kubeconfig)
pulumi.export("cluster_name", cluster.eks_cluster.name)
pulumi.export("node_security_group_id", cluster.node_security_group.id)
```

Let's go through the critical points of this program:

- The `eks.Cluster` is a high-level component that encapsulates the creation of an EKS cluster and its associated compute. By using this, we get a fully managed Kubernetes cluster out of the box.
  
- The `desired_capacity`, `min_size`, and `max_size` parameters are critical for auto-scaling. They control the number of worker nodes running in our cluster. The cluster will scale within these bounds automatically based on the workload.
  
- The `instance_type` parameter is where we specify the type of EC2 instance for our nodes. Here, "p2.xlarge" is a GPU-enabled instance type suitable for AI and machine learning workloads.
  
- The `node_root_volume_size` sets the size of the root EBS volume for each EC2 instance in the node group.

In the outputs, we're exporting the `kubeconfig`, which is needed to interact with the Kubernetes cluster using `kubectl` or similar tools, as well as the cluster name and the node security group ID for reference.

### Additional Configuration

You may want to extend the configuration to, for instance, enable specific Kubernetes addons, set up IAM roles for specific services, or define other resources. The Pulumi EKS package can handle these beyond what's shown here.

Lastly, please note this program assumes you have your AWS credentials configured either as environment variables or via other typical AWS SDK configuration methods. Also, you should have the `pulumi`, `pulumi_aws`, and `pulumi_eks` packages installed in your Python environment.

After deploying this infrastructure with Pulumi CLI by running `pulumi up`, your EKS cluster will be ready, and you can start deploying your high-performance AI applications onto the cluster.