Auto-scaling EKS Clusters for AI Model Training Workloads

Question

Pulumi · Accepted Answer

Auto-scaling EKS Clusters are valuable for AI model training workloads as they can elastically scale to meet the demands of the high computational tasks often needed. To set up an auto-scaling EKS cluster, you need a few components in place:

1. An EKS cluster: This is the backbone of your Kubernetes environment on AWS. You define the version of Kubernetes you want to use, the VPC where it will run, subnets, and security groups.

2. Node Groups: They contain the actual EC2 instances where your Kubernetes workloads run. Node groups can auto-scale by defining the minimum, maximum, and desired number of worker nodes.

3. An IAM role: This allows EKS to make calls to AWS services on your behalf.

4. Auto Scaling Group Tags: This can work in conjunction with the Kubernetes Cluster Autoscaler, which is a tool that automatically adjusts the size of the Kubernetes cluster when one of the following conditions is true: there are pods that failed to run in the cluster due to insufficient resources or there are nodes in the cluster that have been underutilized for an extended period of time and their pods can be placed on other existing nodes.

Below is a Pulumi program that provisions an EKS cluster with an auto-scaling node group for AI model training workloads. The node group is configured to use GPU instances since AI model training is typically GPU-intensive.

Please make sure you have Pulumi installed and AWS CLI configured with access and secret keys. Here's how the Pulumi program looks like:

```python
import pulumi
import pulumi_eks as eks

# Create an EKS cluster with the desired configuration.
cluster = eks.Cluster('ai-model-training-cluster',
    min_size=2,  # Minimum size of the cluster
    max_size=10, # Maximum size of the cluster - it will auto-scale up to this number of nodes
    desired_capacity=4, # Desired capacity of the cluster at start
    instance_type='p2.xlarge', # GPU instance type for AI model training workloads
)

# Create a node group with autoscaling enabled to join the above cluster.
# This autoscaling node group can be dynamically scaled and is optimized for GPU workloads.
node_group = eks.NodeGroup('ai-model-training-nodes',
    cluster=cluster.core, # Reference to the created EKS cluster
    min_size=2,  # Minimum size of the node group
    max_size=10, # Maximum size of the node group - it will auto-scale up to this number of nodes
    desired_capacity=4, # Desired initial capacity of the node group
    instance_type='p2.xlarge', # GPU instance type for AI model training workloads
    labels={'workload-type': 'ai-training'}, # Additional labels which can help in scheduling AI workloads
)

# Export the cluster's kubeconfig and the name of the node group.
pulumi.export('kubeconfig', cluster.kubeconfig)
pulumi.export('node_group', node_group)
```

In the above Pulumi program:

- We define a new EKS cluster with a minimum size of 2 worker nodes and a maximum size of 10. The instance type used here is `p2.xlarge`, which is suitable for GPU-based workloads.
- We also create an auto-scaling node group that is attached to our EKS cluster. This node group also specifies GPU instance types and is labeled with `workload-type: ai-training`, which allows for easier workload management.
- Lastly, we export the `kubeconfig` of the EKS cluster and the name of the node group created. This allows for easy access to the Kubernetes cluster for deployment and management of containerized applications.

Remember, managing Kubernetes clusters and nodes requires an understanding of your particular workload needs to size your cluster and select your instances appropriately. The Kubernetes Cluster Autoscaler can only make automatic scaling decisions based on the resource requests/limits specified by your deployed pods.

When you run this Pulumi program, it will spin up the necessary resources on AWS for your EKS Cluster with auto-scaling enabled, suitable for AI Model Training Workloads. Make sure to adjust the instance types and scaling parameters according to the needs of your workload and the AWS resources you have available.