Rapid Prototyping of AI Environments with Karpenter

Question

Pulumi · Accepted Answer

To rapidly prototype AI environments with Karpenter on AWS, you'll likely want to start by setting up a Kubernetes (EKS) cluster where you can deploy machine learning workloads. Karpenter serves as a Kubernetes-native autoscaling solution that works with Amazon EKS to automatically provision new nodes in response to workload demand.

Below is how you would do this:

1. **Create an EKS Cluster**: You start by creating an EKS cluster. This will be the foundation of your environment where your AI workloads will run.

2. **Install Karpenter**: Once the EKS cluster is up and running, you install Karpenter into the cluster. Karpenter will observe the workloads on your cluster and make decisions about scaling up and down the compute resources based on demand.

3. **Provision a Karpenter Node**: After Karpenter is installed, you provision a node that Karpenter can use. This is typically done through defining `Provisioner` specifications that inform Karpenter how to launch and terminate nodes in response to cluster events.

4. **Deploy Workloads**: Finally, you deploy your AI workloads onto the EKS cluster. Karpenter will watch for unscheduled pods and quickly provision nodes optimized for cost and performance.

Here's an example Pulumi program that sets up an EKS cluster with Karpenter for rapid prototyping of an AI environment:

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

# First, we create an EKS cluster where our workloads will run.
cluster = eks.Cluster("ai-cluster")

# Install Karpenter Helm chart onto the EKS cluster.
# We use the EKS cluster's kubeconfig to interact with the cluster.
# Note that you will need to have the Helm chart available or configure it to be fetched from a repository.
karpenter_chart = aws.helm.v3.Chart("karpenter",
    args=aws.helm.v3.ChartArgs(
        chart="karpenter",
        version="x.y.z", # Replace with the specific version you want to install.
        namespace="kube-system",
        fetch_opts=aws.helm.v3.FetchOpts(
            repo="https://charts.karpenter.sh",
        ),
    ),
    opts=pulumi.ResourceOptions(provider=cluster.provider),
)

# Define the Karpenter Provisioner, which will tell Karpenter how to launch nodes
# based on your AI workloads needs.
karpenter_provisioner = aws.eks.addon.KarpenterProvisioner("ai-provisioner",
    cluster_name=cluster.core.cluster.name,
    provisioner_specs=aws.eks.addon.KarpenterProvisionerSpecArgs(
        # Define your Provisioner spec here, such as instance types, zones, capacity type, etc.
    ),
    opts=pulumi.ResourceOptions(depends_on=[karpenter_chart]),
)

# Export relevant details of the infrastructure
pulumi.export("cluster_name", cluster.core.cluster.name)
pulumi.export("kubeconfig", cluster.kubeconfig)
```

This program creates an EKS cluster and installs Karpenter on it. Please note the `version` in `ChartArgs` needs to be specified with the version of Karpenter you wish to install.

Once the EKS cluster and Karpenter are ready, you can deploy your AI or machine learning workloads on the cluster, and Karpenter will automatically provision and manage compute resources based on the demand of your workloads.

Remember this program does not directly deploy any specific AI workloads. You would add another section to the Pulumi program that uses Kubernetes resources to deploy your specific applications.

Also, keep in mind that for a real-world setup, you would need to configure additional specifics, such as VPCs, IAM roles, security groups, and more, depending on your requirements and the best practices for running secure and efficient workloads on AWS.