Gatekeeper Constraints for Resource Quotas on AI Jobs

Question

Pulumi · Accepted Answer

Resource Quotas in Kubernetes are a way to impose constraints on the resources that can be consumed by namespaces. This helps you manage the compute resources within a Kubernetes cluster effectively. Pulumi allows you to use Kubernetes resources in code form—this is called Infrastructure as Code (IaC). So, if you want to enforce quotas on your AI jobs running in Kubernetes, you would use a `ResourceQuota` object.

To enforce resource quotas using Gatekeeper constraints, you would define constraints in your cluster and create `ConstraintTemplates` that specify the logic for compliance checking against a specified Rego policy (the language used by Open Policy Agent/Gatekeeper). When you apply a `Constraint`, it will enforce the rules defined in the associated `ConstraintTemplate`. However, defining Gatekeeper constraints goes beyond the scope of simple Kubernetes definitions and involves setting up Gatekeeper in your cluster.

The example below demonstrates how you can define a `ResourceQuota` using Pulumi for a namespace that might contain AI jobs. This `ResourceQuota` limits the number of Pods, ConfigMaps, and the amount of CPU and memory that the namespace can use. It does not directly set up Gatekeeper constraints, but it provides the first step toward managing your resources with Kubernetes. To use Gatekeeper for enforcing quotas, you'll also need to set up Gatekeeper in your cluster and write the appropriate constraint templates and constraints.

Here's how you define a `ResourceQuota` in Python using Pulumi:

```python
import pulumi
import pulumi_kubernetes as kubernetes

# Create a Kubernetes Resource Quota
resource_quota = kubernetes.core.v1.ResourceQuota(
    "ai-jobs-resource-quota",
    metadata=kubernetes.meta.v1.ObjectMetaArgs(
        name="ai-jobs-quota",  # The name of the ResourceQuota
        namespace="ai-jobs-namespace",  # The namespace in which this ResourceQuota will be applied
    ),
    spec=kubernetes.core.v1.ResourceQuotaSpecArgs(
        hard={
            # CPU limit across all pods in the namespace
            "limits.cpu": "20",
            # Memory limit across all pods in the namespace
            "limits.memory": "64Gi",
            # Pod count limit in the namespace
            "pods": "10",
            # ConfigMap count limit in the namespace
            "configmaps": "10",
        }
    )
)

# Export the name of the resource quota
pulumi.export('resource_quota_name', resource_quota.metadata.apply(lambda metadata: metadata.name))
```

This code snippet defines a `ResourceQuota` object in Kubernetes using Pulumi. It specifies limits on the amount of CPU and memory resources the namespace can consume, as well as the number of Pods and ConfigMaps it can have.

By exporting the result of `metadata.name`, we can track the name of the `ResourceQuota` using Pulumi's state management functionalities.

It’s important to keep in mind that this is still a part of the puzzle. You'd need additional configuration and resources for using Gatekeeper to add policy enforcements that use this ResourceQuota in a more nuanced way, like limiting the resources on a per-container basis or according to labels.

For more details on `ResourceQuota` and how you can use it with Pulumi, you can check the [documentation](https://www.pulumi.com/registry/packages/kubernetes/api-docs/core/v1/resourcequota/). If you need to enforce your policies with Gatekeeper, I suggest reading through the [Gatekeeper documentation](https://open-policy-agent.github.io/gatekeeper/website/docs/) and using Pulumi to deploy the necessary `ConstraintTemplates` and `Constraints`.