Ensuring Fault Tolerance of Large Language Models on Kubernetes

Question

Pulumi · Accepted Answer

Fault tolerance in a Kubernetes context often involves a few key concepts and resources:

1. **Pod Disruption Budgets (PDBs):** A PDB is a Kubernetes resource that limits the number of Pods of a replicated application that is down simultaneously from voluntary disruptions.

2. **Replica Sets/Deployments:** To ensure high availability, you would usually deploy more than one replica of your pod.

3. **Persistent Storage:** If your models or data need to persist across pod restarts, you would use a PersistentVolume (PV) and a PersistentVolumeClaim (PVC).

4. **Readiness and Liveness Probes:** These are configured for your containers to allow Kubernetes to manage the life cycle of the pods effectively, restarting them if they're not healthy.

5. **Horizontal Pod Autoscaler (HPA):** It automatically scales the number of pods in a replication controller, deployment, replica set or stateful set based on observed CPU/memory utilization.

6. **Node Pools:** Node pools allow having a section within your Kubernetes cluster with different configurations, which might be more suited to certain workloads.

Let's assume you want to deploy a machine learning application responsible for serving a large language model inference API. Your Kubernetes deployment could make use of several replica pods to balance load and ensure high availability. Pods would connect to persistent storage to access the model data and would be monitored via liveness and readiness checks to ensure smooth operation. An HPA would allow the deployment to accommodate varying loads.

Here's a Pulumi program using Python that creates these resources to ensure fault tolerance:

```python
import pulumi
import pulumi_kubernetes as k8s

# The name of your deployment. This will be used as a base for other resources.
app_name = "language-model"

# Define the Pod Disruption Budget to limit the number of Pods that goes down simultaneously.
pdb = k8s.policy.v1beta1.PodDisruptionBudget(
    "pdb",
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name=f"{app_name}-pdb",
    ),
    spec=k8s.policy.v1beta1.PodDisruptionBudgetSpecArgs(
        min_available=1, # Ensure at least one Pod is available during voluntary disruptions.
        selector=k8s.meta.v1.LabelSelectorArgs(
            match_labels={"app": app_name},
        ),
    )
)

# Define the Deployment for the Language Model Service
deployment = k8s.apps.v1.Deployment(
    "deployment",
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name=app_name,
    ),
    spec=k8s.apps.v1.DeploymentSpecArgs(
        replicas=3, # Start with three replicas for availability.
        selector=k8s.meta.v1.LabelSelectorArgs(
            match_labels={"app": app_name},
        ),
        template=k8s.core.v1.PodTemplateSpecArgs(
            metadata=k8s.meta.v1.ObjectMetaArgs(
                labels={"app": app_name},
            ),
            spec=k8s.core.v1.PodSpecArgs(
                containers=[
                    k8s.core.v1.ContainerArgs(
                        name="model-serving-container",
                        image="your-model-serving-image:latest", # Replace with your actual image
                        ports=[k8s.core.v1.ContainerPortArgs(container_port=80)],
                        readiness_probe=k8s.core.v1.ProbeArgs(
                            http_get=k8s.core.v1.HTTPGetActionArgs(
                                path="/healthz",
                                port=80,
                            ),
                            initial_delay_seconds=5,
                            timeout_seconds=3,
                        ),
                        liveness_probe=k8s.core.v1.ProbeArgs(
                            http_get=k8s.core.v1.HTTPGetActionArgs(
                                path="/healthz",
                                port=80,
                            ),
                            initial_delay_seconds=15,
                            timeout_seconds=3,
                        ),
                    ),
                ],
            ),
        ),
    )
)

# Create a Horizontal Pod Autoscaler to automatically scale the deployment based on CPU usage.
hpa = k8s.autoscaling.v1.HorizontalPodAutoscaler(
    "hpa",
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name=f"{app_name}-hpa",
    ),
    spec=k8s.autoscaling.v1.HorizontalPodAutoscalerSpecArgs(
        scale_target_ref=k8s.autoscaling.v1.CrossVersionObjectReferenceArgs(
            kind="Deployment",
            name=app_name,
            api_version="apps/v1",
        ),
        min_replicas=2,
        max_replicas=10,
        target_cpu_utilization_percentage=80,
    )
)

# Export the name of the deployment
pulumi.export("deployment_name", deployment.metadata["name"])
# Export the name of the Pod Disruption Budget
pulumi.export("pdb_name", pdb.metadata["name"])
# Export the name of the Horizontal Pod Autoscaler
pulumi.export("hpa_name", hpa.metadata["name"])
```

In this Pulumi program, we've declared a `PodDisruptionBudget` to ensure minimal downtime, a `Deployment` that includes a readiness and liveness probe to help Kubernetes know when the containers are really ready to serve requests and when they are not healthy. The `HorizontalPodAutoscaler` is set up to scale our deployment based on CPU usage. We are not including PersistentVolumes here because large models typically don't change often and may be bundled with the container or fetched from an object storage upon startup. However, if you need to use persistent storage, you should incorporate `PersistentVolume` and `PersistentVolumeClaim` into your deployment.

You would also need to supply your own container image for model serving (`your-model-serving-image:latest`) which would be built separately from this.

To use this program, you would need to have Pulumi installed and configured with access to a Kubernetes cluster. Once that's set up, you can save the code to a `__main__.py` Python file, then run `pulumi up` to deploy the resources to your cluster.