High-Availability AI Inferencing with Kubernetes

Question

Pulumi · Accepted Answer

Creating a high-availability AI inferencing infrastructure involves setting up a Kubernetes cluster with redundant components to minimize downtime and handle failures gracefully. AI inferencing workloads often require specialized hardware such as GPUs and can benefit from Kubernetes' powerful orchestration capabilities to manage the workloads efficiently.

In this Pulumi program, we will set up a high-availability Kubernetes cluster and deploy a simple AI inferencing application. We'll ensure that our application can tolerate node failures using Kubernetes' PodDisruptionBudget, and we will use Pulumi to manage the cloud resources necessary for our infrastructure.

Here is an outline of what our Pulumi program will achieve:

1. Set up a Kubernetes cluster on Google Kubernetes Engine (GKE).
2. Create a PodDisruptionBudget to ensure that a certain minimum number of replicas of our AI application remain available during voluntary disruptions.
3. Deploy a dummy AI inferencing application with a simple deployment that can be replaced with an actual AI application.

Now, let's go through the Pulumi program.

```python
import pulumi
import pulumi_kubernetes as k8s
from pulumi_gcp import container

# Create a GKE cluster with high-availability settings.
cluster = container.Cluster("high-availability-cluster",
    initial_node_count=3,
    location="us-central1-a",
    remove_default_node_pool=True,
    min_master_version="latest",
    resource_labels={
        "workload": "ai-inferencing",
        "high-availability": "true"
    }
)

# Create a Kubernetes provider instance using the generated kubeconfig.
k8s_provider = k8s.Provider("k8s-provider", kubeconfig=cluster.master_auth[0].cluster_ca_certificate.apply(
    lambda x: """apiVersion: v1
clusters:
- cluster:
    certificate-authority-data: {0}
    server: https://{1}:443
  name: cluster
contexts:
- context:
    cluster: cluster
    user: cluster
  name: cluster
current-context: cluster
kind: Config
preferences: {{}}
users:
- name: cluster
  user:
    client-certificate-data: {2}
    client-key-data: {3}
""".format(cluster.master_auth[0].cluster_ca_certificate, cluster.endpoint, cluster.master_auth[0].client_certificate, cluster.master_auth[0].client_key)))

# Example application deployment.
app_labels = {"app": "ai-inference"}
app_deployment = k8s.apps.v1.Deployment("app-deployment",
    spec=k8s.apps.v1.DeploymentSpecArgs(
        replicas=3,
        selector=k8s.meta.v1.LabelSelectorArgs(match_labels=app_labels),
        template=k8s.core.v1.PodTemplateSpecArgs(
            metadata=k8s.meta.v1.ObjectMetaArgs(labels=app_labels),
            spec=k8s.core.v1.PodSpecArgs(
                containers=[k8s.core.v1.ContainerArgs(
                    name="inference-container",
                    image="my-ai-inference-app:latest" # Replace with your AI inferencing application Docker image.
                )]
            ),
        ),
    ),
    opts=pulumi.ResourceOptions(provider=k8s_provider)
)

# Create PodDisruptionBudget for the AI application.
pdb = k8s.policy.v1beta1.PodDisruptionBudget("app-pdb",
    spec=k8s.policy.v1beta1.PodDisruptionBudgetSpecArgs(
        min_available=2,
        selector=k8s.meta.v1.LabelSelectorArgs(
            match_labels=app_labels
        ),
    ),
    opts=pulumi.ResourceOptions(provider=k8s_provider)
)

# Export the cluster name and kubeconfig.
pulumi.export('cluster_name', cluster.name)
pulumi.export('kubeconfig', cluster.master_auth[0].cluster_ca_certificate)
```

This program does the following:

- It first creates a GKE cluster with the settings suitable for a high-availability deployment.
- With the `kubeconfig`, it configures the Kubernetes provider to interact with our newly created GKE cluster.
- Then it defines an AI inferencing application deployment with three replicas. This number ensures that even if one pod fails, there are still at least two running, maintaining high availability.
- After that, it sets up a PodDisruptionBudget, which specifies that at least two replicas of our AI inferencing application remain running in the event of voluntary disruptions like node upgrades or resizes.
- Lastly, the program exports the cluster name and kubeconfig, which can be used to interact with the Kubernetes cluster via `kubectl` or other tools.

Please note that in `inference-container` you should replace `"my-ai-inference-app:latest"` with the actual container image for the AI inferencing workload you intend to run.

Ensure that you have the right permissions and the GCP credentials configured properly on your system where you're running Pulumi. With that set up, running this program with `pulumi up` will provision the resources as specified.

Implementing a real-world AI inferencing application would involve customizing the container image to include your AI models and inferencing code and potentially configuring GPU or other specialized hardware support in your Kubernetes nodes.