Author: amac2025

🚀 This blog post details how to deploy AI workloads securely and scalably on Kubernetes, leveraging vLLM for high-performance inference and Kyverno for policy enforcement. We focus on a practical implementation using these tools, outlining deployment strategies and security best practices to achieve a robust and efficient AI infrastructure.

🧠 vLLM for High-Performance AI Inference

vLLM (version 0.4.0) is a fast and easy-to-use library for LLM inference and serving. It supports features like continuous batching and memory management, which significantly improve throughput and reduce latency when deploying large language models. Deploying vLLM on Kubernetes offers several benefits, including scalability, resource management, and ease of deployment.

To deploy vLLM, we’ll use a Kubernetes deployment configuration that defines the number of replicas, resource requests and limits, and the container image. Here’s an example deployment manifest:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-deployment
  labels:
    app: vllm
spec:
  replicas: 3
  selector:
    matchLabels:
      app: vllm
  template:
    metadata:
      labels:
        app: vllm
    spec:
      containers:
      - name: vllm-container
        image: vllm/vllm:latest # vLLM image. Ensure the tag is up to date.
        ports:
        - containerPort: 8000
        resources:
          requests:
            cpu: "4"
            memory: "32Gi"
          limits:
            cpu: "8"
            memory: "64Gi"
        args: ["--model", "facebook/opt-1.3b", "--host", "0.0.0.0", "--port", "8000"] # Example model and host settings

This deployment specifies three replicas of the vLLM container, each requesting 4 CPUs and 32GB of memory, with limits set to 8 CPUs and 64GB of memory. The args field defines the command-line arguments passed to the vLLM server, including the model to use (facebook/opt-1.3b in this example) and the host and port to listen on. For other models, such as Mistral 7B or Llama 3, adjust the args.

Once the deployment is created, you can expose the vLLM service using a Kubernetes service:

apiVersion: v1
kind: Service
metadata:
  name: vllm-service
spec:
  selector:
    app: vllm
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8000
  type: LoadBalancer

This service creates a LoadBalancer that exposes the vLLM deployment to external traffic on port 80, forwarding requests to port 8000 on the vLLM containers. For real-world scenarios, consider using more sophisticated networking solutions like Istio for advanced traffic management and security.

⚙️ Kyverno for Policy Enforcement and Security

Kyverno (version 1.14.0) is a policy engine designed for Kubernetes. It allows you to define and enforce policies as code, ensuring that resources deployed to your cluster adhere to your security and compliance requirements. Integrating Kyverno with vLLM deployments enhances security by preventing unauthorized access, limiting resource usage, and enforcing specific configurations.

First, install Kyverno on your Kubernetes cluster following the official documentation. After installation, define policies to govern the deployment of vLLM workloads. Here’s an example Kyverno policy that ensures all vLLM deployments have appropriate resource limits and labels:

apiVersion: kyverno.io/v1
kind: Policy
metadata:
  name: enforce-vllm-resource-limits
spec:
  validationFailureAction: enforce
  rules:
  - name: check-resource-limits
    match:
      any:
      - resources:
          kinds:
          - Deployment
    validate:
      message: "vLLM Deployments must have CPU and memory limits defined."
      pattern:
        spec:
          template:
            spec:
              containers:
              - name: vllm-container
                resources:
                  limits:
                    cpu: "?*"
                    memory: "?*"
                  requests:
                    cpu: "?*"
                    memory: "?*"

This policy checks that all deployments have CPU and memory limits defined for the vllm-container. If a deployment is created without these limits, Kyverno will reject the deployment. Enforce additional policies, such as those that restrict the images that can be used to deploy vLLM workloads. This helps prevent the deployment of untrusted or malicious images.

Another critical aspect of securing vLLM deployments is implementing Network Policies. Network Policies control the network traffic to and from your vLLM pods, ensuring that only authorized traffic is allowed. Here’s an example Network Policy that allows traffic only from specific namespaces:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: vllm-network-policy
spec:
  podSelector:
    matchLabels:
      app: vllm
  ingress:
  - from:
    - namespaceSelector:
        matchLabels:
          name: allowed-namespace # Replace with the allowed namespace
  egress:
  - to:
    - ipBlock:
        cidr: 0.0.0.0/0

This Network Policy ensures that only pods in the allowed-namespace can access the vLLM pods. The egress rule allows all outbound traffic, but you can restrict this further based on your security requirements.

💻 Conclusion

Securing and scaling AI workloads on Kubernetes requires a combination of robust infrastructure and effective policy enforcement. By leveraging vLLM for high-performance inference and Kyverno for policy management, you can achieve a scalable, secure, and resilient AI deployment. Implementing these strategies, combined with continuous monitoring and security audits, will help you maintain a robust AI infrastructure that meets the demands of modern AI applications. Remember to stay updated with the latest versions of vLLM and Kyverno to take advantage of new features and security patches.