model-serving-kubernetes

Model Serving on Kubernetes

Production ML model serving with KServe and Triton — canary deployments, autoscaling, and GPU-aware scheduling.

When to Use This Skill

Use this skill when:

• Serving scikit-learn, PyTorch, TensorFlow, or ONNX models at scale
• Implementing canary deployments and A/B testing for ML models
• Autoscaling inference pods based on request rate or GPU metrics
• Deploying LLMs with Triton or KServe on Kubernetes
• Managing multiple model versions with traffic splitting

Prerequisites

• Kubernetes 1.28+ with GPU nodes
• KServe installed (or Triton standalone)
• kubectl and helm configured
• NVIDIA GPU Operator installed on cluster

KServe Installation

# Install KServe with Helm
helm repo add kserve https://kserve.github.io/helm-charts
helm repo update

helm install kserve kserve/kserve \
  --namespace kserve \
  --create-namespace \
  --set kserve.controller.gateway.ingressGateway.className=nginx

# Verify
kubectl get pods -n kserve
kubectl get crd | grep kserve

Basic InferenceService (KServe)

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: sklearn-iris
  namespace: models
spec:
  predictor:
    sklearn:
      storageUri: gs://kfserving-examples/models/sklearn/1.0/model
      resources:
        requests:
          cpu: "1"
          memory: 2Gi
        limits:
          cpu: "2"
          memory: 4Gi

kubectl apply -f inference-service.yaml

# Get inference service URL
kubectl get inferenceservice sklearn-iris -n models
# NAME           URL                                          READY   ...
# sklearn-iris   http://sklearn-iris.models.example.com       True

# Test prediction
curl -X POST http://sklearn-iris.models.example.com/v1/models/sklearn-iris:predict \
  -H "Content-Type: application/json" \
  -d '{"instances": [[6.8, 2.8, 4.8, 1.4]]}'

GPU-Enabled LLM InferenceService

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: llama-3-8b
  namespace: models
  annotations:
    serving.kserve.io/enable-prometheus-scraping: "true"
spec:
  predictor:
    containers:
    - name: vllm-container
      image: vllm/vllm-openai:latest
      args:
      - "--model"
      - "meta-llama/Llama-3.1-8B-Instruct"
      - "--tensor-parallel-size"
      - "1"
      - "--gpu-memory-utilization"
      - "0.90"
      ports:
      - containerPort: 8080
        protocol: TCP
      resources:
        requests:
          nvidia.com/gpu: "1"
          memory: "20Gi"
          cpu: "4"
        limits:
          nvidia.com/gpu: "1"
          memory: "24Gi"
          cpu: "8"
      readinessProbe:
        httpGet:
          path: /health
          port: 8080
        initialDelaySeconds: 60
        periodSeconds: 10
      env:
      - name: HUGGING_FACE_HUB_TOKEN
        valueFrom:
          secretKeyRef:
            name: hf-token
            key: token
    nodeSelector:
      nvidia.com/gpu.present: "true"
  transformer:
    containers:
    - name: kserve-container
      image: kserve/kserve-transformer:latest