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Kubernetes 与 AI 集成最佳实践

news 2026/6/6 18:27:19

Kubernetes 与 AI 集成最佳实践

一、前言

哥们，别整那些花里胡哨的。Kubernetes 与 AI 集成是现代云原生架构的重要趋势，今天直接上硬货，教你如何在 Kubernetes 中部署和管理 AI 工作负载。

二、AI 工作负载类型

类型	特点	资源需求
训练工作负载	计算密集型	高 GPU 需求
推理工作负载	低延迟要求	中等 GPU 需求
数据处理	存储密集型	高存储 I/O
模型服务	高并发	稳定资源需求

三、实战配置

1. GPU 资源管理

apiVersion: v1 kind: ConfigMap metadata: name: nvidia-device-plugin namespace: kube-system data: config.yaml: | version: v1 flags: migStrategy: single sharing: timeSlicing: renameByDefault: true failRequestsGreaterThanOne: false resources: - name: nvidia.com/gpu replicas: 4 --- apiVersion: apps/v1 kind: DaemonSet metadata: name: nvidia-device-plugin-daemonset namespace: kube-system spec: selector: matchLabels: name: nvidia-device-plugin-ds template: metadata: labels: name: nvidia-device-plugin-ds spec: containers: - name: nvidia-device-plugin-ctr image: nvcr.io/nvidia/k8s-device-plugin:v0.14.1 securityContext: allowPrivilegeEscalation: false capabilities: drop: - ALL volumeMounts: - name: device-plugin mountPath: /var/lib/kubelet/device-plugins volumes: - name: device-plugin hostPath: path: /var/lib/kubelet/device-plugins

2. 训练工作负载部署

apiVersion: batch/v1 kind: Job metadata: name: ai-training-job namespace: default spec: completions: 1 parallelism: 1 template: metadata: labels: app: ai-training spec: restartPolicy: Never containers: - name: training image: pytorch/pytorch:latest command: - python - /app/train.py resources: requests: cpu: "4" memory: "16Gi" nvidia.com/gpu: "1" limits: cpu: "8" memory: "32Gi" nvidia.com/gpu: "1" volumeMounts: - name: data mountPath: /data - name: code mountPath: /app volumes: - name: data persistentVolumeClaim: claimName: ai-data-pvc - name: code configMap: name: training-code

3. 推理服务部署

apiVersion: apps/v1 kind: Deployment metadata: name: ai-inference namespace: default spec: replicas: 3 selector: matchLabels: app: ai-inference template: metadata: labels: app: ai-inference spec: containers: - name: inference image: tensorflow/serving:latest ports: - containerPort: 8501 resources: requests: cpu: "2" memory: "8Gi" nvidia.com/gpu: "1" limits: cpu: "4" memory: "16Gi" nvidia.com/gpu: "1" volumeMounts: - name: model mountPath: /models volumes: - name: model persistentVolumeClaim: claimName: model-pvc --- apiVersion: v1 kind: Service metadata: name: ai-inference-service namespace: default spec: selector: app: ai-inference ports: - port: 8501 targetPort: 8501 type: ClusterIP

4. 自动扩缩容配置

apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: ai-inference-hpa namespace: default spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: ai-inference minReplicas: 2 maxReplicas: 10 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 60 - type: Resource resource: name: memory target: type: Utilization averageUtilization: 70

四、AI 工作负载优化

1. 数据处理优化

apiVersion: apps/v1 kind: StatefulSet metadata: name:>apiVersion: argoproj.io/v1alpha1 kind: Application metadata: name: model-management namespace: argocd spec: project: default source: repoURL: https://github.com/susu/model-repo.git targetRevision: HEAD path: models destination: server: https://kubernetes.default.svc namespace: default syncPolicy: automated: prune: true selfHeal: true

3. 监控与告警

apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: name: ai-workload-metrics namespace: monitoring spec: selector: matchLabels: app: ai-inference endpoints: - port: metrics interval: 15s --- apiVersion: monitoring.coreos.com/v1 kind: PrometheusRule metadata: name: ai-workload-alerts namespace: monitoring spec: groups: - name: ai-workload rules: - alert: GPUUtilizationHigh expr: nvidia_gpu_utilization > 80 for: 5m labels: severity: warning annotations: summary: GPU utilization high description: GPU utilization is above 80% - alert: ModelInferenceLatencyHigh expr: model_inference_latency_seconds > 0.5 for: 5m labels: severity: warning annotations: summary: Model inference latency high description: Model inference latency is above 500ms