Azure AI服务迁移AKS容器化部署

为什么选择AKS作为AI服务载体

Azure Kubernetes Service（AKS）作为微软云原生的托管Kubernetes服务，为AI工作负载提供以下关键优势：

1. 弹性伸缩能力：支持根据GPU/CPU利用率自动扩展Pod副本
2. 异构计算支持：可配置GPU节点池运行深度学习推理
3. 服务网格集成：原生支持Istio实现细粒度流量管理
4. 成本优化：Spot节点池可降低70%计算成本
5. 安全合规：内置Azure AD集成与密钥管理系统

迁移准备阶段

环境预配置

# 安装必要工具链
az extension add --name aks-preview
az feature register --name EnableGPUDedicatedVHD --namespace Microsoft.ContainerService
helm repo add azure-marketplace https://marketplace.azurecr.io/helm/v1/repo

# 创建带GPU支持的AKS集群
az aks create \
  --resource-group ai-prod-rg \
  --name ai-cluster \
  --node-count 3 \
  --node-vm-size Standard_NC6s_v3 \
  --enable-cluster-autoscaler \
  --min-count 1 \
  --max-count 10 \
  --attach-acr ai-registry

容器化改造要点

典型AI服务Dockerfile示例：

FROM nvidia/cuda:11.8.0-base-ubuntu22.04

# 安装Python依赖
RUN apt-get update && \
    apt-get install -y python3.9 python3-pip libgl1

# 配置模型缓存目录
ENV TRANSFORMERS_CACHE=/app/model_cache
RUN mkdir -p $TRANSFORMERS_CACHE

# 安装业务依赖
COPY requirements.txt .
RUN pip install -r requirements.txt --no-cache-dir

# 复制启动脚本
COPY serve.py /app/
COPY entrypoint.sh /

# 配置健康检查
HEALTHCHECK --interval=30s --timeout=5s \
  CMD curl -f http://localhost:8080/health || exit 1

ENTRYPOINT ["/entrypoint.sh"]

AKS部署核心配置

Kubernetes部署清单

apiVersion: apps/v1
kind: Deployment
metadata:
  name: text-analyzer
spec:
  replicas: 3
  selector:
    matchLabels:
      app: nlp
  template:
    metadata:
      labels:
        app: nlp
        ai-type: text-processing
    spec:
      nodeSelector:
        accelerator: nvidia-tesla-v100
      containers:
      - name: analyzer
        image: ai-registry.azurecr.io/text-analyzer:v2.1.3
        resources:
          limits:
            nvidia.com/gpu: 1
            memory: "8Gi"
          requests:
            cpu: "2"
            memory: "4Gi"
        envFrom:
          - secretRef:
              name: ai-secrets
        volumeMounts:
          - name: model-store
            mountPath: /app/model_cache
      volumes:
        - name: model-store
          persistentVolumeClaim:
            claimName: model-pvc

---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: text-analyzer-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: text-analyzer
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

网络流量管理

# 安装NGINX ingress控制器
helm upgrade --install ingress-nginx ingress-nginx \
  --repo https://kubernetes.github.io/ingress-nginx \
  --namespace ingress --create-namespace \
  --set controller.service.annotations."service\.beta\.kubernetes\.io/azure-load-balancer-health-probe-request-path"=/healthz

# 配置证书管理
az aks update -n ai-cluster -g ai-prod-rg --attach-acr ai-registry
kubectl apply -f https://github.com/cert-manager/cert-manager/releases/download/v1.11.0/cert-manager.yaml

存储与数据持久化方案

1. 高性能存储方案选择矩阵：

1. 持久卷声明示例：

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: model-pvc
spec:
  accessModes:
    - ReadWriteMany
  storageClassName: azure-file-premium
  resources:
    requests:
      storage: 200Gi

监控与日志体系构建

1. 监控架构组成：

• Prometheus收集GPU指标
• Grafana展示实时仪表盘
• Azure Monitor集成业务日志
• Kube-state-metrics采集集群状态

1. GPU监控关键指标：

# 显存使用率
sum(container_memory_usage_bytes{container="analyzer", device=~"nvidia.*"}) by (pod) / sum(container_spec_memory_limit_bytes{container="analyzer"}) by (pod)

# GPU利用率
avg(rate(container_accelerator_duty_cycle{container="analyzer"}[5m])) by (pod)

生产环境优化策略

1. 性能调优参数表：

1. 自动缩放配置：

# 安装垂直自动缩放器
helm install vpa recommender \
  --repo https://charts.fairwinds.com/stable \
  --set recommender.extraArgs.v=3

# 配置VPA策略
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: text-analyzer-vpa
spec:
  targetRef:
    apiVersion: "apps/v1"
    kind: Deployment
    name: text-analyzer
  updatePolicy:
    updateMode: "Auto"

迁移后验证流程

1. 混沌工程测试方案：

# 安装chaos-mesh
helm install chaos-mesh chaos-mesh --namespace chaos-testing --create-namespace --set chaosDaemon.runtime=containerd --set chaosDaemon.socketPath=/run/containerd/containerd.sock

# 执行网络延迟测试
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
  name: network-latency-test
spec:
  action: delay
  mode: one
  selector:
    namespaces:
      - ai-services
  delay:
    latency: "500ms"
    correlation: "100"
    jitter: "100ms"
  duration: "5m"

安全加固措施

1. 密钥管理方案：

# 与Azure Key Vault集成
az aks enable-addons --addons azure-keyvault-secrets-provider --name ai-cluster --resource-group ai-prod-rg

# Secrets同步配置
apiVersion: secrets-store.csi.x-k8s.io/v1
kind: SecretProviderClass
metadata:
  name: ai-secrets-class
spec:
  provider: azure
  parameters:
    usePodIdentity: "false"
    keyvaultName: "ai-kv-prod"
    objects: |
      array:
        - |
          objectName: cognitive-services-key
          objectType: secret
    tenantId: "xxxxx-xxxx-xxxx-xxxx-xxxxxxx"

典型问题解决方案

1. GPU节点调度异常处理流程：

检查事件日志 → 验证NVIDIA驱动状态 → 确认设备插件运行 → 
测试nvidia-smi命令 → 验证节点标签 → 检查资源配额 →
验证Pod亲和性设置 → 提交支持请求

1. 模型热更新方案：

# 使用Watch机制监听模型更新
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler

class ModelUpdateHandler(FileSystemEventHandler):
    def on_modified(self, event):
        if event.src_path.endswith(".onnx"):
            reload_model()

observer = Observer()
observer.schedule(ModelUpdateHandler(), path='/models', recursive=True)
observer.start()

持续演进方向

多维度指标采集体系

（1）自定义指标采集架构

# 安装Prometheus适配器
helm install prometheus-adapter prometheus-community/prometheus-adapter \
  --set prometheus.url=http://prometheus-server.monitoring.svc.cluster.local

# 自定义模型请求QPS指标
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: model-qps-monitor
spec:
  endpoints:
  - port: metrics
    interval: 30s
    path: /metrics
  selector:
    matchLabels:
      app-type: ai-model

（2）模型维度监控指标矩阵

智能缩放策略实现

（1）基于流量特征的策略选择

# KEDA缩放配置示例
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: model-a-scaler
spec:
  scaleTargetRef:
    name: model-a-deployment
  triggers:
  - type: prometheus
    metadata:
      serverAddress: http://prometheus-server.monitoring.svc.cluster.local
      metricName: model_a_qps
      threshold: "100"  # 单个Pod处理能力
      query: sum(rate(model_requests_total{model="a"}[2m]))

（2）多模型优先级调度方案

apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: high-priority-model
value: 1000000
description: "关键业务模型优先级"

# Pod配置示例
spec:
  priorityClassName: high-priority-model
  containers:
  - name: model-a
    resources:
      limits:
        nvidia.com/gpu: 2

动态资源分配算法

（1）实时资源分配矩阵

# 基于强化学习的资源分配算法框架
class ResourceAllocator:
    def __init__(self, cluster_state):
        self.models = cluster_state['models']
        self.available_gpu = cluster_state['gpu']
        
    def optimize(self):
        # 实现资源分配策略
        return {
            "model-a": {"replicas": 3, "gpu_per_pod": 1},
            "model-b": {"replicas": 2, "gpu_per_pod": 2}
        }

（2）混合缩放策略对比表

实战优化技巧

（1）冷启动优化方案

# 预热池配置
apiVersion: apps/v1
kind: Deployment
metadata:
  name: warm-pool
spec:
  replicas: 2
  template:
    spec:
      containers:
      - name: preload
        image: model-loader
        args: ["--preload-model=all"]
        resources:
          limits:
            nvidia.com/gpu: 1

（2）动态批处理配置

# 自适应批处理算法
class DynamicBatcher:
    def __init__(self):
        self.max_batch_size = 32
        self.timeout_ms = 100
        
    def process(self, requests):
        batch = []
        start_time = time.time()
        while len(batch) < self.max_batch_size:
            if time.time() - start_time > self.timeout_ms / 1000:
                break
            if incoming_requests:
                batch.append(incoming_requests.pop())
        return self.inference(batch)

典型场景处理策略

（1）模型版本灰度发布

# Istio流量切分配置
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: model-ab-test
spec:
  hosts:
  - ai-service.example.com
  http:
  - route:
    - destination:
        host: model-v1
        subset: v1
      weight: 90
    - destination:
        host: model-v2
        subset: v2
      weight: 10

（2）突发流量应急方案

# 手动扩容命令模板
kubectl scale deployment model-a --replicas=10
az aks nodepool scale \
  --name gpunode \
  --cluster-name ai-cluster \
  --resource-group ai-prod-rg \
  --node-count 15

智能驱逐策略

# 基于优先级的驱逐配置
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: model-a-pdb
spec:
  minAvailable: 80%
  selector:
    matchLabels:
      app: model-a

---
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: model-eviction-priority
value: 100000
globalDefault: false
description: "模型服务驱逐优先级"

通过上述策略的组合运用，可以实现：

1. 关键模型优先保障：当集群资源紧张时，优先保障高优先级模型的资源供给
2. 动态资源再分配：根据实时负载在模型间动态调配GPU资源
3. 预测性扩展：基于历史数据进行容量预规划
4. 混合精度部署：不同精度要求的模型混部实现资源最优化