快速体验Gateway with Inference Extension智能推理路由

LLM应用通常需要使用GPU来启动,而且GPU类型的节点或虚拟节点较CPU节点有着较高的开销。为此,Gateway with Inference Extension组件团队提供了一个使用CPU算力快速体验大语言模型(LLM)推理场景的智能负载均衡能力的方式。本文介绍如何基于Gateway with Inference Extension构建一个mock环境来快速体验推理服务的智能负载均衡能力。

前提条件

已安装Gateway with Inference Extension并勾选启用Gateway API推理扩展。操作入口,请参见安装组件

重要

本文构建的mock环境仅适用于快速体验Gateway with Inference Extension组件的一些基础AI能力,例如灰度发布请求熔断流量镜像等,不适用于需要进行压测的测试场景,也不建议在生产环境中使用。

操作步骤

步骤一:部署mock模型示例应用

  1. 创建mock-vllm.yaml。

    apiVersion: v1
    kind: ServiceAccount
    metadata:
      name: mock-vllm
    ---
    apiVersion: v1
    kind: Service
    metadata:
      name: mock-vllm
      labels:
        app: mock-vllm
        service: mock-vllm
    spec:
      ports:
      - name: http
        port: 8000
        targetPort: 8000
      selector:
        app: mock-vllm
    ---
    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: mock-vllm
    spec:
      replicas: 1
      selector:
        matchLabels:
          app: mock-vllm
      template:
        metadata:
          labels:
            app: mock-vllm
        spec:
          serviceAccountName: mock-vllm
          containers:
          - image: registry-cn-hangzhou.ack.aliyuncs.com/dev/mock-vllm:v0.1.7-g3cffa27-aliyun
            imagePullPolicy: IfNotPresent
            name: mock-vllm
            ports:
            - containerPort: 8000
  2. 部署示例应用。

    kubectl apply -f mock-vllm.yaml
  3. 创建sleep.yaml。

    apiVersion: v1
    kind: ServiceAccount
    metadata:
      name: sleep
    ---
    apiVersion: v1
    kind: Service
    metadata:
      name: sleep
      labels:
        app: sleep
        service: sleep
    spec:
      ports:
      - port: 80
        name: http
      selector:
        app: sleep
    ---
    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: sleep
    spec:
      replicas: 1
      selector:
        matchLabels:
          app: sleep
      template:
        metadata:
          labels:
            app: sleep
        spec:
          terminationGracePeriodSeconds: 0
          serviceAccountName: sleep
          containers:
          - name: sleep
            image:  registry-cn-hangzhou.ack.aliyuncs.com/ack-demo/curl:asm-sleep
            command: ["/bin/sleep", "infinity"]
            imagePullPolicy: IfNotPresent
            volumeMounts:
            - mountPath: /etc/sleep/tls
              name: secret-volume
          volumes:
          - name: secret-volume
            secret:
              secretName: sleep-secret
              optional: true
  4. 部署sleep应用,用于后续对示例应用发起测试请求。

    kubectl apply -f sleep.yaml

步骤二:部署inference资源

  1. 创建inference-rule.yaml。

    apiVersion: inference.networking.x-k8s.io/v1alpha2
    kind: InferencePool
    metadata:
      name: mock-pool
    spec:
      extensionRef:
        group: ""
        kind: Service
        name: mock-ext-proc
      selector:
        app: mock-vllm
      targetPortNumber: 8000
    ---
    apiVersion: inference.networking.x-k8s.io/v1alpha2
    kind: InferenceModel
    metadata:
      name: mock-model
    spec:
      criticality: Critical
      modelName: mock
      poolRef:
        group: inference.networking.x-k8s.io
        kind: InferencePool
        name: mock-pool
      targetModels:
      - name: mock
        weight: 100
  2. 部署inferencePoolinferenceModel。

    kubectl apply -f inference-rule.yaml

步骤三:部署网关和路由规则

  1. 创建gateway.yaml。

    kind: GatewayClass
    apiVersion: gateway.networking.k8s.io/v1
    metadata:
      name: inference-gateway
    spec:
      controllerName: gateway.envoyproxy.io/gatewayclass-controller
    ---
    apiVersion: gateway.networking.k8s.io/v1
    kind: Gateway
    metadata:
      name: mock-gateway
    spec:
      gatewayClassName: inference-gateway
      infrastructure:
        parametersRef:
          group: gateway.envoyproxy.io
          kind: EnvoyProxy
          name: custom-proxy-config
      listeners:
        - name: llm-gw
          protocol: HTTP
          port: 80
    ---
    apiVersion: gateway.envoyproxy.io/v1alpha1
    kind: EnvoyProxy
    metadata:
      name: custom-proxy-config
      namespace: default
    spec:
      provider:
        type: Kubernetes
        kubernetes:
          envoyService:
            type: ClusterIP
    ---
    apiVersion: gateway.envoyproxy.io/v1alpha1
    kind: ClientTrafficPolicy
    metadata:
      name: mock-client-buffer-limit
    spec:
      connection:
        bufferLimit: 20Mi
      targetRefs:
        - group: gateway.networking.k8s.io
          kind: Gateway
          name: mock-gateway
    ---
  2. 创建httproute.yaml。

    apiVersion: gateway.networking.k8s.io/v1
    kind: HTTPRoute
    metadata:
      name: mock-route
    spec:
      parentRefs:
      - group: gateway.networking.k8s.io
        kind: Gateway
        name: mock-gateway
        sectionName: llm-gw
      rules:
      - backendRefs:
        - group: inference.networking.x-k8s.io
          kind: InferencePool
          name: mock-pool
          weight: 1
        matches:
        - path:
            type: PathPrefix
            value: /
  3. 部署网关和路由规则。

    kubectl apply -f gateway.yaml
    kubectl apply -f httproute.yaml

步骤四:发起测试

  1. 获取网关IP。

    export GATEWAY_ADDRESS=$(kubectl get gateway/mock-gateway -o jsonpath='{.status.addresses[0].value}')
    echo ${GATEWAY_ADDRESS}
  2. sleep应用中发起访问。

    kubectl exec deployment/sleep -it -- curl -X POST ${GATEWAY_ADDRESS}/v1/chat/completions \
      -H 'Content-Type: application/json' -H "host: example.com" -v -d '{
        "model": "mock",
        "max_completion_tokens": 100,
        "temperature": 0,
        "messages": [
          {
            "role": "user",
            "content": "introduce yourself"
          }
        ]
    }'

    预期输出:

    *   Trying 192.168.12.230:80...
    * Connected to 192.168.12.230 (192.168.12.230) port 80
    > POST /v1/chat/completions HTTP/1.1
    > Host: example.com
    > User-Agent: curl/8.8.0
    > Accept: */*
    > Content-Type: application/json
    > Content-Length: 184
    > 
    * upload completely sent off: 184 bytes
    < HTTP/1.1 200 OK
    < date: Tue, 27 May 2025 08:21:37 GMT
    < server: uvicorn
    < content-length: 354
    < content-type: application/json
    < 
    * Connection #0 to host 192.168.12.230 left intact
    {"id":"3bcc1fdd-e514-4a06-95aa-36c904015639","object":"chat.completion","created":1748334097.297188,"model":"mock","choices":[{"index":"0","message":{"role":"assistant","content":"As a mock AI Assitant, I can only echo your last message: introduce yourself"},"finish_reason":"stop"}],"usage":{"prompt_tokens":18,"completion_tokens":76,"total_tokens":94}}

步骤五:清理环境

若您不再需要使用此环境,您可以将本文中创建的所有YAML文件移动在一个新的目录下,执行以下命令进行清理:

kubectl delete -f .