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。
操作步骤
步骤一:部署mock模型示例应用
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
部署示例应用。
kubectl apply -f mock-vllm.yaml
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
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
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
部署inferencePool和inferenceModel。
kubectl apply -f inference-rule.yaml
步骤三:部署网关和路由规则
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 ---
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: /
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
获取网关IP。
export GATEWAY_ADDRESS=$(kubectl get gateway/mock-gateway -o jsonpath='{.status.addresses[0].value}') echo ${GATEWAY_ADDRESS}
从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 .
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