微服务环境下大模型部署流程

在大模型微服务化改造中，合理的部署流程是确保系统稳定性和可维护性的关键。本文将分享一个完整的部署流程实践。

部署前准备

首先，在部署前需要确保环境配置正确：

# 检查Docker环境
sudo docker --version
# 检查Kubernetes集群状态
kubectl cluster-info

部署步骤

构建镜像

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

推送镜像到仓库

# 构建并推送
sudo docker build -t registry.example.com/model-service:v1.0 .
sudo docker push registry.example.com/model-service:v1.0

部署到K8s

apiVersion: apps/v1
kind: Deployment
metadata:
  name: model-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: model-service
  template:
    metadata:
      labels:
        app: model-service
    spec:
      containers:
      - name: model-container
        image: registry.example.com/model-service:v1.0
        ports:
        - containerPort: 8000
        resources:
          requests:
            memory: "512Mi"
            cpu: "250m"
          limits:
            memory: "1Gi"
            cpu: "500m"

监控配置

部署后需要配置监控指标：

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: model-monitor
spec:
  selector:
    matchLabels:
      app: model-service
  endpoints:
  - port: http
    path: /metrics

通过以上流程，可以实现大模型服务的标准化部署和监控。