DualFlow/ci/scripts/deploy_model.sh

#!/bin/bash

# 模型部署脚本
# 用法: ./deploy_model.sh <model_name> <environment>

set -e

MODEL_NAME=${1:-"nlp_bert"}
ENVIRONMENT=${2:-"staging"}
DOCKER_IMAGE="dualflow-${MODEL_NAME}:latest"

echo "🚀 开始部署模型: ${MODEL_NAME} 到环境: ${ENVIRONMENT}"

# 检查Docker是否运行
if ! docker info > /dev/null 2>&1; then
    echo "❌ Docker未运行，请启动Docker"
    exit 1
fi

# 检查模型目录是否存在
if [ ! -d "models/${MODEL_NAME}" ]; then
    echo "❌ 模型目录不存在: models/${MODEL_NAME}"
    exit 1
fi

# 构建Docker镜像
echo "📦 构建Docker镜像..."
cd models/${MODEL_NAME}
docker build -t ${DOCKER_IMAGE} .

# 停止现有容器
echo "🛑 停止现有容器..."
docker stop ${MODEL_NAME}-container 2>/dev/null || true
docker rm ${MODEL_NAME}-container 2>/dev/null || true

# 运行新容器
echo "🏃 启动新容器..."
if [ "${ENVIRONMENT}" = "production" ]; then
    # 生产环境配置
    docker run -d \
        --name ${MODEL_NAME}-container \
        --restart unless-stopped \
        -p 8000:8000 \
        -e ENVIRONMENT=production \
        ${DOCKER_IMAGE}
else
    # 开发/测试环境配置
    docker run -d \
        --name ${MODEL_NAME}-container \
        -p 8000:8000 \
        -e ENVIRONMENT=${ENVIRONMENT} \
        ${DOCKER_IMAGE}
fi

# 等待服务启动
echo "⏳ 等待服务启动..."
sleep 10

# 健康检查
echo "🔍 执行健康检查..."
if curl -f http://localhost:8000/health > /dev/null 2>&1; then
    echo "✅ 模型部署成功！"
    echo "🌐 服务地址: http://localhost:8000"
    echo "📊 健康检查: http://localhost:8000/health"
else
    echo "❌ 健康检查失败"
    echo "📋 容器日志:"
    docker logs ${MODEL_NAME}-container
    exit 1
fi

# 显示容器状态
echo "📊 容器状态:"
docker ps | grep ${MODEL_NAME}-container

echo "🎉 部署完成！"