b401ef94
 tangwang
third-party/xinfe...
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#!/bin/bash
# Xinference Docker 部署脚本
# 用于启动 Xinference 服务
set -e
# 检查是否需要 sudo
if sudo -n docker info &> /dev/null 2>&1; then
DOCKER="sudo docker"
# 检测 Docker Compose 的形式（V2: docker compose 或 V1: docker-compose）
if sudo docker compose version &> /dev/null 2>&1; then
DOCKER_COMPOSE="sudo docker compose"
echo "✅ 检测到 Docker Compose V2"
elif command -v docker-compose &> /dev/null; then
DOCKER_COMPOSE="sudo docker-compose"
echo "✅ 检测到 Docker Compose V1"
else
echo "❌ 错误: 未找到 Docker Compose"
exit 1
fi
else
DOCKER="docker"
# 检测 Docker Compose 的形式
if docker compose version &> /dev/null 2>&1; then
DOCKER_COMPOSE="docker compose"
echo "✅ 检测到 Docker Compose V2"
elif command -v docker-compose &> /dev/null; then
DOCKER_COMPOSE="docker-compose"
echo "✅ 检测到 Docker Compose V1"
else
echo "❌ 错误: 未找到 Docker Compose"
exit 1
fi
fi
echo "========================================="
echo " Xinference Docker 部署脚本"
echo "========================================="
echo ""
# 检查 GPU
echo "🔍 检查 GPU 可用性..."
if command -v nvidia-smi &> /dev/null; then
echo "✅ 检测到 NVIDIA GPU:"
nvidia-smi --query-gpu=name,memory.total --format=csv,noheader | head -n 1
# 检查 NVIDIA Container Toolkit
if $DOCKER run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi &> /dev/null; then
GPU_AVAILABLE=true
echo "✅ NVIDIA Container Toolkit 已安装，GPU 可用"
else
GPU_AVAILABLE=false
echo "⚠️ 检测到 GPU 但 NVIDIA Container Toolkit 未安装"
echo "⚠️ 将使用 CPU 模式启动"
echo "💡 如需 GPU 加速，请安装 NVIDIA Container Toolkit（见下方说明）"
fi
else
echo "⚠️ 未检测到 NVIDIA GPU，将使用 CPU 模式"
GPU_AVAILABLE=false
fi
# 创建模型目录
echo ""
echo "📁 创建模型存储目录..."
mkdir -p models
# 拉取镜像
echo ""
echo "🐳 拉取 Xinference Docker 镜像..."
if [ "$GPU_AVAILABLE" = true ]; then
$DOCKER pull xprobe/xinference:latest
else
echo "⚠️ CPU 模式：如需 GPU 支持，请配置好 NVIDIA Docker 运行时"
$DOCKER pull xprobe/xinference:latest
fi
# 停止并删除旧容器
echo ""
echo "🛑 停止并删除旧容器..."
$DOCKER stop xinference 2>/dev/null || true
$DOCKER rm xinference 2>/dev/null || true
$DOCKER_COMPOSE down 2>/dev/null || true
# 启动服务
echo ""
echo "🚀 启动 Xinference 服务..."
if [ "$GPU_AVAILABLE" = true ]; then
echo "🔥 使用 GPU 模式启动..."
$DOCKER_COMPOSE up -d
else
# CPU 模式：直接使用 docker run
echo "💻 使用 CPU 模式启动..."
$DOCKER run -d \
--name xinference \
-p 9997:9997 -p 9998:9998 \
-v "$(pwd)/models:/data" \
-e XINFERENCE_HOME=/data \
--restart unless-stopped \
xprobe/xinference:latest \
xinference-local -H 0.0.0.0
fi
# 等待服务启动
echo ""
echo "⏳ 等待服务启动..."
for i in {1..30}; do
if curl -s http://localhost:9997/v1/models > /dev/null 2>&1; then
echo "✅ Xinference 服务启动成功！"
break
fi
echo " 等待中... ($i/30)"
sleep 2
done
# 检查服务状态
echo ""
echo "📊 服务状态检查..."
if curl -s http://localhost:9997/v1/models > /dev/null 2>&1; then
echo "✅ 服务健康检查通过"
echo ""
echo "========================================="
echo " 🎉 部署成功！"
echo "========================================="
echo ""
echo "📍 服务地址:"
echo " - API: http://localhost:9997"
echo " - Dashboard: http://localhost:9998"
echo ""
echo "📝 下一步操作:"
echo " 1. 查看日志: $DOCKER logs -f xinference"
echo " 2. 部署模型: python deploy_models.py"
echo " 3. 测试搜索: python ecommerce_demo.py"
echo ""
if [ "$GPU_AVAILABLE" = false ]; then
echo "💡 启用 GPU 加速（可选）:"
echo " 如果你想使用 GPU 加速，请安装 NVIDIA Container Toolkit:"
echo ""
echo " # 1. 添加 NVIDIA 仓库"
echo " curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -"
echo " distribution=\$(. /etc/os-release;echo \$ID\$VERSION_ID)"
echo " curl -s -L https://nvidia.github.io/nvidia-docker/\$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list"
echo ""
echo " # 2. 安装 NVIDIA Container Toolkit"
echo " sudo apt-get update"
echo " sudo apt-get install -y nvidia-container-toolkit"
echo ""
echo " # 3. 重启 Docker"
echo " sudo systemctl restart docker"
echo ""
echo " # 4. 重新运行此脚本"
echo " bash start.sh"
echo ""
fi
echo "📚 查看所有已部署模型:"
echo " curl http://localhost:9997/v1/models"
echo ""
else
echo "❌ 服务启动失败，请查看日志："
echo " $DOCKER logs xinference"
exit 1
fi
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