89 lines
2.6 KiB
Bash
Executable File
89 lines
2.6 KiB
Bash
Executable File
#!/bin/bash
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# 织忆 v2.8 部署脚本
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# 用法: bash deploy.sh [--gpu]
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set -e
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echo "=== 织忆 MemoryWeave v2.8 部署脚本 ==="
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# 检测 GPU
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GPU=false
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if [[ "$1" == "--gpu" ]] && command -v nvidia-smi &>/dev/null; then
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GPU=true
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echo "[检测] NVIDIA GPU 可用，将安装 CUDA 版 torch"
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else
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echo "[检测] CPU 模式"
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fi
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# 1. 安装系统依赖（需要 sudo）
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if ! command -v redis-server &>/dev/null; then
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echo "[步骤1] 安装 Redis..."
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if command -v apt &>/dev/null; then
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sudo apt install -y redis-server
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elif command -v pacman &>/dev/null; then
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sudo pacman -S redis
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else
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echo "[警告] 未检测到包管理器，请手动安装 Redis"
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fi
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else
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echo "[步骤1] Redis 已安装 ✓"
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fi
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# 2. 创建虚拟环境
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echo "[步骤2] 创建虚拟环境..."
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if [ ! -d ".venv" ]; then
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python3 -m venv .venv
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fi
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source .venv/bin/activate
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# 3. 安装 Python 依赖
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echo "[步骤3] 安装 Python 依赖..."
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if $GPU; then
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# GPU 机器：从系统 Python 复制 torch CUDA 版
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# （torch 没有官方 cu130 的 pip 包，需要手动处理）
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echo "[警告] GPU 模式需要手动处理 torch CUDA 版本"
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echo "[提示] 从本机 /home/muc/.local/lib/python3.12/site-packages/ 复制 torch + nvidia 到 .venv/lib/python3.12/site-packages/"
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fi
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# 安装基础依赖
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pip install --upgrade pip
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pip install -r requirements.txt
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# 4. 下载 HF 模型
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echo "[步骤4] 下载 moka-ai/m3e-base 模型..."
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export HF_ENDPOINT=https://hf-mirror.com
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python3 -c "
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from sentence_transformers import SentenceTransformer
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print('下载模型中（首次约 400MB）...')
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model = SentenceTransformer('moka-ai/m3e-base')
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print('模型下载完成 ✓')
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"
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# 5. 配置 Redis
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echo "[步骤5] 启动 Redis..."
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if ! pgrep -x redis-server > /dev/null; then
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redis-server --daemonize yes
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echo "Redis 已启动 ✓"
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else
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echo "Redis 已运行 ✓"
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fi
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# 6. 验证部署
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echo "[步骤6] 验证..."
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python3 -c "
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import faiss
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import torch
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from sentence_transformers import SentenceTransformer
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print(f' torch: {torch.__version__} CUDA={torch.cuda.is_available()}')
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print(f' faiss-cpu: {faiss.__version__}')
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print(' 依赖验证通过 ✓')
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"
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echo ""
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echo "=== 部署完成 ==="
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echo ""
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echo "启动服务："
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echo " HF_ENDPOINT=https://hf-mirror.com PYTHONPATH=src .venv/bin/uvicorn api.server:app --host 0.0.0.0 --port 7821"
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echo ""
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echo "或 GPU 模式（需要先手动复制 torch CUDA 版本到 venv）："
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echo " HF_ENDPOINT=https://hf-mirror.com PYTHONPATH=src ZHIYI_PORT=7821 .venv/bin/uvicorn api.server:app --host 0.0.0.0 --port 7821" |