File size: 3,877 Bytes
b6c412f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | #!/bin/bash
# ============================================================
# VetCopilot DAPT v3.1 一键部署脚本
# 适用：GPU 服务器 (A100/RTX 40x0)
# ============================================================
set -e
echo "============================================"
echo " VetCopilot DAPT v3.1 部署"
echo " 引擎: Qwen2.5-3B DAPT + LCPS 2.0 + FAISS RAG"
echo " $(date)"
echo "============================================"
# ─── 配置 ───
MODEL_PATH="${VET_MODEL:-./vet-qwen3b-v3-dapt}"
PORT_AI="${AI_PORT:-8000}"
PORT_APP="${APP_PORT:-8080}"
KB_DIR="${KB_DIR:-./knowledge_base_simple}"
echo ""
echo "[0/4] 检查环境..."
python -c "import torch; print(f' CUDA: {torch.cuda.is_available()}'); print(f' GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"N/A\"}')" || {
echo "[错误] PyTorch CUDA 不可用"
exit 1
}
# ─── 1. 下载 DAPT 模型（如果不存在）───
echo ""
echo "[1/4] 准备 DAPT 模型..."
if [ ! -f "$MODEL_PATH/config.json" ]; then
echo " 模型不存在，从 ModelScope 下载..."
pip install modelscope -q
python -c "
from modelscope.hub.snapshot_download import snapshot_download
snapshot_download('WWSCA123/vet-qwen3b-v3-dapt-inference', local_dir='./vet-qwen3b-v3-dapt')
"
# 补全 tokenizer（从 HF mirror）
python -c "
from huggingface_hub import snapshot_download
import os, shutil
tokenizer_dir = snapshot_download('Qwen/Qwen2.5-3B-Instruct', allow_patterns=['tokenizer*', 'vocab*', 'merges*', 'generation_config*', 'special_tokens*'], local_dir='./_tokenizer_tmp')
for f in os.listdir(tokenizer_dir):
src = os.path.join(tokenizer_dir, f)
dst = os.path.join('$MODEL_PATH', f)
if not os.path.exists(dst):
shutil.copy(src, dst)
rm -rf ./_tokenizer_tmp
echo ' 模型准备完成'
"
else
echo " 模型已存在: $MODEL_PATH"
fi
# ─── 2. 安装依赖 ───
echo ""
echo "[2/4] 安装 Python 依赖..."
pip install -r vetnetv2.0/requirements_gpu.txt -q 2>/dev/null || {
pip install torch transformers accelerate fastapi uvicorn sentence-transformers faiss-gpu -q
}
pip install -r vetcopilot-backend/requirements.txt -q 2>/dev/null || true
echo " 依赖安装完成"
# ─── 3. 启动 DAPT 推理服务 ───
echo ""
echo "[3/4] 启动 DAPT 推理服务 (端口 $PORT_AI)..."
cd vetnetv2.0
nohup python server_gpu.py > ../logs/ai_server.log 2>&1 &
AI_PID=$!
echo " PID: $AI_PID"
# 等待推理服务就绪
echo " 等待推理服务就绪..."
for i in $(seq 1 60); do
if curl -s http://localhost:$PORT_AI/health | grep -q '"status":"ok"'; then
echo " 推理服务就绪"
break
fi
if [ $i -eq 60 ]; then
echo " [警告] 推理服务启动超时，检查日志: logs/ai_server.log"
fi
sleep 5
done
cd ..
# ─── 4. 启动 VetCopilot 应用 ───
echo ""
echo "[4/4] 启动 VetCopilot 应用 (端口 $PORT_APP)..."
cd vetcopilot-backend
nohup python -m uvicorn app.main:app --host 0.0.0.0 --port $PORT_APP > ../logs/app_server.log 2>&1 &
APP_PID=$!
echo " PID: $APP_PID"
# ─── 完成 ───
echo ""
echo "============================================"
echo " 部署完成！"
echo ""
echo " DAPT 推理服务: http://localhost:$PORT_AI"
echo " - 健康检查: GET /health"
echo " - LCPS 诊断: POST /api/v1/diagnose"
echo " - 自由问答: POST /chat"
echo ""
echo " VetCopilot 应用: http://localhost:$PORT_APP"
echo " - 前端: http://localhost:$PORT_APP (Vite dev)"
echo " - API: http://localhost:$PORT_APP/api"
echo ""
echo " 进程 ID:"
echo " 推理服务: $AI_PID"
echo " 应用服务: $APP_PID"
echo ""
echo " 停止服务: kill $AI_PID $APP_PID"
echo " 查看日志: tail -f logs/ai_server.log"
echo " tail -f logs/app_server.log"
echo "============================================"
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