[自动] 云端一键部署脚本

setup_cloud.sh

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+#!/bin/bash
+# -*- coding: utf-8 -*-
+"""
+XiaLi 云 GPU 一键搭建脚本
+=========================
+适用于 AutoDL / 矩池云 / 恒源云 等国内云GPU平台
+
+用法:
+  bash setup_cloud.sh                    # 完整搭建
+  bash setup_cloud.sh --train-only       # 只拉代码，不下载数据
+"""
+
+set -e
+
+echo "========================================"
+echo "  XiaLi 云 GPU 训练环境搭建"
+echo "========================================"
+
+REPO_URL="https://gitee.com/shuiyu1123/xiaoli-llm-chat.git"
+WORK_DIR="/root/xiaoli-llm-chat"
+
+# 1. 克隆代码
+echo ""
+echo "[1/4] 克隆代码..."
+if [ -d "$WORK_DIR" ]; then
+    echo "  目录已存在，拉取更新..."
+    cd "$WORK_DIR" && git pull
+else
+    git clone "$REPO_URL" "$WORK_DIR"
+    cd "$WORK_DIR"
+fi
+
+# 2. 安装依赖
+echo ""
+echo "[2/4] 安装依赖..."
+pip install torch numpy -q
+# 检查 torch 是否有 CUDA
+python3 -c "import torch; print(f'CUDA可用: {torch.cuda.is_available()}, GPU数: {torch.cuda.device_count()}')" 2>/dev/null || {
+    echo "  ⚠️ 检测到无 CUDA，尝试安装 GPU 版 PyTorch..."
+    pip install torch --index-url https://download.pytorch.org/whl/cu118 -q
+}
+pip install modelscope -q
+
+# 确认词表存在
+echo ""
+echo "[3/4] 检查词表..."
+ls -la vocab.json 2>/dev/null || {
+    echo "  ⚠️ vocab.json 不存在！请确认仓库完整"
+    exit 1
+}
+python3 -c "
+import json
+with open('vocab.json') as f:
+    v = json.load(f)
+print(f'  词表: {len(v)} tokens, vocab_size={max(v.values())+1}')
+"
+
+# 4. 确认训练脚本
+echo ""
+echo "[4/4] 确认训练脚本..."
+ls -la train_cloud.py && echo "  ✅ train_cloud.py 就绪"
+
+# 显存信息
+echo ""
+echo "========================================"
+echo "  环境就绪！"
+echo "========================================"
+python3 -c "
+import torch
+if torch.cuda.is_available():
+    total = torch.cuda.get_device_properties(0).total_mem / 1024**3
+    name = torch.cuda.get_device_name(0)
+    print(f'  GPU: {name} ({total:.1f} GiB)')
+else:
+    print('  ⚠️ 未检测到 GPU！')
+"
+echo ""
+echo "启动训练命令:"
+echo "  cd $WORK_DIR"
+echo ""
+echo "  # 单卡训练（完整流程）"
+echo "  python3 train_cloud.py --epochs 5 --align-epochs 20"
+echo ""
+echo "  # 从检查点续训"
+echo "  python3 train_cloud.py --resume --epochs 10"
+echo ""
+echo "  # 24G显存用更小batch"
+echo "  python3 train_cloud.py --batch-size 32"
+echo ""
+echo "========================================"