121 lines
5.8 KiB
Bash
Executable File
121 lines
5.8 KiB
Bash
Executable File
#!/usr/bin/env bash
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# ═══════════════════════════════════════════════════════════
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# 铸渊编程模型训练机 · setup-coding.sh
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# ═══════════════════════════════════════════════════════════
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# 签发: 铸渊 · ICE-GL-ZY001 · 国作登字-2026-A-00037559
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#
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# 在已经跑过母模型 (server/training-agent/setup.sh) 的同一台 GPU 机
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# (zy-gpu-train · 119.45.160.137 · V100×4) 上准备编程模型训练环境。
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#
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# 复用母模型的:
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# - Python venv (/opt/guanghu/training/.venv)
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# - DeepSpeed / transformers / datasets
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# - coscmd
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#
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# 编程模型独立的:
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# - 数据根: /data/guanghu-coding (与母模型 /data/guanghu 隔离)
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# - 模型基座: 从母模型 SFT 产出复制, 不重新下载
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# - 输出: /data/guanghu-coding/checkpoints/zy_coding_v1
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#
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# 用法 (由 coding-model-train.yml 通过 SSH 调用):
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# sudo bash setup-coding.sh
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#
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# 必需环境变量 (写在 /opt/guanghu/coding-training/.env):
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# ZY_BASE_MODEL_DIR 母模型 SFT 产出路径 (默认 /data/guanghu/checkpoints/qwen2_5_7b_sft/best)
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# ZY_BINGSHUO_DIALOG_ZIP 冰朔×铸渊对话 ZIP 路径 (会解压到 raw/bingshuo-dialog/)
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# 可不提供, 但产物质量会下降
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# GH_REPO_OWNER qinfendebingshuo
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# GH_REPO_NAME guanghulab
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# GH_REPO_BRANCH main
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# ═══════════════════════════════════════════════════════════
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set -euo pipefail
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ROOT="${ZY_CODING_TRAIN_ROOT:-/opt/guanghu/coding-training}"
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DATA_DIR="${ZY_CODING_TRAIN_DATA:-/data/guanghu-coding}"
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ENV_FILE="$ROOT/.env"
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MOTHER_VENV="${ZY_MOTHER_VENV:-/opt/guanghu/training/.venv}"
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MOTHER_BASE_MODEL="${ZY_BASE_MODEL_DIR:-/data/guanghu/checkpoints/qwen2_5_7b_sft/best}"
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echo "═════════════════════════════════════════"
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echo " 铸渊编程模型训练机 · setup-coding.sh"
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echo "═════════════════════════════════════════"
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echo " ROOT = $ROOT"
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echo " DATA_DIR = $DATA_DIR"
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echo " VENV = $MOTHER_VENV (复用母模型)"
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echo " 基座模型 = $MOTHER_BASE_MODEL"
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echo "═════════════════════════════════════════"
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# ── Step 1: 检查母模型环境 ──
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echo "[1/6] 检查母模型 venv..."
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if [[ ! -d "$MOTHER_VENV" ]]; then
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echo "❌ 母模型 venv 不存在: $MOTHER_VENV"
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echo " 请先在本机跑 server/training-agent/setup.sh 准备母模型环境。"
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exit 1
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fi
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echo " ✅ venv 存在"
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echo "[2/6] 检查母模型 SFT 产出..."
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if [[ ! -d "$MOTHER_BASE_MODEL" ]]; then
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echo "❌ 母模型 SFT 产出不存在: $MOTHER_BASE_MODEL"
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echo " 编程模型训练必须以母模型为基座. 请先完成母模型训练."
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exit 1
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fi
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# 验证有 config.json + model.safetensors / pytorch_model.bin
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if [[ ! -f "$MOTHER_BASE_MODEL/config.json" ]]; then
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echo "❌ $MOTHER_BASE_MODEL/config.json 缺失, 不像是有效的 transformers 模型目录"
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exit 1
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fi
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echo " ✅ 母模型基座可用"
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# ── Step 3: 创建目录 ──
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echo "[3/6] 创建目录树..."
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sudo mkdir -p "$ROOT" "$DATA_DIR/raw/bingshuo-dialog" "$DATA_DIR/processed" "$DATA_DIR/checkpoints" "$DATA_DIR/logs"
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sudo chown -R "$(id -u):$(id -g)" "$ROOT" "$DATA_DIR"
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echo " ✅ 目录就绪"
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# ── Step 4: 拉仓库脚本 ──
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echo "[4/6] 同步训练脚本到 $ROOT ..."
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# 假定 SCP 已经把 server/coding-model-training/ 的文件拷贝到 $ROOT
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# (由 GitHub workflow 在外部 SCP, 本脚本只校验)
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for f in train_coding.py build_coding_corpus.py start-coding-training.sh configs/ds_zero3_offload.json; do
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if [[ ! -f "$ROOT/$f" ]]; then
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echo "❌ 必需文件 $ROOT/$f 缺失. 应由 workflow SCP 提供."
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exit 1
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fi
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done
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chmod +x "$ROOT/start-coding-training.sh"
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echo " ✅ 脚本就绪"
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# ── Step 5: 解压冰朔×铸渊对话 ZIP (如果有) ──
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echo "[5/6] 处理对话 ZIP..."
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if [[ -n "${ZY_BINGSHUO_DIALOG_ZIP:-}" ]] && [[ -f "$ZY_BINGSHUO_DIALOG_ZIP" ]]; then
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echo " 解压 $ZY_BINGSHUO_DIALOG_ZIP → $DATA_DIR/raw/bingshuo-dialog/"
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unzip -oq "$ZY_BINGSHUO_DIALOG_ZIP" -d "$DATA_DIR/raw/bingshuo-dialog/"
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md_count=$(find "$DATA_DIR/raw/bingshuo-dialog/" -name "*.md" | wc -l)
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echo " ✅ 解压完成, $md_count 个 .md 文件"
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else
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echo " ⚠️ 未提供 ZY_BINGSHUO_DIALOG_ZIP, 跳过 (训练仍可进行, 但只有灵魂语料 + corpus)"
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fi
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# ── Step 6: 跑语料构建器 ──
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echo "[6/6] 构建训练语料..."
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cd "$ROOT"
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ZY_CODING_TRAIN_DATA="$DATA_DIR" \
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ZY_REPO_ROOT="${ZY_REPO_ROOT:-$ROOT/repo}" \
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ZY_BINGSHUO_DIALOG_DIR="$DATA_DIR/raw/bingshuo-dialog" \
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"$MOTHER_VENV/bin/python" "$ROOT/build_coding_corpus.py" 2>&1 | tee "$DATA_DIR/logs/build-corpus.log"
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samples_count=$(wc -l < "$DATA_DIR/processed/coding-sft.jsonl" || echo 0)
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echo ""
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echo "═════════════════════════════════════════"
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echo " ✅ 编程模型训练机就绪"
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echo "═════════════════════════════════════════"
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echo " 数据根 : $DATA_DIR"
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echo " 训练语料 : $DATA_DIR/processed/coding-sft.jsonl"
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echo " 样本数 : $samples_count"
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echo " 输出目录 : $DATA_DIR/checkpoints/zy_coding_v1"
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echo ""
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echo " 下一步: 运行 start-coding-training.sh 启动训练"
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echo "═════════════════════════════════════════"
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