279a83051d
- Previously the script cloned code into /evalstone/, so after mounting the host directory to /opt/evalscope the container could not find bash/run.py and the evalscope package. - Now the script clones directly into root, preserving existing datasets/ and docker/ directories. - Also handles migration from old evalstone/ subdirectory layouts.
399 lines
13 KiB
Bash
Executable File
399 lines
13 KiB
Bash
Executable File
#!/bin/bash
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# ============================================================
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# EvalScope 完整部署脚本
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# 用法: bash deploy_evalscope.sh [OPTIONS] <BASE_DIR> [TOKEN]
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# 示例:
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# bash deploy_evalscope.sh /data1/sora
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# bash deploy_evalscope.sh --download-swe /data1/sora
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#
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# 断点续传：已加载的镜像/tar 包会记录在 <BASE_DIR>/evalscope/docker/.deploy_checkpoint
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# 重复执行时会自动跳过，避免二次加载。
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# ============================================================
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set -euo pipefail
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# --------------------------------------------------
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# 1. 参数解析
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# --------------------------------------------------
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DOWNLOAD_SWE=0
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POSITIONAL_ARGS=()
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while [[ $# -gt 0 ]]; do
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case "$1" in
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--download-swe)
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DOWNLOAD_SWE=1
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shift
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;;
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--help|-h)
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echo "用法: bash deploy_evalscope.sh [OPTIONS] <BASE_DIR> [TOKEN]"
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echo "选项:"
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echo " --download-swe 同时下载 SWE-Bench 镜像包（默认不下载，体积很大）"
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echo " -h, --help 显示帮助"
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exit 0
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;;
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-*)
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echo "未知选项: $1"
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echo "使用 --help 查看用法"
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exit 1
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;;
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*)
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POSITIONAL_ARGS+=("$1")
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shift
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;;
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esac
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done
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set -- "${POSITIONAL_ARGS[@]}"
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BASE_DIR="${1:-/data1/sora}" # 基础目录，默认 /data1/sora
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DEFAULT_TOKEN="ms-3d554a39-6e07-496d-8022-0b0ee64a6389"
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MODELSCOPE_TOKEN="${2:-$DEFAULT_TOKEN}"
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# 派生路径
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EVALSCOPE_DIR="$BASE_DIR/evalscope"
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DOCKER_DIR="$EVALSCOPE_DIR/docker"
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IMAGES_DIR="$DOCKER_DIR/images"
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CONTAINERD_DIR="$DOCKER_DIR/containerd"
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SWE_IMAGES_DIR="$DOCKER_DIR/swe_images"
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DATASETS_DIR="$EVALSCOPE_DIR/datasets"
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CHECKPOINT_FILE="$DOCKER_DIR/.deploy_checkpoint"
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# 断点：记录/检查已加载的镜像或 tar 包
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mark_loaded() {
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local key="$1"
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mkdir -p "$DOCKER_DIR"
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touch "$CHECKPOINT_FILE"
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if ! grep -qx "$key" "$CHECKPOINT_FILE" 2>/dev/null; then
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echo "$key" >> "$CHECKPOINT_FILE"
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fi
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}
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is_loaded() {
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local key="$1"
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if [ -f "$CHECKPOINT_FILE" ] && grep -qx "$key" "$CHECKPOINT_FILE" 2>/dev/null; then
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return 0
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fi
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return 1
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}
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# --------------------------------------------------
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# 2. 安装依赖
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# --------------------------------------------------
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echo "==> 安装 modelscope..."
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pip install modelscope
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# --------------------------------------------------
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# 3. 登录 ModelScope
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# --------------------------------------------------
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if [ -n "$MODELSCOPE_TOKEN" ]; then
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echo "==> 登录 ModelScope..."
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modelscope login --token "$MODELSCOPE_TOKEN"
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fi
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# --------------------------------------------------
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# 4. 下载数据集
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# --------------------------------------------------
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echo "==> 下载 evalscope 数据集..."
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mkdir -p "$DATASETS_DIR"
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python3 -c "
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from modelscope.hub.snapshot_download import snapshot_download
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snapshot_download(
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'SoraAmami/evalscope-datasets',
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repo_type='dataset',
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cache_dir='$EVALSCOPE_DIR',
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local_dir='$DATASETS_DIR'
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)
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"
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# --------------------------------------------------
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# 5. 下载 Docker 镜像包
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# --------------------------------------------------
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echo "==> 下载 evalscope Docker 镜像..."
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mkdir -p "$DOCKER_DIR"
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python3 -c "
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from modelscope.hub.file_download import model_file_download
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model_file_download(
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model_id='SoraAmami/evalscope-docker',
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file_path='evalscope-complete-py312.tar.gz',
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local_dir='$DOCKER_DIR'
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)
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"
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# --------------------------------------------------
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# 6. 配置 Docker 和 containerd 数据目录
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# --------------------------------------------------
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echo "==> 配置 Docker 和 containerd..."
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# 停止服务
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sudo systemctl stop docker.socket 2>/dev/null || true
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sudo systemctl stop docker 2>/dev/null || true
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sudo systemctl stop containerd 2>/dev/null || true
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# 创建数据目录
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mkdir -p "$IMAGES_DIR"
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mkdir -p "$CONTAINERD_DIR"
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# 配置 Docker
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echo "==> 写入 Docker 配置..."
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sudo tee /etc/docker/daemon.json <<EOF
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{
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"data-root": "$IMAGES_DIR",
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"features": {
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"containerd-snapshotter": true
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},
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"registry-mirrors": [
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"https://docker.m.daocloud.io",
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"https://docker.1ms.run",
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"https://hub.rat.dev",
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"https://docker.1panel.live",
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"https://dockerproxy.com",
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"https://hub-mirror.c.163.com",
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"https://mirror.baidubce.com",
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"https://docker.mirrors.ustc.edu.cn",
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"https://docker.mirrors.sjtug.sjtu.edu.cn",
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"https://docker.nju.edu.cn",
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"https://docker.mirrors.tuna.tsinghua.edu.cn"
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]
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}
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EOF
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# 配置 containerd
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echo "==> 写入 containerd 配置..."
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sudo tee /etc/containerd/config.toml <<EOF
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root = "$CONTAINERD_DIR"
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state = "/run/containerd"
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EOF
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# 重启服务
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sudo systemctl reset-failed docker.service 2>/dev/null || true
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sudo systemctl start containerd
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sudo systemctl start docker
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# --------------------------------------------------
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# 7. 加载 evalscope 环境镜像
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# --------------------------------------------------
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echo "==> 加载 evalscope Docker 镜像..."
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if docker images --format '{{.Repository}}:{{.Tag}}' | grep -qx "evalscope-complete-py312:latest"; then
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echo "evalscope-complete-py312:latest 已存在，跳过加载"
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elif is_loaded "evalscope-complete-py312.tar.gz"; then
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echo "evalscope-complete-py312.tar.gz 已加载过（checkpoint），跳过加载"
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else
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docker load -i "$DOCKER_DIR/evalscope-complete-py312.tar.gz"
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mark_loaded "evalscope-complete-py312.tar.gz"
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fi
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# --------------------------------------------------
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# 8. 克隆代码仓库到根目录
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# --------------------------------------------------
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echo "==> 克隆/更新 evalscope 代码..."
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clone_or_update_code() {
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local repo_url="https://git.meta-stone.net/sora/evalstone.git"
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# 1) 如果根目录已经有代码，直接跳过（可手动 git pull 更新）
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if [ -f "$EVALSCOPE_DIR/bash/run.py" ]; then
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echo "代码已存在于 $EVALSCOPE_DIR，跳过克隆"
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return 0
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fi
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# 2) 兼容旧版：如果发现 evalstone/ 子目录，把里面的代码内容提到根目录
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if [ -d "$EVALSCOPE_DIR/evalstone" ] && [ -f "$EVALSCOPE_DIR/evalstone/bash/run.py" ]; then
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echo "发现旧版 evalstone/ 子目录，迁移代码到根目录..."
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local tmp_dir="$EVALSCOPE_DIR/.evalstone_tmp_$(date +%s)"
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mv "$EVALSCOPE_DIR/evalstone" "$tmp_dir"
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for item in "$tmp_dir"/* "$tmp_dir"/.[!.]* "$tmp_dir"/..?*; do
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[ -e "$item" ] || continue
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local basename_item
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basename_item=$(basename "$item")
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# datasets/ 和 docker/ 可能已经存在，跳过同名覆盖
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if [ "$basename_item" = "datasets" ] || [ "$basename_item" = "docker" ]; then
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continue
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fi
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mv "$item" "$EVALSCOPE_DIR/$basename_item"
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done
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rm -rf "$tmp_dir"
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return 0
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fi
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# 3) 全新部署：目录可能非空（已有 datasets/ docker/），先临时移出再克隆
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echo "正在克隆代码到 $EVALSCOPE_DIR ..."
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local tmp_backup="$BASE_DIR/.evalscope_deploy_backup_$(date +%s)"
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mkdir -p "$tmp_backup"
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for item in "$EVALSCOPE_DIR"/* "$EVALSCOPE_DIR"/.[!.]* "$EVALSCOPE_DIR"/..?*; do
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[ -e "$item" ] || continue
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local basename_item
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basename_item=$(basename "$item")
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case "$basename_item" in
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.|..|.git) continue ;;
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datasets|docker)
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mv "$item" "$tmp_backup/$basename_item" ;;
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*)
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# 其他内容一并移走，避免 git clone 因非空目录失败
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mv "$item" "$tmp_backup/$basename_item" ;;
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esac
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done
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git clone "$repo_url" "$EVALSCOPE_DIR"
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# 把 datasets/ docker/ 等移回来
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for item in "$tmp_backup"/* "$tmp_backup"/.[!.]* "$tmp_backup"/..?*; do
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[ -e "$item" ] || continue
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local basename_item
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basename_item=$(basename "$item")
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[ "$basename_item" = "." ] || [ "$basename_item" = ".." ] && continue
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# 如果目标已存在（比如代码仓库里也有 docker/），保留已有内容
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if [ -e "$EVALSCOPE_DIR/$basename_item" ]; then
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continue
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fi
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mv "$item" "$EVALSCOPE_DIR/$basename_item"
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done
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rm -rf "$tmp_backup" 2>/dev/null || true
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}
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clone_or_update_code
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# --------------------------------------------------
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# 9. 准备 sandbox 镜像
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# --------------------------------------------------
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echo "==> 准备 sandbox 镜像..."
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SANDBOX_IMAGES_DIR="$DOCKER_DIR/sandbox_images"
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mkdir -p "$SANDBOX_IMAGES_DIR"
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# 从 ModelScope 下载预打包的 sandbox 镜像（不再使用 docker pull，避免 DockerHub 限速）
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download_sandbox_image_from_modelscope() {
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local file="$1"
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if [ -f "$SANDBOX_IMAGES_DIR/$file" ]; then
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echo "sandbox 镜像包 $file 已存在，跳过下载"
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return 0
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fi
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echo "==> 从 ModelScope 下载 $file ..."
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# 优先使用新版 modelscope_hub API（支持 dataset repo）
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python3 -c "
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from modelscope_hub import HubApi
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api = HubApi()
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api.download_file(
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repo_id='SoraAmami/evalscope-sandbox-images',
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repo_type='dataset',
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path='$file',
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local_dir='$SANDBOX_IMAGES_DIR'
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)
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" && return 0
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echo "警告：从 ModelScope 下载 $file 失败"
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return 1
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}
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load_sandbox_image() {
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local file="$1"
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local expected_image="$2"
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if docker images --format '{{.Repository}}:{{.Tag}}' | grep -qx "$expected_image"; then
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echo "镜像 $expected_image 已存在，跳过加载"
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return 0
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fi
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if is_loaded "$file"; then
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echo "镜像包 $file 已加载过（checkpoint），跳过加载"
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return 0
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fi
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if [ -f "$SANDBOX_IMAGES_DIR/$file" ]; then
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echo "==> 加载 $file ..."
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docker load -i "$SANDBOX_IMAGES_DIR/$file"
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mark_loaded "$file"
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return 0
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fi
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return 1
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}
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# 9.1 bigcodebench-sandbox:latest
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if docker images --format '{{.Repository}}:{{.Tag}}' | grep -qx "bigcodebench-sandbox:latest"; then
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echo "bigcodebench-sandbox:latest 已存在，跳过"
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else
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if ! load_sandbox_image "bigcodebench-sandbox.tar.gz" "bigcodebench-sandbox:latest"; then
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download_sandbox_image_from_modelscope "bigcodebench-sandbox.tar.gz" && \
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load_sandbox_image "bigcodebench-sandbox.tar.gz" "bigcodebench-sandbox:latest"
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fi
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fi
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# 9.2 python:3.11-slim
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if docker images --format '{{.Repository}}:{{.Tag}}' | grep -qx "python:3.11-slim"; then
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echo "python:3.11-slim 已存在，跳过"
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else
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if ! load_sandbox_image "python-3.11-slim.tar.gz" "python:3.11-slim"; then
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download_sandbox_image_from_modelscope "python-3.11-slim.tar.gz" && \
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load_sandbox_image "python-3.11-slim.tar.gz" "python:3.11-slim"
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fi
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fi
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# --------------------------------------------------
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# 10. 下载 SWE-Bench 镜像包（默认不下载，需加 --download-swe）
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# --------------------------------------------------
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if [ "$DOWNLOAD_SWE" -eq 1 ]; then
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echo "==> 下载 SWE-Bench 镜像包..."
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mkdir -p "$SWE_IMAGES_DIR"
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modelscope download \
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--repo-type dataset \
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--local_dir "$SWE_IMAGES_DIR" \
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SoraAmami/swe-bench-verified-images || echo "警告：SWE-Bench 镜像下载失败"
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else
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echo "==> 跳过 SWE-Bench 镜像包下载（如需下载请加 --download-swe）"
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fi
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# --------------------------------------------------
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# 11. 加载 SWE-Bench 镜像（如果存在）
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# --------------------------------------------------
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if [ -d "$SWE_IMAGES_DIR" ] && ls "$SWE_IMAGES_DIR"/swebench_batch_*.tar.gz 1>/dev/null 2>&1; then
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echo "==> 加载 SWE-Bench 镜像..."
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TARS=("$SWE_IMAGES_DIR"/swebench_batch_*.tar.gz)
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TOTAL=${#TARS[@]}
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echo "共找到 $TOTAL 个镜像包"
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IDX=0
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LOADED_COUNT=0
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SKIPPED_COUNT=0
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for TAR in "${TARS[@]}"; do
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IDX=$((IDX + 1))
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BASENAME=$(basename "$TAR")
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if is_loaded "$BASENAME"; then
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echo "[$IDX/$TOTAL] 跳过（已加载过）: $BASENAME"
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SKIPPED_COUNT=$((SKIPPED_COUNT + 1))
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continue
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fi
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echo "[$IDX/$TOTAL] 加载 $BASENAME ..."
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docker load -i "$TAR"
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mark_loaded "$BASENAME"
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LOADED_COUNT=$((LOADED_COUNT + 1))
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done
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echo "SWE-Bench 镜像加载完成：本次加载 $LOADED_COUNT 个，跳过 $SKIPPED_COUNT 个，已加载 $(docker images | grep -c '^swebench/' || true) 个"
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else
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echo "跳过 SWE-Bench 镜像加载（未找到镜像包）"
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fi
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# --------------------------------------------------
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# 12. 验证部署
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# --------------------------------------------------
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echo ""
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echo "========================================"
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echo "部署完成！"
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echo "========================================"
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echo "基础目录: $BASE_DIR"
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echo "EvalScope: $EVALSCOPE_DIR"
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echo "数据集: $DATASETS_DIR"
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echo "Docker 数据: $IMAGES_DIR"
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echo "Containerd: $CONTAINERD_DIR"
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echo ""
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echo "Docker Root Dir:"
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docker info 2>/dev/null | grep "Docker Root Dir" || echo "Docker 未运行"
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echo ""
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echo "已加载镜像:"
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docker images | grep -E "evalscope|swebench|bigcodebench|python" || true
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echo ""
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echo "运行 EvalScope:"
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echo " cd $EVALSCOPE_DIR"
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echo " docker run -it --rm \\"
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echo " --network host \\"
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echo " -v $EVALSCOPE_DIR:/opt/evalscope \\"
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echo " -v /var/run/docker.sock:/var/run/docker.sock \\"
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echo " evalscope-complete-py312:latest \\"
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echo " bash"
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echo "========================================"
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