254 lines
8.1 KiB
Bash
Executable File
254 lines
8.1 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 <BASE_DIR> [TOKEN]
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# 示例: bash deploy_evalscope.sh /data1/sora ms-3d554a39-6e07-496d-8022-0b0ee64a6389
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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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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="$EVALSCOPE_DIR/datasets"
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# --------------------------------------------------
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# 2. 安装依赖
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# --------------------------------------------------
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echo "==> 安装 modelscope..."
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pip install modelscope
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# --------------------------------------------------
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# 3. 登录 ModelScope
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# --------------------------------------------------
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if [ -n "$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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docker load -i "$DOCKER_DIR/evalscope-complete-py312.tar.gz"
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# --------------------------------------------------
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# 8. 克隆代码仓库
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# --------------------------------------------------
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echo "==> 克隆 evalscope 代码..."
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cd "$EVALSCOPE_DIR"
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if [ ! -d "evalstone" ]; then
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git clone https://git.meta-stone.net/sora/evalstone.git
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fi
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# --------------------------------------------------
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# 9. 准备 sandbox 镜像
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# --------------------------------------------------
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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 $expected_image 已存在，跳过加载"
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return 0
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fi
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if [ -f "$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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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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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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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（可选）
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# --------------------------------------------------
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if [ -n "$MODELSCOPE_TOKEN" ]; 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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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 $TOTAL 个镜像包"
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IDX=0
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for TAR in "${TARS[@]}"; do
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IDX=$((IDX + 1))
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echo "[$IDX/$TOTAL] 加载 $(basename "$TAR")..."
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docker load -i "$TAR"
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done
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echo "SWE-Bench 镜像加载完成，已加载 $(docker images | grep '^swebench/' | wc -l) 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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