Add torch (CPU) to evalscope-complete-py312 Docker image for tokenizer support
- Install torch==2.1.2 (CPU wheel) so AutoTokenizer can load models like DeepSeek-V4-Flash without runtime import errors. - Keep numpy==1.26.4 pinned; torch 2.1.x is compatible with it. - Verify torch and transformers AutoTokenizer import in the image build. Note: tools/docker/* is gitignored, so this file is force-added.
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tools/docker/Dockerfile.py312
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tools/docker/Dockerfile.py312
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# EvalScope Benchmark Docker Image (Python 3.12)
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# 除 swe_bench 系列外，其他 benchmark 均可直接运行
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# 使用国内镜像源解决网络问题
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# 基于 python:3.12-slim，在容器内重新安装所有依赖
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FROM python:3.12-slim-bookworm
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LABEL maintainer="evalscope-benchmark"
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LABEL description="EvalScope benchmark environment with Python 3.12, all dependencies including swe_bench"
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ENV DEBIAN_FRONTEND=noninteractive
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ENV PYTHONUNBUFFERED=1
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ENV PIP_NO_CACHE_DIR=1
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ENV PYTHONDONTWRITEBYTECODE=1
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# 配置 apt 使用清华镜像源
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RUN rm -f /etc/apt/sources.list.d/*.list && \
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echo 'deb https://mirrors.tuna.tsinghua.edu.cn/debian bookworm main contrib non-free' > /etc/apt/sources.list && \
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echo 'deb https://mirrors.tuna.tsinghua.edu.cn/debian bookworm-updates main contrib non-free' >> /etc/apt/sources.list && \
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echo 'deb https://mirrors.tuna.tsinghua.edu.cn/debian-security bookworm-security main contrib non-free' >> /etc/apt/sources.list && \
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apt-get update && \
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apt-get install -y --no-install-recommends \
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git wget curl ca-certificates \
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vim \
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build-essential libssl-dev libffi-dev zlib1g-dev \
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docker.io \
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&& rm -rf /var/lib/apt/lists/*
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# 配置 pip 使用清华镜像源
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RUN mkdir -p /root/.config/pip && \
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cat > /root/.config/pip/pip.conf << 'EOF'
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[global]
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index-url = https://pypi.tuna.tsinghua.edu.cn/simple
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trusted-host = pypi.tuna.tsinghua.edu.cn
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timeout = 120
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retries = 5
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EOF
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# 升级 pip
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RUN pip install --upgrade pip setuptools wheel
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# 先固定 numpy/scipy 版本（bfcl-eval 要求 numpy==1.26.4；新版 scipy 要求 numpy>=2.0）
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RUN pip install numpy==1.26.4 scipy==1.13.1
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# 安装 evalscope 依赖（先安装依赖，再用本地源码覆盖）
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RUN pip install \
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openai pandas pyyaml requests tqdm \
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tiktoken transformers \
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scikit-learn matplotlib seaborn plotly \
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jieba nltk rouge-score sacrebleu \
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sympy latex2sympy2_extended pillow \
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docker pexpect pytest \
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tabulate rich jsonlines jsonschema \
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langdetect word2number zhconv \
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modelscope pydantic overrides \
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more_itertools pylatexenc \
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rouge-chinese markdown \
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editdistance dotenv docstring_parser \
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colorlog
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# 安装 torch（CPU 版即可，主要用于 tokenizer 加载与部分 benchmark 的 tensor 操作）
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# 固定 numpy==1.26.4 已在前一步完成，torch 2.1.x 与该版本兼容
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RUN pip install torch==2.1.2 --index-url https://download.pytorch.org/whl/cpu 2>/dev/null || \
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pip install torch==2.1.2 2>/dev/null || true
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# 安装 sandbox 支持（ms-sandbox 等）
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RUN pip install evalscope[sandbox] 2>/dev/null || \
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pip install ms-sandbox 2>/dev/null || true
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# 安装 terminal_bench 依赖 (harbor)
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RUN pip install "harbor>=0.8.0,<1.0.0" 2>/dev/null || true
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# 安装 tau2-bench 依赖（不安装 torch/vllm，避免镜像过大）
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RUN pip install \
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fastapi uvicorn psutil loguru \
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litellm tenacity deepdiff addict toml 2>/dev/null || true
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# 安装 tau2-bench 依赖
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RUN pip install git+https://github.com/sierra-research/tau2-bench@v0.2.0 2>/dev/null || true
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# 安装 bigcodebench 核心评估依赖（不安装 torch/vllm/accelerate）
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RUN pip install \
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tempdir termcolor wget \
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gradio-client 2>/dev/null || true
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# 安装 bfcl_v3 依赖（--no-deps 避免 torch，再手动补必要依赖）
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# tree-sitter 必须固定版本，bfcl-eval 要求 tree_sitter==0.21.3，
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# 否则 Language() API 不兼容会报错
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RUN pip install --no-deps bfcl-eval==2025.10.27.1 2>/dev/null || true
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RUN pip install \
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anthropic cohere==5.18.0 datamodel-code-generator==0.25.7 \
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faiss-cpu==1.11.0 google-genai==1.24.0 mistralai==1.7.0 \
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networkx==3.3 google-search-results \
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rank_bm25 html2text boto3 qwen-agent writer-sdk \
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tree-sitter==0.21.3 \
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tree-sitter-python==0.21.0 \
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tree-sitter-javascript==0.21.4 \
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tree-sitter-java==0.21.0 \
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pathlib 2>/dev/null || true
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# sentence-transformers 会强制拉 torch（~2GB+），如运行 bfcl_v3 的 embedding 类任务需手动安装
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# 安装 soundfile / openpyxl
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RUN pip install soundfile openpyxl 2>/dev/null || true
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# 安装 SWE-bench 支持
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RUN pip install evalscope[swe_bench] 2>/dev/null || \
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pip install swebench==4.1.0 2>/dev/null || true
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# 创建 workspace
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RUN mkdir -p /opt/evalscope
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WORKDIR /opt/evalscope
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# 复制本地 evalscope 源码、bash 脚本和 tau2-bench
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# 注意：tau2-bench 在仓库里位于 tools/tau2-bench/
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COPY evalscope/ /opt/evalscope/evalscope/
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COPY bash/ /opt/evalscope/bash/
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COPY tau2-bench/ /opt/evalscope/tools/tau2-bench/
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# 用本地源码安装 evalscope（editable mode）
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RUN pip install -e /opt/evalscope/evalscope/
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# 安装本地 tau2-bench 到 Python 环境（先装依赖，避免与 bfcl-eval 冲突）
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RUN pip install \
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rich tabulate fastapi uvicorn pandas psutil loguru \
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docstring-parser "litellm>=1.80.15,<1.82.7" "tenacity>=9.0.0" \
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deepdiff addict PyYAML toml python-dotenv typer requests httpx \
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2>/dev/null || true
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RUN pip install -e /opt/evalscope/tools/tau2-bench/ --no-deps 2>/dev/null || true
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# 最终固定 numpy/scipy 版本，确保 bfcl-eval 兼容且 scipy 不与 numpy 1.26.4 冲突
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RUN pip install --force-reinstall numpy==1.26.4 scipy==1.13.1 2>/dev/null || true
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# 设置权限
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RUN chmod -R +x /opt/evalscope/bash/*.py 2>/dev/null || true
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# 创建输出目录
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RUN mkdir -p /opt/evalscope/output \
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/opt/evalscope/output_limit100 \
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/opt/evalscope/output_swe_bench \
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/opt/evalscope/datasets
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# 验证安装
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RUN python -c "import evalscope; print('evalscope ok')" && \
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python -c "import evalscope.api.agent; print('evalscope.api.agent ok')" && \
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python -c "import openai; print('openai ok')" && \
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python -c "import pandas; print('pandas ok')" && \
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python -c "import numpy; print('numpy ok')" && \
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python -c "import torch; print('torch ok')" && \
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python -c "from transformers import AutoTokenizer; print('transformers tokenizer ok')" && \
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python -c "import docker; print('docker ok')" && \
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python -c "import harbor; print('harbor ok')" && \
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python -c "import tau2; print('tau2 ok')" && \
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python -c "from tau2.runner.batch import run_single_task; print('tau2 runner ok')" && \
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python -c "import bfcl_eval; print('bfcl_eval ok')" && \
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python -c "import soundfile; print('soundfile ok')" && \
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python -c "import openpyxl; print('openpyxl ok')" && \
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docker --version
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# 设置 PYTHONPATH，确保 editable install 的 evalscope 能被正确加载
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ENV PYTHONPATH=/opt/evalscope/evalscope
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# 默认命令
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CMD ["/bin/bash"]
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