claude-bot
09ea8a67ce
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01FYqwNMyPUHeTbFWg83GQq6
99 lines
4.4 KiB
Docker
99 lines
4.4 KiB
Docker
# lean-bench: Lean 4 + Mathlib + the ingestion pipeline, in one image.
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#
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# Build from the repository root:
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# docker build -f docker/Dockerfile -t lean-bench:latest .
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#
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# The Mathlib revision and the Lean toolchain are resolved against each other at
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# build time (see docker/resolve-mathlib.sh); override with --build-arg MATHLIB_REV=v4.33.0.
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FROM ubuntu:24.04
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LABEL org.opencontainers.image.title="lean-bench" \
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org.opencontainers.image.description="Detect formalizable mathematics in books and turn it into verified, indexed Lean 4" \
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org.opencontainers.image.source="https://gitea.entroia.com/Zayden-Jung/lean-bench"
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ENV DEBIAN_FRONTEND=noninteractive \
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ELAN_HOME=/opt/elan \
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PATH=/opt/elan/bin:$PATH \
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PYTHONUNBUFFERED=1
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RUN apt-get update && apt-get install -y --no-install-recommends \
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build-essential ca-certificates curl git jq sqlite3 \
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libgmp-dev python3 python3-pip \
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&& rm -rf /var/lib/apt/lists/*
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# -- elan (the Lean toolchain manager) --------------------------------------
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RUN curl -fsSL https://raw.githubusercontent.com/leanprover/elan/master/elan-init.sh -o /tmp/elan-init.sh \
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&& sh /tmp/elan-init.sh -y --no-modify-path --default-toolchain none \
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&& rm /tmp/elan-init.sh \
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&& elan --version
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WORKDIR /opt/lean-bench
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# -- Lean project: toolchain, Mathlib, and its prebuilt cache ---------------
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# One layer per step on purpose. The cache fetch is the expensive one (gigabytes),
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# so it gets a layer of its own: a failure in any later step re-runs that step, never
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# the download. Every long step is wrapped in `timeout` so a stall fails loudly with
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# a diagnosable message instead of hanging until the CI job's own limit.
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ARG MATHLIB_REV=auto
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COPY lean/ lean/
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COPY docker/resolve-mathlib.sh docker/resolve-mathlib.sh
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RUN chmod +x docker/resolve-mathlib.sh && docker/resolve-mathlib.sh lean "$MATHLIB_REV"
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RUN elan toolchain install "$(cat lean/lean-toolchain)" \
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&& elan default "$(cat lean/lean-toolchain)"
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# Resolve dependencies: clones Mathlib and writes the manifest.
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RUN cd lean && timeout 1800 lake update
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# Mathlib's prebuilt artifacts. Slow, large, and cached as its own layer.
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# A handful of the ~9,000 transfers failing is normal; a re-run fetches only what is missing.
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RUN cd lean && for attempt in 1 2 3 4; do \
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timeout 5400 lake exe cache get && break; \
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[ "$attempt" -eq 4 ] && exit 1; \
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echo "cache get attempt $attempt failed; retrying" >&2; sleep $((attempt * 15)); \
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done
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# The honest check that this image can do its job, and it is exactly what
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# verification does at runtime: elaborate a file against Mathlib with `lake env lean`.
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# There is deliberately no `lake build` here -- compiling our own library is something
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# nothing in the pipeline ever uses.
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#
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# Loading Mathlib's environment needs several GB of RAM. On a host that does not have
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# it, this does not fail: it thrashes, and the only symptom is silence. So it runs
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# under `timeout`, and exit 124 is reported for what it almost always is.
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RUN cd lean && timeout 1200 lake env lean LeanBench.lean; status=$?; \
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if [ "$status" -eq 124 ]; then \
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echo "ERROR: elaborating 'import Mathlib' timed out after 20 minutes." >&2; \
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echo "Every olean it needs is already on local disk, so this is not the network." >&2; \
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echo "Loading Mathlib wants ~4-8 GB of RAM. This machine has:" >&2; \
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{ free -h || head -3 /proc/meminfo; } >&2 2>/dev/null; \
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echo "Give the build host more memory, or build this image elsewhere." >&2; \
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fi; \
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[ "$status" -eq 0 ] || exit "$status"; \
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lake env lean --version
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# -- the pipeline -----------------------------------------------------------
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COPY pyproject.toml README.md ./
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COPY leanbench/ leanbench/
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RUN pip install --break-system-packages --no-cache-dir . \
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&& lean-bench --help > /dev/null
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COPY docker/entrypoint.sh /usr/local/bin/leanbench-entrypoint
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RUN chmod +x /usr/local/bin/leanbench-entrypoint
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ENV LEANBENCH_LEAN_PROJECT=/opt/lean-bench/lean \
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LEANBENCH_LEAN_COMMAND='["lake","env","lean"]' \
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LEANBENCH_DATABASE=/data/leanbench.sqlite \
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LEANBENCH_LLM_MODEL=qwen-max \
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LEANBENCH_LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 \
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LEANBENCH_LLM_API_KEY_ENV=DASHSCOPE_API_KEY
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# /books is where you put the source you are submitting; /data holds the database.
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VOLUME ["/data", "/books"]
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WORKDIR /work
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RUN mkdir -p /work
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ENTRYPOINT ["leanbench-entrypoint"]
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CMD ["doctor"]
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