b9e37952e6
Replaces the previous tree entirely. The pipeline is four separable pieces, each
testable on its own:
1. leanbench/llm.py provider-neutral OpenAI-compatible connector, stdlib only,
with retries, JSON coercion and a scripted client so the
rest of the code is testable without a network.
2. leanbench/detect.py per-page detection of formalizable mathematics. Case 1 is
mathematics asserted as fact (definition/theorem/lemma/...,
deliberately not sub-classified); case 2 is a problem left
open to the reader. Hits come back as spans -- start page
and index, end page and index -- so several per page is
normal. A hit cut off by the page end makes the detector
re-read that page together with the next, repeatedly, so an
item spread over three pages is found whole.
Offsets come from verbatim quotes resolved by
leanbench/anchors.py (exact -> whitespace-insensitive ->
fuzzy), because models quote well and count badly.
3. leanbench/formalize.py + leanbench/verify.py
each span becomes one Lean 4 declaration, judged by the real
compiler: full elaboration for case 1, statement legality
only for case 2, which has no proof to check. Lean's own
diagnostics feed back for a repair attempt. "verified" (Lean
accepted it) and "proved" (accepted with no sorry) are kept
strictly apart.
4. leanbench/db.py indexed SQLite plus JSONL export. Every item is indexed by
the types of its inputs, or by its own type when it is not a
proof, and records the source book (sha256) and the page it
starts on -- so `drop --source H --pages A-B` followed by a
re-ingest redoes exactly those pages and nothing else.
Infrastructure:
- lean/ Lake project pinned to Mathlib.
- docker/ image carrying Lean 4 and a prebuilt Mathlib. The Mathlib
revision and the toolchain are resolved against each other at
build time rather than both hardcoded, so the build does not rot.
docker/smoke.py runs the whole pipeline inside the image against
the real compiler, with the LLM scripted.
- .gitea/workflows ci (lint + tests in a venv, since the runner image's Python is
Debian-managed and refuses installs into itself), docker (build,
verify, optionally push), ingest (submit a book by hand).
The docker workflow is not a throwaway build: it leaves the image, the layer cache,
an idle `lean-bench` container and the `lean-bench-data` / `lean-bench-books`
volumes in place on the runner, and never prunes, so the environment is there to be
used afterwards. Pushing to the registry is optional and cannot fail the build --
Gitea's container registry does not accept the built-in Actions token, so that needs
a REGISTRY_TOKEN secret with write:package.
58 tests; they need neither network nor Lean.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ci5T3gWDhhmqDec3e1XtYZ
222 lines
8.9 KiB
YAML
222 lines
8.9 KiB
YAML
name: docker
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# Builds the Lean 4 + Mathlib image and LEAVES IT IN PLACE on the runner host.
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#
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# This is not a throwaway test build. When the job finishes, the runner still has:
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# * the image, tagged lean-bench:latest and lean-bench:sha-<commit>
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# * the Docker layer cache, so the next build reuses Mathlib instead of
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# re-downloading it (nothing here ever calls `docker system prune`)
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# * a long-lived container `lean-bench`, idle and ready for `docker exec`
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# * named volumes `lean-bench-data` (the item database) and `lean-bench-books`
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#
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# so you can submit a book straight afterwards:
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# docker exec -e DASHSCOPE_API_KEY=sk-... lean-bench \
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# lean-bench ingest /books/your-book.pdf --pages 30-60
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#
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# Pushing to the registry is optional and never fails the build: the built-in
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# Actions token is not accepted by Gitea's container registry, so add a personal
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# access token with write:package as the REGISTRY_TOKEN secret to enable it.
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on:
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push:
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branches: [main]
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tags: ["v*"]
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workflow_dispatch:
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inputs:
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mathlib_rev:
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description: "Mathlib revision (tag, branch, commit, or 'auto')"
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required: false
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default: "auto"
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push_image:
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description: "Push to the registry if a token is configured"
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required: false
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default: "true"
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refresh_service:
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description: "Recreate the long-lived lean-bench container even if one is running"
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required: false
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default: "false"
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no_cache:
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description: "Build without the layer cache (slow: refetches Mathlib)"
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required: false
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default: "false"
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env:
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REGISTRY: gitea.entroia.com
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IMAGE_NAME: lean-bench
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LOCAL_IMAGE: lean-bench
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SERVICE_CONTAINER: lean-bench
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DATA_VOLUME: lean-bench-data
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BOOKS_VOLUME: lean-bench-books
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jobs:
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image:
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runs-on: ubuntu-latest
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timeout-minutes: 240
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steps:
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- uses: actions/checkout@v4
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- name: Check the runner can build images
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run: |
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set -eu
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if ! command -v docker >/dev/null; then
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echo "::error::No docker CLI on this runner. Give the act_runner the host"
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echo "::error::docker socket (-v /var/run/docker.sock:/var/run/docker.sock)"
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echo "::error::or run it with the docker-in-docker image."
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exit 1
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fi
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docker version --format 'client {{.Client.Version}} / server {{.Server.Version}}'
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echo "disk on the docker host:"
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docker system df || true
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- name: Work out the tags
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id: meta
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run: |
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set -eu
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owner="$(echo "${{ github.repository_owner }}" | tr '[:upper:]' '[:lower:]')"
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remote="${REGISTRY}/${owner}/${IMAGE_NAME}"
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ref="${GITHUB_REF_NAME:-main}"
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sha="$(echo "${GITHUB_SHA:-unknown}" | cut -c1-12)"
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case "${GITHUB_REF:-}" in
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refs/tags/*) version="${ref}" ;;
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*) version="${ref//\//-}" ;;
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esac
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# Local tags are what stays on the runner; remote tags are for the registry.
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local_tags="${LOCAL_IMAGE}:${version} ${LOCAL_IMAGE}:sha-${sha}"
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remote_tags="${remote}:${version} ${remote}:sha-${sha}"
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if [ "${ref}" = "main" ]; then
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local_tags="${local_tags} ${LOCAL_IMAGE}:latest"
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remote_tags="${remote_tags} ${remote}:latest"
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fi
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{
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echo "remote=${remote}"
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echo "primary=${LOCAL_IMAGE}:${version}"
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echo "local_tags=${local_tags}"
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echo "remote_tags=${remote_tags}"
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} >> "$GITHUB_OUTPUT"
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echo "local: ${local_tags}"
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echo "remote: ${remote_tags}"
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- name: Build
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run: |
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set -eu
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args=""
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for tag in ${{ steps.meta.outputs.local_tags }} ${{ steps.meta.outputs.remote_tags }}; do
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args="$args -t $tag"
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done
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# Reuse the previous build's layers explicitly as well as implicitly, so a
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# rebuild does not refetch Mathlib.
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cache=""
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if [ '${{ inputs.no_cache }}' = 'true' ]; then
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cache="--no-cache"
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elif docker image inspect "${LOCAL_IMAGE}:latest" >/dev/null 2>&1; then
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cache="--cache-from ${LOCAL_IMAGE}:latest"
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fi
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echo "building with: $cache"
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docker build \
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-f docker/Dockerfile \
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--build-arg "MATHLIB_REV=${{ inputs.mathlib_rev || 'auto' }}" \
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--build-arg BUILDKIT_INLINE_CACHE=1 \
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$cache $args .
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- name: Check the image actually works
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run: |
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set -eu
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image='${{ steps.meta.outputs.primary }}'
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docker run --rm "$image" doctor --skip-llm
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docker run --rm --entrypoint bash "$image" -lc \
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'cd "$LEANBENCH_LEAN_PROJECT" && printf "import Mathlib\ntheorem ci_smoke (n : Nat) : n + 0 = n := by simp\n" > /tmp/CI.lean && lake env lean /tmp/CI.lean && echo "lean + mathlib ok"'
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# Whole pipeline, scripted LLM, real compiler. Piped in over stdin so the
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# job container needs no bind mount into the host daemon.
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docker run --rm -i --entrypoint python3 "$image" - < docker/smoke.py
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- name: Log in to the registry
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id: login
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if: ${{ github.event_name != 'workflow_dispatch' || inputs.push_image == 'true' }}
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continue-on-error: true
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run: |
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set -eu
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token='${{ secrets.REGISTRY_TOKEN }}'
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[ -n "$token" ] || token='${{ secrets.GITEA_TOKEN }}'
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[ -n "$token" ] || token='${{ secrets.GITHUB_TOKEN }}'
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user='${{ vars.REGISTRY_USER }}'
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[ -n "$user" ] || user='${{ github.actor }}'
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if [ -z "$token" ]; then
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echo "::warning::No registry token; keeping the image on the runner only."
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exit 1
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fi
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echo "$token" | docker login "${REGISTRY}" -u "$user" --password-stdin
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- name: Push
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id: push
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if: ${{ steps.login.outcome == 'success' }}
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continue-on-error: true
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run: |
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set -eu
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for tag in ${{ steps.meta.outputs.remote_tags }}; do
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echo "pushing $tag"
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docker push "$tag"
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done
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- name: Leave the environment ready for use
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run: |
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set -eu
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image='${{ steps.meta.outputs.primary }}'
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docker volume create "${DATA_VOLUME}" >/dev/null
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docker volume create "${BOOKS_VOLUME}" >/dev/null
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running=""
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if docker ps --format '{{.Names}}' | grep -qx "${SERVICE_CONTAINER}"; then
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running="yes"
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fi
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if [ -n "$running" ] && [ '${{ inputs.refresh_service }}' != 'true' ]; then
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echo "${SERVICE_CONTAINER} is already running; leaving it alone so nothing"
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echo "in flight is interrupted. Re-run with refresh_service=true to move it"
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echo "onto the image just built."
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else
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docker rm -f "${SERVICE_CONTAINER}" >/dev/null 2>&1 || true
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docker run -d \
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--name "${SERVICE_CONTAINER}" \
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--restart unless-stopped \
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-v "${DATA_VOLUME}:/data" \
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-v "${BOOKS_VOLUME}:/books" \
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-e DASHSCOPE_API_KEY \
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--entrypoint sleep \
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"$image" infinity
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echo "${SERVICE_CONTAINER} is up on $image"
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fi
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docker exec "${SERVICE_CONTAINER}" lean-bench doctor --skip-llm || true
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echo "images kept on this runner:"
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docker images "${LOCAL_IMAGE}" --format ' {{.Repository}}:{{.Tag}} {{.Size}} {{.CreatedSince}}'
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env:
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DASHSCOPE_API_KEY: ${{ secrets.DASHSCOPE_API_KEY }}
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- name: Summary
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if: always()
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run: |
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{
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echo "### lean-bench image"
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echo ""
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echo "Kept on the runner (not cleaned up):"
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for tag in ${{ steps.meta.outputs.local_tags }}; do echo "- \`$tag\`"; done
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echo ""
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if [ '${{ steps.push.outcome }}' = 'success' ]; then
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echo "Pushed to the registry:"
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for tag in ${{ steps.meta.outputs.remote_tags }}; do echo "- \`$tag\`"; done
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else
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echo "**Not pushed.** Gitea's container registry rejects the built-in"
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echo "Actions token. Create a personal access token with \`write:package\`"
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echo "and add it as the \`REGISTRY_TOKEN\` secret (and \`REGISTRY_USER\`"
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echo "as a variable if it is not \`${{ github.actor }}\`). The image is on"
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echo "the runner either way."
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fi
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echo ""
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echo "Submit a book to the container that is already running:"
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echo '```'
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echo "docker cp your-book.pdf lean-bench:/books/"
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echo "docker exec -e DASHSCOPE_API_KEY=sk-... lean-bench \\"
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echo " lean-bench ingest /books/your-book.pdf --pages 30-60"
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echo "docker exec lean-bench lean-bench stats"
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echo '```'
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} >> "$GITHUB_STEP_SUMMARY"
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