README.md

metadata

pretty_name: EdgeBench Build Kits

EdgeBench Build Kits

Self-contained, auditable build kits for rebuilding the EdgeBench task environment images from scratch on your own infrastructure — no access to any private registry or network required.

Every released EdgeBench task image is exactly: a public base image + a set of filesystem layers on top. A kit ships that upper-layer content in an inspectable form, so a rebuild covers the same audit surface as a from-scratch build, while you audit the exact shipped bits rather than a build recipe that could drift from them.

Repository layout

bases/<key>/Dockerfile        # reference recipes for the language base images
                              # (generated from EdgeBench tasks/BENCHMARK.yaml)
kits/<task_id>/work/          # one kit per released image
kits/<task_id>/judge/
    Dockerfile                #   FROM <base tag> [+ RUN rm] + ADD context.tar + config replay
    context.tar               #   merged filesystem diff above the base (ownership/modes preserved)
    MANIFEST.sha256           #   per-file sha256 + mode + uid:gid + size — the audit anchor
    kit.json                  #   provenance: source/base image IDs, layer digests, final image name
build_from_kit.py             # standalone builder/verifier (python3 stdlib + docker CLI only)

Notes on the format:

• context.tar is the merged final view of the task layers: files added and later deleted by intermediate layers are collapsed away and never ship.
• Layer whiteouts are materialized as RUN rm only when they delete files that exist in the base image; the pilot kits are purely additive (no RUN rm).
• ADD context.tar / preserves every file's owner, mode, and symlinks exactly as recorded in the released image's layers.

Quick start (with the SForge harness)

Base images build from public official images (ubuntu:22.04, python:3.11, maven:3.9-eclipse-temurin-17, ...). Base tags are deterministic — they hash the base definition in tasks/BENCHMARK.yaml — so a base you build yourself gets exactly the name each kit's FROM line expects.

# builds the base automatically, then work + judge from the kits,
# then re-verifies every file against MANIFEST.sha256
python -m sforge build --task ad_placement_optimization --kits-dir kits/ --verify
python -m sforge run   --task ad_placement_optimization --agent ...

Quick start (standalone, no harness)

# base (tag must match the kit Dockerfile's FROM line, see bases/)
docker build -t edgebench.base.cpp:19685ea8d3f4 bases/cpp/

# task images — the kit directory is the docker build context;
# tag with kit.json's "final_name"
docker build -t edgebench.work.ad_placement_optimization:49747cad3ebd \
    kits/ad_placement_optimization/work
docker build -t edgebench.judge.ad_placement_optimization:56cbfc81cfa1 \
    kits/ad_placement_optimization/judge

# optional: file-by-file verification against the manifest
python3 build_from_kit.py --kit kits/ad_placement_optimization/work --verify

Verifying the kits themselves

Each kit.json records the source image ID the kit was derived from. To cross-check independently: pull the corresponding published EdgeBench image, compute its filesystem diff over the published base, and compare with MANIFEST.sha256 — the manifests are reproducible byte-for-byte.

Current coverage

Pilot batch (3 tasks / 6 kits): ad_placement_optimization (cpp), k12_math_recommendation (python), exchange_core_throughput (java). Remaining tasks will be added incrementally.