apps/trainer (parking-trainer): inspect / train / evaluate / publish. Reads the wash
collector's SQLite + crops read-only off its volume; time split (validation = newest
slice); thin classes dropped; damped class weights; `features` mode (frozen ImageNet
backbone, on-disk feature cache, seconds to retrain) and `finetune` mode (light
augmentation). CPU-only torch from PyTorch's wheel index. ONNX export checked against
the torch model; NO model file below the validation floor (exit 3, report still written);
exit 2 = not enough labels. `evaluate` scores a shipped model on labels reviewed after
training + the unlabelled pile; `publish` PUTs a version folder to a Gitea generic package.
Light core deps; the `train` extra is heavy — CI syncs without it, torch tests skip.
apps/vision: BodyTypeClassifier (bodytype.onnx + sidecar = the preprocessing contract:
crop margin, input size, RGB 0-255, normalisation inside the graph) and
RefinedVehicleDetector over YOLOX — refines only `car` or a class the classifier trained
on, min-confidence, `detector_class` on the result; path set but no file = phase B off
without an error; a broken file is a health detail. models/bodytype.version (tracked,
empty) pins the published version the Dockerfile fetches at build (BuildKit secret;
a pin that cannot be fetched fails the build). Verified: a trainer model gives identical
probabilities inside the vision service; both images built and smoke-tested.
Delivery: parking-trainer image in build-images.yml, the `trainer` compose profile on the
collector stack (CPU, read-only data, TRAINER_OUT), commented TRAINER_OUT/PUBLISH_TOKEN in
the wash-collector stack, .dockerignore for both Python contexts, trainer deps synced in CI.
Wiki: bodytype-classifier-training rewritten as built (+ one fleet model not per site,
secrets/access, where the crops live), opencv-anpr-service §Phase B, vision-review-outbox,
vision-service-packaging, fleet-deployment-komodo, index, log.
Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU
The dev box runs vision as bare `uv run uvicorn`, and a plain uv run/uv sync
re-resolves the venv to the lockfile DEFAULTS, stripping fast-alpr/onnxruntime.
So after any `pnpm dev` real ANPR silently degraded to "snapshot, no plate"
(diagnosed 2026-06-25: real reads through 06-22, venv frozen lean since 06-19,
no other env with fast_alpr). The BOOTH was never affected — it runs the Docker
image, which bakes `uv sync --frozen --extra alpr` at build (immutable, weights
pre-warmed); a booth ModuleNotFoundError is a STALE image (fix: booth.sh update).
Vision package.json dev/start/recognize now run `uv sync --extra alpr &&` first
so pnpm dev is self-healing; added a dev:stub escape hatch for a lean run.
Documented in wiki/decisions/vision-service-packaging.md ("Two runtimes, one
fragile") + a log entry.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
Containerize the two non-desktop apps for the booth appliance. The desktop app stays
on its own tag-only release.yml.
- apps/server/Dockerfile: multi-stage node:22-alpine. `pnpm deploy --legacy --prod`
(NOT prune — the monorepo native better-sqlite3 won't resolve under a root prune)
yields a self-contained bundle; build stage adds node-gyp toolchain, runtime adds
libstdc++; non-root, healthcheck. Migrates the mounted DB on boot via a drizzle-kit-
free runtime migrator (packages/db/scripts/migrate-runtime.mjs) — drizzle-kit is a
devDep, pruned from prod.
- apps/server/src/static-spa.ts: Fastify serves the built React SPA (one container
serves API + UI). GET-only fallback to index.html, excludes /api + /health so it never
shadows the backend; a no-op in dev (no dist). Registered last in server.ts.
- apps/vision/Dockerfile: uv base, --extra alpr, model weights PRE-WARMED into the image
as the runtime user so fast_alpr boots offline (0 downloads at runtime). Engine env-
selected (VISION_RECOGNIZER stub|fast_alpr).
- Branch-aware: docker-compose.yml (base) + .dev.yml (build local, stub, ports) +
.prod.yml (pull pinned, fast_alpr, vision internal, restart always); REGISTRY/TAG from
env so a branch deploy pulls that branch's image.
- .gitea/workflows/build-images.yml: on push to dev/main, run the full turbo build+lint+
test gate, then buildx push both images to git.infra.msai.al/mca/parking_solution with
branch + branch-<sha> tags (registry cache; optional Komodo webhook behind KOMODO_ENABLED).
- .dockerignore excludes **/parking.sqlite* so the signed ledger is NEVER baked.
Verified locally (Docker 29): server image migrates + serves API+SPA (/health 200, /
+ /booth HTML, /api/nope JSON 404, no sqlite outside /data); vision image boots fast_alpr
with 0 runtime downloads; compose stack healthy with server→vision over the private network.
Wiki: new container-deployment.md; vision-service-packaging open Qs resolved; index + log.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
Skeleton of the host-side vision service per the packaging decision: a Python/FastAPI
app at apps/vision/, uv-managed, wired into the Turbo graph via a thin package.json
shim (dev/lint/test/build → uv/uvicorn/ruff/pytest). A per-package turbo.json sets
build outputs [] so the no-op build is warning-free.
Endpoints: GET /health (readiness + model version) and POST /analyze (raw
octet-stream body, so Node POSTs Snapshot.bytes directly; empty→400, oversize→413,
recognizer-not-ready→503). The recognizer is a Protocol with a StubRecognizer (no
models, boots/tests offline — the dev/CI default) and a FastAlprRecognizer (the real
MIT YOLOv9+CCT/ONNX stack, lazily imported; missing models ⇒ ready=False, not a crash)
— the device-adapter pattern applied to the model. fast-alpr + onnxruntime are an
optional `alpr` extra, so `uv sync` needs no model download.
Verified: turbo run lint|test|build includes @parking/vision and stays green; uv run
mypy strict-clean; uvicorn boots and serves /health + /analyze live; pnpm workspace
6→7. Not built yet: the Node VisionClient adapter, a Dockerfile + model fetch, and
Job 2 (vehicle verification). Updates the packaging decision (As-scaffolded) + log.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
Settle WHERE the host-side ANPR service lives and how it joins the build: in this
monorepo at apps/vision/ (not a separate repo), still a separate OS process called
over localhost HTTP, wired into the Turbo graph via a thin package.json shim whose
scripts shell to Python tooling (uv/uvicorn/ruff/pytest). Co-located source honors the
vision-service runtime+license isolation decision (AGPL reach is a linking boundary,
not a folder); the fast-alpr MIT baseline removes most of the split-repo pressure
anyway. New page vision-service-packaging; updates vision-service, opencv-anpr-service,
the CLAUDE.md layout, index, log. Not built yet — packaging decision only.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
julian