Trainer: `parking-trainer serve` — a stdlib HTTP job API on the compose network (never
published): /health, /readiness, /versions, /versions/<v>/report, /jobs. One job at a
time; each job runs the CLI as a subprocess with its output captured, state + log
persisted under /out/jobs/ so a restart keeps history. `publish` takes its URL from
TRAINER_PUBLISH_URL. Dockerfile: CMD serve, EXPOSE 8091, healthcheck.
Collector: COLLECTOR_TRAINER_URL + /api/training/{status,jobs,jobs/:id,versions/:v/report}
— a reviewer-gated proxy that forwards a fixed set of paths and whitelisted knobs and
passes the trainer's status codes through (409 while a job runs; 503 unconfigured, 502
unreachable). /review gains the Training section: labels per class vs the minimum with
Train disabled until two classes clear it, mode / backbone / floor, the running job's
live log, the versions with Report / Evaluate / Publish (publish confirms), and the
reminder that pinning stays a git commit. Fixed on the way: an apostrophe in the page's
inline script broke the whole page — a test now parses the script.
Compose: `trainer` is a service (restart: unless-stopped, read-only data volume, its own
trainer-out volume), the `train` profile and TRAINER_OUT are gone; the Docker-socket
route was rejected (root on the host for a service booths upload to). Verified with both
images running together: a Train started through the proxy finished, version and report
came back, the page rendered.
Wiki: bodytype-classifier-training (loop, running it, operating notes superseded),
vision-review-outbox, fleet-deployment-komodo, log.
Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU
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
julian