julian
b485e9870b
The far end of the Car Wash review outbox (wiki/concepts/vision-review-outbox.md): a small Fastify + SQLite service in the monorepo (shares the payload contract and the class vocabulary via @parking/shared), delivered to art-docker-station by its own stack so nothing booth-side lands there and nothing of it on a booth. - POST /ingest: bearer token per booth (constant-time), X-Booth-Id must match, multipart meta + JPEG (magic checked, 2 MB cap), meta validated against the contract, idempotent on the item id; crop stored at crops/<booth>/<item>.jpg on the volume + one items row. - /review + /api/*: the reviewer's screen served by the process (Basic auth, one login): one pending crop at a time, operator's pick and camera's pick beside it, one button/key per vocabulary class + unusable + skip; stats per booth and per hashed operator (agree / disagree / unusable — disagree = the reviewer's class is outside the operator's category). - GET /export/labels.csv: reviewed usable rows for training; formula-leading cells are neutralised (booth-supplied names). Crops stay on the volume for the trainer on the host. - Booth payload now carries operatorCategory.classes so the comparison needs no site setup. - Delivery: apps/collector/Dockerfile (monorepo context), docker-compose.collector.yml (bind to the overlay IP; commented `trainer` profile seam for the GPU), a third build step in build-images.yml, a `wash-collector` stack in komodo/resources.toml with one secret per booth referenced from both the collector's token list and the booth's own stack (park-2 lines templated, commented, DNS name for the URL). - Tests: app.test.ts (ingest ok/dup/refusals, review + stats + export, config). Image built and smoke-tested locally (health, ingest, duplicate, auth, verdict, export). Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU
48 lines
2.2 KiB
YAML
48 lines
2.2 KiB
YAML
# The Car Wash REVIEW COLLECTOR — deployed on the REVIEWER's host (art-docker-station), NOT
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# on a booth. Its own Komodo stack ("wash-collector" in komodo/resources.toml) points at this
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# file alone, so nothing here reaches a booth and nothing of the booth stack reaches this
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# host. See wiki/concepts/vision-review-outbox.md.
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#
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# Reachability: booths POST to /ingest over the Netbird overlay only. Bind the published
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# port to the host's OVERLAY address (COLLECTOR_BIND), never 0.0.0.0 on a host that also
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# has a public interface. The Netbird policy should allow booths → this host:8090 and
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# nothing else on it.
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services:
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collector:
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image: ${REGISTRY:-git.infra.msai.al/mca/parking_solution}/parking-collector:${TAG:-dev}
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restart: unless-stopped
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ports:
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- "${COLLECTOR_BIND:-127.0.0.1}:8090:8090"
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environment:
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# "<boothId>:<token>" pairs — one per booth, the booth's CARWASH_REVIEW_TOKEN under its
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# pseudonymous CARWASH_REVIEW_BOOTH_ID. A Komodo secret reference in the stack env.
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COLLECTOR_BOOTH_TOKENS: ${COLLECTOR_BOOTH_TOKENS:?set COLLECTOR_BOOTH_TOKENS in the stack env}
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# The single reviewer login (HTTP Basic over the overlay).
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COLLECTOR_REVIEWER_USER: ${COLLECTOR_REVIEWER_USER:-reviewer}
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COLLECTOR_REVIEWER_PASS: ${COLLECTOR_REVIEWER_PASS:?set COLLECTOR_REVIEWER_PASS in the stack env}
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LOG_LEVEL: ${LOG_LEVEL:-info}
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volumes:
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- collector-data:/data
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# Phase B trainer — a one-off job on this host's GPU, NOT a service (profile "train": it
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# only runs when asked: `docker compose --profile train run --rm trainer`). Reads the
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# collector's export + crops straight off the same volume; writes the ONNX classifier the
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# vision image then bakes in. The image/script are the next increment; this is the seam.
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# trainer:
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# image: ${REGISTRY:-git.infra.msai.al/mca/parking_solution}/parking-trainer:${TAG:-dev}
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# profiles: ["train"]
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# deploy:
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# resources:
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# reservations:
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# devices:
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# - driver: nvidia
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# count: all
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# capabilities: [gpu]
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# volumes:
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# - collector-data:/data:ro
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# - ./models:/out
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volumes:
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collector-data:
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