julian
4ff31557a8
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Wiki: bodytype-classifier-training (loop, running it, operating notes superseded),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
37 lines
2.1 KiB
Markdown
37 lines
2.1 KiB
Markdown
# parking-trainer
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The phase-B **body-type classifier** job. Reads the wash collector's volume
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(`collector.sqlite` + `crops/`), trains a classifier on the reviewer's labels, and writes a
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versioned model folder the vision image bakes in — or refuses when validation is below the
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floor. Design and decisions: `wiki/decisions/bodytype-classifier-training.md`.
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```
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parking-trainer inspect --data /data # what a run would train on
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parking-trainer train --data /data --out /out # features mode (minutes)
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parking-trainer train --mode finetune --epochs 12 ... # full fine-tune (about an hour on 4 cores)
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parking-trainer evaluate --model /out/<version>/bodytype.onnx --data /data
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parking-trainer publish /out/<version> --url https://git.infra.msai.al/api/packages/mca/generic/parking-bodytype
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```
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Exit codes: `0` model written · `2` not enough labels · `3` below the floor (report written,
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no model) · `1` other.
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A passing run writes `<out>/<version>/`:
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| file | what |
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| --- | --- |
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| `bodytype.onnx` | the classifier; input `image` = RGB float32 0–255 `[N,3,S,S]`, output `logits` `[N,K]`; normalisation is inside the graph |
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| `bodytype.json` | sidecar: version, class list (in vocabulary order), input size, crop margin, backbone, mode, label counts, validation metrics |
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| `report.md` | the human report: accuracy, per-class recall/precision, confusion matrix, dropped classes, loss weights |
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| `metrics.json` | the same numbers, machine-readable |
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On the reviewer's host the image runs `serve` as the `trainer` service of the
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`wash-collector` stack: a job API (`/health`, `/readiness`, `/versions`, `/jobs`) on the compose
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network that the collector's **Training section** (`/review`) drives — readiness, Train /
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Evaluate / Publish, reports and logs. Jobs run as subprocesses of the CLI, one at a time; state
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and logs persist under `/out/jobs/`. The CLI stays for debugging:
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`docker compose -f docker-compose.collector.yml exec trainer parking-trainer inspect`.
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Local dev: `uv sync --extra train` (CPU torch, ~200 MB), `uv run pytest -q`. The test suite
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runs without the extra (torch tests skip), matching CI.
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