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Wiki: bodytype-classifier-training (loop, running it, operating notes superseded),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
50 lines
2.3 KiB
Docker
50 lines
2.3 KiB
Docker
# syntax=docker/dockerfile:1.7
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# Parking TRAINER image: the phase-B body-type classifier. Build CONTEXT is apps/trainer
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# (self-contained Python package). Runs on the reviewer's host (art-docker-station) beside
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# the collector, never on a booth: by default it SERVES the job API the collector's Training
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# section drives (`serve`); the same image runs the CLI one-off (`train`, `inspect`, …). It
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# reads the wash collector's volume (collector.sqlite + crops/) and writes versioned model
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# folders. CPU-only PyTorch — the host has no usable GPU and a few thousand crops train in
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# minutes/an hour on four Xeon cores.
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# See wiki/decisions/bodytype-classifier-training.md.
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FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS base
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WORKDIR /app
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ENV UV_LINK_MODE=copy \
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UV_COMPILE_BYTECODE=1 \
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PYTHONUNBUFFERED=1
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RUN apt-get update \
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&& apt-get install -y --no-install-recommends libgl1 libglib2.0-0 \
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&& rm -rf /var/lib/apt/lists/*
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COPY pyproject.toml uv.lock .python-version ./
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync --frozen --no-install-project --no-dev --extra train
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COPY trainer/ ./trainer/
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COPY README.md ./
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync --frozen --no-dev --extra train
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# Pre-warm the ImageNet backbone weights INTO the image so a run needs no network (the
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# host has one, but a job that fetches at run time is a job that fails at 2 am). Best-effort:
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# without network at build time torchvision fetches lazily on the first run.
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ENV TORCH_HOME=/app/torch-home
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RUN uv run python -c "import torchvision.models as m; m.resnet18(weights=m.ResNet18_Weights.IMAGENET1K_V1); m.mobilenet_v3_small(weights=m.MobileNet_V3_Small_Weights.IMAGENET1K_V1)" \
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|| echo "[build] backbone weights not pre-warmed (no network) — fetched on first run"
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RUN useradd --system --create-home --uid 999 trainer \
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&& mkdir -p /data /out && chown -R trainer:trainer /app /out
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USER trainer
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ENV TRAINER_DATA_DIR=/data \
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TRAINER_OUT_DIR=/out \
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TRAINER_PORT=8091
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VOLUME ["/out"]
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EXPOSE 8091
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HEALTHCHECK --interval=30s --timeout=5s --start-period=10s --retries=3 \
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CMD python -c "import urllib.request,sys; sys.exit(0 if urllib.request.urlopen('http://localhost:8091/health').status==200 else 1)" || exit 1
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ENTRYPOINT ["uv", "run", "--no-sync", "parking-trainer"]
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CMD ["serve"]
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