leetcrypt
7535feb445
Ships the production serving path (FastAPI upload/map UI + statistical device-category identification) as a lean, reproducible container: - Dockerfile: python:3.10-slim, non-root user, /health HEALTHCHECK, serves uvicorn src.api.main_simple:app. Bakes in the joblib category model and the static rtl_433 protocol table; excludes the 11 GB test corpora and the benched Phase 3B CNN binaries. Built image is 417 MB. - requirements-prod.txt: pins matched to the versions that actually train/serve the model (scikit-learn 1.6.1 / numpy 2.2.6) so joblib.load() stays valid. torch/onnx intentionally absent — the CNN is benched, not on the serving path. - docker-compose.yml: single service, named volume seeds from baked data/ on first run then persists captures_simple.json writes across restarts. - .dockerignore: keeps data/, venv/, vendored firmware, docs out of the image. Validated: image builds, container reports healthy, and the statistical classifier loads + predicts inside the container. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
23 lines
713 B
YAML
23 lines
713 B
YAML
# GigLez — single-service deployment of the live simple-mode API.
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# docker compose up --build → http://localhost:8000
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services:
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giglez:
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build: .
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image: giglez:latest
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ports:
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- "8000:8000"
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volumes:
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# Persist the JSON capture store across restarts. Seeded from the
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# baked-in data/ on first run (see Dockerfile), then app writes survive.
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- giglez_data:/app/data
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restart: unless-stopped
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healthcheck:
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test: ["CMD", "python", "-c", "import urllib.request,sys; sys.exit(0 if urllib.request.urlopen('http://127.0.0.1:8000/health').status==200 else 1)"]
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interval: 30s
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timeout: 3s
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start_period: 15s
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retries: 3
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volumes:
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giglez_data:
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