Dirk Riemann
9e33923f71
First /healthz call on a fresh container triggers Surya to fetch the text-recognition (1.34 GB) and detection (73 MB) models from HuggingFace. Without a volume they land in the container fs and vanish on every rebuild, which is every deploy. Mount named volumes for /root/.cache/datalab (Surya) and /root/.cache/huggingface. Rebuild now keeps /healthz warm. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
40 lines
1.2 KiB
YAML
40 lines
1.2 KiB
YAML
# InfoXtractor Docker Compose stack.
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#
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# Single service. Uses host networking so the container can reach:
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# - Ollama at 127.0.0.1:11434
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# - postgis at 127.0.0.1:5431 (bound to loopback only; security hardening)
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# Both services are LAN-hardened on the host and never exposed publicly,
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# so host-network access stays on-prem. This matches the `goldstein`
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# container pattern on the same server.
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#
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# The GPU reservation block matches immich-ml / the shape Docker Compose
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# expects for GPU allocation on this host.
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services:
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infoxtractor:
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build: .
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container_name: infoxtractor
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network_mode: host
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restart: always
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env_file: .env
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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: 1
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capabilities: [gpu]
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volumes:
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# Persist Surya (datalab) + HuggingFace model caches so rebuilds don't
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# re-download ~1.5 GB of weights every time.
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- ix_surya_cache:/root/.cache/datalab
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- ix_hf_cache:/root/.cache/huggingface
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labels:
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infrastructure.web_url: "http://192.168.68.42:8994"
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backup.enable: "true"
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backup.type: "postgres"
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backup.name: "infoxtractor"
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volumes:
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ix_surya_cache:
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ix_hf_cache:
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