vh
317868dc7e
Prime asked for augaman on fv-ml1's utility card, beside vllm-coder. Mirror augaman-dev's f77164f compose, which parameterises the GPU reservation (GPU_ID, default 0) and the Homepage card name (CARD_SUFFIX). esh-ml1's resolved config is unchanged: same config hash, no recreate. On fv-ml1: augaman:0.1.2 built on-box from the tag, GPU_ID=1, healthy on CUDA at 1264 MiB, and pytest -m gpu tests/vision passes 3/3 on the Blackwell. It has its own gallery and no gallery backup, so it is fixtures-only. The host's raw restic copy of /var/lib/docker/volumes is not a consistent SQLite backup. docs/pfi/augaman-speed-bench/ holds the harness (augaman-dev's recipe plus a no-face control frame and a face-count check on every response), the raw rows and the summary. Server-side, one face: - esh-ml1 GPU 144 ms - fv-ml1 GPU 75 ms - fv-ml1 CPU on 6 cores 152 ms - esh-ml1 CPU 888 ms It agrees with augaman-dev's independent esh-ml1 measurement once each harness's floor is subtracted. This is the before for v0.1.3's detector fix.
72 lines
3.4 KiB
YAML
72 lines
3.4 KiB
YAML
# augaman: the fleet's face-recognition service for Cicada (gitea pfi/augaman). The primary
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# instance is on esh-ml1 (CT 110 on esh-pve, RTX 2000E Ada 16 GB), with the gallery and the
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# backup. A second, fixtures-only instance runs on fv-ml1 (GPU_ID=1, no backup). Enroll,
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# recognize, verify; buffalo_l (SCRFD + ArcFace w600k_r50) on ONNX Runtime CUDA. Canonical copy:
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# this file in pfi/augaman; the eshpfi stack mirrors it as stacks/augaman.
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#
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# ⚠ BIOMETRIC DATA. The gallery volume holds face embeddings and crops of household members.
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# - The live SQLite stays on the local named volume. Never NFS (the service refuses it).
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# - esh-ml1 is OUTSIDE vzdump. The gallery reaches backup only through the backup CLI,
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# writing to BACKUP_DIR, a restic-covered host path. Wire the schedule and VERIFY A
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# RESTORE before real people are enrolled (operator ruling):
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# docker exec augaman python -m augaman.gallery.backup --db /data/gallery.db --dest /backup/gallery.db
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# It prints one JSON line and exits 0, or "backup failed: ..." and exits 1.
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# - BACKUP_DIR must be writable by uid 10001, the container user. Backups are mode 0600.
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#
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# Startup refuses to serve unless CUDA really runs every convolution: a profiled warmup plus
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# ORT's CPU fallbacks turned off. /health reports backend "cuda" only then. Confirm once by the
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# process in `nvidia-smi` on the host. Warmup, including the first CUDA inference, is covered
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# by the healthcheck's start_period.
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#
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# Only AUGAMAN_* variables reach the app, and it refuses any it does not know, so compose's own
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# variables below carry no AUGAMAN_ prefix and are never passed through wholesale (no env_file).
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#
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# .env (tunables): IMAGE, PORT (8040), BACKUP_DIR, HOST_IP (10.0.50.80), AUGAMAN_API_TOKEN
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# (>= 32 visible-ASCII characters; the source of truth is the vault), GPU_ID (the host's card
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# index, default 0), CARD_SUFFIX (appended to the Homepage name, e.g. " (fv-ml1)").
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# AUGAMAN_CUDA_DEVICE_ID stays "0" on every host: the reservation shows the container only the
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# card GPU_ID names, and it sees that card as index 0.
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name: augaman
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services:
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augaman:
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image: ${IMAGE:?set IMAGE}
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container_name: augaman
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restart: unless-stopped
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ports:
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- "${PORT:-8040}:8040"
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environment:
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AUGAMAN_API_TOKEN: ${AUGAMAN_API_TOKEN:?set AUGAMAN_API_TOKEN}
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AUGAMAN_DEVICE: cuda
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AUGAMAN_CUDA_DEVICE_ID: "0"
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volumes:
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- gallery:/data
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- ${BACKUP_DIR:?set BACKUP_DIR}:/backup
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# Multipart spools of large uploads land here: RAM only, never persistent disk (api INV-A07).
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tmpfs:
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- /tmp:size=512m
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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device_ids: ["${GPU_ID:-0}"]
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capabilities: [gpu]
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healthcheck:
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# 200 only when ready and not degraded; a 503 (degraded) fails the check.
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test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://127.0.0.1:8040/health', timeout=5)"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 180s
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labels:
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- homepage.group=AI - Eval & Retrieval
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- homepage.name=augaman — face recognition${CARD_SUFFIX:-}
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- homepage.icon=mdi-face-recognition
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- homepage.description=Enroll, recognize, verify (buffalo_l on CUDA) for Cicada
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- homepage.href=http://${HOST_IP:-10.0.50.80}:${PORT:-8040}/health
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volumes:
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gallery:
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