feat(augaman): second, fixtures-only instance on fv-ml1 GPU 1; CPU vs GPU speed bench (v0.1.2 baseline)

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.

stacks/augaman/compose.yaml

@@ -1,7 +1,8 @@
-# augaman: the fleet's face-recognition service for Cicada (gitea pfi/augaman), on esh-ml1
-# (CT 110 on esh-pve, RTX 2000E Ada 16 GB). Enroll, recognize, verify; buffalo_l (SCRFD +
-# ArcFace w600k_r50) on ONNX Runtime CUDA. Canonical copy: this file in pfi/augaman; the
-# eshpfi stack mirrors it as stacks/augaman.
+# augaman: the fleet's face-recognition service for Cicada (gitea pfi/augaman). The primary
+# instance is on esh-ml1 (CT 110 on esh-pve, RTX 2000E Ada 16 GB), with the gallery and the
+# backup. A second, fixtures-only instance runs on fv-ml1 (GPU_ID=1, no backup). Enroll,
+# recognize, verify; buffalo_l (SCRFD + ArcFace w600k_r50) on ONNX Runtime CUDA. Canonical copy:
+# this file in pfi/augaman; the eshpfi stack mirrors it as stacks/augaman.
 #
 # ⚠ BIOMETRIC DATA. The gallery volume holds face embeddings and crops of household members.
 #   - The live SQLite stays on the local named volume. Never NFS (the service refuses it).
@@ -21,7 +22,10 @@
 # variables below carry no AUGAMAN_ prefix and are never passed through wholesale (no env_file).
 #
 # .env (tunables): IMAGE, PORT (8040), BACKUP_DIR, HOST_IP (10.0.50.80), AUGAMAN_API_TOKEN
-#   (>= 32 visible-ASCII characters; the source of truth is the vault).
+#   (>= 32 visible-ASCII characters; the source of truth is the vault), GPU_ID (the host's card
+#   index, default 0), CARD_SUFFIX (appended to the Homepage name, e.g. " (fv-ml1)").
+# AUGAMAN_CUDA_DEVICE_ID stays "0" on every host: the reservation shows the container only the
+# card GPU_ID names, and it sees that card as index 0.
 
 name: augaman
 
@@ -47,7 +51,7 @@ services:
         reservations:
           devices:
             - driver: nvidia
-              device_ids: ["0"]
+              device_ids: ["${GPU_ID:-0}"]
               capabilities: [gpu]
     healthcheck:
       # 200 only when ready and not degraded; a 503 (degraded) fails the check.
@@ -58,7 +62,7 @@ services:
       start_period: 180s
     labels:
       - homepage.group=AI - Eval & Retrieval
-      - homepage.name=augaman — face recognition
+      - homepage.name=augaman — face recognition${CARD_SUFFIX:-}
       - homepage.icon=mdi-face-recognition
       - homepage.description=Enroll, recognize, verify (buffalo_l on CUDA) for Cicada
       - homepage.href=http://${HOST_IP:-10.0.50.80}:${PORT:-8040}/health