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.
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@@ -179,6 +179,16 @@ embed/rerank/reward trio. GPUs are pinned per container via
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**GPU 1 — light / eval / retrieval + char-RP GGUF (~91/98 GB, on-demand):**
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> ⚠ **This table is stale (checked 2026-09-27).** Live GPU 1 residents were `scriberr`,
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> `vllm-coder`, `vllm-erp-seat`, `vllm-meromero-rp` and now **`augaman`** (below). Read the
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> host (`docker inspect … DeviceRequests`), not this table.
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>
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> **`augaman` :8040 (since 2026-09-27, Prime):** the second instance of the face-recognition service
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> (`stacks/augaman`, `GPU_ID=1`), ~1.3 GB. **Fixtures-only: it has no gallery backup wired.**
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> The primary instance, which holds the gallery and its backup, is on esh-ml1. Note that this host's restic copies
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> `/var/lib/docker/volumes` raw, and that includes `augaman_gallery`: a live SQLite file, so the copy is
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> not guaranteed consistent. That is acceptable for fixtures and not for real faces.
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| Container | Port | Served model | Quant | Ctx |
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|-----------|------|--------------|-------|-----|
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| `vllm-granite` | 8004 | `granite-4.1-8b` — fleet summarizer/classifier | FP8 (compressed-tensors) | 131k |
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