Both hosts are rebuilt from tag v0.1.3 (one ONNX session per detector canvas) and
redeployed. pytest -m gpu tests/vision passes 3/3 on each card.
Server-side for one face, same harness as the v0.1.2 baseline:
- esh-ml1 GPU 144 -> 48 ms
- fv-ml1 GPU 75 -> 27 ms
End to end from nh3-dev: 101.6 and 73.8 ms.
CPU mode got slower on every CPU target: fv-ml1 cpuset 0-5 went 152 -> 205 ms
with a face, and the no-face frame roughly doubled. That is well outside the
run-to-run spread. The suspected cause (not measured) is per-session ORT
thread pools spinning. Reported to augaman-dev. Neither deployment uses CPU
mode.
On esh-ml1 the dependency layer missed the build cache and the rootfs touched
90% until the v0.1.2 image was removed. fv-ml1's build hit the cache, and the
exported requirements are identical, so the stack README now says to check
disk before building on esh-ml1.
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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