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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.
15 lines
683 B
Python
15 lines
683 B
Python
"""The bench frames, per augaman-dev's recipe (2026-09-27). Run in a directory holding
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obama_2012.jpg and obama_biden_2015.jpg from pfi/augaman tests/fixtures/faces/ (public domain).
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uv run --with pillow python make_frames.py
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"""
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from PIL import Image
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o = Image.open("obama_2012.jpg").convert("RGB")
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h = 700
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o = o.resize((round(o.width * h / o.height), h), Image.LANCZOS)
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a = Image.new("RGB", (1920, 1080), (90, 90, 90))
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a.paste(o, (700, 190))
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a.save("frame_a_1080p_oneface.jpg", quality=90) # (a) one face
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Image.new("RGB", (1920, 1080), (90, 90, 90)).save("frame_c_1080p_noface.jpg", quality=90) # (c) control
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# (b) is obama_biden_2015.jpg as-is: 960x1001, two faces
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