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.
2026-09-27 00:28:20 -07:00
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+# augaman speed bench: CPU vs esh-ml1 GPU vs fv-ml1 GPU
+
+Asked by Prime on 2026-09-27. This is the **v0.1.2 "before"** baseline. v0.1.3 gives
+each detector canvas its own ONNX session, removing the ~90 ms CUDA shape-switching
+cost that comfy-dev found. The same harness is to be re-run on v0.1.3 for the "after".
+
+## Harness (it is part of the number)
+
+- **Client:** `bench.py` on nh3-dev, sequential. Each request uses a **fresh
+  connection** (no keep-alive) and is timed around the whole POST.
+- **Protocol:** 3 runs. Per (target, frame, run) there are 3 warm-up requests,
+  then 20 timed ones. Runs are interleaved across targets and frames, so drift
+  spreads over every condition. Time: 0021–0026 PT, 2026-09-27.
+- **Server-side column:** the change in `augaman_pipeline_seconds{op="probe"}`
+  sum/count over the 20 timed requests. It is decode + detect + embed with no
+  network or HTTP, and it is **the device comparison**. It is a mean, not a p50.
+- **Floor:** a `/health` round trip, same client. It came out 28–34 ms, higher than
+  augaman-dev's 16 ms because of the fresh connection per request.
+- **Frames** (`make_frames.py`): (a) 1920x1080 grey canvas with one face;
+  (b) `obama_biden_2015.jpg` as-is, 960x1001, two faces; **(c) the same 1080p grey
+  canvas with no face**, a detector-only control. Every response was checked for
+  the expected face count, and **0 of 900 were wrong**.
+- **Images:** `augaman:0.1.2`, built from the same tag on each host (separate
+  builds, same source).
+- **Targets:**
+
+| target | device | notes |
+|---|---|---|
+| esh-ml1 GPU | RTX 2000E Ada 16 GB (50 W) | the production instance; shares the card with TEI + vllm-reward |
+| fv-ml1 GPU1 | RTX PRO 6000 Blackwell Max-Q | the new second instance; shares GPU 1 with coder, erp and meromero seats |
+| esh-ml1 CPU6 | the LXC's 6 host threads | throwaway `AUGAMAN_DEVICE=cpu` container. pve moved the LXC's cpuset between two reads, so it ran unpinned, with an affinity of 6 |
+| fv-ml1 CPU6 | EPYC 9254, cpuset 0-5 | throwaway container; physical cores on socket 0 |
+| fv-ml1 CPU24 | EPYC 9254, cpuset 0-23 | throwaway container; all physical cores of socket 0 |
+
+GPU utilisation read 0% on fv-ml1 GPU 1 and 0–9% on esh-ml1 across the samples
+(0020–0021, 3 samples each), so the co-resident seats were idle.
+
+## Results (ms): median of the 3 run p50s, [min–max of the run p50s]
+
+| target | (a) 1 face, e2e | (b) 2 faces, e2e | (c) no face, e2e | **server (a)** | **server (b)** | **server (c)** |
+|---|---|---|---|---|---|---|
+| esh-ml1 GPU | 196.5 [195.0–196.6] | 195.1 [194.0–196.1] | 181.0 [177.6–182.9] | **143.8** | **138.7** | **136.2** |
+| fv-ml1 GPU1 | 126.6 [121.8–128.3] | 122.7 [122.4–127.9] | 109.2 [108.7–111.0] | **75.0** | **73.9** | **69.5** |
+| esh-ml1 CPU6 | 954.6 [792.8–1023.3] | 1325.6 [1306.9–1327.6] | 205.1 [202.1–228.0] | **887.6** | **1253.4** | **159.4** |
+| fv-ml1 CPU6 | 203.6 [203.5–204.5] | 222.5 [216.0–236.4] | 111.6 [110.6–112.7] | **152.4** | **179.4** | **72.3** |
+| fv-ml1 CPU24 | 208.1 [204.6–221.6] | 223.7 [219.8–237.1] | 101.1 [100.5–113.4] | **155.9** | **167.3** | **61.9** |
+
+The /health floor was 27.6–33.7 ms (median of run p50s) on every target. The raw
+rows are in `rows-2026-09-27.json`, and `summarize.py` reproduces this table.
+
+## What it says
+
+- **fv-ml1's Blackwell is ~2× esh-ml1's Ada on v0.1.2:** 75 vs 144 ms server-side
+  for one face. End to end from nh3-dev it is 127 vs 197 ms.
+- **On v0.1.2, a GPU buys little over a good CPU.** 6 EPYC cores do one face in
+  152 ms, as fast as esh-ml1's GPU (144 ms). With no face, fv-ml1's CPU (62–72 ms)
+  matches fv-ml1's GPU (70 ms). The GPU pipeline is dominated by something other
+  than GPU compute, and the cards read 0–9% utilisation while it ran. That is
+  consistent with the detector's shape-switching cost that v0.1.3 removes.
+- **esh-ml1's CPU is not a viable home:** 0.9 s for one face and 1.3 s for two. Its
+  embedder costs ~730 ms per face, against ~80 ms on 6 EPYC cores. That gap is much
+  larger than the detector gap (159 vs 72 ms), and its runs were the noisiest
+  (793–1023 ms), so treat the esh CPU number as indicative. It was also sharing its 6
+  threads with TEI.
+- **More EPYC threads do not help one frame:** CPU24 is roughly equal to CPU6 on faces.
+
+## Controls and sensitivity
+
+- **Repeats:** 3 runs × 20 per cell. The A-vs-A spread is the min–max above.
+- **Noise floor:** the GPU cells spread by ≤6 ms across runs, and the fv CPU cells
+  by ≤20 ms. The esh-ml1 CPU (a) cell spread by 230 ms. Differences smaller than
+  those spreads are not findings.
+- **Positive control (instrument agreement):** augaman-dev measured esh-ml1 (a)
+  independently at 186 ms with a 16 ms floor. This harness gets 196.5 ms with a
+  28 ms floor. Minus each floor, 170 vs 169 ms: they agree.
+- **Positive control (detects work):** frame (c) is fastest on every target, and
+  the gap from (a) to (c) is the embedding cost. It is visible on every device.
+- **Negative control:** the face count was checked on all 900 responses, and none
+  were wrong.
@@ -0,0 +1,70 @@
+"""augaman /recognize latency bench, harness per augaman-dev (2026-09-27):
+sequential from nh3-dev; per (target, frame, run): 3 warm-up requests, then 20 timed.
+3 runs, interleaved across targets and frames so drift spreads over every condition.
+Each request is a fresh connection (no keep-alive), timed around the whole POST.
+Server-side share: delta of augaman_pipeline_seconds{op="probe"} sum/count over the
+20 timed requests (a mean, not a p50). Every response is checked for the expected
+face count; a mismatch invalidates the row. Floor: /health, 3 runs x 20, same client.
+
+Targets come from BENCH_TARGETS as JSON [{name, url, token_env}]; tokens are read
+from the environment and never printed.
+"""
+import json, os, re, sys, time
+import httpx
+
+FRAMES = [("a_1080p_1face", "frame_a_1080p_oneface.jpg", 1),
+          ("b_960x1001_2face", "obama_biden_2015.jpg", 2),
+          ("c_1080p_0face", "frame_c_1080p_noface.jpg", 0)]
+RUNS, WARM, N = 3, 3, 20
+targets = json.loads(os.environ["BENCH_TARGETS"])
+data = {f: open(f, "rb").read() for _, f, _ in FRAMES}
+PROBE = re.compile(r'^augaman_pipeline_seconds_(sum|count)\{[^}]*op="probe"[^}]*\}\s+([0-9.eE+-]+)$', re.M)
+
+
+def probe_stats(url):
+    t = httpx.get(url + "/metrics", timeout=10).text
+    vals = {k: float(v) for k, v in PROBE.findall(t)}
+    return vals.get("sum", float("nan")), vals.get("count", float("nan"))
+
+
+def recognize(url, tok, fname):
+    t0 = time.perf_counter()
+    r = httpx.post(url + "/recognize", headers={"Authorization": f"Bearer {tok}"},
+                   files={"frame": (fname, data[fname], "image/jpeg")}, timeout=120)
+    dt = (time.perf_counter() - t0) * 1000
+    r.raise_for_status()
+    return dt, len(r.json()["faces"])
+
+
+def pct(xs, p):
+    xs = sorted(xs); k = (len(xs) - 1) * p; f = int(k); c = min(f + 1, len(xs) - 1)
+    return xs[f] + (xs[c] - xs[f]) * (k - f)
+
+
+rows = []
+for run in range(1, RUNS + 1):
+    for t in targets:
+        url, tok = t["url"], os.environ[t["token_env"]]
+        # floor
+        h = []
+        for _ in range(N):
+            t0 = time.perf_counter(); httpx.get(url + "/health", timeout=10).raise_for_status()
+            h.append((time.perf_counter() - t0) * 1000)
+        rows.append({"target": t["name"], "frame": "health_floor", "run": run,
+                     "p50": pct(h, .5), "p90": pct(h, .9), "server_probe_mean": None, "bad": 0})
+        for label, fname, want in FRAMES:
+            for _ in range(WARM):
+                recognize(url, tok, fname)
+            s0, c0 = probe_stats(url)
+            lat, bad = [], 0
+            for _ in range(N):
+                dt, faces = recognize(url, tok, fname)
+                lat.append(dt); bad += faces != want
+            s1, c1 = probe_stats(url)
+            srv = (s1 - s0) / (c1 - c0) * 1000 if c1 > c0 else None
+            rows.append({"target": t["name"], "frame": label, "run": run, "p50": pct(lat, .5),
+                         "p90": pct(lat, .9), "server_probe_mean": srv, "bad": bad})
+            print(f"run{run} {t['name']:<16} {label:<17} p50 {pct(lat,.5):8.1f}  p90 {pct(lat,.9):8.1f}"
+                  f"  srv {srv if srv is None else round(srv,1)}  bad {bad}", flush=True)
+
+json.dump(rows, open(sys.argv[1] if len(sys.argv) > 1 else "bench_rows.json", "w"), indent=1)
@@ -0,0 +1,14 @@
+"""The bench frames, per augaman-dev's recipe (2026-09-27). Run in a directory holding
+obama_2012.jpg and obama_biden_2015.jpg from pfi/augaman tests/fixtures/faces/ (public domain).
+    uv run --with pillow python make_frames.py
+"""
+from PIL import Image
+
+o = Image.open("obama_2012.jpg").convert("RGB")
+h = 700
+o = o.resize((round(o.width * h / o.height), h), Image.LANCZOS)
+a = Image.new("RGB", (1920, 1080), (90, 90, 90))
+a.paste(o, (700, 190))
+a.save("frame_a_1080p_oneface.jpg", quality=90)          # (a) one face
+Image.new("RGB", (1920, 1080), (90, 90, 90)).save("frame_c_1080p_noface.jpg", quality=90)  # (c) control
+# (b) is obama_biden_2015.jpg as-is: 960x1001, two faces
@@ -0,0 +1,542 @@
+[
+ {
+  "target": "esh-ml1 GPU",
+  "frame": "health_floor",
+  "run": 1,
+  "p50": 28.755177503626328,
+  "p90": 35.665129897824954,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 GPU",
+  "frame": "a_1080p_1face",
+  "run": 1,
+  "p50": 195.03028949839063,
+  "p90": 200.67508020147218,
+  "server_probe_mean": 143.80537764518522,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 GPU",
+  "frame": "b_960x1001_2face",
+  "run": 1,
+  "p50": 195.12132549425587,
+  "p90": 208.15129229886224,
+  "server_probe_mean": 139.3810058987583,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 GPU",
+  "frame": "c_1080p_0face",
+  "run": 1,
+  "p50": 182.8995855030371,
+  "p90": 191.65247399650985,
+  "server_probe_mean": 143.4166395556531,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 GPU1",
+  "frame": "health_floor",
+  "run": 1,
+  "p50": 33.661384499282576,
+  "p90": 38.86325339844916,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 GPU1",
+  "frame": "a_1080p_1face",
+  "run": 1,
+  "p50": 128.3326149932691,
+  "p90": 140.80376060592243,
+  "server_probe_mean": 74.98760224552825,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 GPU1",
+  "frame": "b_960x1001_2face",
+  "run": 1,
+  "p50": 127.89153849735158,
+  "p90": 133.6793933995068,
+  "server_probe_mean": 74.19702970655635,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 GPU1",
+  "frame": "c_1080p_0face",
+  "run": 1,
+  "p50": 111.00122199422913,
+  "p90": 117.57378029578831,
+  "server_probe_mean": 69.54348748549819,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 CPU6",
+  "frame": "health_floor",
+  "run": 1,
+  "p50": 30.578960999264382,
+  "p90": 35.473932592140045,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 CPU6",
+  "frame": "a_1080p_1face",
+  "run": 1,
+  "p50": 954.5748219970847,
+  "p90": 1020.2096714929212,
+  "server_probe_mean": 887.6064898984623,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 CPU6",
+  "frame": "b_960x1001_2face",
+  "run": 1,
+  "p50": 1327.6250180060742,
+  "p90": 1416.4716226019664,
+  "server_probe_mean": 1270.2313813992077,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 CPU6",
+  "frame": "c_1080p_0face",
+  "run": 1,
+  "p50": 202.1412319954834,
+  "p90": 210.3825692931423,
+  "server_probe_mean": 159.44489179673837,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU6",
+  "frame": "health_floor",
+  "run": 1,
+  "p50": 27.60758400108898,
+  "p90": 29.759886700776406,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU6",
+  "frame": "a_1080p_1face",
+  "run": 1,
+  "p50": 203.55955500417622,
+  "p90": 219.8045792974881,
+  "server_probe_mean": 156.9333346793428,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU6",
+  "frame": "b_960x1001_2face",
+  "run": 1,
+  "p50": 236.35765750077553,
+  "p90": 282.70697479892993,
+  "server_probe_mean": 188.06166611611843,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU6",
+  "frame": "c_1080p_0face",
+  "run": 1,
+  "p50": 110.56056749657728,
+  "p90": 114.69427619013004,
+  "server_probe_mean": 72.52865270711482,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU24",
+  "frame": "health_floor",
+  "run": 1,
+  "p50": 31.095413498405833,
+  "p90": 35.30468289682176,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU24",
+  "frame": "a_1080p_1face",
+  "run": 1,
+  "p50": 204.55308200325817,
+  "p90": 235.17365920706652,
+  "server_probe_mean": 159.80161383049563,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU24",
+  "frame": "b_960x1001_2face",
+  "run": 1,
+  "p50": 223.66256099485327,
+  "p90": 244.09297520178376,
+  "server_probe_mean": 171.44956310512498,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU24",
+  "frame": "c_1080p_0face",
+  "run": 1,
+  "p50": 101.08870249678148,
+  "p90": 114.38059099891689,
+  "server_probe_mean": 61.86515123117715,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 GPU",
+  "frame": "health_floor",
+  "run": 2,
+  "p50": 27.849696998600848,
+  "p90": 30.98080779891462,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 GPU",
+  "frame": "a_1080p_1face",
+  "run": 2,
+  "p50": 196.5537174983183,
+  "p90": 208.61720069660805,
+  "server_probe_mean": 144.78365819668397,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 GPU",
+  "frame": "b_960x1001_2face",
+  "run": 2,
+  "p50": 196.06467850098852,
+  "p90": 210.13995000248542,
+  "server_probe_mean": 138.73773735249415,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 GPU",
+  "frame": "c_1080p_0face",
+  "run": 2,
+  "p50": 180.95635149802547,
+  "p90": 188.9815433023614,
+  "server_probe_mean": 136.1989213983179,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 GPU1",
+  "frame": "health_floor",
+  "run": 2,
+  "p50": 27.323893991706427,
+  "p90": 31.146193096356004,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 GPU1",
+  "frame": "a_1080p_1face",
+  "run": 2,
+  "p50": 126.6464070067741,
+  "p90": 133.9685394996195,
+  "server_probe_mean": 74.96715487213805,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 GPU1",
+  "frame": "b_960x1001_2face",
+  "run": 2,
+  "p50": 122.7374875015812,
+  "p90": 126.5989876977983,
+  "server_probe_mean": 73.87235894566402,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 GPU1",
+  "frame": "c_1080p_0face",
+  "run": 2,
+  "p50": 108.72362100053579,
+  "p90": 114.4327986999997,
+  "server_probe_mean": 69.35211137169972,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 CPU6",
+  "frame": "health_floor",
+  "run": 2,
+  "p50": 26.6998285005684,
+  "p90": 31.021830906684055,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 CPU6",
+  "frame": "a_1080p_1face",
+  "run": 2,
+  "p50": 792.7710255025886,
+  "p90": 1075.1574820023964,
+  "server_probe_mean": 795.2748078998411,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 CPU6",
+  "frame": "b_960x1001_2face",
+  "run": 2,
+  "p50": 1325.6497279944597,
+  "p90": 1390.5041895020986,
+  "server_probe_mean": 1253.4432460510288,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 CPU6",
+  "frame": "c_1080p_0face",
+  "run": 2,
+  "p50": 205.08419950056123,
+  "p90": 223.07179619820093,
+  "server_probe_mean": 168.24462699732976,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU6",
+  "frame": "health_floor",
+  "run": 2,
+  "p50": 27.37445900129387,
+  "p90": 32.55561870028032,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU6",
+  "frame": "a_1080p_1face",
+  "run": 2,
+  "p50": 204.45054300216725,
+  "p90": 220.60656060639303,
+  "server_probe_mean": 152.42901378078386,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU6",
+  "frame": "b_960x1001_2face",
+  "run": 2,
+  "p50": 216.0444700057269,
+  "p90": 276.679380304995,
+  "server_probe_mean": 179.3103247997351,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU6",
+  "frame": "c_1080p_0face",
+  "run": 2,
+  "p50": 111.58617900218815,
+  "p90": 116.30833300296217,
+  "server_probe_mean": 72.20571864163503,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU24",
+  "frame": "health_floor",
+  "run": 2,
+  "p50": 27.235380999627523,
+  "p90": 29.255447497416764,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU24",
+  "frame": "a_1080p_1face",
+  "run": 2,
+  "p50": 208.08351850428153,
+  "p90": 221.3218400007463,
+  "server_probe_mean": 150.44436159078032,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU24",
+  "frame": "b_960x1001_2face",
+  "run": 2,
+  "p50": 219.75871399627067,
+  "p90": 259.07196740736254,
+  "server_probe_mean": 166.02391808992252,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU24",
+  "frame": "c_1080p_0face",
+  "run": 2,
+  "p50": 100.5097349989228,
+  "p90": 107.63760179688688,
+  "server_probe_mean": 59.30270317476243,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 GPU",
+  "frame": "health_floor",
+  "run": 3,
+  "p50": 26.72893099952489,
+  "p90": 31.63200489798328,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 GPU",
+  "frame": "a_1080p_1face",
+  "run": 3,
+  "p50": 196.49402699724305,
+  "p90": 204.47104229824618,
+  "server_probe_mean": 142.17575855000177,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 GPU",
+  "frame": "b_960x1001_2face",
+  "run": 3,
+  "p50": 194.01365749945398,
+  "p90": 203.67266629764345,
+  "server_probe_mean": 138.33359165437287,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 GPU",
+  "frame": "c_1080p_0face",
+  "run": 3,
+  "p50": 177.55984649556922,
+  "p90": 184.63658349210164,
+  "server_probe_mean": 135.46126945293508,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 GPU1",
+  "frame": "health_floor",
+  "run": 3,
+  "p50": 34.47381099977065,
+  "p90": 37.53316599468235,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 GPU1",
+  "frame": "a_1080p_1face",
+  "run": 3,
+  "p50": 121.84531000093557,
+  "p90": 126.15792770229746,
+  "server_probe_mean": 75.25073911529034,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 GPU1",
+  "frame": "b_960x1001_2face",
+  "run": 3,
+  "p50": 122.41429199639242,
+  "p90": 125.64870739151957,
+  "server_probe_mean": 73.74720136867836,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 GPU1",
+  "frame": "c_1080p_0face",
+  "run": 3,
+  "p50": 109.15258450404508,
+  "p90": 114.19055700243916,
+  "server_probe_mean": 69.49142985977232,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 CPU6",
+  "frame": "health_floor",
+  "run": 3,
+  "p50": 27.56448650325183,
+  "p90": 34.92588359804358,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 CPU6",
+  "frame": "a_1080p_1face",
+  "run": 3,
+  "p50": 1023.3039910017396,
+  "p90": 1191.5021550987149,
+  "server_probe_mean": 939.7293467947748,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 CPU6",
+  "frame": "b_960x1001_2face",
+  "run": 3,
+  "p50": 1306.9450249968213,
+  "p90": 1427.1712208035751,
+  "server_probe_mean": 1251.0824149969267,
+  "bad": 0
+ },
+ {
+  "target": "esh-ml1 CPU6",
+  "frame": "c_1080p_0face",
+  "run": 3,
+  "p50": 228.02160500577884,
+  "p90": 261.2633642929723,
+  "server_probe_mean": 153.0728507990716,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU6",
+  "frame": "health_floor",
+  "run": 3,
+  "p50": 53.94737450114917,
+  "p90": 107.14866449852707,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU6",
+  "frame": "a_1080p_1face",
+  "run": 3,
+  "p50": 203.5052149949479,
+  "p90": 222.22570729936706,
+  "server_probe_mean": 147.21799310063943,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU6",
+  "frame": "b_960x1001_2face",
+  "run": 3,
+  "p50": 222.4783574929461,
+  "p90": 276.977431504929,
+  "server_probe_mean": 179.41700990777463,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU6",
+  "frame": "c_1080p_0face",
+  "run": 3,
+  "p50": 112.70040849922225,
+  "p90": 125.06614489684581,
+  "server_probe_mean": 72.31316382531077,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU24",
+  "frame": "health_floor",
+  "run": 3,
+  "p50": 30.923282000003383,
+  "p90": 36.041059493436485,
+  "server_probe_mean": null,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU24",
+  "frame": "a_1080p_1face",
+  "run": 3,
+  "p50": 221.6059979982674,
+  "p90": 246.61138239607683,
+  "server_probe_mean": 155.86536910850555,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU24",
+  "frame": "b_960x1001_2face",
+  "run": 3,
+  "p50": 237.05747949861689,
+  "p90": 271.11399889254244,
+  "server_probe_mean": 167.3004158306867,
+  "bad": 0
+ },
+ {
+  "target": "fv-ml1 CPU24",
+  "frame": "c_1080p_0face",
+  "run": 3,
+  "p50": 113.37069750152295,
+  "p90": 157.2230929945363,
+  "server_probe_mean": 71.09609730541706,
+  "bad": 0
+ }
+]
@@ -0,0 +1,23 @@
+"""Summarise bench.py rows: per (target, frame), the median of the 3 run p50s with the
+min-max of those run p50s (the A-vs-A spread, i.e. the noise floor), the median run p90,
+and the median server-side probe mean.
+    python summarize.py rows-2026-09-27.json
+"""
+import json, statistics, sys
+from collections import defaultdict
+
+rows = json.load(open(sys.argv[1]))
+g = defaultdict(list)
+for r in rows:
+    g[(r["target"], r["frame"])].append(r)
+targets = list(dict.fromkeys(r["target"] for r in rows))
+frames = list(dict.fromkeys(r["frame"] for r in rows))
+print(f"{'target':<14} {'frame':<17} {'p50 med':>8} {'p50 run min-max':>16} {'p90 med':>8} {'server med':>10} bad")
+for t in targets:
+    for f in frames:
+        rs = g[(t, f)]
+        p50s = [r["p50"] for r in rs]
+        srv = [r["server_probe_mean"] for r in rs if r["server_probe_mean"] is not None]
+        print(f"{t:<14} {f:<17} {statistics.median(p50s):8.1f} {min(p50s):7.1f}-{max(p50s):<8.1f}"
+              f" {statistics.median(r['p90'] for r in rs):8.1f}"
+              f" {(statistics.median(srv) if srv else float('nan')):10.1f} {sum(r['bad'] for r in rs)}")