diff --git a/tools/bench_latency.py b/tools/bench_latency.py new file mode 100644 index 00000000..715fc3ea --- /dev/null +++ b/tools/bench_latency.py @@ -0,0 +1,85 @@ +"""How fast does a model answer the way the live server asks it? + +The server scales the 2040 px camera frame by ``infer_scale`` (0.5 in production) and +hands it to the model, which letterboxes it to ``imgsz`` 640. This times exactly that call +in PyTorch on this machine (the beamline runs a TensorRT engine on its own GPU, so the +absolute number differs there; the ratio between models carries over): + + python tools/bench_latency.py runs//ultralytics/train/weights/best.pt --out runs//latency.json + +Prints and writes one JSON line: median and p90 milliseconds per frame, frames per second. +""" +from __future__ import annotations + +import argparse +import json +import statistics +import sys +import time +from pathlib import Path + +import cv2 + +ROOT = Path(__file__).resolve().parent.parent + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("weights") + ap.add_argument("--images", default=str(ROOT / "data" / "gold" / "images"), help="frames to time on") + ap.add_argument("--frames", type=int, default=150) + ap.add_argument("--warmup", type=int, default=20) + ap.add_argument("--imgsz", type=int, default=640) + ap.add_argument("--infer-scale", type=float, default=0.5) + ap.add_argument("--device", default="0") + ap.add_argument("--half", action="store_true", default=True) + ap.add_argument("--out", default="") + args = ap.parse_args() + + from ultralytics import YOLO + from ultralytics.utils import LOGGER + LOGGER.setLevel("ERROR") # 'half' is deprecated in 8.4 but still does the job; no warning per frame + + paths = sorted(Path(args.images).glob("*.jpg"))[: args.frames + args.warmup] + if len(paths) < args.warmup + 10: + sys.exit(f"not enough frames under {args.images}") + frames = [] + for p in paths: + img = cv2.imread(str(p)) + if img is None: + continue + if args.infer_scale < 1.0: + img = cv2.resize(img, (max(1, int(img.shape[1] * args.infer_scale)), max(1, int(img.shape[0] * args.infer_scale))), + interpolation=cv2.INTER_AREA) + frames.append(img) + model = YOLO(args.weights) + kw = dict(imgsz=args.imgsz, device=args.device, half=args.half, conf=0.25, verbose=False) + for img in frames[: args.warmup]: + model.predict(img, **kw) + times = [] + for img in frames[args.warmup:]: + t = time.perf_counter() + model.predict(img, **kw) + times.append((time.perf_counter() - t) * 1000.0) + times.sort() + med = statistics.median(times) + out = {"weights": str(args.weights), "frames": len(times), "imgsz": args.imgsz, "infer_scale": args.infer_scale, + "half": bool(args.half), "ms_median": round(med, 2), "ms_p90": round(times[int(0.9 * (len(times) - 1))], 2), + "ms_max": round(times[-1], 2), "fps_median": round(1000.0 / med, 1), "device": args.device, + "backend": "pytorch"} + try: + import torch + out["gpu"] = torch.cuda.get_device_name(0) + import ultralytics + out["ultralytics"] = ultralytics.__version__ + out["torch"] = torch.__version__ + except Exception: + pass + line = json.dumps(out) + print(line) + if args.out: + Path(args.out).write_text(line + "\n", encoding="utf-8") + + +if __name__ == "__main__": + main() diff --git a/tools/spark_cycle.sh b/tools/spark_cycle.sh index 4dc57496..2b8ebf7b 100755 --- a/tools/spark_cycle.sh +++ b/tools/spark_cycle.sh @@ -2,29 +2,39 @@ # One training cycle on the Spark, start to finish, unattended. # # nohup bash ~/AareLC_train/tools/spark_cycle.sh v5 > ~/cycle_v5.log 2>&1 & -# bash ~/AareLC_train/tools/spark_cycle.sh v5 --quick # also the fast settings-A run first +# bash ~/AareLC_train/tools/spark_cycle.sh v5 --quick # also the fast settings-A run first +# MODELS="n s m l" bash ~/AareLC_train/tools/spark_cycle.sh v5 # the promoted recipe at several model sizes # # Steps: wait for a running pipeline batch; grow the gold set by its rule (studio `gold` # stage: gold batches, share, floors); export the reviewed pool as dataset into this # repo; stop vLLM; re-measure every registry model on the grown gold set so the gate and -# the comparison stand on the same frames; train the promoted recipe from the base weights; -# gate (paired bootstrap against the active model), promote on a clear win; compare every -# model on the unseen frames; push; start vLLM again. +# the comparison stand on the same frames; train the promoted recipe from the base weights, +# one model size after the other; gate each (paired bootstrap against the active model); +# only the nano run may promote itself, a bigger model is a candidate until its speed at the +# beamline is known (its latency on this machine is measured and kept next to the run); +# compare every model on the unseen frames; push; start vLLM again if it was running. # # What earlier cycles taught, built in: # - a failed gate is a verdict, not a crash: train_run.py is called once per run; # - a full-resolution-mask training does not fit next to vLLM in 121 GB: vLLM is stopped -# while it runs and started again on exit, whatever happens; +# while the trainings run and started again on exit if it was up before; +# - the trainings run one at a time and each waits for MEM_FLOOR_GB of free memory, so the +# studio server, the inference workers and a reviewers' batch keep their room; +# - an out-of-memory training is tried once more at half the batch size; # - torch's native JIT wants Python headers the Spark lacks: switched off, headers on CPATH; # - a grown gold set invalidates stored predictions: everything is re-measured first. set -eo pipefail TAG="${1:?usage: spark_cycle.sh [--quick]}"; QUICK="${2:-}" +MODELS="${MODELS:-n}" # yolo26 sizes to train, e.g. "n s m l" +MEM_FLOOR_GB="${MEM_FLOOR_GB:-40}" # a training starts only with this much memory available export PATH=$HOME/bin:$PATH export CPATH=$HOME/local/root/usr/include:$HOME/local/root/usr/include/python3.12 export TORCH_DISABLE_NATIVE_JIT=1 +export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True PY=$HOME/AareLC_studio/.venv/bin/python STUDIO=$HOME/AareLC_studio; TRAIN=$HOME/AareLC_train; CFG=pipeline.prod.json say() { echo "== $(date +%H:%M) $*"; } +avail_gb() { free -g | awk 'NR==2{print $7}'; } while pgrep -f "[p]ipeline.run auto" >/dev/null; do sleep 30; done # the reviewers' batch goes first cd "$STUDIO"; set -a; . ~/.aaredb_env; set +a @@ -37,7 +47,19 @@ DS=$(git tag -l "dataset/*_$TAG" | tail -1); [ -n "$DS" ] || { echo "no dataset DSDIR="data/datasets/${DS#dataset/}" say "dataset $DS: $(ls $DSDIR/images/train | wc -l) train / $(ls $DSDIR/images/val | wc -l) val; gold $(ls data/gold/labels | wc -l) frames" +# Base weights for every size asked for (Ultralytics fetches a missing one from its releases). +for size in $MODELS; do + base="models/base/yolo26${size}-seg.pt" + [ -f "$base" ] && continue + say "fetching $base" + (cd models/base && $PY -c "from ultralytics import YOLO; YOLO('yolo26${size}-seg.pt')" >/dev/null 2>&1) || { echo "!! could not fetch $base"; exit 1; } + git add "$base" && git commit -q -m "base weights yolo26${size}-seg" || true +done + +VLLM_WAS_UP=0 +docker ps --format "{{.Names}}" | grep -q "^vllm$" && VLLM_WAS_UP=1 vllm_back() { + [ "$VLLM_WAS_UP" = 1 ] || { echo "$(date) vllm was not running before the cycle: left stopped" >> ~/vllm_restart.log; return 0; } for i in 1 2 3; do docker start vllm >> ~/vllm_restart.log 2>&1 || true; sleep 60 if docker ps --format "{{.Names}}" | grep -q "^vllm$"; then echo "$(date) vllm up (try $i)" >> ~/vllm_restart.log; return 0; fi @@ -46,7 +68,7 @@ vllm_back() { } trap vllm_back EXIT docker stop vllm >/dev/null 2>&1 || true -say "vLLM stopped, $(free -g | awk 'NR==2{print $7}') GB available" +say "vLLM stopped (was up: $VLLM_WAS_UP), $(avail_gb) GB available" say "re-measuring every registry model on the $(ls data/gold/labels | wc -l) gold frames" for d in models/registry/*/; do @@ -57,24 +79,48 @@ BEAMLINE_PT=$STUDIO/models/best_yolo26n-seg-overlap-false_2026-04-12.pt # the [ -f "$BEAMLINE_PT" ] && $PY tools/measure_weights.py "$BEAMLINE_PT" --version yolo26n-seg-overlap-false-2026-04-12 2>&1 | tail -n 1 git add models/registry data/gold && git commit -q -m "gold grown to $(ls data/gold/labels | wc -l) frames; every registry model re-measured on it" || true -run() { # tag suffix, note, extra train_run.py args... - local suffix="$1" note="$2"; shift 2 - local id; id=$(python3 tools/ledger.py new --dataset "$DS" --tag "$TAG-$suffix" --base models/base/yolo26n-seg.pt --note "$note") +wait_for_memory() { # up to two hours for the floor, then go anyway and say so + local waited=0 + while [ "$(avail_gb)" -lt "$MEM_FLOOR_GB" ] && [ $waited -lt 7200 ]; do sleep 60; waited=$((waited + 60)); done + [ "$(avail_gb)" -ge "$MEM_FLOOR_GB" ] || echo " only $(avail_gb) GB available after two hours; starting anyway" +} +run() { # size, tag suffix, note, extra train_run.py args... + local size="$1" suffix="$2" note="$3"; shift 3 + local base="models/base/yolo26${size}-seg.pt" + local id; id=$(python3 tools/ledger.py new --dataset "$DS" --tag "$TAG-$suffix" --base "$base" --note "$note") git add runs && git commit -q -m "run $id: exported on $DS" || true - say "training $id" + wait_for_memory + say "training $id from $base ($(avail_gb) GB available)" set +e $PY tools/train_run.py "$id" --device 0 --workers 8 "$@" > "runs/$id/train.log" 2>&1 < /dev/null local rc=$? # 0 = gate passed (or no baseline), 2 = gate failed: both are verdicts + if [ $rc -ne 0 ] && [ $rc -ne 2 ] && grep -aqi "out of memory" "runs/$id/train.log"; then + say "$id ran out of memory at batch 16; once more at batch 8" + sleep 60 + $PY tools/train_run.py "$id" --device 0 --workers 8 --batch 8 --force "$@" > "runs/$id/train.log" 2>&1 < /dev/null + rc=$? + fi set -e [ $rc -eq 0 ] || [ $rc -eq 2 ] || echo "!! $id ended with exit code $rc: $(grep -aiE "error|out of memory" runs/$id/train.log | tail -1 | cut -c1-160)" local v; v=$(python3 -c "import json;print(json.load(open('runs/$id/run.json')).get('version') or '')") if [ -n "$v" ] && [ -d "runs/$id/gold_predictions" ]; then mkdir -p "runs/_measure_$v" && cp -r "runs/$id/gold_predictions" "runs/_measure_$v/"; fi python3 -c "import json; m=json.load(open('runs/$id/metrics.json')); g=m.get('gate') or {}; b=g.get('bootstrap') or {}; print(' gate:', g.get('pass'), '| rule:', g.get('rule'), '| F1', g.get('f1_delta'), 'mask', round(g.get('mask_score',0)-g.get('mask_score_baseline',0),4), '| win F1', (b.get('f1') or {}).get('win'), 'win mask', (b.get('mask_score') or {}).get('win'))" 2>/dev/null || true + # the answer time the live server would see, in PyTorch on this machine (TensorRT at the beamline is faster) + local best="runs/$id/ultralytics/train/weights/best.pt" + if [ -f "$best" ]; then + $PY tools/bench_latency.py "$best" --out "runs/$id/latency.json" 2>/dev/null | sed 's/^/ latency: /' | cut -c1-200 || true + fi python3 tools/ledger.py render >/dev/null 2>&1 || true git add runs models && git commit -q -m "run $id: $(python3 -c "import json;print(json.load(open('runs/$id/run.json'))['status'])")" || true } -[ "$QUICK" = "--quick" ] && run recipeA "settings A (fliplr 0.5, mask_ratio 4) on dataset $TAG: quick signal, gate only, Spark" --no-promote --set fliplr=0.5 mask_ratio=4 -run noflip-mask1 "promoted recipe (fliplr 0, mask_ratio 1) on dataset $TAG: candidate for the beamlines, Spark" --set fliplr=0 mask_ratio=1 +[ "$QUICK" = "--quick" ] && run n recipeA "settings A (fliplr 0.5, mask_ratio 4) on dataset $TAG: quick signal, gate only, Spark" --no-promote --set fliplr=0.5 mask_ratio=4 +for size in $MODELS; do + if [ "$size" = "n" ]; then + run n noflip-mask1 "promoted recipe (fliplr 0, mask_ratio 1) on dataset $TAG: candidate for the beamlines, Spark" --set fliplr=0 mask_ratio=1 + else + run "$size" "${size}-noflip-mask1" "promoted recipe (fliplr 0, mask_ratio 1) with yolo26${size} on dataset $TAG: gate only until its beamline speed is known, Spark" --no-promote --set fliplr=0 mask_ratio=1 + fi +done say "every model on the unseen gold frames" $PY tools/compare_unseen.py 2>&1 | cut -c1-200 || true