libreyolo-profiling

Testing & Quality

Diagnose and fix slow LibreYOLO with the `libreyolo profile` CLI — both TRAINING throughput (`profile run`) and INFERENCE latency (`profile infer`). Use whenever training feels slow, GPU utilization is low, images/sec is disappointing, inference/predict latency is too high, a run is dataloader- / host-launch- / NMS- / preprocess-bound, or someone wants to optimize step time, batch size, throughput, or p50/p90/p99 latency. Teaches the profile → diagnose → change → compare loop an agent runs to push speed to the max. This is for SPEED, not accuracy (mAP).

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Profile & speed up LibreYOLO (training + inference)

libreyolo profile answers one question fast: where does the time go, and is the GPU starved? It is a measurement tool built to be driven by an agent in a loop — every subcommand takes --json, and both entry points write a self-contained profile.json you can copy and compare.

The profiler only measures. It never changes your model. You read the verdict, you change one thing (a config value or code), then you re-measure. Don't wait for the tool to auto-tune — that's your job.

The CLI is self-describing. Never guess a flag — run libreyolo profile --help and libreyolo profile <sub> --help. Run libreyolo profile get <profile.json> with no field to list the exact metric names for the installed version.

The loop

run|infer  →  summary  →  (kernels|ops|phases)  →  change ONE thing  →  compare  →  repeat

Both run (training) and infer (inference) emit the same profile.json, so summary/get/phases/kernels/ops/compare/what-if work on either.

A. Training throughput — `profile run`

libreyolo profile run <data> --weights LibreYOLO9t.pt --size t --repeat 3 --json

<data> is a dataset yaml/name (e.g. coco128). --batch -1 auto-fits ~70% VRAM.
Always --repeat 3+ — a single run lies when launch-bound; --repeat gives mean ± stdev and is what makes a later compare significant. The aggregate is runs/profile/profile_repeat.json — use that path (not a per-trial prof_0/profile.json sibling).

Read the verdict, then pull the matching lever (then re-measure):

Verdict	Meaning	You change
dataloader	GPU waits on input (~≥20% of step)	more `workers`, `cache='ram'`/`'disk'`, lighter aug, larger batch
host / launch	GPU only partly busy — fed too slowly	larger micro-batch (amortizes launches), fewer per-step `.item()`/syncs, CUDA graphs, op fusion
compute	GPU saturated (~≥80% busy)	already GPU-bound — AMP/bf16, or accept it / change model size
memory-pressure	VRAM thrash (util reads >100%)	lower batch, reduce activation memory — util/img/s here are unreliable

Highest-value, lowest-effort win: host/launch-bound → raise the micro-batch. (yolov9t on an RTX 5070 Ti → micro-batch 36 = +142% img/s, painless.)

B. Inference latency — `profile infer`

libreyolo profile infer <image-or-dir> --weights LibreYOLO9t.pt --size t --batch 1 --json

Source defaults to a bundled sample image, so libreyolo profile infer --weights X works standalone. --half uses fp16 (CUDA). --runs/--warmup size the window.
Reports latency p50/p90/p99, throughput (img/s at --batch), and a stage split: preprocess / forward / postprocess(NMS).
--conf, --iou, --max-det change how much NMS work happens — the knobs to vary when NMS-bound.

Verdict	Meaning	You change
nms / postprocess	NMS dominates the step	lower `--max-det`, higher `--conf`, fewer `classes`, or an end-to-end (NMS-free) model
preprocess	CPU resize/letterbox dominates	larger `--batch`, faster decode, GPU preprocessing
compute	forward dominates (GPU busy, or CPU)	bigger batch, `--half`, a smaller model, or export (ONNX/TensorRT)
host / launch	forward dominates but GPU underused (CUDA)	larger `--batch`, or a bigger model to fill the GPU

Drill (shared, when the verdict isn't enough)

libreyolo profile phases  <profile.json>              # stage/phase split
libreyolo profile kernels <profile.json> --top 20     # worst GPU kernels (--category --grep --tensorcore --phase)
libreyolo profile ops     <profile.json> --top 20     # aten ops by host time
libreyolo profile get     <profile.json> latency_p50_ms   # one metric, tight loops

Low tensorcore_pct on a compute-bound fp32 run → AMP/bf16/--half helps. Big layout / copy share → channels_last. Many tiny elementwise kernels → fusion.

Change one thing, then prove it helped

Change one lever, re-run with the same --repeat/--runs, then:

libreyolo profile compare <before.json> <after.json>

compare reports the delta and a significance call. "single run — use --repeat N" means the delta is noise; repeat before trusting it.

Gotchas

The tool won't change your config — you do, then re-measure.
Always --repeat for training (one run is noise, esp. launch-bound).
Compare the aggregate, not a per-trial sibling.
Under memory-pressure, trust the verdict, not raw util (thrash inflates it).
This measures speed, not accuracy — validate mAP with libreyolo val after changing batch/LR/aug.