Video tag quality: cadence sampling + min-frame aggregation + ML thread cap (#747) #111

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bvandeusen merged 3 commits from dev into main 2026-06-16 14:08:40 -04:00

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Author	SHA1	Message	Date
bvandeusenandClaude Opus 4.8	60a9c9e6ef	refactor(ml): drop GPU code, cap inference threads by default (#747/#872) ... GPU enablement (#872) cancelled — not worth the Pascal-specific build for a modest CPU→GPU win on an old P4. Remove the dead GPU code (device.py, the CUDA provider branch in tagger, the .to('cuda') path in embedder) so nothing carries it forward. Instead, bound CPU inference threads by default so the ml-worker is a predictable core consumer on a SHARED node — the intended scaling model is multiple worker replicas (each --concurrency=1, each its own cgroup limit), not one big container. ONNX Runtime and torch otherwise size their thread pools to ALL host cores, so each replica would grab every core and oversubscribe / starve the co-located DB+web. Cap both to _INTRA_OP_THREADS=4 (matches the prior per-worker cpus:4 unit): run N replicas where N×4 stays within the cores allotted to ML. - tagger: ort.SessionOptions().intra_op_num_threads = 4 (CPUExecutionProvider). - embedder: torch.set_num_threads(4). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-16 13:39:55 -04:00
bvandeusenandClaude Opus 4.8	db7e1f2b59	feat(ml): GPU-capable tagger + embedder with CPU fallback (#872) ... Step 1 of GPU enablement (code only — CPU-safe, CI-green; the CUDA image is a separate step pending the host driver version). - New services/ml/device.py: FC_ML_DEVICE (auto|cuda|cpu) intent + VRAM knobs (FC_ML_ONNX_GPU_MEM_GB, FC_ML_TORCH_MEM_FRACTION). Per-worker-host bootstrap → env, not a DB setting (the GPU host runs CUDA, others CPU). - tagger: use CUDAExecutionProvider (with gpu_mem_limit) when requested AND the provider is actually present (onnxruntime-gpu), else CPUExecutionProvider. Logs the active providers. - embedder: move model + inputs to cuda when requested AND torch.cuda is available; cap torch's VRAM share; .detach().cpu() before numpy. fp32 kept so GPU embeddings stay in the same space as existing CPU ones. Both AND the env intent with the framework's real availability, so on CPU (CI / CPU onnxruntime / no GPU) they fall back cleanly — behavior unchanged. The 8GB P4 is shared by both frameworks, hence the conservative default caps. Tests: device env parsing. (tagger/embedder GPU paths are operator-verified on the GPU host — models aren't in CI.) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-16 12:49:24 -04:00
bvandeusenandClaude Opus 4.8	369e3de684	feat(ml): cadence-based video frame sampling + min-frame tag aggregation (#747) ... Video tag noise root cause: frames were a FIXED count (6) max-pooled — a tag firing on one frame survived at peak confidence, and a fixed count under-samples long multi-scene videos so real scene-local tags looked like noise. Redesign (operator-steered): - Sample at a fixed CADENCE — one frame every `video_frame_interval_seconds` (default 4) across the 5–95% window — so a tag's frame-presence reflects real screen time independent of video length. Capped at `video_max_frames` (default 64): a long video stretches the spacing instead of exploding into hundreds of inferences, bounding per-video cost on the single ml-worker (per-frame ffmpeg timeout also cut 60s→30s). - Aggregate with `_aggregate_video_predictions`: keep a tag only if it appears in >= `video_min_tag_frames` sampled frames (≈ that many × interval seconds on screen — duration-independent noise rejection), with confidence = MEAN over the frames it appears in (not max). Clamps the threshold to the sample count so a 1–2-frame short video still tags. - All three knobs are DB-backed ml_settings (migration 0053), patchable via /api/ml/settings + sliders in the ML settings card — replaces the VIDEO_ML_FRAMES env var (product-not-project). Tests: aggregation drops one-frame noise + means corroborated tags + clamps on short videos; settings round-trip + min>max validation. Replaced the _maxpool_predictions unit test. NOTE: this is the QUALITY half of #747. The perf half — the ml-worker runs CPU-only — is GPU enablement, tracked separately in #872. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-16 11:07:00 -04:00