Make the SigLIP embedder an operator choice (drop-in to SigLIP 2:
google/siglip2-so400m-patch16-512 is a verified 1152-d model at 512px → no
schema change, better small-cue fidelity). A swap = set model + re-embed +
retrain, all operator-driven; the GPU agent does the re-embed so it's fast.
- settings: embedder_model_name is now a setting (migration 0065) alongside the
existing embedder_model_version; both editable + validated (non-empty) in the
ml admin API. The server embedder loads by HF name (AutoImageProcessor/Model,
model-agnostic), preferring the pre-downloaded local dir for the default so
existing deploys don't re-download; rebuilds on a name change.
- agent: new 'embed' job = whole-image SigLIP embedding (mean-pool video frames)
under the lease-announced model → POST /jobs/submit_embedding writes
image_record.siglip_embedding + siglip_model_version. The lease now announces
the model FROM THE SETTING (not a constant).
- re-embed routing: enqueue_gpu_backfill('embed') selects unembedded + stale-
version images; 'siglip' now re-embeds concept crops whose version != current
(so a swap re-triggers crops, not just the never-embedded back-catalogue). The
CPU ml-worker backfill no longer re-embeds on a version mismatch (it can't
churn the library at 512px) — the GPU agent owns version re-embeds. Daily
'embed' + 'siglip' beats self-heal.
- scoring: score_image only bags embeddings in the CURRENT model's space (whole-
image gated by siglip_model_version, concept regions by embedding_version) so a
mid-swap stale vector isn't scored by new-space heads; legacy NULL = current.
- UI: GpuAgentCard "Embedding model (advanced)" — edit name/version, Save, and
"Re-embed library (GPU)" (queues embed + siglip); points at SigLIP 2.
Tests: lease announces model + submit_embedding round-trip; enqueue 'embed'
selects stale/unembedded; stale-version excluded from scoring; embedder model
settable + empty rejected; siglip gate updated to current-version concept.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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>
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>
Direct port of ImageRepo's siglip.py. Lazy torch/transformers import so
the web container can import the module (for enqueue logic) without the
torch cost. EMBED_DIM=1152 asserted against the schema's Vector(1152)
columns. Real inference runs in the local integration suite.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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