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>
