gpu_available() answers "can ANY framework use a GPU" (torch, then ONNX, then
paddle). But torch tools consumed that shared boolean directly as
device = torch.device("cuda" if gpu_available() else "cpu"). On a GPU host where
gpu_available() is True via paddle or ONNX while torch is a CPU-only build, those
tools would route to a CUDA torch cannot use and crash. Transcription had the
mirror problem: it runs on CTranslate2 (not torch), so on a transcription-only
GPU box gpu_available() returned False and Whisper ran on CPU despite a GPU.
Add per-framework helpers to gpu.py:
- torch_gpu_available(): torch.cuda.is_available(), honoring SNAPOTTER_GPU.
- ctranslate2_gpu_available(): ctranslate2.get_cuda_device_count() > 0.
Point each tool at the helper for its own framework: upscale, noise_removal,
enhance_faces and restore use torch_gpu_available(); transcribe uses
ctranslate2_gpu_available(). ocr.py keeps gpu_available() (paddle-aware) and the
dispatcher keeps it for its startup GPU-status line. The SNAPOTTER_GPU override
check is factored into a shared _override_disables_gpu() helper.
TDD: 7 new tests in tests/test_gpu_detection.py cover both helpers (override,
CPU-only, absent framework), including the crux that torch_gpu_available() stays
False on a CPU-only torch build even when a GPU exists for another framework.
Claude-Session: https://claude.ai/code/session_01NfaRxjek8ex5nawvx3mVMf
gpu_available() probed torch, then ONNX Runtime, then nvidia-smi, but never
paddle. The OCR bundle ships paddlepaddle-gpu with no torch or ONNX, so on an
OCR-only GPU host every probe missed the GPU: nvidia-smi saw it but returned
False by design, and OCR silently fell back to Tesseract (CPU, lower quality)
with no signal why.
Add a paddle probe as the last resort in gpu_available(). It runs only after
nvidia-smi confirms a GPU is physically present, and in an isolated subprocess,
because importing paddlepaddle-gpu on a GPU-less host segfaults and would wedge
the shared AI dispatcher. It returns True only when paddle reports both a CUDA
build and a visible device, signalling the result through the exit code so
paddle's own import chatter on stdout cannot corrupt the reading.
CPU-only and torch/ONNX GPU hosts are unaffected: the probe never runs on the
former (nvidia-smi finds nothing) and is never reached on the latter (the torch
step already returns True first).
Claude-Session: https://claude.ai/code/session_01NfaRxjek8ex5nawvx3mVMf
