mirror of
https://github.com/snapotter-hq/SnapOtter.git
synced 2026-08-03 07:46:42 +02:00
SnapOtter
19a607454a
The GPU detection in gpu.py had two issues preventing GPU usage in containers (especially rootless podman with CDI): 1. When torch was installed but torch.cuda.is_available() returned False, the function returned immediately without trying the ONNX Runtime + nvidia-smi fallback. This meant a CPU-only torch build (installed before GPU was available) would block all GPU detection, even for ONNX-based tools. 2. The failure logged a generic "torch loaded but CUDA not available" with no diagnostic information, making it impossible to debug whether the issue was a CPU-only build, missing libraries, or device permissions. The fix restructures gpu_available() into three detection tiers (torch -> ONNX Runtime -> nvidia-smi) that always fall through on failure. When torch CUDA fails, it now checks torch.version.cuda to distinguish CPU-only builds from CUDA builds that can't access the GPU, and logs LD_LIBRARY_PATH, torch.cuda.init() errors, and nvidia-smi results. Also fixes two env var passthrough bugs in buildMinimalEnv(): - SNAPOTTER_GPU was never passed to the Python subprocess, so the user-facing GPU override env var had no effect - MODELS_DIR was a dead entry (never set as env var); replaced with MODELS_PATH which the Dockerfile sets and Python scripts read Closes #134