SnapOtter 19a607454a fix: improve GPU detection diagnostics and fallback for container environments ...

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

2026-05-14 23:17:21 +08:00

..

ai

fix: improve GPU detection diagnostics and fallback for container environments

2026-05-14 23:17:21 +08:00

image-engine

fix: tighten target-size compression tolerance from 5% to 1%

2026-05-11 16:29:14 +08:00

shared

fix: resolve pre-existing test failures for content-aware-crop removal and watermark validation

2026-05-13 21:15:44 +08:00