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Pin Tower-compatible CUDA runtime
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Jens
2026-08-01 16:06:15 +02:00
parent dfcc11b0d5
commit 24dec16744
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docs/DEPENDENCY_POLICY.md
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@@ -46,12 +46,14 @@ not be installed by the default Docker backend image unless an explicit AI image
or profile is introduced later.
The opt-in Unraid all-in-one AI build is NVIDIA-GPU-oriented. It installs the
pinned PyTorch/torchvision pair from the CUDA 13.0 wheel index before installing
pinned PyTorch/torchvision pair from the CUDA 12.8 wheel index before installing
the `ai` extra. The production runtime exposes the NVIDIA device, selects
`YOLO_DEVICE=cuda:0` and sets `YOLO_REQUIRE_CUDA=true`, so missing CUDA fails
closed instead of silently falling back to CPU. The index and versions remain
explicit Docker build arguments and require live driver/runtime validation on
Tower before release promotion.
Tower before release promotion. CUDA 12.8 is deliberately below the Tower
driver's reported CUDA 12.9 capability; a newer wheel index may not be promoted
merely because it exists when `torch.cuda.is_available()` fails on that driver.
Docker dependency metadata is copied before application source. Backend source
changes therefore reuse the dependency layer while changes to `pyproject.toml`