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feat: add governed nationwide AOI orchestration and CUDA enforcement
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Jens
2026-07-26 05:23:33 +02:00
parent 25b6f1ab39
commit 2be72fac58
50 changed files with 1596 additions and 51 deletions
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docs/DEPENDENCY_POLICY.md
+7 -6
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@@ -45,12 +45,13 @@ AI dependencies remain separate in the `ai` optional dependency group and must
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 CPU-oriented because its documented
runtime sets `YOLO_DEVICE=cpu`. It installs the pinned PyTorch/torchvision pair
from PyTorch's CPU wheel index before installing the `ai` extra. This avoids
shipping unused CUDA runtime libraries. The index and versions remain explicit
Docker build arguments so a future, separately validated GPU image can override
them without changing the base dependency group.
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
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.
Docker dependency metadata is copied before application source. Backend source
changes therefore reuse the dependency layer while changes to `pyproject.toml`