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Kept optional so CI (no GPU) can install the base + # package without the model deps. + # surya >= 0.17 is required: the client code uses the + # `surya.foundation` module, which older releases don't expose. + "surya-ocr>=0.17,<0.18", + "torch>=2.7", ] dev = [ "pytest>=8.3", @@ -46,16 +46,10 @@ dev = [ "mypy>=1.13", ] -# Make uv pull torch from the CUDA 12.4 wheel channel to match the -# deploy host's NVIDIA driver (server has CUDA 12.4; pypi torch 2.11 -# ships cu13 wheels which refuse to initialise on an older driver). -[[tool.uv.index]] -name = "pytorch-cu124" -url = "https://download.pytorch.org/whl/cu124" -explicit = true - -[tool.uv.sources] -torch = { index = "pytorch-cu124" } +# Note: the default pypi torch ships cu13 wheels, which emit a +# UserWarning and fall back to CPU against the deploy host's CUDA 12.4 +# driver. Surya then runs on CPU — slower but correct for MVP. A future +# driver upgrade unlocks GPU Surya with no code changes. 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