GPU build of careless (see ../careless/ for the CPU module and the full background). Scaling and merging of X-ray diffraction data with approximate Bayesian inference (variational merging), built on TensorFlow.

Upstream: https://github.com/rs-station/careless
License: MIT

How it differs from the careless module

CUDA is not pip-installed (no [cuda] extra, saves several GB): the cluster cuda/12.8.1 module provides it instead, declared as build_requires/runtime_deps (same pattern as jfjoch_viewer). runtime_deps matters because TensorFlow dlopens the CUDA libraries when careless runs, so cuda must auto-load with this module.
If careless reports missing cuDNN at runtime, the cuda module does not ship it; the fallback is installing careless[cuda] in build instead.

The venv layout, uv usage, and cache handling are identical to careless. To share one download cache between both builds, run each with UV_CACHE_DIR=<shared path> modbuild build <version> — a cache outside PREFIX is kept, an in-PREFIX cache is removed after a successful install.

Adding a new version

Add the version under versions: in files/config.yaml, then on a Ra login node: modbuild build <version> (modbuild/2.1.2).