# careless-gpu

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).