# careless

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 is built

Upstream recommends a fresh conda env, but repo.anaconda.com is not
reachable from Ra, so the build uses uv instead: it fetches a standalone
Python 3.12 into `$PREFIX/python` (upstream supports >=3.9,<3.13), creates
`$PREFIX/venv`, and runs `uv pip install careless==<version>` into it.
This is the CPU build; the GPU build (with the `[cuda]` extra and the
cluster `cuda/12.8.1` module) is the separate `careless-gpu` module.

To share one uv download cache between the careless and careless-gpu
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.

`uv` must be on PATH on the build host (e.g. `pip install --user uv`).

Loading the module puts the venv's `bin/` on `PATH` (the `careless`
command is directly available, no activation needed) and sets
`CARELESS_ENV` to the venv path.

## 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).