duan_jandClaude Fable 5
b55d6c1d91
careless installs plain careless (CPU). careless-gpu also installs plain careless but declares cuda/12.8.1 as build_requires/runtime_deps (jfjoch_viewer pattern) instead of pip-bundling CUDA via the [cuda] extra. Both builds honor UV_CACHE_DIR to share one download cache. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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).