duan_jandClaude Fable 5
f6c2f5a650
The installed careless/0.5.4 venv already contains the NVIDIA wheels ([cuda] extra), which need only the node driver and fall back to CPU, so a separate GPU module and cuda module dep are unnecessary. Revert careless/build to careless[cuda] to match the actual install. 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[cuda]==<version> into
it. The [cuda] extra ships the NVIDIA libraries (CUDA, cuDNN, ...) as
pip wheels, so the one install uses the GPU on GPU nodes (only the node's
NVIDIA driver is needed — no cuda module) and falls back to CPU elsewhere.
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).