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 pulls the NVIDIA libraries as pip wheels, so the
same install works on GPU and CPU-only Ra nodes.
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.
Verifying the install
Run on a Ra login node after building (in order, cheap to expensive):
# 1. module is visible and loads
module search careless
module load careless/<version>
# 2. modulefile wiring: PATH + CARELESS_ENV
which careless # -> .../careless/<version>/venv/bin/careless
echo $CARELESS_ENV # -> .../venv
# 3. venv is self-contained: must resolve inside the module PREFIX,
# not the builder's home cache (which may be cleaned later)
readlink -f "$CARELESS_ENV/bin/python"
# 4. CLI entry point; imports the full TensorFlow stack
careless --help # usage text, no traceback
# 5. installed version matches the one requested
"$CARELESS_ENV/bin/python" -c "import careless; print(careless.__version__)"
# 6. GPU pickup (GPU node only; [] on a CPU node is fine)
"$CARELESS_ENV/bin/python" -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
Optional end-to-end smoke test with a small unmerged mtz (a few seconds; proves the TF graph builds and the optimizer steps):
careless mono dHKL <file.mtz> /tmp/careless_test --iterations 10
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).