Merge branch 'build-careless'
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# careless
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Scaling and merging of X-ray diffraction data with approximate Bayesian
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inference (variational merging), built on TensorFlow.
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- Upstream: https://github.com/rs-station/careless
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- License: MIT
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## How it is built
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Upstream recommends a fresh conda env, but repo.anaconda.com is not
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reachable from Ra, so the build uses uv instead: it fetches a standalone
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Python 3.12 into `$PREFIX/python` (upstream supports >=3.9,<3.13), creates
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`$PREFIX/venv`, and runs `uv pip install careless[cuda]==<version>` into
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it. The `[cuda]` extra pulls the NVIDIA libraries as pip wheels, so the
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same install works on GPU and CPU-only Ra nodes.
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`uv` must be on PATH on the build host (e.g. `pip install --user uv`).
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Loading the module puts the venv's `bin/` on `PATH` (the `careless`
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command is directly available, no activation needed) and sets
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`CARELESS_ENV` to the venv path.
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## Verifying the install
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Run on a Ra login node after building (in order, cheap to expensive):
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```sh
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# 1. module is visible and loads
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module search careless
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module load careless/<version>
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# 2. modulefile wiring: PATH + CARELESS_ENV
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which careless # -> .../careless/<version>/venv/bin/careless
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echo $CARELESS_ENV # -> .../venv
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# 3. venv is self-contained: must resolve inside the module PREFIX,
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# not the builder's home cache (which may be cleaned later)
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readlink -f "$CARELESS_ENV/bin/python"
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# 4. CLI entry point; imports the full TensorFlow stack
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careless --help # usage text, no traceback
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# 5. installed version matches the one requested
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"$CARELESS_ENV/bin/python" -c "import careless; print(careless.__version__)"
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# 6. GPU pickup (GPU node only; [] on a CPU node is fine)
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"$CARELESS_ENV/bin/python" -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
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```
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Optional end-to-end smoke test with a small unmerged mtz (a few seconds;
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proves the TF graph builds and the optimizer steps):
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```sh
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careless mono dHKL <file.mtz> /tmp/careless_test --iterations 10
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```
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## Adding a new version
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Add the version under `versions:` in `files/config.yaml`, then on a Ra
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login node: `modbuild build <version>` (modbuild/2.1.2).
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Executable
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#!/usr/bin/env modbuild
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pbuild::prep() {
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:
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}
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pbuild::configure() {
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# repo.anaconda.com is unreachable from Ra (GnuTLS error), so no conda.
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# uv fetches a standalone CPython instead; keep it inside PREFIX so the
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# venv's interpreter symlinks don't depend on the builder's home cache.
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# modbuild's shell strips ~/.local/bin from PATH; override with UV=/path/to/uv if needed
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UV="${UV:-$HOME/.local/bin/uv}"
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# ~/.cache/uv hits the home disk quota; cache under PREFIX (removed after install)
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export UV_CACHE_DIR="/opt/psi/MX/.uv-cache"
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export UV_PYTHON_INSTALL_DIR="$PREFIX/python"
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"$UV" venv --python 3.12 "$PREFIX/venv"
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}
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pbuild::compile() {
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:
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}
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pbuild::install() {
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UV="${UV:-$HOME/.local/bin/uv}"
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export UV_CACHE_DIR="/opt/psi/MX/.uv-cache"
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# [cuda] extra ships the NVIDIA libs via pip wheels; works on CPU-only nodes too
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"$UV" pip install --python "$PREFIX/venv/bin/python" "careless[cuda]==${V_PKG}"
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}
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---
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format: 1
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careless:
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defaults:
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group: MX
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overlay: base
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relstage: unstable
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versions:
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0.5.4:
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config:
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relstage: unstable
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#%Module1.0
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module-whatis "careless - merging crystallography data with variational inference"
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module-url "https://github.com/rs-station/careless"
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module-license "MIT"
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module-maintainer "MX Data <jiaxin.duan@psi.ch>"
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module-help "
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careless: scaling and merging of X-ray diffraction data using
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approximate Bayesian inference (TensorFlow). Installed with the
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[cuda] extra, so it uses NVIDIA GPUs when available and falls
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back to CPU otherwise.
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"
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setenv CARELESS_ENV $PREFIX/venv
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prepend-path PATH $PREFIX/venv/bin
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