33 lines
1.3 KiB
Markdown
33 lines
1.3 KiB
Markdown
# 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==<version>` into it.
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This is the CPU build; the GPU build (with the `[cuda]` extra and the
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cluster `cuda/12.8.1` module) is the separate `careless-gpu` module.
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To share one uv download cache between the careless and careless-gpu
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builds, run each with `UV_CACHE_DIR=<shared path> modbuild build <version>`
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— a cache outside PREFIX is kept, an in-PREFIX cache is removed after a
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successful install.
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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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## 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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