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>
29 lines
1.1 KiB
Markdown
29 lines
1.1 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[cuda]==<version>` into
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it. The `[cuda]` extra ships the NVIDIA libraries (CUDA, cuDNN, ...) as
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pip wheels, so the one install uses the GPU on GPU nodes (only the node's
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NVIDIA driver is needed — no cuda module) and falls back to CPU elsewhere.
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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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