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## Introduction
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On a recent-enough operating system, installing nabu boils down to `pip install nabu` (along with `pip install pycuda && pip install scikit-cuda` for the Cuda backend).
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Of course, ESRF infrastructure is far from this ideal setting, sometimes for understandable reasons (old hardware, dedicated beamlines machines where upgrading is risky, etc). In this case, conda is often needed to bring a decent version of Python.
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However, conda environments cannot be used between hosts when GPU drivers differ. For example, one node might have Debian 8.11 and a Kepler GPU, while another has Debian 8.11 with a Fermi GPU. In this case, a conda environment must be created for each.
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## Conda environments classification
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The servers can be classified as follows:
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- "Ideal Case": recent operating system (Debian >= 10, Ubuntu >= 20.04). Plain venv is enough. Examples: scisoft 10,11,14,15
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- "venv + PyQt5 patch": venv is almost enough, but some tinkering with `PyQt5` needs to be done to have GUI working. Examples: p9-gpu slurm partition
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- Conda
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- Fermi driver
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- Kepler driver
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- ...
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## ESRF machines classification
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Last updated: 22/02/2021
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