CCU:GPU Cluster

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Revision as of 19:37, 17 May 2019 by Bastian.goldluecke (talk | contribs) (→‎Overview)
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Overview

The CCU provides access to state-of-the-art infrastructure for GPU-based machine learning frameworks based on nVidia GPUs. This page gives a general overview and links to more in-depth tutorials on how to work with the cluster. There is some overhead involved when writing code for your projects and you have to stick to a few guidelines, but there are template projects and scripts provided so that you can get started with minimal knowledge about the technical background of the GPU cluster.


What you need

  • An account for the CCU.
  • Ideally, a desktop PC with an nVidia GPU to test your code before pushing it to the cluster (debugging can otherwise be hard).
  • Your PC ideally runs a flavor of Linux, all example scripts were tested against Ubuntu 18.04 (should also work on derivatives, such as Mint 19).
  • Admin access to your own PC to install lots of stuff (or a friendly administrator).
  • More specific needs will be covered in the in-depth tutorials.


How to get started

  • Step 1:
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