Markus92

Setting up a GPU server with scheduling and containers

Our group recently acquired a new server to do some deep learning: a SuperMicro 4029GP-TRT2, stuffed with 8x NVidia RTX 2080 Ti. Though maybe a bit overpowered, with upcoming networks like BigGAN and fully 3D networks, as well as students joining our group, this machine will be used quite a lot in the future.

One challenge is, is how to manage these GPUs. There are many approaches, but given that most PhD candidates aren't sysadmins, these range from 'free-for-all', leading to one person hogging all GPUs for weeks due to a bug in the code, to Excel sheets that noone understands and noone adheres to because changing GPU ids in code is hard. This leads to a lot of frustration, low productivity and under-utilisation of these expensive servers. Another issue is conflicting software versions. TensorFlow and Keras, for example, tend to do breaking API changes every now and then. As t

TwoLeaves

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-- (http://github.com/asmagill/hammerspoon_asm.undocumented)
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function os.capture(cmd)
  local f = assert(io.popen(cmd, 'r'))
  local s = assert(f:read('*a'))
  f:close()

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