On 03/19/2018 09:43 AM, Joachim Hein wrote:
Hi,
I am currently installing tensorflow via easybuild (I assume many of
us do these days) and am trying to understand EasyBuild’s ideas on
toolchains supporting cuda.
I looked at TensorFlow-1.5.0-goolfc-2017b-Python-3.6.3.eb, which
builds ontop of a toolchain containing GCC, Cuda (installed as a
compiler module), and OpenMPI, Blas, FFTW etc.
I now noticed that there is a new
TensorFlow-1.6.0-foss-2018a-Python-3.6.4-CUDA-9.1.85.eb, which is
accepted into the development branch (PR 6016). This builds ontop a
“vanilla” foss-2018a toolchain, using a Cuda and cuDNN modules
installed as a core module (system compiler).
I am wondering how do we want to organise us in future? Do we want to
continue with the goolfc idea or do we go for a “core” cuda and cuDNN?
I feel this needs standardising soonish. It is also something I feel
I need to document for my users, who want to build their own cuda
based software. What models should be loaded to build software.
Any comments, how we take this further?
Best wishes
Joachim
FWIW ($.02) I'm partial to the latter approach since it allows
more flexibility of CUDA version without redefining an entire
toolchain (which then requres everything to be rebuilt (e.g. Python)
whether they need CUDA or not).
Jack Perdue
Lead Systems Administrator
High Performance Research Computing
TAMU Division of Research
j-per...@tamu.edu http://hprc.tamu.edu
HPRC Helpdesk: h...@hprc.tamu.edu
