Howdy Jakob, The primary difference between fosscuda and foss+CUDA is that fosscuda has an OpenMPI built with CUDA support where as the latter does not.
We run with: EASYBUILD_MINIMAL_TOOLCHAINS which cuts down on the number of things that have be rebuilt here For example for TensorFlow/1.10.1-fosscuda-2018b-Python-3.6.6 we only had to rebuild these packages: Python/3.6.6-fosscuda-2018b protobuf-python/3.6.0-fosscuda-2018b-Python-3.6.6 cuDNN/7.1.4.18-fosscuda-2018b so you might want look at the --minmal-toolchains option. 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 On 3/26/19 8:26 AM, Jakob Schiøtz wrote:
Dear EasyBuilders, I would like to build a TensorFlow module supporting GPUs. Currently, that looks to be TensorFlow-1.12.0-fosscuda-2018b-Python-3.6.6.eb, but this requires building a new toolchain (fosscuda), including rebuilding both OpenMPI and Python with GPU support. In addition, any other software that the user may need alongside TensorFlow will also have to be rebuilt with the fosscuda toolchain to prevent mixing toolchains. That seems to be overkill to me - after all little if anything is gained by rebuilding Python and stuff with GPU support unless the scripts are actually going to use it. I don’t know if numpy will begin offloading computations to the GPU, but presumably moving data back and forth will be expensive, and TensorFlow will allocate all the GPU memory anyway. There used to be a tensorflow variant for the normal toolchain which just depended on CUDA explicitly. Is there a reason not to do it this way? I guess I could try to make a .eb file inspired on the existing ones doing just that - or have I overlooked something? Best regards Jakob -- Jakob Schiøtz, professor, Ph.D. Department of Physics Technical University of Denmark DK-2800 Kongens Lyngby, Denmark http://www.fysik.dtu.dk/~schiotz/
