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{{Project|Has project output=Content|Has sponsor=McNair ProjectsCenter
|Has title=GPU Build
|Has owner=Oliver Chang,Kyran Adams
}}
==Final Decision==
We decided to clone the NVIDIA [[DIGITS DevBox]]: https://developer.nvidia.com/devbox
To start with we are trying to use our existing ASUS Z10 server board, rather than switching to the Asus X99-E WS workstation class motherboard, and rather than Four TITAN X GPUs, we've got a TITAN XP and a TITAN RTX.
Note that the Asus X99-E WS is available from NewEgg for $500 now.
==Single vs. Multi GPU==
*[https://www.nvidia.com/en-us/geforce/products/10series/geforce-gtx-1080-ti/ GTX 1080 Ti Specs]
* Since we are using Tensorflow, it doesn't scale well to multiple GPUs for a single model
Cons of multiple GPUs:
*Adds a lot of complexity.
=== K80, NVLink ===
*NVLink can link between CPU and GPU for increase in speed, but only with the CPU IBM POWER8+.
*NVLink can link between GPU and GPU as a replacement for SLI with other CPUs, but this is not super relevant to tensorflow, even if trying to parallelize across one model.
*[https://www.quora.com/Which-GPU-is-better-for-Deep-Learning-GTX-1080-or-Tesla-K80 This source] says to get the 1080 because the K80 is basically two K40s, which have less memory bandwidth than the 1080. [https://www.reddit.com/r/deeplearning/comments/5mc7s6/performance_difference_between_nvidia_k80_and_gtx/ This source] agrees.
==Misc. Parts==
* [https://www.tooploox.com/blog/deep-learning-with-gpu Another box]
* [http://graphific.github.io/posts/building-a-deep-learning-dream-machine/ Expensive deep learning box]
==Double GPU Server Build==
[https://pcpartpicker.com/user/kyranadams/saved/gDzFdC PC Partpicker build]
*[https://www.quora.com/Can-I-double-the-PCIe-lanes-in-a-dual-CPU-motherboard This article] says that it may be necessary to get both CPUs to get all of the PCI lanes
==Double GPU Build==
===Other things to consider===
* Water cooling? [http://timdettmers.com/2015/03/09/deep-learning-hardware-guide/ this] has a good section on cooling
* Is case actually big enough?Case is not rack mounted
==Software tips==
* Setting up Ubuntu and Docker [https://medium.com/@SocraticDatum/getting-started-with-gpu-driven-deep-learning-part-2-environment-setup-fd1947aab29]
