The document outlines guidelines for building a deep learning machine, highlighting the benefits of DIY hardware, such as cost-effectiveness and customization. Key considerations include the number of GPUs, motherboard compatibility, cooling solutions, and sufficient RAM and storage. It also provides resources and links for further reading and software support.

1. Building a Deep
Learning (Dream)
Machine
DIY hardware hacking for the uninitiated
Roelof Pieters
@graphific http://www.graph-technologies.com
27 June 2016

6. http://graphific.github.io/

7. How: Machine Thank you gamers!

8. Why
- Cheaper *
- Can do things you can’t do in the cloud
- Desktop = faster experiment iteration
- Remote still possible
- Customize to your own needs
- Fun!
* in my own particular case

9. Points to think about
- How many GPUs now and later
- Motherboard (40 lanes / 16x8x8x8 configuration for 4 GPUs)
- Chassis with enough space + air flow
- 4 GPUs: 7 PCIe slots (last GPU can be mounted at the bottom using only one
slot)
- CPU: good enough, as much cores as GPUs, make sure CPU supports 40
PCIe lanes, some new Haswell CPUs only support 32;
- RAM: 2x of total GPU memory
- SSD = nice if data doesn’t fit into GPU+RAM (or hdfs reads from disk): get
larger SSD than your largest dataset
- Mechanical disks: plenty of storage
- PSU: don’t save on efficiency (titanium/platinum)
- Cooling: Water (hard diy) or Fan (easy/cheap but noisy)

11. I: HARDWARE

12. How: Machine

13. How: Machine

14. How: Machine (2)

21. II: SOFTWARE

28. https://github.
com/deeplearningpari
s/dl-machine

29. And so on...

31. http://gitxiv.com/

32. http://www.creativeai.net/

35. GTX 1080

36. Drive PX 2
P100

39. EPGU

40. PGU

42. READ MORE

43. http://graphific.github.io/

44. https:
//hackaday.
io/project/1207
0-32-tflop-
deep-learning-
gpu-box

45. http://timdettmers.
com/2015/03/09/dee
p-learning-
hardware-guide/

46. https://timdettmers.
wordpress.
com/2014/08/14/whi
ch-gpu-for-deep-
learning/