
Building a Deep Learning (Dream) Machine - iamjeff
http://graphific.github.io/posts/building-a-deep-learning-dream-machine/
======
brudgers
Part 2: [https://graphific.github.io/posts/running-a-deep-learning-
dr...](https://graphific.github.io/posts/running-a-deep-learning-dream-
machine/)

------
pilooch
Wrong stance on SSD: always get an SSD where to put the temporary data, i.e.
the form that your dataset takes to be fed into the DL backend. For instance,
working with Caffe, your image dataset is turned into an LMDB or HDF5
database, while you can keep your unprocessed dataset on a regular HDD. The
trick is to always prevent your GPU(s) from starving.

------
dharma1
Good build. If you have cash to burn and don't want to do a build, you can buy
a prebuilt one with 4x new titan X in the UK -
[https://www.scan.co.uk/3xs/configurator/nvidia-deep-
learning...](https://www.scan.co.uk/3xs/configurator/nvidia-deep-learning-box
--3xs-g10)

------
dj-wonk
My projects involve a blend of fast CPU and GPU, so I decided to build a fast
system recently with 1 GeForce GTX 1080, a fast Intel Core i7, and 128 GB of
RAM.

I'll install another GTX 1080 soon. I found that the new Titans are hard to
get and the performance/price ratio seems favorable on the 1080's.

I got Ubuntu 16.04 to work with the latest 1080 graphics drivers and CUDA 8 RC
drivers -- no small feat. Reading all the documentation helps, and even then,
it took some finagling.

~~~
pilooch
See
[https://github.com/beniz/deepdetect/issues/126#issuecomment](https://github.com/beniz/deepdetect/issues/126#issuecomment)
and other links online. The trick is to install CUDA 8 _without_ the attached
driver, then install the new NVidia driver. Once you got that, install on
16.04 is straightforward.

~~~
dj-wonk
Yep. Also, this is a nice write-up for TensorFlow with CUDA 8.0 RC and cuDNN
5.1.5 with Ubuntu 16.04:

[https://alliseesolutions.wordpress.com/2016/09/08/install-
gp...](https://alliseesolutions.wordpress.com/2016/09/08/install-gpu-
tensorflow-from-sources-w-ubuntu-16-04-and-cuda-8-0-rc/)

------
KaiserPro
if CPU isn't that important, then I suggest getting a second had hp Z800, with
the big PSU

lots of space for GPUs lots of space for ram and disks.

~~~
bduerst
Are there any hosting solutions that offer GPUs yet?

~~~
Smerity
Microsoft Azure's K80 based N-series are the best that I've seen so far for by
the hour instances. They're currently in preview availability[1].

Compared to the best AWS GPU, the Azure K80s are (a) cheaper and (b) many
times more performant for the purposes of deep learning.

Hopefully this lights a fire under AWS to become more competitive with their
GPU range.

[1]: [https://azure.microsoft.com/en-us/blog/azure-n-series-
previe...](https://azure.microsoft.com/en-us/blog/azure-n-series-preview-
availability/)

~~~
visarga
I'm still on the waiting list for the new N-series GPU servers. Does anyone
have any idea about when it will be made available more broadly?

------
misiti3780
what was the total price of this box - did i miss the price in the article ?

~~~
jason_slack
I spent almost $2400 on my box. Finished it just a few weeks ago. I used parts
of this article for inspiration. I only have 2 x NVIDIA GTX TITAN-X 12GB
(bought used), not 3. I also used a 1600w power supply.

~~~
pault
How does the Titan compare to the new 1080s for machine learning?

~~~
nightski
Slower but more memory (8GB vs. 12GB). Depending on the problem it could
definitely matter.

There is also a new pascal based titan.

~~~
jason_slack
for my needs these cards are working just fine. I could post a picture of the
rig if anyone wants to see it.

