Hi David,
I use GTS 450 and GTX 980. I use Caffe on ubuntu 14.04.
Caffe's install is difficult. So I recommend using ubuntu 14.04.
time for predicting a position
Detlef44% Detlef54% CUDA cores clock
GTS 450 17.2ms 21 ms 192 783MHz
GTX 980 5.1ms 10.1ms 2,048 1126MHz
GTX 980 cuDNN 6.4ms 5.9ms 2,048 1126MHz
GTX 670 7.9ms 1,344 915MHz
Learning time
MNIST GPU Aya's 10000 iteration (mini-batch=256)
GTS 450 306 sec 9720 sec
GTX 980 169 sec
GTX 980 cuDNN 24 sec 726 sec
GTS 450 is not so slow for predicting a position.
But GTX 980 learning speed is 13 times faster than GTS 450.
And cuDNN, library provided by NVIDIA, is very effective.
cuDNN does not work on GTS 450.
And caffe's page is nice.
http://caffe.berkeleyvision.org/performance_hardware.html
Regards,
Hiroshi Yamashita
----- Original Message -----
From: "David Fotland" <fotl...@smart-games.com>
To: <computer-go@computer-go.org>
Sent: Wednesday, February 03, 2016 3:25 AM
Subject: [Computer-go] What hardware to use to train the DNN
Detlef, Hiroshi, Hideki, and others,
I have caffelib integrated with Many Faces so I can evaluate a DNN. Thank you very much Detlef for sample code to set up the
input layer. Building caffe on windows is painful. If anyone else is doing it and gets stuck I might be able to help.
What hardware are you using to train networks? I don’t have a cuda-capable GPU yet, so I'm going to buy a new box. I'd like
some advice. Caffe is not well supported on Windows, so I plan to use a Linux box for training, but continue to use Windows for
testing and development. For competitions I could use either windows or linux.
Thanks in advance,
David
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