[Computer-go] What hardware to use to train the DNN

Detlef Schmicker ds2 at physik.de
Tue Feb 2 10:34:42 PST 2016


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Hi David,

I use Ubuntu 14.04 LTS with a NVIDIA GTX970 Graphic card (and
i7-4970k, but this is not important for training I think) and
installed CUDNN v4 (important, at least a factor 4 in training speed).

This Ubuntu version is officially supported by Cuda and I did only
have minor problems if an Ubuntu update updated the graphics driver: I
had 2 times in the last year to reinstall cuda (a little ugly, as the
graphic driver did not work after the update and you had to boot into
command line mode).

Detlef

Am 02.02.2016 um 19:25 schrieb David Fotland:
> 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
> 
>> -----Original Message----- From: Computer-go
>> [mailto:computer-go-bounces at computer-go.org] On Behalf Of Hiroshi
>> Yamashita Sent: Monday, February 01, 2016 11:26 PM To:
>> computer-go at computer-go.org Subject: *****SPAM***** Re:
>> [Computer-go] DCNN can solve semeai?
>> 
>> Hi Detlef,
>> 
>> My study heavily depends on your information. Especially Oakfoam
>> code, lenet.prototxt and generate_sample_data_leveldb.py was
>> helpful. Thanks!
>> 
>>> Quite interesting that you do not reach the prediction rate 57%
>>> from the facebook paper by far too! I have the same experience
>>> with the
>> 
>> I'm trying 12 layers 256 filters, but it is around 49.8%. I think
>> 57% is maybe from KGS games.
>> 
>>> Did you strip the games before 1800AD, as mentioned in the FB
>>> paper? I did not do it and was thinking my training is not ok,
>>> but as you have the same result probably this is the only
>>> difference?!
>> 
>> I also did not use before 1800AD. And don't use hadicap games. 
>> Training positions are 15693570 from 76000 games. Test
>> positions are   445693 from  2156 games. All games are shuffled
>> in advance. Each position is randomly rotated. And memorizing
>> 24000 positions, then shuffle and store to LebelDB. At first I
>> did not shuffle games. Then accuracy is down each 61000 iteration
>> (one epoch, 256 mini-batch). http://www.yss-aya.com/20160108.png 
>> It means DCNN understands easily the difference 1800AD games and
>> 2015AD games. I was surprised DCNN's ability. And maybe 1800AD
>> games  are also not good for training?
>> 
>> Regards, Hiroshi Yamashita
>> 
>> ----- Original Message ----- From: "Detlef Schmicker"
>> <ds2 at physik.de> To: <computer-go at computer-go.org> Sent: Tuesday,
>> February 02, 2016 3:15 PM Subject: Re: [Computer-go] DCNN can
>> solve semeai?
>> 
>>> Thanks a lot for sharing this.
>>> 
>>> Quite interesting that you do not reach the prediction rate 57%
>>> from the facebook paper by far too! I have the same experience
>>> with the GoGoD database. My numbers are nearly the same as
>>> yours 49% :) my net is quite simelar, but I use 7,5,5,3,3,....
>>> with 12 layers in total.
>>> 
>>> Did you strip the games before 1800AD, as mentioned in the FB
>>> paper? I did not do it and was thinking my training is not ok,
>>> but as you have the same result probably this is the only
>>> difference?!
>>> 
>>> Best regards,
>>> 
>>> Detlef
>> 
>> _______________________________________________ Computer-go
>> mailing list Computer-go at computer-go.org 
>> http://computer-go.org/mailman/listinfo/computer-go
> 
> _______________________________________________ Computer-go mailing
> list Computer-go at computer-go.org 
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> 
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