Hi David, I've used a GTX 970 for training deep convnets without issue. Depending on your budget, a GTX 980 Ti or TITAN X would be even better (we use some TITAN X's in our lab). The main thing about using smaller GPUs for training these networks is that depending on the implementation of the neural net code, you may have to tune your mini batch size to fit in memory. But this shouldn't be a problem if you are using a lower-memory convolutions such as some of the ones in cuDNN.
If you're willing to wait a bit, the first Nvidia Pascal chip is rumored to be released as early as April. They are supposed to have full support for half precision floating point, which in theory gives a 2x speedup over equivalent single precision performance. Regards, Peter On Tue, Feb 2, 2016 at 10:25 AM, David Fotland <fotl...@smart-games.com> wrote: > 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-boun...@computer-go.org] On Behalf > > Of Hiroshi Yamashita > > Sent: Monday, February 01, 2016 11:26 PM > > To: computer-go@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" <d...@physik.de> > > To: <computer-go@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@computer-go.org > > http://computer-go.org/mailman/listinfo/computer-go > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go
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