-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 you can use caffe with time on the command line.
It gives you forward and backward time for a batch. In my tests the batch size was not too important (I think, because the net is quite large)... cuDNN helps a lot in training, I did not test recently but it was 2 times faster end of last year and improved by a factor of 4 during this year :) Detlef Am 02.03.2016 um 10:22 schrieb Rémi Coulom: > I tried Detlef's 54% NN on my machine. CPU = i7-5930K, GPU = GTX > 980 (not using cuDNN). > > On the CPU, I get 176 ms time, and 10 ms on the GPU (IIRC, someone > reported 6 ms with cuDNN). But it is using only one core on the > CPU, whereas it is using the full GPU. > > If this is correct, then I believe it is still possible to have a > very strong CPU-based program. > > Or is it possible to evaluate faster on the GPU by using a batch? > > Rémi > > On 03/02/2016 09:43 AM, Petr Baudis wrote: >> Also, reading more of that pull request, the guy benchmarking it >> had old nvidia driver version which came with about 50% >> performance hit. So I'm not sure what were the final numbers. >> (And whether current caffe version can actually match these >> numbers, since this pull request wasn't merged.) >> >> On Wed, Mar 02, 2016 at 12:29:41AM -0800, Chaz G. wrote: >>> Rémi, >>> >>> Nvidia launched the K20 GPU in late 2012. Since then, GPUs and >>> their convolution algorithms have improved considerably, while >>> CPU performance has been relatively stagnant. I would expect >>> about a 10x improvement with 2016 hardware. >>> >>> When it comes to training, it's the difference between running >>> a job overnight and running a job for the entire weekend. >>> >>> Best, -Chaz >>> >>> On Tue, Mar 1, 2016 at 1:03 PM, Rémi Coulom >>> <remi.cou...@free.fr> wrote: >>> >>>> How tremendous is it? On that page, I find this data: >>>> >>>> https://github.com/BVLC/caffe/pull/439 >>>> >>>> " These are setup details: >>>> >>>> * Desktop: CPU i7-4770 (Haswell), 3.5 GHz , DRAM - 16 GB; >>>> GPU K20. * Ubuntu 12.04; gcc 4.7.3; MKL 11.1. >>>> >>>> Test:: imagenet, 100 train iteration (batch = 256). >>>> >>>> * GPU: time= 260 sec / memory = 0.8 GB * CPU: time= 752 sec >>>> / memory = 3.5 GiB //Memory data is from system monitor. >>>> >>>> " >>>> >>>> This does not look so tremendous to me. What kind of speed >>>> difference do you get for Go networks? >>>> >>>> Rémi >>>> >>>> On 03/01/2016 06:19 PM, Petr Baudis wrote: >>>> >>>>> On Tue, Mar 01, 2016 at 09:14:39AM -0800, David Fotland >>>>> wrote: >>>>> >>>>>> Very interesting, but it should also mention Aya. >>>>>> >>>>>> I'm working on this as well, but I haven’t bought any >>>>>> hardware yet. My goal is not to get 7 dan on expensive >>>>>> hardware, but to get as much strength as I can on >>>>>> standard PC hardware. I'll be looking at much smaller >>>>>> nets, that don’t need a GPU to run. I'll have to buy a >>>>>> GPU for training. >>>>>> >>>>> But I think most people who play Go are also fans of >>>>> computer games that often do use GPUs. :-) Of course, >>>>> it's something totally different from NVidia Keplers, but >>>>> still the step up from a CPU is tremendous. >>>>> >>>>> Petr Baudis _______________________________________________ >>>>> 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 >>> _______________________________________________ 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 > -----BEGIN PGP SIGNATURE----- Version: GnuPG v2.0.22 (GNU/Linux) iQIcBAEBAgAGBQJW2JVUAAoJEInWdHg+Znf4JGEQAIcuAbKkEihOhjKoH4fXKr1C zsN0nVX5Viyca9Ca8NDxBb8xL1w1MYQl8oO2yiljwZKD6GQiSGV2d+jQTI42HCXn xPn88O9nkzOJh6OKyNmLxZvj/A9XQjFTaDkPgaO0NejoAytdQEjXZrZlDB1dLjvJ 5GYtIZCXpCPsmgV25Iz8Hs1lsMFbUy4GrTx6m+htd3iAZiL+ZmaFMh+aYHVVn9ea j66EAevu3klzCiWoO8A+s0onpvw05LM9gSpC1yPqqR55IOY1ikEAh7MqUSdStz/9 xt7YUdtXm5lrzEIRIMvhDec/FsnVYfmeRE6lKxBus95Y6zcwx9HvwtDZBDWzjIsl 4tgO86N8saK5eG1PqAPmRkg5abp0r3JG7NRCoI/47T9i3NkYC+RtCVZCo0tojzkW gY28blxHtG5yQscob6D1T/2597VWetXBghzcnXCzLe0+aJS2p+S5klW3Q/B4o3t+ fKqICpMQrNzJO3vagefqYkRCzIUdUVXH4/rAnUnu3+pY75iTPNzqAK1PtdYcAQYt XDGUIWH0+Ir9Wk20EzM3dXDR/7rKbHgeuBbIEP4+LnEQsNJ7UE6YSFJHhdJTNGGM 5LXeXJv6YQQO3Gj9yQYuMTZFploVhJKAFf4vIloYwG2ZKE1SkU3aBQsudCr5uhc1 5XK2WAy4gKl/Ih0KxZLm =uWVF -----END PGP SIGNATURE----- _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
