in htop I usually have one CPU running 100% for both cases.
On Saturday, November 12, 2016 at 7:43:16 PM UTC-7, Michael Klachko wrote: > > I'm not sure, but just by looking at CPU usage (top command on Linux) you > should be able to see the difference. > > On Sat, Nov 12, 2016 at 6:19 PM, Ragav Venkatesan <[email protected] > <javascript:>> wrote: > >> Both are using CUdNNs.. I am wondering if some ops are running on the >> CPU, how do I find that out ? >> >> On Friday, November 11, 2016 at 10:00:39 PM UTC-7, Michael Klachko wrote: >>> >>> Do both versions use CuDNN? If gpu0 version didn't use it, that would >>> explain the difference. Also, look at CPU usage for gpu0 version - it could >>> be that some ops are running on CPU instead of GPU. >>> >>> On Fri, Nov 11, 2016 at 2:20 PM, Ragav Venkatesan <[email protected] >>> > wrote: >>> >>>> Running on GTX 1080, cuda0 for device runs for 1.69 minutes at 98% , >>>> gpu0 runs for 5.12 minutes at 34% . Both runs the same code cnn_tutorial >>>> from theano tutorials. The code is not modified or changed at all. >>>> floatX=float32, mode = FAST_RUN, nvcc.fastmath = True and nvcc.allowgc >>>> =True. >>>> >>>> On Thursday, November 10, 2016 at 4:47:38 PM UTC-7, Michael Klachko >>>> wrote: >>>>> >>>>> Yes. It depends on the size of your network/input - the smaller it is, >>>>> the harder it is to keep 3k cores busy all the time. >>>>> Regarding timing, you don't need to write much code: >>>>> >>>>> import time >>>>> start_time = time.time() >>>>> your code here >>>>> print "Code ran for {:.1f} minutes".format((time.time() - >>>>> start_time)/60) >>>>> >>>>> >>>>> >>>>> >>>>> On Thu, Nov 10, 2016 at 3:26 PM, Ragav Venkatesan < >>>>> [email protected]> wrote: >>>>> >>>>>> I'm writing a code to test this, but why do you ask this ? Is there a >>>>>> case where nvidia-smi might give me 35% util when the GPU is actually >>>>>> running the code as fast as it can ? >>>>>> >>>>>> On Wednesday, November 9, 2016 at 5:36:14 PM UTC-7, Michael Klachko >>>>>> wrote: >>>>>>> >>>>>>> Ragav, so when GPU is 98% utilized, is the training faster than when >>>>>>> it's 35% utilized? Have you timed it? >>>>>>> >>>>>>> On Wed, Nov 9, 2016 at 4:09 PM, Ragav Venkatesan < >>>>>>> [email protected]> wrote: >>>>>>> >>>>>>>> After investigating further I don't think this is a speed or slow >>>>>>>> issue. I think the newer version of CUDA/cuDNN using the cuda backend >>>>>>>> is >>>>>>>> not using the GPU fully. The older version (7.5/5103) of CUDA/cuDNN >>>>>>>> produce >>>>>>>> 98% GPU util but the same code on the latest versions (8.0/5105) >>>>>>>> don't. The >>>>>>>> code by the way is the lenet tutorial from theano, so its not some >>>>>>>> weird >>>>>>>> coding error also. Using the libgpuarray backend, I am able to produce >>>>>>>> 98% >>>>>>>> util even with CUDA/cuDNN (8/5105). >>>>>>>> >>>>>>>> On Wednesday, November 9, 2016 at 9:48:40 AM UTC-7, nouiz wrote: >>>>>>>>> >>>>>>>>> It could be that the new back-end (libgpuarray) is faster and more >>>>>>>>> efficient in that cases. So just use that back-end :) >>>>>>>>> >>>>>>>>> The speed difference between both back-end isn't constant, but >>>>>>>>> should be a little bit faster with the new back-end in average. >>>>>>>>> >>>>>>>>> We have found a few speed regression in the new back-end, but they >>>>>>>>> where fixed. If you found one, just tell us and we'll fix it. But the >>>>>>>>> probably is still low of having slowdown in the new back-end. >>>>>>>>> >>>>>>>>> We just merged one such fix with indexing. Make sure to update >>>>>>>>> libgpuarray and recompile it if you want to be sure to have the >>>>>>>>> fastest >>>>>>>>> version. >>>>>>>>> >>>>>>>>> Fred >>>>>>>>> >>>>>>>>> On Tue, Nov 8, 2016 at 1:56 PM, Ragav Venkatesan < >>>>>>>>> [email protected]> wrote: >>>>>>>>> >>>>>>>>>> Ok, here is a problem I'm getting and I am not sure how to solve >>>>>>>>>> this. If I use the libgpuarray backend on the cnn_tutorial I am >>>>>>>>>> getting a >>>>>>>>>> 98% gpu tutilization with cudnn 5105. If I use cuda backend, I am >>>>>>>>>> only >>>>>>>>>> getting about 35% utilization. >>>>>>>>>> Anyidea why this might be so ? >>>>>>>>>> >>>>>>>>>> On Monday, October 24, 2016 at 9:38:17 AM UTC-7, nouiz wrote: >>>>>>>>>>> >>>>>>>>>>> What errors do you have? Delete your Theano cache, just in case >>>>>>>>>>> and be sure to use Theano dev version. The last release don't >>>>>>>>>>> support it I >>>>>>>>>>> think. >>>>>>>>>>> >>>>>>>>>>> Fred >>>>>>>>>>> >>>>>>>>>>> On Mon, Oct 24, 2016 at 12:33 PM, Michael Klachko < >>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>> >>>>>>>>>>>> Yes, it's supported, I'm using it right now (CUDA 8.0 on Ubuntu >>>>>>>>>>>> 14.04): >>>>>>>>>>>> >>>>>>>>>>>> >>> import theano >>>>>>>>>>>> Using gpu device 0: TITAN X (Pascal) (CNMeM is enabled with >>>>>>>>>>>> initial size: 30.0% of memory, cuDNN 5105) >>>>>>>>>>>> >>> print theano.__version__ >>>>>>>>>>>> 0.9.0dev3.dev-20fd30a38d34687e9d944140042762ca9fca6276 >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> On Saturday, October 22, 2016 at 2:54:00 PM UTC-7, Ragav >>>>>>>>>>>> Venkatesan wrote: >>>>>>>>>>>>> >>>>>>>>>>>>> I updated and I'm getting some weird errors. With Cuda >>>>>>>>>>>>> backend, convolutions only run on CPU and with libgpuarray >>>>>>>>>>>>> backend GPUs >>>>>>>>>>>>> only run at about 35% util. >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> -- >>>>>>>>>>>> >>>>>>>>>>>> --- >>>>>>>>>>>> You received this message because you are subscribed to the >>>>>>>>>>>> Google Groups "theano-users" group. >>>>>>>>>>>> To unsubscribe from this group and stop receiving emails from >>>>>>>>>>>> it, send an email to [email protected]. >>>>>>>>>>>> For more options, visit https://groups.google.com/d/optout. >>>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> -- >>>>>>>>>> >>>>>>>>>> --- >>>>>>>>>> You received this message because you are subscribed to the >>>>>>>>>> Google Groups "theano-users" group. >>>>>>>>>> To unsubscribe from this group and stop receiving emails from it, >>>>>>>>>> send an email to [email protected]. >>>>>>>>>> For more options, visit https://groups.google.com/d/optout. >>>>>>>>>> >>>>>>>>> >>>>>>>>> -- >>>>>>>> >>>>>>>> --- >>>>>>>> You received this message because you are subscribed to a topic in >>>>>>>> the Google Groups "theano-users" group. >>>>>>>> To unsubscribe from this topic, visit >>>>>>>> https://groups.google.com/d/topic/theano-users/bSTnP3yLorw/unsubscribe >>>>>>>> . >>>>>>>> To unsubscribe from this group and all its topics, send an email to >>>>>>>> [email protected]. >>>>>>>> For more options, visit https://groups.google.com/d/optout. >>>>>>>> >>>>>>> >>>>>>> -- >>>>>> >>>>>> --- >>>>>> You received this message because you are subscribed to a topic in >>>>>> the Google Groups "theano-users" group. >>>>>> To unsubscribe from this topic, visit >>>>>> https://groups.google.com/d/topic/theano-users/bSTnP3yLorw/unsubscribe >>>>>> . >>>>>> To unsubscribe from this group and all its topics, send an email to >>>>>> [email protected]. >>>>>> For more options, visit https://groups.google.com/d/optout. >>>>>> >>>>> >>>>> -- >>>> >>>> --- >>>> You received this message because you are subscribed to a topic in the >>>> Google Groups "theano-users" group. >>>> To unsubscribe from this topic, visit >>>> https://groups.google.com/d/topic/theano-users/bSTnP3yLorw/unsubscribe. >>>> To unsubscribe from this group and all its topics, send an email to >>>> [email protected]. >>>> For more options, visit https://groups.google.com/d/optout. >>>> >>> >>> -- >> >> --- >> You received this message because you are subscribed to a topic in the >> Google Groups "theano-users" group. >> To unsubscribe from this topic, visit >> https://groups.google.com/d/topic/theano-users/bSTnP3yLorw/unsubscribe. >> To unsubscribe from this group and all its topics, send an email to >> [email protected] <javascript:>. >> For more options, visit https://groups.google.com/d/optout. >> > > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. 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