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]
> <javascript:>> 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.
>>>>>>>>>
>>>>>>>>>
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