I'll create a new thread for this.

On Saturday, November 12, 2016 at 7:45:22 PM UTC-7, Ragav Venkatesan wrote:
>
> 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]> 
>> 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. 
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
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>>

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