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] 
> <javascript:>> 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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