I figured out what happened in my case, it might or might not apply to 
others.
starting nvidia-smi will slow down gpu computation on a headless (no 
monitor, no X11 running) GPU server
(a 
reference 
https://devtalk.nvidia.com/default/topic/482848/multiple-gpus-listing-devices-very-slow/)

and at the beginning, I was watching GPU usage using 
watch-n 0.1 nvidia-smi
which caused the slowness

and once I switch to
nvidia-smi -l 1
It is under normal speed.



On Friday, 12 May 2017 00:20:27 UTC-4, nouiz wrote:
>
> Try to use Theano profiling to know where the time is spent.
>
> Le jeu. 11 mai 2017 15:48, Gavin Weiguang Ding <[email protected] 
> <javascript:>> a écrit :
>
>> hi daksh,
>>
>> have you figured out the difference? i have the same issue here.
>>
>> Thanks,
>> Gavin
>>
>>
>> On Friday, 17 February 2017 02:37:00 UTC-5, Daksh Varshneya wrote:
>>>
>>> Hi,
>>>
>>> I have a piece of code which basically trains a GRU to learn from some 
>>> sequences. I am training the neural network on each individual 
>>> sequence(batch size = 1). When I am running the training code on my local 
>>> machine which has a Nvidia gtx 960m, it takes about 3.09 secs for ~ 10 data 
>>> points. But when I run the same code on aws ec2 p2.xlarge machine which has 
>>> a Nvidia Tesla K80 GPU, surprisingly the time taken for exactly same 
>>> sequences is around 4.93 seconds. This is very disheartening right now. I 
>>> have tried switching AMI's as well. Originally I used a clean install of 
>>> ubuntu 14.04 and installed cuda, cudnn and theano myself. The second image 
>>> I took from amazon marketplace which had all these things installed. 
>>> The code is exactly the same with no difference at all. Any pointers 
>>> which can help?
>>>
>>> Thanks,
>>> Daksh
>>>
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