Speeds up a little, but still not 100% utilization.

On Tuesday, November 15, 2016 at 7:09:52 AM UTC-7, nouiz wrote:
>
> With device=gpu flag also add the flag lib.cnmem=1
>
> This will speed up that code. The new backend so something similar by 
> default.
>
> Fred
>
> Le 14 nov. 2016 12:15, "Michael Klachko" <[email protected] 
> <javascript:>> a écrit :
>
>> I will try testing it on Pascal Titan X card when I have time tomorrow, 
>> and will report back. 
>>
>> On Sat, Nov 12, 2016 at 9:36 PM, Ragav Venkatesan <[email protected] 
>> <javascript:>> wrote:
>>
>>> When I was debugging, I also discovered that if I use ignore_border = 
>>> False for pooling, it doesn't run on GPU in libgpuarray backend. 
>>> ignore_border = True does. Is there anything to 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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