Great then! I thought that this parameter was a % of the available memory. 
Everythiong more clear now. 

Thanks a lot

El miércoles, 3 de agosto de 2016, 16:11:40 (UTC+2), nouiz escribió:
>
> I suppose that your GPU is shared with the monitor?
>
> If so, this is normal that you can't raise the value too high. This % is 
> the % of the total GPU memory. Not of the free memory. So if we take too 
> much, then it don't let enought space for cudnn internal stuff.
>
> So mostly, don't use a too high cnmem value :)
>
> Fred
>
> On Wed, Aug 3, 2016 at 10:05 AM, BorjaH_85 <[email protected] 
> <javascript:>> wrote:
>
>> Hi all,
>>
>> I have been doing a benchmark regarding the gpu-based execution of Theano 
>> (0.8.2) and Keras (1.0.5) on Windows 10. I am comparing the training time 
>> of a CNN over the MNIST dataset in 3 scenarios: CPU, GPU without cuDNN and 
>> GPU with CUDNN. CUDA v7.5 and cuDNN v5.0
>>
>> First two approaches go well, but I have gone into troubles with the 
>> sysenv variables configuration for the GPU+cuDNN trial. My error source is 
>> the CNMeM configuration. If I run the same python code with CNMeM values 
>> from 0 (disbaled) up to 0.75, I have no problems. Moreover, I obtain a drop 
>> in the training time of more than 50% compared to the general GPU execution.
>>
>> But I cannot go behind from 0.8. With 0.8, I receive the same compilation 
>> error.
>>
>> RuntimeError: ('The following error happened while compiling the node', 
>> GpuDnnConv{algo='small', inplace=True}(GpuContiguous.0, GpuContiguous.0, 
>> GpuAllocEmpty.0, GpuDnnConvDesc{border_mode='valid', subsample=(1, 1), 
>> conv_mode='conv', precision='float32'}.0, Constant{1.0}, Constant{0.0}), 
>> '\n', 'could not create cuDNN handle: CUDNN_STATUS_INTERNAL_ERROR')
>>
>> Best regards,
>>
>> Borja
>>
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>

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