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]> 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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