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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