This may help to have Nitish’s deepnet work on your mac. The code is very clean, most important thing is to follow the instructions here https://github.com/nitishsrivastava/deepnet/blob/master/INSTALL.txt
b) CUDA Toolkit and SDK.
Follow the instructions(CUDA5.5): http://docs.nvidia.com/cuda/cuda-getting-started-guide-for-mac-os-x/
NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
I followed both instruction on http://docs.nvidia.com/cuda/cuda-getting-started-guide-for-mac-os-x/
and instruction from the deepnet to set the system paths:
Follow the deepnet instruction: for mac, it is the ‘~.profile’, edit/add to the file:
First make sure CUDA installed right:
install the examples: cuda-install-samples-5.5.sh <dir>
and go to /Developer/NVIDIA/CUDA-5.5/samples, choose any simple example subfolder, go into and do ‘make’, after make completed, you can do a simple test.
(c) Protocol Buffers.
Download the file: http://code.google.com/p/protobuf/
Follow the instructions to compile/install it. It will be install (generally in /usr/local/bin/protoc). It was said that you only need to include the directory that contains ‘proc’, so add to path:
(2) COMPILING CUDAMAT AND CUDAMAT_CONV
For making the cuda work, do ‘make’ in cudamat , but change all the ‘uint’ to ‘unsigned’ in file: cudamat_conv_kernels.cuh
or do a #define uint unsigned
Then run ‘make’ in cudamat folder
(3,4) STEP 3,4
continue follow step 3, and 4 on https://github.com/nitishsrivastava/deepnet/blob/master/INSTALL.txt. and you will get there.
Note (1): I did not install separately for cudamat library by Vlad Mnih and cuda-convnet library by Alex Krizhevsky.
Note (2): If you do NOT have GPU: another alternative is to not use GPU, most recent mac come with NVIDIA 650, but some old version may use intel graphical card. In that case you can still do the deep learning part, but using eigenmat. The drawback is that it will be very slow.
Install eigen from here: http://eigen.tuxfamily.org/index.php?title=Main_Page
if given error <Eigen/..> can not found, change to “Eigen/…”
also you need to change python path, including path to where ‘libeigenmat.dylib’ located. It it still fails to find: libeigenmat.dylib. It may not hurt to give it a direct path, edit the file <eigenmat/eigenmat.py>.
_eigenmat = ct.cdll.LoadLibrary(‘the-path-to/libeigenmat.dylib’)
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Though simple, but color converting is still a complex process which needs a lot of attention for details and the correct understandings. Even PIL does not include a comprehensive package. Well, just come across this website today, which introduces a nice module for color manipulation.