Install and Run cuda-convnet on Windows

转自：http://personal.ie.cuhk.edu.hk/~xy012/others/install_cudaconvnet.html

Install and Run cuda-convnet on Windows OS with VS2010

Xiong Yuanjun

MMLAB, CUHK

Prerequisite

CUDA and a supported NVIDIA GPU. At the time of writing this doc, the CUDA version is
5.5. All following instructions will be based on CUDA 5.5.

Download cuda-convnet. The project site is on the github. To get the code, you can both download the

zip file, or clone from the github using cmd

git clone https://github.com/dnouri/cuda-convnet.git[/code] 
Python 2.7 and Numpy, I suggest using 
WinPython on Windows OS. Why? WinPython contains a python console and numpy-mkl module. Besides, it also embeds many useful packages and a scientific computation friendly Python IDE
- Spyder. The installation experience is far from painful compared to install python and all packages related. Remember to install the
64-bit version.

PThread, for the threading support.
Intel C++ Composer for its intel mkl module.

The Visual Studio Project, kindly provided by
Oriol Vinyals.
Other useful tool: Dependency Walker to look into dll problems.

Installation
1. Prepare the Visual Studio Project
Put the content of the visual studio project into the source folder of
cuda-convnet. However, you may not be able to open the 
.sln
 solution file in VS2010. This is resulted from default directories setting.
Open 
pyconvnet.vcxproj
 with any text editor or xml parser. Since our main execution platform is on a 64-bit windows OS. We only need to modify setting of the profile
.

Search for this profile label, and modify the directory setting to the actual path on your machine. In my experience, labels needing attention include
,

Modify the CUDA version identifier. Search for 
CUDA 4.0
 (the original code was written with cuda 4.0, however we will use cuda 5.5) and modify any occurrence to
CUDA 5.5
.
The 
dll
 and 
lib
 files may change their names over versions. First look into the depencies and modify the project profile to adapt to these changes.

Now you should be able to open the solution in VS2010.

2. Compile the code
Rebuild
 the solution in VS2010. You may come across some errors. Following are some of them, for more help, you can browse the github homepage and its wiki.

First set 
USE_MKL
 in preprocessor difinition. This enables the mkl support.
The compiler may not find the 
cutil_inline.h
. Actually it's missing in newer version of CUDA. Refer to
this gist and add the file to the projects'
Headers
 folder.
Compilation error: 
pthread_muted_t undefined
. Try add 
#include
in 
nvmatrix.cuh
.

After a successful compilation, you will get a 
.pyd
 library called
pyconvnet.pyd
. This is the python module of cuda-convnet.

Run the sample
Download the
cifar-10 dataset. Put the content into 
($ProjectDir$)/storage2/tiny/cifar-10-batches-py-colmajor/
. The zip file should contains 6 data files and 1 meta-file.
Set 
PATH
 in environment variable. You need these 3 dirs.

(pthread directory)\Pre-built.2\dll\x64

(Intel composer directory)\redist\intel64\mkl

(Intel composer directory)\redist\ia32\mkl


However, you may still get dll loading fail. So we can directly copy the related dlls into the project folder
(not recommended). As unfortunate as me, I copied 
mkl_intel_thread.dll
,
pthreadGC2.dll
 and 
pthreadVC2.dll
.
Now let's run the code, type in the command line
python convnet.py --data-path=./storage2/tiny/cifar-10-batches-py-colmajor/ --save-path=./storage2/tmp --test-range=6 --train-range=1-5 --layer-def=./example-layers/layers-19pct.cfg --layer-params=./example-layers/layer-params-19pct.cfg
--data-provider=cifar --test-freq=13 --epoch=200

This commnd will run the convnet for 200 iterations and get you a learned classifier.