CUV Documentation
0.9.201107041204
Summary
CUV is a C++ template and Python library which makes it easy to use NVIDIA(tm)
CUDA.
Features
Supported Platforms:
 • This library was only tested on Ubuntu Karmic, Lucid and Maverick. It uses
 mostly standard components (except PyUBLAS) and should run without major
 modification on any current linux system.
Supported GPUs:
 • By default, code is generated for the lowest compute architecture. We
 recommend you change this to match your hardware. Using ccmake you can set
 the build variable "CUDA_ARCHITECTURE" for example to -arch=compute_20
 • All GT 9800 and GTX 280 and above
 • GT 9200 without convolutions. It might need some minor modifications to
 make the rest work. If you want to use that card and have problems, just
 get in contact.
 • On 8800GTS, random numbers and convolutions wont work.
Structure:
 • Like for example Matlab, CUV assumes that everything is an n-dimensional
 array called "tensor"
 • Tensors can have an arbitrary data-type and can be on the host (CPU-memory)
 or device (GPU-memory)
 • Tensors can be column-major or row-major (1-dimensional tensors are, by
 convention, row-major)
 • The library defines many functions which may or may not apply to all
 possible combinations. Variations are easy to add.
 • For convenience, we also wrap some of the functionality provided by Alex
 Krizhevsky on his website (http://www.cs.utoronto.ca/~kriz/) with
 permission. Thanks Alex for providing your code!
Python Integration
 • CUV plays well with python and numpy. That is, once you wrote your fast GPU
 functions in CUDA/C++, you can export them using Boost.Python. You can use
 Numpy for pre-processing and fancy stuff you have not yet implemented, then
 push the Numpy-matrix to the GPU, run your operations there, pull again to
 CPU and visualize using matplotlib. Great.
Implemented Functionality
 • Simple Linear Algebra for dense vectors and matrices (BLAS level 1,2,3)
 • Helpful functors and abstractions
 • Sparse matrices in DIA format and matrix-multiplication for these matrices
 • I/O functions using boost.serialization
 • Fast Random Number Generator
 • Up to now, CUV was used to build dense and sparse Neural Networks and
 Restricted Boltzmann Machines (RBM), convolutional or locally connected.
Documentation
 • Tutorials are available on http://www.ais.uni-bonn.de/~schulz/tag/cuv
 • The documentation can be generated from the code or accessed on the
 internet: http://www.ais.uni-bonn.de/deep_learning/doc/html/index.html
Contact
 • We are eager to help you getting started with CUV and improve the library
 continuously! If you have any questions, feel free to contact Hannes Schulz
 (schulz at ais dot uni-bonn dot de) or Andreas Mueller (amueller at ais dot
 uni-bonn dot de). You can find the website of our group at http://
 www.ais.uni-bonn.de/deep_learning/index.html.
Installation
Requirements
For C++ libs, you will need:
 • cmake (and cmake-curses-gui for easy configuration)
 • libboost-dev >= 1.37
 • libblas-dev
 • libtemplate-perl -- (we might get rid of this dependency soon)
 • NVIDIA CUDA (tm), including SDK. We support versions 3.X and 4.0
 • thrust library - included in CUDA since 4.0 (otherwise available from http:
 //code.google.com/p/thrust/)
 • doxygen (if you want to build the documentation yourself)
For Python Integration, you additionally have to install
 • pyublas -- from http://mathema.tician.de/software/pyublas
 • python-nose -- for python testing
 • python-dev
Optionally, install dependent libraries
 • cimg-dev for visualization of matrices (grayscale only, ATM)
Obtaining CUV
You should check out the git repository
 $ git clone git://github.com/deeplearningais/CUV.git
Installation Procedure
Building a debug version:
 $ cd cuv-version-source
 $ mkdir -p build/debug
 $ cd build/debug
 $ cmake -DCMAKE_BUILD_TYPE=Debug ../../
 $ ccmake . # adjust paths to your system (cuda, thrust, pyublas, ...)!
 # turn on/off optional libraries (CImg, ...)
 $ make -j
 $ ctest # run tests to see if it went well
 $ sudo make install
 $ export PYTHONPATH=`pwd`/src # only if you want python bindings
Building a release version:
 $ cd cuv-version-source
 $ mkdir -p build/release
 $ cd build/release
 $ cmake -DCMAKE_BUILD_TYPE=Release ../../
 $ ccmake . # adjust paths to your system (cuda, thrust, pyublas, ...)!
 # turn on/off optional libraries (CImg, ...)
 $ make -j
 $ ctest # run tests to see if it went well
 $ sudo make install
 $ export PYTHONPATH=`pwd`/src # only if you want python bindings
On Debian/Ubuntu systems, you can skip the sudo make install step and instead
do
 $ cpack -G DEB
 $ sudo dpkg -i cuv-VERSION.deb
Building the documentation
 $ cd build/debug # change to the build directory
 $ make doc
Sample Code
We show two brief examples. For further inspiration, please take a look at the
test cases implemented in the src/tests directory.
Pushing and pulling of memory
C++ Code:
 #include <cuv.hpp>
 using namespace cuv;
 int main(void){
 tensor<float,host_memory_space> h(256); // reserves space in host memory
 tensor<float,dev_memory_space> d(256); // reserves space in device memory
 fill(h,0); // terse form
 apply_0ary_functor(h,NF_FILL,0.f); // more verbose
 d=h; // push to device
 sequence(d); // fill device vector with a sequence
 h=d; // pull to host
 for(int i=0;i<h.size();i++)
 {
 assert(d[i] == h[i]);
 }
 }
Python Code:
 import cuv_python as cp
 import numpy as np
 h = np.zeros((1,256)) # create numpy matrix
 d = cp.dev_tensor_float(h) # constructs by copying numpy_array
 h2 = np.zeros((1,256)).copy("F") # create numpy matrix
 d2 = cp.dev_tensor_float_cm(h2) # creates dev_tensor_float_cm (column-major float) object
 cp.fill(d,1) # terse form
 cp.apply_nullary_functor(d,cp.nullary_functor.FILL,1) # verbose form
 h = d.np # pull and convert to numpy
 assert(np.sum(h) == 256)
 d.dealloc() # explicitly deallocate memory (optional)
Simple Matrix operations
C++-Code
 #include <cuv.hpp>
 using namespace cuv;
 int main(void){
 tensor<float,dev_memory_space,column_major> C(2048,2048),A(2048,2048),B(2048,2048);
 fill(C,0); // initialize to some defined value, not strictly necessary here
 sequence(A);
 sequence(B);
 apply_binary_functor(A,B,BF_MULT); // elementwise multiplication
 A *= B; // operators also work (elementwise)
 prod(C,A,B, 'n','t'); // matrix multiplication
 }
Python Code
 import cuv_python as cp
 import numpy as np
 C = cp.dev_tensor_float_cm([2048,2048]) # column major tensor
 A = cp.dev_tensor_float_cm([2048,2048])
 B = cp.dev_tensor_float_cm([2048,2048])
 cp.fill(C,0) # fill with some defined values, not really necessary here
 cp.sequence(A)
 cp.sequence(B)
 cp.apply_binary_functor(B,A,cp.binary_functor.MULT) # elementwise multiplication
 B *= A # operators also work (elementwise)
 cp.prod(C,A,B,'n','t') # matrix multiplication
The examples can be found in the "examples/" folder under "python" and "cpp"
Generated on Mon Jul 4 2011 12:04:53 for CUV by doxygen 1.7.1