Open Source Deep Learning Project: CUV

Deep Learning Specialization on Coursera

CUV: Matrix library for CUDA in C++ and Python

Project Website:

Github Link:


CUV is a C++ template and Python library which makes it easy to use NVIDIA(tm)


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.


• 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 ( 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.


• Tutorials are available on
• The documentation can be generated from the code or accessed on the


• 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://

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