The aim of the cudamat project is to make it easy to perform basic matrix calculations on CUDA-enabled GPUs from Python. cudamat provides a Python matrix class that performs calculations on a GPU. At present, some of the operations our GPU matrix class supports include:

• Easy conversion to and from instances of numpy.ndarray.
• Limited slicing support.
• Matrix multiplication and transpose.
• Elementwise addition, subtraction, multiplication, and division.
• Elementwise application of exp, log, pow, sqrt.
• Summation, maximum and minimum along rows or columns.
• Conversion of CUDA errors into Python exceptions.

The current feature set of cudamat is biased towards features needed for implementing some common machine learning algorithms. We have included implementations of feedforward neural networks and restricted Boltzmann machines in the examples that come with cudamat.

Example:

import numpy as np
import cudamat as cm

cm.cublas_init()

# create two random matrices and copy them to the GPU
a = cm.CUDAMatrix(np.random.rand(32, 256))
b = cm.CUDAMatrix(np.random.rand(256, 32))

# perform calculations on the GPU
c = cm.dot(a, b)
d = c.sum(axis = 0)

# copy d back to the host (CPU) and print
print(d.asarray())

An overview of the main features of cudamat can be found in the technical report:

CUDAMat: A CUDA-based matrix class for Python, Volodymyr Mnih, UTML TR 2009-004.

You can obtain the latest release from the repository by typing:

git clone https://github.com/cudamat/cudamat.git

You can also download one of the releases from the releases section.

cudamat uses setuptools and can be installed via pip. For details, please see INSTALL.md.

If you want to contribute new features or improvements, you're welcome to fork cudamat on github and send us your pull requests! Please see CONTRIBUTE.md if you need any help with that.