install.rst

Installation Instructions

Quick Installation

If you have pip installed, you should be able to install the latest stable release of scikits.cuda by running the following:

pip install scikits.cuda

All dependencies should be automatically downloaded and installed if they are not already on your system.

Obtaining the Latest Software

The latest stable and development versions of scikits.cuda can be downloaded from GitHub

Online documentation for scikits.cuda is available at http://lebedov.github.com/scikits.cuda/

Installation Dependencies

scikits.cuda requires that the following software packages be installed:

• Python 2.5 or later.
• setuptools 0.6c10 or later.
• NumPy 1.2.0 or later.
• PyCUDA 0.94.2 or later (some parts of scikits.cuda might not work properly with earlier versions).
• NIVIDIA CUDA Toolkit 4.0 or later.

To run the unit tests, the following packages are also required:

• nose 0.11 or later.
• SciPy 0.8.0 or later.

Some of the linear algebra functionality relies on the CULA Dense toolkit; the single precision release of the toolkit is free of charge, but requires registration. Depending on the version of CULA installed, some functions may not be available:

• CULA R12 or later.

To build the documentation, the following packages are also required:

• Docutils 0.5 or later.
• Jinja2 2.2 or later.
• Pygments 0.8 or later.
• Sphinx 1.0.1 or later.

Platform Support

The software has been developed and tested on Linux; it should also work on other Unix-like platforms supported by the above packages. Parts of the package may work on Windows as well, but remain untested.

Building and Installation

scikits.cuda searches for CUDA libraries in the system library search path when imported. You may have to modify this path (e.g., by adding the path to the CUDA libraries to /etc/ld.so.conf or to the LD_LIBRARY_PATH environmental variable on Linux) if the libraries are not being found.

To build and install the toolbox, download and unpack the source release and run:

python setup.py install

from within the main directory in the release. To rebuild the documentation, run:

python setup.py build_docs

Running the Unit Tests

To run all of the package unit tests, download and unpack the package source tarball and run:

nosetests

from within the main directory in the archive. Tests for individual modules (found in the tests/ subdirectory) can also be run directly.

Getting Started

Sample code demonstrating how to use different parts of the toolbox is located in the demos/ subdirectory of the source release. Most of the high-level functions also contain doctests that describe their usage.