.. Accelerate documentation master file, created by
   sphinx-quickstart on Fri Aug  7 17:43:03 2015.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

Anaconda Accelerate
===================

`Accelerate` provides access to numerical libraries optimized for performance on
Intel CPUs and NVidia GPUs.


Features
--------

* Bindings to CUDA libraries: cuBLAS, cuFFT, cuSPARSE, cuRAND, and sorting
  algorithms from the CUB and Modern GPU libraries
* Speed-boosted linear algebra operations in NumPy, SciPy, scikit-learn and
  NumExpr libraries using Intel's Math Kernel Library (MKL).
* Accelerated variants of Numpy's built-in UFuncs.
* Increased-speed Fast Fourier Transformations (FFT) in NumPy.


Requirements
------------

* 64-bit operating system: Linux, OS X or Windows
* Supported Python and Numpy combinations:
   * Python 2.7 with Numpy 1.9 or 1.10
   * Python 3.4 with Numpy 1.9 or 1.10
   * Python 3.5 with Numpy 1.9 or 1.10
* Numba 0.25

For the CUDA features:

* NVidia driver version 349.00 or later
* CUDA toolkit 7.0
* At least one CUDA GPU with compute capability 2.0 or above


Installation
------------

Accelerate is included with `Anaconda Workgroup and Anaconda Enterprise
subscriptions <https://www.continuum.io/content/anaconda-subscriptions>`_.

To start a 30-day free trial just download and install the Anaconda Accelerate
package.

If you already have `Anaconda <http://continuum.io/downloads.html>`_ (free
Python distribution) installed::

    conda update conda
    conda install accelerate

If you do not have Anaconda installed, you can download it `here
<http://continuum.io/downloads.html>`_.

Anaconda Accelerate can also be installed into your own (non-Anaconda) Python
environment. For more information about Accelerate please contact
`sales@continuum.io <mailto:sales@continuum.io>`_.


Update Instructions
-------------------

If you have Anaconda (free Python distribution) installed::

    conda update conda
    conda update accelerate

If you already have NumbaPro installed, you must manually upgrade NumbaPro to
install the NumbaPro compatibility layer::

    conda update conda
    conda update numbapro


Contents
--------

.. toctree::
   :maxdepth: 3

   releasenotes
   cudalibs
   mkl
   profiling


License Agreement
-----------------

.. toctree::
   :maxdepth: 1

   eula

Documentation for Past Versions
-------------------------------

.. toctree::
   :maxdepth: 1

   Accelerate 2.1 </anaconda/past-versions/accelerate/2.1/index>
   Accelerate 2.0 </anaconda/past-versions/accelerate/2.0/index>
