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# Fast networks for PyBrain

Writing a neural networking framework in Python imposes certain speed restrictions upon it. Python is just not as fast as languages such as C++ or Fortran.

Due to this, PyBrain has its own spin-off called arac, which is a re-implementation of its neural networking facilities that integrates transparently with it.

Depending on the configuration of your system, speedups of 5-10x faster can be expected. This speedup might be even higher (ranging into the hundreds) if you use sophisticated topologies such as MDRNNs. If you want some numbers on your system, there is a comparison script shipped with PyBrain at `examples/arac/benchmark.py`.

## Installation

However, the installation process is less easy than for pure Python PyBrain. On the other hand, it’s mostly about installing an additional library and telling PyBrain to use fast networks.

You will find detailed installation instructions for arac on the arac wiki.

This instructions are work in progress. If you run into difficulties, ask on the PyBrain mailing list.

## Usage

Once you have installed it, there are three ways to use fast networks.

First, the shortcut `buildNetwork` has a keyword `fast` which builds an arac network instead:

    >>> from pybrain.tools.shortcuts import buildNetwork
    >>> n = buildNetwork(2, 3, 1, fast=True)
    >>> n.activate((2, 3))
    array([-0.20781205])

As you can see by examining the network, it is a special class:

    >>> n
    <_FeedForwardNetwork '_FeedForwardNetwork-8'>

which is prefixed with an underscore, the Python convention for naming C implementations of already existing classes. We can import these classes directly from arac and use them in the same way as PyBrain classes:

    >>> from arac.pybrainbridge import _FeedForwardNetwork, _RecurrentNetwork

The third method is to construct a network as a PyBrain network and call the method `convertToFastNetwork` afterwards:

    >>> n = buildNetwork(2, 3, 1, fast=False)
    >>> n.convertToFastNetwork()
    <_FeedForwardNetwork '_FeedForwardNetwork-18'>

However, be cautious with the last method since changes to the PyBrain network are not reflected in the arac network.

## Limitations

Since arac is implemented in C++ and currently maintained by only a single person, arac development is likely to be slower than PyBrain’s. This might lead to certain features (e.g. layer types) to be implemented later than the pure python versions. This also applies to custom layer types. As soon as you have your layer type, you will not be able to use fast networks anymore – except if you chose to also implement them for arac yourself.

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