Pylearn2 is still undergoing rapid development. Don’t expect a clean road without bumps! If you find a bug please write to email@example.com. If you’re a Pylearn2 developer and you find a bug, please write a unit test for it so the bug doesn’t come back!
Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. This means you can write Pylearn2 plugins (new models, algorithms, etc) using mathematical expressions, and theano will optimize and stabilize those expressions for you, and compile them to a backend of your choice (CPU or GPU).
No PyPI download yet. You must checkout the version in github for bleeding-edge/development version, available via:
git clone git://github.com/lisa-lab/pylearn2.git
You also need to set your PYLEARN2_DATA_PATH variable. On linux, the best way to do this is to add a line to your .bashrc file:
Note that this is only an example, and if you are not in the LISA lab, you will need to choose a directory path that is valid on your filesystem. Simply choose a path where it will be convenient for you to store datasets for use with Pylearn2.
Pylearn2 in a box uses Vagrant to easily create a new VM installed with Pylearn2 and the necessary packages.
PyYAML is required for most functionality.
PIL is required for some image-related functionality.
Pylearn2 is released under the 3-claused BSD license, so it may be used for commercial purposes. The license does not require anyone to cite Pylearn2, but if you use Pylearn2 in published research work we encourage you to cite this article:
Pylearn2 is primarily developed by academics, and so citations matter a lot to us. As an added benefit, you increase Pylearn2’s exposure and potential user (and developer) base, which is to the benefit of all users of Pylearn2. Thanks in advance!
Roughly in order of what you’ll want to check out: