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#Theano*

To install the package, see this page

For the documentation, see the project website here

Related Projects

It is recommended that you look at the documentation on the website, as it will be more current than the documentation included with the package.

In order to build the documentation yourself, you will need sphinx. Issue the following command: python ./doc/scripts/docgen.py

Documentation is built into html/

The PDF of the documentation can be found at html/theano.pdf

DIRECTORY LAYOUT

Theano (current directory) is the distribution directory.

  • Theano/theano contains the package
  • Theano/theano has several submodules:
    • gof + compile are the core
    • scalar depends upon core
    • tensor depends upon scalar
    • sparse depends upon tensor
    • sandbox can depend on everything else
  • Theano/examples are copies of the example found on the wiki
  • Theano/benchmark and Theano/examples are in the distribution, but not in the Python package
  • Theano/bin contains executable scripts that are copied to the bin folder when the Python package is installed
  • Tests are distributed and are part of the package, i.e. fall in the appropriate submodules
  • Theano/doc contains files and scripts used to generate the documentation
  • Theano/html is where the documentation will be generated

#Intel Theano

This repo is dedicated to improving Theano performance when running on CPU, in particular Intel® Xeon® processors (HSW+).

  • Branch pcs-theano includes the optimized codes based on Theano version 0.8.0rc1, get and install it via below commands:
git clone -b pcs-theano https://github.com/intel/theano.git pcs-theano
cd pcs-theano
python setup.py build 
python setup.py install
  • Branch pcs-dev includes the optimized codes based on Theano version 0.9.0dev1.
git clone -b pcs-dev https://github.com/intel/theano.git pcs-theano
cd pcs-theano
python setup.py build 
python setup.py install

We also provide an optimized Numpy and some benchmarks/demo cases, you can find optimized Numpy in here, demo cases in here.


* Other names and trademarks may be claimed as the property of others.

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