dyNET 2.0.1.post13

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The Dynamic Neural Network Toolkit

**News!** The master branch is now DyNet version 2.0 (as of 6/28/2017),
which contains a number of changes including a new model format, etc. If
you’re looking for the old version, check out the `v1.1
branch <https://github.com/clab/dynet/tree/v1.1>`__.

- `General <#general>`__
- `Installation <#installation>`__

- `C++ <#c-installation>`__
- `Python <#python-installation>`__

- `Getting Started <#getting-started>`__
- `Citing <#citing>`__
- `Releases and Contributing <#releases-and-contributing>`__

General
-------

DyNet is a neural network library developed by Carnegie Mellon
University and many others. It is written in C++ (with bindings in
Python) and is designed to be efficient when run on either CPU or GPU,
and to work well with networks that have dynamic structures that change
for every training instance. For example, these kinds of networks are
particularly important in natural language processing tasks, and DyNet
has been used to build state-of-the-art systems for `syntactic
parsing <https://github.com/clab/lstm-parser>`__, `machine
translation <https://github.com/neubig/lamtram>`__, `morphological
inflection <https://github.com/mfaruqui/morph-trans>`__, and many other
application areas.

Read the `documentation <http://dynet.readthedocs.io/en/latest/>`__ to
get started, and feel free to contact the `dynet-users
group <https://groups.google.com/forum/#!forum/dynet-users>`__ group
with any questions (if you want to receive email make sure to select
“all email” when you sign up). We greatly appreciate any bug reports and
contributions, which can be made by filing an issue or making a pull
request through the `github page <http://github.com/clab/dynet>`__.

You can also read more technical details in our `technical
report <https://arxiv.org/abs/1701.03980>`__.

Getting started
---------------

You can find tutorials about using DyNet `here
(C++) <http://dynet.readthedocs.io/en/latest/tutorial.html#c-tutorial>`__
and `here
(python) <http://dynet.readthedocs.io/en/latest/tutorial.html#python-tutorial>`__,
and `here (EMNLP 2016
tutorial) <https://github.com/clab/dynet_tutorial_examples>`__.

One aspect that sets DyNet apart from other tookits is the
**auto-batching** feature. See the
`documentation <http://dynet.readthedocs.io/en/latest/minibatch.html>`__
about batching.

The ``example`` folder contains a variety of examples in C++ and python.

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

DyNet relies on a number of external programs/libraries including CMake,
Eigen, and Mercurial (to install Eigen). CMake, and Mercurial can be
installed from standard repositories.

For example on **Ubuntu Linux**:

::

sudo apt-get install build-essential cmake mercurial

Or on **macOS**, first make sure the Apple Command Line Tools are
installed, then get CMake, and Mercurial with either homebrew or
macports:

::

xcode-select --install
brew install cmake hg # Using homebrew.
sudo port install cmake mercurial # Using macports.

On **Windows**, see
`documentation <http://dynet.readthedocs.io/en/latest/install.html#windows-support>`__.

To compile DyNet you also need the `development version of the Eigen
library <https://bitbucket.org/eigen/eigen>`__. **If you use any of the
released versions, you may get assertion failures or compile errors.**
If you don’t have Eigen already, you can get it easily using the
following command:

::

hg clone https://bitbucket.org/eigen/eigen/ -r 699b659

The ``-r NUM`` specified a revision number that is known to work.
Adventurous users can remove it and use the very latest version, at the
risk of the code breaking / not compiling. On macOS, you can install the
latest development of Eigen using Homebrew:

::

brew install --HEAD eigen

C++ installation
~~~~~~~~~~~~~~~~

You can install dynet for C++ with the following commands

::

# Clone the github repository
git clone https://github.com/clab/dynet.git
cd dynet
# Checkout the latest release
git checkout tags/v2.0
mkdir build
cd build
# Run CMake
# -DENABLE_BOOST=ON in combination with -DENABLE_CPP_EXAMPLES=ON also
# compiles the multiprocessing c++ examples
cmake .. -DEIGEN3_INCLUDE_DIR=/path/to/eigen -DENABLE_CPP_EXAMPLES=ON
# Compile using 2 processes
make -j 2
# Test with an example
./examples/train_xor

For more details refer to the
`documentation <http://dynet.readthedocs.io/en/latest/install.html#building>`__

Python installation
~~~~~~~~~~~~~~~~~~~

You can install DyNet for python by using the following command

::

pip install git+https://github.com/clab/dynet#egg=dynet

For more details refer to the
`documentation <http://dynet.readthedocs.io/en/latest/python.html#installing-dynet-for-python>`__

Citing
------

If you use DyNet for research, please cite this report as follows:

::

@article{dynet,
title={DyNet: The Dynamic Neural Network Toolkit},
author={Graham Neubig and Chris Dyer and Yoav Goldberg and Austin Matthews and Waleed Ammar and Antonios Anastasopoulos and Miguel Ballesteros and David Chiang and Daniel Clothiaux and Trevor Cohn and Kevin Duh and Manaal Faruqui and Cynthia Gan and Dan Garrette and Yangfeng Ji and Lingpeng Kong and Adhiguna Kuncoro and Gaurav Kumar and Chaitanya Malaviya and Paul Michel and Yusuke Oda and Matthew Richardson and Naomi Saphra and Swabha Swayamdipta and Pengcheng Yin},
journal={arXiv preprint arXiv:1701.03980},
year={2017}
}

Releases and Contributing
-------------------------

The current release of DyNet is
`v2.0 <https://github.com/clab/dynet/releases/tag/v2.0>`__.

We welcome any contribution to DyNet! You can find the contributing
guidelines
`here <http://dynet.readthedocs.io/en/latest/contributing.html>`__

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