Package: dyNET38
The Dynamic Neural Network ToolkitGeneralInstallationC++PythonGetting StartedCitingReleases and ContributingGeneralDyNet 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 forsyntactic parsing,machine translation,morphological inflection, and many other application areas.Read thedocumentationto get started, and feel free to contact thedynet-users groupgroup 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 thegithub page.You can also read more technical details in ourtechnical report.Getting startedYou can find tutorials about using DyNethere (C++)andhere (python), andhere (EMNLP 2016 tutorial).One aspect that sets DyNet apart from other tookits is theauto-batchingfeature. See thedocumentationabout batching.Theexamplefolder contains a variety of examples in C++ and python.InstallationDyNet relies on a number of external programs/libraries including CMake and
Eigen. CMake can be installed from standard repositories.For example onUbuntu Linux:sudo apt-get install build-essential cmakeOr onmacOS, 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  # Using homebrew.
sudo port install cmake # Using macports.OnWindows, seedocumentation.To compile DyNet you also need aspecific version of the Eigen
library.If you use any of the
released versions, you may get assertion failures or compile errors.You can get it easily using the following command:mkdir eigen
cd eigen
wget https://github.com/clab/dynet/releases/download/2.1/eigen-b2e267dc99d4.zip
unzip eigen-b2e267dc99d4.zipC++ installationYou can install dynet for C++ with the following commands# Clone the github repository
git clone https://github.com/clab/dynet.git
cd dynet
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/xorFor more details refer to thedocumentationPython installationYou can install DyNet for python by using the following commandpip install git+https://github.com/clab/dynet#egg=dynetFor more details refer to thedocumentationCitingIf 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}
}ContributingWe welcome any contribution to DyNet! You can find the contributing guidelineshere