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The Dynamic Neural Network Toolkit

• General
• Installation
  • C++
  • Python
• Getting Started
• Citing
• Releases and Contributing

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 forsyntactic parsing,machine translation,morphological inflection, and many other application areas.

Read thedocumentation to get started, and feel free to contact thedynet-users group 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 thegithub page.

You can also read more technical details in ourtechnical report.

You can find tutorials about using DyNethere (C++) andhere (python), andhere (EMNLP 2016 tutorial).

One aspect that sets DyNet apart from other tookits is theauto-batching feature. See thedocumentation about batching.

Theexample folder contains a variety of examples in C++ and python.

DyNet relies on a number of external programs/libraries including CMake andEigen. CMake can be installed from standard repositories.

For example onUbuntu Linux:

sudo apt-get install build-essential cmake

Or onmacOS, first make sure the Apple Command Line Tools are installed, thenget CMake, and Mercurial with either homebrew or macports:

xcode-select --installbrew install cmake  # Using homebrew.sudo port install cmake # Using macports.

OnWindows, seedocumentation.

To compile DyNet you also need aspecific version of the Eigenlibrary.If you use any of thereleased versions, you may get assertion failures or compile errors.You can get it easily using the following command:

mkdir eigencd eigenwget https://github.com/clab/dynet/releases/download/2.1/eigen-b2e267dc99d4.zipunzip eigen-b2e267dc99d4.zip

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

# Clone the github repositorygit clone https://github.com/clab/dynet.gitcd dynetmkdir buildcd build# Run CMake# -DENABLE_BOOST=ON in combination with -DENABLE_CPP_EXAMPLES=ON also# compiles the multiprocessing c++ examplescmake .. -DEIGEN3_INCLUDE_DIR=/path/to/eigen -DENABLE_CPP_EXAMPLES=ON# Compile using 2 processesmake -j 2# Test with an example./examples/xor

For more details refer to thedocumentation

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 thedocumentation

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}}

We welcome any contribution to DyNet! You can find the contributing guidelineshere