Metadata-Version: 2.1
Name: dyNET
Version: 2.1.2
Summary: The Dynamic Neural Network Toolkit
Home-page: https://github.com/clab/dynet
Author: Graham Neubig
Author-email: dynet-users@googlegroups.com
License: Apache 2.0
Download-URL: https://github.com/clab/dynet/releases
Description: <div align="center">
          <img alt="DyNet" src="doc/source/images/dynet_logo.png"><br><br>
        </div>
        
        ---
        
        [![Build Status (Travis CI)](https://travis-ci.org/clab/dynet.svg?branch=master)](https://travis-ci.org/clab/dynet)
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        [![Build Status (Docs)](https://readthedocs.org/projects/dynet/badge/?version=latest)](http://dynet.readthedocs.io/en/latest/)
        [![PyPI version](https://badge.fury.io/py/dyNET.svg)](https://badge.fury.io/py/dyNET)
        
        The Dynamic Neural Network Toolkit
        
        - [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 and
        Eigen. CMake can be installed from standard repositories.
        
        For example on **Ubuntu Linux**:
        
            sudo apt-get install build-essential cmake
        
        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  # Using homebrew.
            sudo port install cmake # Using macports.
        
        On **Windows**, see [documentation](http://dynet.readthedocs.io/en/latest/install.html#windows-support).
        
        To compile DyNet you also need a [specific version of the Eigen
        library](https://github.com/clab/dynet/releases/download/2.1/eigen-b2e267dc99d4.zip). **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.zip
        
        
        ### 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
            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}
            }
        
        
        ## Contributing
        
        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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Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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