Metadata-Version: 1.1
Name: dyNET
Version: 2.1
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
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        |Build Status (Travis CI)| |Build Status (AppVeyor)| |Build Status
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        The Dynamic Neural Network Toolkit
        
        
        
        
        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 b2e267d
        
        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
           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>`__
        
        .. |Build Status (Travis CI)| image:: https://travis-ci.org/clab/dynet.svg?branch=master
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Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: MacOS X
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Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
