# compiles the multiprocessing c++ examplesĬmake. # -DENABLE_BOOST=ON in combination with -DENABLE_CPP_EXAMPLES=ON also I'm also unclear whether this will > result in a change to the installed files or not. You can install dynet for C++ with the following commands # Clone the github repository I'm > unclear whether this was intentional, and whether it will overwrite the > previously set value of PYTHONEXTRALIBS. You can get it easily using the following command: mkdir eigen Released versions, you may get assertion failures or compile errors. To compile DyNet you also need a specific version of the Eigen Sudo port install cmake # Using macports. Get CMake, and Mercurial with either homebrew or macports: xcode-select -install Or on macOS, first make sure the Apple Command Line Tools are installed, then CMake can be installed from standard repositories.įor example on Ubuntu Linux: sudo apt-get install build-essential cmake InstallationĭyNet relies on a number of external programs/libraries including CMake andĮigen. The example folder contains a variety of examples in C++ and python. One aspect that sets DyNet apart from other tookits is the auto-batching feature. You can find tutorials about using DyNet here (C++) and here (python), and here (EMNLP 2016 tutorial). You can also read more technical details in our technical report. 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. Read the documentation to get started, and feel free to contact the dynet-users group group with any questions (if you want to receive email make sure to select "all email" when you sign up). 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, machine translation, morphological inflection, and many other application areas. 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. ![]() DyNet is a neural network library developed by Carnegie Mellon University and many others.
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