Installing DyNet for C++¶
How to build DyNet and link it with your C++ programs.
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 Windows Support.
To compile DyNet you also need the development version of the Eigen library. 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
To get and build DyNet, clone the repository
git clone https://github.com/clab/dynet.git
then enter the directory and use cmake
to generate the makefiles. When you run
cmake, you will need to specify
the path to Eigen, and will probably want to specify
to compile the C++ examples.
cd dynet mkdir build cd build cmake .. -DEIGEN3_INCLUDE_DIR=/path/to/eigen -DENABLE_CPP_EXAMPLES=ON
Then compile, where “2” can be replaced by the number of cores on your machine
make -j 2
To see that things have built properly, you can run
which will train a multilayer perceptron to predict the xor function.
If any process here fails, please see Asking Questions/Reporting Bugs for help.
GPU (CUDA) support¶
DyNet supports running programs on GPUs with CUDA. If you have CUDA
installed, you can build DyNet with GPU support by adding
-DBACKEND=cuda to your cmake options. The linking method is exactly
the same as with the CPU backend case.
If you know the CUDA architecture supported by your GPU (e.g. by referencing
you can speed compilation significantly by adding
XXX is your architecture number.
When running DyNet with CUDA on GPUs, some of DyNet’s functionality
(e.g. conv2d) depends on the NVIDIA cuDNN libraries.
CMake will automatically detect cuDNN in the CUDA installation path
/usr/local/cuda) and enable it if detected.
If CMake is unable to find cuDNN automatically, try setting CUDNN_ROOT, such as
However, if you don’t have cuDNN installed, the dependent functionality will be automatically disabled and an error will be throwed during runtime if you try to use them.
DyNet can leverage Intel’s MKL library to speed up computation on the CPU. As an example, we’ve seen 3x speedup in seq2seq training when using MKL. To use MKL, include the following cmake option:
If CMake is unable to find MKL automatically, try setting MKL_ROOT, such as
One common install location is
If either MKL or MKL_ROOT are set, CMake will look for MKL.
By default, MKL will use all CPU cores. You can control how many cores MKL uses by setting the environment variable MKL_NUM_THREADS to the desired number. The following is the total time to process 250 training examples running the example encdec (on a 6 core Intel Xeon E5-1650):
encdec.exe --dynet-seed 1 --dynet-mem 1000 train-hsm.txt dev-hsm.txt
+-----------------+------------+---------+ | MKL_NUM_THREADS | Cores Used | Time(s) | +-----------------+------------+---------+ | <Without MKL> | 1 | 28.6 | | 1 | 1 | 13.3 | | 2 | 2 | 9.5 | | 3 | 3 | 8.1 | | 4 | 4 | 7.8 | | 6 | 6 | 8.2 | +-----------------+------------+---------+
As you can see, for this particular example, using MKL roughly doubles the speed of computation while still using only one core. Increasing the number of cores to 2 or 3 is quite beneficial, but beyond that there are diminishing returns or even slowdown.
Compiling with Boost¶
DyNet requires Boost for a few pieces of less-commonly-used functionality
to be enabled (unit tests and multi-processing). Boost can be enabled by using the
-DENABLE_BOOST=ON flag to
cmake. In general, DyNet will find
Boost it if it is in the standard
location. If Boost is in a non-standard location, say
you can specify the location by adding the following to your CMake
-DBOOST_ROOT:PATHNAME=$HOME/boost -DBoost_LIBRARY_DIRS:FILEPATH=$HOME/boost/lib -DBoost_NO_BOOST_CMAKE=TRUE -DBoost_NO_SYSTEM_PATHS=TRUE
Note that you will also have to set your
LD_LIBRARY_PATH``(``DYLD_LIBRARY_PATH instead for osx) to point to
Note also that Boost must be compiled with the same compiler version as
you are using to compile DyNet.
DyNet has been tested to build in Windows using Microsoft Visual Studio 2015. You may be able to build with MSVC 2013 by slightly modifying the instructions below.
First, install Eigen following the above instructions.
To generate the MSVC solution and project files, run cmake, pointing it to the location you installed Eigen (for example, at c:\libs\Eigen):
mkdir build cd build cmake .. -DEIGEN3_INCLUDE_DIR=c:/libs/Eigen -G"Visual Studio 14 2015 Win64"
This will generate dynet.sln. Simply open this and build all. Note: multi-process functionality is currently not supported in Windows, so the multi-process examples (`*-mp`) will not be included in the generated solution
The Windows build also supports MKL and CUDA with the latest version of Eigen. If you build with CUDA and/or cuDNN, ensure their respective DLLs are in your PATH environment variable when you use dynet (whether in native C++ or Python). For example:
set PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin;c:\libs\cudnn-8.0-windows10-x64-v5.1\bin;%PATH%