How to build DyNet and link it with your programs


DyNet relies on a number of external libraries including Boost, CMake, Eigen, and Mercurial (to install Eigen). Boost, CMake, and Mercurial can be installed from standard repositories.

For example on Ubuntu Linux:

sudo apt-get install libboost-all-dev cmake mercurial

Or on macOS, first make sure the Apple Command Line Tools are installed, then get Boost, CMake, and Mercurial with either homebrew or macports:

xcode-select --install
brew install boost cmake hg  # Using homebrew.
sudo port install boost cmake mercurial # Using macports.

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 installed already, you can get it easily using the following command:

hg clone -r 346ecdb
cd eigen
mkdir build && cd build
cmake ..
make install # sudo permissions might be necessary on Linux.
cd ../..

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

then enter the directory and use `cmake <>`__ to generate the makefiles

cd dynet
mkdir build
cd build
cmake .. -DEIGEN3_INCLUDE_DIR=/path/to/eigen

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.

Compiling/linking external programs

When you want to use DyNet in an external program, you will need to add the dynet directory to the compile path:


and link with the DyNet library:

-L/path/to/dynet/build/dynet -ldynet

Debugging build problems

If you have a build problem and want to debug, please run

make clean
make VERBOSE=1 &> make.log

then examine the commands in the make.log file to see if anything looks fishy. If you would like help, send this make.log file via the “Issues” tab on GitHub, or to the dynet-users mailing list.

GPU/MKL support and build options

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. This will result in three libraries named “libdynet” and “libgdynet” being created. When you want to run a program on CPU, you can link to the “libdynet” library as shown above. When you want to run a program on GPU, you can link to the “libgdynet” library.

-L/path/to/dynet/build/dynet -lgdynet

(Eventually you will be able to use a single library to run on either CPU or GPU, but this is not fully implemented yet.)

MKL support

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 /opt/intel/mkl/.

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.

Non-standard Boost location

DyNet requires Boost, and will find it if it is in the standard location. If Boost is in a non-standard location, say $HOME/boost, you can specify the location by adding the following to your CMake options:


Note that you will also have to set your LD_LIBRARY_PATH to point to the boost/lib directory. Note also that Boost must be compiled with the same compiler version as you are using to compile DyNet.

Building for Windows

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.

Second, install Boost for your compiler and platform. Follow the instructions for compiling Boost or just download the already-compiled binaries.

To generate the MSVC solution and project files, run cmake, pointing it to the location you installed Eigen and Boost (for example, at c:\libs\Eigen and c:\libs\boost_1_61_0):

mkdir build
cd build
cmake .. -DEIGEN3_INCLUDE_DIR=c:\libs\Eigen -DBOOST_ROOT=c:\libs\boost_1_61_0 -DBOOST_LIBRARYDIR=c:\libs\boost_1_61_0\lib64-msvc-14.0 -DBoost_NO_BOOST_CMAKE=ON -G"Visual Studio 14 2015 Win64"

This will generate dynet.sln and a bunch of *.vcxproj files (one for the DyNet library, and one per example). You should be able to just open dynet.sln 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 CUDA with the latest version of Eigen (as of Oct 28, 2016), with the following code change:

  • TensorDeviceCuda.h: Change sleep(1) to Sleep(1000)