Installing DyNet for Python

Installing a Release CPU Version

Python bindings to DyNet are supported for both Python 2.x and 3.x. If you want to install a release version of DyNet and don’t need to run on GPU, you can simply run

pip install dynet

Installing a Cutting-edge and/or GPU Version

If you want the most recent features of DyNet from the development branch, or want GPU compute capability, you’ll want to install DyNet from source.

Before doing so, you will need to make sure that several packages are installed. For example on Ubuntu Linux:

sudo apt-get update
sudo apt-get install python-pip 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 python # Using homebrew.
sudo port install cmake mercurial py-pip # Using macports.

On Windows, see Windows Support.

(Currently, since the pip installation will build from source, you need to install
cython ahead: pip install cython.)

Once these packages are installed, the following will download, build and install DyNet. Note that compiling DyNet may take a long time, up to 10 minutes or more, but as long as you see “Running install for dynet” with the moving progress wheel, things should be running.

pip install git+

If you have CUDA installed on your system and want to install with GPU support, you can instead run the following command.

BACKEND=cuda pip install git+

Alternatively, you can add the following to your requirements.txt (for CUDA support you will need to make sure that BACKEND=cuda is in your environmental variables when DyNet is installed):


You can also manually set the directory of the cuDNN library as follows:

CUDNN_ROOT=/path/to/cudnn BACKEND=cuda pip install git+

If installation using pip fails, if you copy-and-paste the entire log that you get after running the pip command into a github issue, we will help you debug. You can also try to install DyNet manually as listed below.

Manual Installation

The following is a list of all the commands needed to perform a manual install:

# Installing Python DyNet:

pip install cython  # if you don't have it already.
mkdir dynet-base
cd dynet-base
# getting dynet and eigen
git clone
hg clone -r b2e267d  # -r NUM specified a known working revision
cd dynet
mkdir build
cd build
# without GPU support (if you get an error that Eigen cannot be found, try using the full path to Eigen)
cmake .. -DEIGEN3_INCLUDE_DIR=../../eigen -DPYTHON=`which python`
# or with GPU support (if you get an error that Eigen cannot be found, try using the full path to Eigen)
cmake .. -DEIGEN3_INCLUDE_DIR=../../eigen -DPYTHON=`which python` -DBACKEND=cuda

make -j 2 # replace 2 with the number of available cores
cd python
python ../../ build --build-dir=.. --skip-build install # add `--user` for a user-local install.

# this should suffice, but on some systems you may need to add the following line to your
# init files in order for the compiled .so files be accessible to Python.
# /path/to/dynet/build/dynet is the location in which libdynet.dylib resides.
export DYLD_LIBRARY_PATH=/path/to/dynet/build/dynet/:$DYLD_LIBRARY_PATH
# if the environment is Linux, use LD_LIBRARY_PATH instead.
export LD_LIBRARY_PATH=/path/to/dynet/build/dynet/:$LD_LIBRARY_PATH

To explain these one-by-one, first we get DyNet:

cd $HOME
mkdir dynet-base
cd dynet-base
git clone
cd dynet
git submodule init # To be consistent with DyNet's installation instructions.
git submodule update # To be consistent with DyNet's installation instructions.

Then get Eigen:

cd $HOME
cd dynet-base
hg clone -r b2e267d

(-r NUM specifies a known working revision of Eigen. You can remove this in order to get the bleeding edge Eigen, with the risk of some compile breaks, and the possible benefit of added optimizations.)

We also need to make sure the cython module is installed. (you can replace pip with your favorite package manager, such as conda, or install within a virtual environment)

pip install cython

To simplify the following steps, we can set a bash variable to hold where we have saved the main directories of DyNet and Eigen. In case you have gotten DyNet and Eigen differently from the instructions above and saved them in different location(s), these variables will be helpful:


Compile DyNet.

This is pretty much the same process as compiling DyNet, with the addition of the -DPYTHON= flag, pointing to the location of your Python interpreter.

Assuming that the cmake command found all the needed libraries and didn’t fail, the make command will take a while, and compile DyNet as well as the Python bindings. You can change make -j 2 to a higher number, depending on the available cores you want to use while compiling.

You now have a working Python binding inside of build/dynet. To verify this is working:

cd $PATH_TO_DYNET/build/python

then, within Python:

import dynet as dy
print dy.__version__
pc = dy.ParameterCollection()

In order to install the module so that it is accessible from everywhere in the system, run the following:

cd $PATH_TO_DYNET/build/python
python ../../ EIGEN3_INCLUDE_DIR=$PATH_TO_EIGEN build --build-dir=.. --skip-build install --user

The --user switch will install the module in your local site-packages, and works without root privileges. To install the module to the system site-packages (for all users), or to the current virtualenv (if you are on one), run python ../../ EIGEN3_INCLUDE_DIR=$PATH_TO_EIGEN build --build-dir=.. --skip-build install without this switch.

You should now have a working python binding (the dynet module).

Note however that the installation relies on the compiled DyNet library being in $PATH_TO_DYNET/build/dynet, so make sure not to move it from there.

Now, check that everything works:

cd examples/python

Alternatively, if the following script works for you, then your installation is likely to be working:

import dynet as dy
pc = dy.ParameterCollection()

If it doesn’t work and you get an error similar to the following:

ImportError: dlopen(/Users/sneharajana/.python-eggs/dyNET-0.0.0-py2.7-macosx-10.11-intel.egg-tmp/, 2): Library not loaded: @rpath/libdynet.dylib
Referenced from: /Users/sneharajana/.python-eggs/dyNET-0.0.0-py2.7-macosx-10.11-intel.egg-tmp/
Reason: image not found``

then you may need to run the following (and add it to your shell init files):

export DYLD_LIBRARY_PATH=/path/to/dynet/build/dynet/:$DYLD_LIBRARY_PATH
# Linux
export LD_LIBRARY_PATH=/path/to/dynet/build/dynet/:$LD_LIBRARY_PATH

# /path/to/dynet/build/dynet is the location in which under osx) resides.

Anaconda Support

Anaconda is a popular package management system for Python, and DyNet can be installed into this environment. First, make sure that you install all the necessary packages according to the instructions at the top of this page. Then create an Anaconda environment and activate it as below:

source activate my_environment_name

After this, you should be able to install using pip or manual installation as normal.

Windows Support

You can also use Python on Windows, including GPU and MKL support. For simplicity, we recommend using a Python distribution that already has Cython installed. The following has been tested to work:

  1. Install WinPython 2.7.10 (comes with Cython already installed).
  2. Compile DyNet according to the directions in the Windows C++ documentation (Windows Support), and additionally add the following flag when executing cmake: -DPYTHON=/path/to/your/python.exe.
  3. Open a command prompt and set VS90COMNTOOLS to the path to your Visual Studio “Common7/Tools” directory. One easy way to do this is a command such as:
  1. Open dynet.sln from this command prompt and build the “Release” version of the solution.
  2. Follow the rest of the instructions above for testing the build and installing it for other users

Note, currently only the Release version works. Also, if you compile with CUDA and/or cuDNN, ensure their respective DLLs are in your PATH environment variable when you run Python.

GPU/MKL Support

Installing on GPU

For installing on a computer with GPU, first install CUDA. The following instructions assume CUDA is installed.

The installation process is pretty much the same, while adding the -DBACKEND=cuda flag to the cmake stage:


If you know the CUDA architecture supported by your GPU (e.g. by referencing this page) you can speed compilation significantly by adding -DCUDA_ARCH=XXX where XXX is your architecture number. If CUDA is installed in a non-standard location and cmake cannot find it, you can specify also -DCUDA_TOOLKIT_ROOT_DIR=/path/to/cuda.

Now, build the Python modules (as above, we assume Cython is installed):

After running make -j 2, you should have the file in the build/python folder.

As before, cd build/python followed by python ../../ EIGEN3_INCLUDE_DIR=$PATH_TO_EIGEN build --build-dir=.. --skip-build install --user will install the module.

cuDNN support

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 (i.e. /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.

Using the GPU from Python

The preferred way to make DyNet use the GPU under Python is to import dynet as usual:

import dynet

Then tell it to use the GPU by using the commandline switch --dynet-gpu or the GPU switches detailed here when invoking the program. This option lets the same code work with either the GPU or the CPU version depending on how it is invoked.

Alternatively, you can also select whether the CPU or GPU should be used by using dynet_config module:

import dynet_config
import dynet

This may be useful if you want to decide programmatically whether to use the CPU or GPU. Importantly, it is not suggested to use import _dynet any more.

Running with MKL

If you’ve built DyNet to use MKL (using -DMKL or -DMKL_ROOT), Python sometimes has difficulty finding the MKL shared libraries. You can try setting LD_LIBRARY_PATH to point to your MKL library directory. If that doesn’t work, try setting the following environment variable (supposing, for example, your MKL libraries are located at /opt/intel/mkl/lib/intel64):

export LD_PRELOAD=/opt/intel/mkl/lib/intel64/