Installing DyNet for Python¶
Python bindings to DyNet are supported for both Python 2.x and 3.x. Before installing DyNet, 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.
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 setup.py install for dynet” with the moving progress wheel, things should be running.
pip install git+https://github.com/clab/dynet#egg=dynet
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+https://github.com/clab/dynet#egg=dynet
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):
In case 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.
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 https://github.com/clab/dynet.git hg clone https://bitbucket.org/eigen/eigen -r 346ecdb # -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 ../../setup.py 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
To explain these one-by-one, first we get DyNet:
cd $HOME mkdir dynet-base cd dynet-base git clone https://github.com/clab/dynet.git 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 https://bitbucket.org/eigen/eigen/ -r 346ecdb
(-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
pip with your favorite package manager, such as
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:
This is pretty much the same process as compiling DyNet, with the
addition of the
-DPYTHON= flag, pointing to the location of your
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
You now have a working Python binding inside of
verify this is working:
cd $PATH_TO_DYNET/build/python 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 $PATH_TO_DYNET/build/python python ../../setup.py EIGEN3_INCLUDE_DIR=$PATH_TO_EIGEN build --build-dir=.. --skip-build install --user
--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 ../../setup.py EIGEN3_INCLUDE_DIR=$PATH_TO_EIGEN build --build-dir=.. --skip-build install without this switch.
You should now have a working python binding (the
Note however that the installation relies on the compiled DyNet library
$PATH_TO_DYNET/build/dynet, so make sure not to move it
Now, check that everything works:
cd $PATH_TO_DYNET cd examples/python python xor.py python rnnlm.py rnnlm.py
Alternatively, if the following script works for you, then your installation is likely to be working:
from dynet import * pc = 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/_dynet.so, 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/_dynet.so Reason: image not found``
then you may need to run the following (and add it to your shell init files):
# /path/to/dynet/build/dynet is the location in which libdynet.dylib resides.
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.
You can also use Python on Windows. For simplicity, we recommend using a Python distribution that already has Cython installed. The following has been tested to work:
- Install WinPython 2.7.10 (comes with Cython already installed).
- Compile DyNet according to the directions in the Windows C++ documentation (Windows Support), and additionally add the following flag when executing
- Open a command prompt and set
VS90COMNTOOLSto the path to your Visual Studio “Common7/Tools” directory. One easy way to do this is a command such as:
- Open dynet.sln from this command prompt and build the “Release” version of the solution.
- Follow the rest of the instructions above for testing the build and installing it for other users
Note, currently only the Release version works.
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 .. -DEIGEN3_INCLUDE_DIR=$PATH_TO_EIGEN -DPYTHON=$PATH_TO_PYTHON -DBACKEND=cuda
(if CUDA is installed in a non-standard location and
find it, you can specify also
Now, build the Python modules (as above, we assume Cython is installed):
make -j 2, you should have the files
_gdynet.so in the
cd build/python followed by
python ../../setup.py EIGEN3_INCLUDE_DIR=$PATH_TO_EIGEN build --build-dir=.. --skip-build install --user will install the module.
Using the GPU from Python¶
The preferred way to make dynet use the GPU under Python is to import dynet as usual:
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 one of the following more specific import statements:
import _dynet # or import _gdynet # For GPU
This may be useful if you want to decide programmatically whether to
use the CPU or GPU. Importantly, importing
will not initialize the global parameters. If you forget to initialize
these, dynet may abort with a segmentation fault. Instead, make sure
to initialize the global parameters, as follows:
# Same as import dynet as dy import _dynet as dy dy.init()
Running with MKL¶
If you’ve built DyNet to use MKL (using
-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