1.2. Installation of ANNarchy

ANNarchy is designed to run on GNU/Linux and OSX. It relies mostly on a C++ compiler, Cython (C for Python extension) and Python (NumPy, Sympy) libraries. Installation on Windows is not yet possible.

1.2.1. Download

The source code of ANNarchy can be downloaded on Bitbucket:

git clone http://bitbucket.org/annarchy/annarchy.git

As ANNarchy is under heavy development, you should update the repository regularly:

git pull origin master

1.2.2. Installation on GNU/Linux systems Dependencies

ANNarchy depends on a number of packages which should be easily accessible on recent GNU/Linux distributions. The classical way to install these dependencies is through your package manager, or using full Python distributions such as Anaconda. Older versions of these packages may work but have not been tested.

  • g++ >= 4.6 (4.7 or above is recommended)
  • make >= 3.0
  • python == 2.7 or >= 3.3 (with the development files, e.g. python-dev or python-devel)
  • cython >= 0.19
  • setuptools >= 0.6
  • numpy >= 1.8
  • sympy >= 0.7.4
  • scipy >= 0.12
  • matplotlib >= 1.3.0

Additionally, the following packages are optional but strongly recommended:

  • pyqtgraph >= 0.9.8 (to visualize some of the provided examples)
  • lxml >= 3.0 (to save the networks in .xml format)

To use the CUDA backend:

  • the CUDA-SDK is available on the official website (we recommend to use at least a SDK version > 6.x). For further details on installation etc., please consider the corresponding Quickstart guides ( Quickstart_8.0 for the current SDK 8.x).

If your distribution is of the “stable” kind (Debian), the Python packages would benefit strongly from being installed using pip to get recent versions:

sudo pip install cython numpy sympy pyqtgraph matplotlib lxml scipy

ANNarchy works with full Python distributions such as Anaconda, as well as in virtual environments. Installation Using pip

Stable releases of ANNarchy are available on PyPi:

sudo pip install ANNarchy


pip install ANNarchy --user

if you do not have administrator permissions. Using the source code

Installation of ANNarchy is possible through one of the three following methods:

Local installation in home directory

If you want to install ANNarchy in your home directory, type:

python setup.py install --user

The ANNarchy code will be installed in $HOME/.local/lib/python2.7/site-packages/ and automatically added to your PYTHONPATH.

Global installation

If you have superuser permissions, you can install ANNarchy in /usr/local by typing in the top-level directory:

sudo python setup.py install

This simply installs the code in /usr/local/lib/python2.7/dist-packages/ (replace ‘2.7’ with your Python version).

Specific installation

If you want to install ANNarchy in another directory (let’s say in /path/to/repertory), you should first set your Python path to this directory:

export PYTHONPATH=$PYTHONPATH:/path/to/repertory/lib/python2.7/site-packages

Again, replace ‘2.7’ with your Python version. If this directory does not exist, you should create it now. Don’t forget to set this value in your ~/.bash_profile or ~/.bashrc to avoid typing this command before every session. You can then install ANNarchy by typing:

python setup.py install --prefix=/path/to/repertory

If you have multiple Python installations on your system (e.g. through Anaconda), you should update your LD_LIBRARY_PATH environment variable in .bashrc or bash_profile to point at the location of libpython2.7.so (or whatever version):


ANNarchy tries to detect which python installation you are currently using, but helping it does not hurt… CUDA

If ANNarchy detects the CUDA SDK during installation, it will prepare the required modules. You need to make sure that the CUDA compiler nvcc is accessible in your path.

The main problem with CUDA is that the binaries, headers and libraries are installed at different locations depending on the version: /usr/local/cuda, /usr/local/cuda-7.0 or /usr/local/cuda-8.0. There is unfortunately no way for ANNarchy to guess the installation path.

A first thing to help ANNarchy find the CUDA libraries is to define the LD_LIBRARY_PATH environment variable and have point at the lib64/ subfolder:

export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64/:$LD_LIBRARY_PATH

This should in most cases work if you have only one CUDA installation. Otherwise, it is needed that you indicate where the CUDA libraries are, by modifying the ANNarchy configuration file located at ~/.config/ANNarchy/annarchy.json:

    "openmp": {
        "compiler": "g++",
        "flags": "-march=native -O2"
    "cuda": {
        "compiler": "nvcc",
        "flags": "",
        "device": 0,
        "path": "/usr/local/cuda"

Simply point the ['cuda']['path'] field to the right location (without lib64/). If the nvcc compiler binary is at a different location, the absolute path to the nvcc can be provided by ['cuda']['compiler'] field.

It can happen that the detection of CUDA fails during installation, as some environment variables are not set. In this case try:

sudo env "PATH=$PATH" "LIBRARY_PATH=$LIBRARY_PATH" python setup.py install

1.2.3. Installation on MacOS X systems

Installation on MacOS X is in principle similar to GNU/Linux:

python setup.py install (--user or --prefix)

We advise using a full Python distribution such as Anaconda, which installs automatically all dependencies of ANNarchy, rather than using the old python provided by Apple.

The only problem with Anaconda (and potentially other Python distributions, not tested) is that the compiler will use by default the Python shared library provided by Apple, leading to the following crash when simulating:

Fatal Python error: PyThreadState_Get: no current thread
Abort trap: 6

The solution is to set the environment variable DYLD_FALLBACK_LIBRARY_PATH to point at the correct library libpython2.7.dylib in your .bash_profile. For a standard Anaconda installation, this should be:



The default compiler on OS X is clang-llvm. You should install the command_line_tools together with XCode in order to use it.

For some reasons, this compiler is not compatible with OpenMP, so the models will only run sequentially.

The CUDA backend is not available on OS X.