This documentation is for a development version. Click here for the latest stable release (v0.1.0).


Installing KerasSpiking

We recommend using pip to install KerasSpiking:

pip install keras-spiking

That’s it!


KerasSpiking works with Python 3.6 or later. pip will do its best to install all of KerasSpiking’s requirements automatically. However, if anything goes wrong during this process you can install the requirements manually and then try to pip install keras-spiking again.

Developer installation

If you want to modify KerasSpiking, or get the very latest updates, you will need to perform a developer installation:

git clone
pip install -e ./keras-spiking

Installing TensorFlow

Use pip install tensorflow to install the latest version of TensorFlow. GPU support is included in this package as of version 2.1.0.

Note that if you are using one of the non-standard TensorFlow packages (e.g. tensorflow-gpu, tensorflow-cpu, or tf-nightly), then pip install keras-spiking will install the tensorflow package over top of your existing TensorFlow installation, which is probably not what you want. To avoid this, you can install with the --no-deps option:

pip install --no-deps keras-spiking

This will install only the KerasSpiking package, and you will need to manually pip install any other requirements. This option can also be used with the developer installation method above.

In order to use TensorFlow with GPU support you will need to install the appropriate Nvidia drivers and CUDA/cuDNN. The precise steps for accomplishing this will depend on your system. On Linux the correct Nvidia drivers (as of TensorFlow 2.2.0) can be installed via sudo apt install nvidia-driver-440, and on Windows simply using the most up-to-date drivers should work. For CUDA/cuDNN we recommend using conda to simplify the process. conda install tensorflow-gpu will install TensorFlow as well as all the CUDA/cuDNN requirements. If you run into any problems, see the TensorFlow GPU installation instructions for more details.

It is also possible to build TensorFlow from source. This is significantly more complicated but allows you to customize the installation to your computer, which can improve simulation speeds.

Instructions for installing on Ubuntu or Mac OS.

Instructions for installing on Windows.