This documentation is for a development version. Click here for the latest stable release (v0.1.0).
We recommend using
pip to install KerasSpiking:
pip install keras-spiking
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.
If you want to modify KerasSpiking, or get the very latest updates, you will need to perform a developer installation:
git clone https://github.com/nengo/keras-spiking.git pip install -e ./keras-spiking
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.
pip install keras-spiking will install the
over top of your existing TensorFlow installation,
which is probably not what you want.
To avoid this, you can install with the
pip install --no-deps keras-spiking
This will install only the KerasSpiking package, and you will need to manually
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
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
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.