This documentation is for a development version. Click here for the latest stable release (v3.4.0).
We recommend using
pip to install NengoDL:
pip install nengo-dl
NengoDL works with Python 3.6 or later.
pip will do its best to install
all of NengoDL’s requirements when it installs NengoDL. However, if anything
goes wrong during this process you can install the requirements manually and
then try to
pip install nengo-dl again.
See the Nengo documentation
for instructions on installing
nengo, and the
installation instructions below.
If you want to modify NengoDL, or get the very latest updates, you will need to perform a developer installation:
git clone https://github.com/nengo/nengo-dl.git pip install -e ./nengo-dl
pip install tensorflow to install the latest version of TensorFlow. GPU support
is included in this package as of version 2.1.0.
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 2.4.0) can be
sudo apt install nvidia-driver-450, 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.