This documentation is for a development version. Click here for the latest stable release (v3.4.0).


Installing NengoDL

We recommend using pip to install NengoDL:

pip install nengo-dl

That’s it!


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 numpy and nengo, and the tensorflow installation instructions below.

Developer installation

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

git clone
pip install -e ./nengo-dl

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.

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 installed via 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 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.