Getting started¶
Installation¶
To install NengoLMU, we recommend using pip
.
pip install lmu
Before running this command, please ensure your system meets the NengoLMU requirements.
Requirements¶
NengoLMU works with Python 3.6 or later. After installing NumPy and TensorFlow, pip
will do its best to install all of the package’s other requirements when it installs
NengoLMU. However, if anything goes wrong during this process, you can install each
required package manually and then try to pip install lmu
again.
Developer installation¶
If you want to modify NengoLMU, or get the very latest updates, you will need to perform a developer installation:
git clone https://github.com/abr/lmu
pip install -e ./lmu
Installing TensorFlow¶
NengoLMU is designed to work within TensorFlow. Assuming you have the required libraries
installed, the latest version of TensorFlow can be using pip install tensorflow
To use TensorFlow with GPU support, you will need to have the CUDA/cuDNN libraries
installed on your system. For this, we recommend you use
conda
to simplify the installation process. conda install tensorflow-gpu
will install
the TensorFlow package as well as all the CUDA/cuDNN requirements. If you run into
any problems, see the
TensorFlow GPU installation instructions
for more details.
In addition to CUDA/cuDNN, TensorFlow’s GPU acceleration is only supported with Nvidia
GPUs. Acquiring the appropriate drivers for your Nvidia GPU depends on your system.
On Linux, the correct Nvidia drivers (as of TensorFlow 2.2.0) can be installed via the
command sudo apt install nvidia-driver-440
. On Windows, Nvidia drivers can be
downloaded from their
website.
It is also possible to build TensorFlow from source. This is significantly more complicated but allows you to customize the installation to your specific system configuration, which can improve simulation speeds. See the system specific instructions below:
Installing other packages¶
The steps above will only install NengoLMU’s required dependencies. Optional NengoLMU features require additional packages to be installed.
Running the test suite requires pytest.
Building the documentation requires Sphinx, NumPyDoc, nengo_sphinx_theme, and a few other packages.
These additional dependencies can also be installed through pip
when
installing NengoLMU.
pip install lmu[tests] # Needed to run unit tests
pip install lmu[docs] # Needed to build docs
pip install lmu[all] # All of the above
Next steps¶
If you want to learn how to use NengoLMU in your models, read through the basic usage page.
For a more detailed understanding of the various classes and functions in the NengoLMU package, refer to the API reference.
If you are interested to learn the theoretical background behind how the Legendre Memory Unit works, we recommend reading this technical overview.
If you would like to see how NengoLMU is incorporated into various models, check out our examples.