A deep learning library for spiking neural networks which is based on PyTorch, focuses on fast training and supports inference on neuromorphic hardware.
Currently, the installation of requirements for building docs (for instance) is done as a pre-step for all the scripts, even the ones that will run only the test setup. This slows down the pipeline since many libraries need to be installed, and they will not be used. This issue is to list possible ways to improve the whole pipeline and work on it.
Currently, the installation of requirements for building docs (for instance) is done as a pre-step for all the scripts, even the ones that will run only the test setup. This slows down the pipeline since many libraries need to be installed, and they will not be used. This issue is to list possible ways to improve the whole pipeline and work on it.