DarwiNN: efficient distributed neuroevolution under communication constraints

Neuroevolution (NE), defined as the application of evolution-based training methods to Deep Neural Networks (DNNs), has recently demonstrated encouraging results on a variety of learning tasks. NE is highly parallel and relies on DNN inference as its main computational kernel and therefore can potentially leverage large-scale distributed inference-specific hardware infrastructure in the cloud or edge. We introduce chromosome updates (CU), a novel communication-optimized method for distributing NE computation, and DarwiNN, an open-source, GPU-accelerated distributed NE toolbox based on PyTorch, which implements CU and other algorithms for distributing NE.