  We present a lightweight Python framework for distributed training of neural
networks on multiple GPUs or CPUs. The framework is built on the popular Keras
machine learning library. The Message Passing Interface (MPI) protocol is used
to coordinate the training process, and the system is well suited for job
submission at supercomputing sites. We detail the software's features, describe
its use, and demonstrate its performance on systems of varying sizes on a
benchmark problem drawn from high-energy physics research.
