  Restricted Boltzmann Machine (RBM) is a generative stochastic neural network
that can be applied to collaborative filtering technique used by recommendation
systems. Prediction accuracy of the RBM model is usually better than that of
other models for recommendation systems. However, training the RBM model
involves Markov-Chain Monte Carlo (MCMC) method, which is computationally
expensive. In this paper, we have successfully applied distributed parallel
training using Horovod framework to improve the training time of the RBM model.
Our tests show that the distributed training approach of the RBM model has a
good scaling efficiency. We also show that this approach effectively reduces
the training time to little over 12 minutes on 64 CPU nodes compared to 5 hours
on a single CPU node. This will make RBM models more practically applicable in
recommendation systems.
