Changelog

November 2019

• Initial release

Known issues

Multi-GPU scaling

We benchmark this implementation on the ML-20m dataset so that our results are comparable to the original VAE-CF paper. We also use the same neural network architecture. As a consequence, the ratio of communication to computation is relatively large. This means that although using multiple GPUs speeds up the training substantially, the scaling efficiency is worse from what one would expect if using a larger model and a more realistic dataset.