Accelerating recommender system training 15x with RAPIDS

Abstract

In this paper we present the novel aspects of our 15th place solution to the RecSys Challenge 2019 which are focused on the acceleration of feature generation and model training time. In our final solution we sped up training of our model by a factor of 15.6x, from a workflow of 891.8s (14m52s) to 57.2s, through a combination of the RAPIDS.AI cuDF library for preprocessing, a custom batch dataloader, LAMB and extreme batch sizes, and an update to the kernel responsible for calculating the embedding gradient in PyTorch. Using cuDF we also accelerated our feature generation by a factor of 9.7x by performing the computations on the GPU, reducing the time taken to generate the features used in our model from 51 minutes to 5. We demonstrate these optimizations on the fastai tabular model which we relied on extensively in our final ensemble. With training time so drastically reduced the iteration involved in generating new features and training new models is much more fluid, allowing for the rapid prototyping of deep learning based recommender systems in hours as opposed to days.