Click-through rate (CTR) estimation is one of the most critical components of modern recommender systems. As the volume of data and its complexity grow rapidly, the use of deep learning (DL) models to improve the quality of estimations has become widespread. They generally have greater expressive power than traditional machine learning (ML) approaches. Frequently evolving data also implies that the lifespan of a trained model tends to be short. Fast and iterative training of a model is important to sustain the competitiveness of a service.
In this post, we introduce HugeCTR, a GPU-accelerated training framework for CTR estimation. It is open-sourced and highly optimized for performance on NVIDIA GPUs while allowing users to customize their models in JSON format.
HugeCTR, on a single NVIDIA V100 GPU, achieves a speedup of up to 114X over TensorFlow on a 40-core CPU node, and up to 8.3X that of TensorFlow on the same V100 GPU. HugeCTR, in its version 2.1, facilitates the training of the three state of the art models: Wide & Deep, DCN, and DeepFM. DLRM support will be available soon.
HugeCTR is also a pillar of NVIDIA Merlin, a framework and ecosystem created to facilitate all phases of recommender system development, accelerated on NVIDIA GPUs.
Read the full blog, Introducing NVIDIA Merlin HugeCTR: A Training Framework Dedicated to Recommender Systems, on the NVIDIA Developer Blog.