NVIDIA Merlin is a framework for building high-performance, deep learning-based recommender systems.
Figure 1: NVIDIA Merlin Recommender System Framework
Merlin includes tools for building deep learning-based recommendation systems that provide better predictions than traditional methods and increase click-through rates. Each stage of the pipeline is optimized to support hundreds of terabytes of data, all accessible through easy-to-use APIs.
NVTabular reduces data preparation time by GPU-accelerating feature transformations and preprocessing.
HugeCTR is a deep neural network training framework that is capable of distributed training across multiple GPUs and nodes for maximum performance.
NVIDIA Triton™ Inference Server and NVIDIA® TensorRT™ accelerate production inference on GPUs for feature transforms and neural network execution.
Features APIs built specifically for managing the massive tabular datasets used in recommendation systems.
Specifically designed for 100+ terabyte recommender datasets and terabyte embedding tables with 10X the inference performance of other approaches.
Supports state-of-the-art hybrid models such as Wide and Deep, Neural Collaborative Filtering (NCF), Variational Autoencoder (VAE), Deep Cross Network, DeepFM, and xDeepFM.
An End-to-End System Architecture
NVIDIA Merlin accelerates the entire pipeline from ingesting and training to deploying GPU-accelerated recommender systems. Models and tools simplify building and deploying a production-quality pipeline. We invite you to share some information about your recommender pipeline in this survey to influence the Merlin Roadmap.
HugeCTR is a highly efficient C++ framework designed for distributed training with model-parallel embedding tables and data-parallel neural networks. HugeCTR covers common and recent architectures such as Wide and Deep, Deep Cross Network, and DeepFM. Deep Learning Recommendation Model (DLRM) support is coming soon.