NVIDIA has launched several products and technologies to fit the market needs. Currently, the company is releasing products in gaming, AI, data centers, Merlin Inference, and others. Merlin Inference Container is a library that provides a way to inject custom logic into the CUDA kernel launch process. It can be used to insert, log, profile, or debug codes.
The library is implemented as a set of hooks that are called at various points in the launch process. Merlin provides data researchers, supervised learning computer vision engineers, and academicians with the tools to create high-performance recommenders on a large scale.
Merlin comprises libraries, techniques, and features that make deep learning recommender development more accessible. It solves the typical pre-processes, algorithm development, mentoring, and inference difficulties. Each component of the Merlin pipeline is designed to handle thousands of petabytes, along with APIs that are simple to use. Merlin can make better forecasts than standard approaches and boost click-through levels.
Merlin inference container is a new technology introduced by NVIDIA, which helps in improving the performance of virtual machines running on the GPU cloud. The GPU Direct RDMA feature provides low latency communication between VMs and GPUs. However, this feature can cause inference when multiple VMs are running on the same host.
Merlin inference container helps in resolving this issue by creating a separate physical network for each VM. This results in improved performance and reduced inference. Merlin Interface consists of various components such as Merlin NVTabular, HugeCTR, cuDNN, RAPIDS, TensorRT, Triton, and others. Merlin NVTabular is a library that pre-processes the data and performs feature engineering. It reduces the data preparation time and also helps the researchers in modifying the recommender system.
Merlin interface consists of HugeCTR which is a neural network framework used for training the system on the existing data. It enhances the prediction task. It is embedded on multiple GPUs and provides high-end predictions. Merlin enhances the throughput by combining latency and utilizing the power of GPUs.
Corporate organizations train the recommendation systems on huge datasets. Training and processing huge data sets requires a long period. Merlin Interface consists of the feature engineering library that enhances data processing. With its help, terabytes of data can be manipulated in a short period and with higher accuracy.
Large-scale data training
Organizations face difficulties due to the bottleneck created while loading vast data on the recommendation systems. HugeCTR provides high-end training to the system on large-scale datasets. Terabytes of data can be trained easily to make the recommender system more efficient. Deep learning algorithms are implemented through HugeCTR to make the system accurate.
Deep learning inference pipelines
Merlin has embedded inference pipelines through Triton. Merlin Triton comprises Vertex AI Prediction. HugeCTR and NVTabular provide Merlin-accelerated pipelines through the GPE inference. The deployment process is simple—users can access the power of the Merlin interface in a few easy steps.
Merlin allows researchers to accelerate the entire cloud pipeline. It performs tasks like ingesting, training, and launching GPU-related recommendation systems. Its components are open source, which enables the users to easily build and deploy high-quality production.
NVIDIA Merlin is used to building large-scale recommender systems that require huge datasets to train, especially for deep learning solutions.
Leaders in media, entertainment, and on-demand distribution use an open-source recommendation framework for accelerated deep learning on GPUs. NVIDIA Merlin is a comprehensive recommender system that accelerates every step of recommender development, from data pre-processing to training and inference.
The NVIDIA Merlin team is developing open-source software (OSS) libraries such as NVTabular and HugeCTR. It aims to improve the feasibility and efficiency of GPU-based recommendations for feature engineering, data loading, training, and inference. NVIDIA works on next-generation recommender tools by pushing the boundaries of ETL acceleration, training, and GPU inference.
NVIDIA Merlin includes tools that democratize deep learning advice by solving common ETL, learning, and inference problems. The Merlin Collection of Models, Methods, and Libraries includes tools for building deep learning systems capable of processing terabytes of data that can provide more accurate predictions and increase clicks.
The purpose of Jupyter Notebooks in introductory examples/movies is to show how NVIDIA Merlin uses NVIDIA NVTabular to perform ETL, then train TensorFlow, PyTorch, or HugeCTR models, and then infer with Triton.
Merlin is a comprehensive GPU platform that offers fast-feature engineering and high learning rates. Merlin Training is a collection of DL recommender templates and training tools. Recommendation systems are one of the most practical categories of machine learning. They are used to provide millions of recommendations to company users every day.