NVIDIA Merlin is an open source library that accelerates recommender systems on NVIDIA GPUs. The library enables data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes tools to address common feature engineering, training, and inference challenges. Each stage of the Merlin pipeline is optimized to support hundreds of terabytes of data, which is all accessible through easy-to-use APIs. For more information, see NVIDIA Merlin on the NVIDIA developer web site.
NVIDIA Merlin is a scalable and GPU-accelerated solution, making it easy to build recommender systems from end to end. With NVIDIA Merlin, you can:
Transform data (ETL) for preprocessing and engineering features.
Accelerate your existing training pipelines in TensorFlow, PyTorch, or FastAI by leveraging optimized, custom-built data loaders.
Scale large deep learning recommender models by distributing large embedding tables that exceed available GPU and CPU memory.
Deploy data transformations and trained models to production with only a few lines of code.
Components of NVIDIA Merlin
NVIDIA Merlin consists of the following open source libraries:
NVTabular is a feature engineering and preprocessing library for tabular data. The library can quickly and easily manipulate terabyte-size datasets that are used to train deep learning based recommender systems. The library offers a high-level API that can define complex data transformation workflows. With NVTabular, you can:
Prepare datasets quickly and easily for experimentation so that you can train more models.
Process datasets that exceed GPU and CPU memory without having to worry about scale.
Focus on what to do with the data and not how to do it by using abstraction at the operation level.
HugeCTR is a GPU-accelerated training framework that can scale large deep learning recommendation models by distributing training across multiple GPUs and nodes. HugeCTR contains optimized data loaders with GPU-acceleration and provides strategies for scaling large embedding tables beyond available memory. With HugeCTR, you can:
Scale embedding tables over multiple GPUs or nodes.
Load a subset of an embedding table into a GPU in a coarse-grained, on-demand manner during the training stage.
The Merlin Models library provides standard models for recommender systems with an aim for high-quality implementations that range from classic machine learning models to highly-advanced deep learning models. With Merlin Models, you can:
Accelerate your ranking model training by up to 10x by using performant data loaders for TensorFlow, PyTorch, and HugeCTR.
Iterate rapidly on featuring engineering and model exploration by mapping datasets created with NVTabular into a model input layer automatically. The model input layer enables you to change either without impacting the other.
Assemble connectable building blocks for common RecSys architectures so that you can create of new models quickly and easily.
The Transformers4Rec library provides sequential and session-based recommendation. The library provides modular building blocks that are compatible with standard PyTorch modules. You can use the building blocks to design custom architectures such as multiple towers, multiple heads and tasks, and losses. With Transformers4Rec, you can:
Build sequential and session-based recommenders from any sequential tabular data.
Take advantage of the integration with NVTabular for seamless data preprocessing and feature engineering.
Perform next-item prediction as well as classic binary classification or regression tasks.
Merlin Systems provides tools for combining recommendation models with other elements of production recommender systems like feature stores, nearest neighbor search, and exploration strategies into end-to-end recommendation pipelines that can be served with Triton Inference Server. With Merlin Systems, you can:
Start with an integrated platform for serving recommendations built on Triton Inference Server.
Create graphs that define the end-to-end process of generating recommendations.
Benefit from existing integrations with popular tools that are commonly found in recommender system pipelines.
Merlin Core provides functionality that is used throughout the Merlin ecosystem. With Merlin Core, you can:
Use a standard dataset abstraction for processing large datasets across multiple GPUs and nodes.
Benefit from a common schema that identifies key dataset features and enables Merlin to automate routine modeling and serving tasks.
Simplify your code by using a shared API for constructing graphs of data transformation operators.
The simplest way to use Merlin is to run a docker container. NVIDIA GPU Cloud (NGC) provides containers that include all the Merlin component libraries, dependencies, and receive unit and integration testing. For more information, see the Containers page.
To develop and contribute to Merlin, review the installation documentation for each component library. The development environment for each Merlin component is easily set up with
Example Notebooks and Tutorials
A collection of end-to-end examples are available in the form of Jupyter notebooks. The example notebooks demonstrate how to:
Download and prepare a dataset.
Use preprocessing and engineering features.
Train deep-learning recommendation models with TensorFlow, PyTorch, FastAI, HugeCTR or Merlin Models.
Deploy the models to production with Triton Inference Server.
These examples are based on different datasets and provide a wide range of real-world use cases.
Merlin Is Built On
Merlin relies on cuDF for GPU-accelerated DataFrame operations used in feature engineering.
Merlin relies on Dask to distribute and scale feature engineering and preprocessing within NVTabular and to accelerate dataloading in Merlin Models and HugeCTR.
Triton Inference Server
Merlin leverages Triton Inference Server to provide GPU-accelerated serving for recommender system pipelines.