examples

NVIDIA Merlin Example Notebooks

We have a collection of Jupyter example notebooks that show how to build an end-to-end recommender system with NVIDIA Merlin. The notebooks use different datasets to demonstrate different feature engineering workflows that might help you to adapt your data for a recommender system.

These example notebooks demonstrate how to use NVTabular with TensorFlow, PyTorch, HugeCTR and Merlin Models. Each example provides additional details about the end-to-end workflow, such as includes ETL, training, and inference.

Inventory

Building and Deploying a multi-stage RecSys

Recommender system pipelines are often based on multiple stages: Retrieval, Filtering, Scoring and Ordering. This example provides an end-to-end pipeline that leverages the Merlin framework:

• Processing the dataset using NVTabular.
• Training a scoring model using Merlin Models.
• Training a retrieval model using Merlin Models.
• Building a feature store with Feast and ANN index with Faiss.
• Deploying an end-to-end pipeline of retrieval, scoring, and ANN search to Triton Inference Server.

Getting Started with MovieLens

The MovieLens25M is a popular dataset for recommender systems and is used in academic publications. Many users are familiar with this dataset, so the notebooks focus primarily on the basic concepts of NVTabular:

• Learning NVTabular to GPU-accelerate ETL (Preprocess and Feature Engineering).
• Getting familiar with NVTabular's high-level API.
• Using single-hot and multi-hot categorical input features with NVTabular.
• Using the NVTabular dataloader with the TensorFlow Keras model.
• Using the NVTabular dataloader with PyTorch.

Scaling Large Datasets with Criteo

Criteo provides the largest publicly available dataset for recommender systems with a size of 1TB of uncompressed click logs that contain 4 billion examples.

These notebooks demonstrate how to scale NVTabular as well as the following:

• Use multiple GPUs and nodes with NVTabular for feature engineering.
• Train recommender system models with the Merlin Models for TensorFlow.
• Train recommender system models with HugeCTR using multiple GPUs.
• Inference with the Triton Inference Server and Merlin Models for TensorFlow or HugeCTR.

Training and Serving with Merlin on AWS SageMaker

The notebook and scripts demonstrate how to use Merlin components like NVTabular, Merlin Models, and Merlin Systems with Triton Inference Server to build and deploy a sample end-to-end recommender system in AWS SageMaker.

• Use the Amazon SageMaker Python SDK to interact with the SageMaker environment.
• Create a sample NVTabular workflow to prepare data for binary classification.
• Train a DLRMModel with Merlin Models for click and conversion prediction.
• Create a Merlin Systems ensemble for use with Triton Inference Server.
• Build a container and store it in AWS ECR that is based on Merlin and includes the training script.
• Use the Python SDK to run the container and train the model.
• Use the boto3 library locally to make inference requests to Triton Inference Server in the container that is running in the SageMaker environment.

Running the Example Notebooks

You can run the examples with Docker containers. Docker containers are available from the NVIDIA GPU Cloud catalog. Access the catalog of containers at https://catalog.ngc.nvidia.com/containers.

Depending on which example you want to run, you should use any one of these Docker containers:

• merlin-hugectr (contains Merlin Core, Merlin Models, Merlin Systems, NVTabular, HugeCTR)
• merlin-tensorflow (contains Merlin Core, Merlin Models, Merlin Systems, NVTabular and TensorFlow)
• merlin-pytorch (contains Merlin Core, Merlin Models, Merlin Systems, NVTabular and PyTorch)

All the containers include Triton Inference Server and are capable of training models and performing inference.

To run the example notebooks using Docker containers, perform the following steps:

1. Pull and start the container by running the following command:

   docker run --gpus all --rm -it \
     -p 8888:8888 -p 8797:8787 -p 8796:8786 --ipc=host \
     <docker container> /bin/bash

   The container opens a shell when the run command execution is completed. Your shell prompt should look similar to the following example:

   root@2efa5b50b909:

2. Start the JupyterLab server by running the following command:

   jupyter-lab --allow-root --ip='0.0.0.0'

   View the messages in your terminal to identify the URL for JupyterLab. The messages in your terminal show similar lines to the following example:

   Or copy and paste one of these URLs:
   http://2efa5b50b909:8888/lab?token=9b537d1fda9e4e9cadc673ba2a472e247deee69a6229ff8d
   or http://127.0.0.1:8888/lab?token=9b537d1fda9e4e9cadc673ba2a472e247deee69a6229ff8d

3. Open a browser and use the 127.0.0.1 URL provided in the messages by JupyterLab.

4. After you log in to JupyterLab, navigate to the /Merlin/examples directory to try out the example notebooks.