NVIDIA Merlin Example Notebooks

We have a collection of Jupyter example notebooks that are based on different datasets to provide end-to-end examples for NVIDIA Merlin. These example notebooks demonstrate how to use NVTabular with TensorFlow, PyTorch, and HugeCTR. 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 Fiass.

  • 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:

  • Use multiple GPUs and nodes with NVTabular for ETL.

  • Train recommender system models with the NVTabular dataloader for PyTorch.

  • Train recommender system models with the NVTabular dataloader for TensorFlow.

  • Train recommender system models with HugeCTR using a multi-GPU.

  • Inference with the Triton Inference Server and TensorFlow or HugeCTR.

Running the Example Notebooks

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

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

  • Merlin-Training (contains Merlin Core, Merlin Models, Merlin Systems, NVTabular and HugeCTR)

  • Merlin-PyTorch-Training (contains Merlin Core, Merlin Models, Merlin Systems, NVTabular and PyTorch)

  • Merlin-Tensorflow-Training (contains Merlin Core, Merlin Models, Merlin Systems, NVTabular and TensorFlow)

  • Merlin-Tensorflow-Inference (contains Merlin Core, Merlin Models, Merlin Systems, NVTabular, TensorFlow and Triton Inference Server)

  • Merlin-Inference (contains Merlin Core, Merlin Models, Merlin Systems, NVTabular, TensorFlow, PyTorch, HugeCTR and Triton Inference Server)

There are example docker-compose files referenced in Scaling to large Datasets with Criteo that you can get from our GitHub repository.

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. Install JupyterLab with pip by running the following command:

    pip install jupyterlab

    For more information, see the JupyterLab Installation Guide.

  3. 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

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

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