Training and Deploying Ranking models with Merlin

Training and Deploying Ranking models with Merlin#

Ranking models are probably the most common use case in recommender systems. The examples under this folder are designed to demonstrate how to build, train and evaluate a ranking model (e.g. DLRM) using Merlin Models and deploy on Triton Inference Server with Merlin Systems. Currently we support models built with TensorFlow framework, and traditional-ml models like XGBoost and python-based models with implicit datasets. Examples built with PyTorch framework are being developed and will be added here soon.

To learn more about ranking models, please visit this documentation page.

Running the Example Notebooks#

Docker containers are available from the NVIDIA GPU Cloud. We use the latest stable version of the merlin-tensorflow container to run the example notebooks. 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 \
  nvcr.io/nvidia/merlin/merlin-tensorflow:23.XX /bin/bash

    You can find the release tags and more information on the merlin-tensorflow container page.

    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/ranking directory to try out the example notebooks.

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