Hierarchical Parameter Server Notebooks

This directory contains a set of Jupyter notebooks that demonstrate how to use HPS in PyTorch.


The simplest way to run a one of our notebooks is with a Docker container. A container provides a self-contained, isolated, and reproducible environment for repetitive experiments. Docker images are available from the NVIDIA GPU Cloud (NGC). If you prefer to build the HugeCTR Docker image on your own, refer to Set Up the Development Environment With Merlin Containers.

Pull the NGC Docker

Pull the container using the following command:

docker pull nvcr.io/nvidia/merlin/merlin-hugectr:24.04

Clone the HugeCTR Repository

Use the following command to clone the HugeCTR repository:

git clone https://github.com/NVIDIA/HugeCTR

Start the Jupyter Notebook

  1. Launch the container in interactive mode (mount the HugeCTR root directory into the container for your convenience) by running this command:

    docker run --runtime=nvidia --rm -it --cap-add SYS_NICE -u $(id -u):$(id -g) -v $(pwd):/hugectr -w /hugectr -p 8888:8888 nvcr.io/nvidia/merlin/merlin-hugectr:24.04
  2. Start Jupyter using these commands:

    cd /hugectr/hps_torch/notebooks
    jupyter-notebook --allow-root --ip --port 8888 --NotebookApp.token='hugectr'
  3. Connect to your host machine using the 8888 port by accessing its IP address or name from your web browser: http://[host machine]:8888

    Use the token available from the output by running the command above to log in. For example:

    http://[host machine]:8888/?token=aae96ae9387cd28151868fee318c3b3581a2d794f3b25c6b

Notebook List

Here’s a list of notebooks that you can run:

  • hps_torch_demo.ipynb: Demonstrates how to use the HPS plugin for Torch to conduct embedding lookup for inference.

System Specifications

The specifications of the system on which each notebook can run successfully are summarized in the table. The notebooks are verified on the system below but it does not mean the minimum requirements.







Intel® Xeon® CPU E5-2698 v4 @ 2.20GHz
512 GB Memory

Tesla V100-SXM2-32GB
32 GB Memory


Kingsley Liu