These example notebooks demonstrate how to use NVTabular with HugeCTR. Each example provides details about the end-to-end workflow, which includes Download data, ETL, and Training.

Each example notebook is structured as follows:

01-Download-Convert.ipynb: Demonstrates how to download the dataset and convert it into the correct format so that it can be consumed.
02-ETL-with-NVTabular.ipynb: Demonstrates how to execute the preprocessing and feature engineering pipeline (ETL) with NVTabular on the GPU.
03-Training-with-TF.ipynb: Demonstrates how to train a model with TensorFlow based on the ETL output.
MM-01-Getting-started.ipynb: Demonstrates Merlin Models by training Facebook's DLRM architecture with only 3 commands.
MM-02-Merlin-Models-and-NVTabular-integration.ipynb: Demonstrates how NVTabular and Merlin Models can be integrated together.
MM-03-Exploring-different-models.ipynb: Demonstrate how to build and train several popular deep learning-based ranking model architectures.
MM-04-Exporting-ranking-models.ipynb: Demonstrates how to export (save) NVTabular workflow and a ranking model for model deployment with Merlin Systems library.
MM-05-Retrieval-Model.ipynb: Demonstrates how to build a Two-Tower model for Item Retrieval task using synthetic datasets
MM-06-Define-your-own-architecture-with-Merlin-Models.ipynb: Demonstrates how to combine pre-existing blocks and create a custom DLRM architecture.
MM-07-Train-traditional-ML-models-using-the-Merlin-Models-API.ipynb: Demonstrates how to train several traditional ML models using Merlin Models.

Reading the notebook without leaving NGC

If you want to read through the notebook example without leaving our website, follow these steps:

Navigate to the File Browser tab of the asset in NGC
Select the version you'd like to see
Next to the .ipynb file select \"View Jupyter\"
There you have it! You can read a notebook for documentation and copy code samples without ever leaving NGC.
All the instructions you need to get started are in the resource - so head over and see how to get up and running.

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Each example provides details about the end-to-end workflow, which includes Download data, ETL, and Training. \n\n\n\n\n## Each example notebook is structured as follows:\n1. 01-Download-Convert.ipynb: Demonstrates how to download the dataset and convert it into the correct format so that it can be consumed.\n2. 02-ETL-with-NVTabular.ipynb: Demonstrates how to execute the preprocessing and feature engineering pipeline (ETL) with NVTabular on the GPU.\n3. 03-Training-with-TF.ipynb: Demonstrates how to train a model with TensorFlow based on the ETL output.\n4. MM-01-Getting-started.ipynb: Demonstrates Merlin Models by training Facebook's DLRM architecture with only 3 commands.\n5. MM-02-Merlin-Models-and-NVTabular-integration.ipynb: Demonstrates how NVTabular and Merlin Models can be integrated together.\n6. MM-03-Exploring-different-models.ipynb: Demonstrate how to build and train several popular deep learning-based ranking model architectures.\n7. MM-04-Exporting-ranking-models.ipynb: Demonstrates how to export (save) NVTabular workflow and a ranking model for model deployment with Merlin Systems library.\n8. MM-05-Retrieval-Model.ipynb: Demonstrates how to build a Two-Tower model for Item Retrieval task using synthetic datasets\n9. MM-06-Define-your-own-architecture-with-Merlin-Models.ipynb: Demonstrates how to combine pre-existing blocks and create a custom DLRM architecture.\n10. MM-07-Train-traditional-ML-models-using-the-Merlin-Models-API.ipynb: Demonstrates how to train several traditional ML models using Merlin Models.\n\n\n## Reading the notebook without leaving NGC\nIf you want to read through the notebook example without leaving our website, follow these steps:\n\n1. Navigate to the File Browser tab of the asset in NGC\n2. Select the version you'd like to see\n3. Next to the .ipynb file select \"View Jupyter\"\n4. There you have it! 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