getting-started-movielens

Getting Started with Merlin and the MovieLens Dataset

The MovieLens25M is a popular dataset for recommender systems and is used in academic publications. Most users are familiar with the dataset and we will teach the basic concepts of Merlin:

• Learn to use NVTabular for using GPU-accelerated feature engineering and data preprocessing.
• Become familiar with the high-level API for NVTabular.
• Use single-hot/multi-hot categorical input features with NVTabular.
• Train a Merlin Model with Tensorflow.
• Use the Merlin Dataloader with PyTorch.
• Train a HugeCTR model.
• Serve recommendations from the Tensorflow model with the Triton Inference Server.

Explore the following notebooks:

• Download and Convert
• Feature Engineering with NVTabular
• Training with TensorFlow
• Training with PyTorch
• Training with HugeCTR
• Serve Recommendations with Triton Inference Server (Tensorflow)