Deep learning based recommender systems
AI Runtime for single-node tasks is in Public Preview. The distributed training API for multi-GPU workloads remain in Beta.
This page provides notebook examples for building recommendation systems using AI Runtime. These examples demonstrate how to create efficient recommendation models using modern deep learning approaches.
Tutorial | Description |
|---|---|
Learn how to convert recommendation data into Mosaic Data Shard (MDS) format and then use that data to create a two-tower recommendation model. |
Two-tower recommendation model
These notebooks demonstrate how to convert your recommendation data into Mosaic Data Shard (MDS) format and then use that data to create a two-tower recommendation model. This approach is particularly effective for large-scale recommendation systems.
Data preparation: Convert recommendation model dataset to MDS format
First, convert your recommendation dataset to the MDS format for efficient data loading:
Convert data
Model training: Two-tower recommender model using PyTorch Lightning
Train the two-tower recommender model using the prepared dataset and the PyTorch Lightning Trainer API on multiple GPU nodes (A10 or H100 GPUs).