---
type: concept
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  contentHash: b42a5f9f4dca7e5972a961cfba33689cd61e096ce7a0a321c7d18399bbebf523
  pageDirectory: concepts
  sources:
    - deep-learning-based-recommender-systems-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - pytorch-lightning-for-distributed-recommendation-training
    - PLFDRT
  citations:
    - file: deep-learning-based-recommender-systems-databricks-on-aws.md
title: PyTorch Lightning for Distributed Recommendation Training
description: Use of PyTorch Lightning to implement distributed training of recommendation models, enabling multi-GPU and multi-node scaling on Databricks AI Runtime.
tags:
  - pytorch-lightning
  - distributed-training
  - recommender-systems
timestamp: "2026-06-19T09:57:45.304Z"
---

# PyTorch Lightning for Distributed Recommendation Training

**PyTorch Lightning for Distributed Recommendation Training** refers to the use of the PyTorch Lightning framework to build and train recommendation models across multiple GPUs in a distributed setting. This approach simplifies the implementation of distributed training logic while leveraging deep learning architectures such as two‑tower models for large‑scale recommendation systems. ^[deep-learning-based-recommender-systems-databricks-on-aws.md]

## Overview

PyTorch Lightning provides a high‑level interface for PyTorch that abstracts away boilerplate distributed training code. When applied to recommendation systems, it enables data scientists and engineers to focus on model architecture, data pipelines, and evaluation rather than on the mechanics of multi‑GPU synchronization or gradient accumulation. ^[deep-learning-based-recommender-systems-databricks-on-aws.md]

Databricks AI Runtime supports PyTorch Lightning for distributed training workloads. A dedicated tutorial walks through the creation of a two‑tower recommendation model using PyTorch Lightning, demonstrating how to scale training across multiple GPUs in a Databricks cluster. ^[deep-learning-based-recommender-systems-databricks-on-aws.md]

## Key Concepts

- **Two‑tower model**: A common architecture for retrieval‑style recommenders, where user features and item features are encoded into separate embedding towers and compared via a similarity function. PyTorch Lightning can manage the distributed training of both towers simultaneously. ^[deep-learning-based-recommender-systems-databricks-on-aws.md]
- **Distributed training**: PyTorch Lightning’s `LightningModule` and `Trainer` handle sharding of data and model across GPUs, reducing training time for large‑scale recommendation datasets. ^[deep-learning-based-recommender-systems-databricks-on-aws.md]

## Usage on Databricks

The Databricks AI Runtime includes pre‑configured PyTorch Lightning environments. Users can create a two‑tower recommendation model by following the [two‑tower recommendation model tutorial](https://docs.databricks.com/aws/en/machine-learning/ai-runtime/examples/tutorials/sgc-recommender-system-lightning). The tutorial covers:

- Setting up a PyTorch Lightning `LightningModule`.
- Configuring distributed data parallelism with the `Trainer`.
- Training and evaluating the recommendation model on GPU clusters.

^[deep-learning-based-recommender-systems-databricks-on-aws.md]

## Related Concepts

- [Two‑tower recommendation model](/concepts/two-tower-recommendation-model.md)
- [Distributed training](/concepts/workload-yaml-for-distributed-training.md)
- [AI Runtime](/concepts/ai-runtime.md)
- Recommender systems
- [PyTorch Lightning](/concepts/pytorch-lightning-for-ddp.md)

## Sources

- deep-learning-based-recommender-systems-databricks-on-aws.md

# Citations

1. [deep-learning-based-recommender-systems-databricks-on-aws.md](/references/deep-learning-based-recommender-systems-databricks-on-aws-9c825c28.md)
