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May 20, 2022 · Artificial Intelligence

Building a Collaborative Denoising Autoencoder with PyTorch Lightning

This article explains the collaborative denoising autoencoder (CDAE) for recommendation, walks through data preparation with MovieLens, shows a full PyTorch Lightning implementation, tunes hyper‑parameters using Ray Tune and CometML, and reports detailed evaluation metrics.

AutoencoderCDAECometML
0 likes · 11 min read
Building a Collaborative Denoising Autoencoder with PyTorch Lightning
Code DAO
Code DAO
Dec 17, 2021 · Artificial Intelligence

How to Scale XGBoost with Ray for Distributed Multi‑GPU Training

XGBoost‑Ray provides a fault‑tolerant, multi‑node, multi‑GPU backend for XGBoost that integrates seamlessly with Ray Tune, supports distributed data loading, and can be enabled with only three code changes, enabling scalable training and inference on large clusters.

GPURayRay Tune
0 likes · 8 min read
How to Scale XGBoost with Ray for Distributed Multi‑GPU Training