Deep learning based recommender systems

These notebooks build deep learning recommender systems on AI Runtime. They show how to create a two-tower recommendation model using PyTorch Lightning for distributed training.

Example Interface Description
Two-tower recommendation model Notebook Learn how to create a two-tower recommendation model using PyTorch Lightning for distributed training.
Two-tower recommendation with PyTorch CLI Train and evaluate a two-tower movie recommender on MovieLens 1M on one A10 GPU.
Search technical documents with Qwen3 embeddings CLI Create Qwen3 text embeddings for technical documents on one A10 GPU and return semantically similar documents for each query.
Deep learning recommendation model with TorchRec CLI Train a click-through-rate model on Criteo data across four A10 nodes with TorchRec sharding.
Learning to rank with XGBoost CLI Train an XGBoost LambdaMART model on one A10 GPU to rank search results by relevance.
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