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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. |
| Learning to rank with XGBoost | CLI | Train an XGBoost LambdaMART model on one A10 GPU to rank search results by relevance. |