Scaling to Large Datasets with Criteo
Criteo provides the largest publicly available dataset for recommender systems. The dataset is 1 TB uncompressed click logs of 4 billion examples. The example notebooks show how to scale NVTabular in the following ways:
Using multiple GPUs and multiple nodes with NVTabular for ETL.
Training recommender system model with NVTabular dataloader for PyTorch.
Refer to the following notebooks: