The Well provides two main class WellDataset and WellDataModule to handle the raw data that are stored in .hdf5 files. The WellDataset implements a map-style PyTorch Dataset. The WellDataModule provides dataloaders for training, validation, and test. The tutorial provides a guide on how to use these classes in a training pipeline.

The WellDataset is a map-style dataset. It converts the .hdf5 file structure expected by the Well into torch.Tensor data. It first processes metadata from the .hdf5 attributes to allow for retrieval of individual samples.

::: the_well.data.WellDataset options: show_root_heading: true heading_level: 4

The WellDataModule provides the different dataloaders required for training, validation, and testing. It has two kinds of dataloaders: the default one that yields batches of a fixed time horizon, and rollout ones that yields batches to evaluate rollout performances.

::: the_well.data.WellDataModule options: show_root_heading: true heading_level: 4

The Well package implements a series of metrics to assess the performances of a trained model.

::: the_well.benchmark.metrics options: show_root_heading: true heading_level: 3