Training

Training utilities expect PyTorch Geometric Data samples whose target is stored as data.y. Datasets, splits, and loaders are caller-controlled; TOML configuration builds models and trainer settings but does not discover data.

Trainer setup

TrainingConfig controls epochs, batch size, optimizer, scheduler, device, checkpoint directory, early stopping, gradient clipping, and DataLoader options. Trainer validates the config, creates the optimizer and scheduler, writes config.json, stores training history, and saves checkpoints.

TOML setup

load_training_config() returns a ConfiguredTrainingSetup containing a GATBiLSTMNet, TrainingConfig, loss, metrics, tracking configuration, and sanitized tracking metadata. create_trainer_from_config() creates a Trainer from the same TOML file.

Model data contract

Model inputs follow the graph contract in Graph Conversion. Batched graph-level training needs data.batch. Temporal modes of GATBiLSTMNet require data.time_bin as a node-aligned tensor. The training dataset must provide data.y for loss computation.

Outputs

Training writes checkpoints and history.json under save_dir. The final checkpoint is always saved; best-checkpoint behavior is controlled by save_best_only. Checkpoints are complete PyTorch project artifacts, so resume only from trusted checkpoint files produced by your own training runs.

Related pages

See Datasets And Splits, Training Configuration, Metrics And Tracking, Training Reference, and Troubleshooting.