scvi.train.TrainRunner#
- class scvi.train.TrainRunner(model, training_plan, data_splitter, max_epochs, use_gpu=None, **trainer_kwargs)[source]#
Bases:
object
TrainRunner calls Trainer.fit() and handles pre and post training procedures.
- Parameters:
model (BaseModelClass) – model to train
training_plan (LightningModule) – initialized TrainingPlan
data_splitter (Union[SemiSupervisedDataSplitter, DataSplitter]) – initialized
SemiSupervisedDataSplitter
orDataSplitter
max_epochs (int) – max_epochs to train for
use_gpu (Optional[Union[str, int, bool]]) – Use default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str, e.g.,
'cuda:0'
), or use CPU (if False).trainer_kwargs – Extra kwargs for
Trainer
Examples
>>> # Following code should be within a subclass of BaseModelClass >>> data_splitter = DataSplitter(self.adata) >>> training_plan = TrainingPlan(self.module, len(data_splitter.train_idx)) >>> runner = TrainRunner( >>> self, >>> training_plan=trianing_plan, >>> data_splitter=data_splitter, >>> max_epochs=max_epochs) >>> runner()