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 traintraining_plan (
LightningModule
) – initialized TrainingPlandata_splitter (
Union
[SemiSupervisedDataSplitter
,DataSplitter
]) – initializedSemiSupervisedDataSplitter
orDataSplitter
max_epochs (
int
) – max_epochs to train foruse_gpu (
Union
[str
,int
,bool
,None
] (default:None
)) – 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()