Merge branch 'features-sfluegel' into feature-pretraining

chebai/trainer/InnerCVTrainer.py

@@ -26,7 +26,6 @@ def __init__(self, *args, **kwargs):
         self.init_kwargs = kwargs
         super().__init__(*args, **kwargs)
         # instantiation custom logger connector
-        self._logger_connector = _LoggerConnectorCVSupport(self)
         self._logger_connector.on_trainer_init(self.logger, 1)
 
     def cv_fit(self, datamodule: XYBaseDataModule, n_splits: int = -1, *args, **kwargs):
@@ -47,8 +46,6 @@ def cv_fit(self, datamodule: XYBaseDataModule, n_splits: int = -1, *args, **kwar
                                                 version=self.logger.version, fold=fold)
                 init_kwargs['logger'] = new_logger
                 new_trainer = Trainer(*self.init_args, **init_kwargs)
-                self._logger_connector = _LoggerConnectorCVSupport(self)
-                self._logger_connector.on_trainer_init(self.logger, 1)
                 print(f'Logging this fold at {new_trainer.logger.log_dir}')
                 new_trainer.fit(train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, *args, **kwargs)
 
@@ -128,31 +125,3 @@ def __resolve_ckpt_dir(self, trainer: "Trainer") -> _PATH:
 
         print(f'Now using checkpoint path {ckpt_path}')
         return ckpt_path
-
-
-warning_cache = WarningCache()
-
-
-class _LoggerConnectorCVSupport(_LoggerConnector):
-    def configure_logger(self, logger: Union[bool, Logger, Iterable[Logger]]) -> None:
-        if not logger:
-            # logger is None or logger is False
-            self.trainer.loggers = []
-        elif logger is True:
-            # default logger
-            if _TENSORBOARD_AVAILABLE or _TENSORBOARDX_AVAILABLE:
-                logger_ = TensorBoardLogger(save_dir=self.trainer.default_root_dir, version=SLURMEnvironment.job_id())
-            else:
-                warning_cache.warn(
-                    "Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch`"
-                    " package, due to potential conflicts with other packages in the ML ecosystem. For this reason,"
-                    " `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard`"
-                    " or `tensorboardX` packages are found."
-                    " Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default"
-                )
-                logger_ = CSVLogger(save_dir=self.trainer.default_root_dir)  # type: ignore[assignment]
-            self.trainer.loggers = [logger_]
-        elif isinstance(logger, Iterable):
-            self.trainer.loggers = list(logger)
-        else:
-            self.trainer.loggers = [logger]

configs/training/default_callbacks.yml

@@ -1,4 +1,4 @@
-- class_path: chebai.trainer.InnerCVTrainer.ModelCheckpointCVSupport
+- class_path: chebai.trainer.InnerCVTrainer.ModelCheckpoint
   init_args:
     monitor: val_micro-f1
     mode: 'max'