Add LightningCLI(save_config_overwrite=False|True)

CHANGELOG.md

@@ -71,6 +71,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `save_config_filename` init argument to `LightningCLI` to ease resolving name conflicts ([#7741](https://github.com/PyTorchLightning/pytorch-lightning/pull/7741))
 
 
+- Added `save_config_overwrite` init argument to `LightningCLI` to ease overwriting existing config files ([#8059](https://github.com/PyTorchLightning/pytorch-lightning/pull/8059))
+
+
 - Added reset dataloader hooks to Training Plugins and Accelerators ([#7861](https://github.com/PyTorchLightning/pytorch-lightning/pull/7861))
 
 

pytorch_lightning/utilities/cli.py

@@ -89,20 +89,24 @@ def __init__(
         parser: LightningArgumentParser,
         config: Union[Namespace, Dict[str, Any]],
         config_filename: str,
+        overwrite: bool = False,
     ) -> None:
         self.parser = parser
         self.config = config
         self.config_filename = config_filename
+        self.overwrite = overwrite
 
     def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
         log_dir = trainer.log_dir or trainer.default_root_dir
         config_path = os.path.join(log_dir, self.config_filename)
-        if os.path.isfile(config_path):
+        if not self.overwrite and os.path.isfile(config_path):
             raise RuntimeError(
-                f'{self.__class__.__name__} expected {config_path} to not exist. '
-                'Aborting to avoid overwriting results of a previous run.'
+                f'{self.__class__.__name__} expected {config_path} to NOT exist. Aborting to avoid overwriting'
+                ' results of a previous run. You can delete the previous config file,'
+                ' set `LightningCLI(save_config_callback=None)` to disable config saving,'
+                ' or set `LightningCLI(save_config_overwrite=True)` to overwrite the config file.'
             )
-        self.parser.save(self.config, config_path, skip_none=False)
+        self.parser.save(self.config, config_path, skip_none=False, overwrite=self.overwrite)
 
 
 class LightningCLI:
@@ -112,8 +116,9 @@ def __init__(
         self,
         model_class: Type[LightningModule],
         datamodule_class: Type[LightningDataModule] = None,
-        save_config_callback: Type[SaveConfigCallback] = SaveConfigCallback,
+        save_config_callback: Optional[Type[SaveConfigCallback]] = SaveConfigCallback,
         save_config_filename: str = 'config.yaml',
+        save_config_overwrite: bool = False,
         trainer_class: Type[Trainer] = Trainer,
         trainer_defaults: Dict[str, Any] = None,
         seed_everything_default: int = None,
@@ -150,6 +155,8 @@ def __init__(
             model_class: :class:`~pytorch_lightning.core.lightning.LightningModule` class to train on.
             datamodule_class: An optional :class:`~pytorch_lightning.core.datamodule.LightningDataModule` class.
             save_config_callback: A callback class to save the training config.
+            save_config_filename: Filename for the config file.
+            save_config_overwrite: Whether to overwrite an existing config file.
             trainer_class: An optional subclass of the :class:`~pytorch_lightning.trainer.trainer.Trainer` class.
             trainer_defaults: Set to override Trainer defaults or add persistent callbacks.
             seed_everything_default: Default value for the :func:`~pytorch_lightning.utilities.seed.seed_everything`
@@ -173,6 +180,7 @@ def __init__(
         self.datamodule_class = datamodule_class
         self.save_config_callback = save_config_callback
         self.save_config_filename = save_config_filename
+        self.save_config_overwrite = save_config_overwrite
         self.trainer_class = trainer_class
         self.trainer_defaults = {} if trainer_defaults is None else trainer_defaults
         self.seed_everything_default = seed_everything_default
@@ -246,7 +254,9 @@ def instantiate_trainer(self) -> None:
             else:
                 self.config_init['trainer']['callbacks'].append(self.trainer_defaults['callbacks'])
         if self.save_config_callback and not self.config_init['trainer']['fast_dev_run']:
-            config_callback = self.save_config_callback(self.parser, self.config, self.save_config_filename)
+            config_callback = self.save_config_callback(
+                self.parser, self.config, self.save_config_filename, overwrite=self.save_config_overwrite
+            )
             self.config_init['trainer']['callbacks'].append(config_callback)
         self.trainer = self.trainer_class(**self.config_init['trainer'])
 

tests/utilities/test_cli.py

@@ -603,3 +603,14 @@ def add_arguments_to_parser(self, parser):
 
     assert cli.model.batch_size == 8
     assert cli.model.num_classes == 5
+
+
+def test_cli_config_overwrite(tmpdir):
+    trainer_defaults = {'default_root_dir': str(tmpdir), 'logger': False, 'max_steps': 1, 'max_epochs': 1}
+
+    with mock.patch('sys.argv', ['any.py']):
+        LightningCLI(BoringModel, trainer_defaults=trainer_defaults)
+    with mock.patch('sys.argv', ['any.py']), pytest.raises(RuntimeError, match='Aborting to avoid overwriting'):
+        LightningCLI(BoringModel, trainer_defaults=trainer_defaults)
+    with mock.patch('sys.argv', ['any.py']):
+        LightningCLI(BoringModel, save_config_overwrite=True, trainer_defaults=trainer_defaults)