Sort Trainer arguments based on importance (#17022)

src/lightning/pytorch/trainer/trainer.py

@@ -89,19 +89,15 @@ class Trainer:
     @_defaults_from_env_vars
     def __init__(
         self,
-        logger: Optional[Union[Logger, Iterable[Logger], bool]] = None,
+        *,
-        enable_checkpointing: Optional[bool] = None,
+        accelerator: Union[str, Accelerator] = "auto",
-        callbacks: Optional[Union[List[Callback], Callback]] = None,
+        strategy: Union[str, Strategy] = "auto",
-        default_root_dir: Optional[_PATH] = None,
-        gradient_clip_val: Optional[Union[int, float]] = None,
-        gradient_clip_algorithm: Optional[str] = None,
-        num_nodes: int = 1,
         devices: Union[List[int], str, int] = "auto",
-        enable_progress_bar: Optional[bool] = None,
+        num_nodes: int = 1,
-        overfit_batches: Union[int, float] = 0.0,
+        precision: _PRECISION_INPUT = "32-true",
-        check_val_every_n_epoch: Optional[int] = 1,
+        logger: Optional[Union[Logger, Iterable[Logger], bool]] = None,
+        callbacks: Optional[Union[List[Callback], Callback]] = None,
         fast_dev_run: Union[int, bool] = False,
-        accumulate_grad_batches: int = 1,
         max_epochs: Optional[int] = None,
         min_epochs: Optional[int] = None,
         max_steps: int = -1,
@@ -111,23 +107,28 @@ class Trainer:
         limit_val_batches: Optional[Union[int, float]] = None,
         limit_test_batches: Optional[Union[int, float]] = None,
         limit_predict_batches: Optional[Union[int, float]] = None,
+        overfit_batches: Union[int, float] = 0.0,
         val_check_interval: Optional[Union[int, float]] = None,
-        log_every_n_steps: Optional[int] = None,
+        check_val_every_n_epoch: Optional[int] = 1,
-        accelerator: Union[str, Accelerator] = "auto",
-        strategy: Union[str, Strategy] = "auto",
-        sync_batchnorm: bool = False,
-        precision: _PRECISION_INPUT = "32-true",
-        enable_model_summary: Optional[bool] = None,
         num_sanity_val_steps: Optional[int] = None,
-        profiler: Optional[Union[Profiler, str]] = None,
+        log_every_n_steps: Optional[int] = None,
-        benchmark: Optional[bool] = None,
+        enable_checkpointing: Optional[bool] = None,
+        enable_progress_bar: Optional[bool] = None,
+        enable_model_summary: Optional[bool] = None,
+        accumulate_grad_batches: int = 1,
+        gradient_clip_val: Optional[Union[int, float]] = None,
+        gradient_clip_algorithm: Optional[str] = None,
         deterministic: Optional[Union[bool, _LITERAL_WARN]] = None,
-        reload_dataloaders_every_n_epochs: int = 0,
+        benchmark: Optional[bool] = None,
-        use_distributed_sampler: bool = True,
-        detect_anomaly: bool = False,
-        plugins: Optional[Union[PLUGIN_INPUT, List[PLUGIN_INPUT]]] = None,
         inference_mode: bool = True,
+        use_distributed_sampler: bool = True,
+        profiler: Optional[Union[Profiler, str]] = None,
+        detect_anomaly: bool = False,
         barebones: bool = False,
+        plugins: Optional[Union[PLUGIN_INPUT, List[PLUGIN_INPUT]]] = None,
+        sync_batchnorm: bool = False,
+        reload_dataloaders_every_n_epochs: int = 0,
+        default_root_dir: Optional[_PATH] = None,
     ) -> None:
         r"""
         Customize every aspect of training via flags.

tests/tests_pytorch/callbacks/progress/test_rich_progress_bar.py

@@ -102,7 +102,7 @@ def test_rich_progress_bar_import_error(monkeypatch):
 
 
 @RunIf(rich=True)
-def test_rich_progress_bar_custom_theme(tmpdir):
+def test_rich_progress_bar_custom_theme():
     """Test to ensure that custom theme styles are used."""
     with mock.patch.multiple(
         "lightning.pytorch.callbacks.progress.rich_progress",
@@ -114,7 +114,7 @@ def test_rich_progress_bar_custom_theme(tmpdir):
         theme = RichProgressBarTheme()
 
         progress_bar = RichProgressBar(theme=theme)
-        progress_bar.on_train_start(Trainer(tmpdir), BoringModel())
+        progress_bar.on_train_start(Trainer(), BoringModel())
 
         assert progress_bar.theme == theme
         args, kwargs = mocks["CustomBarColumn"].call_args

tests/tests_pytorch/trainer/flags/test_env_vars.py

@@ -43,7 +43,7 @@ def test_passing_env_variables_only():
 @mock.patch.dict(os.environ, {"PL_TRAINER_LOGGER": "True", "PL_TRAINER_MAX_STEPS": "7"})
 def test_passing_env_variables_defaults():
     """Testing overwriting trainer arguments."""
-    trainer = Trainer(False, max_steps=42)
+    trainer = Trainer(logger=False, max_steps=42)
     assert trainer.logger is None
     assert trainer.max_steps == 42