Prune deprecated Trainer(checkpoint_callback=ModelCheckpoint()) (#6166)

CHANGELOG.md

@@ -26,6 +26,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed support for passing a bool value to `profiler` argument of Trainer ([#6164](https://github.com/PyTorchLightning/pytorch-lightning/pull/6164))
 
 
+- Removed passing a `ModelCheckpoint` instance to `Trainer(checkpoint_callback)` ([#6166](https://github.com/PyTorchLightning/pytorch-lightning/pull/6166))
+
+
 - Removed deprecated Trainer argument `enable_pl_optimizer` and `automatic_optimization` ([#6163](https://github.com/PyTorchLightning/pytorch-lightning/pull/6163))
 
 

pytorch_lightning/trainer/connectors/callback_connector.py

@@ -16,7 +16,7 @@ from typing import List, Union
 
 from pytorch_lightning.callbacks import Callback, ModelCheckpoint, ProgressBar, ProgressBarBase
 from pytorch_lightning.core.lightning import LightningModule
-from pytorch_lightning.utilities import rank_zero_info, rank_zero_warn
+from pytorch_lightning.utilities import rank_zero_info
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 
@@ -63,15 +63,6 @@ class CallbackConnector:
         self.trainer.callbacks = self._reorder_callbacks(self.trainer.callbacks)
 
     def configure_checkpoint_callbacks(self, checkpoint_callback: Union[ModelCheckpoint, bool]):
-        if isinstance(checkpoint_callback, ModelCheckpoint):
-            # TODO: deprecated, remove this block in v1.3.0
-            rank_zero_warn(
-                "Passing a ModelCheckpoint instance to Trainer(checkpoint_callbacks=...)"
-                " is deprecated since v1.1 and will no longer be supported in v1.3."
-                " Use `callbacks` argument instead.", DeprecationWarning
-            )
-            self.trainer.callbacks.append(checkpoint_callback)
-
         if self._trainer_has_checkpoint_callbacks() and checkpoint_callback is False:
             raise MisconfigurationException(
                 "Trainer was configured with checkpoint_callback=False but found ModelCheckpoint"

pytorch_lightning/trainer/trainer.py

@@ -177,9 +177,6 @@ class Trainer(
                 It will configure a default ModelCheckpoint callback if there is no user-defined ModelCheckpoint in
                 :paramref:`~pytorch_lightning.trainer.trainer.Trainer.callbacks`.
 
-                .. warning:: Passing a ModelCheckpoint instance to this argument is deprecated since
-                    v1.1 and will be unsupported from v1.3. Use `callbacks` argument instead.
-
             check_val_every_n_epoch: Check val every n train epochs.
 
             default_root_dir: Default path for logs and weights when no logger/ckpt_callback passed.

tests/checkpointing/test_model_checkpoint.py

@@ -899,16 +899,6 @@ def test_configure_model_checkpoint(tmpdir):
     assert trainer.checkpoint_callback == callback1
     assert trainer.checkpoint_callbacks == [callback1, callback2]
 
-    with pytest.warns(DeprecationWarning, match='will no longer be supported in v1.3'):
-        trainer = Trainer(checkpoint_callback=callback1, **kwargs)
-        assert [c for c in trainer.callbacks if isinstance(c, ModelCheckpoint)] == [callback1]
-        assert trainer.checkpoint_callback == callback1
-
-    with pytest.warns(DeprecationWarning, match="will no longer be supported in v1.3"):
-        trainer = Trainer(checkpoint_callback=callback1, callbacks=[callback2], **kwargs)
-        assert trainer.checkpoint_callback == callback2
-        assert trainer.checkpoint_callbacks == [callback2, callback1]
-
     with pytest.raises(MisconfigurationException, match="checkpoint_callback=False but found ModelCheckpoint"):
         Trainer(checkpoint_callback=False, callbacks=[callback1], **kwargs)