Remove ModelCheckpoint.on_train_end

pytorch_lightning/callbacks/model_checkpoint.py

@@ -315,19 +315,6 @@ def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModul
             return
         self.save_checkpoint(trainer)
 
-    def on_train_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        """Save a checkpoint when training stops.
-
-        This will only save a checkpoint if `save_last` is also enabled as the monitor metrics logged during
-        training/validation steps or end of epochs are not guaranteed to be available at this stage.
-        """
-        if self._should_skip_saving_checkpoint(trainer) or not self.save_last:
-            return
-        if self.verbose:
-            rank_zero_info("Saving latest checkpoint...")
-        monitor_candidates = self._monitor_candidates(trainer, trainer.current_epoch, trainer.global_step)
-        self._save_last_checkpoint(trainer, monitor_candidates)
-
     def on_save_checkpoint(
         self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", checkpoint: Dict[str, Any]
     ) -> Dict[str, Any]:

tests/checkpointing/test_checkpoint_callback_frequency.py

@@ -81,8 +81,8 @@ def training_step(self, batch, batch_idx):
     trainer.fit(model)
 
     if save_last:
-        # last epochs are saved every step (so double the save calls) and once `on_train_end`
-        expected = expected * 2 + 1
+        # last epochs are saved every step (so double the save calls)
+        expected = expected * 2
     assert save_mock.call_count == expected
 
 

tests/checkpointing/test_model_checkpoint.py

@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import logging
 import math
 import os
 import pickle
@@ -776,30 +775,6 @@ def test_default_checkpoint_behavior(tmpdir):
     assert ckpts[0] == "epoch=2-step=15.ckpt"
 
 
-@pytest.mark.parametrize("max_epochs", [1, 2])
-@pytest.mark.parametrize("should_validate", [True, False])
-@pytest.mark.parametrize("save_last", [True, False])
-@pytest.mark.parametrize("verbose", [True, False])
-def test_model_checkpoint_save_last_warning(
-    tmpdir, caplog, max_epochs: int, should_validate: bool, save_last: bool, verbose: bool
-):
-    """Tests 'Saving latest checkpoint...' log."""
-    # set a high `every_n_epochs` to avoid saving in `on_train_epoch_end`. the message is only printed `on_train_end`
-    # but it would get skipped because it got already saved in `on_train_epoch_end` for the same global step
-    ckpt = ModelCheckpoint(dirpath=tmpdir, save_top_k=0, save_last=save_last, verbose=verbose, every_n_epochs=123)
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        callbacks=[ckpt],
-        max_epochs=max_epochs,
-        limit_train_batches=1,
-        limit_val_batches=int(should_validate),
-    )
-    model = BoringModel()
-    with caplog.at_level(logging.INFO):
-        trainer.fit(model)
-    assert caplog.messages.count("Saving latest checkpoint...") == (verbose and save_last)
-
-
 def test_model_checkpoint_save_last_checkpoint_contents(tmpdir):
     """Tests that the save_last checkpoint contains the latest information."""
     seed_everything(100)