Deprecate checkpoint_callback from the Trainer constructor in favour of enable_checkpointing (#9754)

* enable_chekpointing

* update codebase

* chlog

* update tests

* fix warning

* Apply suggestions from code review

Co-authored-by: ananthsub <[email protected]>

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* Apply suggestions from code review

Co-authored-by: ananthsub <[email protected]>

* Apply suggestions from code review

Co-authored-by: ananthsub <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: 3 people

CHANGELOG.md

@@ -322,6 +322,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated Accelerator collective API `barrier`, `broadcast`, and `all_gather`, call `TrainingTypePlugin` collective API directly ([#9677](https://github.com/PyTorchLightning/pytorch-lightning/pull/9677))
 
 
+- Deprecated `checkpoint_callback` from the `Trainer` constructor in favour of `enable_checkpointing` ([#9754](https://github.com/PyTorchLightning/pytorch-lightning/pull/9754))
+
+
 - Deprecated the `LightningModule.on_post_move_to_device` method ([#9525](https://github.com/PyTorchLightning/pytorch-lightning/pull/9525))
 
 

docs/source/common/hyperparameters.rst

@@ -201,7 +201,7 @@ To recap, add ALL possible trainer flags to the argparser and init the ``Trainer
     trainer = Trainer.from_argparse_args(hparams)
 
     # or if you need to pass in callbacks
-    trainer = Trainer.from_argparse_args(hparams, checkpoint_callback=..., callbacks=[...])
+    trainer = Trainer.from_argparse_args(hparams, enable_checkpointing=..., callbacks=[...])
 
 ----------
 

docs/source/common/trainer.rst

@@ -528,6 +528,38 @@ Example::
 checkpoint_callback
 ^^^^^^^^^^^^^^^^^^^
 
+Deprecated: This has been deprecated in v1.5 and will be removed in v.17. Please use ``enable_checkpointing`` instead.
+
+default_root_dir
+^^^^^^^^^^^^^^^^
+
+.. raw:: html
+
+    <video width="50%" max-width="400px" controls
+    poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/default%E2%80%A8_root_dir.jpg"
+    src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/default_root_dir.mp4"></video>
+
+|
+
+Default path for logs and weights when no logger or
+:class:`pytorch_lightning.callbacks.ModelCheckpoint` callback passed.  On
+certain clusters you might want to separate where logs and checkpoints are
+stored. If you don't then use this argument for convenience. Paths can be local
+paths or remote paths such as `s3://bucket/path` or 'hdfs://path/'. Credentials
+will need to be set up to use remote filepaths.
+
+.. testcode::
+
+    # default used by the Trainer
+    trainer = Trainer(default_root_dir=os.getcwd())
+
+distributed_backend
+^^^^^^^^^^^^^^^^^^^
+Deprecated: This has been renamed ``accelerator``.
+
+enable_checkpointing
+^^^^^^^^^^^^^^^^^^^^
+
 .. raw:: html
 
     <video width="50%" max-width="400px" controls
@@ -542,11 +574,11 @@ To disable automatic checkpointing, set this to `False`.
 
 .. code-block:: python
 
-    # default used by Trainer
-    trainer = Trainer(checkpoint_callback=True)
+    # default used by Trainer, saves the most recent model to a single checkpoint after each epoch
+    trainer = Trainer(enable_checkpointing=True)
 
     # turn off automatic checkpointing
-    trainer = Trainer(checkpoint_callback=False)
+    trainer = Trainer(enable_checkpointing=False)
 
 
 You can override the default behavior by initializing the :class:`~pytorch_lightning.callbacks.ModelCheckpoint`
@@ -563,38 +595,6 @@ See :doc:`Saving and Loading Weights <../common/weights_loading>` for how to cus
     # Add your callback to the callbacks list
     trainer = Trainer(callbacks=[checkpoint_callback])
 
-
-.. warning:: Passing a ModelCheckpoint instance to this argument is deprecated since
-    v1.1 and will be unsupported from v1.3. Use `callbacks` argument instead.
-
-
-default_root_dir
-^^^^^^^^^^^^^^^^
-
-.. raw:: html
-
-    <video width="50%" max-width="400px" controls
-    poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/default%E2%80%A8_root_dir.jpg"
-    src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/default_root_dir.mp4"></video>
-
-|
-
-Default path for logs and weights when no logger or
-:class:`pytorch_lightning.callbacks.ModelCheckpoint` callback passed.  On
-certain clusters you might want to separate where logs and checkpoints are
-stored. If you don't then use this argument for convenience. Paths can be local
-paths or remote paths such as `s3://bucket/path` or 'hdfs://path/'. Credentials
-will need to be set up to use remote filepaths.
-
-.. testcode::
-
-    # default used by the Trainer
-    trainer = Trainer(default_root_dir=os.getcwd())
-
-distributed_backend
-^^^^^^^^^^^^^^^^^^^
-Deprecated: This has been renamed ``accelerator``.
-
 fast_dev_run
 ^^^^^^^^^^^^
 

pytorch_lightning/trainer/connectors/callback_connector.py

@@ -38,7 +38,8 @@ def __init__(self, trainer):
     def on_trainer_init(
         self,
         callbacks: Optional[Union[List[Callback], Callback]],
-        checkpoint_callback: bool,
+        checkpoint_callback: Optional[bool],
+        enable_checkpointing: bool,
         enable_progress_bar: bool,
         progress_bar_refresh_rate: Optional[int],
         process_position: int,
@@ -67,7 +68,7 @@ def on_trainer_init(
 
         # configure checkpoint callback
         # pass through the required args to figure out defaults
-        self._configure_checkpoint_callbacks(checkpoint_callback)
+        self._configure_checkpoint_callbacks(checkpoint_callback, enable_checkpointing)
 
         # configure swa callback
         self._configure_swa_callbacks()
@@ -140,22 +141,31 @@ def _configure_accumulated_gradients(
         self.trainer.accumulate_grad_batches = grad_accum_callback.get_accumulate_grad_batches(0)
         self.trainer.accumulation_scheduler = grad_accum_callback
 
-    def _configure_checkpoint_callbacks(self, checkpoint_callback: bool) -> None:
+    def _configure_checkpoint_callbacks(self, checkpoint_callback: Optional[bool], enable_checkpointing: bool) -> None:
+        if checkpoint_callback is not None:
+            rank_zero_deprecation(
+                f"Setting `Trainer(checkpoint_callback={checkpoint_callback})` is deprecated in v1.5 and will "
+                f"be removed in v1.7. Please consider using `Trainer(enable_checkpointing={checkpoint_callback})`."
+            )
+            # if both are set then checkpoint only if both are True
+            enable_checkpointing = checkpoint_callback and enable_checkpointing
+
         # TODO: Remove this error in v1.5 so we rely purely on the type signature
-        if not isinstance(checkpoint_callback, bool):
+        if not isinstance(enable_checkpointing, bool):
             error_msg = (
-                "Invalid type provided for checkpoint_callback:"
-                f" Expected bool but received {type(checkpoint_callback)}."
+                "Invalid type provided for `enable_checkpointing`: "
+                f"Expected bool but received {type(enable_checkpointing)}."
             )
-            if isinstance(checkpoint_callback, Callback):
+            if isinstance(enable_checkpointing, Callback):
                 error_msg += " Pass callback instances to the `callbacks` argument in the Trainer constructor instead."
             raise MisconfigurationException(error_msg)
-        if self._trainer_has_checkpoint_callbacks() and checkpoint_callback is False:
+        if self._trainer_has_checkpoint_callbacks() and enable_checkpointing is False:
             raise MisconfigurationException(
-                "Trainer was configured with checkpoint_callback=False but found ModelCheckpoint in callbacks list."
+                "Trainer was configured with `enable_checkpointing=False`"
+                " but found `ModelCheckpoint` in callbacks list."
             )
 
-        if not self._trainer_has_checkpoint_callbacks() and checkpoint_callback is True:
+        if not self._trainer_has_checkpoint_callbacks() and enable_checkpointing is True:
             self.trainer.callbacks.append(ModelCheckpoint())
 
     def _configure_model_summary_callback(self, weights_summary: Optional[str] = None) -> None:

pytorch_lightning/trainer/trainer.py

@@ -120,7 +120,8 @@ class Trainer(
     def __init__(
         self,
         logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool] = True,
-        checkpoint_callback: bool = True,
+        checkpoint_callback: Optional[bool] = None,
+        enable_checkpointing: bool = True,
         callbacks: Optional[Union[List[Callback], Callback]] = None,
         default_root_dir: Optional[str] = None,
         gradient_clip_val: Union[int, float] = 0.0,
@@ -215,6 +216,12 @@ def __init__(
             callbacks: Add a callback or list of callbacks.
 
             checkpoint_callback: If ``True``, enable checkpointing.
+
+                .. deprecated:: v1.5
+                    ``checkpoint_callback`` has been deprecated in v1.5 and will be removed in v1.7.
+                    Please consider using ``enable_checkpointing`` instead.
+
+            enable_checkpointing: If ``True``, enable checkpointing.
                 It will configure a default ModelCheckpoint callback if there is no user-defined ModelCheckpoint in
                 :paramref:`~pytorch_lightning.trainer.trainer.Trainer.callbacks`.
 
@@ -465,6 +472,7 @@ def __init__(
         self.callback_connector.on_trainer_init(
             callbacks,
             checkpoint_callback,
+            enable_checkpointing,
             enable_progress_bar,
             progress_bar_refresh_rate,
             process_position,

tests/accelerators/test_tpu_backend.py

@@ -51,7 +51,7 @@ def test_resume_training_on_cpu(tmpdir):
     """Checks if training can be resumed from a saved checkpoint on CPU."""
     # Train a model on TPU
     model = BoringModel()
-    trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=8)
+    trainer = Trainer(max_epochs=1, tpu_cores=8)
     trainer.fit(model)
 
     model_path = trainer.checkpoint_callback.best_model_path
@@ -62,9 +62,7 @@ def test_resume_training_on_cpu(tmpdir):
     assert weight_tensor.device == torch.device("cpu")
 
     # Verify that training is resumed on CPU
-    trainer = Trainer(
-        resume_from_checkpoint=model_path, checkpoint_callback=True, max_epochs=1, default_root_dir=tmpdir
-    )
+    trainer = Trainer(resume_from_checkpoint=model_path, max_epochs=1, default_root_dir=tmpdir)
     trainer.fit(model)
     assert trainer.state.finished, f"Training failed with {trainer.state}"
 

tests/callbacks/test_callbacks.py

@@ -35,7 +35,7 @@ def configure_callbacks(self):
 
     model = TestModel()
     trainer_options = dict(
-        default_root_dir=tmpdir, checkpoint_callback=False, fast_dev_run=True, enable_progress_bar=False
+        default_root_dir=tmpdir, enable_checkpointing=False, fast_dev_run=True, enable_progress_bar=False
     )
 
     def assert_expected_calls(_trainer, model_callback, trainer_callback):
@@ -86,7 +86,7 @@ def configure_callbacks(self):
             return [model_callback_mock]
 
     model = TestModel()
-    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, checkpoint_callback=False)
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, enable_checkpointing=False)
 
     callbacks_before_fit = trainer.callbacks.copy()
     assert callbacks_before_fit

tests/callbacks/test_early_stopping.py

@@ -111,7 +111,7 @@ def test_early_stopping_no_extraneous_invocations(tmpdir):
         limit_train_batches=4,
         limit_val_batches=4,
         max_epochs=expected_count,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
     trainer.fit(model, datamodule=dm)
 

tests/callbacks/test_lr_monitor.py

@@ -390,7 +390,7 @@ def finetune_function(self, pl_module, epoch: int, optimizer, opt_idx: int):
         callbacks=[TestFinetuning(), lr_monitor, Check()],
         enable_progress_bar=False,
         weights_summary=None,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
     model = TestModel()
     model.training_epoch_end = None

tests/callbacks/test_progress_bar.py

@@ -263,7 +263,7 @@ def on_validation_epoch_end(self, *args):
         limit_val_batches=limit_val_batches,
         callbacks=[progress_bar],
         logger=False,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
     trainer.fit(model)
 
@@ -342,7 +342,7 @@ def test_main_progress_bar_update_amount(
         limit_val_batches=val_batches,
         callbacks=[progress_bar],
         logger=False,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
     trainer.fit(model)
     if train_batches > 0:
@@ -362,7 +362,7 @@ def test_test_progress_bar_update_amount(tmpdir, test_batches: int, refresh_rate
         limit_test_batches=test_batches,
         callbacks=[progress_bar],
         logger=False,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
     trainer.test(model)
     progress_bar.test_progress_bar.update.assert_has_calls([call(delta) for delta in test_deltas])
@@ -379,7 +379,7 @@ def training_step(self, batch, batch_idx):
             return super().training_step(batch, batch_idx)
 
     trainer = Trainer(
-        default_root_dir=tmpdir, max_epochs=1, limit_train_batches=2, logger=False, checkpoint_callback=False
+        default_root_dir=tmpdir, max_epochs=1, limit_train_batches=2, logger=False, enable_checkpointing=False
     )
     trainer.fit(TestModel())