diff --git a/CHANGELOG.md b/CHANGELOG.md
index 01076b46ec073..a25271ed3d2a0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -308,6 +308,9 @@ 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))
 
 
diff --git a/docs/source/common/hyperparameters.rst b/docs/source/common/hyperparameters.rst
index 1781a26a9189f..41a99e022ae95 100644
--- a/docs/source/common/hyperparameters.rst
+++ b/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=[...])
 
 ----------
 
diff --git a/docs/source/common/trainer.rst b/docs/source/common/trainer.rst
index e8f78864b1ddf..42615383b5c1f 100644
--- a/docs/source/common/trainer.rst
+++ b/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
 ^^^^^^^^^^^^
 
diff --git a/pytorch_lightning/trainer/connectors/callback_connector.py b/pytorch_lightning/trainer/connectors/callback_connector.py
index 4b6fe7feac4d7..0e5ba3170064a 100644
--- a/pytorch_lightning/trainer/connectors/callback_connector.py
+++ b/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:
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index fa018a345262b..6f7c95a68e74f 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/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,
@@ -214,6 +215,12 @@ 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`.
 
@@ -456,6 +463,7 @@ def __init__(
         self.callback_connector.on_trainer_init(
             callbacks,
             checkpoint_callback,
+            enable_checkpointing,
             enable_progress_bar,
             progress_bar_refresh_rate,
             process_position,
diff --git a/tests/accelerators/test_tpu_backend.py b/tests/accelerators/test_tpu_backend.py
index d9b9e67aeb4ab..5d021851bad4e 100644
--- a/tests/accelerators/test_tpu_backend.py
+++ b/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}"
 
diff --git a/tests/callbacks/test_callbacks.py b/tests/callbacks/test_callbacks.py
index 78e21d821b810..34beca2021933 100644
--- a/tests/callbacks/test_callbacks.py
+++ b/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
diff --git a/tests/callbacks/test_early_stopping.py b/tests/callbacks/test_early_stopping.py
index fe6873c8f43bf..b44dee9abb0b6 100644
--- a/tests/callbacks/test_early_stopping.py
+++ b/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)
 
diff --git a/tests/callbacks/test_lr_monitor.py b/tests/callbacks/test_lr_monitor.py
index df84a1b186231..ff92c83871162 100644
--- a/tests/callbacks/test_lr_monitor.py
+++ b/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
diff --git a/tests/callbacks/test_progress_bar.py b/tests/callbacks/test_progress_bar.py
index 746a9717dbbca..cd6a5cf2a0cad 100644
--- a/tests/callbacks/test_progress_bar.py
+++ b/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())
 
diff --git a/tests/callbacks/test_pruning.py b/tests/callbacks/test_pruning.py
index 5c2f2d4d7b3de..0f79132f9748b 100644
--- a/tests/callbacks/test_pruning.py
+++ b/tests/callbacks/test_pruning.py
@@ -109,7 +109,7 @@ def train_with_pruning_callback(
         default_root_dir=tmpdir,
         enable_progress_bar=False,
         weights_summary=None,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
         logger=False,
         limit_train_batches=10,
         limit_val_batches=2,
@@ -227,7 +227,7 @@ def apply_lottery_ticket_hypothesis(self):
         default_root_dir=tmpdir,
         enable_progress_bar=False,
         weights_summary=None,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
         logger=False,
         limit_train_batches=10,
         limit_val_batches=2,
@@ -254,7 +254,7 @@ def test_multiple_pruning_callbacks(tmpdir, caplog, make_pruning_permanent: bool
         default_root_dir=tmpdir,
         enable_progress_bar=False,
         weights_summary=None,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
         logger=False,
         limit_train_batches=10,
         limit_val_batches=2,
diff --git a/tests/callbacks/test_quantization.py b/tests/callbacks/test_quantization.py
index 73d1a458b52ed..f548d4d98adcc 100644
--- a/tests/callbacks/test_quantization.py
+++ b/tests/callbacks/test_quantization.py
@@ -56,7 +56,7 @@ def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):
     assert torch.allclose(org_score, quant_score, atol=0.45)
     model_path = trainer.checkpoint_callback.best_model_path
 
-    trainer_args.update(dict(max_epochs=1, checkpoint_callback=False))
+    trainer_args.update(dict(max_epochs=1, enable_checkpointing=False))
     if not convert:
         trainer = Trainer(callbacks=[QuantizationAwareTraining()], **trainer_args)
         trainer.fit(qmodel, datamodule=dm)
diff --git a/tests/checkpointing/test_checkpoint_callback_frequency.py b/tests/checkpointing/test_checkpoint_callback_frequency.py
index cb38ae8f0573f..46551df660b8d 100644
--- a/tests/checkpointing/test_checkpoint_callback_frequency.py
+++ b/tests/checkpointing/test_checkpoint_callback_frequency.py
@@ -22,9 +22,9 @@
 from tests.helpers.runif import RunIf
 
 
-def test_checkpoint_callback_disabled(tmpdir):
+def test_disabled_checkpointing(tmpdir):
     # no callback
-    trainer = Trainer(max_epochs=3, checkpoint_callback=False)
+    trainer = Trainer(max_epochs=3, enable_checkpointing=False)
     assert not trainer.checkpoint_callbacks
     trainer.fit(BoringModel())
     assert not trainer.checkpoint_callbacks
diff --git a/tests/checkpointing/test_legacy_checkpoints.py b/tests/checkpointing/test_legacy_checkpoints.py
index 0910959fc7e7c..af7836590c2eb 100644
--- a/tests/checkpointing/test_legacy_checkpoints.py
+++ b/tests/checkpointing/test_legacy_checkpoints.py
@@ -79,7 +79,6 @@ def test_resume_legacy_checkpoints(tmpdir, pl_version: str):
             default_root_dir=str(tmpdir),
             gpus=int(torch.cuda.is_available()),
             precision=(16 if torch.cuda.is_available() else 32),
-            checkpoint_callback=True,
             callbacks=[es, stop],
             max_epochs=21,
             accumulate_grad_batches=2,
diff --git a/tests/checkpointing/test_model_checkpoint.py b/tests/checkpointing/test_model_checkpoint.py
index ed2da81dbdeda..5363766b5969d 100644
--- a/tests/checkpointing/test_model_checkpoint.py
+++ b/tests/checkpointing/test_model_checkpoint.py
@@ -1007,17 +1007,17 @@ def test_configure_model_checkpoint(tmpdir):
     callback2 = ModelCheckpoint()
 
     # no callbacks
-    trainer = Trainer(checkpoint_callback=False, callbacks=[], **kwargs)
+    trainer = Trainer(enable_checkpointing=False, callbacks=[], **kwargs)
     assert not any(isinstance(c, ModelCheckpoint) for c in trainer.callbacks)
     assert trainer.checkpoint_callback is None
 
     # default configuration
-    trainer = Trainer(checkpoint_callback=True, callbacks=[], **kwargs)
+    trainer = Trainer(callbacks=[], **kwargs)
     assert sum(1 for c in trainer.callbacks if isinstance(c, ModelCheckpoint)) == 1
     assert isinstance(trainer.checkpoint_callback, ModelCheckpoint)
 
-    # custom callback passed to callbacks list, checkpoint_callback=True is ignored
-    trainer = Trainer(checkpoint_callback=True, callbacks=[callback1], **kwargs)
+    # custom callback passed to callbacks list, enable_checkpointing=True is ignored
+    trainer = Trainer(enable_checkpointing=True, callbacks=[callback1], **kwargs)
     assert [c for c in trainer.callbacks if isinstance(c, ModelCheckpoint)] == [callback1]
     assert trainer.checkpoint_callback == callback1
 
@@ -1026,8 +1026,8 @@ def test_configure_model_checkpoint(tmpdir):
     assert trainer.checkpoint_callback == callback1
     assert trainer.checkpoint_callbacks == [callback1, callback2]
 
-    with pytest.raises(MisconfigurationException, match="checkpoint_callback=False but found ModelCheckpoint"):
-        Trainer(checkpoint_callback=False, callbacks=[callback1], **kwargs)
+    with pytest.raises(MisconfigurationException, match="`enable_checkpointing=False` but found `ModelCheckpoint`"):
+        Trainer(enable_checkpointing=False, callbacks=[callback1], **kwargs)
 
 
 def test_val_check_interval_checkpoint_files(tmpdir):
@@ -1198,8 +1198,8 @@ def test_model_checkpoint_mode_options():
 
 def test_trainer_checkpoint_callback_bool(tmpdir):
     mc = ModelCheckpoint(dirpath=tmpdir)
-    with pytest.raises(MisconfigurationException, match="Invalid type provided for checkpoint_callback"):
-        Trainer(checkpoint_callback=mc)
+    with pytest.raises(MisconfigurationException, match="Invalid type provided for `enable_checkpointing`"):
+        Trainer(enable_checkpointing=mc)
 
 
 def test_check_val_every_n_epochs_top_k_integration(tmpdir):
diff --git a/tests/deprecated_api/test_remove_1-6.py b/tests/deprecated_api/test_remove_1-6.py
index f580d9b89f7f8..40326eebc1d08 100644
--- a/tests/deprecated_api/test_remove_1-6.py
+++ b/tests/deprecated_api/test_remove_1-6.py
@@ -30,7 +30,7 @@
 
 def test_v1_6_0_trainer_model_hook_mixin(tmpdir):
     model = BoringModel()
-    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, checkpoint_callback=False, logger=False)
+    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, enable_checkpointing=False, logger=False)
     trainer.fit(model)
     with pytest.deprecated_call(match="is deprecated in v1.4 and will be removed in v1.6"):
         trainer.is_function_implemented("training_step", model)
diff --git a/tests/deprecated_api/test_remove_1-7.py b/tests/deprecated_api/test_remove_1-7.py
index 30cb0269d72c5..58ffd65c6c318 100644
--- a/tests/deprecated_api/test_remove_1-7.py
+++ b/tests/deprecated_api/test_remove_1-7.py
@@ -257,6 +257,11 @@ def test_v1_7_0_deprecate_lightning_distributed(tmpdir):
         _ = LightningDistributed()
 
 
+def test_v1_7_0_checkpoint_callback_trainer_constructor(tmpdir):
+    with pytest.deprecated_call(match=r"Setting `Trainer\(checkpoint_callback=True\)` is deprecated in v1.5"):
+        _ = Trainer(checkpoint_callback=True)
+
+
 def test_v1_7_0_old_on_train_batch_start(tmpdir):
     class OldSignature(Callback):
         def on_train_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
diff --git a/tests/loggers/test_all.py b/tests/loggers/test_all.py
index 6cc8daa75f773..cc8a8e5b99809 100644
--- a/tests/loggers/test_all.py
+++ b/tests/loggers/test_all.py
@@ -338,7 +338,6 @@ def _test_logger_created_on_rank_zero_only(tmpdir, logger_class):
         accelerator="ddp_cpu",
         num_processes=2,
         max_steps=1,
-        checkpoint_callback=True,
         callbacks=[RankZeroLoggerCheck()],
     )
     trainer.fit(model)
diff --git a/tests/loggers/test_base.py b/tests/loggers/test_base.py
index 878ea1362b9ed..d535905930dd2 100644
--- a/tests/loggers/test_base.py
+++ b/tests/loggers/test_base.py
@@ -318,7 +318,7 @@ class _Test:
         limit_train_batches=0.1,
         limit_val_batches=0.1,
         num_sanity_val_steps=0,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
         enable_progress_bar=False,
         weights_summary=None,
     )
@@ -342,7 +342,7 @@ class _Test:
         limit_train_batches=0.1,
         limit_val_batches=0.1,
         num_sanity_val_steps=0,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
         enable_progress_bar=False,
         weights_summary=None,
     )
@@ -359,7 +359,7 @@ class _Test:
         limit_train_batches=0.1,
         limit_val_batches=0.1,
         num_sanity_val_steps=0,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
         enable_progress_bar=False,
         weights_summary=None,
     )
diff --git a/tests/loops/batch/test_truncated_bptt.py b/tests/loops/batch/test_truncated_bptt.py
index 874a621f8a485..de38800917398 100644
--- a/tests/loops/batch/test_truncated_bptt.py
+++ b/tests/loops/batch/test_truncated_bptt.py
@@ -101,7 +101,7 @@ def on_train_batch_start(self, *_, **__) -> None:
         max_epochs=2,
         weights_summary=None,
         logger=False,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
     trainer.fit(model)
 
@@ -142,7 +142,7 @@ def training_epoch_end(self, training_step_outputs):
         max_epochs=1,
         weights_summary=None,
         logger=False,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
     trainer.fit(model, train_dataloaders=train_dataloader)
 
@@ -166,7 +166,7 @@ def training_step(self, *args, **kwargs):
         max_epochs=2,
         log_every_n_steps=2,
         weights_summary=None,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
     trainer.fit(model)
     assert set(trainer.logged_metrics) == {"loss_step", "loss_epoch", "epoch"}
diff --git a/tests/loops/optimization/test_optimizer_loop.py b/tests/loops/optimization/test_optimizer_loop.py
index 4f7f8ac7d48ce..66a58524a399b 100644
--- a/tests/loops/optimization/test_optimizer_loop.py
+++ b/tests/loops/optimization/test_optimizer_loop.py
@@ -194,7 +194,7 @@ def configure_optimizers(self):
         limit_val_batches=0,
         num_sanity_val_steps=0,
         logger=False,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
     trainer.fit(model)
     weights_complete = model.parameters()
@@ -213,7 +213,7 @@ def configure_optimizers(self):
         limit_val_batches=0,
         num_sanity_val_steps=0,
         logger=False,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
     with pytest.raises(CustomException):
         trainer.fit(model)
@@ -234,7 +234,7 @@ def configure_optimizers(self):
         limit_val_batches=0,
         num_sanity_val_steps=0,
         logger=False,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
     trainer.fit(model)
     weights_resumed = model.parameters()
diff --git a/tests/loops/test_loops.py b/tests/loops/test_loops.py
index 23a709e3e8c94..e22e653827a0f 100644
--- a/tests/loops/test_loops.py
+++ b/tests/loops/test_loops.py
@@ -367,7 +367,7 @@ def configure_optimizers_multiple(self):
         accumulate_grad_batches=accumulate_grad_batches,
         enable_progress_bar=False,
         logger=False,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
 
     # simulate a failure
@@ -567,7 +567,6 @@ def train_dataloader(self):
         accumulate_grad_batches=accumulate_grad_batches,
         enable_progress_bar=False,
         logger=False,
-        checkpoint_callback=True,
     )
     trainer.fit(model)
 
diff --git a/tests/loops/test_training_loop_flow_scalar.py b/tests/loops/test_training_loop_flow_scalar.py
index 4f64a906646ba..d7dbecc82eaaf 100644
--- a/tests/loops/test_training_loop_flow_scalar.py
+++ b/tests/loops/test_training_loop_flow_scalar.py
@@ -297,7 +297,7 @@ def training_step(self, batch, batch_idx):
         max_epochs=4,
         weights_summary=None,
         logger=False,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
 
     Closure.warning_cache.clear()
@@ -343,7 +343,7 @@ def train_dataloader(self):
         max_epochs=4,
         weights_summary=None,
         logger=False,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
 
     with pytest.warns(UserWarning, match=r".*train_dataloader yielded None.*"):
diff --git a/tests/models/test_tpu.py b/tests/models/test_tpu.py
index 77fddd239bc47..b320842f5ae81 100644
--- a/tests/models/test_tpu.py
+++ b/tests/models/test_tpu.py
@@ -399,11 +399,11 @@ def test_tpu_precision_16_clip_gradients(mock_clip_grad_norm, clip_val, tmpdir):
 @RunIf(tpu=True)
 @pl_multi_process_test
 def test_if_test_works_with_checkpoint_false(tmpdir):
-    """Ensure that model trains properly when `checkpoint_callback` is set to False."""
+    """Ensure that model trains properly when `enable_checkpointing` is set to False."""
 
     # Train a model on TPU
     model = BoringModel()
-    trainer = Trainer(max_epochs=1, tpu_cores=8, default_root_dir=tmpdir, fast_dev_run=True, checkpoint_callback=False)
+    trainer = Trainer(max_epochs=1, tpu_cores=8, default_root_dir=tmpdir, fast_dev_run=True, enable_checkpointing=False)
     trainer.fit(model)
     assert trainer.state.finished, f"Training failed with {trainer.state}"
 
diff --git a/tests/trainer/connectors/test_callback_connector.py b/tests/trainer/connectors/test_callback_connector.py
index 41b3faa82f70d..34d5b8fa97afa 100644
--- a/tests/trainer/connectors/test_callback_connector.py
+++ b/tests/trainer/connectors/test_callback_connector.py
@@ -144,7 +144,7 @@ def _attach_callbacks(trainer_callbacks, model_callbacks):
         model = LightningModule()
         model.configure_callbacks = lambda: model_callbacks
         trainer = Trainer(
-            checkpoint_callback=False, enable_progress_bar=False, weights_summary=None, callbacks=trainer_callbacks
+            enable_checkpointing=False, enable_progress_bar=False, weights_summary=None, callbacks=trainer_callbacks
         )
         trainer.model = model
         cb_connector = CallbackConnector(trainer)
@@ -196,7 +196,7 @@ def test_attach_model_callbacks_override_info(caplog):
     """Test that the logs contain the info about overriding callbacks returned by configure_callbacks."""
     model = LightningModule()
     model.configure_callbacks = lambda: [LearningRateMonitor(), EarlyStopping()]
-    trainer = Trainer(checkpoint_callback=False, callbacks=[EarlyStopping(), LearningRateMonitor(), ProgressBar()])
+    trainer = Trainer(enable_checkpointing=False, callbacks=[EarlyStopping(), LearningRateMonitor(), ProgressBar()])
     trainer.model = model
     cb_connector = CallbackConnector(trainer)
     with caplog.at_level(logging.INFO):
diff --git a/tests/trainer/connectors/test_checkpoint_connector.py b/tests/trainer/connectors/test_checkpoint_connector.py
index 60ed5182b63f8..ff938598a4ada 100644
--- a/tests/trainer/connectors/test_checkpoint_connector.py
+++ b/tests/trainer/connectors/test_checkpoint_connector.py
@@ -41,7 +41,7 @@ def on_hpc_load(self, checkpoint):
 
 def test_hpc_hook_calls(tmpdir):
     model = HPCHookdedModel()
-    trainer = Trainer(default_root_dir=tmpdir, max_steps=1, checkpoint_callback=False, logger=False)
+    trainer = Trainer(default_root_dir=tmpdir, max_steps=1, enable_checkpointing=False, logger=False)
     trainer.fit(model)
     connector = trainer.checkpoint_connector
     connector.hpc_save(tmpdir, logger=Mock())
@@ -50,7 +50,7 @@ def test_hpc_hook_calls(tmpdir):
 
     # new training run, restore from hpc checkpoint file automatically
     assert set(os.listdir(tmpdir)) == {"hpc_ckpt_1.ckpt"}
-    trainer = Trainer(default_root_dir=tmpdir, max_steps=1, checkpoint_callback=False, logger=False)
+    trainer = Trainer(default_root_dir=tmpdir, max_steps=1, enable_checkpointing=False, logger=False)
     trainer.fit(model)
     assert model.hpc_save_called == 1
     assert model.hpc_load_called == 1
@@ -90,7 +90,7 @@ def test_preloaded_checkpoint_lifecycle(tmpdir):
 def test_hpc_restore_attempt(tmpdir):
     """Test that restore() attempts to restore the hpc_ckpt with highest priority."""
     model = BoringModel()
-    trainer = Trainer(default_root_dir=tmpdir, max_steps=1, checkpoint_callback=False, logger=False)
+    trainer = Trainer(default_root_dir=tmpdir, max_steps=1, enable_checkpointing=False, logger=False)
     trainer.fit(model)
 
     hpc_ckpt_path = tmpdir / "hpc_ckpt_3.ckpt"
@@ -102,7 +102,7 @@ def test_hpc_restore_attempt(tmpdir):
         torch.nn.init.constant_(param, 0)
 
     # case 1: restore hpc first, no explicit resume path provided
-    trainer = Trainer(default_root_dir=tmpdir, max_steps=2, checkpoint_callback=False, logger=False)
+    trainer = Trainer(default_root_dir=tmpdir, max_steps=2, enable_checkpointing=False, logger=False)
     trainer.fit(model)
 
     for param in model.parameters():
diff --git a/tests/trainer/logging_/test_logger_connector.py b/tests/trainer/logging_/test_logger_connector.py
index 690acff6ad5fa..818f67a9be20f 100644
--- a/tests/trainer/logging_/test_logger_connector.py
+++ b/tests/trainer/logging_/test_logger_connector.py
@@ -512,7 +512,7 @@ def _assert_called(model, fn, stage):
         max_epochs=1,
         enable_progress_bar=False,
         num_sanity_val_steps=2,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
     )
 
     trainer.fit(model)
diff --git a/tests/trainer/logging_/test_train_loop_logging.py b/tests/trainer/logging_/test_train_loop_logging.py
index 83a07ed7adcc5..57776c05efd72 100644
--- a/tests/trainer/logging_/test_train_loop_logging.py
+++ b/tests/trainer/logging_/test_train_loop_logging.py
@@ -481,7 +481,7 @@ def on_epoch_end(self, trainer: Trainer, model: LightningModule):
         max_epochs=2,
         limit_train_batches=1,
         limit_val_batches=0,
-        checkpoint_callback=False,
+        enable_checkpointing=False,
         logger=False,
         weights_summary=None,
     )
diff --git a/tests/trainer/properties/test_log_dir.py b/tests/trainer/properties/test_log_dir.py
index 0623cf0097280..277a2f105efd6 100644
--- a/tests/trainer/properties/test_log_dir.py
+++ b/tests/trainer/properties/test_log_dir.py
@@ -47,7 +47,7 @@ def test_logdir_no_checkpoint_cb(tmpdir):
     expected = os.path.join(tmpdir, "lightning_logs", "version_0")
     model = TestModel(expected)
 
-    trainer = Trainer(default_root_dir=tmpdir, max_steps=2, checkpoint_callback=False)
+    trainer = Trainer(default_root_dir=tmpdir, max_steps=2, enable_checkpointing=False)
 
     assert trainer.log_dir == expected
     trainer.fit(model)
@@ -71,7 +71,7 @@ def test_logdir_no_logger_no_checkpoint(tmpdir):
     expected = os.path.join(tmpdir)
     model = TestModel(expected)
 
-    trainer = Trainer(default_root_dir=tmpdir, max_steps=2, logger=False, checkpoint_callback=False)
+    trainer = Trainer(default_root_dir=tmpdir, max_steps=2, logger=False, enable_checkpointing=False)
 
     assert trainer.log_dir == expected
     trainer.fit(model)
diff --git a/tests/trainer/test_dataloaders.py b/tests/trainer/test_dataloaders.py
index 5c9aacc92a8ac..3b2b0e687aebb 100644
--- a/tests/trainer/test_dataloaders.py
+++ b/tests/trainer/test_dataloaders.py
@@ -335,11 +335,11 @@ def test_dataloaders_with_limit_val_batches(tmpdir, dataset, limit_val_batches):
 
     epoch_cb = Counter()
     callbacks = [epoch_cb]
-    checkpoint_callback = True
+    enable_checkpointing = False
     if limit_val_batches > 0:
         callbacks.append(ModelCheckpoint(monitor="val_log", save_top_k=1, mode="max", verbose=False))
-    else:
-        checkpoint_callback = False
+        enable_checkpointing = True
+
     epochs = 2
     trainer = Trainer(
         default_root_dir=tmpdir,
@@ -347,7 +347,7 @@ def test_dataloaders_with_limit_val_batches(tmpdir, dataset, limit_val_batches):
         max_epochs=epochs,
         callbacks=callbacks,
         limit_val_batches=limit_val_batches,
-        checkpoint_callback=checkpoint_callback,
+        enable_checkpointing=enable_checkpointing,
     )
     model = DummyModel()
 
diff --git a/tests/trainer/test_trainer.py b/tests/trainer/test_trainer.py
index 021812464e972..abf42085d3f93 100644
--- a/tests/trainer/test_trainer.py
+++ b/tests/trainer/test_trainer.py
@@ -1307,7 +1307,7 @@ def setup(self, trainer, model, stage):
 
     model = CurrentModel()
     callback = CurrentCallback()
-    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, checkpoint_callback=False, callbacks=[callback])
+    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, enable_checkpointing=False, callbacks=[callback])
 
     if stage == "fit":
         trainer.fit(model)
@@ -1884,7 +1884,7 @@ def test_on_load_checkpoint_missing_callbacks(tmpdir):
 def test_module_current_fx_attributes_reset(tmpdir):
     """Ensure that lightning module's attributes related to current fx are reset at the end of execution."""
     model = BoringModel()
-    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=1, checkpoint_callback=False, logger=False)
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=1, enable_checkpointing=False, logger=False)
 
     trainer.fit(model)
     assert model._current_fx_name is None
diff --git a/tests/trainer/test_trainer_cli.py b/tests/trainer/test_trainer_cli.py
index d2a4b462ceb77..6512170084a94 100644
--- a/tests/trainer/test_trainer_cli.py
+++ b/tests/trainer/test_trainer_cli.py
@@ -190,7 +190,7 @@ def test_argparse_args_parsing_gpus(cli_args, expected_parsed, expected_device_i
         ({}, {}),
         ({"logger": False}, {}),
         ({"logger": False}, {"logger": True}),
-        ({"logger": False}, {"checkpoint_callback": True}),
+        ({"logger": False}, {"enable_checkpointing": True}),
     ],
 )
 def test_init_from_argparse_args(cli_args, extra_args):
diff --git a/tests/utilities/test_cli.py b/tests/utilities/test_cli.py
index ace76bd9374c8..0e8e0dba768eb 100644
--- a/tests/utilities/test_cli.py
+++ b/tests/utilities/test_cli.py
@@ -202,7 +202,7 @@ def test_parse_args_parsing_gpus(monkeypatch, cli_args, expected_gpu):
         ({}, {}),
         (dict(logger=False), {}),
         (dict(logger=False), dict(logger=True)),
-        (dict(logger=False), dict(checkpoint_callback=True)),
+        (dict(logger=False), dict(enable_checkpointing=True)),
     ],
 )
 def test_init_from_argparse_args(cli_args, extra_args):