update usage of deprecated checkpoint_callback

tests/backends/test_tpu_backend.py

@@ -39,7 +39,12 @@ 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,
+        checkpoint_callback=True,
+        max_epochs=1,
+        default_root_dir=tmpdir,
+    )
     result = trainer.fit(model)
 
     assert result == 1

tests/callbacks/test_early_stopping.py

@@ -56,8 +56,7 @@ def test_resume_early_stopping_from_checkpoint(tmpdir):
     early_stop_callback = EarlyStoppingTestRestore()
     trainer = Trainer(
         default_root_dir=tmpdir,
-        checkpoint_callback=checkpoint_callback,
-        callbacks=[early_stop_callback],
+        callbacks=[early_stop_callback, checkpoint_callback],
         num_sanity_val_steps=0,
         max_epochs=4,
     )

tests/checkpointing/test_checkpoint_callback_frequency.py

@@ -129,7 +129,7 @@ def training_step(self, batch, batch_idx):
 
     model = TestModel()
     trainer = Trainer(
-        checkpoint_callback=callbacks.ModelCheckpoint(dirpath=tmpdir, monitor='my_loss', save_top_k=k),
+        callbacks=[callbacks.ModelCheckpoint(dirpath=tmpdir, monitor='my_loss', save_top_k=k)],
         default_root_dir=tmpdir,
         max_epochs=epochs,
         weights_summary=None,

tests/checkpointing/test_model_checkpoint.py

@@ -897,7 +897,7 @@ def test_configure_model_checkpoint(tmpdir):
     assert trainer.checkpoint_callbacks == [callback1, callback2]
 
     with pytest.warns(DeprecationWarning, match='will no longer be supported in v1.3'):
-        trainer = Trainer(checkpoint_callback=callback1, callbacks=[], **kwargs)
+        trainer = Trainer(checkpoint_callback=callback1, **kwargs)
         assert [c for c in trainer.callbacks if isinstance(c, ModelCheckpoint)] == [callback1]
         assert trainer.checkpoint_callback == callback1
 

tests/core/test_datamodules.py

@@ -243,7 +243,7 @@ def test_dm_checkpoint_save(tmpdir):
         default_root_dir=tmpdir,
         max_epochs=3,
         weights_summary=None,
-        checkpoint_callback=ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on')
+        callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on')],
     )
 
     # fit model

tests/models/data/horovod/train_default_model.py

@@ -49,7 +49,7 @@ def run_test_from_config(trainer_options):
     reset_seed()
 
     ckpt_path = trainer_options['weights_save_path']
-    trainer_options.update(checkpoint_callback=ModelCheckpoint(dirpath=ckpt_path))
+    trainer_options.update(callbacks=[ModelCheckpoint(dirpath=ckpt_path)])
 
     model = EvalModelTemplate()
 

tests/models/test_amp.py

@@ -129,7 +129,7 @@ def test_amp_gpu_ddp_slurm_managed(tmpdir):
         gpus=[0],
         accelerator='ddp_spawn',
         precision=16,
-        checkpoint_callback=checkpoint,
+        callbacks=[checkpoint],
         logger=logger,
     )
     trainer.is_slurm_managing_tasks = True

tests/models/test_cpu.py

@@ -43,7 +43,7 @@ def test_cpu_slurm_save_load(enable_pl_optimizer, tmpdir):
         logger=logger,
         limit_train_batches=0.2,
         limit_val_batches=0.2,
-        checkpoint_callback=ModelCheckpoint(dirpath=tmpdir),
+        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
         enable_pl_optimizer=enable_pl_optimizer,
     )
     result = trainer.fit(model)
@@ -80,7 +80,7 @@ def test_cpu_slurm_save_load(enable_pl_optimizer, tmpdir):
         default_root_dir=tmpdir,
         max_epochs=1,
         logger=logger,
-        checkpoint_callback=ModelCheckpoint(dirpath=tmpdir),
+        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
         enable_pl_optimizer=enable_pl_optimizer,
     )
     model = EvalModelTemplate(**hparams)
@@ -208,7 +208,7 @@ def test_running_test_after_fitting(tmpdir):
         limit_train_batches=0.4,
         limit_val_batches=0.2,
         limit_test_batches=0.2,
-        checkpoint_callback=checkpoint,
+        callbacks=[checkpoint],
         logger=logger,
     )
     result = trainer.fit(model)
@@ -239,7 +239,7 @@ def test_running_test_no_val(tmpdir):
         limit_train_batches=0.4,
         limit_val_batches=0.2,
         limit_test_batches=0.2,
-        checkpoint_callback=checkpoint,
+        callbacks=[checkpoint],
         logger=logger,
     )
     result = trainer.fit(model)

tests/models/test_restore.py

@@ -159,7 +159,7 @@ def test_running_test_pretrained_model_distrib_dp(tmpdir):
         max_epochs=2,
         limit_train_batches=0.4,
         limit_val_batches=0.2,
-        checkpoint_callback=checkpoint,
+        callbacks=[checkpoint],
         logger=logger,
         gpus=[0, 1],
         accelerator='dp',
@@ -209,7 +209,7 @@ def test_running_test_pretrained_model_distrib_ddp_spawn(tmpdir):
         max_epochs=2,
         limit_train_batches=0.4,
         limit_val_batches=0.2,
-        checkpoint_callback=checkpoint,
+        callbacks=[checkpoint],
         logger=logger,
         gpus=[0, 1],
         accelerator='ddp_spawn',
@@ -257,7 +257,7 @@ def test_running_test_pretrained_model_cpu(tmpdir):
         max_epochs=3,
         limit_train_batches=0.4,
         limit_val_batches=0.2,
-        checkpoint_callback=checkpoint,
+        callbacks=[checkpoint],
         logger=logger,
         default_root_dir=tmpdir,
     )
@@ -288,7 +288,7 @@ def test_load_model_from_checkpoint(tmpdir, model_template):
         max_epochs=2,
         limit_train_batches=0.4,
         limit_val_batches=0.2,
-        checkpoint_callback=ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on', save_top_k=-1),
+        callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on', save_top_k=-1)],
         default_root_dir=tmpdir,
     )
 
@@ -404,8 +404,10 @@ def test_model_saving_loading(tmpdir):
 
     # fit model
     trainer = Trainer(
-        max_epochs=1, logger=logger,
-        checkpoint_callback=ModelCheckpoint(dirpath=tmpdir), default_root_dir=tmpdir,
+        max_epochs=1,
+        logger=logger,
+        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
+        default_root_dir=tmpdir,
     )
     result = trainer.fit(model)
 
@@ -460,7 +462,7 @@ def test_strict_model_load_more_params(monkeypatch, tmpdir, tmpdir_server, url_c
     # fit model
     trainer = Trainer(
         default_root_dir=tmpdir, max_epochs=1, logger=logger,
-        checkpoint_callback=ModelCheckpoint(dirpath=tmpdir),
+        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
     )
     result = trainer.fit(model)
 
@@ -500,7 +502,7 @@ def test_strict_model_load_less_params(monkeypatch, tmpdir, tmpdir_server, url_c
     # fit model
     trainer = Trainer(
         default_root_dir=tmpdir, max_epochs=1, logger=logger,
-        checkpoint_callback=ModelCheckpoint(dirpath=tmpdir),
+        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
     )
     result = trainer.fit(model)
 

tests/trainer/logging_tests/test_eval_loop_logging_1_0.py

@@ -26,6 +26,7 @@
 from torch.utils.data import DataLoader, Dataset
 
 from pytorch_lightning import Trainer, callbacks, seed_everything
+from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.loggers import TensorBoardLogger
 from tests.base import BoringModel, RandomDataset, SimpleModule
@@ -291,7 +292,7 @@ def validation_epoch_end(self, outputs) -> None:
         max_epochs=1,
         log_every_n_steps=1,
         weights_summary=None,
-        checkpoint_callback=callbacks.ModelCheckpoint(dirpath='val_loss')
+        callbacks=[ModelCheckpoint(dirpath='val_loss')],
     )
     trainer.fit(model)
 
@@ -358,7 +359,7 @@ def test_monitor_val_epoch_end(tmpdir):
     trainer = Trainer(
         max_epochs=epoch_min_loss_override + 2,
         logger=False,
-        checkpoint_callback=checkpoint_callback,
+        callbacks=[checkpoint_callback],
     )
     trainer.fit(model)
 

tests/trainer/logging_tests/test_train_loop_logging_1_0.py

@@ -27,6 +27,7 @@
 
 import pytorch_lightning as pl
 from pytorch_lightning import Trainer, callbacks
+from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.core.lightning import LightningModule
 from tests.base.boring_model import BoringModel, RandomDictDataset, RandomDictStringDataset
 from tests.base.deterministic_model import DeterministicModel
@@ -88,7 +89,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         max_epochs=2,
         log_every_n_steps=1,
         weights_summary=None,
-        checkpoint_callback=callbacks.ModelCheckpoint(monitor='l_se')
+        callbacks=[ModelCheckpoint(monitor='l_se')],
     )
     trainer.fit(model)
 

tests/trainer/test_trainer.py

@@ -55,7 +55,7 @@ def test_no_val_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
         default_root_dir=tmpdir,
         max_epochs=1,
         logger=logger,
-        checkpoint_callback=ModelCheckpoint(dirpath=tmpdir),
+        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
     )
     # fit model
     result = trainer.fit(model)
@@ -101,7 +101,7 @@ def test_no_val_end_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
         default_root_dir=tmpdir,
         max_epochs=1,
         logger=logger,
-        checkpoint_callback=ModelCheckpoint(dirpath=tmpdir),
+        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
     )
     result = trainer.fit(model)
 
@@ -145,7 +145,7 @@ def test_strict_model_load(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
         default_root_dir=tmpdir,
         max_epochs=1,
         logger=logger,
-        checkpoint_callback=ModelCheckpoint(dirpath=tmpdir),
+        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
     )
     result = trainer.fit(model)
 
@@ -462,7 +462,7 @@ def test_model_checkpoint_only_weights(tmpdir):
     trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=1,
-        checkpoint_callback=ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on', save_weights_only=True),
+        callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on', save_weights_only=True)],
     )
     # fit model
     result = trainer.fit(model)
@@ -539,7 +539,7 @@ def increment_on_load_checkpoint(self, _):
         max_epochs=2,
         limit_train_batches=0.65,
         limit_val_batches=1,
-        checkpoint_callback=ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on', save_top_k=-1),
+        callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on', save_top_k=-1)],
         default_root_dir=tmpdir,
         val_check_interval=1.0,
         enable_pl_optimizer=enable_pl_optimizer,
@@ -718,7 +718,7 @@ def test_test_checkpoint_path(tmpdir, ckpt_path, save_top_k):
         max_epochs=2,
         progress_bar_refresh_rate=0,
         default_root_dir=tmpdir,
-        checkpoint_callback=ModelCheckpoint(monitor="early_stop_on", save_top_k=save_top_k),
+        callbacks=[ModelCheckpoint(monitor="early_stop_on", save_top_k=save_top_k)],
     )
     trainer.fit(model)
     if ckpt_path == "best":