Reduce log output size in special tests (#7481)

Author: awaelchli

tests/callbacks/test_stochastic_weight_avg.py

@@ -117,6 +117,7 @@ def train_with_swa(tmpdir, batchnorm=True, accelerator=None, gpus=None, num_proc
 
     trainer = Trainer(
         default_root_dir=tmpdir,
+        progress_bar_refresh_rate=0,
         max_epochs=max_epochs,
         limit_train_batches=5,
         limit_val_batches=0,

tests/checkpointing/test_checkpoint_callback_frequency.py

@@ -131,6 +131,7 @@ def training_epoch_end(self, outputs) -> None:
     trainer = Trainer(
         callbacks=[callbacks.ModelCheckpoint(dirpath=tmpdir, monitor='my_loss_step', save_top_k=k, mode="max")],
         default_root_dir=tmpdir,
+        progress_bar_refresh_rate=0,
         max_epochs=epochs,
         weights_summary=None,
         val_check_interval=val_check_interval,

tests/plugins/test_deepspeed_plugin.py

@@ -392,11 +392,12 @@ def on_before_accelerator_backend_setup(self, trainer, pl_module) -> None:
 
     model = BoringModel()
     trainer = Trainer(
+        default_root_dir=tmpdir,
+        progress_bar_refresh_rate=0,
         max_epochs=1,
         plugins=[DeepSpeedPlugin(config=deepspeed_zero_config)],
         precision=16,
         gpus=1,
-        default_root_dir=tmpdir,
         callbacks=[TestCallback()]
     )
     with pytest.raises(SystemExit):
@@ -410,8 +411,8 @@ def test_deepspeed_multigpu(tmpdir, deepspeed_config):
     """
     model = BoringModel()
     trainer = Trainer(
-        plugins=[DeepSpeedPlugin(zero_optimization=False, stage=2)],
         default_root_dir=tmpdir,
+        plugins=[DeepSpeedPlugin(zero_optimization=False, stage=2)],
         gpus=2,
         fast_dev_run=True,
         precision=16,
@@ -489,8 +490,8 @@ def test_deepspeed_multigpu_stage_3(tmpdir, deepspeed_config):
     """
     model = ModelParallelBoringModel()
     trainer = Trainer(
-        plugins=[DeepSpeedPlugin(stage=3)],
         default_root_dir=tmpdir,
+        plugins=[DeepSpeedPlugin(stage=3)],
         gpus=2,
         fast_dev_run=True,
         precision=16,
@@ -507,9 +508,10 @@ def run_checkpoint_test(tmpdir, save_full_weights):
     dm = ClassifDataModule()
     ck = ModelCheckpoint(monitor="val_acc", mode="max", save_last=True, save_top_k=-1)
     trainer = Trainer(
+        default_root_dir=tmpdir,
+        progress_bar_refresh_rate=0,
         max_epochs=10,
         plugins=[DeepSpeedPlugin(stage=3, save_full_weights=save_full_weights)],
-        default_root_dir=tmpdir,
         gpus=2,
         precision=16,
         accumulate_grad_batches=2,
@@ -525,9 +527,9 @@ def run_checkpoint_test(tmpdir, save_full_weights):
     assert saved_results == results
 
     trainer = Trainer(
+        default_root_dir=tmpdir,
         max_epochs=10,
         plugins=[DeepSpeedPlugin(stage=3, save_full_weights=save_full_weights)],
-        default_root_dir=tmpdir,
         gpus=2,
         precision=16,
         accumulate_grad_batches=2,
@@ -579,6 +581,8 @@ def on_train_batch_start(
     model = ModelParallelClassificationModel()
     dm = ClassifDataModule()
     trainer = Trainer(
+        default_root_dir=tmpdir,
+        progress_bar_refresh_rate=0,
         max_epochs=5,
         plugins=[DeepSpeedPlugin(stage=2, cpu_offload=cpu_offload)],
         gpus=2,
@@ -597,8 +601,8 @@ def test_deepspeed_multigpu_test(tmpdir, deepspeed_config):
     """
     model = ModelParallelBoringModel()
     trainer = Trainer(
-        plugins=[DeepSpeedPlugin(stage=3)],
         default_root_dir=tmpdir,
+        plugins=[DeepSpeedPlugin(stage=3)],
         gpus=2,
         fast_dev_run=True,
         precision=16,

tests/test_profiler.py

@@ -303,6 +303,7 @@ def test_pytorch_profiler_trainer_ddp(tmpdir, pytorch_profiler):
     model = BoringModel()
     trainer = Trainer(
         default_root_dir=tmpdir,
+        progress_bar_refresh_rate=0,
         max_epochs=1,
         limit_train_batches=5,
         limit_val_batches=5,