tests: add default_root_dir=tmpdir

tests/callbacks/test_callbacks.py

@@ -165,6 +165,7 @@ def on_test_end(self, trainer, pl_module):
         limit_val_batches=0.1,
         limit_train_batches=0.2,
         progress_bar_refresh_rate=0,
+        default_root_dir=tmpdir,
     )
 
     assert not test_callback.setup_called

tests/callbacks/test_progress_bar.py

@@ -54,7 +54,7 @@ def test_progress_bar_misconfiguration():
         Trainer(callbacks=callbacks)
 
 
-def test_progress_bar_totals():
+def test_progress_bar_totals(tmpdir):
     """Test that the progress finishes with the correct total steps processed."""
 
     model = EvalModelTemplate()
@@ -63,6 +63,7 @@ def test_progress_bar_totals():
         progress_bar_refresh_rate=1,
         limit_val_batches=1.0,
         max_epochs=1,
+        default_root_dir=tmpdir,
     )
     bar = trainer.progress_bar_callback
     assert 0 == bar.total_train_batches
@@ -136,7 +137,7 @@ def test_progress_bar_fast_dev_run():
 
 
 @pytest.mark.parametrize('refresh_rate', [0, 1, 50])
-def test_progress_bar_progress_refresh(refresh_rate):
+def test_progress_bar_progress_refresh(tmpdir, refresh_rate):
     """Test that the three progress bars get correctly updated when using different refresh rates."""
 
     model = EvalModelTemplate()
@@ -177,6 +178,7 @@ def on_test_batch_end(self, trainer, pl_module):
         limit_train_batches=1.0,
         num_sanity_val_steps=2,
         max_epochs=3,
+        default_root_dir=tmpdir,
     )
     assert trainer.progress_bar_callback.refresh_rate == refresh_rate
 

tests/loggers/test_all.py

@@ -54,6 +54,7 @@ def log_metrics(self, metrics, step):
         limit_train_batches=0.2,
         limit_val_batches=0.5,
         fast_dev_run=True,
+        default_root_dir=tmpdir,
     )
     trainer.fit(model)
 

tests/loggers/test_base.py

@@ -102,7 +102,7 @@ def test_multiple_loggers(tmpdir):
     assert logger2.finalized_status == "success"
 
 
-def test_multiple_loggers_pickle(tmpdir):
+def test_multiple_loggers_pickle():
     """Verify that pickling trainer with multiple loggers works."""
 
     logger1 = CustomLogger()

tests/loggers/test_tensorboard.py

@@ -17,7 +17,10 @@
 def test_tensorboard_hparams_reload(tmpdir):
     model = EvalModelTemplate()
 
-    trainer = Trainer(max_epochs=1, default_root_dir=tmpdir)
+    trainer = Trainer(
+        max_epochs=1,
+        default_root_dir=tmpdir,
+    )
     trainer.fit(model)
 
     folder_path = trainer.logger.log_dir

tests/models/test_amp.py

@@ -21,7 +21,7 @@ def test_amp_single_gpu(tmpdir, backend):
         max_epochs=1,
         gpus=1,
         distributed_backend=backend,
-        precision=16
+        precision=16,
     )
 
     model = EvalModelTemplate()
@@ -39,18 +39,15 @@ def test_amp_multi_gpu(tmpdir, backend):
     tutils.set_random_master_port()
 
     model = EvalModelTemplate()
-
-    trainer_options = dict(
+    # tutils.run_model_test(trainer_options, model)
+    trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=1,
         # gpus=2,
         gpus='0, 1',  # test init with gpu string
         distributed_backend=backend,
         precision=16,
     )
-
-    # tutils.run_model_test(trainer_options, model)
-    trainer = Trainer(**trainer_options)
     result = trainer.fit(model)
     assert result
 
@@ -66,17 +63,15 @@ def test_multi_gpu_wandb(tmpdir, backend):
     model = EvalModelTemplate()
     logger = WandbLogger(name='utest')
 
-    trainer_options = dict(
+    # tutils.run_model_test(trainer_options, model)
+    trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=1,
         gpus=2,
         distributed_backend=backend,
         precision=16,
         logger=logger,
-
     )
-    # tutils.run_model_test(trainer_options, model)
-    trainer = Trainer(**trainer_options)
     result = trainer.fit(model)
     assert result
     trainer.test(model)
@@ -106,6 +101,7 @@ def test_amp_gpu_ddp_slurm_managed(tmpdir):
         precision=16,
         checkpoint_callback=checkpoint,
         logger=logger,
+        default_root_dir=tmpdir,
     )
     trainer.is_slurm_managing_tasks = True
     result = trainer.fit(model)

tests/models/test_cpu.py

@@ -29,6 +29,7 @@ def test_cpu_slurm_save_load(tmpdir):
         limit_train_batches=0.2,
         limit_val_batches=0.2,
         checkpoint_callback=ModelCheckpoint(tmpdir),
+        default_root_dir=tmpdir,
     )
     result = trainer.fit(model)
     real_global_step = trainer.global_step
@@ -64,6 +65,7 @@ def test_cpu_slurm_save_load(tmpdir):
         max_epochs=1,
         logger=logger,
         checkpoint_callback=ModelCheckpoint(tmpdir),
+        default_root_dir=tmpdir,
     )
     model = EvalModelTemplate(**hparams)
 
@@ -220,6 +222,7 @@ def test_running_test_no_val(tmpdir):
         checkpoint_callback=checkpoint,
         logger=logger,
         early_stop_callback=False,
+        default_root_dir=tmpdir,
     )
     result = trainer.fit(model)
 

tests/models/test_gpu.py

@@ -39,18 +39,16 @@ def test_multi_gpu_model(tmpdir, backend):
     """Make sure DDP works."""
     tutils.set_random_master_port()
 
-    trainer_options = dict(
+    model = EvalModelTemplate()
+    # tutils.run_model_test(trainer_options, model)
+    trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=1,
         limit_train_batches=0.4,
         limit_val_batches=0.2,
         gpus=[0, 1],
         distributed_backend=backend,
     )
-
-    model = EvalModelTemplate()
-    # tutils.run_model_test(trainer_options, model)
-    trainer = Trainer(**trainer_options)
     result = trainer.fit(model)
     assert result
 
@@ -64,7 +62,11 @@ def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
     """Make sure DDP works with dataloaders passed to fit()"""
     tutils.set_random_master_port()
 
-    trainer_options = dict(
+    model = EvalModelTemplate()
+    fit_options = dict(train_dataloader=model.train_dataloader(),
+                       val_dataloaders=model.val_dataloader())
+
+    trainer = Trainer(
         default_root_dir=tmpdir,
         progress_bar_refresh_rate=0,
         max_epochs=1,
@@ -73,12 +75,6 @@ def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
         gpus=[0, 1],
         distributed_backend='ddp'
     )
-
-    model = EvalModelTemplate()
-    fit_options = dict(train_dataloader=model.train_dataloader(),
-                       val_dataloaders=model.val_dataloader())
-
-    trainer = Trainer(**trainer_options)
     result = trainer.fit(model, **fit_options)
     assert result == 1, "DDP doesn't work with dataloaders passed to fit()."
 

tests/models/test_grad_norm.py

@@ -88,6 +88,7 @@ def test_grad_tracking(tmpdir, norm_type, rtol=5e-3):
         logger=logger,
         track_grad_norm=norm_type,
         row_log_interval=1,  # request grad_norms every batch
+        default_root_dir=tmpdir,
     )
     result = trainer.fit(model)
 

tests/models/test_hooks.py

@@ -8,7 +8,7 @@
 
 
 @pytest.mark.parametrize('max_steps', [1, 2, 3])
-def test_on_before_zero_grad_called(max_steps):
+def test_on_before_zero_grad_called(tmpdir, max_steps):
 
     class CurrentTestModel(EvalModelTemplate):
         on_before_zero_grad_called = 0
@@ -21,6 +21,7 @@ def on_before_zero_grad(self, optimizer):
     trainer = Trainer(
         max_steps=max_steps,
         num_sanity_val_steps=5,
+        default_root_dir=tmpdir,
     )
     assert 0 == model.on_before_zero_grad_called
     trainer.fit(model)

tests/models/test_horovod.py

@@ -146,7 +146,8 @@ def validation_step(self, batch, *args, **kwargs):
 def test_horovod_multi_optimizer(tmpdir):
     model = TestGAN(**EvalModelTemplate.get_default_hparams())
 
-    trainer_options = dict(
+    # fit model
+    trainer = Trainer(
         default_root_dir=str(tmpdir),
         progress_bar_refresh_rate=0,
         max_epochs=1,
@@ -155,9 +156,6 @@ def test_horovod_multi_optimizer(tmpdir):
         deterministic=True,
         distributed_backend='horovod',
     )
-
-    # fit model
-    trainer = Trainer(**trainer_options)
     result = trainer.fit(model)
     assert result == 1, 'model failed to complete'
 

tests/models/test_restore.py

@@ -38,6 +38,7 @@ def test_running_test_pretrained_model_distrib(tmpdir, backend):
         logger=logger,
         gpus=[0, 1],
         distributed_backend=backend,
+        default_root_dir=tmpdir,
     )
 
     # fit model
@@ -84,6 +85,7 @@ def test_running_test_pretrained_model_cpu(tmpdir):
         limit_val_batches=0.2,
         checkpoint_callback=checkpoint,
         logger=logger,
+        default_root_dir=tmpdir,
     )
 
     # fit model
@@ -154,6 +156,7 @@ def test_dp_resume(tmpdir):
         max_epochs=1,
         gpus=2,
         distributed_backend='dp',
+        default_root_dir=tmpdir,
     )
 
     # get logger
@@ -224,14 +227,13 @@ def test_model_saving_loading(tmpdir):
     # logger file to get meta
     logger = tutils.get_default_logger(tmpdir)
 
-    trainer_options = dict(
+    # fit model
+    trainer = Trainer(
         max_epochs=1,
         logger=logger,
-        checkpoint_callback=ModelCheckpoint(tmpdir)
+        checkpoint_callback=ModelCheckpoint(tmpdir),
+        default_root_dir=tmpdir,
     )
-
-    # fit model
-    trainer = Trainer(**trainer_options)
     result = trainer.fit(model)
 
     # traning complete

tests/trainer/test_dataloaders.py

@@ -199,7 +199,7 @@ def test_all_dataloaders_passed_to_fit(tmpdir, ckpt_path):
         default_root_dir=tmpdir,
         max_epochs=1,
         limit_val_batches=0.1,
-        limit_train_batches=0.2
+        limit_train_batches=0.2,
     )
     fit_options = dict(train_dataloader=model.dataloader(train=True),
                        val_dataloaders=model.dataloader(train=False))
@@ -232,7 +232,7 @@ def test_multiple_dataloaders_passed_to_fit(tmpdir, ckpt_path):
         default_root_dir=tmpdir,
         max_epochs=1,
         limit_val_batches=0.1,
-        limit_train_batches=0.2
+        limit_train_batches=0.2,
     )
     fit_options = dict(train_dataloader=model.dataloader(train=True),
                        val_dataloaders=[model.dataloader(train=False),
@@ -336,7 +336,7 @@ def test_mixing_of_dataloader_options(tmpdir, ckpt_path):
         default_root_dir=tmpdir,
         max_epochs=1,
         limit_val_batches=0.1,
-        limit_train_batches=0.2
+        limit_train_batches=0.2,
     )
 
     # fit model
@@ -401,7 +401,7 @@ def test_inf_train_dataloader(tmpdir, check_interval):
     trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=1,
-        val_check_interval=check_interval
+        val_check_interval=check_interval,
     )
     result = trainer.fit(model)
     # verify training completed
@@ -440,7 +440,7 @@ def test_error_on_zero_len_dataloader(tmpdir):
             max_epochs=1,
             limit_train_batches=0.1,
             limit_val_batches=0.1,
-            limit_test_batches=0.1
+            limit_test_batches=0.1,
         )
         trainer.fit(model)
 
@@ -453,13 +453,6 @@ def test_warning_with_few_workers(tmpdir, ckpt_path):
     model = EvalModelTemplate()
 
     # logger file to get meta
-    trainer_options = dict(
-        default_root_dir=tmpdir,
-        max_epochs=1,
-        limit_val_batches=0.1,
-        limit_train_batches=0.2
-    )
-
     train_dl = model.dataloader(train=True)
     train_dl.num_workers = 0
 
@@ -471,7 +464,12 @@ def test_warning_with_few_workers(tmpdir, ckpt_path):
 
     fit_options = dict(train_dataloader=train_dl,
                        val_dataloaders=val_dl)
-    trainer = Trainer(**trainer_options)
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=1,
+        limit_val_batches=0.1,
+        limit_train_batches=0.2,
+    )
 
     # fit model
     with pytest.warns(UserWarning, match='train'):
@@ -488,7 +486,7 @@ def test_warning_with_few_workers(tmpdir, ckpt_path):
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason='Test requires multiple GPUs')
-def test_dataloader_reinit_for_subclass():
+def test_dataloader_reinit_for_subclass(tmpdir):
 
     class CustomDataLoader(torch.utils.data.DataLoader):
         def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None,
@@ -505,6 +503,7 @@ def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None,
         gpus=[0, 1],
         num_nodes=1,
         distributed_backend='ddp',
+        default_root_dir=tmpdir,
     )
 
     class CustomDummyObj:
@@ -534,7 +533,7 @@ class CustomSampler(torch.utils.data.Sampler):
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 3, reason='Test requires multiple GPUs')
-def test_batch_size_smaller_than_num_gpus():
+def test_batch_size_smaller_than_num_gpus(tmpdir):
     # we need at least 3 gpus for this test
     num_gpus = 3
     batch_size = 3
@@ -576,6 +575,7 @@ def train_dataloader(self):
         limit_train_batches=0.1,
         limit_val_batches=0,
         gpus=num_gpus,
+        default_root_dir=tmpdir,
     )
 
     # we expect the reduction for the metrics also to happen on the last batch