tests: add default_root_dir=tmpdir

tests/callbacks/test_callbacks.py

@@ -166,6 +166,7 @@ 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

@@ -66,6 +66,7 @@ def test_progress_bar_totals(tmpdir):
         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
@@ -182,6 +183,7 @@ 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

@@ -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)
@@ -107,6 +102,7 @@ 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

@@ -30,6 +30,7 @@ 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
@@ -66,6 +67,7 @@ def test_cpu_slurm_save_load(tmpdir):
         max_epochs=1,
         logger=logger,
         checkpoint_callback=ModelCheckpoint(tmpdir),
+        default_root_dir=tmpdir,
     )
     model = EvalModelTemplate(**hparams)
 
@@ -223,6 +225,7 @@ 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

@@ -89,6 +89,7 @@ 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

@@ -23,6 +23,7 @@ def on_before_zero_grad(self, optimizer):
         max_steps=max_steps,
         max_epochs=2,
         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
@@ -225,14 +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

@@ -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
@@ -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:
@@ -577,6 +576,7 @@ 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

tests/trainer/test_trainer.py

@@ -168,11 +168,13 @@ def _optimizer_step(self, epoch, batch_idx, optimizer,
     model = EvalModelTemplate()
     schedule = {1: 2, 3: 4}
 
-    trainer = Trainer(accumulate_grad_batches=schedule,
-                      limit_train_batches=0.1,
-                      limit_val_batches=0.1,
-                      max_epochs=2,
-                      default_root_dir=tmpdir)
+    trainer = Trainer(
+        accumulate_grad_batches=schedule,
+        limit_train_batches=0.1,
+        limit_val_batches=0.1,
+        max_epochs=2,
+        default_root_dir=tmpdir,
+    )
 
     # for the test
     trainer.optimizer_step = _optimizer_step
@@ -435,7 +437,7 @@ def test_trainer_max_steps_and_epochs(tmpdir):
     trainer_options.update(
         default_root_dir=tmpdir,
         max_epochs=3,
-        max_steps=num_train_samples + 10
+        max_steps=num_train_samples + 10,
     )
 
     # fit model
@@ -449,7 +451,7 @@ def test_trainer_max_steps_and_epochs(tmpdir):
     # define less train epochs than steps
     trainer_options.update(
         max_epochs=2,
-        max_steps=trainer_options['max_epochs'] * 2 * num_train_samples
+        max_steps=trainer_options['max_epochs'] * 2 * num_train_samples,
     )
 
     # fit model
@@ -472,7 +474,7 @@ def test_trainer_min_steps_and_epochs(tmpdir):
         early_stop_callback=EarlyStopping(monitor='val_loss', min_delta=1.0),
         val_check_interval=2,
         min_epochs=1,
-        max_epochs=7
+        max_epochs=7,
     )
 
     # define less min steps than 1 epoch