missing tests default_root_dir=tmpdir (#6314)

* default_root_dir=tmpdir

* miss

tests/models/test_restore.py

@@ -167,10 +167,7 @@ def test_callbacks_state_resume_from_checkpoint(tmpdir):
     def get_trainer_args():
         checkpoint = ModelCheckpoint(dirpath=tmpdir, monitor="val_loss", save_last=True)
         trainer_args = dict(
-            default_root_dir=tmpdir, max_steps=1, logger=False, callbacks=[
-                checkpoint,
-                callback_capture,
-            ]
+            default_root_dir=tmpdir, max_steps=1, logger=False, callbacks=[checkpoint, callback_capture]
         )
         assert checkpoint.best_model_path == ""
         assert checkpoint.best_model_score is None

tests/models/test_sync_batchnorm.py

@@ -105,8 +105,16 @@ def test_sync_batchnorm_ddp(tmpdir):
     dm.setup(stage=None)
 
     model = SyncBNModule(gpu_count=2, bn_targets=bn_outputs)
+    ddp = DDPSpawnPlugin(
+        parallel_devices=[torch.device("cuda", 0), torch.device("cuda", 1)],
+        num_nodes=1,
+        sync_batchnorm=True,
+        cluster_environment=TorchElasticEnvironment(),
+        find_unused_parameters=True
+    )
 
     trainer = Trainer(
+        default_root_dir=tmpdir,
         gpus=2,
         num_nodes=1,
         accelerator='ddp_spawn',
@@ -115,15 +123,7 @@ def test_sync_batchnorm_ddp(tmpdir):
         sync_batchnorm=True,
         num_sanity_val_steps=0,
         replace_sampler_ddp=False,
-        plugins=[
-            DDPSpawnPlugin(
-                parallel_devices=[torch.device("cuda", 0), torch.device("cuda", 1)],
-                num_nodes=1,
-                sync_batchnorm=True,
-                cluster_environment=TorchElasticEnvironment(),
-                find_unused_parameters=True
-            )
-        ]
+        plugins=[ddp]
     )
 
     trainer.fit(model, dm)

tests/plugins/test_deepspeed_plugin.py

@@ -133,7 +133,11 @@ def test_deepspeed_precision_choice(amp_backend, tmpdir):
     """
 
     trainer = Trainer(
-        fast_dev_run=True, default_root_dir=tmpdir, plugins='deepspeed', amp_backend=amp_backend, precision=16
+        fast_dev_run=True,
+        default_root_dir=tmpdir,
+        plugins='deepspeed',
+        amp_backend=amp_backend,
+        precision=16,
     )
 
     assert isinstance(trainer.accelerator.training_type_plugin, DeepSpeedPlugin)
@@ -178,13 +182,11 @@ def test_deepspeed_defaults(tmpdir):
 
 @RunIf(deepspeed=True)
 def test_invalid_deepspeed_defaults_no_precision(tmpdir):
-    """
-        Test to ensure that using defaults, if precision is not set to 16, we throw an exception.
-    """
+    """Test to ensure that using defaults, if precision is not set to 16, we throw an exception."""
     model = BoringModel()
     trainer = Trainer(
-        fast_dev_run=True,
         default_root_dir=tmpdir,
+        fast_dev_run=True,
         plugins='deepspeed',
     )
     with pytest.raises(
@@ -195,27 +197,29 @@ def test_invalid_deepspeed_defaults_no_precision(tmpdir):
 
 @RunIf(min_gpus=1, deepspeed=True)
 def test_warn_deepspeed_override_backward(tmpdir):
-    """
-        Test to ensure that if the backward hook in the LightningModule is overridden, we throw a warning.
-    """
+    """Test to ensure that if the backward hook in the LightningModule is overridden, we throw a warning."""
 
     class TestModel(BoringModel):
 
         def backward(self, loss: Tensor, optimizer: Optimizer, optimizer_idx: int, *args, **kwargs) -> None:
             return loss.backward()
 
     model = TestModel()
-    trainer = Trainer(fast_dev_run=True, default_root_dir=tmpdir, plugins=DeepSpeedPlugin(), gpus=1, precision=16)
+    trainer = Trainer(
+        fast_dev_run=True,
+        default_root_dir=tmpdir,
+        plugins=DeepSpeedPlugin(),
+        gpus=1,
+        precision=16,
+    )
     with pytest.warns(UserWarning, match='Overridden backward hook in the LightningModule will be ignored'):
         trainer.fit(model)
 
 
 @RunIf(min_gpus=1, deepspeed=True)
 def test_deepspeed_run_configure_optimizers(tmpdir):
-    """
-        Test end to end that deepspeed works with defaults (without ZeRO as that requires compilation),
-        whilst using configure_optimizers for optimizers and schedulers.
-    """
+    """Test end to end that deepspeed works with defaults (without ZeRO as that requires compilation),
+        whilst using configure_optimizers for optimizers and schedulers."""
 
     class TestModel(BoringModel):
 
@@ -234,7 +238,7 @@ def on_train_start(self) -> None:
         default_root_dir=tmpdir,
         gpus=1,
         fast_dev_run=True,
-        precision=16
+        precision=16,
     )
 
     trainer.fit(model)
@@ -267,7 +271,7 @@ def on_train_start(self) -> None:
         default_root_dir=tmpdir,
         gpus=1,
         fast_dev_run=True,
-        precision=16
+        precision=16,
     )
 
     trainer.fit(model)
@@ -278,9 +282,7 @@ def on_train_start(self) -> None:
 
 @RunIf(min_gpus=1, deepspeed=True)
 def test_deepspeed_custom_precision_params(tmpdir):
-    """
-        Ensure if we modify the FP16 parameters via the DeepSpeedPlugin, the deepspeed config contains these changes.
-    """
+    """Ensure if we modify the FP16 parameters via the DeepSpeedPlugin, the deepspeed config contains these changes."""
 
     class TestModel(BoringModel):
 
@@ -293,24 +295,15 @@ def on_train_start(self) -> None:
             raise SystemExit()
 
     model = TestModel()
-    trainer = Trainer(
-        plugins=[
-            DeepSpeedPlugin(
-                loss_scale=10, initial_scale_power=10, loss_scale_window=10, hysteresis=10, min_loss_scale=10
-            )
-        ],
-        precision=16,
-        gpus=1
-    )
+    ds = DeepSpeedPlugin(loss_scale=10, initial_scale_power=10, loss_scale_window=10, hysteresis=10, min_loss_scale=10)
+    trainer = Trainer(default_root_dir=tmpdir, plugins=[ds], precision=16, gpus=1)
     with pytest.raises(SystemExit):
         trainer.fit(model)
 
 
 @RunIf(min_gpus=1, deepspeed=True)
 def test_deepspeed_assert_config_zero_offload_disabled(tmpdir, deepspeed_zero_config):
-    """
-        Ensure if we use a config and turn off cpu_offload, that this is set to False within the config.
-    """
+    """Ensure if we use a config and turn off cpu_offload, that this is set to False within the config."""
 
     deepspeed_zero_config['zero_optimization']['cpu_offload'] = False
 
@@ -321,7 +314,12 @@ def on_train_start(self) -> None:
             raise SystemExit()
 
     model = TestModel()
-    trainer = Trainer(plugins=[DeepSpeedPlugin(config=deepspeed_zero_config)], precision=16, gpus=1)
+    trainer = Trainer(
+        plugins=[DeepSpeedPlugin(config=deepspeed_zero_config)],
+        precision=16,
+        gpus=1,
+        default_root_dir=tmpdir,
+    )
     with pytest.raises(SystemExit):
         trainer.fit(model)
 

tests/plugins/test_rpc_plugin.py

@@ -38,6 +38,7 @@ def on_fit_start(self, trainer, pl_module):
 
     model = BoringModel()
     trainer = Trainer(
+        default_root_dir=str(tmpdir),
         fast_dev_run=True,
         gpus=gpus,
         num_processes=num_processes,
@@ -76,7 +77,8 @@ def test_rpc_function_calls_ddp(tmpdir):
         max_epochs=max_epochs,
         gpus=2,
         distributed_backend='ddp',
-        plugins=[plugin]
+        plugins=[plugin],
+        default_root_dir=tmpdir,
     )
 
     trainer.fit(model)

tests/trainer/legacy_deprecate_flow_log/test_trainer_steps_scalar_return.py

@@ -75,7 +75,7 @@ def training_step_scalar_with_step_end(tmpdir):
     model.training_step_end = model.training_step_end__scalar
     model.val_dataloader = None
 
-    trainer = Trainer(fast_dev_run=True, weights_summary=None)
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, weights_summary=None)
     trainer.fit(model)
 
     # make sure correct steps were called
@@ -165,7 +165,11 @@ def test_train_step_epoch_end_scalar(tmpdir):
     model.training_epoch_end = model.training_epoch_end__scalar
     model.val_dataloader = None
 
-    trainer = Trainer(max_epochs=1, weights_summary=None)
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=1,
+        weights_summary=None,
+    )
     trainer.fit(model)
 
     # make sure correct steps were called
@@ -222,13 +226,13 @@ def test_dpp_reduce_mean_pbar(tmpdir):
 
     trainer = Trainer(
         max_epochs=1,
-        default_root_dir=os.getcwd(),
+        default_root_dir=tmpdir,
         limit_train_batches=10,
         limit_test_batches=2,
         limit_val_batches=2,
         accelerator=distributed_backend,
         gpus=2,
-        precision=32
+        precision=32,
     )
 
     trainer.fit(model)

tests/trainer/logging_/test_logger_connector.py

@@ -478,6 +478,7 @@ def test_auto_add_dataloader_idx(tmpdir, add_dataloader_idx):
     """ test that auto_add_dataloader_idx argument works """
 
     class TestModel(BoringModel):
+
         def val_dataloader(self):
             dl = super().val_dataloader()
             return [dl, dl]
@@ -495,10 +496,7 @@ def validation_step(self, *args, **kwargs):
     model = TestModel()
     model.validation_epoch_end = None
 
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        max_steps=5
-    )
+    trainer = Trainer(default_root_dir=tmpdir, max_steps=5)
     trainer.fit(model)
     logged = trainer.logged_metrics
 

tests/trainer/logging_/test_train_loop_logging_1_0.py

@@ -411,7 +411,7 @@ def val_dataloader(self):
     assert generated == expected
 
 
-def test_validation_step_with_string_data_logging():
+def test_validation_step_with_string_data_logging(tmpdir):
 
     class TestModel(BoringModel):
 
@@ -436,7 +436,7 @@ def validation_step(self, batch, batch_idx):
     # model
     model = TestModel()
     trainer = Trainer(
-        default_root_dir=os.getcwd(),
+        default_root_dir=tmpdir,
         limit_train_batches=1,
         limit_val_batches=1,
         max_epochs=1,
@@ -491,7 +491,7 @@ def validation_step(self, batch, batch_idx):
     # model
     model = TestModel()
     trainer = Trainer(
-        default_root_dir=os.getcwd(),
+        default_root_dir=tmpdir,
         limit_train_batches=1,
         limit_val_batches=1,
         max_epochs=1,

tests/trainer/test_trainer.py

@@ -472,7 +472,11 @@ def on_load_checkpoint(self, _):
         state = pl_load(ckpt)
 
         # Resume training
-        new_trainer = Trainer(resume_from_checkpoint=ckpt, max_epochs=2)
+        new_trainer = Trainer(
+            default_root_dir=tmpdir,
+            resume_from_checkpoint=ckpt,
+            max_epochs=2,
+        )
         new_trainer.fit(next_model)
         assert state["global_step"] + next_model.num_batches_seen == trainer.num_training_batches * trainer.max_epochs
         assert next_model.num_on_load_checkpoint_called == 1

tests/trainer/test_trainer_tricks.py

@@ -34,7 +34,12 @@ def test_num_training_batches(tmpdir):
     """
     # when we have fewer batches in the dataloader we should use those instead of the limit
     model = EvalModelTemplate()
-    trainer = Trainer(limit_val_batches=100, limit_train_batches=100, max_epochs=1)
+    trainer = Trainer(
+        limit_val_batches=100,
+        limit_train_batches=100,
+        max_epochs=1,
+        default_root_dir=tmpdir,
+    )
     trainer.fit(model)
 
     assert len(model.train_dataloader()) == 10
@@ -45,7 +50,12 @@ def test_num_training_batches(tmpdir):
 
     # when we have more batches in the dataloader we should limit them
     model = EvalModelTemplate()
-    trainer = Trainer(limit_val_batches=7, limit_train_batches=7, max_epochs=1)
+    trainer = Trainer(
+        limit_val_batches=7,
+        limit_train_batches=7,
+        max_epochs=1,
+        default_root_dir=tmpdir,
+    )
     trainer.fit(model)
 
     assert len(model.train_dataloader()) == 10