diff --git a/pytorch_lightning/callbacks/model_checkpoint.py b/pytorch_lightning/callbacks/model_checkpoint.py
index 672670da72b55f..6bc64bdcbd8a85 100644
--- a/pytorch_lightning/callbacks/model_checkpoint.py
+++ b/pytorch_lightning/callbacks/model_checkpoint.py
@@ -16,6 +16,7 @@
 from pytorch_lightning import _logger as log
 from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.utilities import rank_zero_warn, rank_zero_only
+from pytorch_lightning.utilities.io import gfile
 
 
 class ModelCheckpoint(Callback):
@@ -97,7 +98,9 @@ def __init__(self, filepath: Optional[str] = None, monitor: str = 'val_loss', ve
                  save_last: bool = False, save_top_k: int = 1, save_weights_only: bool = False,
                  mode: str = 'auto', period: int = 1, prefix: str = ''):
         super().__init__()
-        if save_top_k > 0 and filepath is not None and os.path.isdir(filepath) and len(os.listdir(filepath)) > 0:
+        if(filepath):
+            filepath = str(filepath)  # the tests pass in a py.path.local but we want a str
+        if save_top_k > 0 and filepath is not None and gfile.isdir(filepath) and len(gfile.listdir(filepath)) > 0:
             rank_zero_warn(
                 f"Checkpoint directory {filepath} exists and is not empty with save_top_k != 0."
                 "All files in this directory will be deleted when a checkpoint is saved!"
@@ -109,12 +112,13 @@ def __init__(self, filepath: Optional[str] = None, monitor: str = 'val_loss', ve
         if filepath is None:  # will be determined by trainer at runtime
             self.dirpath, self.filename = None, None
         else:
-            if os.path.isdir(filepath):
+            if gfile.isdir(filepath):
                 self.dirpath, self.filename = filepath, '{epoch}'
             else:
                 filepath = os.path.realpath(filepath)
                 self.dirpath, self.filename = os.path.split(filepath)
-            os.makedirs(self.dirpath, exist_ok=True)
+            if not gfile.exists(self.dirpath):
+                gfile.makedirs(self.dirpath)
         self.save_last = save_last
         self.save_top_k = save_top_k
         self.save_weights_only = save_weights_only
@@ -156,12 +160,19 @@ def kth_best_model(self):
         return self.kth_best_model_path
 
     def _del_model(self, filepath):
-        if os.path.isfile(filepath):
-            os.remove(filepath)
+        if gfile.exists(filepath):
+            try:
+                # in compat mode, remove is not implemented so if running this
+                # against an actual remove file system and the correct remote
+                # dependencies exist then this will work fine.
+                gfile.remove(filepath)
+            except AttributeError:
+                os.remove(filepath)
 
     def _save_model(self, filepath):
         # make paths
-        os.makedirs(os.path.dirname(filepath), exist_ok=True)
+        if not gfile.exists(os.path.dirname(filepath)):
+            gfile.makedirs(os.path.dirname(filepath))
 
         # delegate the saving to the model
         if self.save_function is not None:
@@ -249,7 +260,7 @@ def on_validation_end(self, trainer, pl_module):
 
         filepath = self.format_checkpoint_name(epoch, metrics)
         version_cnt = 0
-        while os.path.isfile(filepath):
+        while gfile.exists(filepath):
             filepath = self.format_checkpoint_name(epoch, metrics, ver=version_cnt)
             # this epoch called before
             version_cnt += 1
diff --git a/pytorch_lightning/core/saving.py b/pytorch_lightning/core/saving.py
index ed299bb8a816e0..542bcb230aa9a5 100644
--- a/pytorch_lightning/core/saving.py
+++ b/pytorch_lightning/core/saving.py
@@ -12,6 +12,11 @@
 from pytorch_lightning.utilities import rank_zero_warn, AttributeDict
 from pytorch_lightning.utilities.io import load as pl_load
 
+# we want this for tf.iogfile, which if tf is installed gives full tf,
+# otherwise gives a pruned down version which works for some file backends but
+# not all
+from tensorboard.compat import tf
+
 PRIMITIVE_TYPES = (bool, int, float, str)
 ALLOWED_CONFIG_TYPES = (AttributeDict, dict, Namespace)
 try:
@@ -269,11 +274,11 @@ def load_hparams_from_tags_csv(tags_csv: str) -> Dict[str, Any]:
     True
     >>> os.remove(path_csv)
     """
-    if not os.path.isfile(tags_csv):
+    if not tf.io.gfile.exists(tags_csv):
         rank_zero_warn(f'Missing Tags: {tags_csv}.', RuntimeWarning)
         return {}
 
-    with open(tags_csv) as fp:
+    with tf.io.gfile.GFile(tags_csv, "r") as fp:
         csv_reader = csv.reader(fp, delimiter=',')
         tags = {row[0]: convert(row[1]) for row in list(csv_reader)[1:]}
 
@@ -281,13 +286,13 @@ def load_hparams_from_tags_csv(tags_csv: str) -> Dict[str, Any]:
 
 
 def save_hparams_to_tags_csv(tags_csv: str, hparams: Union[dict, Namespace]) -> None:
-    if not os.path.isdir(os.path.dirname(tags_csv)):
+    if not tf.io.gfile.isdir(os.path.dirname(tags_csv)):
         raise RuntimeError(f'Missing folder: {os.path.dirname(tags_csv)}.')
 
     if isinstance(hparams, Namespace):
         hparams = vars(hparams)
 
-    with open(tags_csv, 'w') as fp:
+    with tf.io.gfile.GFile(tags_csv, 'w') as fp:
         fieldnames = ['key', 'value']
         writer = csv.DictWriter(fp, fieldnames=fieldnames)
         writer.writerow({'key': 'key', 'value': 'value'})
@@ -306,24 +311,24 @@ def load_hparams_from_yaml(config_yaml: str) -> Dict[str, Any]:
     True
     >>> os.remove(path_yaml)
     """
-    if not os.path.isfile(config_yaml):
+    if not tf.io.gfile.exists(config_yaml):
         rank_zero_warn(f'Missing Tags: {config_yaml}.', RuntimeWarning)
         return {}
 
-    with open(config_yaml) as fp:
+    with tf.io.gfile.GFile(config_yaml, "r") as fp:
         tags = yaml.load(fp, Loader=yaml.SafeLoader)
 
     return tags
 
 
 def save_hparams_to_yaml(config_yaml, hparams: Union[dict, Namespace]) -> None:
-    if not os.path.isdir(os.path.dirname(config_yaml)):
+    if not tf.io.gfile.isdir(os.path.dirname(config_yaml)):
         raise RuntimeError(f'Missing folder: {os.path.dirname(config_yaml)}.')
 
     if isinstance(hparams, Namespace):
         hparams = vars(hparams)
 
-    with open(config_yaml, 'w', newline='') as fp:
+    with tf.io.gfile.GFile(config_yaml, 'w') as fp:
         yaml.dump(hparams, fp)
 
 
diff --git a/pytorch_lightning/loggers/tensorboard.py b/pytorch_lightning/loggers/tensorboard.py
index 12ce47398d3b37..a82c601176bc31 100644
--- a/pytorch_lightning/loggers/tensorboard.py
+++ b/pytorch_lightning/loggers/tensorboard.py
@@ -17,6 +17,7 @@
 from pytorch_lightning.core.saving import save_hparams_to_yaml
 from pytorch_lightning.loggers.base import LightningLoggerBase
 from pytorch_lightning.utilities import rank_zero_only
+from pytorch_lightning.utilities.io import gfile
 
 
 class TensorBoardLogger(LightningLoggerBase):
@@ -97,7 +98,8 @@ def experiment(self) -> SummaryWriter:
         if self._experiment is not None:
             return self._experiment
 
-        os.makedirs(self.root_dir, exist_ok=True)
+        if not gfile.exists(self.root_dir):
+            gfile.makedirs(self.root_dir)
         self._experiment = SummaryWriter(log_dir=self.log_dir, **self._kwargs)
         return self._experiment
 
@@ -145,7 +147,7 @@ def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) ->
     def save(self) -> None:
         super().save()
         dir_path = self.log_dir
-        if not os.path.isdir(dir_path):
+        if not gfile.isdir(dir_path):
             dir_path = self.save_dir
 
         # prepare the file path
@@ -171,13 +173,13 @@ def version(self) -> int:
     def _get_next_version(self):
         root_dir = os.path.join(self.save_dir, self.name)
 
-        if not os.path.isdir(root_dir):
+        if not gfile.isdir(root_dir):
             log.warning('Missing logger folder: %s', root_dir)
             return 0
 
         existing_versions = []
-        for d in os.listdir(root_dir):
-            if os.path.isdir(os.path.join(root_dir, d)) and d.startswith("version_"):
+        for d in gfile.listdir(root_dir):
+            if gfile.isdir(os.path.join(root_dir, d)) and d.startswith("version_"):
                 existing_versions.append(int(d.split("_")[1]))
 
         if len(existing_versions) == 0:
diff --git a/pytorch_lightning/trainer/callback_config.py b/pytorch_lightning/trainer/callback_config.py
index 5e490a106826bd..a42043255d13f1 100644
--- a/pytorch_lightning/trainer/callback_config.py
+++ b/pytorch_lightning/trainer/callback_config.py
@@ -6,6 +6,7 @@
 from pytorch_lightning.callbacks import Callback, ModelCheckpoint, EarlyStopping, ProgressBarBase, ProgressBar
 from pytorch_lightning.loggers import LightningLoggerBase
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from pytorch_lightning.utilities.io import gfile
 
 
 class TrainerCallbackConfigMixin(ABC):
@@ -67,7 +68,8 @@ def configure_checkpoint_callback(self):
             monitor_key = 'loss' if train_step_only else 'val_loss'
 
             if self.checkpoint_callback is True:
-                os.makedirs(ckpt_path, exist_ok=True)
+                if not gfile.exists(ckpt_path):
+                    gfile.makedirs(ckpt_path)
                 self.checkpoint_callback = ModelCheckpoint(
                     filepath=ckpt_path,
                     monitor=monitor_key
@@ -77,7 +79,9 @@ def configure_checkpoint_callback(self):
                     and self.checkpoint_callback.dirpath is None:
                 self.checkpoint_callback.dirpath = ckpt_path
                 self.checkpoint_callback.filename = '{epoch}'
-                os.makedirs(self.checkpoint_callback.dirpath, exist_ok=True)
+                if not gfile.exists(self.checkpoint_callback.dirpath):
+                    gfile.makedirs(self.checkpoint_callback.dirpath)
+
         elif self.checkpoint_callback is False:
             self.checkpoint_callback = None
 
diff --git a/pytorch_lightning/trainer/training_io.py b/pytorch_lightning/trainer/training_io.py
index 6f4e85d5b28e44..39e45a134a4cb4 100644
--- a/pytorch_lightning/trainer/training_io.py
+++ b/pytorch_lightning/trainer/training_io.py
@@ -102,6 +102,7 @@
 )
 from pytorch_lightning.utilities import rank_zero_warn, parsing
 from pytorch_lightning.utilities.io import load as pl_load
+from pytorch_lightning.utilities.io import gfile
 
 try:
     import torch_xla
@@ -375,8 +376,8 @@ def restore_hpc_weights_if_needed(self, model: LightningModule):
 
         # look for hpc weights
         folderpath = self.weights_save_path
-        if os.path.exists(folderpath):
-            files = os.listdir(folderpath)
+        if gfile.exists(folderpath):
+            files = gfile.listdir(folderpath)
             hpc_weight_paths = [x for x in files if 'hpc_ckpt' in x]
 
             # if hpc weights exist restore model
@@ -451,15 +452,16 @@ def restore_training_state(self, checkpoint):
     # ----------------------------------
     def hpc_save(self, folderpath: str, logger):
         # make sure the checkpoint folder exists
-        os.makedirs(folderpath, exist_ok=True)
+        if not gfile.exists(folderpath):
+            gfile.makedirs(folderpath)
 
         # save logger to make sure we get all the metrics
         logger.save()
 
         ckpt_number = self.max_ckpt_in_folder(folderpath) + 1
 
-        if not os.path.exists(folderpath):
-            os.makedirs(folderpath, exist_ok=True)
+        if not gfile.exists(folderpath):
+            gfile.makedirs(folderpath)
         filepath = os.path.join(folderpath, f'hpc_ckpt_{ckpt_number}.ckpt')
 
         # give model a chance to do something on hpc_save
@@ -509,7 +511,7 @@ def hpc_load(self, folderpath, on_gpu):
         log.info(f'restored hpc model from: {filepath}')
 
     def max_ckpt_in_folder(self, path, name_key='ckpt_'):
-        files = os.listdir(path)
+        files = gfile.listdir(path)
         files = [x for x in files if name_key in x]
         if len(files) == 0:
             return 0
diff --git a/pytorch_lightning/utilities/io.py b/pytorch_lightning/utilities/io.py
index b2f9ffa788541f..c24ba2ae8edf46 100644
--- a/pytorch_lightning/utilities/io.py
+++ b/pytorch_lightning/utilities/io.py
@@ -2,6 +2,13 @@
 
 from urllib.parse import urlparse
 
+# we want this for tf.io.gfile, which if tf is installed gives full tf,
+# otherwise gives a pruned down version which works for some file backends but
+# not all
+from tensorboard.compat import tf
+
+gfile = tf.io.gfile
+
 
 def load(path_or_url: str, map_location=None):
     parsed = urlparse(path_or_url)
diff --git a/tests/loggers/test_all.py b/tests/loggers/test_all.py
index f8a8fead41f586..a6d0f683516e11 100644
--- a/tests/loggers/test_all.py
+++ b/tests/loggers/test_all.py
@@ -46,7 +46,7 @@ def log_metrics(self, metrics, step):
             super().log_metrics(metrics, step)
             self.history.append((step, metrics))
 
-    logger_args = _get_logger_args(logger_class, tmpdir)
+    logger_args = _get_logger_args(logger_class, str(tmpdir))
     logger = StoreHistoryLogger(**logger_args)
 
     trainer = Trainer(
@@ -82,7 +82,7 @@ def test_loggers_pickle(tmpdir, monkeypatch, logger_class):
     import atexit
     monkeypatch.setattr(atexit, 'register', lambda _: None)
 
-    logger_args = _get_logger_args(logger_class, tmpdir)
+    logger_args = _get_logger_args(logger_class, str(tmpdir))
     logger = logger_class(**logger_args)
 
     # test pickling loggers
@@ -109,7 +109,7 @@ def test_logger_reset_correctly(tmpdir, extra_params):
     model = EvalModelTemplate()
 
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         **extra_params
     )
     logger1 = trainer.logger
diff --git a/tests/models/test_cpu.py b/tests/models/test_cpu.py
index 051db81d1b1657..0656723b3f334c 100644
--- a/tests/models/test_cpu.py
+++ b/tests/models/test_cpu.py
@@ -19,7 +19,7 @@ def test_cpu_slurm_save_load(tmpdir):
     model = EvalModelTemplate(**hparams)
 
     # logger file to get meta
-    logger = tutils.get_default_logger(tmpdir)
+    logger = tutils.get_default_logger(str(tmpdir))
     version = logger.version
 
     # fit model
@@ -28,7 +28,7 @@ def test_cpu_slurm_save_load(tmpdir):
         logger=logger,
         train_percent_check=0.2,
         val_percent_check=0.2,
-        checkpoint_callback=ModelCheckpoint(tmpdir)
+        checkpoint_callback=ModelCheckpoint(str(tmpdir)),
     )
     result = trainer.fit(model)
     real_global_step = trainer.global_step
@@ -54,17 +54,13 @@ def test_cpu_slurm_save_load(tmpdir):
 
     # test HPC saving
     # simulate snapshot on slurm
-    saved_filepath = trainer.hpc_save(tmpdir, logger)
+    saved_filepath = trainer.hpc_save(str(tmpdir), logger)
     assert os.path.exists(saved_filepath)
 
     # new logger file to get meta
-    logger = tutils.get_default_logger(tmpdir, version=version)
+    logger = tutils.get_default_logger(str(tmpdir), version=version)
 
-    trainer = Trainer(
-        max_epochs=1,
-        logger=logger,
-        checkpoint_callback=ModelCheckpoint(tmpdir),
-    )
+    trainer = Trainer(max_epochs=1, logger=logger, checkpoint_callback=ModelCheckpoint(str(tmpdir)),)
     model = EvalModelTemplate(**hparams)
 
     # set the epoch start hook so we can predict before the model does the full training
@@ -87,7 +83,7 @@ def test_early_stopping_cpu_model(tmpdir):
     """Test each of the trainer options."""
     stopping = EarlyStopping(monitor='val_loss', min_delta=0.1)
     trainer_options = dict(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         early_stop_callback=stopping,
         max_epochs=2,
         gradient_clip_val=1.0,
@@ -116,7 +112,7 @@ def test_multi_cpu_model_ddp(tmpdir):
     tutils.set_random_master_port()
 
     trainer_options = dict(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         progress_bar_refresh_rate=0,
         max_epochs=1,
         train_percent_check=0.4,
@@ -133,7 +129,7 @@ def test_multi_cpu_model_ddp(tmpdir):
 def test_lbfgs_cpu_model(tmpdir):
     """Test each of the trainer options."""
     trainer_options = dict(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1,
         progress_bar_refresh_rate=0,
         weights_summary='top',
@@ -152,7 +148,7 @@ def test_lbfgs_cpu_model(tmpdir):
 def test_default_logger_callbacks_cpu_model(tmpdir):
     """Test each of the trainer options."""
     trainer_options = dict(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1,
         gradient_clip_val=1.0,
         overfit_pct=0.20,
@@ -174,14 +170,14 @@ def test_running_test_after_fitting(tmpdir):
     model = EvalModelTemplate()
 
     # logger file to get meta
-    logger = tutils.get_default_logger(tmpdir)
+    logger = tutils.get_default_logger(str(tmpdir))
 
     # logger file to get weights
     checkpoint = tutils.init_checkpoint_callback(logger)
 
     # fit model
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         progress_bar_refresh_rate=0,
         max_epochs=2,
         train_percent_check=0.4,
@@ -205,7 +201,7 @@ def test_running_test_no_val(tmpdir):
     model = EvalModelTemplate()
 
     # logger file to get meta
-    logger = tutils.get_default_logger(tmpdir)
+    logger = tutils.get_default_logger(str(tmpdir))
 
     # logger file to get weights
     checkpoint = tutils.init_checkpoint_callback(logger)
@@ -284,7 +280,7 @@ def test_simple_cpu(tmpdir):
 
     # fit model
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1,
         val_percent_check=0.1,
         train_percent_check=0.1,
@@ -298,7 +294,7 @@ def test_simple_cpu(tmpdir):
 def test_cpu_model(tmpdir):
     """Make sure model trains on CPU."""
     trainer_options = dict(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         progress_bar_refresh_rate=0,
         max_epochs=1,
         train_percent_check=0.4,
@@ -313,7 +309,7 @@ def test_cpu_model(tmpdir):
 def test_all_features_cpu_model(tmpdir):
     """Test each of the trainer options."""
     trainer_options = dict(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         gradient_clip_val=1.0,
         overfit_pct=0.20,
         track_grad_norm=2,
@@ -387,7 +383,7 @@ def train_dataloader(self):
 
     # fit model
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1,
         truncated_bptt_steps=truncated_bptt_steps,
         val_percent_check=0,
diff --git a/tests/models/test_gpu.py b/tests/models/test_gpu.py
index 80249a727ccbbd..90f543bc2fb1c2 100644
--- a/tests/models/test_gpu.py
+++ b/tests/models/test_gpu.py
@@ -18,7 +18,7 @@
 def test_single_gpu_model(tmpdir, gpus):
     """Make sure single GPU works (DP mode)."""
     trainer_options = dict(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         progress_bar_refresh_rate=0,
         max_epochs=1,
         train_percent_check=0.1,
@@ -38,7 +38,7 @@ def test_multi_gpu_model(tmpdir, backend):
     tutils.set_random_master_port()
 
     trainer_options = dict(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1,
         train_percent_check=0.4,
         val_percent_check=0.2,
@@ -84,7 +84,7 @@ def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
 def test_multi_gpu_none_backend(tmpdir):
     """Make sure when using multiple GPUs the user can't use `distributed_backend = None`."""
     trainer_options = dict(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         progress_bar_refresh_rate=0,
         max_epochs=1,
         train_percent_check=0.1,
diff --git a/tests/trainer/test_lr_finder.py b/tests/trainer/test_lr_finder.py
index d0becff0918c65..c635d87788363b 100755
--- a/tests/trainer/test_lr_finder.py
+++ b/tests/trainer/test_lr_finder.py
@@ -14,7 +14,7 @@ def test_error_on_more_than_1_optimizer(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1
     )
 
@@ -29,7 +29,7 @@ def test_model_reset_correctly(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1
     )
 
@@ -51,7 +51,7 @@ def test_trainer_reset_correctly(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1
     )
 
@@ -81,7 +81,7 @@ def test_trainer_arg_bool(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=2,
         auto_lr_find=True
     )
@@ -100,7 +100,7 @@ def test_trainer_arg_str(tmpdir):
     before_lr = model.my_fancy_lr
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=2,
         auto_lr_find='my_fancy_lr'
     )
@@ -120,7 +120,7 @@ def test_call_to_trainer_method(tmpdir):
     before_lr = hparams.get('learning_rate')
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=2,
     )
 
@@ -144,7 +144,7 @@ def test_accumulation_and_early_stopping(tmpdir):
     before_lr = hparams.get('learning_rate')
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         accumulate_grad_batches=2,
     )
 
@@ -167,7 +167,7 @@ def test_suggestion_parameters_work(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=3,
     )
 
@@ -187,7 +187,7 @@ def test_suggestion_with_non_finite_values(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=3
     )
 
diff --git a/tests/trainer/test_trainer.py b/tests/trainer/test_trainer.py
index f391260c139f42..0c1063ea4ca8ee 100644
--- a/tests/trainer/test_trainer.py
+++ b/tests/trainer/test_trainer.py
@@ -30,12 +30,12 @@ def test_no_val_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
     model = EvalModelTemplate()
 
     # logger file to get meta
-    logger = tutils.get_default_logger(tmpdir)
+    logger = tutils.get_default_logger(str(tmpdir))
 
     trainer = Trainer(
         max_epochs=1,
         logger=logger,
-        checkpoint_callback=ModelCheckpoint(tmpdir)
+        checkpoint_callback=ModelCheckpoint(str(tmpdir))
     )
     # fit model
     result = trainer.fit(model)
@@ -51,7 +51,7 @@ def test_no_val_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
     assert LightningModule.CHECKPOINT_KEY_HYPER_PARAMS in ckpt.keys(), 'module_arguments missing from checkpoints'
 
     # load new model
-    hparams_path = tutils.get_data_path(logger, path_dir=tmpdir)
+    hparams_path = tutils.get_data_path(logger, path_dir=str(tmpdir))
     hparams_path = os.path.join(hparams_path, 'hparams.yaml')
     ckpt_path = f'http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}' if url_ckpt else new_weights_path
     model_2 = EvalModelTemplate.load_from_checkpoint(
@@ -65,18 +65,18 @@ def test_no_val_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
 def test_no_val_end_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
     """Tests use case where trainer saves the model, and user loads it from tags independently."""
     # set $TORCH_HOME, which determines torch hub's cache path, to tmpdir
-    monkeypatch.setenv('TORCH_HOME', tmpdir)
+    monkeypatch.setenv('TORCH_HOME', str(tmpdir))
 
     model = EvalModelTemplate()
 
     # logger file to get meta
-    logger = tutils.get_default_logger(tmpdir)
+    logger = tutils.get_default_logger(str(tmpdir))
 
     # fit model
     trainer = Trainer(
         max_epochs=1,
         logger=logger,
-        checkpoint_callback=ModelCheckpoint(tmpdir)
+        checkpoint_callback=ModelCheckpoint(str(tmpdir))
     )
     result = trainer.fit(model)
 
@@ -88,7 +88,7 @@ def test_no_val_end_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
     trainer.save_checkpoint(new_weights_path)
 
     # load new model
-    hparams_path = tutils.get_data_path(logger, path_dir=tmpdir)
+    hparams_path = tutils.get_data_path(logger, path_dir=str(tmpdir))
     hparams_path = os.path.join(hparams_path, 'hparams.yaml')
     ckpt_path = f'http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}' if url_ckpt else new_weights_path
     model_2 = EvalModelTemplate.load_from_checkpoint(
@@ -163,7 +163,7 @@ def _optimizer_step(self, epoch, batch_idx, optimizer,
                       train_percent_check=0.1,
                       val_percent_check=0.1,
                       max_epochs=2,
-                      default_root_dir=tmpdir)
+                      default_root_dir=str(tmpdir))
 
     # for the test
     trainer.optimizer_step = _optimizer_step
@@ -179,13 +179,13 @@ def test_loading_meta_tags(tmpdir):
     hparams = EvalModelTemplate.get_default_hparams()
 
     # save tags
-    logger = tutils.get_default_logger(tmpdir)
+    logger = tutils.get_default_logger(str(tmpdir))
     logger.log_hyperparams(Namespace(some_str='a_str', an_int=1, a_float=2.0))
     logger.log_hyperparams(hparams)
     logger.save()
 
     # load hparams
-    path_expt_dir = tutils.get_data_path(logger, path_dir=tmpdir)
+    path_expt_dir = tutils.get_data_path(logger, path_dir=str(tmpdir))
     hparams_path = os.path.join(path_expt_dir, TensorBoardLogger.NAME_HPARAMS_FILE)
     hparams = load_hparams_from_yaml(hparams_path)
 
@@ -204,13 +204,13 @@ def test_loading_yaml(tmpdir):
     hparams = EvalModelTemplate.get_default_hparams()
 
     # save tags
-    logger = tutils.get_default_logger(tmpdir)
+    logger = tutils.get_default_logger(str(tmpdir))
     logger.log_hyperparams(Namespace(some_str='a_str', an_int=1, a_float=2.0))
     logger.log_hyperparams(hparams)
     logger.save()
 
     # load hparams
-    path_expt_dir = tutils.get_data_path(logger, path_dir=tmpdir)
+    path_expt_dir = tutils.get_data_path(logger, path_dir=str(tmpdir))
     hparams_path = os.path.join(path_expt_dir, 'hparams.yaml')
     tags = load_hparams_from_yaml(hparams_path)
 
@@ -262,7 +262,7 @@ def mock_save_function(filepath, *args):
     # simulated losses
     losses = [10, 9, 2.8, 5, 2.5]
 
-    checkpoint_callback = ModelCheckpoint(tmpdir, save_top_k=save_top_k, save_last=save_last,
+    checkpoint_callback = ModelCheckpoint(str(tmpdir), save_top_k=save_top_k, save_last=save_last,
                                           prefix=file_prefix, verbose=1)
     checkpoint_callback.save_function = mock_save_function
     trainer = Trainer()
@@ -291,7 +291,7 @@ def test_model_checkpoint_only_weights(tmpdir):
 
     trainer = Trainer(
         max_epochs=1,
-        checkpoint_callback=ModelCheckpoint(tmpdir, save_weights_only=True)
+        checkpoint_callback=ModelCheckpoint(str(tmpdir), save_weights_only=True)
     )
     # fit model
     result = trainer.fit(model)
@@ -367,8 +367,8 @@ def increment_on_load_checkpoint(self, _):
         max_epochs=2,
         train_percent_check=0.65,
         val_percent_check=1,
-        checkpoint_callback=ModelCheckpoint(tmpdir, save_top_k=-1),
-        default_root_dir=tmpdir,
+        checkpoint_callback=ModelCheckpoint(str(tmpdir), save_top_k=-1),
+        default_root_dir=str(tmpdir),
         early_stop_callback=False,
         val_check_interval=1.,
     )
@@ -423,7 +423,7 @@ def test_trainer_max_steps_and_epochs(tmpdir):
 
     # define less train steps than epochs
     trainer_options.update(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=3,
         max_steps=num_train_samples + 10
     )
@@ -458,7 +458,7 @@ def test_trainer_min_steps_and_epochs(tmpdir):
 
     # define callback for stopping the model and default epochs
     trainer_options.update(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         early_stop_callback=EarlyStopping(monitor='val_loss', min_delta=1.0),
         val_check_interval=2,
         min_epochs=1,
@@ -501,7 +501,7 @@ def test_benchmark_option(tmpdir):
 
     # fit model
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1,
         benchmark=True,
     )
@@ -609,7 +609,7 @@ def training_step(self, batch, batch_idx, optimizer_idx=None):
 
     # fit model
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_steps=(model.test_batch_inf_loss + 1),
         terminate_on_nan=True
     )
@@ -634,7 +634,7 @@ def on_after_backward(self):
 
     model = CurrentModel()
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_steps=(model.test_batch_nan + 1),
         terminate_on_nan=True
     )
@@ -669,7 +669,7 @@ def on_batch_start(self, trainer, pl_module):
         train_percent_check=0.2,
         progress_bar_refresh_rate=0,
         logger=False,
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
     )
     assert not trainer.interrupted
     trainer.fit(model)
@@ -693,7 +693,7 @@ def _optimizer_step(*args, **kwargs):
         max_steps=1,
         max_epochs=1,
         gradient_clip_val=1.0,
-        default_root_dir=tmpdir
+        default_root_dir=str(tmpdir)
     )
 
     # for the test
@@ -705,7 +705,7 @@ def _optimizer_step(*args, **kwargs):
 
 def test_gpu_choice(tmpdir):
     trainer_options = dict(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
     )
     # Only run if CUDA is available
     if not torch.cuda.is_available():
@@ -849,7 +849,7 @@ def __init__(self, **kwargs):
 def test_trainer_pickle(tmpdir):
     trainer = Trainer(
         max_epochs=1,
-        default_root_dir=tmpdir
+        default_root_dir=str(tmpdir)
     )
     pickle.dumps(trainer)
     cloudpickle.dumps(trainer)
diff --git a/tests/trainer/test_trainer_tricks.py b/tests/trainer/test_trainer_tricks.py
index 973ed32e7cd927..5f301aa8e0ced3 100755
--- a/tests/trainer/test_trainer_tricks.py
+++ b/tests/trainer/test_trainer_tricks.py
@@ -15,7 +15,7 @@ def test_model_reset_correctly(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1
     )
 
@@ -38,7 +38,7 @@ def test_trainer_reset_correctly(tmpdir):
 
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1
     )
 
@@ -77,7 +77,7 @@ def test_trainer_arg(tmpdir, scale_arg):
     before_batch_size = hparams.get('batch_size')
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1,
         auto_scale_batch_size=scale_arg,
     )
@@ -99,7 +99,7 @@ def test_call_to_trainer_method(tmpdir, scale_method):
     before_batch_size = hparams.get('batch_size')
     # logger file to get meta
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1,
     )
 
@@ -118,7 +118,7 @@ def test_error_on_dataloader_passed_to_fit(tmpdir):
     # only train passed to fit
     model = EvalModelTemplate()
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=str(tmpdir),
         max_epochs=1,
         val_percent_check=0.1,
         train_percent_check=0.2,