diff --git a/requirements/extra.txt b/requirements/extra.txt
index 291813e05e..47d3a66e0d 100644
--- a/requirements/extra.txt
+++ b/requirements/extra.txt
@@ -4,7 +4,8 @@ matplotlib>3.1
 horovod>=0.21.2  # no need to install with [pytorch] as pytorch is already installed
 omegaconf>=2.0.1
 torchtext>=0.5
-# onnx>=1.7.0
+onnx>=1.7.0
 onnxruntime>=1.3.0
 hydra-core>=1.0
 jsonargparse[signatures]>=3.15.0
+gcsfs>=2021.5.0
diff --git a/tests/utilities/test_remote_filesystem.py b/tests/utilities/test_remote_filesystem.py
new file mode 100644
index 0000000000..15a7dbb4c9
--- /dev/null
+++ b/tests/utilities/test_remote_filesystem.py
@@ -0,0 +1,106 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+
+import fsspec
+import pytest
+
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.loggers import TensorBoardLogger
+from tests.helpers import BoringModel
+
+GCS_BUCKET_PATH = os.getenv("GCS_BUCKET_PATH", None)
+_GCS_BUCKET_PATH_AVAILABLE = GCS_BUCKET_PATH is not None
+
+gcs_fs = fsspec.filesystem("gs") if _GCS_BUCKET_PATH_AVAILABLE else None
+
+
+def gcs_path_join(dir_path):
+    return GCS_BUCKET_PATH + str(dir_path)
+
+
+def gcs_rm_dir(dir_path):
+    gcs_fs.rm(dir_path, recursive=True)
+    return True
+
+
+@pytest.mark.skipif(not _GCS_BUCKET_PATH_AVAILABLE, reason="Test requires GCS bucket path")
+def test_gcs_model_checkpoint_contents(tmpdir):
+    dir_path = gcs_path_join(tmpdir)
+
+    model = BoringModel()
+    checkpoint_callback = ModelCheckpoint(dirpath=dir_path, save_top_k=-1, save_last=True)
+    epochs = 2
+
+    trainer = Trainer(
+        default_root_dir=dir_path,
+        callbacks=[checkpoint_callback],
+        limit_train_batches=10,
+        limit_val_batches=10,
+        max_epochs=2,
+        logger=False,
+    )
+
+    trainer.fit(model)
+
+    assert checkpoint_callback.best_model_path == os.path.join(dir_path, 'epoch=1-step=19.ckpt')
+    assert checkpoint_callback.last_model_path == os.path.join(dir_path, 'last.ckpt')
+
+    expected = [f'epoch={i}-step={j}.ckpt' for i, j in zip(range(epochs), [9, 19])]
+    expected.append('last.ckpt')
+
+    gcs_ckpt_paths = [os.path.basename(path) for path in gcs_fs.listdir(dir_path, detail=False)]
+    assert gcs_ckpt_paths == expected
+
+    assert gcs_rm_dir(dir_path)
+
+
+@pytest.mark.skipif(not _GCS_BUCKET_PATH_AVAILABLE, reason="Test requires GCS bucket path")
+def test_gcs_logging(tmpdir):
+    dir_path = gcs_path_join(tmpdir)
+
+    name = "tb_versioning"
+    log_dir = os.path.join(dir_path, name)
+    gcs_fs.mkdir(log_dir)
+    expected_version = "101"
+
+    logger = TensorBoardLogger(save_dir=dir_path, name=name, version=expected_version)
+    logger.log_hyperparams({"a": 1, "b": 2, 123: 3, 3.5: 4, 5j: 5})
+
+    assert logger.version == expected_version
+
+    gcs_paths = [os.path.basename(path) for path in gcs_fs.listdir(log_dir, detail=False)]
+    gcs_paths = list(filter(lambda x: len(x) > 0, gcs_paths))
+
+    assert gcs_paths == [expected_version]
+    assert gcs_fs.listdir(os.path.join(log_dir, expected_version), detail=False)
+
+    assert gcs_rm_dir(dir_path)
+
+
+@pytest.mark.skipif(not _GCS_BUCKET_PATH_AVAILABLE, reason="Test requires GCS bucket path")
+def test_gcs_save_hparams_to_yaml_file(tmpdir):
+    dir_path = gcs_path_join(tmpdir)
+
+    model = BoringModel()
+    logger = TensorBoardLogger(save_dir=dir_path, default_hp_metric=False)
+    trainer = Trainer(max_steps=1, default_root_dir=dir_path, logger=logger)
+    assert trainer.log_dir == trainer.logger.log_dir
+    trainer.fit(model)
+
+    hparams_file = "hparams.yaml"
+    assert gcs_fs.isfile(os.path.join(trainer.log_dir, hparams_file))
+
+    assert gcs_rm_dir(dir_path)