Refine remote fs doc (#11393)

docs/source/common/checkpointing.rst

@@ -385,3 +385,13 @@ Custom Checkpoint IO Plugin
 .. note::
 
     Some ``TrainingTypePlugins`` like ``DeepSpeedStrategy`` do not support custom ``CheckpointIO`` as checkpointing logic is not modifiable.
+
+-----------
+
+***************************
+Managing Remote Filesystems
+***************************
+
+Lightning supports saving and loading checkpoints from a variety of filesystems, including local filesystems and several cloud storage providers.
+
+Check out :ref:`Remote Filesystems <remote_fs>` document for more info.

docs/source/common/remote_fs.rst

@@ -1,16 +1,19 @@
-Remote filesystems
-==================
+.. _remote_fs:
 
-PyTorch Lightning enables working with data from a variety of filesystems, including local filesystems and several cloud storage providers
-such as ``s3`` on AWS, ``gcs`` on Google Cloud, or ``adl`` on Azure.
+##################
+Remote Filesystems
+##################
+
+PyTorch Lightning enables working with data from a variety of filesystems, including local filesystems and several cloud storage providers such as
+`S3 <https://aws.amazon.com/s3/>`_ on `AWS <https://aws.amazon.com/>`_, `GCS <https://cloud.google.com/storage>`_ on `Google Cloud <https://cloud.google.com/>`_,
+or `ADL <https://azure.microsoft.com/solutions/data-lake/>`_ on `Azure <https://azure.microsoft.com/>`_.
 
 This applies to saving and writing checkpoints, as well as for logging.
 Working with different filesystems can be accomplished by appending a protocol like "s3:/" to file paths for writing and reading data.
 
-
 .. code-block:: python
 
-    # `default_root_dir` is the default path used for logs and weights
+    # `default_root_dir` is the default path used for logs and checkpoints
     trainer = Trainer(default_root_dir="s3://my_bucket/data/")
     trainer.fit(model)
 
@@ -32,7 +35,7 @@ Additionally, you could also resume training with a checkpoint stored at a remot
     trainer = Trainer(default_root_dir=tmpdir, max_steps=3)
     trainer.fit(model, ckpt_path="s3://my_bucket/ckpts/classifier.ckpt")
 
-PyTorch Lightning uses `fsspec <https://filesystem-spec.readthedocs.io/en/latest/>`__ internally to handle all filesystem operations.
+PyTorch Lightning uses `fsspec <https://filesystem-spec.readthedocs.io/>`_ internally to handle all filesystem operations.
 
 The most common filesystems supported by Lightning are:
 

docs/source/extensions/logging.rst

@@ -14,8 +14,9 @@
 Logging
 #######
 
+*****************
 Supported Loggers
-=================
+*****************
 
 The following are loggers we support:
 
@@ -101,6 +102,7 @@ Lightning offers automatic log functionalities for logging scalars, or manual lo
 
 Automatic Logging
 =================
+
 Use the :meth:`~pytorch_lightning.core.lightning.LightningModule.log`
 method to log from anywhere in a :doc:`lightning module <../common/lightning_module>` and :doc:`callbacks <../extensions/callbacks>`.
 
@@ -182,6 +184,7 @@ If your work requires to log in an unsupported method, please open an issue with
 
 Manual Logging Non-Scalar Artifacts
 ===================================
+
 If you want to log anything that is not a scalar, like histograms, text, images, etc., you may need to use the logger object directly.
 
 .. code-block:: python
@@ -388,3 +391,13 @@ in the `hparams tab <https://pytorch.org/docs/stable/tensorboard.html#torch.util
             self.log("hp/metric_2", some_scalar_2)
 
     In the example, using ``"hp/"`` as a prefix allows for the metrics to be grouped under "hp" in the tensorboard scalar tab where you can collapse them.
+
+-----------
+
+***************************
+Managing Remote Filesystems
+***************************
+
+Lightning supports saving logs to a variety of filesystems, including local filesystems and several cloud storage providers.
+
+Check out :ref:`Remote Filesystems <remote_fs>` document for more info.