make the fs variable private

pytorch_lightning/callbacks/model_checkpoint.py

@@ -120,10 +120,10 @@ def __init__(self, filepath: Optional[str] = None, monitor: str = 'val_loss', ve
                  mode: str = 'auto', period: int = 1, prefix: str = ''):
         super().__init__()
         if filepath:
-            self.fs = get_filesystem(filepath)
+            self._fs = get_filesystem(filepath)
         else:
-            self.fs = get_filesystem("")  # will give local fileystem
-        if save_top_k > 0 and filepath is not None and self.fs.isdir(filepath) and len(self.fs.ls(filepath)) > 0:
+            self._fs = get_filesystem("")  # will give local fileystem
+        if save_top_k > 0 and filepath is not None and self._fs.isdir(filepath) and len(self._fs.ls(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!"
@@ -135,13 +135,13 @@ 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 self.fs.isdir(filepath):
+            if self._fs.isdir(filepath):
                 self.dirpath, self.filename = filepath, "{epoch}"
             else:
-                if self.fs.protocol == "file":  # dont normalize remote paths
+                if self._fs.protocol == "file":  # dont normalize remote paths
                     filepath = os.path.realpath(filepath)
                 self.dirpath, self.filename = os.path.split(filepath)
-            self.fs.makedirs(self.dirpath, exist_ok=True)
+            self._fs.makedirs(self.dirpath, exist_ok=True)
         self.save_last = save_last
         self.save_top_k = save_top_k
         self.save_weights_only = save_weights_only
@@ -184,16 +184,16 @@ def kth_best_model(self):
         return self.kth_best_model_path
 
     def _del_model(self, filepath):
-        if self.fs.exists(filepath):
-            self.fs.rm(filepath)
+        if self._fs.exists(filepath):
+            self._fs.rm(filepath)
 
     def _save_model(self, filepath, trainer, pl_module):
 
         # in debugging, track when we save checkpoints
         trainer.dev_debugger.track_checkpointing_history(filepath)
 
         # make paths
-        self.fs.makedirs(os.path.dirname(filepath), exist_ok=True)
+        self._fs.makedirs(os.path.dirname(filepath), exist_ok=True)
 
         # delegate the saving to the model
         if self.save_function is not None:
@@ -299,7 +299,7 @@ def on_pretrain_routine_start(self, trainer, pl_module):
         self.dirpath = ckpt_path
 
         assert trainer.global_rank == 0, "tried to make a checkpoint from non global_rank=0"
-        self.fs.makedirs(self.dirpath, exist_ok=True)
+        self._fs.makedirs(self.dirpath, exist_ok=True)
 
     def __warn_deprecated_monitor_key(self):
         using_result_obj = os.environ.get('PL_USING_RESULT_OBJ', None)
@@ -348,7 +348,7 @@ def on_validation_end(self, trainer, pl_module):
         ckpt_name_metrics = trainer.logged_metrics
         filepath = self.format_checkpoint_name(epoch, ckpt_name_metrics)
         version_cnt = 0
-        while self.fs.exists(filepath):
+        while self._fs.exists(filepath):
             filepath = self.format_checkpoint_name(epoch, ckpt_name_metrics, ver=version_cnt)
             # this epoch called before
             version_cnt += 1