use gfile to support remote directories

Tests all use the `tmpfile` fixture which provides a py.path.local which is
incompatible with the compat.gfile. The contract in many places is type str or
Optional[str] which py.path.local is not.

I hope that folks are not passing in path.local objects, if so this change will
break them. The type annotations say to use str, so this should be ok. The
other option is to just explicitly convert to str as to not break people using
an incorrect type (like the tests were doing)

Author: f4hy

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

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:
@@ -269,25 +274,25 @@ 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:]}
 
     return tags
 
 
 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)
 
 

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:

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
 

pytorch_lightning/trainer/trainer.py

@@ -371,10 +371,11 @@ def __init__(
                            ' val and test loop using a single batch')
 
         # set default save path if user didn't provide one
-        self.default_root_dir = default_root_dir
-
-        if self.default_root_dir is None:
+        if default_root_dir is None:
             self.default_root_dir = os.getcwd()
+        else:
+            # we have to do str() because the unit tests violate type annotation and pass path objecto
+            self.default_root_dir = str(default_root_dir)
 
         # training bookeeping
         self.total_batch_idx = 0

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
@@ -374,9 +375,9 @@ def restore_hpc_weights_if_needed(self, model: LightningModule):
         did_restore = False
 
         # look for hpc weights
-        folderpath = self.weights_save_path
-        if os.path.exists(folderpath):
-            files = os.listdir(folderpath)
+        folderpath = str(self.weights_save_path)
+        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 +452,17 @@ 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)
+        folderpath = str(folderpath)  # because the tests pass a path object
+        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 +512,7 @@ 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(str(path))
         files = [x for x in files if name_key in x]
         if len(files) == 0:
             return 0

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)