[air] Use checkpoint.as_directory() instead of cleaning up manually

python/ray/ml/checkpoint.py

@@ -228,30 +228,21 @@ def to_dict(self) -> dict:
             return ray.get(self._obj_ref)
         elif self._local_path or self._uri:
             # Else, checkpoint is either on FS or external storage
-            cleanup = False
-
-            local_path = self._local_path
-            if not local_path:
-                # Checkpoint does not exist on local path. Save
-                # in temporary directory, but clean up later
-                local_path = self.to_directory()
-                cleanup = True
-
-            checkpoint_data_path = os.path.join(local_path, _DICT_CHECKPOINT_FILE_NAME)
-            if os.path.exists(checkpoint_data_path):
-                # If we are restoring a dict checkpoint, load the dict
-                # from the checkpoint file.
-                with open(checkpoint_data_path, "rb") as f:
-                    checkpoint_data = pickle.load(f)
-            else:
-                data = _pack(local_path)
-
-                checkpoint_data = {
-                    _FS_CHECKPOINT_KEY: data,
-                }
-
-            if cleanup:
-                shutil.rmtree(local_path)
+            with self.as_directory() as local_path:
+                checkpoint_data_path = os.path.join(
+                    local_path, _DICT_CHECKPOINT_FILE_NAME
+                )
+                if os.path.exists(checkpoint_data_path):
+                    # If we are restoring a dict checkpoint, load the dict
+                    # from the checkpoint file.
+                    with open(checkpoint_data_path, "rb") as f:
+                        checkpoint_data = pickle.load(f)
+                else:
+                    data = _pack(local_path)
+
+                    checkpoint_data = {
+                        _FS_CHECKPOINT_KEY: data,
+                    }
 
             return checkpoint_data
         else:
@@ -406,17 +397,8 @@ def to_uri(self, uri: str) -> str:
                 f"Hint: {fs_hint(uri)}"
             )
 
-        cleanup = False
-
-        local_path = self._local_path
-        if not local_path:
-            cleanup = True
-            local_path = self.to_directory()
-
-        upload_to_uri(local_path=local_path, uri=uri)
-
-        if cleanup:
-            shutil.rmtree(local_path)
+        with self.as_directory() as local_path:
+            upload_to_uri(local_path=local_path, uri=uri)
 
         return uri
 

python/ray/ml/predictors/integrations/lightgbm/lightgbm_predictor.py

@@ -1,6 +1,5 @@
 from typing import Optional, List, Union
 import os
-import shutil
 import numpy as np
 import pandas as pd
 
@@ -40,15 +39,15 @@ def from_checkpoint(cls, checkpoint: Checkpoint) -> "LightGBMPredictor":
                 ``LightGBMTrainer`` run.
 
         """
-        path = checkpoint.to_directory()
-        bst = lightgbm.Booster(model_file=os.path.join(path, MODEL_KEY))
-        preprocessor_path = os.path.join(path, PREPROCESSOR_KEY)
-        if os.path.exists(preprocessor_path):
-            with open(preprocessor_path, "rb") as f:
-                preprocessor = cpickle.load(f)
-        else:
-            preprocessor = None
-        shutil.rmtree(path)
+        with checkpoint.as_directory() as path:
+            bst = lightgbm.Booster(model_file=os.path.join(path, MODEL_KEY))
+            preprocessor_path = os.path.join(path, PREPROCESSOR_KEY)
+            if os.path.exists(preprocessor_path):
+                with open(preprocessor_path, "rb") as f:
+                    preprocessor = cpickle.load(f)
+            else:
+                preprocessor = None
+
         return LightGBMPredictor(model=bst, preprocessor=preprocessor)
 
     def predict(

python/ray/ml/predictors/integrations/xgboost/xgboost_predictor.py

@@ -1,6 +1,5 @@
 from typing import Optional, List, Union, Dict, Any
 import os
-import shutil
 import numpy as np
 import pandas as pd
 
@@ -40,16 +39,15 @@ def from_checkpoint(cls, checkpoint: Checkpoint) -> "XGBoostPredictor":
                 ``XGBoostTrainer`` run.
 
         """
-        path = checkpoint.to_directory()
-        bst = xgboost.Booster()
-        bst.load_model(os.path.join(path, MODEL_KEY))
-        preprocessor_path = os.path.join(path, PREPROCESSOR_KEY)
-        if os.path.exists(preprocessor_path):
-            with open(preprocessor_path, "rb") as f:
-                preprocessor = cpickle.load(f)
-        else:
-            preprocessor = None
-        shutil.rmtree(path)
+        with checkpoint.as_directory() as path:
+            bst = xgboost.Booster()
+            bst.load_model(os.path.join(path, MODEL_KEY))
+            preprocessor_path = os.path.join(path, PREPROCESSOR_KEY)
+            if os.path.exists(preprocessor_path):
+                with open(preprocessor_path, "rb") as f:
+                    preprocessor = cpickle.load(f)
+            else:
+                preprocessor = None
         return XGBoostPredictor(model=bst, preprocessor=preprocessor)
 
     def predict(

python/ray/ml/tests/test_checkpoints.py

@@ -325,13 +325,11 @@ def testLocalCheckpointSerde(self):
         # Local checkpoints are converted to bytes on serialization. Currently
         # this is a pickled dict, so we compare with a dict checkpoint.
         source_checkpoint = Checkpoint.from_dict({"checkpoint_data": 5})
-        tmpdir = source_checkpoint.to_directory()
-        self.addCleanup(shutil.rmtree, tmpdir)
-
-        checkpoint = Checkpoint.from_directory(tmpdir)
-        self._testCheckpointSerde(
-            checkpoint, *source_checkpoint.get_internal_representation()
-        )
+        with source_checkpoint.as_directory() as tmpdir:
+            checkpoint = Checkpoint.from_directory(tmpdir)
+            self._testCheckpointSerde(
+                checkpoint, *source_checkpoint.get_internal_representation()
+            )
 
     def testBytesCheckpointSerde(self):
         # Bytes checkpoints are just dict checkpoints constructed