[Tune][Fix]Remove the clear_checkpoint function during Trial restor…

…ation error handling. (ray-project#48532)

This PR removes the `clear_checkpoint` function,
so that Tune doesn't try to "restart trials from scratch.
`clear_checkpoint` solved for a legacy use case that doesn't
apply anymore, and "restoration failures" are also now an
edge case for function Trainables and Ray Train usage.

---------

Signed-off-by: Hongpeng Guo <hpguo@anyscale.com>
Signed-off-by: mohitjain2504 <mohit.jain@dream11.com>

python/ray/tune/experiment/trial.py

@@ -793,11 +793,11 @@ def get_error(self) -> Optional[TuneError]:
         return None
 
     def _handle_restore_error(self, exc: Exception):
+        # For Restoration errors, we only increment the restore failure count
+        # if the number of failures exceeds the restore retry limit.
         if self.temporary_state.num_restore_failures >= int(
             os.environ.get("TUNE_RESTORE_RETRY_NUM", 0)
         ):
-            # Restore was unsuccessful, try again without checkpoint.
-            self.clear_checkpoint()
             self.run_metadata.num_failures += 1
         else:
             self.temporary_state.num_restore_failures += 1
@@ -883,12 +883,6 @@ def should_checkpoint(self):
     def has_checkpoint(self) -> bool:
         return self.checkpoint is not None
 
-    def clear_checkpoint(self):
-        if self.latest_checkpoint_result:
-            self.latest_checkpoint_result.checkpoint = None
-        self.temporary_state.restoring_from = None
-        self.run_metadata.invalidate_cache()
-
     def on_checkpoint(self, checkpoint_result: _TrainingResult):
         """Hook for handling checkpoints taken by the Trainable.
 

python/ray/tune/tests/test_tuner_restore.py

@@ -537,7 +537,21 @@ def test_tuner_restore_latest_available_checkpoint(
 
 @pytest.mark.parametrize("retry_num", [0, 2])
 def test_restore_retry(ray_start_2_cpus, tmpdir, retry_num):
-    """Test retrying restore on a trial level by setting `TUNE_RESTORE_RETRY_NUM`."""
+    """
+    Test retrying restore on a trial level by setting `TUNE_RESTORE_RETRY_NUM`.
+
+    This unit test holds the following hyperparameters:
+    - `retry_num`: Maximum number of retry attempts for restoring a trial.
+        This value is assigned to the environment variable `TUNE_RESTORE_RETRY_NUM`.
+        If the restoration fails after retry_num attempts, the trial increments its
+        counter of total number of failures by 1.
+
+    - `retry_num_to_fail`: Number of restore attempts to fail. In this test,
+        retry_num_to_fail is set to 2, causing the first two restore attempts to fail.
+
+    - `max_failures`: Maximum allowable failures during training. Here, max_failures is
+        set to 2, meaning the training process will terminate after two total failures.
+    """
 
     class MockTrainable(Trainable):
         """A trainable that can generate one failure during training and
@@ -546,7 +560,7 @@ class MockTrainable(Trainable):
         def setup(self, config):
             self.idx = 0
             self.tag_file_path = config["tag_file_path"]
-            self.retry_num_to_fail = config.get("retry_num_to_fail", 2)
+            self.retry_num_to_fail = 2
             self._is_restored = False
 
         def step(self):
@@ -592,7 +606,7 @@ def load_checkpoint(self, checkpoint_dir):
                 name="tryout_restore",
                 stop={"training_iteration": 5},
                 storage_path=str(tmpdir),
-                failure_config=FailureConfig(max_failures=1),
+                failure_config=FailureConfig(max_failures=2),
                 checkpoint_config=CheckpointConfig(checkpoint_frequency=1),
             ),
             param_space={"tag_file_path": tag_file},