[tune] Cleanup path-related properties in experiment classes

python/ray/_private/test_utils.py

@@ -1595,13 +1595,22 @@ def simulate_storage(
     elif storage_type == "s3":
         from moto.server import ThreadedMotoServer
 
+        old_env = os.environ
+        os.environ["AWS_ACCESS_KEY_ID"] = "testing"
+        os.environ["AWS_SECRET_ACCESS_KEY"] = "testing"
+        os.environ["AWS_SECURITY_TOKEN"] = "testing"
+        os.environ["AWS_SESSION_TOKEN"] = "testing"
+
         root = root or uuid.uuid4().hex
         s3_server = f"http://localhost:{port}"
         server = ThreadedMotoServer(port=port)
         server.start()
         url = f"s3://{root}?region={region}&endpoint_override={s3_server}"
         yield url
         server.stop()
+
+        os.environ = old_env
+
     else:
         raise NotImplementedError(f"Unknown storage type: {storage_type}")
 

python/ray/air/integrations/mlflow.py

@@ -310,7 +310,7 @@ def log_trial_end(self, trial: "Trial", failed: bool = False):
 
         # Log the artifact if set_artifact is set to True.
         if self.should_save_artifact:
-            self.mlflow_util.save_artifacts(run_id=run_id, dir=trial.logdir)
+            self.mlflow_util.save_artifacts(run_id=run_id, dir=trial.local_path)
 
         # Stop the run once trial finishes.
         status = "FINISHED" if not failed else "FAILED"

python/ray/air/integrations/wandb.py

@@ -653,7 +653,7 @@ def _start_logging_actor(
             actor_options={"num_cpus": 0, **_force_on_current_node()}
         )
         self._trial_logging_actors[trial] = self._remote_logger_class.remote(
-            logdir=trial.logdir,
+            logdir=trial.local_path,
             queue=self._trial_queues[trial],
             exclude=exclude_results,
             to_config=self.AUTO_CONFIG_KEYS,

python/ray/air/tests/mocked_wandb_integration.py

@@ -20,7 +20,7 @@ class Trial(
             "trial_name",
             "experiment_dir_name",
             "placement_group_factory",
-            "logdir",
+            "local_path",
         ],
     )
 ):

python/ray/air/tests/test_integration_mlflow.py

@@ -19,7 +19,7 @@
 
 
 class MockTrial(
-    namedtuple("MockTrial", ["config", "trial_name", "trial_id", "logdir"])
+    namedtuple("MockTrial", ["config", "trial_name", "trial_id", "local_path"])
 ):
     def __hash__(self):
         return hash(self.trial_id)

python/ray/air/tests/test_integration_wandb.py

@@ -334,7 +334,7 @@ def test_wandb_logger_exclude_config(self):
             trial_name="trial_0",
             experiment_dir_name="trainable",
             placement_group_factory=PlacementGroupFactory([{"CPU": 1}]),
-            logdir=tempfile.gettempdir(),
+            local_path=tempfile.gettempdir(),
         )
         logger = WandbTestExperimentLogger(
             project="test_project",

python/ray/tune/analysis/experiment_analysis.py

@@ -100,8 +100,7 @@ def __init__(
             # If only a mode was passed, use anonymous metric
             self.default_metric = DEFAULT_METRIC
 
-        self._local_base_dir = self._checkpoints_and_paths[0][1].parent
-
+        self._local_experiment_path = self._checkpoints_and_paths[0][1]
         if not pd:
             logger.warning(
                 "pandas not installed. Run `pip install pandas` for "
@@ -110,15 +109,19 @@ def __init__(
         else:
             self.fetch_trial_dataframes()
 
-        self._sync_config = sync_config
+        remote_storage_path = None
+        if sync_config and sync_config.upload_dir:
+            remote_storage_path = sync_config.upload_dir
+
+        self._remote_storage_path = remote_storage_path
 
     def _parse_cloud_path(self, local_path: str):
         """Convert local path into cloud storage path"""
-        if not self._sync_config or not self._sync_config.upload_dir:
+        if not self._remote_storage_path:
             return None
 
         return local_path.replace(
-            str(self._local_base_dir), self._sync_config.upload_dir
+            str(Path(self._local_experiment_path).parent), self._remote_storage_path
         )
 
     def _load_checkpoints(self, experiment_checkpoint_path: str) -> List[str]:
@@ -686,7 +689,7 @@ def get_best_logdir(
                 based on `mode`, and compare trials based on `mode=[min,max]`.
         """
         best_trial = self.get_best_trial(metric, mode, scope)
-        return best_trial.logdir if best_trial else None
+        return best_trial.local_path if best_trial else None
 
     def get_last_checkpoint(self, trial=None, metric="training_iteration", mode="max"):
         """Gets the last persistent checkpoint path of the provided trial,
@@ -783,8 +786,8 @@ def runner_data(self) -> Dict:
     def _get_trial_paths(self) -> List[str]:
         if self.trials:
             # We do not need to set the relative path here
-            # Maybe assert that t.logdir is in local_base_path?
-            _trial_paths = [str(t.logdir) for t in self.trials]
+            # Maybe assert that t.local_path is in local_base_path?
+            _trial_paths = [str(t.local_path) for t in self.trials]
         else:
             logger.info(
                 "No `self.trials`. Drawing logdirs from checkpoint "
@@ -795,7 +798,7 @@ def _get_trial_paths(self) -> List[str]:
             for trial_json_state, path in self._checkpoints_and_paths:
                 try:
                     trial = Trial.from_json_state(trial_json_state, stub=True)
-                    trial.local_dir = str(path)
+                    trial.local_experiment_path = str(path)
                 except Exception:
                     logger.warning(
                         f"Could not load trials from experiment checkpoint. "
@@ -808,7 +811,7 @@ def _get_trial_paths(self) -> List[str]:
                 self.trials.append(trial)
 
             self.trials.sort(key=lambda trial: trial.trial_id)
-            _trial_paths = [str(trial.logdir) for trial in self.trials]
+            _trial_paths = [str(trial.local_path) for trial in self.trials]
 
         if not _trial_paths:
             raise TuneError("No trials found.")

python/ray/tune/callback.py

@@ -108,7 +108,7 @@ def train(config):
     """
 
     # File templates for any artifacts written by this callback
-    # These files should live in the `trial.logdir` for each trial.
+    # These files should live in the `trial.local_path` for each trial.
     # TODO(ml-team): Make this more visible to users to override. Internal use for now.
     _SAVED_FILE_TEMPLATES = []
 

python/ray/tune/execution/ray_trial_executor.py

@@ -747,7 +747,7 @@ def reset_trial(
                         trainable.reset.remote(
                             extra_config,
                             logger_creator=logger_creator,
-                            remote_checkpoint_dir=trial.remote_checkpoint_dir,
+                            remote_checkpoint_dir=trial.remote_path,
                         ),
                         timeout=DEFAULT_GET_TIMEOUT,
                     )
@@ -938,8 +938,8 @@ def save(
                     metrics=result,
                     local_to_remote_path_fn=partial(
                         TrainableUtil.get_remote_storage_path,
-                        logdir=trial.logdir,
-                        remote_checkpoint_dir=trial.remote_checkpoint_dir,
+                        logdir=trial.local_path,
+                        remote_checkpoint_dir=trial.remote_path,
                     )
                     if trial.uses_cloud_checkpointing
                     else None,
@@ -1070,7 +1070,7 @@ def _change_working_directory(self, trial):
         if ray._private.worker._mode() == ray._private.worker.LOCAL_MODE:
             old_dir = os.getcwd()
             try:
-                os.chdir(trial.logdir)
+                os.chdir(trial.local_path)
                 yield
             finally:
                 os.chdir(old_dir)