[Tune] Don't recommend tune.run API in logging messages when using …

…the `Tuner` (#33642)

Signed-off-by: Justin Yu <justinvyu@berkeley.edu>

python/ray/tune/execution/trial_runner.py

@@ -4,6 +4,7 @@
 import json
 import logging
 import os
+from pathlib import Path
 import time
 import traceback
 import warnings
@@ -378,14 +379,14 @@ def restore_from_dir(self, experiment_dir: Optional[str] = None) -> List[Trial]:
             )
 
         # Set checkpoint file to load
-        logger.info(
-            f"Using following experiment state file to resume: " f"{newest_state_path}"
-        )
-
         logger.warning(
             f"Attempting to resume experiment from {self._local_experiment_path}. "
             "This will ignore any new changes to the specification."
         )
+        logger.info(
+            "Using the newest experiment state file found within the "
+            f"experiment directory: {Path(newest_state_path).name}"
+        )
 
         # Actually load data
         with open(newest_state_path, "r") as f:

python/ray/tune/impl/tuner_internal.py

@@ -595,6 +595,7 @@ def _get_tune_run_arguments(self, trainable: TrainableType) -> Dict[str, Any]:
             trial_name_creator=self._tune_config.trial_name_creator,
             trial_dirname_creator=self._tune_config.trial_dirname_creator,
             chdir_to_trial_dir=self._tune_config.chdir_to_trial_dir,
+            _tuner_api=True,
         )
 
     def _fit_internal(

python/ray/tune/tune.py

@@ -4,6 +4,7 @@
 import datetime
 import logging
 import os
+from pathlib import Path
 import signal
 import sys
 import threading
@@ -208,6 +209,19 @@ def signal_interrupt_tune_run(sig: int, frame):
     return experiment_interrupted_event
 
 
+def _ray_auto_init(entrypoint: str):
+    """Initialize Ray unless already configured."""
+    if os.environ.get("TUNE_DISABLE_AUTO_INIT") == "1":
+        logger.info("'TUNE_DISABLE_AUTO_INIT=1' detected.")
+    elif not ray.is_initialized():
+        ray.init()
+        logger.info(
+            "Initializing Ray automatically. "
+            "For cluster usage or custom Ray initialization, "
+            f"call `ray.init(...)` before `{entrypoint}`."
+        )
+
+
 class _Config(abc.ABC):
     def to_dict(self) -> dict:
         """Converts this configuration to a dict format."""
@@ -259,6 +273,7 @@ def run(
     _remote: Optional[bool] = None,
     # Passed by the Tuner.
     _remote_string_queue: Optional[Queue] = None,
+    _tuner_api: bool = False,
 ) -> ExperimentAnalysis:
     """Executes training.
 
@@ -466,6 +481,21 @@ class and registered trainables.
     remote_run_kwargs = locals().copy()
     remote_run_kwargs.pop("_remote")
 
+    error_message_map = (
+        {
+            "entrypoint": "Tuner(...)",
+            "search_space_arg": "param_space",
+            "restore_entrypoint": 'Tuner.restore(path="{path}", trainable=...)',
+        }
+        if _tuner_api
+        else {
+            "entrypoint": "tune.run(...)",
+            "search_space_arg": "config",
+            "restore_entrypoint": "tune.run(..., resume=True)",
+        }
+    )
+    _ray_auto_init(entrypoint=error_message_map["entrypoint"])
+
     if _remote is None:
         _remote = ray.util.client.ray.is_connected()
 
@@ -478,9 +508,6 @@ class and registered trainables.
             DeprecationWarning,
         )
 
-    if not trial_executor or isinstance(trial_executor, RayTrialExecutor):
-        _ray_auto_init()
-
     if _remote:
         if get_air_verbosity():
             logger.warning(
@@ -517,8 +544,8 @@ class and registered trainables.
 
     if mode and mode not in ["min", "max"]:
         raise ValueError(
-            "The `mode` parameter passed to `tune.run()` has to be one of "
-            "['min', 'max']"
+            f"The `mode` parameter passed to `{error_message_map['entrypoint']}` "
+            "must be one of ['min', 'max']"
         )
 
     air_verbosity = get_air_verbosity()
@@ -536,7 +563,8 @@ class and registered trainables.
         config = config.to_dict()
     if not isinstance(config, dict):
         raise ValueError(
-            "The `config` passed to `tune.run()` must be a dict. "
+            f"The `{error_message_map['search_space_arg']}` passed to "
+            f"`{error_message_map['entrypoint']}` must be a dict. "
             f"Got '{type(config)}' instead."
         )
 
@@ -669,8 +697,6 @@ class and registered trainables.
                 max_failures=max_failures,
                 restore=restore,
             )
-    else:
-        logger.debug("Ignoring some parameters passed into tune.run.")
 
     if fail_fast and max_failures != 0:
         raise ValueError("max_failures must be 0 if fail_fast=True.")
@@ -734,7 +760,8 @@ class and registered trainables.
     ):
         if _has_unresolved_values(config):
             raise ValueError(
-                "You passed a `config` parameter to `tune.run()` with "
+                f"You passed a `{error_message_map['search_space_arg']}` parameter to "
+                f"`{error_message_map['entrypoint']}` with "
                 "unresolved parameters, but the search algorithm was already "
                 "instantiated with a search space. Make sure that `config` "
                 "does not contain any more parameter definitions - include "
@@ -745,10 +772,11 @@ class and registered trainables.
         scheduler.set_search_properties, metric, mode, **experiments[0].public_spec
     ):
         raise ValueError(
-            "You passed a `metric` or `mode` argument to `tune.run()`, but "
+            "You passed a `metric` or `mode` argument to "
+            f"`{error_message_map['entrypoint']}`, but "
             "the scheduler you are using was already instantiated with their "
             "own `metric` and `mode` parameters. Either remove the arguments "
-            "from your scheduler or from your call to `tune.run()`"
+            f"from your scheduler or from `{error_message_map['entrypoint']}` args."
         )
 
     progress_metrics = _detect_progress_metrics(_get_trainable(run_or_experiment))
@@ -830,9 +858,10 @@ class and registered trainables.
         for exp in experiments:
             search_alg.add_configurations([exp])
     else:
-        logger.info(
-            "TrialRunner resumed, ignoring new add_experiment but "
-            "updating trial resources."
+        logger.debug(
+            "You have resumed the Tune run, which means that any newly specified "
+            "`Experiment`s will be ignored. "
+            "Tune will just continue what was previously running."
         )
         if resources_per_trial:
             runner.update_pending_trial_resources(resources_per_trial)
@@ -919,10 +948,12 @@ class and registered trainables.
         )
 
     if experiment_interrupted_event.is_set():
+        restore_entrypoint = error_message_map["restore_entrypoint"].format(
+            path=Path(experiment_checkpoint).parent,
+        )
         logger.warning(
-            "Experiment has been interrupted, but the most recent state was "
-            "saved. You can continue running this experiment by passing "
-            "`resume=True` to `tune.run()`"
+            "Experiment has been interrupted, but the most recent state was saved.\n"
+            f"Continue running this experiment with: {restore_entrypoint}"
         )
     ea = ExperimentAnalysis(
         experiment_checkpoint,
@@ -972,7 +1003,7 @@ def run_experiments(
         raise ValueError("cannot use custom trial executor")
 
     if not trial_executor or isinstance(trial_executor, RayTrialExecutor):
-        _ray_auto_init()
+        _ray_auto_init(entrypoint="tune.run_experiments(...)")
 
     if _remote:
         if get_air_verbosity():
@@ -1036,16 +1067,3 @@ def run_experiments(
                 callbacks=callbacks,
             ).trials
         return trials
-
-
-def _ray_auto_init():
-    """Initialize Ray unless already configured."""
-    if os.environ.get("TUNE_DISABLE_AUTO_INIT") == "1":
-        logger.info("'TUNE_DISABLE_AUTO_INIT=1' detected.")
-    elif not ray.is_initialized():
-        logger.info(
-            "Initializing Ray automatically."
-            "For cluster usage or custom Ray initialization, "
-            "call `ray.init(...)` before `tune.run`."
-        )
-        ray.init()