[air/output] Print experiment information at experiment start (#34952)

Includes #34788

This PR prints experiment information at the start of the experiment.

Signed-off-by: Kai Fricke <[email protected]>

python/ray/tune/experimental/output.py

@@ -1,3 +1,4 @@
+import sys
 from typing import List, Dict, Optional, Tuple, Any, TYPE_CHECKING, Collection
 
 import contextlib
@@ -463,6 +464,26 @@ def __init__(self, verbosity: AirVerbosity):
         self._start_time = time.time()
         self._last_heartbeat_time = 0
 
+    def experiment_started(
+        self,
+        experiment_name: str,
+        experiment_path: str,
+        searcher_str: str,
+        scheduler_str: str,
+        total_num_samples: int,
+        tensorboard_path: Optional[str] = None,
+        **kwargs,
+    ):
+        print(f"\nView detailed results here: {experiment_path}")
+
+        if tensorboard_path:
+            print(
+                f"To visualize your results with TensorBoard, run: "
+                f"`tensorboard --logdir {tensorboard_path}`"
+            )
+
+        print("")
+
     @property
     def _time_heartbeat_str(self):
         current_time_str, running_for_str = _get_time_str(self._start_time, time.time())
@@ -557,6 +578,42 @@ def _print_heartbeat(self, trials, *sys_args):
 
 
 class TuneTerminalReporter(TuneReporterBase):
+    def experiment_started(
+        self,
+        experiment_name: str,
+        experiment_path: str,
+        searcher_str: str,
+        scheduler_str: str,
+        total_num_samples: int,
+        tensorboard_path: Optional[str] = None,
+        **kwargs,
+    ):
+        if total_num_samples > sys.maxsize:
+            total_num_samples_str = "infinite"
+        else:
+            total_num_samples_str = str(total_num_samples)
+
+        print(
+            tabulate(
+                [
+                    ["Search algorithm", searcher_str],
+                    ["Scheduler", scheduler_str],
+                    ["Number of trials", total_num_samples_str],
+                ],
+                headers=["Configuration for experiment", experiment_name],
+                tablefmt=AIR_TABULATE_TABLEFMT,
+            )
+        )
+        super().experiment_started(
+            experiment_name=experiment_name,
+            experiment_path=experiment_path,
+            searcher_str=searcher_str,
+            scheduler_str=scheduler_str,
+            total_num_samples=total_num_samples,
+            tensorboard_path=tensorboard_path,
+            **kwargs,
+        )
+
     def _print_heartbeat(self, trials, *sys_args):
         if self._verbosity < self._heartbeat_threshold:
             return
@@ -575,7 +632,7 @@ def _print_heartbeat(self, trials, *sys_args):
             tabulate(
                 all_infos,
                 headers=header,
-                tablefmt="simple",
+                tablefmt=AIR_TABULATE_TABLEFMT,
                 showindex=False,
             )
         )

python/ray/tune/tune.py

@@ -37,6 +37,7 @@
 )
 
 from ray.tune.impl.placeholder import create_resolvers_map, inject_placeholders
+from ray.tune.logger import TBXLoggerCallback
 from ray.tune.progress_reporter import (
     ProgressReporter,
     _detect_reporter,
@@ -957,10 +958,24 @@ class and registered trainables.
     with contextlib.ExitStack() as stack:
         from ray.tune.experimental.output import TuneRichReporter
 
+        if any(isinstance(cb, TBXLoggerCallback) for cb in callbacks):
+            tensorboard_path = runner._local_experiment_path
+        else:
+            tensorboard_path = None
+
         if air_progress_reporter and isinstance(
             air_progress_reporter, TuneRichReporter
         ):
             stack.enter_context(air_progress_reporter.with_live())
+        elif air_progress_reporter:
+            air_progress_reporter.experiment_started(
+                experiment_name=runner._experiment_dir_name,
+                experiment_path=runner.experiment_path,
+                searcher_str=search_alg.__class__.__name__,
+                scheduler_str=scheduler.__class__.__name__,
+                total_num_samples=search_alg.total_samples,
+                tensorboard_path=tensorboard_path,
+            )
 
         try:
             while (