[tune] Add Tuner.get_results() to retrieve results after restore

python/ray/tune/impl/tuner_internal.py

@@ -26,6 +26,7 @@
 _TUNER_PKL = "tuner.pkl"
 _TRAINABLE_KEY = "_trainable"
 _PARAM_SPACE_KEY = "_param_space"
+_EXPERIMENT_ANALYSIS_KEY = "_experiment_analysis"
 
 
 class TunerInternal:
@@ -104,6 +105,8 @@ def __init__(
             self._run_config
         )
 
+        self._experiment_analysis = None
+
         # Not used for restored Tuner.
         self._param_space = param_space or {}
         self._process_scaling_config()
@@ -219,6 +222,13 @@ def _restore_from_path_or_uri(
             shutil.rmtree(experiment_checkpoint_path)
             self._experiment_checkpoint_dir = str(new_exp_path)
 
+        try:
+            self._experiment_analysis = ExperimentAnalysis(
+                self._experiment_checkpoint_dir
+            )
+        except Exception:
+            self._experiment_analysis = None
+
     def _maybe_sync_down_tuner_state(self, restore_path: str) -> Tuple[bool, str]:
         """Sync down trainable state from remote storage.
 
@@ -284,7 +294,17 @@ def fit(self) -> ResultGrid:
         else:
             analysis = self._fit_resume(trainable)
 
-        return ResultGrid(analysis)
+        self._experiment_analysis = analysis
+
+        return ResultGrid(self._experiment_analysis)
+
+    def get_results(self) -> ResultGrid:
+        if not self._experiment_analysis:
+            raise RuntimeError(
+                "Can't return results as experiment has not been run, yet. "
+                "Call `Tuner.fit()` to run the experiment first."
+            )
+        return ResultGrid(self._experiment_analysis)
 
     def _get_tune_run_arguments(self, trainable) -> Dict[str, Any]:
         """Get tune.run arguments common for both new and resumed runs."""
@@ -413,6 +433,7 @@ def __getstate__(self):
         state = self.__dict__.copy()
         state.pop(_TRAINABLE_KEY, None)
         state.pop(_PARAM_SPACE_KEY, None)
+        state.pop(_EXPERIMENT_ANALYSIS_KEY, None)
         return state
 
     def __setstate__(self, state):

python/ray/tune/tests/test_tuner_restore.py

@@ -100,6 +100,11 @@ def test_tuner_restore_num_trials(ray_start_4_cpus, tmpdir):
 
     del tuner
     tuner = Tuner.restore(str(tmpdir / "test_tuner_restore_num_trials"))
+
+    # Check restored results
+    results = tuner.get_results()
+    assert len(results) == 4
+
     results = tuner.fit()
     assert len(results) == 4
 
@@ -137,7 +142,17 @@ def test_tuner_restore_resume_errored(ray_start_4_cpus, tmpdir):
     tuner = Tuner.restore(
         str(tmpdir / "test_tuner_restore_resume_errored"), resume_errored=True
     )
+
+    # Check restored results
+    results = tuner.get_results()
+    assert len(results) == 4
+    assert len(results.errors) == 2
+    # Second and third trial are at iter 1 because they failed after first report
+    assert [r.metrics["it"] for r in results] == [2, 1, 2, 1]
+
+    # Get new results
     results = tuner.fit()
+
     assert len(results) == 4
     assert len(results.errors) == 0
     # Since the errored trials are being resumed from previous state and then report
@@ -176,6 +191,14 @@ def test_tuner_restore_restart_errored(ray_start_4_cpus, tmpdir):
     tuner = Tuner.restore(
         str(tmpdir / "test_tuner_restore_restart_errored"), restart_errored=True
     )
+
+    # Check restored results
+    results = tuner.get_results()
+    assert len(results) == 4
+    assert len(results.errors) == 2
+    assert [r.metrics["it"] for r in results] == [2, 1, 2, 1]
+
+    # Get new results
     results = tuner.fit()
     assert len(results) == 4
     assert len(results.errors) == 0

python/ray/tune/tuner.py

@@ -257,3 +257,28 @@ def fit(self) -> ResultGrid:
                 raise TuneError(
                     _TUNER_FAILED_MSG.format(path=experiment_checkpoint_dir)
                 ) from e
+
+    def get_results(self) -> ResultGrid:
+        """Get results of a hyperparameter tuning run.
+
+        This method returns the same results as :meth:`fit() <ray.tune.tuner.Tuner.fit>`
+        and can be used to retrieve the results after restoring a tuner without
+        calling ``fit()`` again.
+
+        If the tuner has not been fit before, an error will be raised.
+
+        .. code-block:: python
+
+            from ray.tune import Tuner
+
+            tuner = Tuner.restore("/path/to/experiment')
+            results = tuner.get_results()
+
+        Returns:
+            Result grid of a previously fitted tuning run.
+
+        """
+        if not self._is_ray_client:
+            return self._local_tuner.get_results()
+        else:
+            return ray.get(self._remote_tuner.fit.remote())