[tune] Cancel Experiment via Client (#7719)

* init cancel

* testing

* Update python/ray/tune/tests/test_tune_server.py

Co-Authored-By: Richard Liaw <rliaw@berkeley.edu>

* Apply suggestions from code review

* Apply suggestions from code review

* finished

* set_finished

Co-authored-by: ijrsvt <ian.rodney@gmail.com>

python/ray/tune/suggest/basic_variant.py

@@ -73,7 +73,7 @@ class BasicVariantGenerator(SearchAlgorithm):
         trials = list(self._trial_generator)
         if self._shuffle:
             random.shuffle(trials)
-        self._finished = True
+        self.set_finished()
         return trials
 
     def _generate_trials(self, num_samples, unresolved_spec, output_path=""):
@@ -104,6 +104,3 @@ class BasicVariantGenerator(SearchAlgorithm):
                     evaluated_params=flatten_resolved_vars(resolved_vars),
                     trial_id=trial_id,
                     experiment_tag=experiment_tag)
-
-    def is_finished(self):
-        return self._finished

python/ray/tune/suggest/search.py

@@ -10,6 +10,7 @@ class SearchAlgorithm:
 
     See also: `ray.tune.suggest.BasicVariantGenerator`.
     """
+    _finished = False
 
     def add_configurations(self, experiments):
         """Tracks given experiment specifications.
@@ -62,4 +63,8 @@ class SearchAlgorithm:
 
         Can return True before all trials have finished executing.
         """
-        raise NotImplementedError
+        return self._finished
+
+    def set_finished(self):
+        """Marks the search algorithm as finished."""
+        self._finished = True

python/ray/tune/suggest/suggestion.py

@@ -34,11 +34,11 @@ class SuggestionAlgorithm(SearchAlgorithm):
         self._parser = make_parser()
         self._trial_generator = []
         self._counter = 0
-        self._finished = False
         self._metric = metric
         assert mode in ["min", "max"]
         self._mode = mode
         self._use_early_stopped = use_early_stopped_trials
+        self._finished = False
 
     def add_configurations(self, experiments):
         """Chains generator given experiment specifications.
@@ -69,7 +69,7 @@ class SuggestionAlgorithm(SearchAlgorithm):
                 return trials
             trials += [trial]
 
-        self._finished = True
+        self.set_finished()
         return trials
 
     def _generate_trials(self, num_samples, experiment_spec, output_path=""):
@@ -105,9 +105,6 @@ class SuggestionAlgorithm(SearchAlgorithm):
                 experiment_tag=tag,
                 trial_id=trial_id)
 
-    def is_finished(self):
-        return self._finished
-
     def suggest(self, trial_id):
         """Queries the algorithm to retrieve the next set of parameters.