[tune] Fix up Ax Search and Examples

doc/source/tune-searchalg.rst

@@ -171,7 +171,9 @@ This algorithm requires specifying a search space and objective. You can use `Ax
 
 .. code-block:: python
 
-    tune.run(... , search_alg=AxSearch(parameter_dicts, ... ))
+    client = AxClient(enforce_sequential_optimization=False)
+    client.create_experiment( ... )
+    tune.run(... , search_alg=AxSearch(client))
 
 An example of this can be found in `ax_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/ax_example.py>`__.
 

python/ray/tune/examples/ax_example.py

@@ -51,11 +51,13 @@ def easy_objective(config, reporter):
 
 if __name__ == "__main__":
     import argparse
+    from ax.service.ax_client import AxClient
 
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "--smoke-test", action="store_true", help="Finish quickly for testing")
     args, _ = parser.parse_known_args()
+
     ray.init()
 
     config = {
@@ -101,13 +103,14 @@ def easy_objective(config, reporter):
             "bounds": [0.0, 1.0],
         },
     ]
-    algo = AxSearch(
+    client = AxClient(enforce_sequential_optimization=False)
+    client.create_experiment(
         parameters=parameters,
         objective_name="hartmann6",
-        max_concurrent=4,
         minimize=True,  # Optional, defaults to False.
         parameter_constraints=["x1 + x2 <= 2.0"],  # Optional.
         outcome_constraints=["l2norm <= 1.25"],  # Optional.
     )
+    algo = AxSearch(client, max_concurrent=4)
     scheduler = AsyncHyperBandScheduler(reward_attr="hartmann6")
     run(easy_objective, name="ax", search_alg=algo, **config)

python/ray/tune/suggest/ax.py

@@ -6,16 +6,19 @@
     import ax
 except ImportError:
     ax = None
+import logging
 
 from ray.tune.suggest.suggestion import SuggestionAlgorithm
 
+logger = logging.getLogger(__name__)
+
 
 class AxSearch(SuggestionAlgorithm):
     """A wrapper around Ax to provide trial suggestions.
 
-    Requires Ax to be installed.
-    Ax is an open source tool from Facebook for configuring and
-    optimizing experiments. More information can be found in https://ax.dev/.
+    Requires Ax to be installed. Ax is an open source tool from
+    Facebook for configuring and optimizing experiments. More information
+    can be found in https://ax.dev/.
 
     Parameters:
         parameters (list[dict]): Parameters in the experiment search space.
@@ -48,40 +51,27 @@ class AxSearch(SuggestionAlgorithm):
         >>>     objective_name="hartmann6", max_concurrent=4)
     """
 
-    def __init__(self,
-                 parameters,
-                 objective_name,
-                 max_concurrent=10,
-                 minimize=False,
-                 parameter_constraints=None,
-                 outcome_constraints=None,
-                 **kwargs):
+    def __init__(self, ax_client, max_concurrent=10, **kwargs):
         assert ax is not None, "Ax must be installed!"
-        from ax.service import ax_client
         assert type(max_concurrent) is int and max_concurrent > 0
-        self._ax = ax_client.AxClient(enforce_sequential_optimization=False)
-        self._ax.create_experiment(
-            name="ax",
-            parameters=parameters,
-            objective_name=objective_name,
-            minimize=minimize,
-            parameter_constraints=parameter_constraints or [],
-            outcome_constraints=outcome_constraints or [],
-        )
+        self._ax = ax_client
+        exp = self._ax.experiment
+        self._objective_name = exp.optimization_config.objective.metric.name
+        if self._ax._enforce_sequential_optimization:
+            logger.warning("Detected sequential enforcement. Setting max "
+                           "concurrency to 1.")
+            max_concurrent = 1
         self._max_concurrent = max_concurrent
-        self._parameters = [d["name"] for d in parameters]
-        self._objective_name = objective_name
+        self._parameters = list(exp.parameters)
         self._live_index_mapping = {}
-
         super(AxSearch, self).__init__(**kwargs)
 
     def _suggest(self, trial_id):
         if self._num_live_trials() >= self._max_concurrent:
             return None
         parameters, trial_index = self._ax.get_next_trial()
-        suggested_config = list(parameters.values())
         self._live_index_mapping[trial_id] = trial_index
-        return dict(zip(self._parameters, suggested_config))
+        return parameters
 
     def on_trial_result(self, trial_id, result):
         pass