[hotfix] Lint formatting for new Tune optimizer ZOOpt (#8040)

* formatting

* removedill

* lint

doc/source/conf.py

@@ -32,7 +32,8 @@ MOCK_MODULES = [
     "tensorflow.contrib.rnn", "tensorflow.contrib.slim", "tensorflow.core",
     "tensorflow.core.util", "tensorflow.python", "tensorflow.python.client",
     "tensorflow.python.util", "torch", "torch.distributed", "torch.nn",
-    "torch.nn.parallel", "torch.utils.data", "torch.utils.data.distributed"
+    "torch.nn.parallel", "torch.utils.data", "torch.utils.data.distributed",
+    "zoopt"
 ]
 for mod_name in MOCK_MODULES:
     sys.modules[mod_name] = mock.Mock()

doc/source/tune/api_docs/suggestion.rst

@@ -48,6 +48,11 @@ SkOptSearch
 
 .. autoclass:: ray.tune.suggest.skopt.SkOptSearch
 
+ZOOptSearch
+-----------
+
+.. autoclass:: ray.tune.suggest.zoopt.ZOOptSearch
+
 SearchAlgorithm
 ---------------
 

docker/tune_test/requirements.txt

@@ -28,4 +28,3 @@ torch
 torchvision
 xgboost
 zoopt>=0.4.0
-dill

python/ray/tune/examples/zoopt_example.py

@@ -1,8 +1,7 @@
-"""This test checks that ZOOpt is functional.
+"""This test checks that ZOOptSearch is functional.
 
 It also checks that it is usable with a separate scheduler.
 """
-
 import ray
 from ray.tune import run
 from ray.tune.suggest.zoopt import ZOOptSearch
@@ -39,7 +38,7 @@ if __name__ == "__main__":
     }
 
     config = {
-        "num_samples": 200 if args.smoke_test else 1000,
+        "num_samples": 10 if args.smoke_test else 1000,
         "config": {
             "iterations": 10,  # evaluation times
         },

python/ray/tune/suggest/zoopt.py

@@ -1,8 +1,12 @@
 import copy
 import logging
-import dill as pickle
-from zoopt import Dimension2, Parameter
-from zoopt.algos.opt_algorithms.racos.sracos import SRacosTune
+import ray.cloudpickle as pickle
+
+try:
+    import zoopt
+except ImportError:
+    zoopt = None
+
 from ray.tune.suggest.suggestion import SuggestionAlgorithm
 
 logger = logging.getLogger(__name__)
@@ -76,7 +80,7 @@ class ZOOptSearch(SuggestionAlgorithm):
                  metric="episode_reward_mean",
                  mode="min",
                  **kwargs):
-
+        assert zoopt is not None, "Zoopt not found - please install zoopt."
         assert budget is not None, "`budget` should not be None!"
         assert dim_dict is not None, "`dim_list` should not be None!"
         assert type(max_concurrent) is int and max_concurrent > 0
@@ -99,9 +103,10 @@ class ZOOptSearch(SuggestionAlgorithm):
             self._dim_keys.append(k)
             _dim_list.append(dim_dict[k])
 
-        dim = Dimension2(_dim_list)
-        par = Parameter(budget=budget)
+        dim = zoopt.Dimension2(_dim_list)
+        par = zoopt.Parameter(budget=budget)
         if _algo == "sracos" or _algo == "asracos":
+            from zoopt.algos.opt_algorithms.racos.sracos import SRacosTune
             self.optimizer = SRacosTune(dimension=dim, parameter=par)
 
         self.solution_dict = {}

python/ray/tune/tests/test_tune_restore.py

@@ -7,6 +7,7 @@ import skopt
 import numpy as np
 from hyperopt import hp
 from nevergrad.optimization import optimizerlib
+from zoopt import ValueType
 
 import ray
 from ray import tune
@@ -18,7 +19,6 @@ from ray.tune.suggest.skopt import SkOptSearch
 from ray.tune.suggest.nevergrad import NevergradSearch
 from ray.tune.suggest.sigopt import SigOptSearch
 from ray.tune.suggest.zoopt import ZOOptSearch
-from zoopt import ValueType
 from ray.tune.utils import validate_save_restore