tests confirmed, fixed bugs+style (ray-project#4120)

ci/long_running_tests/workloads/pbt.py

@@ -23,26 +23,24 @@
 
 cluster = Cluster()
 for i in range(num_nodes):
-    cluster.add_node(
-        redis_port=6379 if i == 0 else None,
-        num_redis_shards=num_redis_shards if i == 0 else None,
-        num_cpus=10,
-        num_gpus=0,
-        resources={str(i): 2},
-        object_store_memory=object_store_memory,
-        redis_max_memory=redis_max_memory)
+    cluster.add_node(redis_port=6379 if i == 0 else None,
+                     num_redis_shards=num_redis_shards if i == 0 else None,
+                     num_cpus=10,
+                     num_gpus=0,
+                     resources={str(i): 2},
+                     object_store_memory=object_store_memory,
+                     redis_max_memory=redis_max_memory)
 ray.init(redis_address=cluster.redis_address)
 
 # Run the workload.
 
-pbt = PopulationBasedTraining(
-    time_attr="training_iteration",
-    metric="episode_reward_mean",
-    mode="max",
-    perturbation_interval=10,
-    hyperparam_mutations={
-        "lr": [0.1, 0.01, 0.001, 0.0001],
-    })
+pbt = PopulationBasedTraining(time_attr="training_iteration",
+                              metric="episode_reward_mean",
+                              mode="max",
+                              perturbation_interval=10,
+                              hyperparam_mutations={
+                                  "lr": [0.1, 0.01, 0.001, 0.0001],
+                              })
 
 run_experiments(
     {

python/ray/tune/examples/async_hyperband_example.py

@@ -48,21 +48,21 @@ def _restore(self, checkpoint_path):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--smoke-test", action="store_true", help="Finish quickly for testing")
+    parser.add_argument("--smoke-test",
+                        action="store_true",
+                        help="Finish quickly for testing")
     args, _ = parser.parse_known_args()
     ray.init()
 
     # asynchronous hyperband early stopping, configured with
     # `episode_reward_mean` as the
     # objective and `training_iteration` as the time unit,
     # which is automatically filled by Tune.
-    ahb = AsyncHyperBandScheduler(
-        time_attr="training_iteration",
-        metric="episode_reward_mean",
-        mode="max",
-        grace_period=5,
-        max_t=100)
+    ahb = AsyncHyperBandScheduler(time_attr="training_iteration",
+                                  metric="episode_reward_mean",
+                                  mode="max",
+                                  grace_period=5,
+                                  max_t=100)
 
     run(MyTrainableClass,
         name="asynchyperband_test",
@@ -77,8 +77,8 @@ def _restore(self, checkpoint_path):
                 "gpu": 0
             },
             "config": {
-                "width": sample_from(
-                    lambda spec: 10 + int(90 * random.random())),
+                "width": sample_from(lambda spec: 10 + int(90 * random.random(
+                ))),
                 "height": sample_from(lambda spec: int(100 * random.random())),
             },
         })

python/ray/tune/examples/ax_example.py

@@ -42,19 +42,19 @@ def easy_objective(config, reporter):
     time.sleep(0.2)
     for i in range(config["iterations"]):
         x = np.array([config.get(f"x{i+1}") for i in range(6)])
-        reporter(
-            timesteps_total=i,
-            hartmann6=hartmann6(x),
-            l2norm=np.sqrt((x**2).sum()))
+        reporter(timesteps_total=i,
+                 hartmann6=hartmann6(x),
+                 l2norm=np.sqrt((x**2).sum()))
         time.sleep(0.02)
 
 
 if __name__ == "__main__":
     import argparse
 
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--smoke-test", action="store_true", help="Finish quickly for testing")
+    parser.add_argument("--smoke-test",
+                        action="store_true",
+                        help="Finish quickly for testing")
     args, _ = parser.parse_known_args()
     ray.init()
 

python/ray/tune/examples/bayesopt_example.py

@@ -16,19 +16,19 @@ def easy_objective(config, reporter):
     import time
     time.sleep(0.2)
     for i in range(config["iterations"]):
-        reporter(
-            timesteps_total=i,
-            neg_mean_loss=-(config["height"] - 14)**2 +
-            abs(config["width"] - 3))
+        reporter(timesteps_total=i,
+                 neg_mean_loss=-(config["height"] - 14)**2 +
+                 abs(config["width"] - 3))
         time.sleep(0.02)
 
 
 if __name__ == "__main__":
     import argparse
 
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--smoke-test", action="store_true", help="Finish quickly for testing")
+    parser.add_argument("--smoke-test",
+                        action="store_true",
+                        help="Finish quickly for testing")
     args, _ = parser.parse_known_args()
     ray.init()
 
@@ -43,16 +43,15 @@ def easy_objective(config, reporter):
             "timesteps_total": 100
         }
     }
-    algo = BayesOptSearch(
-        space,
-        max_concurrent=4,
-        metric="neg_mean_loss",
-        mode="max",
-        utility_kwargs={
-            "kind": "ucb",
-            "kappa": 2.5,
-            "xi": 0.0
-        })
+    algo = BayesOptSearch(space,
+                          max_concurrent=4,
+                          metric="neg_mean_loss",
+                          mode="max",
+                          utility_kwargs={
+                              "kind": "ucb",
+                              "kappa": 2.5,
+                              "xi": 0.0
+                          })
     scheduler = AsyncHyperBandScheduler(metric="neg_mean_loss", mode="max")
     run(easy_objective,
         name="my_exp",

python/ray/tune/examples/genetic_example.py

@@ -31,8 +31,9 @@ def michalewicz_function(config, reporter):
     import argparse
 
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--smoke-test", action="store_true", help="Finish quickly for testing")
+    parser.add_argument("--smoke-test",
+                        action="store_true",
+                        help="Finish quickly for testing")
     args, _ = parser.parse_known_args()
     ray.init()
 
@@ -45,11 +46,10 @@ def michalewicz_function(config, reporter):
     })
 
     config = {"stop": {"training_iteration": 100}}
-    algo = GeneticSearch(
-        space,
-        reward_attr="neg_mean_loss",
-        max_generation=2 if args.smoke_test else 10,
-        population_size=10 if args.smoke_test else 50)
+    algo = GeneticSearch(space,
+                         reward_attr="neg_mean_loss",
+                         max_generation=2 if args.smoke_test else 10,
+                         population_size=10 if args.smoke_test else 50)
     scheduler = AsyncHyperBandScheduler(metric="neg_mean_loss", mode="max")
     run(michalewicz_function,
         name="my_exp",

python/ray/tune/examples/hyperband_example.py

@@ -48,19 +48,19 @@ def _restore(self, checkpoint_path):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--smoke-test", action="store_true", help="Finish quickly for testing")
+    parser.add_argument("--smoke-test",
+                        action="store_true",
+                        help="Finish quickly for testing")
     args, _ = parser.parse_known_args()
     ray.init()
 
     # Hyperband early stopping, configured with `episode_reward_mean` as the
     # objective and `training_iteration` as the time unit,
     # which is automatically filled by Tune.
-    hyperband = HyperBandScheduler(
-        time_attr="training_iteration",
-        metric="episode_reward_mean",
-        mode="max",
-        max_t=100)
+    hyperband = HyperBandScheduler(time_attr="training_iteration",
+                                   metric="episode_reward_mean",
+                                   mode="max",
+                                   max_t=100)
 
     exp = Experiment(
         name="hyperband_test",

python/ray/tune/examples/hyperopt_example.py

@@ -18,10 +18,9 @@ def easy_objective(config, reporter):
     assert type(config["activation"]) == str, \
         "Config is incorrect: {}".format(type(config["activation"]))
     for i in range(config["iterations"]):
-        reporter(
-            timesteps_total=i,
-            neg_mean_loss=-(config["height"] - 14)**2 +
-            abs(config["width"] - 3))
+        reporter(timesteps_total=i,
+                 neg_mean_loss=-(config["height"] - 14)**2 +
+                 abs(config["width"] - 3))
         time.sleep(0.02)
 
 
@@ -30,8 +29,9 @@ def easy_objective(config, reporter):
     from hyperopt import hp
 
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--smoke-test", action="store_true", help="Finish quickly for testing")
+    parser.add_argument("--smoke-test",
+                        action="store_true",
+                        help="Finish quickly for testing")
     args, _ = parser.parse_known_args()
     ray.init()
 
@@ -63,11 +63,10 @@ def easy_objective(config, reporter):
             "timesteps_total": 100
         },
     }
-    algo = HyperOptSearch(
-        space,
-        max_concurrent=4,
-        metric="neg_mean_loss",
-        mode="min",
-        points_to_evaluate=current_best_params)
+    algo = HyperOptSearch(space,
+                          max_concurrent=4,
+                          metric="neg_mean_loss",
+                          mode="min",
+                          points_to_evaluate=current_best_params)
     scheduler = AsyncHyperBandScheduler(metric="neg_mean_loss", mode="max")
     run(easy_objective, search_alg=algo, scheduler=scheduler, **config)