[tune] Add more intensive tests (#7667)

* make_heavier_tests

* help

python/ray/tune/examples/bohb_example.py

@@ -1,6 +1,5 @@
 #!/usr/bin/env python
 
-import argparse
 import json
 import os
 
@@ -11,14 +10,6 @@ from ray.tune import Trainable, run
 from ray.tune.schedulers.hb_bohb import HyperBandForBOHB
 from ray.tune.suggest.bohb import TuneBOHB
 
-parser = argparse.ArgumentParser()
-parser.add_argument(
-    "--smoke-test", action="store_true", help="Finish quickly for testing")
-parser.add_argument(
-    "--ray-address",
-    help="Address of Ray cluster for seamless distributed execution.")
-args, _ = parser.parse_known_args()
-
 
 class MyTrainableClass(Trainable):
     """Example agent whose learning curve is a random sigmoid.
@@ -52,7 +43,7 @@ class MyTrainableClass(Trainable):
 
 if __name__ == "__main__":
     import ConfigSpace as CS
-    ray.init(address=args.ray_address)
+    ray.init(num_cpus=8)
 
     # BOHB uses ConfigSpace for their hyperparameter search space
     config_space = CS.ConfigurationSpace()
@@ -75,4 +66,4 @@ if __name__ == "__main__":
         scheduler=bohb_hyperband,
         search_alg=bohb_search,
         num_samples=10,
-        stop={"training_iteration": 10 if args.smoke_test else 100})
+        stop={"training_iteration": 100})

python/ray/tune/examples/hyperband_example.py

@@ -47,7 +47,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--smoke-test", action="store_true", help="Finish quickly for testing")
     args, _ = parser.parse_known_args()
-    ray.init()
+    ray.init(num_cpus=4 if args.smoke_test else None)
 
     # Hyperband early stopping, configured with `episode_reward_mean` as the
     # objective and `training_iteration` as the time unit,
@@ -56,7 +56,7 @@ if __name__ == "__main__":
         time_attr="training_iteration",
         metric="episode_reward_mean",
         mode="max",
-        max_t=100)
+        max_t=200)
 
     run(MyTrainableClass,
         name="hyperband_test",

python/ray/tune/examples/pbt_example.py

@@ -110,9 +110,9 @@ if __name__ == "__main__":
         reuse_actors=True,
         verbose=False,
         stop={
-            "training_iteration": 2000,
+            "training_iteration": 200,
         },
-        num_samples=4,
+        num_samples=8,
         config={
             "lr": 0.0001,
             # note: this parameter is perturbed but has no effect on