Replace --redis-address with --address in test, docs, tune, rllib (#5602)

* wip

* add tests and tune

* add ci

* test fix

* lint

* fix tests

* wip

* sugar dep

python/ray/tune/examples/async_hyperband_example.py

@@ -51,10 +51,10 @@ if __name__ == "__main__":
     parser.add_argument(
         "--smoke-test", action="store_true", help="Finish quickly for testing")
     parser.add_argument(
-        "--ray-redis-address",
+        "--ray-address",
         help="Address of Ray cluster for seamless distributed execution.")
     args, _ = parser.parse_known_args()
-    ray.init(redis_address=args.ray_redis_address)
+    ray.init(address=args.ray_address)
 
     # asynchronous hyperband early stopping, configured with
     # `episode_reward_mean` as the

python/ray/tune/examples/bohb_example.py

@@ -19,7 +19,7 @@ parser = argparse.ArgumentParser()
 parser.add_argument(
     "--smoke-test", action="store_true", help="Finish quickly for testing")
 parser.add_argument(
-    "--ray-redis-address",
+    "--ray-address",
     help="Address of Ray cluster for seamless distributed execution.")
 args, _ = parser.parse_known_args()
 
@@ -56,7 +56,7 @@ class MyTrainableClass(Trainable):
 
 if __name__ == "__main__":
     import ConfigSpace as CS
-    ray.init(redis_address=args.ray_redis_address)
+    ray.init(address=args.ray_address)
 
     # BOHB uses ConfigSpace for their hyperparameter search space
     config_space = CS.ConfigurationSpace()

python/ray/tune/examples/mnist_pytorch.py

@@ -116,11 +116,11 @@ if __name__ == "__main__":
     parser.add_argument(
         "--smoke-test", action="store_true", help="Finish quickly for testing")
     parser.add_argument(
-        "--ray-redis-address",
+        "--ray-address",
         help="Address of Ray cluster for seamless distributed execution.")
     args = parser.parse_args()
-    if args.ray_redis_address:
-        ray.init(redis_address=args.ray_redis_address)
+    if args.ray_address:
+        ray.init(address=args.ray_address)
     sched = AsyncHyperBandScheduler(
         time_attr="training_iteration", metric="mean_accuracy")
     analysis = tune.run(

python/ray/tune/examples/mnist_pytorch_trainable.py

@@ -25,7 +25,7 @@ parser.add_argument(
     default=False,
     help="enables CUDA training")
 parser.add_argument(
-    "--ray-redis-address", type=str, help="The Redis address of the cluster.")
+    "--ray-address", type=str, help="The Redis address of the cluster.")
 parser.add_argument(
     "--smoke-test", action="store_true", help="Finish quickly for testing")
 
@@ -64,7 +64,7 @@ class TrainMNIST(tune.Trainable):
 
 if __name__ == "__main__":
     args = parser.parse_args()
-    ray.init(redis_address=args.ray_redis_address)
+    ray.init(address=args.ray_address)
     sched = ASHAScheduler(metric="mean_accuracy")
     analysis = tune.run(
         TrainMNIST,

python/ray/tune/tests/example.py

@@ -6,9 +6,9 @@
 # import ray
 # import argparse
 # parser = argparse.ArgumentParser()
-# parser.add_argument("--redis-address")
+# parser.add_argument("--address")
 # args = parser.parse_args()
-# ray.init(redis_address=args.redis_address)
+# ray.init(address=args.address)
 
 # __quick_start_begin__
 import torch.optim as optim

python/ray/tune/tests/test_cluster.py

@@ -357,7 +357,7 @@ import time
 import ray
 from ray import tune
 
-ray.init(redis_address="{redis_address}")
+ray.init(address="{address}")
 
 
 tune.run(
@@ -372,7 +372,7 @@ tune.run(
     dict(experiment=kwargs),
     raise_on_failed_trial=False)
 """.format(
-        redis_address=cluster.redis_address, checkpoint_dir=dirpath)
+        address=cluster.address, checkpoint_dir=dirpath)
     run_string_as_driver_nonblocking(script)
     # Wait until the right checkpoint is saved.
     # The trainable returns every 0.5 seconds, so this should not miss
@@ -446,7 +446,7 @@ import time
 import ray
 from ray import tune
 
-ray.init(redis_address="{redis_address}")
+ray.init(address="{address}")
 
 {fail_class_code}
 
@@ -460,7 +460,7 @@ tune.run(
     max_failures=1,
     raise_on_failed_trial=False)
 """.format(
-        redis_address=cluster.redis_address,
+        address=cluster.address,
         checkpoint_dir=dirpath,
         fail_class_code=reformatted,
         fail_class=_Mock.__name__)

python/ray/tune/tests/tutorial.py

@@ -40,7 +40,7 @@ search_space = {
 }
 
 # Uncomment this to enable distributed execution
-# `ray.init(redis_address=...)`
+# `ray.init(address=...)`
 
 analysis = tune.run(train_mnist, config=search_space)
 # __eval_func_end__