wassname/ray
1
0
Fork 0
mirror of https://github.com/wassname/ray.git synced 2026-09-12 12:51:15 +08:00
Code Issues Packages Projects Releases Wiki Activity

Update named actor API (#8559)

Browse Source
This commit is contained in:
Edward Oakes
2020-05-24 20:08:03 -05:00
committed by GitHub
parent 92c2e41dfd
commit 860eb6f13a
10 changed files with 109 additions and 140 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/source/rllib-training.rst
+3 -6
View File
@@ -467,12 +467,10 @@ For even finer-grained control over training, you can use RLlib's lower-level `b
Global Coordination
~~~~~~~~~~~~~~~~~~~
Sometimes, it is necessary to coordinate between pieces of code that live in different processes managed by RLlib. For example, it can be useful to maintain a global average of a certain variable, or centrally control a hyperparameter used by policies. Ray provides a general way to achieve this through *named actors* (learn more about Ray actors `here <actors.html>`__). As an example, consider maintaining a shared global counter that is incremented by environments and read periodically from your driver program:
Sometimes, it is necessary to coordinate between pieces of code that live in different processes managed by RLlib. For example, it can be useful to maintain a global average of a certain variable, or centrally control a hyperparameter used by policies. Ray provides a general way to achieve this through *detached actors* (learn more about Ray actors `here <actors.html>`__). These actors are assigned a global name and handles to them can be retrieved using these names. As an example, consider maintaining a shared global counter that is incremented by environments and read periodically from your driver program:
.. code-block:: python
from ray.util import named_actors
@ray.remote
class Counter:
def __init__(self):
@@ -483,12 +481,11 @@ Sometimes, it is necessary to coordinate between pieces of code that live in dif
return self.count
# on the driver
counter = Counter.remote()
named_actors.register_actor("global_counter", counter)
counter = Counter.options(name="global_counter").remote()
print(ray.get(counter.get.remote())) # get the latest count
# in your envs
counter = named_actors.get_actor("global_counter")
counter = ray.get_actor("global_counter")
counter.inc.remote(1) # async call to increment the global count
Ray actors provide high levels of performance, so in more complex cases they can be used implement communication patterns such as parameter servers and allreduce.