Implement a first pass at actors in the API. (#242)

* Implement actor field for tasks

* Implement actor management in local scheduler.

* initial python frontend for actors

* import actors on worker

* IPython code completion and tests

* prepare creating actors through local schedulers

* add actor id to PyTask

* submit actor calls to local scheduler

* starting to integrate

* simple fix

* Fixes from rebasing.

* more work on python actors

* Improve local scheduler actor handlers.

* Pass actor ID to local scheduler when connecting a client.

* first working version of actors

* fixing actors

* fix creating two copies of the same actor

* fix actors

* remove sleep

* get rid of export synchronization

* update

* insert actor methods into the queue in the right order

* remove print statements

* make it compile again after rebase

* Minor updates.

* fix python actor ids

* Pass actor_id to start_worker.

* add test

* Minor changes.

* Update actor tests.

* Temporary plan for import counter.

* Temporarily fix import counters.

* Fix some tests.

* Fixes.

* Make actor creation non-blocking.

* Fix test?

* Fix actors on Python 2.

* fix rare case.

* Fix python 2 test.

* More tests.

* Small fixes.

* Linting.

* Revert tensorflow version to 0.12.0 temporarily.

* Small fix.

* Enhance inheritance test.

python/ray/experimental/state.py

@@ -0,0 +1,13 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import ray.worker
+
+def get_local_schedulers():
+  local_schedulers = []
+  for client in ray.worker.global_worker.redis_client.keys("CL:*"):
+    client_type, ray_client_id = ray.worker.global_worker.redis_client.hmget(client, "client_type", "ray_client_id")
+    if client_type == b"photon":
+      local_schedulers.append(ray_client_id)
+  return local_schedulers