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/__init__.py

@@ -16,5 +16,6 @@ if hasattr(ctypes, "windll"):
 import ray.experimental
 import ray.serialization
 from ray.worker import register_class, error_info, init, connect, disconnect, get, put, wait, remote, log_event, log_span, flush_log
+from ray.actor import actor
 from ray.worker import EnvironmentVariable, env
 from ray.worker import SCRIPT_MODE, WORKER_MODE, PYTHON_MODE, SILENT_MODE