[tune] Remove hack to serve pin requests off thread (#2680)

* nopin

* fix

python/ray/tune/util.py

@@ -2,15 +2,12 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from six.moves import queue
 import base64
 import numpy as np
-import threading
 
 import ray
 
 _pinned_objects = []
-_fetch_requests = queue.Queue()
 PINNED_OBJECT_PREFIX = "ray.tune.PinnedObject:"
 
 
@@ -33,36 +30,11 @@ def get_pinned_object(pinned_id):
 
     from ray.local_scheduler import ObjectID
 
-    if threading.current_thread().getName() != "MainThread":
-        placeholder = queue.Queue()
-        _fetch_requests.put((placeholder, pinned_id))
-        print("Requesting main thread to fetch pinned object", pinned_id)
-        return placeholder.get()
-
     return _from_pinnable(
         ray.get(
             ObjectID(base64.b64decode(pinned_id[len(PINNED_OBJECT_PREFIX):]))))
 
 
-def _serve_get_pin_requests():
-    """This is hack to avoid ray.get() on the function runner thread.
-
-    The issue is that we run trainable functions on a separate thread,
-    which cannot access Ray API methods. So instead, that thread puts the
-    fetch in a queue that is periodically checked from the main thread.
-    """
-
-    assert threading.current_thread().getName() == "MainThread"
-
-    try:
-        while not _fetch_requests.empty():
-            (placeholder, pinned_id) = _fetch_requests.get_nowait()
-            print("Fetching pinned object from main thread", pinned_id)
-            placeholder.put(get_pinned_object(pinned_id))
-    except queue.Empty:
-        pass
-
-
 def _to_pinnable(obj):
     """Converts obj to a form that can be pinned in object store memory.