Use our own implementation of parallel_memcopy (#7254)

python/ray/worker.py

@@ -66,12 +66,6 @@ ERROR_KEY_PREFIX = b"Error:"
 # entry/init points.
 logger = logging.getLogger(__name__)
 
-# Whether we should warn about slow put performance.
-if os.environ.get("OMP_NUM_THREADS") == "1":
-    should_warn_of_slow_puts = True
-else:
-    should_warn_of_slow_puts = False
-
 
 class ActorCheckpointInfo:
     """Information used to maintain actor checkpoints."""
@@ -275,22 +269,10 @@ class Worker:
                 "do this, you can wrap the ray.ObjectID in a list and "
                 "call 'put' on it (or return it).")
 
-        global should_warn_of_slow_puts
-        if should_warn_of_slow_puts:
-            start = time.perf_counter()
-
         serialized_value = self.get_serialization_context().serialize(value)
-        result = self.core_worker.put_serialized_object(
+        return self.core_worker.put_serialized_object(
             serialized_value, object_id=object_id, pin_object=pin_object)
 
-        if should_warn_of_slow_puts:
-            delta = time.perf_counter() - start
-            if delta > 0.1:
-                logger.warning("OMP_NUM_THREADS=1 is set, this may slow down "
-                               "ray.put() for large objects (issue #6998).")
-                should_warn_of_slow_puts = False
-        return result
-
     def deserialize_objects(self, data_metadata_pairs, object_ids):
         context = self.get_serialization_context()
         return context.deserialize_objects(data_metadata_pairs, object_ids)