adding context parameter for pool with a warning for not being supported (#6776)

doc/source/multiprocessing.rst

@@ -40,6 +40,9 @@ instructions to run on a multi-node Ray cluster instead.
 The full ``multiprocessing.Pool`` API is currently supported. Please see the
 `multiprocessing documentation`_ for details.
 
+.. warning::
+  The ``context`` argument in the ``Pool`` constructor is ignored when using Ray.
+
 .. _`multiprocessing documentation`: https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.pool
 
 Run on a Cluster

python/ray/experimental/multiprocessing/pool.py

@@ -1,3 +1,4 @@
+import logging
 from multiprocessing import TimeoutError
 import os
 import time
@@ -9,6 +10,8 @@ import copy
 
 import ray
 
+logger = logging.getLogger(__name__)
+
 RAY_ADDRESS_ENV = "RAY_ADDRESS"
 
 
@@ -319,6 +322,7 @@ class Pool:
                  initializer=None,
                  initargs=None,
                  maxtasksperchild=None,
+                 context=None,
                  ray_address=None):
         self._closed = False
         self._initializer = initializer
@@ -326,6 +330,11 @@ class Pool:
         self._maxtasksperchild = maxtasksperchild or -1
         self._actor_deletion_ids = []
 
+        if context:
+            logger.warning("The 'context' argument is not supported using "
+                           "ray. Please refer to the documentation for how "
+                           "to control ray initialization.")
+
         processes = self._init_ray(processes, ray_address)
         self._start_actor_pool(processes)
 
@@ -339,12 +348,12 @@ class Pool:
 
             # Cluster mode.
             if ray_address is not None:
-                print("Connecting to ray cluster at address='{}'".format(
+                logger.info("Connecting to ray cluster at address='{}'".format(
                     ray_address))
                 ray.init(address=ray_address)
             # Local mode.
             else:
-                print("Starting local ray cluster")
+                logger.info("Starting local ray cluster")
                 ray.init(num_cpus=processes)
 
         ray_cpus = int(ray.state.cluster_resources()["CPU"])