Fix DeprecationWarning (#5608)

python/ray/experimental/sgd/pytorch/pytorch_trainer.py

@@ -91,7 +91,7 @@ class PyTorchTrainer(object):
             batch_size_per_replica = batch_size // num_replicas
             if batch_size % num_replicas > 0:
                 new_batch_size = batch_size_per_replica * num_replicas
-                logger.warn(
+                logger.warning(
                     ("Changing batch size from {old_batch_size} to "
                      "{new_batch_size} to evenly distribute batches across "
                      "{num_replicas} replicas.").format(

python/ray/memory_monitor.py

@@ -141,7 +141,7 @@ class MemoryMonitor(object):
                             self.worker_name, round(heap_size / (1024**3), 4),
                             round(self.heap_limit / (1024**3), 4)))
                 elif heap_size > 0.8 * self.heap_limit:
-                    logger.warn(
+                    logger.warning(
                         "Heap memory usage for {} is {} / {} GiB limit".format(
                             self.worker_name, round(heap_size / (1024**3), 4),
                             round(self.heap_limit / (1024**3), 4)))

python/ray/projects/scripts.py

@@ -84,7 +84,7 @@ def create(project_name, cluster_yaml, requirements):
     os.makedirs(PROJECT_DIR)
 
     if cluster_yaml is None:
-        logger.warn("Using default autoscaler yaml")
+        logger.warning("Using default autoscaler yaml")
 
         with open(CLUSTER_TEMPLATE) as f:
             template = f.read().replace(r"{{name}}", project_name)
@@ -94,7 +94,7 @@ def create(project_name, cluster_yaml, requirements):
         cluster_yaml = CLUSTER_YAML
 
     if requirements is None:
-        logger.warn("Using default requirements.txt")
+        logger.warning("Using default requirements.txt")
         # no templating required, just copy the file
         copyfile(REQUIREMENTS_TXT_TEMPLATE, REQUIREMENTS_TXT)
 

python/ray/tune/ray_trial_executor.py

@@ -325,7 +325,7 @@ class RayTrialExecutor(TrialExecutor):
         if wait_time > NONTRIVIAL_WAIT_TIME_THRESHOLD_S:
             self._last_nontrivial_wait = time.time()
         if time.time() - self._last_nontrivial_wait > BOTTLENECK_WARN_PERIOD_S:
-            logger.warn(
+            logger.warning(
                 "Over the last {} seconds, the Tune event loop has been "
                 "backlogged processing new results. Consider increasing your "
                 "period of result reporting to improve performance.".format(

python/ray/tune/sample.py

@@ -26,8 +26,9 @@ class sample_from(object):
 
 
 def function(func):
-    logger.warn("DeprecationWarning: wrapping {} with tune.function() is no "
-                "longer needed".format(func))
+    logger.warning(
+        "DeprecationWarning: wrapping {} with tune.function() is no "
+        "longer needed".format(func))
     return func
 
 

python/ray/worker.py

@@ -1794,7 +1794,7 @@ def listen_error_messages_raylet(worker, task_error_queue, threads_stopped):
                 # Delay it a bit to see if we can suppress it
                 task_error_queue.put((error_message, time.time()))
             else:
-                logger.warn(error_message)
+                logger.warning(error_message)
     except (OSError, redis.exceptions.ConnectionError) as e:
         logger.error("listen_error_messages_raylet: {}".format(e))
     finally: