Reduce Ray / RLlib startup messages (#5368)

python/ray/rllib/agents/ppo/ppo.py

@@ -142,10 +142,6 @@ def validate_config(config):
         logger.warning(
             "Using the simple non-minibatch optimizer. This will greatly "
             "reduce performance, consider simple_optimizer=False.")
-    if not config["vf_share_layers"]:
-        logger.warning(
-            "FYI: By default, the value function will not share layers "
-            "with the policy model ('vf_share_layers': False).")
 
 
 PPOTrainer = build_trainer(

python/ray/rllib/evaluation/rollout_worker.py

@@ -317,9 +317,9 @@ class RolloutWorker(EvaluatorInterface):
             if (ray.is_initialized()
                     and ray.worker._mode() != ray.worker.LOCAL_MODE
                     and not ray.get_gpu_ids()):
-                logger.info("Creating policy evaluation worker {}".format(
+                logger.debug("Creating policy evaluation worker {}".format(
                     worker_index) +
-                            " on CPU (please ignore any CUDA init errors)")
+                             " on CPU (please ignore any CUDA init errors)")
             if not tf:
                 raise ImportError("Could not import tensorflow")
             with tf.Graph().as_default():

python/ray/rllib/policy/dynamic_tf_policy.py

@@ -321,7 +321,7 @@ class DynamicTFPolicy(TFPolicy):
             batch_tensors[k] = placeholder
 
         if log_once("loss_init"):
-            logger.info(
+            logger.debug(
                 "Initializing loss function with dummy input:\n\n{}\n".format(
                     summarize(batch_tensors)))
 

python/ray/rllib/utils/__init__.py

@@ -65,7 +65,10 @@ def try_import_tf():
         return None
 
     try:
+        if "TF_CPP_MIN_LOG_LEVEL" not in os.environ:
+            os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
         import tensorflow.compat.v1 as tf
+        tf.logging.set_verbosity(tf.logging.ERROR)
         tf.disable_v2_behavior()
         return tf
     except ImportError: