[RLlib] Change config.fault_tolerance default behavior (from recreate_failed_env_runners=False to True).

doc/source/rllib/rllib-advanced-api.rst

@@ -362,7 +362,7 @@ of such environment behavior:
 
 Note that with or without parallel evaluation, all
 :ref:`fault tolerance settings <rllib-scaling-guide>`, such as
-``ignore_env_runner_failures`` or ``recreate_failed_env_runners`` are respected and applied
+``ignore_env_runner_failures`` or ``restart_failed_env_runners`` are respected and applied
 to the failed evaluation workers.
 
 Here's an example:

doc/source/rllib/rllib-fault-tolerance.rst

@@ -31,7 +31,7 @@ The two properties that RLlib supports here are self-recovery and elasticity:
 * **Self-Recovery**: When possible, RLlib will attempt to restore any :py:class:`~ray.rllib.env.env_runner.EnvRunner` that was previously removed. During restoration, RLlib syncs the latest state over to the restored :py:class:`~ray.rllib.env.env_runner.EnvRunner` before new episodes can be sampled.
 
 
-Worker fault tolerance can be turned on by setting config ``recreate_failed_env_runners`` to True.
+Worker fault tolerance can be turned on by setting ``config.fault_tolerance(restart_failed_env_runners=True)``.
 
 RLlib achieves this by utilizing a
 `state-aware and fault tolerant actor manager <https://github.com/ray-project/ray/blob/master/rllib/utils/actor_manager.py>`__. Under the hood, RLlib relies on Ray Core :ref:`actor fault tolerance <actor-fault-tolerance>` to automatically recover failed worker actors.
@@ -57,7 +57,7 @@ errors to higher level components. You can do that easily by turning on config
     So for on-policy algorithms, it may be better to recover at worker level to make sure
     training progresses with elastic worker set while the environments are being reconstructed.
     More specifically, use configs ``num_envs_per_env_runner=1``, ``restart_failed_sub_environments=False``,
-    and ``recreate_failed_env_runners=True``.
+    and ``restart_failed_env_runners=True``.
 
 
 Fault Tolerance and Recovery Provided by Ray Tune

doc/source/rllib/rllib-training.rst

@@ -412,16 +412,16 @@ inference. Make sure to set ``num_gpus: 1`` if you want to use a GPU. If the lea
 4. Finally, if both model and environment are compute intensive, then enable `remote worker envs <rllib-env.html#vectorized>`__ with `async batching <rllib-env.html#vectorized>`__ by setting ``remote_worker_envs: True`` and optionally ``remote_env_batch_wait_ms``. This batches inference on GPUs in the rollout workers while letting envs run asynchronously in separate actors, similar to the `SEED <https://ai.googleblog.com/2020/03/massively-scaling-reinforcement.html>`__ architecture. The number of workers and number of envs per worker should be tuned to maximize GPU utilization.
 
 In case you are using lots of workers (``num_env_runners >> 10``) and you observe worker failures for whatever reasons, which normally interrupt your RLlib training runs, consider using
-the config settings ``ignore_env_runner_failures=True``, ``recreate_failed_env_runners=True``, or ``restart_failed_sub_environments=True``:
+the config settings ``ignore_env_runner_failures=True``, ``restart_failed_env_runners=True``, or ``restart_failed_sub_environments=True``:
 
-``ignore_env_runner_failures``: When set to True, your Algorithm will not crash due to a single worker error but continue for as long as there is at least one functional worker remaining.
-``recreate_failed_env_runners``: When set to True, your Algorithm will attempt to replace/recreate any failed worker(s) with newly created one(s). This way, your number of workers will never decrease, even if some of them fail from time to time.
-``restart_failed_sub_environments``: When set to True and there is a failure in one of the vectorized sub-environments in one of your workers, the worker will try to recreate only the failed sub-environment and re-integrate the newly created one into your vectorized env stack on that worker.
+``restart_failed_env_runners``: When set to True (default), your Algorithm will attempt to restart any failed EnvRunner and replace it with a newly created one. This way, your number of workers will never decrease, even if some of them fail from time to time.
+``ignore_env_runner_failures``: When set to True, your Algorithm will not crash due to an EnvRunner error, but continue for as long as there is at least one functional worker remaining. This setting is ignored when ``restart_failed_env_runners=True``.
+``restart_failed_sub_environments``: When set to True and there is a failure in one of the vectorized sub-environments in one of your EnvRunners, RLlib tries to recreate only the failed sub-environment and re-integrate the newly created one into your vectorized env stack on that EnvRunner.
 
-Note that only one of ``ignore_env_runner_failures`` or ``recreate_failed_env_runners`` may be set to True (they are mutually exclusive settings). However,
+Note that only one of ``ignore_env_runner_failures`` or ``restart_failed_env_runners`` should be set to True (they are mutually exclusive settings). However,
 you can combine each of these with the ``restart_failed_sub_environments=True`` setting.
-Using these options will make your training runs much more stable and more robust against occasional OOM or other similar "once in a while" errors on your workers
-themselves or inside your environments.
+Using these options will make your training runs much more stable and more robust against occasional OOM or other similar "once in a while" errors on the EnvRunners
+themselves or inside your custom environments.
 
 
 Debugging RLlib Experiments

rllib/algorithms/algorithm.py

@@ -1362,7 +1362,7 @@ def _env_runner_remote(worker, num, round, iter):
                 " too unstable, b) you have enough evaluation workers "
                 "(`config.evaluation(evaluation_num_env_runners=...)`) to cover for "
                 "occasional losses, and c) you use the `config.fault_tolerance("
-                "recreate_failed_env_runners=True)` setting."
+                "restart_failed_env_runners=True)` setting."
             )
 
         if not self.config.enable_env_runner_and_connector_v2:
@@ -1552,7 +1552,7 @@ def _env_runner_remote(worker, num, round, iter):
                 " too unstable, b) you have enough evaluation workers "
                 "(`config.evaluation(evaluation_num_env_runners=...)`) to cover for "
                 "occasional losses, and c) you use the `config.fault_tolerance("
-                "recreate_failed_env_runners=True)` setting."
+                "restart_failed_env_runners=True)` setting."
             )
 
         if not self.config.enable_env_runner_and_connector_v2:

rllib/algorithms/algorithm_config.py

@@ -528,26 +528,22 @@ def __init__(self, algo_class: Optional[type] = None):
         self._evaluation_parallel_to_training_wo_thread = False
 
         # `self.fault_tolerance()`
-        # TODO (sven): Rename to `restart_..` to match other attributes AND ray's
-        #  `ray.remote(max_num_restarts=..)([class])` setting.
-        self.recreate_failed_env_runners = False
+        self.restart_failed_env_runners = True
         self.ignore_env_runner_failures = False
         # By default, restart failed worker a thousand times.
         # This should be enough to handle normal transient failures.
-        # This also prevents infinite number of restarts in case
-        # the worker or env has a bug.
+        # This also prevents infinite number of restarts in case the worker or env has
+        # a bug.
         self.max_num_env_runner_restarts = 1000
-        # Small delay between worker restarts. In case rollout or
-        # evaluation workers have remote dependencies, this delay can be
-        # adjusted to make sure we don't flood them with re-connection
-        # requests, and allow them enough time to recover.
-        # This delay also gives Ray time to stream back error logging
-        # and exceptions.
+        # Small delay between worker restarts. In case EnvRunners or eval EnvRunners
+        # have remote dependencies, this delay can be adjusted to make sure we don't
+        # flood them with re-connection requests, and allow them enough time to recover.
+        # This delay also gives Ray time to stream back error logging and exceptions.
         self.delay_between_env_runner_restarts_s = 60.0
         self.restart_failed_sub_environments = False
         self.num_consecutive_env_runner_failures_tolerance = 100
-        self.env_runner_health_probe_timeout_s = 30
-        self.env_runner_restore_timeout_s = 1800
+        self.env_runner_health_probe_timeout_s = 30.0
+        self.env_runner_restore_timeout_s = 1800.0
 
         # `self.rl_module()`
         self._model_config = {}
@@ -3230,7 +3226,7 @@ def debugging(
     def fault_tolerance(
         self,
         *,
-        recreate_failed_env_runners: Optional[bool] = NotProvided,
+        restart_failed_env_runners: Optional[bool] = NotProvided,
         ignore_env_runner_failures: Optional[bool] = NotProvided,
         max_num_env_runner_restarts: Optional[int] = NotProvided,
         delay_between_env_runner_restarts_s: Optional[float] = NotProvided,
@@ -3239,6 +3235,7 @@ def fault_tolerance(
         env_runner_health_probe_timeout_s: Optional[float] = NotProvided,
         env_runner_restore_timeout_s: Optional[float] = NotProvided,
         # Deprecated args.
+        recreate_failed_env_runners=DEPRECATED_VALUE,
         ignore_worker_failures=DEPRECATED_VALUE,
         recreate_failed_workers=DEPRECATED_VALUE,
         max_num_worker_restarts=DEPRECATED_VALUE,
@@ -3250,8 +3247,8 @@ def fault_tolerance(
         """Sets the config's fault tolerance settings.
 
         Args:
-            recreate_failed_env_runners: Whether - upon an EnvRunner failure - RLlib
-                tries to recreate the lost EnvRunner(s) as an identical copy of the
+            restart_failed_env_runners: Whether - upon an EnvRunner failure - RLlib
+                tries to restart the lost EnvRunner(s) as an identical copy of the
                 failed one(s). You should set this to True when training on SPOT
                 instances that may preempt any time. The new, recreated EnvRunner(s)
                 only differ from the failed one in their `self.recreated_worker=True`
@@ -3260,11 +3257,11 @@ def fault_tolerance(
                 setting is ignored.
             ignore_env_runner_failures: Whether to ignore any EnvRunner failures
                 and continue running with the remaining EnvRunners. This setting is
-                ignored, if `recreate_failed_env_runners=True`.
+                ignored, if `restart_failed_env_runners=True`.
             max_num_env_runner_restarts: The maximum number of times any EnvRunner
-                is allowed to be restarted (if `recreate_failed_env_runners` is True).
+                is allowed to be restarted (if `restart_failed_env_runners` is True).
             delay_between_env_runner_restarts_s: The delay (in seconds) between two
-                consecutive EnvRunner restarts (if `recreate_failed_env_runners` is
+                consecutive EnvRunner restarts (if `restart_failed_env_runners` is
                 True).
             restart_failed_sub_environments: If True and any sub-environment (within
                 a vectorized env) throws any error during env stepping, the
@@ -3274,7 +3271,7 @@ def fault_tolerance(
             num_consecutive_env_runner_failures_tolerance: The number of consecutive
                 times an EnvRunner failure (also for evaluation) is tolerated before
                 finally crashing the Algorithm. Only useful if either
-                `ignore_env_runner_failures` or `recreate_failed_env_runners` is True.
+                `ignore_env_runner_failures` or `restart_failed_env_runners` is True.
                 Note that for `restart_failed_sub_environments` and sub-environment
                 failures, the EnvRunner itself is NOT affected and won't throw any
                 errors as the flawed sub-environment is silently restarted under the
@@ -3290,6 +3287,12 @@ def fault_tolerance(
         Returns:
             This updated AlgorithmConfig object.
         """
+        if recreate_failed_env_runners != DEPRECATED_VALUE:
+            deprecation_warning(
+                old="AlgorithmConfig.fault_tolerance(recreate_failed_env_runners)",
+                new="AlgorithmConfig.fault_tolerance(restart_failed_env_runners)",
+                error=True,
+            )
         if ignore_worker_failures != DEPRECATED_VALUE:
             deprecation_warning(
                 old="AlgorithmConfig.fault_tolerance(ignore_worker_failures)",
@@ -3299,7 +3302,7 @@ def fault_tolerance(
         if recreate_failed_workers != DEPRECATED_VALUE:
             deprecation_warning(
                 old="AlgorithmConfig.fault_tolerance(recreate_failed_workers)",
-                new="AlgorithmConfig.fault_tolerance(recreate_failed_env_runners)",
+                new="AlgorithmConfig.fault_tolerance(restart_failed_env_runners)",
                 error=True,
             )
         if max_num_worker_restarts != DEPRECATED_VALUE:
@@ -3339,8 +3342,8 @@ def fault_tolerance(
 
         if ignore_env_runner_failures is not NotProvided:
             self.ignore_env_runner_failures = ignore_env_runner_failures
-        if recreate_failed_env_runners is not NotProvided:
-            self.recreate_failed_env_runners = recreate_failed_env_runners
+        if restart_failed_env_runners is not NotProvided:
+            self.restart_failed_env_runners = restart_failed_env_runners
         if max_num_env_runner_restarts is not NotProvided:
             self.max_num_env_runner_restarts = max_num_env_runner_restarts
         if delay_between_env_runner_restarts_s is not NotProvided:
@@ -5150,6 +5153,22 @@ def rollouts(self, *args, **kwargs):
     def exploration(self, *args, **kwargs):
         return self.env_runners(*args, **kwargs)
 
+    @property
+    @Deprecated(
+        new="AlgorithmConfig.fault_tolerance(restart_failed_env_runners=..)",
+        error=False,
+    )
+    def recreate_failed_env_runners(self):
+        return self.restart_failed_env_runners
+
+    @recreate_failed_env_runners.setter
+    def recreate_failed_env_runners(self, value):
+        deprecation_warning(
+            old="AlgorithmConfig.recreate_failed_env_runners",
+            new="AlgorithmConfig.restart_failed_env_runners",
+            error=True,
+        )
+
     @property
     @Deprecated(new="AlgorithmConfig._enable_new_api_stack", error=False)
     def _enable_new_api_stack(self):
@@ -5225,18 +5244,18 @@ def ignore_worker_failures(self, value):
         self.ignore_env_runner_failures = value
 
     @property
-    @Deprecated(new="AlgorithmConfig.recreate_failed_env_runners", error=False)
+    @Deprecated(new="AlgorithmConfig.restart_failed_env_runners", error=False)
     def recreate_failed_workers(self):
-        return self.recreate_failed_env_runners
+        return self.restart_failed_env_runners
 
     @recreate_failed_workers.setter
     def recreate_failed_workers(self, value):
         deprecation_warning(
             old="AlgorithmConfig.recreate_failed_workers",
-            new="AlgorithmConfig.recreate_failed_env_runners",
+            new="AlgorithmConfig.restart_failed_env_runners",
             error=False,
         )
-        self.recreate_failed_env_runners = value
+        self.restart_failed_env_runners = value
 
     @property
     @Deprecated(new="AlgorithmConfig.max_num_env_runner_restarts", error=False)

rllib/algorithms/impala/impala.py

@@ -913,7 +913,7 @@ def _process_data(_actor, _episodes):
             waiting_processed_sample_batches,
             ignore_ray_errors=(
                 self.config.ignore_env_runner_failures
-                or self.config.recreate_failed_env_runners
+                or self.config.restart_failed_env_runners
             ),
         )
 
@@ -1153,7 +1153,7 @@ def _process_episodes(actor, batch):
             waiting_processed_sample_batches,
             ignore_ray_errors=(
                 self.config.ignore_env_runner_failures
-                or self.config.recreate_failed_env_runners
+                or self.config.restart_failed_env_runners
             ),
         )
 
@@ -1223,7 +1223,7 @@ def aggregate_into_larger_batch():
             if (
                 self.config.batch_mode == "truncate_episodes"
                 and self.config.enable_connectors
-                and self.config.recreate_failed_env_runners
+                and self.config.restart_failed_env_runners
             ):
                 if any(
                     SampleBatch.VF_PREDS in pb

rllib/algorithms/tests/test_callbacks_on_algorithm.py

@@ -59,7 +59,7 @@ def test_on_workers_recreated_callback(self):
             .callbacks(OnWorkersRecreatedCallbacks)
             .env_runners(num_env_runners=3)
             .fault_tolerance(
-                recreate_failed_env_runners=True,
+                restart_failed_env_runners=True,
                 delay_between_env_runner_restarts_s=0,
             )
         )