[Train] Skip incrementing failure counter on preemption node died failures

python/ray/air/config.py

@@ -382,29 +382,28 @@ class FailureConfig:
             Will recover from the latest checkpoint if present.
             Setting to -1 will lead to infinite recovery retries.
             Setting to 0 will disable retries. Defaults to 0.
-        fail_fast: Whether to fail upon the first error. Only used for
-            Ray Tune - this does not apply
-            to single training runs (e.g. with ``Trainer.fit()``).
-            If fail_fast='raise' provided, Ray Tune will automatically
-            raise the exception received by the Trainable. fail_fast='raise'
-            can easily leak resources and should be used with caution (it
-            is best used with `ray.init(local_mode=True)`).
+        fail_fast: Whether to fail upon the first error.
+            If fail_fast='raise' provided, the original error during training will be
+            immediately raised. fail_fast='raise' can easily leak resources and
+            should be used with caution.
     """
 
     max_failures: int = 0
     fail_fast: Union[bool, str] = False
 
     def __post_init__(self):
-        # Same check as in tune.run
-        if self.fail_fast and self.max_failures != 0:
-            raise ValueError("max_failures must be 0 if fail_fast=True.")
-
         # Same check as in TuneController
         if not (isinstance(self.fail_fast, bool) or self.fail_fast.upper() == "RAISE"):
             raise ValueError(
                 "fail_fast must be one of {bool, 'raise'}. " f"Got {self.fail_fast}."
             )
 
+        # Same check as in tune.run
+        if self.fail_fast and self.max_failures != 0:
+            raise ValueError(
+                f"max_failures must be 0 if fail_fast={repr(self.fail_fast)}."
+            )
+
     def __repr__(self):
         return _repr_dataclass(self)
 

python/ray/air/tests/test_errors.py

@@ -1,5 +1,5 @@
 """
-This test suite covers error handling and propagation in Ray AIR.
+This test suite covers error handling and propagation in Ray Train/Tune.
 
 There are two main error types to test:
 1. Trainable errors: These happen in the remote actor itself.
@@ -16,16 +16,26 @@
 - Assert how errors from the Tune driver get propagated to the user.
 """
 import gc
-import pytest
+import threading
+import time
 from tempfile import TemporaryDirectory
 
+import pytest
+
 import ray
 from ray import train, tune
+from ray._private.test_utils import wait_for_condition
+from ray._raylet import GcsClient
+from ray.cluster_utils import Cluster
+from ray.core.generated import autoscaler_pb2
 from ray.train import Checkpoint, FailureConfig, RunConfig, ScalingConfig
 from ray.train.data_parallel_trainer import DataParallelTrainer
 from ray.train.trainer import BaseTrainer, TrainingFailedError
 from ray.tune import Tuner, TuneConfig, TuneError
 
+from ray.tests.conftest import *  # noqa
+from ray.train.tests.util import create_dict_checkpoint, load_dict_checkpoint
+
 
 @pytest.fixture(scope="module")
 def ray_start_4_cpus():
@@ -43,6 +53,34 @@ def gc_collect():
     gc.collect()
 
 
+@pytest.fixture
+def cluster_setup(ray_start_cluster_head: Cluster):
+    # Sets up a cluster with 4 nodes: head node + 3 workers
+    cluster = ray_start_cluster_head
+    nodes = []
+    nodes.append(cluster.add_node(resources={"worker1": 1, "coordinator": 1}))
+    nodes.append(cluster.add_node(resources={"worker2": 1, "cpu": 1}))
+    nodes.append(cluster.add_node(resources={"worker3": 1, "cpu": 1}))
+    cluster.wait_for_nodes()
+
+    @ray.remote
+    def get_node_id():
+        return ray.get_runtime_context().get_node_id()
+
+    worker1_node_id = ray.get(get_node_id.options(resources={"worker1": 1}).remote())
+    worker2_node_id = ray.get(get_node_id.options(resources={"worker2": 1}).remote())
+    worker3_node_id = ray.get(get_node_id.options(resources={"worker3": 1}).remote())
+    wait_for_condition(
+        lambda: len({node["NodeID"] for node in ray.nodes() if (node["Alive"])}) == 4
+    )
+
+    yield cluster, nodes, [
+        worker1_node_id,
+        worker2_node_id,
+        worker3_node_id,
+    ]
+
+
 class _TestSpecificError(RuntimeError):
     pass
 
@@ -195,6 +233,90 @@ def test_driver_error_with_trainer(ray_start_4_cpus, tmp_path, error_on):
     assert TrainingFailedError._FAILURE_CONFIG_MSG not in str(exc_info.value)
 
 
+@pytest.mark.parametrize("error_at_level", ["worker", "coordinator"])
+def test_preemption_handling(
+    cluster_setup,
+    tmp_path,
+    error_at_level: str,
+):
+    """Integration test for node preemption handling in Ray Train/Tune.
+    Even though `max_failures=0`, preemption errors should still be retried."""
+    cluster, nodes, node_ids = cluster_setup
+    # node 1 = coordinator, node 2 = worker, node 3 = worker
+    coordinator_node, worker_node, _ = nodes
+    coordinator_node_id, worker_node_id, _ = node_ids
+
+    num_workers = 2
+    tmp_path.joinpath("markers").mkdir()
+
+    def train_fn(config):
+        checkpoint = train.get_checkpoint()
+        start_iter = 0
+        if checkpoint:
+            start_iter = load_dict_checkpoint(checkpoint)["iter"] + 1
+            print(f"Restored at iter = {start_iter}")
+
+        for iter in range(start_iter, 6):
+            with create_dict_checkpoint({"iter": iter}) as checkpoint:
+                ray.train.report({"iter": iter}, checkpoint=checkpoint)
+
+            if iter == 2:
+                # Write a "done marker" to tell the driver to simulate a preemption.
+                tmp_path.joinpath(
+                    "markers", str(ray.train.get_context().get_world_rank())
+                ).touch()
+                # Await execution.
+                time.sleep(120)
+
+    def launch_training():
+        trainer = DataParallelTrainer(
+            train_loop_per_worker=train_fn,
+            scaling_config=ScalingConfig(
+                num_workers=num_workers,
+                trainer_resources={"coordinator": 1},
+                resources_per_worker={"cpu": 1},  # worker2 and worker3
+            ),
+            run_config=RunConfig(
+                storage_path=str(tmp_path),
+                name="test_preemption_error",
+                failure_config=train.FailureConfig(fail_fast=False, max_failures=0),
+            ),
+        )
+        result = trainer.fit()
+        assert result.metrics["iter"] == 5
+
+    t = threading.Thread(target=launch_training)
+    t.start()
+
+    # Wait until the workers are ready for preemption (after a few checkpoints).
+    while len(list(tmp_path.joinpath("markers").glob("*"))) < num_workers:
+        time.sleep(0.5)
+
+    if error_at_level == "coordinator":
+        node, node_id = coordinator_node, coordinator_node_id
+    elif error_at_level == "worker":
+        node, node_id = worker_node, worker_node_id
+    else:
+        raise NotImplementedError(f"Invalid error_at_level = {error_at_level}")
+
+    # Preempt a node.
+    gcs_client = GcsClient(address=ray.get_runtime_context().gcs_address)
+    print("Draining node...")
+    is_accepted = gcs_client.drain_node(
+        node_id,
+        autoscaler_pb2.DrainNodeReason.Value("DRAIN_NODE_REASON_PREEMPTION"),
+        "preemption",
+    )
+    assert is_accepted
+    print("Killing node...")
+    cluster.remove_node(node, allow_graceful=True)
+    print("Adding new node..")  # so that the job can be rescheduled
+    # New node can replace a preempted coordinator or worker
+    # NOTE: `cluster.add_node` only works in the main thread.
+    cluster.add_node(resources={"coordinator": 1, "cpu": 1})
+    t.join()  # Assert no errors during training.
+
+
 if __name__ == "__main__":
     import sys
 

python/ray/train/constants.py

@@ -82,6 +82,10 @@ def _get_defaults_results_dir() -> str:
 # or Train worker to the trial directory. Defaults to 1.
 RAY_CHDIR_TO_TRIAL_DIR = "RAY_CHDIR_TO_TRIAL_DIR"
 
+# Set this to 1 to count preemption errors toward `FailureConfig(max_failures)`.
+# Defaults to 0, which always retries on node preemption failures.
+RAY_TRAIN_COUNT_PREEMPTION_ERRORS = "RAY_TRAIN_COUNT_PREEMPTION_ERRORS"
+
 # NOTE: When adding a new environment variable, please track it in this list.
 TRAIN_ENV_VARS = {
     ENABLE_DETAILED_AUTOFILLED_METRICS_ENV,

python/ray/train/data_parallel_trainer.py

@@ -1,6 +1,6 @@
 import inspect
 import logging
-from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Type, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Type, Union
 
 import ray
 from ray._private.thirdparty.tabulate.tabulate import tabulate
@@ -357,61 +357,71 @@ def _validate_scaling_config(cls, scaling_config: ScalingConfig) -> ScalingConfi
 
         return scaling_config
 
-    def _report(self, training_iterator: TrainingIterator) -> None:
-        for results in training_iterator:
+    def _run_training(self, training_iterator: TrainingIterator) -> None:
+        """This method loops over the `TrainingIterator`:
+        The actual iteration (for ... in ...) waits for the training function
+        on each worker to report a result and supplies it as a list of results.
+        Afterwards (in the body of the loop), it will report the result
+        to the Tune session.
+        The iterator ends after the training function on each worker has finished.
+        """
+        for training_results in training_iterator:
             # TODO(ml-team): add ability to report results from multiple workers.
-            first_worker_result = results[0]
-            assert all(isinstance(result, _TrainingResult) for result in results)
-
-            tune_session = get_session()
-
-            # Check if any workers reported a checkpoint.
-            # If so, report a checkpoint pointing to the persisted location
-            # to Tune for book-keeping.
-            # NOTE: This removes the restriction for any individual worker
-            # (ex: global rank 0 worker) from needing to report a checkpoint.
-            # All workers reported a checkpoint to the same fs path, so there's
-            # no need to report multiple checkpoints to Tune.
-            worker_checkpoints = [
-                result.checkpoint for result in results if result.checkpoint is not None
-            ]
-            at_least_one_reported_checkpoint = len(worker_checkpoints) > 0
-
-            if at_least_one_reported_checkpoint:
-                # Update the coordinator's checkpoint index to the latest.
-                # This is what keeps the checkpoint index in line with the workers.
-                tune_session.storage._update_checkpoint_index(
-                    first_worker_result.metrics
-                )
-
-            # Make sure that all workers uploaded to the same location.
-            assert all(
-                checkpoint.path == tune_session.storage.checkpoint_fs_path
-                for checkpoint in worker_checkpoints
-            )
+            self._propagate_results(training_results)
+
+    def _propagate_results(self, training_results: List[_TrainingResult]):
+        first_worker_result = training_results[0]
+        assert all(isinstance(result, _TrainingResult) for result in training_results)
+
+        tune_session = get_session()
+
+        # Check if any workers reported a checkpoint.
+        # If so, report a checkpoint pointing to the persisted location
+        # to Tune for book-keeping.
+        # NOTE: This removes the restriction for any individual worker
+        # (ex: global rank 0 worker) from needing to report a checkpoint.
+        # All workers reported a checkpoint to the same fs path, so there's
+        # no need to report multiple checkpoints to Tune.
+        worker_checkpoints = [
+            result.checkpoint
+            for result in training_results
+            if result.checkpoint is not None
+        ]
+        at_least_one_reported_checkpoint = len(worker_checkpoints) > 0
+
+        if at_least_one_reported_checkpoint:
+            # Update the coordinator's checkpoint index to the latest.
+            # This is what keeps the checkpoint index in line with the workers.
+            tune_session.storage._update_checkpoint_index(first_worker_result.metrics)
+
+        # Make sure that all workers uploaded to the same location.
+        assert all(
+            checkpoint.path == tune_session.storage.checkpoint_fs_path
+            for checkpoint in worker_checkpoints
+        )
 
-            checkpoint = (
-                Checkpoint(
-                    filesystem=tune_session.storage.storage_filesystem,
-                    path=tune_session.storage.checkpoint_fs_path,
-                )
-                if at_least_one_reported_checkpoint
-                else None
+        checkpoint = (
+            Checkpoint(
+                filesystem=tune_session.storage.storage_filesystem,
+                path=tune_session.storage.checkpoint_fs_path,
             )
+            if at_least_one_reported_checkpoint
+            else None
+        )
 
-            tracked_training_result = _TrainingResult(
-                checkpoint=checkpoint,
-                metrics=first_worker_result.metrics,
-            )
+        tracked_training_result = _TrainingResult(
+            checkpoint=checkpoint,
+            metrics=first_worker_result.metrics,
+        )
 
-            logger.debug(
-                "Report (metrics, checkpoint) to the Tune session:\n"
-                f"  metrics={tracked_training_result.metrics}\n"
-                f"  checkpoint={tracked_training_result.checkpoint}"
-            )
+        logger.debug(
+            "Report (metrics, checkpoint) to the Tune session:\n"
+            f"  metrics={tracked_training_result.metrics}\n"
+            f"  checkpoint={tracked_training_result.checkpoint}"
+        )
 
-            # Report the metrics and checkpoint to Tune.
-            tune_session._report_training_result(tracked_training_result)
+        # Report the metrics and checkpoint to Tune.
+        tune_session._report_training_result(tracked_training_result)
 
     def training_loop(self) -> None:
         scaling_config = self._validate_scaling_config(self.scaling_config)
@@ -457,7 +467,7 @@ def training_loop(self) -> None:
             checkpoint=self.starting_checkpoint,
         )
 
-        self._report(training_iterator)
+        self._run_training(training_iterator)
 
         # Shutdown workers.
         backend_executor.shutdown()

python/ray/tune/error.py

@@ -46,10 +46,3 @@ class _TuneNoNextExecutorEventError(_SubCategoryTuneError):
     this category. This category is for everything else."""
 
     pass
-
-
-class _TuneRestoreError(_SubCategoryTuneError):
-    """Error that happens in restoring a remote trainable."""
-
-    def __init__(self, exc: Exception):
-        self.exc = exc