Ensure that adaptive only stops once

distributed/deploy/adaptive.py

@@ -109,8 +109,6 @@ def __init__(
 
         self.target_duration = parse_timedelta(target_duration)
 
-        logger.info("Adaptive scaling started: minimum=%s maximum=%s", minimum, maximum)
-
         super().__init__(
             minimum=minimum, maximum=maximum, wait_count=wait_count, interval=interval
         )

distributed/deploy/adaptive_core.py

@@ -5,7 +5,7 @@
 from collections import defaultdict, deque
 from collections.abc import Iterable
 from datetime import timedelta
-from typing import TYPE_CHECKING, cast
+from typing import TYPE_CHECKING, Literal, cast
 
 import tlz as toolz
 from tornado.ioloop import IOLoop
@@ -17,12 +17,21 @@
 from distributed.metrics import time
 
 if TYPE_CHECKING:
-    from distributed.scheduler import WorkerState
+    from typing_extensions import TypeAlias
 
+    from distributed.scheduler import WorkerState
 
 logger = logging.getLogger(__name__)
 
 
+AdaptiveStateState: TypeAlias = Literal[
+    "starting",
+    "running",
+    "stopped",
+    "inactive",
+]
+
+
 class AdaptiveCore:
     """
     The core logic for adaptive deployments, with none of the cluster details
@@ -89,6 +98,8 @@ class AdaptiveCore:
     observed: set[WorkerState]
     close_counts: defaultdict[WorkerState, int]
     _adapting: bool
+    #: Whether this adaptive strategy is periodically adapting
+    _state: AdaptiveStateState
     log: deque[tuple[float, dict]]
 
     def __init__(
@@ -107,12 +118,6 @@ def __init__(
         self.interval = parse_timedelta(interval, "seconds")
         self.periodic_callback = None
 
-        def f():
-            try:
-                self.periodic_callback.start()
-            except AttributeError:
-                pass
-
         if self.interval:
             import weakref
 
@@ -124,8 +129,10 @@ async def _adapt():
                     await core.adapt()
 
             self.periodic_callback = PeriodicCallback(_adapt, self.interval * 1000)
-            self.loop.add_callback(f)
-
+            self.loop.add_callback(self._start)
+            self._state = "starting"
+        else:
+            self._state = "inactive"
         try:
             self.plan = set()
             self.requested = set()
@@ -140,12 +147,34 @@ async def _adapt():
             maxlen=dask.config.get("distributed.admin.low-level-log-length")
         )
 
+    def _start(self) -> None:
+        if self._state != "starting":
+            return
+
+        assert self.periodic_callback is not None
+        self.periodic_callback.start()
+        self._state = "running"
+        logger.info(
+            "Adaptive scaling started: minimum=%s maximum=%s",
+            self.minimum,
+            self.maximum,
+        )
+
     def stop(self) -> None:
-        logger.info("Adaptive stop")
+        if self._state in ("inactive", "stopped"):
+            return
 
-        if self.periodic_callback:
+        if self._state == "running":
+            assert self.periodic_callback is not None
             self.periodic_callback.stop()
-            self.periodic_callback = None
+            logger.info(
+                "Adaptive scaling stopped: minimum=%s maximum=%s",
+                self.minimum,
+                self.maximum,
+            )
+
+        self.periodic_callback = None
+        self._state = "stopped"
 
     async def target(self) -> int:
         """The target number of workers that should exist"""

distributed/deploy/tests/test_adaptive_core.py

@@ -104,11 +104,12 @@ def safe_target(self):
             raise OSError()
 
     with captured_logger("distributed.deploy.adaptive_core") as log:
-        adapt = BadAdaptive(minimum=1, maximum=4)
-        await adapt.adapt()
+        adapt = BadAdaptive(minimum=1, maximum=4, interval="10ms")
+        while adapt._state != "stopped":
+            await asyncio.sleep(0.01)
     text = log.getvalue()
     assert "Adaptive stopping due to error" in text
-    assert "Adaptive stop" in text
+    assert "Adaptive scaling stopped" in text
     assert not adapt._adapting
     assert not adapt.periodic_callback
 
@@ -147,6 +148,18 @@ async def scale_down(self, workers=None):
     adapt.stop()
 
 
+@gen_test()
+async def test_adaptive_logs_stopping_once():
+    with captured_logger("distributed.deploy.adaptive_core") as log:
+        adapt = MyAdaptive(interval="100ms")
+        while not adapt.periodic_callback.is_running():
+            await asyncio.sleep(0.01)
+        adapt.stop()
+        adapt.stop()
+    lines = log.getvalue().splitlines()
+    assert sum("Adaptive scaling stopped" in line for line in lines) == 1
+
+
 @gen_test()
 async def test_adapt_stop_del():
     adapt = MyAdaptive(interval="100ms")