StreamClosedError when calling client.shutdown()

Inizia con l’esempio minimo di SageMaker nell’issue e riproduci la sequenza di arresto usando Dask 2025.3.0 su Python 3.12. Leggi distributed/comm/tcp.py e distributed/batched.py nei punti relativi ai traceback di StreamClosedError e CommClosedError, quindi segui come client.shutdown() chiude le connessioni dello scheduler e dei worker. Il lavoro è completato quando un arresto normale non emette più questi errori imprevisti né provoca avvisi fuorvianti sulla perdita di attività.

Describe the issue:

I'm trying to run Dask in a SageMaker training/processing job and everything is working fine except that I'm getting an error in the scheduler and workers when calling client.shutdown()

Worker Error:

2025-03-27 00:16:54,367 - distributed.worker - INFO - Stopping worker at tcp://10.0.146.252:43319. Reason: scheduler-close
2025-03-27 00:16:54,367 - distributed.worker - INFO - Removing Worker plugin shuffle
2025-03-27 00:16:54,368 - distributed.batched - INFO - Batched Comm Closed <TCP (closed) Worker->Scheduler local=tcp://10.0.146.252:32892 remote=tcp://algo-2:8786>
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/site-packages/distributed/comm/tcp.py", line 298, in write
    raise StreamClosedError()
tornado.iostream.StreamClosedError: Stream is closed
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/site-packages/distributed/batched.py", line 115, in _background_send
    nbytes = yield coro
             ^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/tornado/gen.py", line 766, in run
    value = future.result()
            ^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/distributed/comm/tcp.py", line 308, in write
    convert_stream_closed_error(self, e)
  File "/usr/local/lib/python3.12/site-packages/distributed/comm/tcp.py", line 137, in convert_stream_closed_error
    raise CommClosedError(f"in {obj}: {exc}") from exc
distributed.comm.core.CommClosedError: in <TCP (closed) Worker->Scheduler local=tcp://10.0.146.252:32892 remote=tcp://algo-2:8786>: Stream is closed
2025-03-27 00:16:54,372 - distributed.core - INFO - Received 'close-stream' from tcp://algo-2:8786; closing.

Scheduler Error:

2025-03-27 00:16:54,365 - distributed.scheduler - INFO - Closing scheduler. Reason: unknown
2025-03-27 00:16:54,366 - distributed.scheduler - INFO - Scheduler closing all comms
2025-03-27 00:16:54,367 - distributed.core - INFO - Connection to tcp://10.0.172.211:47714 has been closed.
2025-03-27 00:16:54,367 - distributed.scheduler - INFO - Remove worker addr: tcp://10.0.172.211:46717 name: tcp://10.0.172.211:46717 (stimulus_id='handle-worker-cleanup-1743034614.3673875')
2025-03-27 00:16:54,367 - distributed.scheduler - WARNING - Removing worker 'tcp://10.0.172.211:46717' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {'compute-370249c7007c98cd91d71a31c040470f', 'compute-09b07314d50d8280d4975166465796dd', 'compute-3e7069bd725a0d319b3c4f74619d1a4c', 'compute-a026990cf4b6bd2562eb91ae24fd9257', 'compute-634bdcb70319d9d55105495cf828dc9f'} (stimulus_id='handle-worker-cleanup-1743034614.3673875')
2025-03-27 00:16:54,368 - distributed.core - INFO - Connection to tcp://10.0.146.252:32892 has been closed.
2025-03-27 00:16:54,368 - distributed.scheduler - INFO - Remove worker addr: tcp://10.0.146.252:43319 name: tcp://10.0.146.252:43319 (stimulus_id='handle-worker-cleanup-1743034614.3685637')
2025-03-27 00:16:54,368 - distributed.scheduler - WARNING - Removing worker 'tcp://10.0.146.252:43319' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {'compute-a298a11d0634ccbe5bd1502400ad2a94', 'compute-e5c305c5a3f20519438085130e73ff4d', 'compute-ab4f324c0dbfa011c899b004ba886942', 'compute-85c7289d7ac8c7d7dfaf09bc8546e9ef', 'compute-2de9831faeee24c2827286de6550358d'} (stimulus_id='handle-worker-cleanup-1743034614.3685637')
2025-03-27 00:16:54,369 - distributed.scheduler - INFO - Lost all workers
2025-03-27 00:16:54,370 - distributed.batched - INFO - Batched Comm Closed <TCP (closed) Scheduler->Client local=tcp://10.0.185.14:8786 remote=tcp://10.0.162.85:34804>
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/site-packages/distributed/batched.py", line 115, in _background_send
    nbytes = yield coro
             ^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/tornado/gen.py", line 766, in run
    value = future.result()
            ^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/distributed/comm/tcp.py", line 263, in write
    raise CommClosedError()
distributed.comm.core.CommClosedError

Minimal Complete Verifiable Example:

import asyncio
import json
import os
import time
from typing import Optional

from dask.distributed import Client
from distributed import Scheduler, Worker

SCHEDULER_PORT = 8786


def setup_dask_cluster() -> Optional[Client]:
    hosts = json.loads(os.environ["SM_HOSTS"])
    current_host = os.environ["SM_CURRENT_HOST"]

    client_host, scheduler_host = hosts[0], hosts[1]
    scheduler_address = f"tcp://{scheduler_host}:{SCHEDULER_PORT}"

    if current_host == client_host:
        client = Client(scheduler_address)
        client.wait_for_workers(len(hosts) - 2, timeout=30)
        return client

    async def run_scheduler():
        async with Scheduler(host=scheduler_host, port=SCHEDULER_PORT) as scheduler:
            await scheduler.finished()

    async def run_worker():
        async with Worker(scheduler_address) as worker:
            await worker.finished()

    asyncio.run(run_scheduler() if current_host == scheduler_host else run_worker())


def main():
    client = setup_dask_cluster()
    if not client:
        return  # Scheduler or worker node

    def compute(x):
        time.sleep(1)
        return 2 * x

    futures = client.map(compute, range(10))
    results = client.gather(futures)
    print(sum(results))

    client.shutdown()


if __name__ == '__main__':
    main()

Anything else we need to know?:

Environment:
AWS SageMaker processing job

• Dask version: 2025.3.0
• Python version: 3.12
• Operating System: Linux
• Install method (conda, pip, source): pip