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fix(train): stop EvaluationPipelineExecution.wait() on a Stopped execution - #6437
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…ution wait() returned on the target status and raised on statuses containing 'failed', but did not treat Stopped as terminal. With the default timeout=None, waiting on a stopped evaluation (execution.stop() or a stop from the console) polled forever; with a timeout it eventually raised TimeoutExceededError saying the job is still running. Raise FailedStatusError for Stopped (unless it is the target status), in both the Jupyter and the terminal branches, as trainer_wait already does for Stopped training jobs.
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Issue #, if available: N/A
Description of changes:
EvaluationPipelineExecution.wait()returns on the target status and raises on statuses containing "failed", but doesn't treatStoppedas terminal:timeout=None, waiting on a stopped evaluation polls forever, whether it was stopped withexecution.stop()or from the console;TimeoutExceededErrorsaying the job is still running.This change raises
FailedStatusErrorforStopped, unlessStoppedis the target status. It does this in both the Jupyter and the terminal branches, ascommon_utils/trainer_wait.pyalready does for stopped training jobs. #6423 is the equivalent fix forPipelineExecution.wait()in sagemaker-mlops.Testing:
test_wait_stops_on_stopped_status, wheretime.sleepraises ifwait()keeps polling. It fails before this change, and also checks thatwait(target_status="Stopped")returns.tests/unit/train/evaluate: no new failures. The 2 MTRL handshake tests also fail on master.By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.