amoghrajesh opened a new pull request, #76033:
URL: https://github.com/apache/airflow/pull/76033

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   `SageMakerTrainingOperator` has no `on_kill`. When a task is killed, times 
out, or is cleared, the SageMaker training job it submitted keeps running and 
keeps billing, with nothing left in Airflow pointing at it. On GPU or 
multi-instance training that is expensive, and the orphaned job has to be found 
and stopped by hand in the console.
   
   This adds an opt-in `stop_training_job_on_kill` flag that stops the job 
instead, matching `stop_job_run_on_kill` on `GlueJobOperator`. It defaults to 
`False`, so nothing changes for existing Dags.
   
   The job name is recorded before the submit call rather than after, so a kill 
landing in that window still has a name to act on. It is also recorded after 
the timestamp rename that `_get_unique_job_name` may apply, so `on_kill` stops 
the job that was actually submitted rather than the name in the config.
   
   `SageMakerHook.stop_training_job` swallows `ValidationException`, which is 
what SageMaker returns when the job already reached a terminal state or never 
existed. That race is expected when a task is killed as its job finishes, and 
it should not surface as a traceback from `on_kill`. Any other `ClientError` is 
re raised.
   
   This is the first half of durable execution for this operator. Reconnecting 
to a running job on retry, instead of submitting a duplicate, is a follow-up 
that will port it to `ResumableJobMixin`.
   
   ### Testing
   
   Ran a real SageMaker in `eu-west-1` with a KNN training job with 
`stop_training_job_on_kill=True`, marking the task failed moved the job to 
`Stopping` and then `Stopped`. With the flag off, the same kill left the job 
`InProgress` after the
   task was gone.
   
   
   <img width="2478" height="1190" alt="image" 
src="https://github.com/user-attachments/assets/1f76924f-7716-4717-b068-4d11ffcab32e";
 />
   
   
   ```shell
   aws sagemaker list-training-jobs --region eu-west-1
   
   {
       "TrainingJobSummaries": [
           {
               "TrainingJobName": "airflow-on-kill-test",
               "TrainingJobArn": 
"arn:aws:sagemaker:eu-west-1:396807896376:training-job/airflow-on-kill-test",
               "CreationTime": "2026-10-30T17:54:15.332000+05:30",
               "TrainingEndTime": "2026-10-30T17:54:52.582000+05:30",
               "LastModifiedTime": "2026-10-30T17:54:52.864000+05:30",
               "TrainingJobStatus": "Stopped",
               "SecondaryStatus": "Stopped"
           }
       ]
   }
   ```
   
   
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   ##### Was generative AI tooling used to co-author this PR?
   
   - [X] Yes (please specify the tool below)
   
   Generated-by: Claude Code (Opus 5) following [the 
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   ---
   
   * Read the **[Pull Request 
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 for more information. Note: commit author/co-author name and email in commits 
become permanently public when merged.
   * For fundamental code changes, an Airflow Improvement Proposal 
([AIP](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Improvement+Proposals))
 is needed.
   * When adding dependency, check compliance with the [ASF 3rd Party License 
Policy](https://www.apache.org/legal/resolved.html#category-x).
   * For significant user-facing changes create newsfragment: 
`{pr_number}.significant.rst`, in 
[airflow-core/newsfragments](https://github.com/apache/airflow/tree/main/airflow-core/newsfragments).
 You can add this file in a follow-up commit after the PR is created so you 
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