llama-stack
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Published
20 hours ago •
meta-llama
meta-llama
fix: cancel scheduler tasks on shutdown
What does this PR do?
Scheduler: cancel tasks on shutdown.
Otherwise the currently running tasks will never exit (before they actually complete), which means the process can't be properly shut down (only with SIGKILL).
Ideally, we let tasks know that they are about to shutdown and give them some time to do so; but in the lack of the mechanism, it's better to cancel than linger forever.
Test Plan
Start a long running task (e.g. torchtune or external kfp-provider training). Ctr-C the process in TTY. Confirm it exits in reasonable time.
^CINFO: Shutting down
INFO: Waiting for application shutdown.
13:32:26.187 - INFO - Shutting down
13:32:26.187 - INFO - Shutting down DatasetsRoutingTable
13:32:26.187 - INFO - Shutting down DatasetIORouter
13:32:26.187 - INFO - Shutting down TorchtuneKFPPostTrainingImpl
Traceback (most recent call last):
File "/opt/homebrew/Cellar/[email protected]/3.12.4/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/runners.py", line 118, in run
return self._loop.run_until_complete(task)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/Cellar/[email protected]/3.12.4/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/base_events.py", line 687, in run_until_complete
return future.result()
^^^^^^^^^^^^^^^
asyncio.exceptions.CancelledError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "/Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/executor_main.py", line 109, in <module>
executor_main()
File "/Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/executor_main.py", line 101, in executor_main
output_file = executor.execute()
^^^^^^^^^^^^^^^^^^
File "/Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/executor.py", line 361, in execute
result = self.func(**func_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/var/folders/45/1q1rx6cn7jbcn2ty852w0g_r0000gn/T/tmp.RKpPrvTWDD/ephemeral_component.py", line 118, in component
asyncio.run(recipe.setup())
File "/opt/homebrew/Cellar/[email protected]/3.12.4/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/runners.py", line 194, in run
return runner.run(main)
^^^^^^^^^^^^^^^^
File "/opt/homebrew/Cellar/[email protected]/3.12.4/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/runners.py", line 123, in run
raise KeyboardInterrupt()
KeyboardInterrupt
13:32:31.219 - ERROR - Task 'component' finished with status FAILURE
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
INFO 2025-05-09 13:32:31,221 llama_stack.providers.utils.scheduler:221 scheduler: Job
test-jobc3c2e1e4-859c-4852-a41d-ef29e55e3efa: Pipeline [1m[95m'test-jobc3c2e1e4-859c-4852-a41d-ef29e55e3efa'[1m[0m
finished with status [1m[91mFAILURE[1m[0m. Inner task failed: [1m[96m'component'[1m[0m.
ERROR 2025-05-09 13:32:31,223 llama_stack_provider_kfp_trainer.scheduler:54 scheduler: Job
test-jobc3c2e1e4-859c-4852-a41d-ef29e55e3efa failed.
╭───────────────────────────────────── Traceback (most recent call last) ─────────────────────────────────────╮
│ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/src/llama_stack_provider_kfp_trainer/scheduler.py:45 │
│ in do │
│ │
│ 42 │ │ │ │
│ 43 │ │ │ job.status = JobStatus.running │
│ 44 │ │ │ try: │
│ ❱ 45 │ │ │ │ artifacts = self._to_artifacts(job.handler().output) │
│ 46 │ │ │ │ for artifact in artifacts: │
│ 47 │ │ │ │ │ on_artifact_collected_cb(artifact) │
│ 48 │
│ │
│ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/base_compon │
│ ent.py:101 in __call__ │
│ │
│ 98 │ │ │ │ f'{self.name}() missing {len(missing_arguments)} required ' │
│ 99 │ │ │ │ f'{argument_or_arguments}: {arguments}.') │
│ 100 │ │ │
│ ❱ 101 │ │ return pipeline_task.PipelineTask( │
│ 102 │ │ │ component_spec=self.component_spec, │
│ 103 │ │ │ args=task_inputs, │
│ 104 │ │ │ execute_locally=pipeline_context.Pipeline.get_default_pipeline() is │
│ │
│ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/pipeline_ta │
│ sk.py:187 in __init__ │
│ │
│ 184 │ │ ]) │
│ 185 │ │ │
│ 186 │ │ if execute_locally: │
│ ❱ 187 │ │ │ self._execute_locally(args=args) │
│ 188 │ │
│ 189 │ def _execute_locally(self, args: Dict[str, Any]) -> None: │
│ 190 │ │ """Execute the pipeline task locally. │
│ │
│ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/dsl/pipeline_ta │
│ sk.py:197 in _execute_locally │
│ │
│ 194 │ │ from kfp.local import task_dispatcher │
│ 195 │ │ │
│ 196 │ │ if self.pipeline_spec is not None: │
│ ❱ 197 │ │ │ self._outputs = pipeline_orchestrator.run_local_pipeline( │
│ 198 │ │ │ │ pipeline_spec=self.pipeline_spec, │
│ 199 │ │ │ │ arguments=args, │
│ 200 │ │ │ ) │
│ │
│ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/local/pipeline_ │
│ orchestrator.py:43 in run_local_pipeline │
│ │
│ 40 │ │
│ 41 │ # validate and access all global state in this function, not downstream │
│ 42 │ config.LocalExecutionConfig.validate() │
│ ❱ 43 │ return _run_local_pipeline_implementation( │
│ 44 │ │ pipeline_spec=pipeline_spec, │
│ 45 │ │ arguments=arguments, │
│ 46 │ │ raise_on_error=config.LocalExecutionConfig.instance.raise_on_error, │
│ │
│ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/local/pipeline_ │
│ orchestrator.py:108 in _run_local_pipeline_implementation │
│ │
│ 105 │ │ │ ) │
│ 106 │ │ return outputs │
│ 107 │ elif dag_status == status.Status.FAILURE: │
│ ❱ 108 │ │ log_and_maybe_raise_for_failure( │
│ 109 │ │ │ pipeline_name=pipeline_name, │
│ 110 │ │ │ fail_stack=fail_stack, │
│ 111 │ │ │ raise_on_error=raise_on_error, │
│ │
│ /Users/ihrachys/src/llama-stack-provider-kfp-trainer/.venv/lib/python3.12/site-packages/kfp/local/pipeline_ │
│ orchestrator.py:137 in log_and_maybe_raise_for_failure │
│ │
│ 134 │ │ logging_utils.format_task_name(task_name) for task_name in fail_stack) │
│ 135 │ msg = f'Pipeline {pipeline_name_with_color} finished with status │
│ {status_with_color}. Inner task failed: {task_chain_with_color}.' │
│ 136 │ if raise_on_error: │
│ ❱ 137 │ │ raise RuntimeError(msg) │
│ 138 │ with logging_utils.local_logger_context(): │
│ 139 │ │ logging.error(msg) │
│ 140 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
RuntimeError: Pipeline [1m[95m'test-jobc3c2e1e4-859c-4852-a41d-ef29e55e3efa'[1m[0m finished with status
[1m[91mFAILURE[1m[0m. Inner task failed: [1m[96m'component'[1m[0m.
INFO 2025-05-09 13:32:31,266 llama_stack.distribution.server.server:136 server: Shutting down
DistributionInspectImpl
INFO 2025-05-09 13:32:31,266 llama_stack.distribution.server.server:136 server: Shutting down ProviderImpl
INFO: Application shutdown complete.
INFO: Finished server process [26648]
