Source code for mlflow.projects.submitted_run

from abc import abstractmethod

import os
import signal
import logging

from mlflow.entities import RunStatus

_logger = logging.getLogger(__name__)

[docs]class SubmittedRun(object): """ Wrapper around an MLflow project run (e.g. a subprocess running an entry point command or a Databricks job run) and exposing methods for waiting on and cancelling the run. This class defines the interface that the MLflow project runner uses to manage the lifecycle of runs launched in different environments (e.g. runs launched locally or on Databricks). ``SubmittedRun`` is not thread-safe. That is, concurrent calls to wait() / cancel() from multiple threads may inadvertently kill resources (e.g. local processes) unrelated to the run. NOTE: Subclasses of ``SubmittedRun`` must expose a ``run_id`` member containing the run's MLflow run ID. """
[docs] @abstractmethod def wait(self): """ Wait for the run to finish, returning True if the run succeeded and false otherwise. Note that in some cases (e.g. remote execution on Databricks), we may wait until the remote job completes rather than until the MLflow run completes. """ pass
[docs] @abstractmethod def get_status(self): """ Get status of the run. """ pass
[docs] @abstractmethod def cancel(self): """ Cancel the run (interrupts the command subprocess, cancels the Databricks run, etc) and waits for it to terminate. The MLflow run status may not be set correctly upon run cancellation. """ pass
@property @abstractmethod def run_id(self): pass
class LocalSubmittedRun(SubmittedRun): """ Instance of ``SubmittedRun`` corresponding to a subprocess launched to run an entry point command locally. """ def __init__(self, run_id, command_proc): super(LocalSubmittedRun, self).__init__() self._run_id = run_id self.command_proc = command_proc @property def run_id(self): return self._run_id def wait(self): return self.command_proc.wait() == 0 def cancel(self): # Interrupt child process if it hasn't already exited if self.command_proc.poll() is None: # Kill the the process tree rooted at the child if it's the leader of its own process # group, otherwise just kill the child try: if == os.getpgid( os.killpg(, signal.SIGTERM) else: self.command_proc.terminate() except OSError: # The child process may have exited before we attempted to terminate it, so we # ignore OSErrors raised during child process termination "Failed to terminate child process (PID %s) corresponding to MLflow " "run with ID %s. The process may have already exited.",, self._run_id, ) self.command_proc.wait() def _get_status(self): exit_code = self.command_proc.poll() if exit_code is None: return RunStatus.RUNNING if exit_code == 0: return RunStatus.FINISHED return RunStatus.FAILED def get_status(self): return RunStatus.to_string(self._get_status())