Add API calls for checking workflow completeness

Skip grading/training tasks if the workflow is already complete.

openassessment/assessment/api/ai_worker.py

@@ -291,3 +291,65 @@ def create_classifiers(training_workflow_uuid, classifier_set):
         ).format(uuid=training_workflow_uuid, ex=ex)
         logger.exception(msg)
         raise AITrainingInternalError(msg)
+
+
+def is_training_workflow_complete(workflow_uuid):
+    """
+    Check whether the training workflow is complete.
+
+    Args:
+        workflow_uuid (str): The UUID of the training workflow
+
+    Returns:
+        bool
+
+    Raises:
+        AITrainingRequestError
+        AITrainingInternalError
+
+    """
+    try:
+        return AITrainingWorkflow.is_workflow_complete(workflow_uuid)
+    except AITrainingWorkflow.DoesNotExist:
+        msg = (
+            u"Could not retrieve training workflow "
+            u"with uuid {uuid} to check whether it's complete."
+        ).format(uuid=workflow_uuid)
+        raise AITrainingRequestError(msg)
+    except DatabaseError:
+        msg = (
+            u"An unexpected error occurred while checking "
+            u"the training workflow with uuid {uuid} for completeness"
+        ).format(uuid=workflow_uuid)
+        raise AITrainingInternalError(msg)
+
+
+def is_grading_workflow_complete(workflow_uuid):
+    """
+    Check whether the grading workflow is complete.
+
+    Args:
+        workflow_uuid (str): The UUID of the grading workflow
+
+    Returns:
+        bool
+
+    Raises:
+        AIGradingRequestError
+        AIGradingInternalError
+
+    """
+    try:
+        return AIGradingWorkflow.is_workflow_complete(workflow_uuid)
+    except AIGradingWorkflow.DoesNotExist:
+        msg = (
+            u"Could not retrieve grading workflow "
+            u"with uuid {uuid} to check whether it's complete."
+        ).format(uuid=workflow_uuid)
+        raise AIGradingRequestError(msg)
+    except DatabaseError:
+        msg = (
+            u"An unexpected error occurred while checking "
+            u"the grading workflow with uuid {uuid} for completeness"
+        ).format(uuid=workflow_uuid)
+        raise AIGradingInternalError(msg)

openassessment/assessment/models/ai.py

@@ -481,6 +481,25 @@ class AIWorkflow(models.Model):
             workflow = cls.objects.get(uuid=workflow_uuid)
             yield workflow
 
+    @classmethod
+    def is_workflow_complete(cls, workflow_uuid):
+        """
+        Check whether the workflow with a given UUID has been marked complete.
+
+        Args:
+            workflow_uuid (str): The UUID of the workflow to check.
+
+        Returns:
+            bool
+
+        Raises:
+            DatabaseError
+            cls.DoesNotExist
+
+        """
+        workflow = cls.objects.get(uuid=workflow_uuid)
+        return workflow.is_complete
+
     def _log_start_workflow(self):
         """
         A logging operation called at the beginning of an AI Workflows life.

openassessment/assessment/test/test_ai_worker.py

@@ -188,6 +188,22 @@ class AIWorkerTrainingTest(CacheResetTest):
         with self.assertRaises(AITrainingInternalError):
             ai_worker_api.create_classifiers(workflow.uuid, CLASSIFIERS)
 
+    def test_is_workflow_complete(self):
+        self.assertFalse(ai_worker_api.is_training_workflow_complete(self.workflow_uuid))
+        workflow = AITrainingWorkflow.objects.get(uuid=self.workflow_uuid)
+        workflow.mark_complete_and_save()
+        self.assertTrue(ai_worker_api.is_training_workflow_complete(self.workflow_uuid))
+
+    def test_is_workflow_complete_no_such_workflow(self):
+        with self.assertRaises(AITrainingRequestError):
+            ai_worker_api.is_training_workflow_complete('no such workflow')
+
+    @mock.patch.object(AITrainingWorkflow.objects, 'get')
+    def test_is_workflow_complete_database_error(self, mock_call):
+        mock_call.side_effect = DatabaseError("Oh no!")
+        with self.assertRaises(AITrainingInternalError):
+            ai_worker_api.is_training_workflow_complete(self.workflow_uuid)
+
 
 class AIWorkerGradingTest(CacheResetTest):
     """
@@ -301,3 +317,19 @@ class AIWorkerGradingTest(CacheResetTest):
         mock_call.side_effect = DatabaseError("KABOOM!")
         with self.assertRaises(AIGradingInternalError):
             ai_worker_api.create_assessment(self.workflow_uuid, self.SCORES)
+
+    def test_is_workflow_complete(self):
+        self.assertFalse(ai_worker_api.is_grading_workflow_complete(self.workflow_uuid))
+        workflow = AIGradingWorkflow.objects.get(uuid=self.workflow_uuid)
+        workflow.mark_complete_and_save()
+        self.assertTrue(ai_worker_api.is_grading_workflow_complete(self.workflow_uuid))
+
+    def test_is_workflow_complete_no_such_workflow(self):
+        with self.assertRaises(AIGradingRequestError):
+            ai_worker_api.is_grading_workflow_complete('no such workflow')
+
+    @mock.patch.object(AIGradingWorkflow.objects, 'get')
+    def test_is_workflow_complete_database_error(self, mock_call):
+        mock_call.side_effect = DatabaseError("Oh no!")
+        with self.assertRaises(AIGradingInternalError):
+            ai_worker_api.is_grading_workflow_complete(self.workflow_uuid)

openassessment/assessment/test/test_worker.py

@@ -18,7 +18,9 @@ from openassessment.assessment.worker.algorithm import (
 from openassessment.assessment.serializers import (
     deserialize_training_examples, rubric_from_dict
 )
-from openassessment.assessment.errors import AITrainingRequestError, AIGradingInternalError
+from openassessment.assessment.errors import (
+    AITrainingRequestError, AIGradingInternalError, AIGradingRequestError
+)
 from openassessment.assessment.test.constants import (
     EXAMPLES, RUBRIC, STUDENT_ITEM, ANSWER
 )
@@ -135,6 +137,26 @@ class AITrainingTaskTest(CeleryTaskTest):
             train_classifiers(self.workflow_uuid)
 
     @override_settings(ORA2_AI_ALGORITHMS=AI_ALGORITHMS)
+    def test_skip_completed_workflow(self):
+        # Mark the grading workflow as complete
+        workflow = AITrainingWorkflow.objects.get(uuid=self.workflow_uuid)
+        workflow.mark_complete_and_save()
+
+        # The training task should short-circuit immediately, skipping calls
+        # to get parameters for the task.
+        actual_call = ai_worker_api.get_training_task_params
+        patched = 'openassessment.assessment.worker.grading.ai_worker_api.get_training_task_params'
+        with mock.patch(patched) as mock_call:
+            mock_call.side_effect = actual_call
+            train_classifiers(self.workflow_uuid)
+            self.assertFalse(mock_call.called)
+
+    @override_settings(ORA2_AI_ALGORITHMS=AI_ALGORITHMS)
+    def test_check_complete_error(self):
+        with self.assert_retry(train_classifiers, AITrainingRequestError):
+            train_classifiers("no such workflow uuid")
+
+    @override_settings(ORA2_AI_ALGORITHMS=AI_ALGORITHMS)
     def test_unable_to_load_algorithm_class(self):
         # The algorithm is defined in the settings, but the class does not exist.
         self._set_algorithm_id(UNDEFINED_CLASS_ALGORITHM_ID)
@@ -266,6 +288,26 @@ class AIGradingTaskTest(CeleryTaskTest):
         workflow.classifier_set = classifier_set
         workflow.save()
 
+    @override_settings(ORA2_AI_ALGORITHMS=AI_ALGORITHMS)
+    def test_skip_completed_workflow(self):
+        # Mark the grading workflow as complete
+        workflow = AIGradingWorkflow.objects.get(uuid=self.workflow_uuid)
+        workflow.mark_complete_and_save()
+
+        # The grading task should short-circuit immediately, skipping calls
+        # to get parameters for the task.
+        actual_call = ai_worker_api.get_grading_task_params
+        patched = 'openassessment.assessment.worker.grading.ai_worker_api.get_grading_task_params'
+        with mock.patch(patched) as mock_call:
+            mock_call.side_effect = actual_call
+            grade_essay(self.workflow_uuid)
+            self.assertFalse(mock_call.called)
+
+    @override_settings(ORA2_AI_ALGORITHMS=AI_ALGORITHMS)
+    def test_check_complete_error(self):
+        with self.assert_retry(grade_essay, AIGradingRequestError):
+            grade_essay("no such workflow uuid")
+
     @mock.patch('openassessment.assessment.api.ai_worker.create_assessment')
     @override_settings(ORA2_AI_ALGORITHMS=AI_ALGORITHMS)
     def test_algorithm_gives_invalid_score(self, mock_create_assessment):

openassessment/assessment/worker/grading.py

@@ -48,6 +48,21 @@ def grade_essay(workflow_uuid):
         AIAlgorithmError: An error occurred while retrieving or using an AI algorithm.
 
     """
+    # Short-circuit if the workflow is already marked complete
+    # This is an optimization, but grading tasks could still
+    # execute multiple times depending on when they get picked
+    # up by workers and marked complete.
+    try:
+        if ai_worker_api.is_grading_workflow_complete(workflow_uuid):
+            return
+    except AIError:
+        msg = (
+            u"An unexpected error occurred while checking the "
+            u"completion of grading workflow with UUID {uuid}"
+        ).format(uuid=workflow_uuid)
+        logger.exception(msg)
+        raise grade_essay.retry()
+
     # Retrieve the task parameters
     try:
         params = ai_worker_api.get_grading_task_params(workflow_uuid)

openassessment/assessment/worker/training.py

@@ -65,6 +65,21 @@ def train_classifiers(workflow_uuid):
         InvalidExample: The training examples provided by the AI API were not valid.
 
     """
+    # Short-circuit if the workflow is already marked complete
+    # This is an optimization, but training tasks could still
+    # execute multiple times depending on when they get picked
+    # up by workers and marked complete.
+    try:
+        if ai_worker_api.is_training_workflow_complete(workflow_uuid):
+            return
+    except AIError:
+        msg = (
+            u"An unexpected error occurred while checking the "
+            u"completion of training workflow with UUID {uuid}"
+        ).format(uuid=workflow_uuid)
+        logger.exception(msg)
+        raise train_classifiers.retry()
+
     # Retrieve task parameters
     try:
         params = ai_worker_api.get_training_task_params(workflow_uuid)