def post(self):
"""Handles POST requests."""
payload_proto = (
training_job_response_payload_pb2.TrainingJobResponsePayload())
payload_proto.ParseFromString(self.request.body)
if not validate_job_result_message_proto(payload_proto.job_result):
raise self.InvalidInputException
job_id = payload_proto.job_result.job_id
classifier_training_job = (
classifier_services.get_classifier_training_job_by_id(job_id))
if classifier_training_job.status == (
feconf.TRAINING_JOB_STATUS_FAILED):
# Send email to admin and admin-specified email recipients.
# Other email recipients are specified on admin config page.
email_manager.send_job_failure_email(job_id)
raise self.InternalErrorException(
'The current status of the job cannot transition to COMPLETE.')
classifier_data_proto = getattr(
payload_proto.job_result,
payload_proto.job_result.WhichOneof('classifier_frozen_model'))
classifier_services.store_classifier_data(
job_id, classifier_data_proto)
# Update status of the training job to 'COMPLETE'.
classifier_services.mark_training_job_complete(job_id)
return self.render_json({})
def test_mark_training_job_complete(self):
"""Test the mark_training_job_complete method."""
exp_id = u'1'
next_scheduled_check_time = datetime.datetime.utcnow()
state_name = 'Home'
interaction_id = 'TextInput'
job_id = self._create_classifier_training_job(
feconf.INTERACTION_CLASSIFIER_MAPPING['TextInput']['algorithm_id'],
interaction_id, exp_id, 1, next_scheduled_check_time, [],
state_name, feconf.TRAINING_JOB_STATUS_PENDING, {}, 1)
classifier_training_job = (
classifier_services.get_classifier_training_job_by_id(job_id))
self.assertEqual(classifier_training_job.status,
feconf.TRAINING_JOB_STATUS_PENDING)
classifier_services.mark_training_job_complete(job_id)
classifier_training_job = (
classifier_services.get_classifier_training_job_by_id(job_id))
self.assertEqual(classifier_training_job.status,
feconf.TRAINING_JOB_STATUS_COMPLETE)
# Test that invalid status changes cannot be made.
with self.assertRaisesRegexp(
Exception, ('The status change %s to %s is not valid.' %
(feconf.TRAINING_JOB_STATUS_COMPLETE,
feconf.TRAINING_JOB_STATUS_COMPLETE))):
classifier_services.mark_training_job_complete(job_id)
def test_can_not_mark_training_jobs_complete_due_to_invalid_job_id(
self) -> None:
with self.assertRaisesRegex( # type: ignore[no-untyped-call]
Exception,
'The ClassifierTrainingJobModel corresponding to the '
'job_id of the ClassifierTrainingJob does not exist.'):
classifier_services.mark_training_job_complete('invalid_job_id')
def post(self):
"""Handles POST requests."""
signature = self.payload.get('signature')
message = self.payload.get('message')
vm_id = self.payload.get('vm_id')
if vm_id == feconf.DEFAULT_VM_ID and not feconf.DEV_MODE:
raise self.UnauthorizedUserException
if not validate_job_result_message_dict(message):
raise self.InvalidInputException
if not verify_signature(message, vm_id, signature):
raise self.UnauthorizedUserException
job_id = message['job_id']
classifier_data = message['classifier_data']
classifier_training_job = (
classifier_services.get_classifier_training_job_by_id(job_id))
if classifier_training_job.status == (
feconf.TRAINING_JOB_STATUS_FAILED):
raise self.InternalErrorException(
'The current status of the job cannot transition to COMPLETE.')
try:
classifier_services.store_classifier_data(job_id, classifier_data)
except Exception as e:
raise self.InternalErrorException(e)
# Update status of the training job to 'COMPLETE'.
classifier_services.mark_training_job_complete(job_id)
return self.render_json({})
def post(self):
"""Handles POST requests."""
signature = self.payload.get('signature')
message = self.payload.get('message')
vm_id = self.payload.get('vm_id')
if vm_id == feconf.DEFAULT_VM_ID and not constants.DEV_MODE:
raise self.UnauthorizedUserException
if not validate_job_result_message_dict(message):
raise self.InvalidInputException
if not verify_signature(message, vm_id, signature):
raise self.UnauthorizedUserException
job_id = message['job_id']
# The classifier data received in the payload has all floating point
# values stored as strings. This is because floating point numbers
# are represented differently on GAE(Oppia) and GCE(Oppia-ml).
# Therefore, converting all floating point numbers to string keeps
# signature consistent on both Oppia and Oppia-ml.
# For more info visit: https://stackoverflow.com/q/40173295
classifier_data = (
classifier_services.
convert_strings_to_float_numbers_in_classifier_data( #pylint: disable=line-too-long
message['classifier_data_with_floats_stringified']))
classifier_training_job = (
classifier_services.get_classifier_training_job_by_id(job_id))
if classifier_training_job.status == (
feconf.TRAINING_JOB_STATUS_FAILED):
# Send email to admin and admin-specified email recipients.
# Other email recipients are specified on admin config page.
email_manager.send_job_failure_email(job_id)
raise self.InternalErrorException(
'The current status of the job cannot transition to COMPLETE.')
try:
classifier_services.store_classifier_data(job_id, classifier_data)
except Exception as e:
raise self.InternalErrorException(e)
# Update status of the training job to 'COMPLETE'.
classifier_services.mark_training_job_complete(job_id)
return self.render_json({})
