def save_machine_learning_model_info(self):
try:
record = MLModel.get_or_none(MLModel.f_model_version == self.job_id,
MLModel.f_role == self.role,
MLModel.f_model_id == self.model_id,
MLModel.f_party_id == self.party_id)
if not record:
job = Job.get_or_none(Job.f_job_id == self.job_id)
pipeline = self.pipelined_model.read_pipeline_model()
if job:
job_data = job.to_dict()
model_info = {
'job_id': job_data.get("f_job_id"),
'role': self.role,
'party_id': self.party_id,
'roles': job_data.get("f_roles"),
'model_id': self.model_id,
'model_version': self.model_version,
'initiator_role': job_data.get('f_initiator_role'),
'initiator_party_id': job_data.get('f_initiator_party_id'),
'runtime_conf': job_data.get('f_runtime_conf'),
'work_mode': job_data.get('f_work_mode'),
'train_dsl': job_data.get('f_dsl'),
'train_runtime_conf': job_data.get('f_train_runtime_conf'),
'size': self.get_model_size(),
'job_status': job_data.get('f_status'),
'parent': pipeline.parent,
'fate_version': pipeline.fate_version,
'runtime_conf_on_party': json_loads(pipeline.runtime_conf_on_party),
'parent_info': json_loads(pipeline.parent_info),
'inference_dsl': json_loads(pipeline.inference_dsl)
}
model_utils.save_model_info(model_info)
schedule_logger(self.job_id).info(
'save {} model info done. model id: {}, model version: {}.'.format(self.job_id,
self.model_id,
self.model_version))
else:
schedule_logger(self.job_id).info(
'save {} model info failed, no job found in db. '
'model id: {}, model version: {}.'.format(self.job_id,
self.model_id,
self.model_version))
else:
schedule_logger(self.job_id).info('model {} info has already existed in database.'.format(self.job_id))
except Exception as e:
schedule_logger(self.job_id).exception(e)
def save_machine_learning_model_info(self):
try:
record = MLModel.get_or_none(
MLModel.f_model_version == self.job_id)
if not record:
job = Job.get_or_none(Job.f_job_id == self.job_id)
if job:
job_data = job.to_json()
MLModel.create(
f_role=self.role,
f_party_id=self.party_id,
f_roles=job_data.get("f_roles"),
f_model_id=self.model_id,
f_model_version=self.model_version,
f_job_id=job_data.get("f_job_id"),
f_create_time=current_timestamp(),
f_initiator_role=job_data.get('f_initiator_role'),
f_initiator_party_id=job_data.get(
'f_initiator_party_id'),
f_runtime_conf=job_data.get('f_runtime_conf'),
f_work_mode=job_data.get('f_work_mode'),
f_dsl=job_data.get('f_dsl'),
f_train_runtime_conf=job_data.get(
'f_train_runtime_conf'),
f_size=self.get_model_size(),
f_job_status=job_data.get('f_status'))
schedule_logger(self.job_id).info(
'save {} model info done. model id: {}, model version: {}.'
.format(self.job_id, self.model_id,
self.model_version))
else:
schedule_logger(self.job_id).info(
'save {} model info failed, no job found in db. '
'model id: {}, model version: {}.'.format(
self.job_id, self.model_id, self.model_version))
else:
schedule_logger(self.job_id).info(
'model {} info has already existed in database.'.format(
self.job_id))
except Exception as e:
schedule_logger(self.job_id).exception(e)
