def _load_pipeline(self): buffer_type = "Pipeline" pipeline_obj = pipeline_pb2.Pipeline() pipeline_obj = model_manager.read_model(buffer_type=buffer_type, proto_buffer=pipeline_obj, name=self.workflow_param.model_table, namespace=self.workflow_param.model_namespace) pipeline_obj.node_meta = list(pipeline_obj.node_meta) pipeline_obj.node_param = list(pipeline_obj.node_param) self.pipeline = pipeline_obj
def save_pipeline(job_id, role, party_id, model_id, model_version): job_dsl, job_runtime_conf, train_runtime_conf = job_utils.get_job_configuration(job_id=job_id, role=role, party_id=party_id) job_parameters = job_runtime_conf.get('job_parameters', {}) job_type = job_parameters.get('job_type', '') if job_type == 'predict': return dag = job_utils.get_job_dsl_parser(dsl=job_dsl, runtime_conf=job_runtime_conf, train_runtime_conf=train_runtime_conf) predict_dsl = dag.get_predict_dsl(role=role) pipeline = pipeline_pb2.Pipeline() pipeline.inference_dsl = json_dumps(predict_dsl, byte=True) pipeline.train_dsl = json_dumps(job_dsl, byte=True) pipeline.train_runtime_conf = json_dumps(job_runtime_conf, byte=True) job_tracker = Tracking(job_id=job_id, role=role, party_id=party_id, model_id=model_id, model_version=model_version) job_tracker.save_output_model({'Pipeline': pipeline}, 'pipeline')
def _init_pipeline(self): pipeline_obj = pipeline_pb2.Pipeline() # pipeline_obj.node_meta = [] # pipeline_obj.node_param = [] self.pipeline = pipeline_obj LOGGER.debug("finish init pipeline")