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")