# Copyright 2021 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os.path
import json
from sedna.common.log import LOGGER
from sedna.common.file_ops import FileOps
from sedna.common.config import BaseConfig
from sedna.common.config import Context
from sedna.common.constant import K8sResourceKind
from sedna.service.client import LCClient
from sedna.backend import set_backend
from sedna.common.class_factory import ClassFactory, ClassType
__all__ = ('JobBase',)
[docs]class JobBase:
""" sedna feature base class """
def __init__(self, estimator, config=None):
super(JobBase, self).__init__()
self.config = BaseConfig()
if config:
self.config.from_json(config)
self.log = LOGGER
self.estimator = set_backend(estimator=estimator, config=self.config)
self.job_kind = K8sResourceKind.DEFAULT.value
self.job_name = self.config.job_name or self.config.service_name
self.worker_name = self.config.worker_name or self.job_name
@property
[docs] def model_path(self):
if os.path.isfile(self.config.model_url):
return self.config.model_url
return self.get_parameters('model_path') or FileOps.join_path(
self.config.model_url, self.estimator.model_name)
[docs] def train(self, **kwargs):
raise NotImplementedError
[docs] def inference(self, x=None, post_process=None, **kwargs):
res = self.estimator.predict(x, kwargs=kwargs)
callback_func = None
if callable(post_process):
callback_func = post_process
elif post_process is not None:
callback_func = ClassFactory.get_cls(
ClassType.CALLBACK, post_process)
return callback_func(res) if callback_func else res
[docs] def evaluate(self, data, post_process=None, **kwargs):
callback_func = None
if callable(post_process):
callback_func = post_process
elif post_process is not None:
callback_func = ClassFactory.get_cls(
ClassType.CALLBACK, post_process)
res = self.estimator.evaluate(data=data, **kwargs)
return callback_func(res) if callback_func else res
[docs] def get_parameters(self, param, default=None):
return self.parameters.get_parameters(param=param, default=default)
[docs] def report_task_info(self, task_info, status, results, kind="train"):
message = {
"name": self.worker_name,
"namespace": self.config.namespace,
"ownerName": self.job_name,
"ownerKind": self.job_kind,
"kind": kind,
"status": status,
"results": results
}
if task_info:
message["ownerInfo"] = task_info
try:
LCClient.send(self.config.lc_server, self.worker_name, message)
except Exception as err:
self.log.error(err)