Created by: ChuRao

    def _train_by_any_executor(self, event_handler, exe, num_epochs, reader):
        if self.checkpoint_cfg:
            epochs = [
                epoch_id for epoch_id in range(num_epochs)
                if epoch_id >= self.checkpoint_cfg.epoch_id
            ]
        else:
            epochs = [epoch_id for epoch_id in range(num_epochs)]

        for epoch_id in epochs:
            event_handler(BeginEpochEvent(epoch_id))
            for step_id, data in enumerate(reader()):
                if self.__stop:
                    if self.checkpoint_cfg:
                        self._clean_checkpoint()
                    return

                if self.checkpoint_cfg and self.checkpoint_cfg.load_serial \
                    and self.checkpoint_cfg.step_id >= step_id and self.checkpoint_cfg.epoch_id == epoch_id:
                    continue

                begin_event = BeginStepEvent(epoch_id, step_id)
                event_handler(begin_event)
                if begin_event.fetch_metrics:
                    metrics = exe.run(feed=data,
                                      fetch_list=[
                                          var.name
                                          for var in self.train_func_outputs
                                      ])
                else:
                    metrics = exe.run(feed=data, fetch_list=[])

                if self.checkpoint_cfg:
                    self._save_checkpoint(epoch_id, step_id)
                event_handler(EndStepEvent(epoch_id, step_id, metrics))
            event_handler(EndEpochEvent(epoch_id))
        if self.checkpoint_cfg:
            self._clean_checkpoint()

if self.checkpoint_cfg and self.checkpoint_cfg.load_serial \ and self.checkpoint_cfg.step_id >= step_id and self.checkpoint_cfg.epoch_id == epoch_id: continue 这一段非常影响性能。在读取样本较慢、训练总数较大的时候（比如1秒读取一个batch数据的时候，有2万个batch数据）。一个存档点step_id在5000，加载了存档点岂不是要当空读数据5000秒之后才真正开始训练？ 在有shuffle的时候重复一个epoch里一些数据重复用于训练问题不是很大吧？

我还以为怎么回事，一下午怎么event_handler一点输出也没有。 建议给个参数用来遗弃存档点的step_id