def run_train()

in train_extractive_reader.py [0:0]

• 36 lines of code
• 4 McCabe index (conditional complexity)

    def run_train(self):
        cfg = self.cfg

        train_iterator = self.get_data_iterator(
            cfg.train_files,
            cfg.train.batch_size,
            True,
            shuffle=True,
            shuffle_seed=cfg.seed,
            offset=self.start_batch,
        )

        # num_train_epochs = cfg.train.num_train_epochs - self.start_epoch

        logger.info("Total iterations per epoch=%d", train_iterator.max_iterations)
        updates_per_epoch = train_iterator.max_iterations // cfg.train.gradient_accumulation_steps

        total_updates = updates_per_epoch * cfg.train.num_train_epochs
        logger.info(" Total updates=%d", total_updates)

        warmup_steps = cfg.train.warmup_steps

        if self.scheduler_state:
            logger.info("Loading scheduler state %s", self.scheduler_state)
            shift = int(self.scheduler_state["last_epoch"])
            logger.info("Steps shift %d", shift)
            scheduler = get_schedule_linear(
                self.optimizer,
                warmup_steps,
                total_updates,
            )
        else:
            scheduler = get_schedule_linear(self.optimizer, warmup_steps, total_updates)

        eval_step = cfg.train.eval_step
        logger.info("  Eval step = %d", eval_step)
        logger.info("***** Training *****")

        global_step = self.start_epoch * updates_per_epoch + self.start_batch

        for epoch in range(self.start_epoch, cfg.train.num_train_epochs):
            logger.info("***** Epoch %d *****", epoch)
            global_step = self._train_epoch(scheduler, epoch, eval_step, train_iterator, global_step)

        if cfg.local_rank in [-1, 0]:
            logger.info("Training finished. Best validation checkpoint %s", self.best_cp_name)

        return