def run_train()

in training/trainer.py [0:0]

• 26 lines of code
• 5 McCabe index (conditional complexity)

    def run_train(self):

        while self.epoch < self.max_epochs:
            dataloader = self.train_dataset.get_loader(epoch=int(self.epoch))
            barrier()
            outs = self.train_epoch(dataloader)
            self.logger.log_dict(outs, self.epoch)  # Logged only on rank 0

            # log train to text file.
            if self.distributed_rank == 0:
                with g_pathmgr.open(
                    os.path.join(self.logging_conf.log_dir, "train_stats.json"),
                    "a",
                ) as f:
                    f.write(json.dumps(outs) + "\n")

            # Save checkpoint before validating
            self.save_checkpoint(self.epoch + 1)

            del dataloader
            gc.collect()

            # Run val, not running on last epoch since will run after the
            # loop anyway
            if self.is_intermediate_val_epoch(self.epoch):
                self.run_val()

            if self.distributed_rank == 0:
                self.best_meter_values.update(self._get_trainer_state("train"))
                with g_pathmgr.open(
                    os.path.join(self.logging_conf.log_dir, "best_stats.json"),
                    "a",
                ) as f:
                    f.write(json.dumps(self.best_meter_values) + "\n")

            self.epoch += 1
        # epoch was incremented in the loop but the val step runs out of the loop
        self.epoch -= 1