def train()

in mdl.py [0:0]

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

def train(args, trainer, task, epoch_itr):
    """Train the model for one epoch."""
    # Update parameters every N batches
    update_freq = 1

    # Initialize data iterator
    itr = epoch_itr.next_epoch_itr(
        fix_batches_to_gpus=args.fix_batches_to_gpus,
        shuffle=False, # TODO: changed
    )

    itr = iterators.GroupedIterator(itr, update_freq)
    progress = progress_bar.build_progress_bar(
        args, itr, epoch_itr.epoch, no_progress_bar='simple',
    )

    for i, samples in enumerate(progress, start=epoch_itr.iterations_in_epoch):
        log_output = trainer.train_step(samples)
        if log_output is None:
            continue

        # log mid-epoch stats
        stats = get_training_stats(trainer)
        progress.log(stats, tag='train', step=stats['num_updates'])

    stats = get_training_stats(trainer)
    progress.print(stats, tag='train', step=stats['num_updates'])

    # reset training meters
    for k in ['train_loss', 'train_nll_loss', 'wps', 'ups', 'wpb', 'bsz', 'gnorm', 'clip']:
        meter = trainer.get_meter(k)
        if meter is not None:
            meter.reset()