Train the model using Caffe operator in MXNet

def fit(args, network, data_loader, eval_metrics=None, batch_end_callback=None):
    """Train the model using Caffe operator in MXNet"""
    # kvstore
    kv = mx.kvstore.create(args.kv_store)

    # logging
    head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s'
    if 'log_file' in args and args.log_file is not None:
        log_file = args.log_file
        log_dir = args.log_dir
        log_file_full_name = os.path.join(log_dir, log_file)
        if not os.path.exists(log_dir):
            os.mkdir(log_dir)
        logger = logging.getLogger()
        handler = logging.FileHandler(log_file_full_name)
        formatter = logging.Formatter(head)
        handler.setFormatter(formatter)
        logger.addHandler(handler)
        logger.setLevel(logging.DEBUG)
        logger.info('start with arguments %s', args)
    else:
        logging.basicConfig(level=logging.DEBUG, format=head)
        logging.info('start with arguments %s', args)

    # load model
    model_prefix = args.model_prefix
    if model_prefix is not None:
        model_prefix += "-%d" % (kv.rank)
    model_args = {}
    if args.load_epoch is not None:
        assert model_prefix is not None
        tmp = mx.model.FeedForward.load(model_prefix, args.load_epoch)
        model_args = {'arg_params' : tmp.arg_params,
                      'aux_params' : tmp.aux_params,
                      'begin_epoch' : args.load_epoch}
    # save model
    save_model_prefix = args.save_model_prefix
    if save_model_prefix is None:
        save_model_prefix = model_prefix
    checkpoint = None if save_model_prefix is None else mx.callback.do_checkpoint(save_model_prefix)

    # data
    (train, val) = data_loader(args, kv)

    # train
    devs = mx.cpu() if args.gpus is None else [
        mx.gpu(int(i)) for i in args.gpus.split(',')]

    epoch_size = args.num_examples / args.batch_size

    if args.kv_store == 'dist_sync':
        epoch_size /= kv.num_workers
        model_args['epoch_size'] = epoch_size

    if 'lr_factor' in args and args.lr_factor < 1:
        model_args['lr_scheduler'] = mx.lr_scheduler.FactorScheduler(
            step=max(int(epoch_size * args.lr_factor_epoch), 1),
            factor=args.lr_factor)

    if 'clip_gradient' in args and args.clip_gradient is not None:
        model_args['clip_gradient'] = args.clip_gradient

    # disable kvstore for single device
    if 'local' in kv.type and (
            args.gpus is None or len(args.gpus.split(',')) is 1):
        kv = None

    mod = mx.mod.Module(network, context=devs)

    if eval_metrics is None:
        eval_metrics = ['accuracy']
        # TopKAccuracy only allows top_k > 1
        for top_k in [5, 10, 20]:
            eval_metrics.append(mx.metric.create('top_k_accuracy', top_k=top_k))

    if batch_end_callback is not None:
        if not isinstance(batch_end_callback, list):
            batch_end_callback = [batch_end_callback]
    else:
        batch_end_callback = []
    batch_end_callback.append(mx.callback.Speedometer(args.batch_size, 50))

    mod.fit(train_data=train, eval_metric=eval_metrics, eval_data=val, optimizer='sgd',
            optimizer_params={'learning_rate':args.lr, 'momentum': 0.9, 'wd': 0.00001},
            num_epoch=args.num_epochs, batch_end_callback=batch_end_callback,
            initializer=mx.init.Xavier(factor_type="in", magnitude=2.34),
            kvstore=kv, epoch_end_callback=checkpoint, **model_args)