Does "log_save_interval" control training log?
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Question
Does "log_save_interval" control training log? If not, what controls the log of training? I changed this tag, but training log (such as loss) will be written to disk at every step no matter how I changed it.
Code (adapted from sample)
   
"""
Runs a model on a single node across multiple gpus.
"""
import os
from argparse import ArgumentParser

from pytorch_lightning import Trainer, seed_everything
from pl_examples.models.lightning_template import LightningTemplateModel

seed_everything(234)


def main(args):
    """ Main training routine specific for this project. """
    # ------------------------
    # 1 INIT LIGHTNING MODEL
    # ------------------------
    model = LightningTemplateModel(**vars(args))

    # ------------------------
    # 2 INIT TRAINER
    # ------------------------
    trainer = Trainer.from_argparse_args(args)

    # ------------------------
    # 3 START TRAINING
    # ------------------------
    trainer.fit(model)


def run_cli():
    # ------------------------
    # TRAINING ARGUMENTS
    # ------------------------
    # these are project-wide arguments
    root_dir = os.path.dirname(os.path.realpath(__file__))
    parent_parser = ArgumentParser(add_help=False)

    # each LightningModule defines arguments relevant to it
    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
    parser = Trainer.add_argparse_args(parser)
    parser.set_defaults(gpus=1,log_save_interval=1000,limit_train_batches=1,check_val_every_n_epoch=1000,max_epochs=10000, num_worker=0)
    args = parser.parse_args()

    # ---------------------
    # RUN TRAINING
    # ---------------------
    main(args)


if __name__ == '__main__':
    run_cli()

What have you tried?
I tried to change row_log_interval and log_save_interval, but it seems that neither of them worked. Training log (e.g. loss) will be recorded at every step.
What's your environment?

OS: Win 10
Packaging: Anaconda
Version: 0.8.4