def run_experiment_worker()

in main.py [0:0]

• 10 lines of code
• 1 McCabe index (conditional complexity)

def run_experiment_worker(subprocess_index, world_size, model_mico, hparams, log_queue):
    """This is the function in each sub-process for multi-process training with DistributedDataParallel.

    Parameters
    ----------
    subprocess_index : int
        The index for the current sub-process
    world_size : int
        Total number of all the sub-processes
    model_mico : `MutualInfoCotrain` object
        The same initialized model for all the sub-processes
    hparams : dictionary
        The hyper-parameters for MICO
    log_queue : `torch.multiprocessing.Queue`
        For the logging with multi-process

    """
    setup_worker_logging(subprocess_index, log_queue)
    logging.info("Running experiment on GPU %d" % subprocess_index)
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '12355'
    dist.init_process_group("nccl", rank=subprocess_index, world_size=world_size)

    # For reproducible random runs
    random.seed(hparams.seed * world_size + subprocess_index)
    np.random.seed(hparams.seed * world_size + subprocess_index)
    torch.manual_seed(hparams.seed * world_size + subprocess_index)

    train_mico(subprocess_index, model_mico)