def restore_log()

in train_helpers.py [0:0]

• 9 lines of code
• 12 McCabe index (conditional complexity)

def restore_log(path, local_rank, mpi_size):
    loaded = [json.loads(l) for l in open(distributed_maybe_download(path, local_rank, mpi_size))]
    try:
        cur_eval_loss = min([z['elbo'] for z in loaded if 'type' in z and z['type'] == 'eval_loss'])
    except ValueError:
        cur_eval_loss = float('inf')
    starting_epoch = max([z['epoch'] for z in loaded if 'type' in z and z['type'] == 'train_loss'])
    iterate = max([z['step'] for z in loaded if 'type' in z and z['type'] == 'train_loss'])
    return cur_eval_loss, iterate, starting_epoch