def _initialize_arguments(p: configargparse.ArgParser):
p.add('--model_storage_directory', help='The directory caching all model runs')
p.add('--bert_model_path', help='Model path to BERT')
p.add('--labels', help='Numbers of labels to predict over', type=str)
p.add('--architecture', help='Training architecture', type=str)
p.add('--freeze_bert', help='Whether to freeze bert', type=bool)
p.add('--batch_size', help='Batch size for training multi-label document classifier', type=int)
p.add('--bert_batch_size', help='Batch size for feeding 510 token subsets of documents through BERT', type=int)
p.add('--epochs', help='Epochs to train', type=int)
#Optimizer arguments
p.add('--learning_rate', help='Optimizer step size', type=float)
p.add('--weight_decay', help='Adam regularization', type=float)
p.add('--evaluation_interval', help='Evaluate model on test set every evaluation_interval epochs', type=int)
p.add('--checkpoint_interval', help='Save a model checkpoint to disk every checkpoint_interval epochs', type=int)
#Non-config arguments
p.add('--cuda', action='store_true', help='Utilize GPU for training or prediction')
p.add('--device')
p.add('--timestamp', help='Run specific signature')
p.add('--model_directory', help='The directory storing this model run, a sub-directory of model_storage_directory')
p.add('--use_tensorboard', help='Use tensorboard logging', type=bool)
args = p.parse_args()
args.labels = [x for x in args.labels.split(', ')]
#Set run specific envirorment configurations
args.timestamp = time.strftime("run_%Y_%m_%d_%H_%M_%S") + "_{machine}".format(machine=socket.gethostname())
args.model_directory = os.path.join(args.model_storage_directory, args.timestamp) #directory
os.makedirs(args.model_directory, exist_ok=True)
#Handle logging configurations
log.handlers.clear()
formatter = logging.Formatter('%(message)s')
fh = logging.FileHandler(os.path.join(args.model_directory, "log.txt"))
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
log.addHandler(fh)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
ch.setFormatter(formatter)
log.setLevel(logging.INFO)
log.addHandler(ch)
log.info(p.format_values())
#Set global GPU state
#if torch.cuda.is_available() and args.cuda:
# if torch.cuda.device_count() > 1:
# log.info("Using %i CUDA devices" % torch.cuda.device_count() )
# else:
# log.info("Using CUDA device:{0}".format(torch.cuda.current_device()))
#args.device = args.device #'cuda:1'
#else:
# log.info("Not using CUDA :(")
# args.dev = 'cpu'
return args
