train rasa core, and rasa nlu models programmatically rasa.core 0.14.3 nlu 0.15

example.py

from rasa_core.agent import Agent
from rasa_core.policies import KerasPolicy
from rasa_nlu.training_data import load_data
from rasa_nlu import config
from rasa_nlu.model import Trainer
import datetime

def _archive_name (type, environment):
  t = datetime.datetime.now()
  timestring = t.strftime('%Y%m%d-%H%M%S')
  return '%s__model_%s' % (prefix, timestring)

def train_core (params):
  
  domain = params.get('domain') if 'domain' in params else ''
  stories = params.get('stories') if 'stories' in params else ''
  environment = params.get('environment') if 'environment' in params else None
  model_name = _archive_name('core', environment)
  
  additional_arguments = {
        "epochs": 100,
        "batch_size": 20,
        "validation_split": 0.1,
        "augmentation_factor": 50,
        "debug_plots": True,
        "max_history": 5
    }
    agent = Agent(domain_path,
                  policies=[KerasPolicy(**additional_arguments)])
    
   training_data = agent.load_data(md_stories_file_path if stories_in_json else stories_path)
    agent.train(training_data)
    # persist
    agent.persist(model_dir)
    
def train_nlu (params):
    intents_file = params.get('intents') if 'intents' in params else ''
    config_file = params.get('config') if 'config' in params else {}
    environment = params.get('environment') if 'environment' in params else None
    model_name = _archive_name('nlu', environment)
    model_dir = '%s/%s' % (BASE_DIR, model_name)

    nlu_config = config.load(intents_file)
    data = load_data(intents_file)
    trainer = Trainer(nlu_config)

    trainer.train(data)
    trainer.persist(BASE_DIR, project_name= '', fixed_model_name = model_name)