Hi, I am making a migration from Rasa 0.14 to Rasa 1.1.4 right now. I’ll explain my current set up. I have multiple models with different data (these correspond to the different modules of my chatbot) that I train using a script (manually doing it would be a mess) and store all the models in a single folder. I am storing them in a single folder so that I can run a single rasa HTTP server using the command python -m rasa_nlu.server --path nlu_models Here’s a snippet of the trainer script: builder = ComponentBuilder(use_cache=True) models_path = NLU_MODELS_DIRECTORY for module_name in modules: module_directory = os.path.join(MODULES_BASE_DIR, module_name) rasa_config_file = DEFAULT_CONFIG_FILE model_config_file = module_directory + MODEL_CONFIG_FILE_BASE config_model = get_config(model_config_file) data_path = module_directory + config_model["TRAINING_DATA"] print("Training '" + module_name + "' module interpreter") training_data = load_data(data_path) trainer = Trainer(config.load(rasa_config_file), builder) trainer.train(training_data) model_directory = trainer.persist( path=models_path, project_name=config_model["PROJECT_NAME"], fixed_model_name=config_model["FIXED_MODEL_NAME"], ) print("Done training '" + module_name + "' module interpreter") I read the docs for Rasa 1 and I don’t see a python API mentioned anywhere. In Rasa 1, how do I Write a similar single script to train all the models and store them in a single folder Run a Rasa NLU HTTP server from this folder which has all the models from that folder

Training models from a python script: Python API in the new Rasa 1.0

Tanja (Tanja Bunk) July 8, 2019, 1:37pm 4

In the thread Removing projects for Rasa NLU server some approaches are discussed. But I guess you named the most promising ones.

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