Reloading a model in a running system using rasa_nlu.train


Im trying to write a script that collects .md files, combines them and retrains an existing model. If I use the Web API erverything works fine (using /train).

But if I try to use:

python -m rasa_nlu.train -v --config ${CONFIG} --data --path projects --project ${PROJECT} --fixed_model_name ${MODELNAME}

it only creates a model but the server wont reload it.

Is there any way to trigger this process ? Did I miss something in the docs ?

Version: 0.13.1-full (Docker image)


language: "de"

- name: "tokenizer_whitespace"
- name: "intent_entity_featurizer_regex"
- name: "ner_crf"
- name: "ner_synonyms"
- name: "intent_featurizer_count_vectors"
- name: "intent_classifier_tensorflow_embedding"

Thank you in advance

see: stackoverflow

If you want the server to load it, you’d have to restart the server. Or do it with the /trainendpoint. Also what do you mean retrains an existing model?

If I call host:5000/train?model=${MODEL}&project=${PROJECT} it seems to replace the existing model. Thats what i expected when i used the python based approach.

Are you using a new model name or an already existing one? Because yes it will overwrite if it already exists

Yes the model already exists but when using the python approach it wont load the overriden model.