Changing duckling url shouldn't require a model retrain

Rasa version: Rasa core 0.14.0 Rasa nlu 0.14.4 Python version: 3.6.8 Operating system (windows, osx, …): osx Issue: rasa nlu model creation takes the duckling URL from the config.yml file and puts it into the metadata.json file of the trained model. we use docker-compose for local testing and k8s for cloud test/prod. docker and k8s use different way to network between containers; docker uses named containers eg duckling and k8s uses localhost. So we need different duckling url in local vs cloud testing. we’ve separated the URL’s in environment files but the Rasa training puts the URL into the metadata.json file of the model. This means that the model has to be retrained between local (docker-compose) and cloud (k8s-docker) testing. It makes more sense to have the URL outside of the model in a config file that can be controlled with environment and build processes so that the trained model can be copied rather than retrained (for no reason other than URL change due to environment). eg. for docker-compose “url”: “http://duckling:8000”, for k8s “url”: “http://localhost:8000”,

Content of configuration file (config.yml):

for docker-compose:

pipeline:
# other stuff
  - name: ner_duckling_http
    url: http://duckling:8000

for cloud k8s:
pipeline:
# other stuff
  - name: ner_duckling_http
    url: http://localhost:8000

Content of domain file (domain.yml) (if used & relevant):

not relevant

[c12k]

We have to change the duckling url regularly because dev and prod environments are different. So frequency of needing to change this is daily. for docker-compose “url”: “http://duckling:8000”, for k8s “url”: “http://localhost:8000”

[wochinge]

Because

• it’s messy in the code
• it’s only a tiny tiny advancement if we don’t retrain in case the duckling url is changed (how often are you changing your duckling url?)

So basically the relation between benefit and effort is very bad.