Rasa NLU model loading takes significant amount of time

Rasa Open Source
1

Hi,

I am using rasa nlu alone for my usecase. I am training the rasa model using rasa.nlu.model.Trainer and loading the model using rasa.nlu.model.Interpreter . I have 5+ different nlu models.

I have below issues,

  1. Model loading takes nearly 35 to 40 secs. So for 5 models, its 200 secs. I would like to load model faster.
  2. So, i planned to dump using pickle. And tried to load. In that case i am getting the following error.

TypeError: can’t pickle _thread.RLock objects

i am using Rasa 1.5.3.

Any help would be highly appreciated. Thanks in advance

2

@seenimhd are you loading all the models in one server? And how big are your models?

3

Hi @akelad,

Thank you for the reply

are you loading all the models in one server?

Yea, I am trying to.

And how big are your models

Each model will have 2 to 3 intents with 10 to 15 examples each.

4

Rasa isn’t designed to load multiple models at one time though, each model should have a different server running. Right, I suspect you’re maybe using spacy in your pipeline? That takes a little longer to load. However, is the loading time a big issue? Because that should only need to be done once on startup

1 Like
5

Yea, we are using spacy. We are using flask framework, which doesn’t hold rasa model in global level. Anyway, thanks for the reply.

Why can’t able to dump the rasa nlu model?

6

What do you mean by dump the model?

7

I am trying to dump the rasa.nlu.model.Interpreter as serialised object using pickle/joblib.

8

This is how our models are persisted anyways. For example: rasa/sklearn_intent_classifier.py at master · RasaHQ/rasa · GitHub