I have 10 chatbot running in different ports with 10 different action server.(even tried with 1 action server not a big change in memory)
Each chatbot have 2000 or more intents.
I am using fallback and the response will be ‘$no out$’
I will hit all the 10 application api and from where I get response which does not starts with ‘$’ is my response.
When all the 10 chatbot runs in background it uses more than 25 gb in which 15gb takes from free memory and other from available memory.
Thanks for your quick response Alan. According to my understanding I cannot load multiple projects to same appliacation as I am using rasa 1.x.So I continued to run 10 different API for 10 applications using run --enable-api command and by specifying 10 different port number.
Since it runs 10 times it loads spacy 10 times in the memory so it eats more memory.Is it possible to load spacy only once and make all other api to use the same spacy but different models(core and nlu) since my config.yml is same for all?
I even tried to concat all applications together and train as one project. I m endingup with memoryerror after training for 2 hours.
I am not sure if I miss anything. I have installed
tensorboard==1.13.1
tensorflow==1.13.1
tensorflow-estimator==1.13.0
tensorflow-hub==0.7.0
tensorflow-text==1.15.1
I am getting the following error.
Failed to find component class for 'ConveRTFeaturizer’Unknown component name. Check your configured pipeline and make sure the mentioned component is not misspelled.
I have found solution to load from local thanks.But after using Convert I am getting response correct but my confidence level is too low
Initially with spacy I got 0.85 - 0.98. for matching values
Not i am getting only 0.25 - 0.4
