I have an issue in regards to next action prediction for multiple instances of RASA on docker.
I am on rasa:2.3.4-spacy-en version on a Docker container (i create 2 docker images for 2 different enviroments build and release).
at the moment both the environments have same config.yml, domain.yml, data/nlu.yml, data/stories.yml, data/rules.yml, actions.py etc.
In the Dockerfile i RUN a train command and then build the image with docker-compose.
When i use to use both the instances for a scenario. One instance is using policy_1_TEDPolicy & another instance is using policy_2_RulePolicy to predict the next action.
In my case the Rulepolicy is wrong.
Whats the correct process for making all containers instances have same behaviour. Use the same model file?
Any help is much appreciated
My config.yml file is as below,
# No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
# If you’d like to customize it, uncomment and adjust the pipeline.
# See Tuning Your NLU Model for more information.
- name: WhitespaceTokenizer
- name: RegexFeaturizer case_sensitive: false
- name: LexicalSyntacticFeaturizer
- name: CountVectorsFeaturizer
- name: CountVectorsFeaturizer analyzer: char_wb min_ngram: 1 max_ngram: 4
- name: DIETClassifier epochs: 100 constrain_similarities: true
- name: EntitySynonymMapper
- name: ResponseSelector
- name: FallbackClassifier threshold: 0.3 ambiguity_threshold: 0.1
- name: SpacyNLP model: en_core_web_md
- name: SpacyEntityExtractor dimensions: [“PERSON”]
- name: SpacyTokenizer
- name: SpacyFeaturizer
Configuration for Rasa Core.
# No configuration for policies was provided. The following default policies were used to train your model.
# If you’d like to customize them, uncomment and adjust the policies.
# See Policies for more information.
- name: MemoizationPolicy
- name: TEDPolicy max_history: 5 epochs: 100 constrain_similarities: true
- name: RulePolicy