Rasa 3.x version is taking too long to train


I am using Rasa’s latest version 3.0.4 on Apple M1 Mac. The time it takes for training is too long. Each time I make some minor changes in nlu or core rasa files, it takes 4-5 hours of training which is not acceptable. However, this issue didn’t occurred when I used Rasa version < 3.0 in my Intel chip Mac. Please help me in getting this issue resolved.

Here is my config.yml file. It’s the default one when we create a new rasa project.

# The config recipe.
# https://rasa.com/docs/rasa/model-configuration/
recipe: default.v1

# Configuration for Rasa NLU.
# https://rasa.com/docs/rasa/nlu/components/
language: en

# # 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 https://rasa.com/docs/rasa/tuning-your-model for more information.
  - name: WhitespaceTokenizer
  - name: RegexFeaturizer
  - 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
    epochs: 100
    constrain_similarities: true
  - name: FallbackClassifier
    threshold: 0.3
    ambiguity_threshold: 0.1

# Configuration for Rasa Core.
# https://rasa.com/docs/rasa/core/policies/
# # 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 https://rasa.com/docs/rasa/policies for more information.
  - name: MemoizationPolicy
  - name: RulePolicy
  - name: UnexpecTEDIntentPolicy
    max_history: 10
    epochs: 100
  - name: TEDPolicy
    max_history: 10
    epochs: 100
    constrain_similarities: true

Team, Please reply and provide a solution.

How does the training time compare on x86?

It takes nearly 10-15 minutes to train on x86

I saw 4x times longer for some of my bots.