NLU training taking lot of time

Rasa Open Source
1

Hi,

NLU training on my data is consuming a lot of time.

Details are as following -

Data size - 3.7 MB
RAM size - 32 GB
No. of cores - 12
Training time - 40 mins

NLU config is as follows -

language: en

pipeline:

- name: nlp_spacy
- name: tokenizer_spacy
- name: intent_entity_featurizer_regex
- name: ner_synonyms
- name: tokenizer_whitespace
- name: ner_crf
- name: intent_featurizer_count_vectors
     analyzer: 'WORD'
     min_ngram: 1  # int
     max_ngram: 1 # int
- name: intent_classifier_tensorflow_embedding
     epochs: 100

Can anybody tell me why is training taking a lot of time on such small data?

Thanks

2

Does anybody have info about NLU training time?

3

That’s strange ! I think you should use proper pipeline configuration(config.yml).

My Data size - 16 MB stores, and 0.5MB max for nlu.md

RAM - 8GB

i7 CORS_2.8GHz - 8

Trainign time -  ~30 min

NLU Config :

language: en
pipeline:
- name: WhitespaceTokenizer
- name: RegexFeaturizer
- name: CRFEntityExtractor
- name: CountVectorsFeaturizer
  token_pattern: (?u)\b\w+\b
- name: EmbeddingIntentClassifier
  intent_tokenization_flag: true
  intent_split_symbol: "+"
- name: DucklingHTTPExtractor
  url: http://localhost:8000
  dimensions:
    - number
    - location
    - time
- name: EntitySynonymMapper

policies:
- batch_size: 50
  epochs: 500
  max_training_samples: 300
  name: KerasPolicy
- max_history: 5
  name: MemoizationPolicy
  #- name: FallbackPolicy
  #  core_threshold: 0.3
  #  fallback_action_name: "action_restart"
- name: FormPolicy
- name: MappingPolicy
- name: TwoStageFallbackPolicy
  nlu_threshold: 0.3
  core_threshold: 0.3
  fallback_core_action_name: "action_restart"
  fallback_nlu_action_name: "utter_default"
  deny_suggestion_intent_name: "out_of_scope"
4

@shivangpatel. Thanks for the reply. I will check on this pipeline and then revert back to you

5

@shivangpatel on how many epochs you trained your nlu model?

6

I didn’t change. I was used default values. Use maximum default value initially for development.

Tips 1: After complete development and production (architecture + flow) ready, you need to worry about to tune hyper param for production model to get maximum accuracy.

Tips 2: One more thing, Add good and quality data to get good results/output.

7

@shivangpatel My rasa core training is not taking much time. I asked for NLU training time only. The data size you mentioned for NLU is just 0.5 MB. Wheres as I tested for NLU data of 3.7 MB.

8

Then, maybe It’s required that much time…
Try to tag any one senior level rasa developer to get response.

Anyway, How many Intent you required ? And how many examples you wrote for each intent !?

9

Can you explain why do you need the spacy tokenizer and a white space tokenizer? also which english model are you using sm, medium or large

as far as i remember, tensorflow embedding don’t require spacy embeddings, so perhaps you don’t need to load the spacy vectors(nlp_spacy)

10

I am using only spacy tokeniser in the pipeline. I have edited above pipeline.

I have removed nlp_spacy from pipeline now as you mentioned tensorflow embedding does not require nlp_spacy.

How can we specify small or mdium or large english model in pipeline?

11

hey @r4sn4, you can specify the model as mentioned the docs:

pipeline:
- name: "SpacyNLP"
  # language model to load
  model: "en_core_web_md"
1 Like
12

your last question is only valid if you are using the spacy NLP as explained by Jitesh

13

Ok…