How to use GPU to train my model

[Deprecated] Rasa X Community Edition
1

rasa-x 0.42.5 rasa-sdk :2.8.4

I have about 5000 nul data, everytime train model in rasa-x is too slow, How do I speed up my training

It has some methods use GPU to train my model ? if have ,hao to config

Looking forward to your reply

2

@yangli

you mean training examples right? and can you tell us what time it’s taking to train the model? please also share your system configuration i.e RAM, CPU etc.

3

Yes

train the model taking more than 2 hours and popup error “Training failed”

cpu

Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                4
On-line CPU(s) list:   0-3
Thread(s) per core:    2
Core(s) per socket:    2
Socket(s):             1
NUMA node(s):          1
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 85
Model name:            Intel(R) Xeon(R) Platinum 8269CY CPU @ 2.50GHz
Stepping:              7
CPU MHz:               2500.002
BogoMIPS:              5000.00
Hypervisor vendor:     KVM
Virtualization type:   full
L1d cache:             32K
L1i cache:             32K
L2 cache:              1024K
L3 cache:              36608K
NUMA node0 CPU(s):     0-3

Mem

               total        used        free      shared  buff/cache   available
Mem:          31490       17902        4076          14        9510       13182

and there is my rasa config

language: zh

pipeline:
  - name: components.nlu.tokenizers.bert_tokenizer.CustomBertTokenizer
    cache_dir: ./tmp
    model_weights: pre_models
  - name: LanguageModelFeaturizer
    cache_dir: ./tmp
    model_name: bert
    model_weights: pre_models
  - name: DIETClassifier
    epochs: 100
    constrain_similarities: True
    model_confidence: linear_norm
    entity_recognition: false
    evaluate_on_number_of_examples: 1000
    evaluate_every_number_of_epochs: 5
    tensorboard_log_directory: "./tensorboard"
    tensorboard_log_level: "epoch"
    number_of_negative_examples: 20
  - name: RegexEntityExtractor
  - name: EntitySynonymMapper
  - name: ResponseSelector
    retrieval_intent: faq
    scale_loss: false
    epochs: 100
  - name: ResponseSelector
    retrieval_intent: 闲聊
    scale_loss: false
    epochs: 100
  - name: FallbackClassifier
    threshold: 0.5
#    ambiguity_threshold: 0.1

policies:
   - name: AugmentedMemoizationPolicy
   - name: TEDPolicy
     max_history: 5
     epochs: 100
   - name: RulePolicy
     core_fallback_threshold: 0.3
     core_fallback_action_name: "action_default_fallback"
     enable_fallback_prediction: True
4

when i train about 3000 examples ,it take about 40 minutes and train success

5

Strange, can you train the same by using rasa open-source, please and check the time of training?

6

use rasa open-source also too slowly. it take more than two hours and training is not over

7

phew again strange :frowning: