title: Runtime Error when running a LDA model of gensim, how can I fix it?
body: <p>I have an runtime error: </p>
  
  <pre><code>RuntimeError: 
          An attempt has been made to start a new process before the
          current process has finished its bootstrapping phase.
  
          This probably means that you are not using fork to start your
          child processes and you have forgotten to use the proper idiom
          in the main module:
  
              if __name__ == '__main__':
                  freeze_support()
                  ...
  
          The "freeze_support()" line can be omitted if the program
          is not going to be frozen to produce an executable.
    0%|          | 0/29 [00:48&lt;?, ?it/s]
  </code></pre>
  
  <p>When I try run this code: </p>
  
  <pre><code>def topic_model_coherence_generator (corpus, texts, dictionary, start_topic_count=2, end_topic_count=10, step=1, cpus=1):
      models=[]
      coherence_scores = []
      for topic_nums in tqdm(range(start_topic_count, end_topic_count+1, step)):
          lda_model = gensim.models.LdaModel(corpus=bow_corpus, id2word=dictionary, chunksize=1740, alpha='auto', eta='auto',
                                     random_state=42, iterations=500, num_topics=topic_nums, passes=20, eval_every=None)
  
          cv_coherence_model_lda = gensim.models.CoherenceModel(model=lda_model, corpus=bow_corpus,
                                                        texts=norm_corpus_bigrams, dictionary=dictionary,
                                                        coherence='c_v')
  
          coherence_score= cv_coherence_model_lda.get_coherence()
          coherence_scores.append(coherence_score)
          models.append(lda_model)
      return models, coherence_scores
  
  lda_models, coherence_scores = topic_model_coherence_generator(corpus=bow_corpus,
                                                                 texts=norm_corpus_bigrams,
                                                                 dictionary= dictionary,
                                                                 start_topic_count=2,
                                                                 end_topic_count=30,
                                                                 step=1, cpus=16)
  </code></pre>
  
  <p>That I want is obtain the optimal number of topics of my corpus for obtain then the topics and interpreting topic model results. 
  I'm biologist so I don't know how can I fix it. 
  Thanks for your help</p>