title: CRITICAL WORKER TIMEOUT error on gunicorn django
body: <p>I am trying to tarined word2vec model and save it and then create some cluster based on that modal, it run locally fine but when I create the docker image and run with gunicorn, It always giving me timeout error, I tried the described solutions <a href="https://stackoverflow.com/questions/10855197/gunicorn-worker-timeout-error">here</a> but it didn't workout for me</p>
  
  <p>I am using </p>
  
  <pre><code>python 3.5
  gunicorn 19.7.1
  gevent 1.2.2
  eventlet 0.21.0
  </code></pre>
  
  <p>here is my gunicorn.conf file</p>
  
  <pre><code>#!/bin/bash
  
  # Start Gunicorn processes
  echo Starting Gunicorn.
  exec gunicorn ReviewsAI.wsgi:application \
      --bind 0.0.0.0:8000 \
      --worker-class eventlet
      --workers 1
      --timeout 300000
      --graceful-timeout 300000
      --keep-alive 300000
  </code></pre>
  
  <p>I also tried worker classes of <code>gevent,sync</code> also but it didn't work. can anybody tell me why this timeout error keep occuring. thanks</p>
  
  <p>Here is my log</p>
  
  <pre><code>Starting Gunicorn.
  [2017-11-10 06:03:45 +0000] [1] [INFO] Starting gunicorn 19.7.1
  [2017-11-10 06:03:45 +0000] [1] [INFO] Listening at: http://0.0.0.0:8000 (1)
  [2017-11-10 06:03:45 +0000] [1] [INFO] Using worker: eventlet
  [2017-11-10 06:03:45 +0000] [8] [INFO] Booting worker with pid: 8
  2017-11-10 06:05:00,307 : INFO : collecting all words and their counts
  2017-11-10 06:05:00,309 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types
  2017-11-10 06:05:00,737 : INFO : collected 11927 word types from a corpus of 1254665 raw words and 126 sentences
  2017-11-10 06:05:00,738 : INFO : Loading a fresh vocabulary
  2017-11-10 06:05:00,916 : INFO : min_count=1 retains 11927 unique words (100% of original 11927, drops 0)
  2017-11-10 06:05:00,917 : INFO : min_count=1 leaves 1254665 word corpus (100% of original 1254665, drops 0)
  2017-11-10 06:05:00,955 : INFO : deleting the raw counts dictionary of 11927 items
  2017-11-10 06:05:00,957 : INFO : sample=0.001 downsamples 59 most-common words
  2017-11-10 06:05:00,957 : INFO : downsampling leaves estimated 849684 word corpus (67.7% of prior 1254665)
  2017-11-10 06:05:00,957 : INFO : estimated required memory for 11927 words and 200 dimensions: 25046700 bytes
  2017-11-10 06:05:01,002 : INFO : resetting layer weights
  2017-11-10 06:05:01,242 : INFO : training model with 4 workers on 11927 vocabulary and 200 features, using sg=0 hs=0 sample=0.001 negative=5 window=4
  2017-11-10 06:05:02,294 : INFO : PROGRESS: at 6.03% examples, 247941 words/s, in_qsize 0, out_qsize 7
  2017-11-10 06:05:03,423 : INFO : PROGRESS: at 13.65% examples, 269423 words/s, in_qsize 0, out_qsize 7
  2017-11-10 06:05:04,670 : INFO : PROGRESS: at 23.02% examples, 286330 words/s, in_qsize 8, out_qsize 11
  2017-11-10 06:05:05,745 : INFO : PROGRESS: at 32.70% examples, 310218 words/s, in_qsize 0, out_qsize 7
  2017-11-10 06:05:07,054 : INFO : PROGRESS: at 42.06% examples, 308128 words/s, in_qsize 8, out_qsize 11
  2017-11-10 06:05:08,123 : INFO : PROGRESS: at 51.75% examples, 320675 words/s, in_qsize 0, out_qsize 7
  2017-11-10 06:05:09,355 : INFO : PROGRESS: at 61.11% examples, 320556 words/s, in_qsize 8, out_qsize 11
  2017-11-10 06:05:10,436 : INFO : PROGRESS: at 70.79% examples, 328012 words/s, in_qsize 0, out_qsize 7
  2017-11-10 06:05:11,663 : INFO : PROGRESS: at 80.16% examples, 327237 words/s, in_qsize 8, out_qsize 11
  2017-11-10 06:05:12,752 : INFO : PROGRESS: at 89.84% examples, 332298 words/s, in_qsize 0, out_qsize 7
  2017-11-10 06:05:13,784 : INFO : PROGRESS: at 99.21% examples, 336724 words/s, in_qsize 0, out_qsize 9
  2017-11-10 06:05:13,784 : INFO : worker thread finished; awaiting finish of 3 more threads
  2017-11-10 06:05:13,784 : INFO : worker thread finished; awaiting finish of 2 more threads
  2017-11-10 06:05:13,784 : INFO : worker thread finished; awaiting finish of 1 more threads
  2017-11-10 06:05:13,784 : INFO : worker thread finished; awaiting finish of 0 more threads
  2017-11-10 06:05:13,784 : INFO : training on 6273325 raw words (4248672 effective words) took 12.5s, 339100 effective words/s
  2017-11-10 06:05:13,785 : INFO : saving Word2Vec object under trained_models/mobile, separately None
  2017-11-10 06:05:13,785 : INFO : not storing attribute syn0norm
  2017-11-10 06:05:13,785 : INFO : not storing attribute cum_table
  2017-11-10 06:05:14,026 : INFO : saved trained_models/mobile
  [2017-11-10 06:05:43 +0000] [1] [CRITICAL] WORKER TIMEOUT (pid:8)
  2017-11-10 06:05:43,712 : INFO : precomputing L2-norms of word weight vectors
  [2017-11-10 06:05:44 +0000] [14] [INFO] Booting worker with pid: 14
  </code></pre>