Python gunicorn django上的关键工作人员超时错误

我正在尝试创建word2vec模型并保存它，然后基于该模型创建一些集群，它在本地运行良好，但当我创建docker映像并使用gunicorn运行时，它总是给我超时错误，我尝试了所述的解决方案，但没有为我解决问题

我正在使用

python 3.5
gunicorn 19.7.1
gevent 1.2.2
eventlet 0.21.0

这是我的gunicorn.conf文件

#!/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

我还尝试了

gevent、sync

的工人类，但没有成功。谁能告诉我为什么这个超时错误一直发生。谢谢

这是我的日志

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

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我也有类似的问题。我解决了将gunicorn版本更新到19.9.0的问题

gunicorn 19.9.0

对于可能遇到相同问题的其他人，请确保添加超时。我个人使用

---

gunicorn app.wsgi:application-w2-b:8000--timeout 120

在“gunicorn.conf”文件中，是否为

exec gunicorn…

参数的行继续添加了反斜杠？它们只出现在您发布的代码片段的前两行中。这取决于训练模型所需的时间。您可以尝试增加超时。该超时值

--timeout 120

标记为我修复了问题的标志。谢谢！！超时做什么？我在Heroku的Python Plotly Dash应用程序上遇到了一些类似的问题。默认值：30名工作人员在超过此数秒的时间内保持沉默，然后被杀死并重新启动。值为正数或0。将其设置为0会完全禁用所有工作人员的超时，从而产生无限超时的效果。一般来说，默认值为30秒就足够了。只有当您确定同步工作者的影响时，才能将其设置得明显更高。对于非同步工作进程，这只意味着工作进程仍在通信，并且与处理单个请求所需的时间长度无关。