问题描述
我正在尝试将word2vec模型保存起来，然后基于该模式创建一些集群，它在本地运行良好，但是当我创建docker映像并与gunicorn一起运行时，它总是给我超时错误，我尝试了描述解决方案，但它对我没有帮助
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 here but it didn't workout for me
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
here is my gunicorn.conf file
#!/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
我也尝试了 gevent，sync 的工作者类，但是没有用。谁能告诉我为什么持续发生此超时错误。谢谢
I also tried worker classes of gevent,sync also but it didn't work. can anybody tell me why this timeout error keep occuring. thanks
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
推荐答案
我遇到了类似的问题。它为我解决了将gunicorn的版本更新为19.9.0
I got similar problem. It solved for me to update the version of gunicorn to 19.9.0
gunicorn 19.9.0
以及可能遇到相同问题的其他人-确保添加超时。我个人使用
and for others that might experience the same problem - make sure to add the timeout. I personally use
gunicorn app.wsgi：application -w 2 -b：8000 --timeout 120
这篇关于Gunicorn django上的关键工作器超时错误的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！