I want to run a Flask web services app with gunicorn in Docker. Upon startup, the app loads a large machine learning model.
However, when I run gunicorn within Docker I received the following timeouts and it just keeps spawning workers.
[2019-12-12 21:52:42 +0000] [1] [CRITICAL] WORKER TIMEOUT (pid:1198)
[2019-12-12 21:52:42 +0000] [1] [CRITICAL] WORKER TIMEOUT (pid:1204)
[2019-12-12 21:52:42 +0000] [1] [CRITICAL] WORKER TIMEOUT (pid:1210)
[2019-12-12 21:52:42 +0000] [1] [CRITICAL] WORKER TIMEOUT (pid:1211)
[2019-12-12 21:52:42 +0000] [1] [CRITICAL] WORKER TIMEOUT (pid:1222)
[2019-12-12 21:52:42 +0000] [1] [CRITICAL] WORKER TIMEOUT (pid:1223)
[2019-12-12 21:52:42 +0000] [1264] [INFO] Booting worker with pid: 1264
[2019-12-12 21:52:42 +0000] [1265] [INFO] Booting worker with pid: 1265
[2019-12-12 21:52:42 +0000] [1276] [INFO] Booting worker with pid: 1276
[2019-12-12 21:52:42 +0000] [1277] [INFO] Booting worker with pid: 1277
[2019-12-12 21:52:42 +0000] [1278] [INFO] Booting worker with pid: 1278
[2019-12-12 21:52:42 +0000] [1289] [INFO] Booting worker with pid: 1289
Running it as a flask app within Docker or running the flask app with (or without) gunicorn from the command line works fine. It also works with gunicorn if I remove the machine learning model.
For example:
$python app.py
$gunicorn -b 0.0.0.0:8080 --workers=2 --threads=4 app:app
$gunicorn app:app
Here is my Dockerfile with the Flask development server. Works fine.
ADD . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD python app.py
If I run gunicorn as follows it just keeps spawning workers:
CMD gunicorn -b 0.0.0.0:8080 --workers=2 --threads=4 app:app
or
CMD ["gunicorn", "app:app"]
gunicorn has a --timeout=30 parameter. Defaults to 30 seconds which I increased to 300. This did not appear to have an affect.
Note: I rewrote the app for the Starlette library and received the same results!
Any guidance is appreciated.
Thanks, Jay
I needed to add the gunicorn --timeout as follows:
CMD gunicorn --timeout 1000 --workers 1 --threads 4 --log-level debug --bind 0.0.0.0:8000 app:app
I also ran into problems deploy on Google Cloud Platform. The log only showed a kill message. Increasing the memory in the compute instance solved that problem.
try this
CMD["gunicorn", "--timeout", "1000", "--workers=1","-b", "0.0.0.0:8000","--log-level", "debug", "manage"]
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