Hi Adam,
Here's a few ideas for diagnosis:
1. Can you try printing or logging repr(os.environ) and checking for the
GAE_MEMORY_MB environment variable? I just tried deploying a test app with
instance_class: F4 and service: xyz, and print(repr(os.environ)) gives me
something like this:
environ({'GAE_MEMORY_MB': '512', 'GAE_INSTANCE': ...etc etc...})
The python37 runtime *should* start one worker if GAE_MEMORY_MB <= 128, two
if <= 256, four if <= 512, otherwise eight.
If you see 'GAE_MEMORY_MB': '512' in there but anything other than four
workers starting up, I'm extremely interested in figuring out why :)
Likewise if you see anything other than 512 for an F4 instance.
2. Check that you're looking at the logs for the correct service -- make
sure you see "GAE Application, xyz" and not "GAE Application, Default
Service" or just "GAE Application" (which combines logs from all services)
in the logs viewer.
3. Once you've done those two, if nothing sticks out, you can manually
configure the worker count by adding a line like this to app.yaml:
entrypoint: gunicorn main:app --workers 2 -c /config/gunicorn.py
This will start up two workers. You can also configure the number of
threads per worker:
entrypoint: gunicorn main:app --threads 8 --workers 2 -c
/config/gunicorn.py
(The default if you just using 'gunicorn main:app -c /config/gunicorn.py'
is four workers with four threads each.)
Cheers,
Phil
On Monday, September 17, 2018 at 10:30:06 AM UTC-7, Adam Lugowski wrote:
>
> Hi Rahul,
>
> I'm not manually configuring workers. Happy to learn of how to do that. My
> app.yaml is simple:
>
> runtime: python37
> service : xyz
>
> inbound_services:
> - warmup
>
> instance_class: F4
>
> handlers:
> ....
>
> On Sunday, September 16, 2018 at 6:59:38 AM UTC-7, [email protected]
> wrote:
>>
>> Hello Adam,
>> Could you paste your app.yaml? Are you explicitly configuring the
>> number of workers?
>> Thanks,
>> ~Rahul.
>>
>>
>> On Friday, September 14, 2018 at 3:47:41 PM UTC-7, Adam Lugowski wrote:
>>>
>>> The only theory I have is informed by these log messages on startup:
>>>
>>> A [2018-09-14 19:28:38 +0000] [1] [INFO] Starting gunicorn 19.9.0
>>> A [2018-09-14 19:28:38 +0000] [1] [INFO] Listening at:
>>> http://0.0.0.0:8080 (1)
>>> A [2018-09-14 19:28:38 +0000] [1] [INFO] Using worker: threads
>>> A [2018-09-14 19:28:38 +0000] [8] [INFO] Booting worker with pid: 8
>>> A [2018-09-14 19:28:38 +0000] [9] [INFO] Booting worker with pid: 9
>>> A [2018-09-14 19:28:38 +0000] [10] [INFO] Booting worker with pid: 10
>>> A [2018-09-14 19:28:38 +0000] [11] [INFO] Booting worker with pid: 11
>>> A [2018-09-14 19:28:38 +0000] [12] [INFO] Booting worker with pid: 12
>>> A [2018-09-14 19:28:38 +0000] [13] [INFO] Booting worker with pid: 13
>>> A [2018-09-14 19:28:39 +0000] [14] [INFO] Booting worker with pid: 14
>>> A [2018-09-14 19:28:39 +0000] [15] [INFO] Booting worker with pid: 15
>>>
>>>
>>> It looks like multiple workers are started, so maybe the fixed memory
>>> overhead is just duplicated by each worker.
>>> This is also supported by the fact that some of my requests load
>>> matplotlib. Locally this makes a slow first request then fast on subsequent
>>> ones. On GAE F4 the first few are slow, then it stays fast, as if multiple
>>> processes have to be warmed up.
>>> If this is true, then going to a larger instance is poor advice;
>>> doubling memory size also doubles CPU count which doubles fixed overhead
>>> and we're back at square one.
>>>
>>
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