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, rah...@google.com 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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