Hi,

I've tinkered around a bit more and found that the problem is actually with
Native mode vs Standalone mode. In the standalone mode, the pod definition
doesn't get a Resource request for nvidia/gpu, whereas in the Native mode
it does. I'll open another question since this isn't related to autoscaler
at all. Thanks.


Regards,
Sunny



On Tue, Aug 1, 2023 at 3:34 PM Gyula Fóra <gyula.f...@gmail.com> wrote:

> The autoscaler only works for FlinkDeployments in Native mode. You should
> turn off the reactive scheduler mode as well because that's something
> completely different.
> After that you can check the autoscaler logs for more info.
>
> Gyula
>
> On Tue, Aug 1, 2023 at 10:33 AM Raihan Sunny via user <
> user@flink.apache.org> wrote:
>
>> Hi,
>>
>> I have a workload that depends on the GPU. I have only 1 GPU card. As per
>> the documentation I have added the necessary configurations and can run the
>> GPU workload in standalone REACTIVE mode with as many taskmanager instances
>> as required.
>>
>> I have set the number of task slots to 1 so that a raise in parallelism
>> causes a new pod to be created. I can scale up the job just fine in this
>> mode, however when I add autoscaling configurations to the FlinkDeployment
>> manifest, scaling up doesn't work. This is because with the autoscaling
>> manifest, there seems to be resource requests and limits are being
>> automatically set to the pods for the gpu. This is not the case with the
>> standalone mode which is why I guess scaling up doesn't cause any issues.
>>
>> So, what can I do to get the autoscaler working? I'm using Flink version
>> 1.17.1 with PyFlink and Flink Kubernetes Operator version 1.5.0.
>>
>>
>> Regards,
>> Sunny
>>
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Floor, Dubai Design District, Dubai, United Arab Emirates
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