Pod overrides not working

Apache Airflow version: 2.0.1 Kubernetes version (if you are using kubernetes) (use kubectl version): 1.17 Environment:

• Cloud provider or hardware configuration: GKE, some CPU some GPU nodes

What happened: Hey I’m having some issues using the executor_config in the python operator when running with kubernetes_executor, i have tried both the option of using the pod_template_file (to point out a specific file) and the pod_override option to do some changes to the pod spec among others set specific resource limits and request (a requirment in our namespace) however when I deploy it and start the task I get an error in the scheduler that I cannot create the worker since I have not specified the resource limits and requests. This new pod spec contained resources (and other necessary changes e.g. node selectors) for finding a node with GPU and allocation a GPU. I could see that when the scheduler scheduled the worker it used the correct gpu pod_template_file. One thing I did to debug was to use the pod_template_file with the GPU spec as the default pod template (by setting it in the airflow.cfg). This works which means that there is no issue with the pod_template_file. What you expected to happen: It seems like the pod overrides are not applied correctly How to reproduce it: Use pod_template_file or pod_override in a cluster of namespace that requires certain resource limits.

[CatarinaSilva]

hey @kaxil I can confirm the requests and limits are working with the new way in 2.0.2

[CatarinaSilva]

Thanks @kaxil currently testing with the new format, will let you know if it works