Jaxing opened a new issue #15670:
URL: https://github.com/apache/airflow/issues/15670


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   **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.
   
   


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