[Python] How to have a mix of both Celery Executor and Kubernetes Executor in Apache Airflow?

I have multiple dags using Celery Executor but I want one particular dag to run using Kubernetes Executor. I am unable to deduce a good and reliable way to achieve this.

I have an airflow.cfg in which I have declared CeleryExecutor to be used. And I don't want to change it since it is really needed in all the dags but one.

# The executor class that airflow should use. Choices include
# SequentialExecutor, LocalExecutor, CeleryExecutor
executor = CeleryExecutor


My dag code:

from datetime import datetime, timedelta

from airflow import DAG
from airflow.contrib.operators.kubernetes_pod_operator import \
KubernetesPodOperator
from airflow.operators.dummy_operator import DummyOperator

default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime.utcnow(),
'email': ['airflow@example.com'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5)
}

dag = DAG(
'kubernetes_sample_1', default_args=default_args)


start = DummyOperator(task_id='run_this_first', dag=dag)

passing = KubernetesPodOperator(namespace='default',
image="Python:3.6",
cmds=["Python", "-c"],
arguments=["print('hello world')"],
labels={"foo": "bar"},
name="passing-test",
task_id="passing-task",
get_logs=True,
dag=dag
)

failing = KubernetesPodOperator(namespace='default',
image="ubuntu:1604",
cmds=["Python", "-c"],
arguments=["print('hello world')"],
labels={"foo": "bar"},
name="fail",
task_id="failing-task",
get_logs=True,
dag=dag
)

passing.set_upstream(start)
failing.set_upstream(start)



I can put an if-else condition and then change the value from the point where Airflow picks up the configuration. If this sounds right, please tell me the paths and the files. Although I was hoping to get a more mature method, if it exists.

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