dags/package_requirements.py

from airflow import DAG
from datetime import datetime
from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator
from airflow.configuration import conf
# get the current Kubernetes namespace Airflow is running in
namespace = conf.get("kubernetes", "NAMESPACE")

# set the name that will be printed
name = 'luoyulong'

python_requirements = ['sphinx>=1.8','sphinx_rtd_theme','recommonmark>=0.6.0','markdown>=3.4.1']

# instantiate the DAG
with DAG(
        start_date=datetime(2022,6,1),
        catchup=False,
        schedule_interval='@daily',
        dag_id='lyl_package_test'
) as dag:
    package_python_libs = KubernetesPodOperator(
        # unique id of the task within the DAG
        task_id='package',
        # the Docker image to launch
        image='SXKJ:32775/jupyter:1.0',
        # launch the Pod on the same cluster as Airflow is running on
        in_cluster=True,
        # launch the Pod in the same namespace as Airflow is running in
        namespace=namespace,
        # Pod configuration
        # name the Pod
        name='my_fucking_pod',
        # give the Pod name a random suffix, ensure uniqueness in the namespace
        random_name_suffix=True,
        # attach labels to the Pod, can be used for grouping
        labels={'app':'backend', 'env':'dev'},
        # reattach to worker instead of creating a new Pod on worker failure
        reattach_on_restart=True,
        # delete Pod after the task is finished
        is_delete_operator_pod=True,
        # get log stdout of the container as task logs
        get_logs=True,
        # log events in case of Pod failure
        log_events_on_failure=True,
        cmds=["/bin/bash", "-c",'/home/sxkj/bigdata/install.sh'],
        # pass your name as an environment var
        env_vars={"PYTHON_REQUIREMENTS": ' '.join(python_requirements),
                  "UPLOAD_PATH":'/tmp/x'
                  }
    )