diff --git a/dags_airflow_ale_kopfocal.py b/dags_airflow_ale_kopfocal.py
new file mode 100644
index 0000000..92e2554
--- /dev/null
+++ b/dags_airflow_ale_kopfocal.py
@@ -0,0 +1,68 @@
+import airflow.utils.dates
+from airflow.utils.log.logging_mixin import LoggingMixin
+from airflow import DAG
+from airflow.operators.dummy import DummyOperator
+
+from airflow.contrib.operators.kubernetes_pod_operator import KubernetesPodOperator
+from kubernetes.client import models as k8s
+
+import json
+from datetime import timedelta
+
+
+
+
+
+dag = DAG(
+    dag_id="kubeoptest",
+    start_date=airflow.utils.dates.days_ago(3),
+    schedule_interval=None,
+    description="Test for kubernetes operator",
+    
+)
+
+
+##DAG CALLABLES
+
+
+    
+### DAG DEFS   
+Ini = DummyOperator(task_id="start_training", dag=dag)
+
+
+
+Op =KubernetesPodOperator(
+        # unique id of the task within the DAG
+        task_id="kubeop",
+        # the Docker image to launch
+        image="ubuntu:20.04",
+        # 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="glitchflow",
+        # Pod configuration
+        # name the Pod
+        name="airflow_op",
+        
+        # attach labels to the Pod, can be used for grouping
+        labels={"app": "preq", "backend": "airflow"},
+        # 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,
+        # enable xcom
+        do_xcom_push=True,
+        dag=dag,
+        
+        #env_vars={"NAME_TO_GREET": f"{name}"},
+    )
+
+ 
+
+
+
+Ini>>Op