xcom_dag.py

from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from airflow.operators.subdag import SubDagOperator
from airflow.utils.task_group import TaskGroup

from random import uniform
from datetime import datetime

default_args = {
    'start_date': datetime(2020, 1, 1)
}

def _training_model(ti):
    accuracy = uniform(0.1, 10.0)
    print(f'model\'s accuracy: {accuracy}')

    ti.xcom_push(key='model_accuracy', value=str(accuracy))

def _choose_best_model(**context):
    task_instance = context['task_instance']
    accuracies = task_instance.xcom_pull(key='model_accuracy', 
                              task_ids=['processing_tasks.training_model_a'
                                        'processing_tasks.training_model_b'
                                        'processing_tasks.training_model_c'])
    print('aaaaaaaaaaaa')
    print(accuracies)

with DAG('xcom_dag', schedule_interval='@daily', default_args=default_args, catchup=False) as dag:

    downloading_data = BashOperator(
        task_id='downloading_data',
        bash_command='sleep 3',
        do_xcom_push=False
    )

    with TaskGroup('processing_tasks') as processing_tasks:
        training_model_a = PythonOperator(
            task_id='training_model_a',
            python_callable=_training_model,
            provide_context=True
        )

        training_model_b = PythonOperator(
            task_id='training_model_b',
            python_callable=_training_model,
            provide_context=True
        )

        training_model_c = PythonOperator(
            task_id='training_model_c',
            python_callable=_training_model,
            provide_context=True
        )

    choose_model = PythonOperator(
        task_id='choose_best_model',
        python_callable=_choose_best_model,
        provide_context=True
    )

    downloading_data >> processing_tasks >> choose_model