damp-lion-88352
CLI Runner
pyflyte run
from airflow.sensors.filesystem import FileSensor from airflow.sensors.time_sensor import TimeSensor from datetime import datetime, timedelta from pytz import UTC from flytekit import task, workflow @task() def t1(): print("flyte") @workflow def wf_time_sensor(): # sensor = FileSensor(task_id="id", filepath="/tmp/1234") sensor = TimeSensor(task_id="fire_immediately", target_time=(datetime.now(tz=UTC)+timedelta(seconds=1)).time()) sensor >> t1() @workflow def wf_file_sensor(): sensor = FileSensor(task_id="id", filepath="/tmp/1234") sensor >> t1() if __name__ == '__main__': from click.testing import CliRunner from flytekit.clis.sdk_in_container import pyflyte runner = CliRunner() # result = runner.invoke(cli, "--help") result = runner.invoke(pyflyte.main, ["run", "airflow_task.py", "wf_file_sensor"])
result.output
Flyte enables production-grade orchestration for machine learning workflows and data processing created to accelerate local workflows to production.