# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
import asyncio
from typing import Any, AsyncIterator, Sequence
from airflow.providers.google.cloud.hooks.mlengine import MLEngineAsyncHook
from airflow.triggers.base import BaseTrigger, TriggerEvent
[docs]class MLEngineStartTrainingJobTrigger(BaseTrigger):
"""
MLEngineStartTrainingJobTrigger run on the trigger worker to perform starting training job operation.
:param conn_id: Reference to google cloud connection id
:param job_id: The ID of the job. It will be suffixed with hash of job configuration
:param project_id: Google Cloud Project where the job is running
:param poll_interval: polling period in seconds to check for the status
"""
def __init__(
self,
conn_id: str,
job_id: str,
region: str,
poll_interval: float = 4.0,
package_uris: list[str] | None = None,
training_python_module: str | None = None,
training_args: list[str] | None = None,
runtime_version: str | None = None,
python_version: str | None = None,
job_dir: str | None = None,
project_id: str | None = None,
labels: dict[str, str] | None = None,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
):
super().__init__()
self.log.info("Using the connection %s .", conn_id)
self.conn_id = conn_id
self.job_id = job_id
self._job_conn = None
self.project_id = project_id
self.region = region
self.poll_interval = poll_interval
self.runtime_version = runtime_version
self.python_version = python_version
self.job_dir = job_dir
self.package_uris = package_uris
self.training_python_module = training_python_module
self.training_args = training_args
self.labels = labels
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serializes MLEngineStartTrainingJobTrigger arguments and classpath."""
return (
"airflow.providers.google.cloud.triggers.mlengine.MLEngineStartTrainingJobTrigger",
{
"conn_id": self.conn_id,
"job_id": self.job_id,
"poll_interval": self.poll_interval,
"region": self.region,
"project_id": self.project_id,
"runtime_version": self.runtime_version,
"python_version": self.python_version,
"job_dir": self.job_dir,
"package_uris": self.package_uris,
"training_python_module": self.training_python_module,
"training_args": self.training_args,
"labels": self.labels,
},
)
[docs] async def run(self) -> AsyncIterator[TriggerEvent]: # type: ignore[override]
"""Gets current job execution status and yields a TriggerEvent."""
hook = self._get_async_hook()
try:
while True:
# Poll for job execution status
response_from_hook = await hook.get_job_status(job_id=self.job_id, project_id=self.project_id)
if response_from_hook == "success":
yield TriggerEvent(
{
"job_id": self.job_id,
"status": "success",
"message": "Job completed",
}
)
elif response_from_hook == "pending":
self.log.info("Job is still running...")
self.log.info("Sleeping for %s seconds.", self.poll_interval)
await asyncio.sleep(self.poll_interval)
else:
yield TriggerEvent({"status": "error", "message": response_from_hook})
except Exception as e:
self.log.exception("Exception occurred while checking for query completion")
yield TriggerEvent({"status": "error", "message": str(e)})
def _get_async_hook(self) -> MLEngineAsyncHook:
return MLEngineAsyncHook(
gcp_conn_id=self.conn_id,
impersonation_chain=self.impersonation_chain,
)
