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from __future__ import annotations
from functools import cached_property
from typing import TYPE_CHECKING, Any, AsyncIterator, Sequence
from google.cloud.aiplatform_v1 import (
BatchPredictionJob,
HyperparameterTuningJob,
JobState,
PipelineState,
types,
)
from airflow.exceptions import AirflowException
from airflow.providers.google.cloud.hooks.vertex_ai.batch_prediction_job import BatchPredictionJobAsyncHook
from airflow.providers.google.cloud.hooks.vertex_ai.hyperparameter_tuning_job import (
HyperparameterTuningJobAsyncHook,
)
from airflow.providers.google.cloud.hooks.vertex_ai.pipeline_job import PipelineJobAsyncHook
from airflow.triggers.base import BaseTrigger, TriggerEvent
if TYPE_CHECKING:
from proto import Message
[docs]class BaseVertexAIJobTrigger(BaseTrigger):
"""Base class for Vertex AI job triggers.
This trigger polls the Vertex AI job and checks its status.
In order to use it properly, you must:
- implement the following methods `_wait_job()`.
- override required `job_type_verbose_name` attribute to provide meaningful message describing your
job type.
- override required `job_serializer_class` attribute to provide proto.Message class that will be used
to serialize your job with `to_dict()` class method.
"""
[docs] job_type_verbose_name: str = "Vertex AI Job"
[docs] job_serializer_class: Message = None
[docs] statuses_success = {
JobState.JOB_STATE_PAUSED,
JobState.JOB_STATE_SUCCEEDED,
}
def __init__(
self,
conn_id: str,
project_id: str,
location: str,
job_id: str,
poll_interval: int,
impersonation_chain: str | Sequence[str] | None = None,
):
super().__init__()
self.conn_id = conn_id
self.project_id = project_id
self.location = location
self.job_id = job_id
self.poll_interval = poll_interval
self.impersonation_chain = impersonation_chain
self.trigger_class_path = (
f"airflow.providers.google.cloud.triggers.vertex_ai.{self.__class__.__name__}"
)
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
return (
self.trigger_class_path,
{
"conn_id": self.conn_id,
"project_id": self.project_id,
"location": self.location,
"job_id": self.job_id,
"poll_interval": self.poll_interval,
"impersonation_chain": self.impersonation_chain,
},
)
[docs] async def run(self) -> AsyncIterator[TriggerEvent]:
try:
job = await self._wait_job()
except AirflowException as ex:
yield TriggerEvent(
{
"status": "error",
"message": str(ex),
}
)
return
status = "success" if job.state in self.statuses_success else "error"
message = f"{self.job_type_verbose_name} {job.name} completed with status {job.state.name}"
yield TriggerEvent(
{
"status": status,
"message": message,
"job": self._serialize_job(job),
}
)
async def _wait_job(self) -> Any:
"""Awaits a Vertex AI job instance for a status examination."""
raise NotImplementedError
def _serialize_job(self, job: Any) -> Any:
return self.job_serializer_class.to_dict(job)
[docs]class CreateHyperparameterTuningJobTrigger(BaseVertexAIJobTrigger):
"""CreateHyperparameterTuningJobTrigger run on the trigger worker to perform create operation."""
[docs] job_type_verbose_name = "Hyperparameter Tuning Job"
[docs] job_serializer_class = HyperparameterTuningJob
@cached_property
[docs] def async_hook(self) -> HyperparameterTuningJobAsyncHook:
return HyperparameterTuningJobAsyncHook(
gcp_conn_id=self.conn_id, impersonation_chain=self.impersonation_chain
)
async def _wait_job(self) -> types.HyperparameterTuningJob:
job: types.HyperparameterTuningJob = await self.async_hook.wait_hyperparameter_tuning_job(
project_id=self.project_id,
location=self.location,
job_id=self.job_id,
poll_interval=self.poll_interval,
)
return job
[docs]class CreateBatchPredictionJobTrigger(BaseVertexAIJobTrigger):
"""CreateBatchPredictionJobTrigger run on the trigger worker to perform create operation."""
[docs] job_type_verbose_name = "Batch Prediction Job"
[docs] job_serializer_class = BatchPredictionJob
@cached_property
[docs] def async_hook(self) -> BatchPredictionJobAsyncHook:
return BatchPredictionJobAsyncHook(
gcp_conn_id=self.conn_id, impersonation_chain=self.impersonation_chain
)
async def _wait_job(self) -> types.BatchPredictionJob:
job: types.BatchPredictionJob = await self.async_hook.wait_batch_prediction_job(
project_id=self.project_id,
location=self.location,
job_id=self.job_id,
poll_interval=self.poll_interval,
)
return job
[docs]class RunPipelineJobTrigger(BaseVertexAIJobTrigger):
"""Make async calls to Vertex AI to check the state of a Pipeline Job."""
[docs] job_type_verbose_name = "Pipeline Job"
[docs] job_serializer_class = types.PipelineJob
[docs] statuses_success = {
PipelineState.PIPELINE_STATE_PAUSED,
PipelineState.PIPELINE_STATE_SUCCEEDED,
}
@cached_property
[docs] def async_hook(self) -> PipelineJobAsyncHook:
return PipelineJobAsyncHook(gcp_conn_id=self.conn_id, impersonation_chain=self.impersonation_chain)
async def _wait_job(self) -> types.PipelineJob:
job: types.PipelineJob = await self.async_hook.wait_for_pipeline_job(
project_id=self.project_id,
location=self.location,
job_id=self.job_id,
poll_interval=self.poll_interval,
)
return job