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from __future__ import annotations

import asyncio
from collections import Counter
from enum import IntEnum
from functools import cached_property
from typing import Any, AsyncIterator

from botocore.exceptions import WaiterError

from airflow.exceptions import AirflowException
from import SageMakerHook
from import async_wait
from airflow.triggers.base import BaseTrigger, TriggerEvent

[docs]class SageMakerTrigger(BaseTrigger): """ SageMakerTrigger is fired as deferred class with params to run the task in triggerer. :param job_name: name of the job to check status :param job_type: Type of the sagemaker job whether it is Transform or Training :param poke_interval: polling period in seconds to check for the status :param max_attempts: Number of times to poll for query state before returning the current state, defaults to None. :param aws_conn_id: AWS connection ID for sagemaker """ def __init__( self, job_name: str, job_type: str, poke_interval: int = 30, max_attempts: int = 480, aws_conn_id: str = "aws_default", ): super().__init__() self.job_name = job_name self.job_type = job_type self.poke_interval = poke_interval self.max_attempts = max_attempts self.aws_conn_id = aws_conn_id
[docs] def serialize(self) -> tuple[str, dict[str, Any]]: """Serialize SagemakerTrigger arguments and classpath.""" return ( "", { "job_name": self.job_name, "job_type": self.job_type, "poke_interval": self.poke_interval, "max_attempts": self.max_attempts, "aws_conn_id": self.aws_conn_id, }, )
[docs] def hook(self) -> SageMakerHook: return SageMakerHook(aws_conn_id=self.aws_conn_id)
@staticmethod def _get_job_type_waiter(job_type: str) -> str: return { "training": "TrainingJobComplete", "transform": "TransformJobComplete", "processing": "ProcessingJobComplete", "tuning": "TuningJobComplete", "endpoint": "endpoint_in_service", # this one is provided by boto }[job_type.lower()] @staticmethod def _get_waiter_arg_name(job_type: str) -> str: return { "training": "TrainingJobName", "transform": "TransformJobName", "processing": "ProcessingJobName", "tuning": "HyperParameterTuningJobName", "endpoint": "EndpointName", }[job_type.lower()] @staticmethod def _get_response_status_key(job_type: str) -> str: return { "training": "TrainingJobStatus", "transform": "TransformJobStatus", "processing": "ProcessingJobStatus", "tuning": "HyperParameterTuningJobStatus", "endpoint": "EndpointStatus", }[job_type.lower()]
[docs] async def run(self):"job name is %s and job type is %s", self.job_name, self.job_type) async with self.hook.async_conn as client: waiter = self.hook.get_waiter( self._get_job_type_waiter(self.job_type), deferrable=True, client=client ) await async_wait( waiter=waiter, waiter_delay=self.poke_interval, waiter_max_attempts=self.max_attempts, args={self._get_waiter_arg_name(self.job_type): self.job_name}, failure_message=f"Error while waiting for {self.job_type} job", status_message=f"{self.job_type} job not done yet", status_args=[self._get_response_status_key(self.job_type)], ) yield TriggerEvent({"status": "success", "message": "Job completed."})
[docs]class SageMakerPipelineTrigger(BaseTrigger): """Trigger to wait for a sagemaker pipeline execution to finish."""
[docs] class Type(IntEnum): """Type of waiter to use."""
[docs] COMPLETE = 1
[docs] STOPPED = 2
def __init__( self, waiter_type: Type, pipeline_execution_arn: str, waiter_delay: int, waiter_max_attempts: int, aws_conn_id: str, ): self.waiter_type = waiter_type self.pipeline_execution_arn = pipeline_execution_arn self.waiter_delay = waiter_delay self.waiter_max_attempts = waiter_max_attempts self.aws_conn_id = aws_conn_id
[docs] def serialize(self) -> tuple[str, dict[str, Any]]: return ( self.__class__.__module__ + "." + self.__class__.__qualname__, { "waiter_type": self.waiter_type.value, # saving the int value here "pipeline_execution_arn": self.pipeline_execution_arn, "waiter_delay": self.waiter_delay, "waiter_max_attempts": self.waiter_max_attempts, "aws_conn_id": self.aws_conn_id, }, )
_waiter_name = { Type.COMPLETE: "PipelineExecutionComplete", Type.STOPPED: "PipelineExecutionStopped", }
[docs] async def run(self) -> AsyncIterator[TriggerEvent]: hook = SageMakerHook(aws_conn_id=self.aws_conn_id) async with hook.async_conn as conn: waiter = hook.get_waiter(self._waiter_name[self.waiter_type], deferrable=True, client=conn) for _ in range(self.waiter_max_attempts): try: await waiter.wait( PipelineExecutionArn=self.pipeline_execution_arn, WaiterConfig={"MaxAttempts": 1} ) # we reach this point only if the waiter met a success criteria yield TriggerEvent({"status": "success", "value": self.pipeline_execution_arn}) return except WaiterError as error: if "terminal failure" in str(error): raise "Status of the pipeline execution: %s", error.last_response["PipelineExecutionStatus"] ) res = await conn.list_pipeline_execution_steps( PipelineExecutionArn=self.pipeline_execution_arn ) count_by_state = Counter(s["StepStatus"] for s in res["PipelineExecutionSteps"]) running_steps = [ s["StepName"] for s in res["PipelineExecutionSteps"] if s["StepStatus"] == "Executing" ]"State of the pipeline steps: %s", count_by_state)"Steps currently in progress: %s", running_steps) await asyncio.sleep(int(self.waiter_delay)) raise AirflowException("Waiter error: max attempts reached")

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