Source code for airflow.providers.amazon.aws.triggers.sagemaker

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from __future__ import annotations

from functools import cached_property
from typing import Any

from airflow.providers.amazon.aws.hooks.sagemaker import SageMakerHook
from airflow.providers.amazon.aws.utils.waiter_with_logging 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]]:
        """Serializes SagemakerTrigger arguments and classpath."""
        return (
            "airflow.providers.amazon.aws.triggers.sagemaker.SageMakerTrigger",
            {
                "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,
            },
        )

    @cached_property
[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):
        self.log.info("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."})