chore: import upstream snapshot with attribution
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from typing import Dict, Union
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from ray.train.v2._internal.execution.controller.state import TrainControllerStateType
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from ray.train.v2._internal.metrics.base import (
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RUN_ID_TAG_KEY,
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RUN_NAME_TAG_KEY,
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EnumMetric,
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TimeMetric,
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)
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class ControllerMetrics:
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"""Factory for creating controller-specific metrics.
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This class defines all metrics used to track the state and performance of the
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training controller. Each metric is defined with its name, type, default value,
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description, and required tags.
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"""
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# ===== Metric Names =====
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CONTROLLER_STATE = "train_controller_state"
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WORKER_GROUP_START_TOTAL_TIME_S = "train_worker_group_start_total_time_s"
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WORKER_GROUP_SHUTDOWN_TOTAL_TIME_S = "train_worker_group_shutdown_total_time_s"
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# ===== Tag Keys =====
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CONTROLLER_STATE_TAG_KEY = "ray_train_controller_state"
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@classmethod
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def _create_time_metric(
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cls, name: str, description: str, base_tags: Dict[str, str]
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) -> TimeMetric:
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return TimeMetric(
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name=name,
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description=description,
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base_tags=base_tags,
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)
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@classmethod
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def _create_controller_state_metric(
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cls, base_tags: Dict[str, str]
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) -> EnumMetric[TrainControllerStateType]:
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return EnumMetric[TrainControllerStateType](
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name=cls.CONTROLLER_STATE,
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description="Current state of the Ray Train controller",
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base_tags=base_tags,
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enum_tag_key=cls.CONTROLLER_STATE_TAG_KEY,
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)
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@classmethod
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def get_controller_metrics(
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cls, run_name: str, run_id: str
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) -> Dict[str, Union[TimeMetric, EnumMetric[TrainControllerStateType]]]:
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base_tags = {RUN_NAME_TAG_KEY: run_name, RUN_ID_TAG_KEY: run_id}
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return {
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cls.WORKER_GROUP_START_TOTAL_TIME_S: cls._create_time_metric(
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cls.WORKER_GROUP_START_TOTAL_TIME_S,
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"Total time taken to start the worker group",
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base_tags,
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),
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cls.WORKER_GROUP_SHUTDOWN_TOTAL_TIME_S: cls._create_time_metric(
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cls.WORKER_GROUP_SHUTDOWN_TOTAL_TIME_S,
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"Total time taken to shutdown the worker group",
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base_tags,
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),
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cls.CONTROLLER_STATE: cls._create_controller_state_metric(base_tags),
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}
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