chore: import upstream snapshot with attribution
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import logging
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from dataclasses import dataclass
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from ray.train._internal.syncer import SyncConfig as TrainSyncConfig
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from ray.util.annotations import PublicAPI
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logger = logging.getLogger(__name__)
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@PublicAPI(stability="beta")
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@dataclass
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class SyncConfig(TrainSyncConfig):
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"""Configuration object for Tune file syncing to `RunConfig(storage_path)`.
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In Ray Tune, here is where syncing (mainly uploading) happens:
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The experiment driver (on the head node) syncs the experiment directory to storage
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(which includes experiment state such as searcher state, the list of trials
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and their statuses, and trial metadata).
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It's also possible to sync artifacts from the trial directory to storage
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by setting `sync_artifacts=True`.
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For a Ray Tune run with many trials, each trial will upload its trial directory
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to storage, which includes arbitrary files that you dumped during the run.
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Args:
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sync_period: Minimum time in seconds to wait between two sync operations.
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A smaller ``sync_period`` will have the data in storage updated more often
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but introduces more syncing overhead. Defaults to 5 minutes.
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sync_timeout: Maximum time in seconds to wait for a sync process
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to finish running. A sync operation will run for at most this long
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before raising a `TimeoutError`. Defaults to 30 minutes.
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sync_artifacts: [Beta] Whether or not to sync artifacts that are saved to the
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trial directory (accessed via `ray.tune.get_context().get_trial_dir()`)
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to the persistent storage configured via `tune.RunConfig(storage_path)`.
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The trial or remote worker will try to launch an artifact syncing
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operation every time `tune.report` happens, subject to `sync_period`
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and `sync_artifacts_on_checkpoint`.
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Defaults to False -- no artifacts are persisted by default.
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sync_artifacts_on_checkpoint: If True, trial/worker artifacts are
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forcefully synced on every reported checkpoint.
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This only has an effect if `sync_artifacts` is True.
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Defaults to True.
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"""
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pass
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