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