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28 lines
1.5 KiB
ReStructuredText
28 lines
1.5 KiB
ReStructuredText
Ray.tune: Hyperparameter Optimization Framework
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===============================================
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Ray.tune is a hyperparameter tuning framework for long-running tasks such as RL and deep learning training.
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User documentation can be `found here <http://ray.readthedocs.io/en/latest/tune.html>`__.
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Implementation overview
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-----------------------
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At a high level, Ray.tune takes in JSON experiment configs (e.g. that defines the grid or random search)
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and compiles them into a number of `Trial` objects. It schedules trials on the Ray cluster using a given
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`TrialScheduler` implementation (e.g. median stopping rule or HyperBand).
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This is implemented as follows:
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- `variant_generator.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/variant_generator.py>`__
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parses the config and generates the trial variants.
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- `trial.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/trial.py>`__ manages the lifecycle
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of the Ray actor responsible for executing the trial.
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- `trial_runner.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/tune.py>`__ tracks scheduling
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state for all the trials of an experiment. TrialRunners are usually
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created automatically by ``run_experiments(experiment_json)``, which parses and starts the experiments.
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- `trial_scheduler.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/trial_scheduler.py>`__
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plugs into TrialRunner to implement custom prioritization or early stopping algorithms.
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