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* Move setup script out * some changes * Finished Contributor guide * some comments to the design * move * Apply suggestions from code review Co-Authored-By: richardliaw <rliaw@berkeley.edu> * sourcecode * comments
75 lines
3.4 KiB
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75 lines
3.4 KiB
ReStructuredText
Tune Design Guide
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=================
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In this part of the documentation, we overview the design and architecture
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of Tune.
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.. image:: images/tune-arch.png
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The blue boxes refer to internal components, and green boxes are public-facing.
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Please refer to the package reference for `user-facing APIs <tune-package-ref.html>`__.
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Main Components
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---------------
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Tune's main components consist of TrialRunner, Trial objects, TrialExecutor, SearchAlg, TrialScheduler, and Trainable.
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TrialRunner
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~~~~~~~~~~~
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[`source code <https://github.com/ray-project/ray/blob/master/python/ray/tune/trial_runner.py>`__]
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This is the main driver of the training loop. This component
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uses the TrialScheduler to prioritize and execute trials,
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queries the SearchAlgorithm for new
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configurations to evaluate, and handles the fault tolerance logic.
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**Fault Tolerance**: The TrialRunner executes checkpointing if ``checkpoint_freq``
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is set, along with automatic trial restarting in case of trial failures (if ``max_failures`` is set).
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For example, if a node is lost while a trial (specifically, the corresponding
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Trainable of the trial) is still executing on that node and checkpointing
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is enabled, the trial will then be reverted to a ``"PENDING"`` state and resumed
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from the last available checkpoint when it is run.
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The TrialRunner is also in charge of checkpointing the entire experiment execution state
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upon each loop iteration. This allows users to restart their experiment
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in case of machine failure.
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Trial objects
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~~~~~~~~~~~~~
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[`source code <https://github.com/ray-project/ray/blob/master/python/ray/tune/trial.py>`__]
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This is an internal data structure that contains metadata about each training run. Each Trial
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object is mapped one-to-one with a Trainable object but are not themselves
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distributed/remote. Trial objects transition among
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the following states: ``"PENDING"``, ``"RUNNING"``, ``"PAUSED"``, ``"ERRORED"``, and
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``"TERMINATED"``.
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TrialExecutor
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~~~~~~~~~~~~~
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[`source code <https://github.com/ray-project/ray/blob/master/python/ray/tune/trial_executor.py>`__]
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The TrialExecutor is a component that interacts with the underlying execution framework.
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It also manages resources to ensure the cluster isn't overloaded. By default, the TrialExecutor uses Ray to execute trials.
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SearchAlg
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~~~~~~~~~
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[`source code <https://github.com/ray-project/ray/tree/master/python/ray/tune/suggest>`__] The SearchAlgorithm is a user-provided object
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that is used for querying new hyperparameter configurations to evaluate.
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SearchAlgorithms will be notified every time a trial finishes
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executing one training step (of ``train()``), every time a trial
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errors, and every time a trial completes.
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TrialScheduler
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~~~~~~~~~~~~~~
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[`source code <https://github.com/ray-project/ray/blob/master/python/ray/tune/schedulers>`__] TrialSchedulers operate over a set of possible trials to run,
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prioritizing trial execution given available cluster resources.
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TrialSchedulers are given the ability to kill or pause trials,
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and also are given the ability to reorder/prioritize incoming trials.
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Trainables
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~~~~~~~~~~
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[`source code <https://github.com/ray-project/ray/blob/master/python/ray/tune/trainable.py>`__]
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These are user-provided objects that are used for
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the training process. If a class is provided, it is expected to conform to the
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Trainable interface. If a function is provided. it is wrapped into a
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Trainable class, and the function itself is executed on a separate thread.
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Trainables will execute one step of ``train()`` before notifying the TrialRunner.
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