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[tune] Improve PBT example (#4575)

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Eric Liang
2019-04-09 20:59:17 -07:00
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doc/source/tune-schedulers.rst
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@@ -36,7 +36,7 @@ Tune includes a distributed implementation of `Population Based Training (PBT) <
When the PBT scheduler is enabled, each trial variant is treated as a member of the population. Periodically, top-performing trials are checkpointed (this requires your Trainable to support `checkpointing <tune-usage.html#trial-checkpointing>`__). Low-performing trials clone the checkpoints of top performers and perturb the configurations in the hope of discovering an even better variation.
You can run this `toy PBT example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_example.py>`__ to get an idea of how how PBT operates. When training in PBT mode, a single trial may see many different hyperparameters over its lifetime, which is recorded in its ``result.json`` file. The following figure generated by the example shows PBT discovering new hyperparams over the course of a single experiment:
You can run this `toy PBT example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_example.py>`__ to get an idea of how how PBT operates. When training in PBT mode, a single trial may see many different hyperparameters over its lifetime, which is recorded in its ``result.json`` file. The following figure generated by the example shows PBT with optimizing a LR schedule over the course of a single experiment:
.. image:: pbt.png