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@@ -71,7 +71,7 @@ Early Stopping with ASHA
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Let's integrate an early stopping algorithm to our search - ASHA, a scalable algorithm for principled early stopping.
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How does it work? On a high level, it terminates trials that are less promising and
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allocates more time and resources to more promising trials. See `this blog post <ttps://blog.ml.cmu.edu/2018/12/12/massively-parallel-hyperparameter-optimization/>`__ for more details.
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allocates more time and resources to more promising trials. See `this blog post <https://blog.ml.cmu.edu/2018/12/12/massively-parallel-hyperparameter-optimization/>`__ for more details.
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We can afford to **increase the search space by 5x**, by adjusting the parameter ``num_samples``. See the `Trial Scheduler section <tune-schedulers.html>`__ for more details of available schedulers and library integrations.
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