From 0224731f9e98026d8507bd3f75da1c04261753a8 Mon Sep 17 00:00:00 2001 From: Yifan Zhao <yifanz16@illinois.edu> Date: Thu, 8 Apr 2021 15:57:55 -0500 Subject: [PATCH] Use 'kept' configs consistently throughout --- doc/getting_started.rst | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/doc/getting_started.rst b/doc/getting_started.rst index aefdf71..cc8b7cb 100644 --- a/doc/getting_started.rst +++ b/doc/getting_started.rst @@ -125,15 +125,17 @@ We will be using the term QoS throughout the tutorials. :py:meth:`tuner.tune <predtuner.modeledapp.ApproxModeledTuner.tune>` is the main method for running a tuning session. It accepts a few parameters which controls the behavior of tuning. -`max_iter` defines the number of iterations to use in autotuning. -Within 1000 iterations, PredTuner should find about 200 valid configurations. -PredTuner will also automatically mark out `Pareto-optimal -<https://en.wikipedia.org/wiki/Pareto_efficiency>`_ -configurations. -These are called "best" configurations (`tuner.best_configs`), -in contrast to "valid" configurations which are the configurations that satisfy our accuracy requirements -(`tuner.kept_configs`). -`take_best_n` allows taking some extra close-optimal configurations in addition to Pareto-optimal ones. + +* `qos_keep_threshold` decides the QoS threshold above which the found configuration is kept. + These are called the "kept" configurations and are accessible from `tuner.kept_configs`. + +* `max_iter` defines the number of iterations to use in autotuning. + Within 1000 iterations, PredTuner should be about to find about 200 "kept" configurations. + +* PredTuner will also automatically mark out + `Pareto-optimal <https://en.wikipedia.org/wiki/Pareto_efficiency>`_ configurations. + These are called "best" configurations (`tuner.best_configs`) + `take_best_n` allows taking some extra close-optimal configurations in addition to Pareto-optimal ones. 1000 iterations is for demonstration; in practice, at least 10000 iterations are necessary on VGG16-sized models to converge to a set of good configurations. -- GitLab