Improved autotuning scoring and parameter search process #813
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tgaddair
commented
Feb 6, 2019
tgaddair
commented
- Use time between samples instead of aggregated time spent in the background thread for computing sample score
- Normalized scores after each sample to focus the Bayesian optimization more on certain ranges of parameters
- Increased noise parameter from 0.2 -> 0.8 to reflect apriori entropy expectations in samples
alsrgv
reviewed
Feb 6, 2019
| *mu = sum / v.size(); | ||
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| std::vector<double> diff(v.size()); | ||
| std::transform(v.begin(), v.end(), diff.begin(), std::bind2nd(std::minus<double>(), *mu)); |
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Does it take lambda instead of std::bind2nd? Seems more reader-friendly :-)
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| VectorXd y_i(1); | ||
| y_i(0) = norm_score; | ||
| y_sample.row(i) = y_i; |
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If I read this correctly, all observations are re-normalized every time, right?
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Yes, we only call BayesianOptimization::NextSample() once after each call to AddSample(), so it should be performant. This is also idempotent, because we store the raw value in y_samples_, while the matrix y_sample is normalized.
apeforest
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to apeforest/horovod
that referenced
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Feb 12, 2019
Signed-off-by: Lin Yuan <apeforest@gmail.com>
jeffdaily
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to ROCm/horovod
that referenced
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Nov 27, 2019
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