<p>A good python library for hyper-parameter tuning is <a href="https://arxiv.org/pdf/1603.06560.pdf" rel="nofollow noreferrer"><code>keras tuner</code></a>. You can utilize different tuners in this library, but for the large data, as you've mentioned, <a href="https://arxiv.org/pdf/1603.06560.pdf" rel="nofollow noreferrer"><code>Hyperband Optimization</code></a> can be state-of-the-art and appropriate one.</p>
how to do hyperparameter optimization in large data?
<p>I almost finished my time series model, collected enough data and now I am stuck at hyperparameter optimization.</p>
<p>And after lots of googling I found new &amp; good library called ultraopt, but problem is that how much amount of fragment of data should I use from my total data (~150 GB) for hyperparameter tuning. And I want to try lots of algorithm and combinations, is there any faster and easy way?</p>
<p>Or</p>
<p>Is there any math involved, something like,
mydata = 100%size</p>
<p>hyperparameter optimization with 5% of mydatasize,</p>
<p>optimized hyperparameter *or+ or something with left 95% of datasize   #something like this</p>
<p>To get a similar result as full data used for optimization at a time. Is there any shortcut for these?</p>
<p>I am using Python 3.7,
CPU: AMD ryzen5 3400g,
GPU: AMD Vega 11,
RAM: 16 GB</p>