Faster grid search with small dataset to derive best params instead of full dataset? I have a dataset of 300 000 rows and an ensemble model, which include grid search to find the best params of every algorithm. Unfortunately the grid search needs to long and I have problems to implement gpu running for the different algorithms (xgb,lightgbm..). Also the models which are from scikit-learn like random-forest dont run on gpu.
The idea is now, instead of 300 000 rows, I will create a small dataset with max. 500 rows, which need less time than the full dataset.

Could the minimal required sample size calculation help?

How big should the sample be? to get a good data distribution of the big dataset?
This idea of using a small sample to the data set to search for the hyperparameters is called multi-fidelity methods.  A good starting point is the book by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren Automated machine learning: methods, systems, challenges which is open access.