[r/ML] [D] how to parallelize optimal parameter search for DL NNs on multiple datasets?

Summary

The user is seeking advice on how to efficiently parallelize a large-scale hyperparameter optimization task for deep learning models. They aim to test various combinations of non-DL parameters across five different neural networks and eleven datasets, looking for methods to execute these numerous experiments non-sequentially and in parallel to speed up the process.

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