Hyperparameter optimization for Pytorch model

Question

What is the best way to perform hyperparameter optimization for a Pytorch model? Implement e.g. Random Search myself? Use Skicit Learn? Or is there anything else I am not aware

Answer 1

You can use Bayesian optimization (full disclosure, I've contributed to this package) or Hyperband. Both of these methods attempt to automate the hyperparameter tuning stage. Hyperband is supposedly the state of the art in this space. Hyperband is the only parameter-free method that I've heard of other than random search. You can also look into using reinforcement learning to learn the optimal hyperparameters if you prefer.