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Population Based Training of Neural Networks, Jaderberg et al. @ DeepMind

A simple PyTorch implementation of PBT.

Finding a good hyperparameter schedule.

PBT trains each model partially and assesses them on the validation set. It then transfers the parameters and hyperparameters from the top performing models to the bottom performing models (exploitation). After transferring the hyperparameters, PBT perturbs them (exploration). Each model is then trained some more, and the process repeats. This allows PBT to learn a hyperparameter schedule instead of only a fixed hyperparameter configuration. PBT can be used with different selection methods (e.g. different ways of defining "top" and "bottom" (e.g. top 5, top 5%, etc.)).

For more information, see the paper or blog post.

• PyTorch >= 1.0.0

$ python main.py --device cuda --population_size 10 --batch_size 20