nni

I'm using NNI[1] with decent success for hyperparameter optimization. It implements a number of different approaches, from a simple random search to a Tree Parzen Estimator (TPE) and specialized algorithms for automatically designing networks. It's very powerful and gives you a lot of freedom (it can minimize/maximize the output of fundamentally any python program). The main drawback is that you are on your own to figure out which paramters go well together: For example using an assesor to stop underperforming attempts early is great for random search, but devastating for TPE. You have to figure that out on your own. You inevitably spend some time tuning your hyperparameter tuner. It's still a big win in terms of human effort, at the expense of doing a lot more computing. 1: https://github.com/Microsoft/nni

In typical ML you just learn the weights. But the architecture stays fixed, and since we don't have an architecture that can realistically solve well enough all tasks, it shouldn't count as Godel machine. Meta-learning, where you also learn hyper-parameters (including potentially the architecture) are closer to that, see for example: - Google Vizier: https://ai.google/research/pubs/pub46180 - Microsoft neural architecture search: https://github.com/Microsoft/nni But this is still far from a true Godel machine.