Deep learning models are literally full of hyper-parameters. Finding the best configuration for these variables in a high-dimensional space is not trivial.
Searching for hyper-parameters is an iterative process constrained by computing power, money, and time. Therefore, it would be really useful to have software that helps you search over hyper-parameter settings.
Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.
SigOpt is an optimization-as-a-service API that allows users to seamlessly tune the configuration parameters in AI and ML models.
Ray Tune is a Python library for hyperparameter tuning at any scale, integrating seamlessly with optimization libraries such as Hyperopt and SigOpt.
Weights & Biases has a nice feature called “Hyperparameter Sweeps” — a way to efficiently select the right model for a given dataset using the tool.