[D] Which hyperparameters search library to use?

An ML engineer is benchmarking four major hyperparameter optimization libraries for real-world experiments: Hyperopt, Optuna, sklearn's GridSearchCV, and sklearn's RandomizedSearchCV. They seek an ecosystem-agnostic tool (works with PyTorch, TensorFlow, JAX) with low performance overhead, rich features, and long-term stability. The community discussion highlights Optuna's modern API and efficiency versus sklearn's simplicity, helping practitioners choose the right tool to automate model tuning and accelerate experimentation.