Strengths
- Supports dynamic search space definition at runtime without requiring predefined grids.
- Integrates with multiple popular machine learning libraries including PyTorch, TensorFlow, and XGBoost.
- Enables easy parallelization to scale optimization across multiple workers.
- Provides visualization tools and a real-time dashboard for monitoring optimization progress.
- Supports multi-objective and constrained optimization.
Limitations
- Primarily focused on Python, limiting usability in other programming languages.
- Requires manual definition of objective functions, which adds code overhead.