HyperParameter Optimisation (HPO) Tools
- 01. Google Vizier Service - A Python-based research interface for blackbox and hyperparameter optimization
- 02. Hyperopt: Distributed Asynchronous Hyper-parameter Optimization
- 03. Optuna works with any machine or deep learning framework. An open source hyperparameter optimization framework to automate hyperparameter search
- 04. scikit-optimize - Sequential model-based optimization in Python
- 05. Talos radically changes the ordinary Keras, TensorFlow (tf.keras), and PyTorch workflow by fully automating hyperparameter tuning and model evaluation.
- 06. Bayesian Optimization - Pure Python implementation of bayesian global optimization with gaussian processes.
- 07. KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search.
- 08. NNI automates feature engineering, neural architecture search, hyperparameter tuning, and model compression for deep learning.
- 09. Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for simplifying ML compute
- 10. SHERPA is a Python library for hyperparameter tuning of machine learning models.
- 11. Polyaxon supports random search and grid search, and provides a simple interface for advanced approaches, such as Hyperband and Bayesian Optimization.
- 12. mlmachine is a Python library that organizes and accelerates notebook-based machine learning experiments.
- 13. Dragonfly is an open source python library for scalable Bayesian optimisation.
- 14. flaml.tune is a module for economical hyperparameter tuning. It frees users from manually tuning many hyperparameters for a software, such as machine learning training procedures.
- 15. HEBO: Heteroscedastic Evolutionary Bayesian Optimisation
- 16. Nevergrad - A gradient-free optimization platform
- 17. SigOpt is a model development platform that makes it easy to track runs, visualize training, and scale hyperparameter optimization for any type of model built with any library on any infrastructure.
- 18. ZOOpt - Zeroth-order optimization does not rely on the gradient of the objective function, but instead, learns from samples of the search space
- 19. GPyOpt is a Python open-source library for Bayesian Optimization developed by the Machine Learning group of the University of Sheffield.
- 20. Spearmint is a software package to perform Bayesian optimization and automatically run experiments and adjusts a number of parameters so as to minimize some objective in as few runs as possible.