For questions on hyperparameter tuning and experiment management. Use Tune to search for the best model configurations by running and scheduling many parallel trials.
Tune is a Python library for experiment execution and hyperparameter tuning at any scale. You can tune your favorite machine learning framework (PyTorch, XGBoost, TensorFlow and Keras, and more) by running state of the art algorithms such as Population Based Training (PBT) and HyperBand/ASHA. Tune further integrates with a wide range of additional hyperparameter optimization tools, including Ax, BayesOpt, BOHB, Nevergrad, and Optuna.
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