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Hyperparameter tuning is a way to find the best machine learning model. We make it ridiculously easy to run hyperparameter sweeps using simple algorithms like grid search, to more modern approaches like bayesian optimization and early stopping.
SweepsScalable, customizable hyperparameter tuning
Carey Phelpsleft a comment
This simple system of record automatically saves logs from every experiment, making it easy to look over the history of your progress and compare new models with existing baselines. Pros: Easy, fast, and lightweight experiment tracking Cons: Only available for Python projects
Weights & BiasesDeveloper tools for deep learning & machine learning