Finding the right mix of hyperparameters to achieve your goals can be difficult and time-consuming. Kubeflow includes a hyperparameter optimization tool called Katib, and it is fully integrated into the DKube platform.
Katib sweeps through a range of hyperparameter combinations on a specific code and dataset, and chooses the best metrics based on your goals. An input configuration file selects the hyperparameters, the target metrics, and the algorithm to use for the input parameters. Once the combination has been identified to maximize the metric goals, a model is created for that combination.
The impact of each hyperparameter on the target metrics can be viewed graphically to better understand overall trends.
The output of Katib is fully integrated into the DKube workflow, including the ability to operate on the models that are created, and to use MLFlow to compare the metrics generated from training.