Better ML models with Katib

Better ML models with Katib

Katib advantages Katib is the only HP tuning system that can realistically run as a hosted service in on-premise data centers or private/public clouds. There are various reasons why this is true: Multi-tenancy: Katib supports multi-tenancy, which facilitates cross-team collaboration.

In Machine Learning, a hyperparameter is a user-defined value that is kept fixed during training. Examples of hyperparameters are the value of k in k-means clustering, the learning rate, the batch size, or the number of hidden nodes in neural networks.

While there are now techniques that rely on changing those values while our model is learning (e.g., Leslie Smith’s one cycle policy, simulated annealing, etc.), these are not learnable parameters such as the weights of a neural network.

However, hyperparameters can greatly impact the quality of the model generated by the training process and the time and memory requirements of the algorithm. Thus, hyperparameters have to be tuned to get the optimal configuration for a given problem.

We can tune the hyperparameters of a model either manually or automatically. In most cases, it would be impractical to execute this task by hand. Thus, an automatic hyperparameter tuning mechanism would facilitate discovering the optimal values for each hyperparameter in a reasonable timeframe.

Many systems automate this process. This story examines Katib, an open-source, framework-agnostic, cloud-native, scalable, and production-ready hyperparameter tuning system. But why do we need another hyperparameter optimization engine? We have a multitude of options already: OptunaHyperOptNNIVizier, or Ray. So, first, let’s see what Katib does to improve things in the real world.

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