Katib is a an open-source Kubernetes-native project for automated machine learning (AutoML). Katib supports hyperparameter tuning, early stopping, and neural architecture search (NAS). It helps to tune your hyperparameters by testing a range of values for each one. You define what your objective is and set up, and experiment so Katib can try different hyperparameter values.
- Natively supports many ML frameworks, such as TensorFlow, MXNet, PyTorch, XGBoost, and others
- Supports a lot of various AutoML algorithms, such as Bayesian optimization, Tree of Parzen Estimators, Random Search, Covariance Matrix Adaptation Evolution Strategy, Hyperband, Efficient Neural Architecture Search, Differentiable Architecture Search and many more
- Neural architecture search (NAS) feature
- Different interfaces that interact with Katib: a web UI, gRPC API, command-line interfaces (CLIs), and Katib Python SDK
- Support any Kubernetes CRDs or Kubernetes workloads as a Katib Trial template
- Early stopping of the experiment’s trials
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