Katib is an open source project which uses Kubernetes CRD to run Automated Machine Learning (AutoML) tasks. To know more about Katib follow the official guides.
This directory contains examples of Katib Experiments in action. To install Katib on your Kubernetes cluster check the setup guide. You can use various Katib interfaces to run these examples.
For a complete description of the Katib Experiment specification follow the configuration guide
Get started with Katib Experiments from your local laptop and Kind cluster by following this example.
The following examples show various AutoML algorithms in Katib.
Check the Hyperparameter Tuning Experiments for the following algorithms:
Check the Neural Architecture Search Experiments for the following algorithms:
Improve your Hyperparameter Tuning Experiments with the following Early Stopping algorithms:
To learn more about Katib Python SDK check this directory.
You can use different resume policies in Katib Experiments. Follow this guide to know more about it. Check the following examples:
Katib supports the various metrics collectors and metrics strategies. Check the official guide to know more about it. In this directory you can find the following examples:
You can specify different settings for your Trial template. To know more about it follow this guide. Check the following examples:
Check the following images for the Trial containers:
Katib has out of the box support for the Kubeflow Training Operators to perform Trial's Worker job. Check the following examples for the various distributed operators:
To run Katib with Kubeflow Pipelines check these examples.
To know more about using Argo Workflows in Katib check this directory.
To know more about using Tekton Pipelines in Katib check this directory.
You can run Katib Experiments on FPGA based instances. For more information check these examples.