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Hyperparameter Tuning with Kubeflow and Kale

In this course, we will use Kale to unify the workflow across the above components, and present a seamless process to create ML pipelines for HP tuning, starting from your Jupyter Notebook. We will use Kale to convert a Jupyter Notebook to a Kubeflow Pipeline without any modification to the original Python code. Pipeline definition and deployment is achieved via an intuitive GUI, provided by Kale’s JupyterLab extension. As a next step, Kale will scale up the resulting pipeline to multiple parallel runs for hyperparameter tuning using Kubeflow Katib. Kale also integrates with Arrikto’s Rok data management platform to efficiently make the data available across Kubeflow components in a versioned way, and snapshot every step of each pipeline, making all pipelines completely reproducible.

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    Course
  • Self-Paced

About This Course

This is part two in our series. In Part 1 we presented an innovative way of simplifying your complex multi-step ML workflows, so that you save time and iterate faster with your teammates. Starting from a notebook, you can run your Python code as a Kubeflow Pipeline with the click of a button using MiniKF and Kale.

In this course, we focus on bringing together popular Kubeflow components to deploy a complete and easy to use workflow based on Jupyter Notebooks, MiniKF, Kale, and Katib. Also, you will build a complex data science pipeline with hyperparameter tuning on Kubeflow Pipelines, without using any CLI commands or SDKs.

This course was presented as a workshop by Google & Arrikto during KubeCon San Diego 2019. Here are the slides, and the video of the workshop.

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