Hyperparameter tuning for TensorFlow using Katib and Kubeflow
Who is this presentation for?
- Data scientists, machine learning engineers, and DevOps engineers
Neelima Mukiri and Meenakshi Kaushik provide an introduction to model building and optimization for TensorFlow in any Kubernetes environment. Katib is a scalable and flexible hyperparameter tuning framework and is tightly integrated with Kubernetes. It can be easily run on a laptop or in a distributed production deployment, and Katib jobs and configuration can be easily ported to any Kubernetes cluster.
You’ll go hands-on and set up a single-node Kubernetes on a laptop using VirtualBox. Neelima and Meenakshi provide a sample dataset and an example configuration and Kubeflow Pipeline that demonstrates hyperparameter tuning automation. They’ll walk you through Katib and Kubeflow, discussing functionality and usage, and explain how to port the tutorial to an enterprise environment for production deployment.
- A basic understanding of machine learning (what a model is and how to evaluate and compare models) and Linux command line usage
Materials or downloads needed in advance
- A laptop with VirtualBox installed (please make sure to have VirtualBox installed BEFORE arriving onsite)
What you'll learn
- Gain hands-on experience creating and optimizing models using Katib on Kubeflow
- Understand hyperparameter tuning and how to transfer the system to an on-premises or cloud environment for production use cases
Neelima Mukiri is a principal engineer in the Cloud Platform Solutions Group at Cisco, working on the architecture and development of Cisco’s Container Platform. Previously, she worked on the core virtualization layer at VMware and systems software in Samsung Electronics.
Meenakshi Kaushik is a product manager for Cisco Container Platform, an enterprise-grade Kubernetes offering that supports GPU and Kubeflow for hybrid AI and ML workloads. Meenakshi is interested in the AI and ML space and is excited to see how the technology can enhance human well-being and productivity.
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