Kubeflow is the de facto standard for running Machine Learning (ML) workflows on Kubernetes. Its goal is to simplify the day-to-day operations of the data scientists and accelerate the production deployment of models.

Kubeflow comes with all of the tools and technologies that end users are accustomed to like Jupyter Notebooks, Tensorflow, and Tensorboard. It also provides intuitive UIs for managing and consuming the data of the cluster.

In this session you will:

1) learn the basics of Kubeflow, including configuring a Jupyter Notebook on a K8s cluster

2) upload data from your local machine directly to the cluster using Kubeflow’s UIs

3) tackle a real world ML problem using Keras and GPUs to train a dog breed identifier

4) track and visualize training metrics using Tensorboard.


New on-demand courses are added weekly

Tutorial Overview

  • 1

    ODSC Europe 2020: Model Training with GPUs and Live Metrics Tracking with Tensorboard on Kubeflow

    • Tutorial Overview and Author Bio

    • Before you get started: Prerequisites and Resources

    • Tutorial slides

    • Model Training with GPUs and Live Metrics Tracking with Tensorboard on Kubeflow

Instructor Bio:

Software Engineer | Arrikto

Kimonas Sotirchos

Kimonas is a Software Engineer at Arrikto, working on storage solutions on the cloud. He loves Open Source and has been a core contributor to the Kubeflow project for more than a year. Kimonas is the owner of the platform's Jupyter infrastructure and his main goal is to improve the way users manage the lifecycle of their ML tools, like Notebooks, and data on top of Kubeflow. He is also a mentor at the Kubeflow project at Google Summer of Code 2020 providing guidance for adding seamless support for launching Tensorboard instances.

Student | National Technical University of Athens

Konstantinos Andriopoulos

Konstantinos is an undergraduate student at the National Technical University of Athens in the school of Electrical and Computer Engineering. Currently, he is a member of the Kubeflow organization for which he developed the Tensorboard web-app as a part of his Google Summer of Code proposal. His other interests involve speech signal processing, machine learning and distributed systems.