Abstract: 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.
* Lesson 1: Learn the basics of Kubeflow
Discover the different tools and services of Kubeflow. Configure a Jupyter Notebook, including injecting Object Store credentials and assigning GPUs. And all of this within Kubeflow’s UIs, without the need to access any terminal.
* Lesson 2: Learn how to upload your local data to the Kubeflow cluster
You will learn how to use Kubeflow’s intuitive UIs and applications to upload files and folder from your local machine directly to the cloud with simple drag and drop mechanisms. You will also be able to navigate and play around with the data that lives inside your cluster’s volumes.
* Lesson 3: Train a Keras model using GPUs
Create and train a Keras CNN model with GPUs. Given a dog image, the final model should be able to identify the dog breed reliably.
* Lesson 4: Launch Tensorboard to visualize your training metrics
You will learn how to launch and use Tensorboard with Kubeflow to track and visualize your training metrics while they are generated live from the Jupyter Notebook.
Attendees should be familiar with Kubernetes, Jupyter Notebooks, and Tensorboard.
Bio: Konstantinos is an undergraduate student at the National Technical University of Athens in the school of Electronic and Computer Engineering.
His current interests involve the constant development and optimization of emoFeatExtract: an open-source Python package and feature set for Speech Emotional Recognition. Also, he's currently a Google Summer of Code student for Kubeflow. In his free time he enjoys playing and listening to music, as well as swimming.