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.
Tutorial Overview and Author Bio
Model Training with GPUs and Live Metrics Tracking with Tensorboard on Kubeflow
Software Engineer | Arrikto
Student | National Technical University of Athens