The goal of this project is to get an overview of the state-of-the-art technology on training and deploying machine learning projects with kubernetes and apply that to a SUSE CaaSP cluster.
With that in mind, we will train and deploy a model for summarizing github issues:
This example, will make use of the following technology:
- kubeflow: Machine Learning Toolkit for Kubernetes
- Keras: The Python Deep Learning library
- Seldon Core: Machine Learning Deployment for Kubernetes
- Tensorfow: An open source machine learning framework for everyone
- cri-o: Lightweight Container Runtime for Kubernetes
- SUSE CaaSP: SUSE Container as a Service Platform
- Nvidia container engine
Looking for mad skills in:
machinelearning kubeflow keras seldoncore tensorflow cri-o kubernetes caasp nvidia cuda gpu containers
This project is part of:
Hack Week 17
Throughout this project, I will learn about Kub...
SUSE is well known for the standard enterprise ...
The goal of this project is two fold.
A common challenge for OpenStack and K8S deploy...