KubeCon + CloudNativeCon Europe 2020

An introduction to Kubeflow, the ML toolkit for K8s and the workflows you can use as a data scientist to scale up your ML code effortlessly.

Ever thought how hard it is to convert your Jupyter Notebooks into deployable and composable pipelines, scale up computation and run hyperparameter tuning? With Kubeflow, this process becomes extremely easy as you make use of the many components of this ML toolkit: Pipelines, Kale, Katib, Snapshot Store.

You will learn how to deploy Kubeflow in minutes, explore your ML code inside a Jupyter Notebook, convert it to a composable and scalable workflow with the click of a button, make the pipeline reproducible using immutable snapshots, go back in history and debug it, run hyperparameter tuning and distribute your computation.

Did we mention you won’t need any specific SDK or CLI command to do this? Sounds like magic? Come and see for yourself!

This tutorial will get you off the ground with Kubernetes security basics, using live demos and examples to work through yourself.

We’ll start with possible attack vectors, to help you map out the threat model that applies to your cluster, so you can figure out where you need to focus your efforts for security.

We’ll show you how to compromise a deployment with a pod running with a known vulnerability. Once you’ve had the attacker’s eye-view, we’ll walk you through the most important techniques and open source tools to prevent compromise.

· Using secure Kubernetes settings
· Including vulnerability scanning in your workflow
· Configuring pods to run securely (eg avoiding unnecessary privileges)
· Using GitOps to restrict user access to your cluster and provide an audit trail

You’ll leave this tutorial armed with practical actions for securing your deployment.

Kubernetes provides a high-level abstraction layer that makes it easy to deploy distributed computing resources without knowing what’s happening in the kernel and applications. But when debugging, Kubernetes does not provide any help in inspecting these low-level details.

We showcase the following tools specifically designed for running on Kubernetes:
- Inspektor Gadget, built on BPF Compiler Collection (BCC) and traceloop
- kubectl-trace, built on bpftrace

These allow devops teams to answer debugging questions such as:
- What were the last system calls executed before the crash?
- Was this function called? With which arguments and return value?
- Which TCP packets were retransmitted?
- Which queries run slow?
- Was this file opened?

You'll need access to a Linux machine and a Minikube installation. Please follow the instructions at https://github.com/kinvolk/cloud-native-bpf-workshop

Learn how to build applications that span across more than one Kubernetes region by using a NATS based global communications network. In this talk, it will be covered how to setup a globally available NATS cluster using multiple Kubernetes regions from 4 different clouds (AWS, Digital Ocean, GKE & Azure) using NATS gateways and leafnode connections, as well as how to create applications that take advantage of the NATS decentralized authorization model by showing how to implement a simple Slack-like clone that runs under your terminal.

Attendees of this session will be able to follow through the implementation of the service (and interact with each other!) during the talk, to run the examples it is recommended to have Docker installed.

Networking in Kubernetes has several aspects, including DNS, iptables, routing, software bridges, IP assignment, network policies, etc. While the practices for understanding the network were fairly easy to translate from physical servers to virtual machines, the level of complexity increases greatly when moving to containers in Kubernetes.

This tutorial will explain several of the networking concepts used in Kubernetes with accompanying lab exercises in a virtualized environment so that participants will become comfortable looking under the hood at how a Kubernetes cluster is working (or not working, as the case may be).

The material will be designed for people comfortable with SSH, bash, kubectl, and basic networking concepts, and will fill in the more advanced networking knowledge as the tutorial progresses. Please have Vagrant + VirtualBox installed to run the labs locally.

This hands-on tutorial introduces the key concepts of Tekton, a flexible, Kubernetes-native CI/CD framework that enables the automation of deployments across multiple platforms - including Kubernetes, serverless, and VMs - by abstracting away the underlying details.

In this tutorial, participants will:

- Learn about fundamental Tekton concepts and benefits and how to use the tkn CLI
- Install tasks and learn about task catalogs
- Create a pipeline for building, testing, and deploying an application on Kubernetes
- Add pipeline resources
- Trigger a pipelinerun
- Deploy the application using a pipelinerun

Pre-requisites:
Install the following prior to the tutorial:

• minikube:https://kubernetes.io/docs/tasks/tools/install-minikube/ (Kubernetes v1.16 or higher)
• tkn CLI: https://github.com/tektoncd/cli
• (optional) VS Code Tekton extension: https://marketplace.visualstudio.com/items?itemName=redhat.vscode-tekton-pipelines

Here a hands-on KubeEdge workshop is proposed which ideally will run through the full length of the conference.

This workshop is intend to invite participants to get hands on experience building a real edge computing solution with KubeEdge, end-to-end.

Starting from deploying and provisioning an edge node(e.g. raspberry Pi), followed with device modeling and connectivity setup, then building a video stream machine learning based solution.

Through this exercise, participants will get first hand experience to understand the orchestration engine build on top of kurbenetes, understand the edge computing node setup mechanism, learn the device modeling concept for IoT Edge scenarios. And develop a state-of-art AI based video stream processing flow, all in a 30 minutes session.