So we got our cluster and we got our Jupyter Notebook ready. Let's practice a bit with that.

Hello

A Jupyter Notebook is an interactive python environment. It's pretty useful because you may "play" with it. Let's see a couple of examples:

Print something

1. Open your notebook

2. Click on the option "New" and select "Python 3"

3. In the first cell type:

Press CTRL+Enter on your keyboard you should see something similar to the image below.

If you want to learn more about how to use a Jupyter Notebook there's a great user guide on their site, just click

Hello Universe

So The cluster is up, the notebook is ready it's time to add some spice to the recipe. Whenever you want to develop a real AI application you will have to take care of three major areas: DataSets, Training, Inference.

DataSet is your data, where it comes from, how you prepare it, where you store it. We do not currently take care of this in k3ai. We focus on model training and model inference.

Model Training is about how you "teach" your mathematical model to identify specifics patterns or provide you results out of your dataset. For the sake of this guide, we will use Kubeflow pipelines for our training.

Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers -

So the flow will be this:

1. Add Kubeflow pipelines to our existing cluster

2. Download and use the public Kubeflow minimal pipeline example to learn how to use them

3. Run a slightly more complex pipeline against our fresh Kubeflow pipeline environment

To add Kubeflow pipelines to our existing environment we will type in our terminal

or if we are in K3s/K30 and we want to make use of traefik

Once the deployment is done we may reach out to the pipelines UI on port 80. In case we did not use traefik we may expose the UI with this command:

Now let's proceed to download a sample notebook from the Kubeflow repo. You may grab it from

Open the notebook in raw mode, select all and save it as file with .ipynb extension (i.e.: demo.ipynb)

1. Open your Jupyter Notebook UI (http://<YOUR CLUSTER IP>:8888)

2. Click on Upload and load the Notebook you just saved

3. Once the Notebook has been upload it open it

4. Execute the first cell to install KFP SDK library. This is needed to interact with Kubeflow pipelines

and change it to

That's all.. execute all cells one after another and you'll see after the cell we just changed a result similar to this

At this point click on the "Run Details" and you should see something like this

Congratulation you have now a full pipeline running on your k3ai playground!

Identify the problem

Artificial Intelligence platforms are complex. They combine a multitude of tools and frameworks that help Data Scientist and Data Engineers to solve the problem of building end-to-end pipelines.

But those AI platforms, by inheritance, have a degree of complexity. Let take at the use case of some of them:

The end goal of every organization that utilizes machine learning (ML) is to have their ML models successfully run in production and generate value to the business. But what does it take to reach that point?

Before a model ends up in production, there are potentially many steps required to build and deploy an ML model: data loading, verification, splitting, processing, feature engineering, model training and verification, hyperparameter tuning, and model serving.

In addition, ML models can require more observation than traditional applications, because your data inputs can drift over time. Manually rebuilding models and data sets is time consuming and error prone.

Kubeflow project -

See the elephant in the room? We all have to struggle with the complexity of a process that looks like the one below

So here the first problem we identified (yes I said first): Remove the complexity and give you a straight solution.

Now there are plenty of alternatives when it comes to the infrastructure (local infrastructure) like:

• Minikube

• Kind

• Docker for Windows (Kubernetes)

• MicroK8s

And some of them even allow you to install some platforms like Kubeflow but.. could you cherry-picking AI tools and/or solutions and running them on top of an infrastructure that does not suck up your entire laptop RAM? Let say you start from learning the basics of training a model on different platforms and later move to learn serving models. You won't have everything running but move from one configuration to the other quickly.

Identify the other problem

If experimentation is one face of the coin the other is using K3ai in the context of CI/CD.

Data Engineers, DevOps or in a more fancy definition AIOps have to face the challenge of building infrastructure pipelines that satisfy the following requirements:

• Must be FAST to be built and EASY to be destroyed

• Must be AVAILABLE everywhere no matter if it's on-prem, on-cloud, or in the remote universe

• Must be REPRODUCIBLE you want to be able to replicate the scenario again and again without having every time to re-configure things from scratch

Solving the problem

K3ai goal is to provide a micro-infrastructure that removes the complexity of the installation, configuration, and execution of any AI platform so that the user may focus on experimentation.

We want to satisfy the need for AI citizens and Corporate Scientists to be able to focus on what matters to them and forget the complexity attached to it.

To do so we have to satisfy a few requirements:

• Everything we code has to be SIMPLE enough that anybody can contribute back

• Everything must live within ONE single command. This way may easily be integrated within any automation script

• Everything must be MODULAR. We want to provide the greatest list of AI tools/solution ever so people may cherry-picking and create their own AI infrastructure combinations

K3ai is for the community by the community we want to be the reference to learn, grow for AI professionals, students and researchers.

First things First

Start by installing K3Ai, what you DON'T need to start:

• A fancy super-duper computer/server with GPU's etc..(we managed to install everything in a 4 GB RAM laptop)

• A cluster: don't worry we will take care of it if you don't have anything

• Linux, Mac, Win: we got them all (and working to support ARM too)

• Do I need to go through 1000 pages of documentation? Nah just go for the below command and move to Hello-Start

Ready? Let's go, first pick up your flavor, we have a utility script but f for any reason it fails just go straight away to and download the binary. Place it in your path and that's it.

NOTE: Unfortunately not all plugins work with ARM. We will take care of this and make a way to let you know before installing them

All you have to do is, download the binary for your Operating System, move it to your path (if you like easy things), and use it.

Linux (Including Microsoft WSL)

once downloaded untar the file and move it to your path

Windows

once downloaded unzip the file and move it to your path or execute it from a folder of your choice (i.e.: k3ai.exe -h)

Mac

once downloaded untar the file and move it to your path

Arm64

once downloaded untar the file and move it to your path

Alternative method for Linux

If for any reason it fails just go straight away to and download the binary. Place it in your path and that's it.

or use the following

Note: sometimes things take longer than expected resulting in the error below:

Don't worry! Sometimes the installation takes a few minutes, especially the Vagrant version or if you have limited bandwidth.

Errors in the utility script? Use this (on Linux)

This the list of all command available within k3ai

To get the list simply type k3ai -h

 k3ai is a lightweight infrastructure-in-a-box solution specifically built to
 install and configure AI tools and platforms in production environments on Edge
 and IoT devices as easily as local test environments.

Usage:
  k3ai [command]

Available Commands:
  apply       Apply a plugin or a plugin group
  delete      Delete a plugin or a plugin group
  help        Help about any command
  init        Initialize K3ai Client
  list        List all plugins or plugin groups
  version     Print CLI version

Flags:
  -h, --help          help for k3ai-cli
      --repo string   URI for the plugins repository.  

Use "k3ai-cli [command] --help" for more information about a command.

Apply

with apply you deploy the various plugins or group-plugins.

Init

With init you may deploy a cluster either local or remote

List

List is used to retrieve the available plugins or group-plugins

Delete

Delete is used to remove a deployed plugin

k3ai use a config file to support auomated deployment of cluster through the init command.

The sample template install by defauly KinD

What I will learn?

Let start with the basics. Here's how we structured the examples section:

Hello-Home: this is the first step. We suppose you don't have anything set up so let's start by enabling you to install a cluster you may use and a Jupyter Notebook to work with.

Hello-Earth: this is the second step. Let's introduce some more concepts, We will teach you how to create a basic pipeline with Kubeflow, but first, let's install it and see how to combine notebooks with pipelines.

Hello-Universe: this is the third step. After building a pipeline, and learning to write and train a model, we move on to the inference server side. Here you'll learn to interact with the model serving use cases

Hello-All: this is the final step. Let's add a mini web-app and create an end-to-end scenario in order to see our results live!

We are still working on this so be patient...

Getting started as a K3ai contributor‌

This document is the single source of truth for how to contribute to the code base. We'd love to accept your patches and contributions to this project. There are just a few small guidelines you need to follow.‌

As you will notice we do not, currently, require any CLA signature. This may change in the future anyway but if so even that change will follow the contributing guidelines and processes.‌

Follow the code of conduct‌

Please make sure to read and observe our and .‌

Joining the community‌

Follow these instructions if you want to‌

• Become a member of the K3ai GitHub org (see below)

• Be recognized as an individual or organization contributing to K3ai

‌

Joining the K3ai GitHub Org‌

Before asking to join the community, we ask that you first make a small number of contributions to demonstrate your intent to continue contributing to K3ai.‌

There are a number of ways to contribute to K3ai‌

• Submit PRs

• File issues reporting bugs or providing feedback

• Answer questions on Slack or GitHub issues

‌

When you are ready to join‌

• Send a PR adding yourself as a member in ​

• After the PR is merged an admin will send you an invitation

  • This is a manual process we are a very small team so please be patient

‌

Your first contribution‌

Find something to work on‌

Help is always welcome! For example, documentation (like the text you are reading now) can always use improvement. There's always code that can be clarified and variables or functions that can be renamed or commented on. There's always a need for more test coverage. You get the idea - if you ever see something you think should be fixed, you should own it. Here is how you get started.‌

Starter issues‌

To find K3ai issues that make good entry points:‌

• Start with issues labeled good first issue.

• For issues that require deeper knowledge of one or more technical aspects,

  look at issues labeled help wanted.

• Examine the issues in any of the

  ​.

‌Owners files and PR workflow‌

Our PR workflow goal is to become almost nearly identical to Kubernetes'. Most of these instructions are a modified version of Kubernetes' and guides.‌

Overview of OWNERS files‌

Nov. 2020 We are not yet to the point where we use OWNERS and/or REVIEWERS but we plan things in advance so the below represents the idea of future workflows.‌

OWNERS files are used to designate responsibility for different parts of the K3ai codebase. Today, we use them to assign the reviewer and approver roles used in our two-phase code review process.‌

The velocity of a project that uses code review is limited by the number of people capable of reviewing code. The quality of a person's code review is limited by their familiarity with the code under review. Our goal is to address both of these concerns through the prudent use and maintenance of OWNERS files‌

OWNERS‌

Each directory that contains a unit of independent code or content may also contain an OWNERS file. This file applies to everything within the directory, including the OWNERS file itself, sibling files, and child directories.‌

OWNERS files are in YAML format and support the following keys:‌

• approvers: a list of GitHub usernames or aliases that can /approve a PR

• labels: a list of GitHub labels to automatically apply to a PR

• options: a map of options for how to interpret this OWNERS file, currently only one:

‌All users are expected to be assignable. In GitHub terms, this means they are either collaborators of the repo, or members of the organization to which the repo belongs.‌

A typical OWNERS file looks like:

‌OWNERS_ALIASES‌

Each repo may contain at its root an OWNERS_ALIAS file.‌

OWNERS_ALIAS files are in YAML format and support the following keys:‌

• aliases: a mapping of alias name to a list of GitHub usernames

‌We use aliases for groups instead of GitHub Teams, because changes to GitHub Teams are not publicly auditable.‌

A sample OWNERS_ALIASES file looks like:

‌GitHub usernames and aliases listed in OWNERS files are case-insensitive.‌

The code review process

‌The author submits a PR

• [FUTURE]Phase 0: Automation suggests reviewers and approvers for the PR

  • Determine the set of OWNERS files nearest to the code being changed

  • Choose at least two suggested reviewers, trying to find a unique reviewer for every leaf

‌

Quirks of the process

‌There are a number of behaviors we've observed that while possible are discouraged, as they go against the intent of this review process. Some of these could be prevented in the future, but this is the state of today.‌

• An approver's /lgtm is simultaneously interpreted as an /approve

  • While a convenient shortcut for some, it can be surprising that the same command is interpreted

    in one of two ways depending on who the commenter is

‌

Automation using OWNERS files‌

​​

‌Prow receives events from GitHub, and reacts to them. It is effectively stateless. The following pieces of prow are used to implement the code review process above.‌

• ​​

  • per-repo configuration:

    • labels: list of labels required to be present for merge (eg:

‌

Maintaining OWNERS files

‌OWNERS files should be regularly maintained.‌

We encourage people to self-nominate or self-remove from OWNERS files via PR's. Ideally in the future we could use metrics-driven automation to assist in this process.‌

We should strive to:‌

• grow the number of OWNERS files

• add new people to OWNERS files

• ensure OWNERS files only contain org members and repo collaborators

• ensure OWNERS files only contain people are actively contributing to or reviewing the code they own

‌

Bad examples of OWNERS usage:‌

• directories that lack OWNERS files, resulting in too many hitting root OWNERS

• OWNERS files that have a single person as both approver and reviewer

• OWNERS files that haven't been touched in over 6 months

• OWNERS files that have non-collaborators present

‌Good examples of OWNERS usage:‌

• there are more reviewers than approvers

• the approvers are not i

K3ai is a lightweight infrastructure-in-a-box specifically built to install and configure AI tools and platforms to quickly experiment and/or run in production over edge devices.

Ready to experiment?

All you have to do is, download the binary for your Operating System, move it to your path (if you like easy things), and use it.

Linux (including Microsoft WSL)

once downloaded untar the file and move it to your path

Windows

once downloaded unzip the file and move it to your path or execute it from a folder of your choice (i.e.: k3ai.exe -h)

Mac

once downloaded untar the file and move it to your path

Arm64

once downloaded untar the file and move it to your path

Alternative method for Linux

If for any reason it fails just go straight away to and download the binary. Place it in your path and that's it.

or use the following

Looking for more interaction? join our Slack channel ****

What we do support:

• Windows

• Linux

• Mac

• ARM

NOTE: Unfortunately not all plugins work with ARM. We will take care of this and make a way to let you know before installing them

Components of K3ai

Currently, we install the following components (the list is changing and growing):

• Kubernetes based on K3s from Rancher:

• Kubernetes based on K0s from Mirantis: https://k0sproject.io

• Kubernetes KinD:

• Kubeflow pipelines:

Okay if everything worked as expected you're now a (not yet completely) happy owner. And I say not yet completely happy customer because there's not yet any AI tool there.

So, let's do a recap, the cluster is up and running but you probably don't know:

1. How to interact with that

2. How to install things on it.

First things first. Do you remember that utility we asked you to download and install named k9s? Okay now just type in your terminal k9s

Hello

Let's check if we got everything ready:

• A laptop/device/server with a minimum of 4 GB of RAM (if you got 8 is perfect)

Quick Start Guide

To install a GPU-enabled cluster there are few mandatory steps to prepare in advance.

Please follow this guide from NVIDIA to install the pre-requisites:

https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#install-guide

Once you completed the pre-req's you may install everything with the following command:

k3ai apply nvidia-gpu

The MPI Operator makes it easy to run allreduce-style distributed training on Kubernetes. Please check out this blog post for an introduction to MPI Operator and its industry adoption - https://www.kubeflow.org/docs/components/training/mpi/

To install the MPI operator simply type:

k3ai apply mpi-op

Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers.

Quick Start Guide

We offer to flavors: Kubeflow pipelines based on Argo Workflows and based on TektonCD engine

#To install KF based on Argo Workflows
k3ai apply kubeflow-pipelines

#To install KF based on TektonCD
k3ai apply kf-pipelines-tekton

What is Kubeflow Pipelines?

The Kubeflow Pipelines platform consists of:

• A user interface (UI) for managing and tracking experiments, jobs, and runs.

• An engine for scheduling multi-step ML workflows.

• An SDK for defining and manipulating pipelines and components.

• Notebooks for interacting with the system using an SDK.

The following are the goals of Kubeflow Pipelines:

• End-to-end orchestration: enabling and simplifying the orchestration of machine learning pipelines.

• Easy experimentation: making it easy for you to try numerous ideas and techniques and manage your various trials/experiments.

• Easy re-use: enabling you to re-use components and pipelines to quickly create end-to-end solutions without having to rebuild each time.

Learn more on the Kubeflow website: ****

H2O is an open source, in-memory, distributed, fast, and scalable machine learning and predictive analytics platform that allows you to build machine learning models on big data and provides easy productionalization of those models in an enterprise environment. -

In order to install H2O simply type:

This will deploy a single node instance of the h2o platform.

You may monitor the status of the pod with:

To access it type:

Point your browser to either localhost or your cluster ip (i.e.: http://localhost:54321) you should see something like this

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. - **[https://jupyter.org/**](https://jupyter.org/)****

We do support the current list of Jupyter Stacks as indicated in:

https://jupyter-docker-stacks.readthedocs.io/****

In order to run Jupyter Notebooks just run the following command:

k3ai apply jupyter-minimal

The following notebooks plugins are available:

• --plugin_jupyter-minimal: (jupyter-minimal) for more information please see here****

We are currently working on:

• --plugin_jupyter-r: (jupyter-r-notebook) for more information please see ****

• --plugin_jupyter-scipy: (jupyter-scipy-notebook) for more information please see ****

• --plugin_jupyter-tf: (jupyter-tensorflow-notebook) for more information please see ****

We are working on this.

In the meantime just remove the cli from:

• Linux/WSL: /usr/local/bin

• Windows: C:\Windows\System32

• Mac: ideally you placed it in /usr/local/bin if not delete it from where you placed it.

Work in Progress (do you like it? Let us know with an )

In order to uninstall k3ai, we provide a simple command to remove all components. All you have to do is launch the following command:

K3ai Inclusivity

As per CoC the inclusivity document is a direct derivation of Kubeflow inclusivity document ()

This document is a guide to inclusivity for leaders involved in the K3ai project. It describes conscious efforts and behavior patterns that contribute to an inclusive environment where K3ai members feel safe to collaborate.

The K3ai community is a global group of contributors with a wide variety of backgrounds. As such, leaders are expected to take purposeful steps to ensure sustainability of the collaborative space. This is essential for project health and future growth. We expect all community members, and especially leaders, to practice and grow in the areas covered in this document.

Starting from K3ai 0.2.0 we introduced a configuration file to allow a user to deploy various flavors of Kubernetes both locally and remotely.

The config file is typically saved in <yourhomedir>/.k3ai but could be moved around and/or hosted on other locations. We currently do not support remote config files.

A single config file may host multiple configurations, hence is capable to deploy multiple clusters at the same time.

Generic Section

The kind definition at the beginning of the config file indicates that all the pieces of information below are relative to the infrastructure deployment. We did this with the intention later to have the capability to split the config behavior and possibly call other config files.

Common Sections

• name: is the instance name as an internal reference for K3ai. Is not currently used so it act as a placeholder right now

• enabled: if set to true the section will be used and the cluster will be deployed

• type: represent the cluster to be installed: k3s,k3d,k0s,kinD

Rancher K3s Config Specifics

Rancher K3d Config Specifics

Mirantis K0s Config Specifics

Do not copy the above, has been truncated to make it more readable.

KinD Config Specifics

Remote Clusters Specifics

We currently support only Civo Cloud. Notice that the cloudSecretPath is a placeholder we are going to add this feature but for the time being, you'll need to pass the key through the terminal directly.

Building plugins for k3ai is very simple. This guide will walk you through the steps required to build a plugin and contribute to the project.

The first step is to learn the structure of the plugins repository: ****

The repo is structured in a very simple way.

• core

  • groups

Quick Start Guide

You only have to decide if you want CPU support:

What is Argo Workflows?

K3ai Code of Conduct

This CoC has been derived from Kubeflow CoC you may read here

Our Pledge

In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.

Our Standards

Examples of behavior that contributes to creating a positive environment include:

• Using welcoming and inclusive language

• Being respectful of differing viewpoints and experiences

• Gracefully accepting constructive criticism

• Focusing on what is best for the community

Examples of unacceptable behavior by participants include:

• The use of sexualized language or imagery and unwelcome sexual attention or advances

• Trolling, insulting/derogatory comments, and personal or political attacks

• Public or private harassment

• Publishing others’ private information, such as a physical or electronic address, without explicit permission

Our Responsibilities

Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.

Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.

Scope

This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.

Conflict Resolution

We do not believe that all conflict is bad; healthy debate and disagreement often yield positive results. However, it is never okay to be disrespectful or to engage in behavior that violates the project’s code of conduct.

If you see someone violating the code of conduct, you are encouraged to address the behavior directly with those involved. Many issues can be resolved quickly and easily, and this gives people more control over the outcome of their dispute. If you are unable to resolve the matter for any reason, or if the behavior is threatening or harassing, report it. We are dedicated to providing an environment where participants feel welcome and safe.

Attribution

This Code of Conduct is adapted from the Contributor Covenant, version 1.4, available at

TFJob provides a Kubernetes custom resource that makes it easy to run distributed or non-distributed TensorFlow jobs on Kubernetes.

More on the Tensorflow Operator at https://github.com/kubeflow/tf-operator****

Quick Start

All you have to run is:

k3ai apply tensorflow-op

Test your installation

We present here a sample from Tensorflow Operator on ****

Step 1

We first need to add a persistent volume and claim, to do so let's add the two YAML file we need, copy and paste each command in order.

now we add the PVC.

Note: Because we are using local-path as storage volume and we are on a single node cluster we can't use ReadWriteMany as per Rancher local-path provisioner issue __

Step 2

Now we deploy the example

You can observe the result of the example with

It should output something similar to this (we show just partially the output here)

PyTorch is a Python package that provides two high-level features:

• Tensor computation (like NumPy) with strong GPU acceleration

• Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed. More information at **or the PyTorch site

In 2019 we went all in and took Civo in a new direction, launching the world’s first k3s-powered, managed Kubernetes service into beta.

As easy as can be, K3ai works perfectly on Civo. Here it is the simplest guide ever to run k3ai on Civo - three steps and your k3ai is ready!

Installing k3ai on Civo

Ready? It requires less than 5 minutes!

You'll need an account on Civo.com. To do so simply register on Civo here:

****

Installing

All you have to do is simply type:

Wait for the instance to finish the deployment

Step 2

Download the kubeconfig file, move it to your preferred location, and set your environment to use it:

Step 3

One last thing and then we're done:

enjoy your k3ai on****

K3ai Inventors

• Alessandro Festa

  • GitHub: @alfsuse

  • Twitter: @bringyourownai

  • Org: SUSE

• Gabriele Santomaggio

  • GitHub: @Gsantomaggio

  • Twitter: @GSantomaggio

  • Org: SUSE

Design and documentation

• Kenneth Wimer

  • GitHub: @kwwii

  • Twitter: @kwwii

  • Org: SUSE

Contributors

• Saiyam Pathak

  • Github: @saiyam1814

  • Twitter: @SaiyamPathak

  • Org: CIVO Cloud

The below roadmap is a short version of what we release based on monthly updates

December 2020

Released

• Init command to create local and remote clusters

• Support for Mirantis K0s clusters

• Support for KinD clusters

• Support for Rancher K3d clusters

October 2020

Released

• Kubeflow pipelines support

• Argo Workflows support

• GPU support

• Tensorflow Serving for ResNet models support

Planned

• WSL improvements

• Cloud Deployment initial support

• Tensorflow Serving - MNIST support

• KFP SDK support

• Civo Cloud Blog: https://www.civo.com/learn/running-kubeflow-pipelines

• Coffe and Cloud Native E51: https://youtu.be/I02GIAKwLBU?t=1314

• Kubeflow Official Documentation: https://www.kubeflow.org/docs/pipelines/installation/localcluster-deployment/#k3ai-alpha

• Cloud native Samachaar - Ep2:

• Europe Cloud Conference 2020:

• All Things Open 2020:

• HubStation October Blog: