This the list of all command available within k3ai

To get the list simply type k3ai -h

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

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

October 2020

Released

• Kubeflow pipelines support

• Argo Workflows support

• GPU support

Planned

• WSL improvements

• Cloud Deployment initial support

• Tensorflow Serving - MNIST support

K3ai Inclusivity

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

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.

Carve out space

Carve out well-defined spaces for contribution. Encourage members of the community to engage in these spaces. Reach out to specific individuals to let them know you think they would be a good fit.

Get out of the way

Get out of the way when someone steps up. Give them ownership and set expectations for delivery and accountability. Follow up on those expectations. If you have concerns or need more information, increase the frequency of communication rather than taking over or overstepping.

Make opportunities

Seek out situations that provide opportunities for members of the community. Examples of this include connecting event organizers with potential speakers, introducing leaders to individual contributors, and inviting others to collaborate. Consciously drive the creation of opportunities in areas that community members want to grow in.

Ask members where they want to grow

Find out which areas community members want to grow in. This could be in the form of 1:1 conversations, small groups, or weekly meetings. Ask how you can help.

Rather than making assumptions and assigning tasks, ask people where they want to contribute and help them figure out how to make the most impact. Stretch them just enough that they can see progress and sustained growth.

Empower members to say no

Make it clear that members are empowered to turn down opportunities. Encourage them to define their own boundaries and give them space to assert those boundaries. Communicate that it is their responsibility to balance their commitments and that they will be supported in doing so. Before presenting a specific opportunity to an individual, provide a disclaimer that it is perfectly acceptable to say no.

Encourage members to ask for what they want

Encourage members of the community to make requests. That could be for improvements to the product, community, or their own personal growth. Respond to these requests with kindness and fairness.

Ask for volunteers and make time for the community to bring up topics they care about.

Explicitly call out challenges

Name specific challenges that affect members of the community and state your position on how to resolve them. Make statements such as, "I understand how difficult it must be to X," and "I wish you didn't have to face such blatant challenges doing Y." Offer advice on how to deal with them or just be there to commiserate.

Simply acknowledging the struggle is an act of empathy that makes it easier to face these challenges. This is a means of lightening the load on underrepresented groups by not requiring them to shoulder these burdens silently.

Give clear, specific, and actionable feedback

Be proactive about providing feedback, but ask first and be kind. Include concrete steps that can be taken to improve the outcome and steer clear of criticism involving something that cannot be reasonably changed.

Treat everyone with respect

Set an example and uphold that standard. Do not tolerate double standards or casual deprecation, even in jest. Ensure that community members understand the group is open to everyone.

Follow up on complaints

When you observe a violation or become aware of one, follow through on enforcing community standards. Do this with care, showing respect and kindness for everyone involved. These instances have a broader impact than just the involved parties, since they set downstream expectations for the entire community.

This is a responsibility that the Kubeflow project does not take lightly, since it directly impacts the ability of members to feel safe in the community.

Indicators of success

It can be difficult to assess whether these efforts are effective. In many ways, success can be invisible since it involves the prevention of conflict. A few indicators are:

• Diverse membership across various dimensions (geographic, corporate, level of experience, etc.)

• Presence of members from frequently marginalized groups

• Continued engagement by long-term members

Attribution

The origins of this document are an enumeration of efforts by Kubeflow project cofounder . This was not a solo effort and included support from , , , and other members of the Kubeflow team.

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 , 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!

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.

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:

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

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)

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)

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

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:

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 k9sand you'll see your Kubernetes cluster appear. To close it down CTRL+C

In case you saw us printing something like export=/path/path/file just copy&paste that before typing K9s. That is needed to explain to your laptop how to reach out your freshly installed cluster.

Hello Notebooks

In order to start playing with AI you need a workspace right? So the first way to get one is using the popular Jupyter Notebooks.

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. -

K3ai as an amazing way to get things done that is a simple rule:

everything needs to be done in no more than 3 steps

That's why we developed the plugins and group-plugins concept, to take shortcuts and get the job done.

So let's take a look at the option we got first. Type the following in your terminal

You should get a result similar to the one below

That's the list of the current plugins we got, each one represents a single application. Now let's try a different command:

The result will be different (something similar to):

What we asked was: "Hey k3ai give me the list of the group plugins please". What is a group plugin? It's a combination of plugins. For example, let's take our Jupyter Notebook: we want the notebook itself but we also want to publish it through an ingress (traefik) so that we may reach out immediately to the application.

Kubernetes Ingress builds on top of Kubernetes to provide load balancing at the application layer, mapping HTTP and HTTPS requests with particular domains or URLs to Kubernetes services. Ingress can also be used to terminate SSL / TLS before load balancing to the service.

NOTE: Not all group plugins work with every cluster because not all of them deploy the same configuration to make things easier check the below table a reference.

Earth call Notebook

Okay, we are ready, let's go for the notebook. Type:

Or if you want also the ingress (like in K3s/k3d)

Once everything has been deployed the notebook is reachable at http://<YOUR CLUSTER IP>:8888

Note: if did not use the ingress you have first to expose the port, simply type the following in your terminal to reach it out

Don't close the terminal, open your browser and point yourself to http://localhost:8888 or http://<YOUR CLUSTER IP>:8888

Note: We do not use authentication for our notebook because we expect you to use if for experimentation but it's a standard configuration so in case head to section to learn how to change that

If everything went right you should see something similar to this

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)

• Passion for AI in general

• 30 mins of your time for each section

• Did you already got k3ai-cli installed? If not go to the section

• kubectl (don't ask yet just download it from

• k9s (you'll thank us later... download it from )

Okay if we got everything let's start. We will use k3ai-cli to do everything so you don't really have to learn how to do things over than learn k3ai. The diagram below shows you how the various k3ai-cli command will drive you through the Hello* guides

First Step

The first step is to learn how to install the cluster (unless you don't have one already). K3ai support various configurations:

• Local deployments

• Cloud deployments

We make use of a configuration file to drive the various installation steps so, while the main goal of K3ai is not to deploy Kubernetes clusters we aim to make the life of our users as simple as possible.

Note: are you an expert in automation and cluster deployment (K8s)? Help us and add some nice tooling to K3ai.

K3ai currently support the following local clusters:

• Rancher K3s -

• Mirantis K0s -

• KinD -

On the cloud side we do offer support for:

• Civo Cloud -

• AWS - (Work In Progress)

This guide will use the local installation.

Home

Open a terminal window and simply type the following:

What will happen is the following:

1. If it does not exist a folder named .k3ai will be created under your home directory (i.e.: in Linux under /home/yourusername/) inside this directory we will download a sample config.yaml file.

2. The config.yaml has a default installation cluster: KinD that requires docker to be installed.

Home Rebuild

An alternative way to install a cluster and be able to choose the favorite flavor is to use a slightly different command

Let's go into more details here's the full list of options:

• k3ai init --local k3s

• k3ai init --local k0s

• k3ai init --local kind

In case of Cloud:

• k3ai init --cloud civo

Now to sum it up here's a video that shows how it works.

Home Rebuild - Foundation

As we mentioned at the beginning of this guide k3ai support a config file as well. The config file looks like the one below and is located at <home user folder>/.k3ai/config.yaml but k3ai support also a custom location through k3ai init --config <yourpath to config file>

For cloud there a couple of extra configs like the one below

Done your Hello Home is ready! You may proceed to the section

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

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!

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.

‌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

‌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

‌

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:

‌

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

‌

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

‌Good examples of OWNERS usage:‌

• there are more reviewers than approvers

• the approvers are not i

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

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:

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)

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: https://github.com/kf5i/k3ai-plugins****

The repo is structured in a very simple way.

• core

  • groups

  • plugins

• common

• community

Coreis the root folder it includes plugins and groups

Plugins are the actual application to be deployed, for each plugin folder there is a plugin.yaml file.

Groups are a combination of various plugins to be installed altogether

Under common you'll find all the manifests or files needed by more than one plugin. Those are sort of reusable components (i.e.: treafik ingress definitions for plugins).

k3ai supports custom repositories for plugins and groups so this means you may have your own instead of using our public ones.

Let's create a local repo and deploy a "hello-world" plugin.

First, we have to create the basic structure. So let's create anywhere on your laptop a structure like this:

• demo

  • core

    • groups

Now let's open the plugin.yaml file and copy the below content in it.

Save the file and open the deployment.yaml. Copy&Paste the following content.

We are ready let's check the plugin list with

You should get something like this

Great! Now let's apply the plugin to our environment

Now let's check if the pod is running with

We are ready so let's execute a command inside our plugin. Copy and Paste the below command into your terminal.

If everything goes right you should see something like this

Congratulation you created your first plugin. Now to delete it simply execute

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

Quick Start

As usual, let's deploy PyTorch with one single line command

Test You PyTorch-Job installation

We will use the MNISE example from the Kubeflow PyTorch-Job repo at ****

As usual, we want to avoid complexity so we re-worked a bit the sample and make it way much more easier.

Step 1

You'll see tha in the example a container need to be created before running the sample, we merged the container commands directly in the YAML file so now it's one-click job.

For CPU only

If you have GPU enabled you may run it this way

Step 2

Check if pod are deployed correctly with

It should ouput something like this

Step 3

Check logs result of your training job

You should observe an output similar to this (since we are using 1 Master and 1 worker in this case)

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:

Quick Start Guide

You only have to decide if you want CPU support:

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****

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

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.

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: ****

K3ai Code of Conduct

This CoC has been derived from Kubeflow CoC you may read

• Civo Cloud Blog:

• Coffe and Cloud Native E51:

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 ****

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 ****

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. - http://docs.h2o.ai/h2o/latest-stable/h2o-docs/welcome.html

In order to install H2O simply type:

k3ai apply h2o-single

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

You may monitor the status of the pod with:

kubectl get pod -n h2o

#Output should be similar to this
NAME                 READY   STATUS    RESTARTS   AGE
h2o-stateful-set-0   1/1     Running   0          2m19s

To access it type:

 kubectl port-forward -n h2o svc/h2o-service 54321:54321

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

K3ai Inventors

• Alessandro Festa