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K3ai V1 Docs

K3ai (keɪ3ai)

This is the Version 1 of k3ai. This version is now deprecated so it should not be used anymore

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?

curl -sfL https://get-core.k3ai.in | bash -

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

Components of K3ai

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

Kubernetes based on K3s from Rancher:
Kubeflow pipelines:
Argo Workflows:

What we are trying to solve (a.k.a Our Goals)

"The great danger for most of us lies not in setting our aim too high and falling short, but in setting our aim too low, and achieving our mark." –Michelangelo

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.

Quick Start

First things First

Start by installing K3Ai from the V1 channel with this:

curl -sfL https://get-core.k3ai.in | bash -

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.

Still curious? Here's a short demo:

Remove k3ai

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

k3s-uninstall.sh

Contributing

Welcome to the K3ai project! We took the freedom to take these rules from other great OSS projects like Kubeflow, Kubernetes, and so on.‌

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.‌

Plugins

GPU support

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:

Kubeflow Pipelines

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

Quick Start Guide

You only have to decide if you want CPU support:

or if you prefer GPU support:

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: ****

Kubeflow SDK library

To help K3ai users to interact with Kubeflow we are introducing the support of Kubeflow SDK library (kfp).

The Kubeflow Pipelines SDK provides a set of Python packages that you can use to specify and run your machine learning (ML) workflows. A pipeline is a description of an ML workflow, including all of the components that make up the steps in the workflow and how the components interact with each other - https://www.kubeflow.org/docs/pipelines/sdk/sdk-overview/****

We offer two different way to consume kfp within k3ai:

by a virtual environment on the local computer of the user
by one of our Jupyter notebooks directly within k3ai

KFP with VirtualEnv

virtualenv is a tool to create isolated Python environments. Since Python 3.3, a subset of it has been integrated into the standard library under the . -

Step 1

Please check you have virtualenv installed on your machine. Depending on the OS you are using you may use different approaches. Please follow the official guides to install virtualenv at ****

Step 2

Run the following command:

Once the installer has finished please proceed to the "How to use KFP SDK" section.

KFP within K3ai environment

We leverage Jupyter Notebooks to provide a pre-installed kfp environment so that one may immediately experiment with this.

If you are using WSL

If you already deployed the pipelines simply run:

Once the installer has finished please proceed to the "How to use KFP SDK" section.

How to use KFP SDK

We present here a simple example to explain how the KFP SDK may be used. More examples may be found at ****

Testing KFP SDK from virtualenv

This procedure will require you the K3ai IP address in case you forgot it the simplest way to grab it is execute the following command:

In your terminal create a file called demo.py, use your favorite IDE to copy and paste the below example, and change it accordingly to your K3ai environment.

save the file and execute it with

You should get a result like in the "Checking the results" section.

Testing with Jupyter Notebooks

Open your Jupyter Notebook at the address provided during the plugin installation. If you forgot the ip you may retrieve it this way

Once the Notebook is open click on top right of the notebook to create a new ipython environment

In the first cell paste the following script

Pres CTRL+ENTER to execute the cell.

Checking the results

If everything went well in both virtualenv and jupyter notebooks samples you should have a result similar to this:

Tensorflow Operator

Kubeflow Tensorflow-Job Training Operator

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

Quick Start

All you have to run is with CPU support

PyTorch Operator

Kubeflow PyTorch-Job Training Operator

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

If you leverage CPU only

if you like to use PyTorch with GPU

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)

Argo Workflows

Quick Start Guide

You only have to decide if you want CPU support:

curl -sfL https://get.k3ai.in | bash -s -- --cpu --plugin_argo_workflow

or you prefer GPU support:

curl -sfL https://get.k3ai.in | bash -s -- --gpu --plugin_argo_workflow

What is Argo Workflows?

Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD.

Define workflows where each step in the workflow is a container.
Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a graph (DAG).
Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes.

Learn more on the Kubeflow website: ****

Tensorflow Serving - ResNet

TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. Learn more about Tensorflow on their site: https://www.tensorflow.org/tfx/guide/serving****

Quick Start Guide

Running TensorFlow Serving to serve the TensorFlow ResNet model is, as usual, a single line trick.

CPU support:

GPU support:

Test the installation

For a full explanation of how to use Tensorflow Serving please take a look at the documentation site:

Step 1 - Prepare your client environment

To run any experiment against a remote inference server you have to have tensorflow-serving-api installed on your machine. As per official documentation here:

As reference

Step 2

Clone the TensorFlow repository where we will find the test scripts

Step 3

Find your cluster IP where Tensorflow Serving service is exposed

You should have a similar output:

Take note of LoadBalancer Ingress IP

Step 4

We can now query the service at its external address from our local host.

Using gRPC:

Using REST Api:

You should have an output similar to this:

WSL (Windows Subsystem for Linux)

Yep, you get it right we also have WSL support!

Note: GPU is not currently supported in k3ai withing WSL. The reason is simply that GPU capability is still in development by NVIDIA and Microsoft so we will wait for it to reach a more stable grade.

Quick Start

The Windows Subsystem for Linux lets developers run a GNU/Linux environment -- including most command-line tools, utilities, and applications -- directly on Windows, unmodified, without the overhead of a traditional virtual machine or the dual-boot setup.

Step 1

Install any Linux distro supported in WSL. For a quick how-to please follow the guide at

Step 2

Once ready simply run the following command:

Note: the command above is slightly different from the other commands we typically use. It will change to the usual once the feature will be merged in the main code.

(Optional) Step 3

Once the installation is finished you may run any other plugin as usual, but with the --skipk3s flag. As an example the Tensorflow Serving - ResNet:

The following plugins are immediately supported:

Argo
Kubeflow pipelines
Tensorflow Serving - Resnet

Troubleshooting

Restart WSL

If you re-login in your WSL session after a "wsl --shutdown" or simply because you restarted/shutdown your computer the k3ai environment will not automatically restart.

We created a utility file for you to re-start k3ai every time. In order to do so execute in WSL:

wait for a couple of minutes for the cluster to restart all the pods and you're good to go.

The connection to the server localhost:8080 was refused

If the above error happens please use the following command to install k3ai

Jupyter Notebook

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:

The following notebooks plugins are available:

--plugin_jupyter-minimal: (jupyter-minimal) for more information please see ****
--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 ****
--plugin_jupyter-datascience: (jupyter-datascience-notebook) for more information please see ****
--plugin_jupyter-pyspark: (jupyter-pyspark-notebook) for more information please see ****
--plugin_jupyter-allspark: (jupyter-all-spark-notebook)for more information please see ****

Note: all Jupyter Notebooks container Kubeflow SDK (kfp) library to interact with Kubeflow pipelines. In order to install pipelines and Jupyter at the same time use:

other guides

Civo Cloud

Civo was born when our small team first came together to create an OpenStack-based cloud for a shared hosting provider. Read their story here: https://www.civo.com/blog/kube100-so-far

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:

****

Step 1

Launch your k3s cluster using the default options (Traefik and Metric-server selected)

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****

{'next_page_token': None, 'pipelines': [{'created_at': datetime.datetime(2020, 10, 14, 13, 27, 18, tzinfo=tzlocal()), 'default_version': {'code_source_url': None, 'created_at': datetime.datetime(2020, 10, 14, 13, 27, 18, tzinfo=tzlocal()), 'id': '8a53981e-7c3e-4897-8c75-26f710c20f7a', 'name': '[Demo] TFX - Iris classification ' 'pipeline', 'package_url': None, 'parameters': [{'name': 'pipeline-root', 'value': 'gs://{{kfp-default-bucket}}/tfx_iris/{{workflow.uid}}'}, {'name': 'data-root', 'value': 'gs://ml-pipeline/sample-data/iris/data'}, {'name': 'module-file', 'value': '/tfx-src/tfx/examples/iris/iris_utils_native_keras.py'}], 'resource_references': [{'key': {'id': '8a53981e-7c3e-4897-8c75-26f710c20f7a', 'type': 'PIPELINE'}, 'name': None, 'relationship': 'OWNER'}]}, 'description': '[source ' 'code](https://github.com/kubeflow/pipelines/tree/c84f4da0f7b534e1884f6696f161dc1375206ec2/samples/core/iris). ' 'Example pipeline that classifies Iris flower ' 'subspecies and how to use native Keras within ' 'TFX.', 'error': None, 'id': '8a53981e-7c3e-4897-8c75-26f710c20f7a', 'name': '[Demo] TFX - Iris classification pipeline', 'parameters': [{'name': 'pipeline-root', 'value': 'gs://{{kfp-default-bucket}}/tfx_iris/{{workflow.uid}}'}, {'name': 'data-root', 'value': 'gs://ml-pipeline/sample-data/iris/data'}, {'name': 'module-file', 'value': '/tfx-src/tfx/examples/iris/iris_utils_native_keras.py'}], 'url': None}], 'total_size': 1}

K3ai V1 Docs

K3ai (keɪ3ai)

hashtagReady to experiment?

hashtagComponents of K3ai

What we are trying to solve (a.k.a Our Goals)

hashtagIdentify the problem

hashtagIdentify the other problem

hashtagSolving the problem

Quick Start

hashtagFirst things First

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

Remove k3ai

Contributing

hashtagGetting started as a K3ai contributor‌

Plugins

GPU support

hashtagQuick Start Guide

Kubeflow Pipelines

hashtagQuick Start Guide

hashtagWhat is Kubeflow Pipelines?

Kubeflow SDK library

hashtagKFP with VirtualEnv

hashtagStep 1

hashtagStep 2

hashtagKFP within K3ai environment

hashtagHow to use KFP SDK

hashtagTesting KFP SDK from virtualenv

hashtagTesting with Jupyter Notebooks

hashtagChecking the results

Tensorflow Operator

hashtagQuick Start

PyTorch Operator

hashtagQuick Start

hashtagTest You PyTorch-Job installation

hashtagStep 1

hashtagStep 2

hashtagStep 3

Argo Workflows

hashtagQuick Start Guide

hashtagWhat is Argo Workflows?

Tensorflow Serving - ResNet

hashtagQuick Start Guide

hashtagTest the installation

hashtagStep 1 - Prepare your client environment

hashtagStep 2

hashtagStep 3

hashtagStep 4

WSL (Windows Subsystem for Linux)

hashtagYep, you get it right we also have WSL support!

hashtagQuick Start

hashtagStep 1

hashtagStep 2

hashtag(Optional) Step 3

hashtagTroubleshooting

hashtagRestart WSL

hashtagThe connection to the server localhost:8080 was refused

Jupyter Notebook

other guides

Civo Cloud

hashtagInstalling k3ai on Civo

hashtagStep 1

hashtagStep 2

hashtagStep 3

Remove k3ai

GPU support

hashtagQuick Start Guide

What we are trying to solve (a.k.a Our Goals)

hashtagIdentify the problem

hashtagIdentify the other problem

hashtagSolving the problem

Quick Start

hashtagFirst things First

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

Kubeflow SDK library

hashtagKFP with VirtualEnv

hashtagStep 1

hashtagStep 2

hashtagKFP within K3ai environment

hashtagHow to use KFP SDK

hashtagTesting KFP SDK from virtualenv

Ready to experiment?

Components of K3ai

Identify the problem

Identify the other problem

Solving the problem

First things First

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

Getting started as a K3ai contributor‌

Quick Start Guide

Quick Start Guide

What is Kubeflow Pipelines?

KFP with VirtualEnv

Step 1

Step 2

KFP within K3ai environment

How to use KFP SDK

Testing KFP SDK from virtualenv

Testing with Jupyter Notebooks

Checking the results

Quick Start

Quick Start

Test You PyTorch-Job installation

Step 1

Step 2

Step 3

Quick Start Guide

What is Argo Workflows?

Quick Start Guide

Test the installation

Step 1 - Prepare your client environment

Step 2

Step 3

Step 4

Yep, you get it right we also have WSL support!

Quick Start

Step 1

Step 2

(Optional) Step 3

Troubleshooting

Restart WSL

The connection to the server localhost:8080 was refused

Installing k3ai on Civo

Step 1

Step 2

Step 3

Quick Start Guide

Identify the problem

Identify the other problem

Solving the problem

First things First

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

KFP with VirtualEnv

Step 1

Step 2

KFP within K3ai environment

How to use KFP SDK

Testing KFP SDK from virtualenv

Testing with Jupyter Notebooks

Checking the results

Quick Start Guide

Test the installation

Step 1 - Prepare your client environment

Step 2

Step 3

Step 4

Installing k3ai on Civo

Step 1

Step 2

Step 3

Yep, you get it right we also have WSL support!

Quick Start

Step 1

Step 2

(Optional) Step 3

Troubleshooting

Restart WSL

The connection to the server localhost:8080 was refused

Quick Start

Test You PyTorch-Job installation

Step 1