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 code of conduct 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

• Sentiment within the community that ideas are heard and contributions valued

• Accountability of leaders by members

Attribution

The origins of this document are an enumeration of efforts by Kubeflow project cofounder David Aronchick. This was not a solo effort and included support from Jeremy Lewi, Michelle Casbon, Edd Wilder-James, and other members of the Kubeflow team.

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)

curl -sfL "https://get.k3ai.in" -o k3ai.tar.gz

once downloaded untar the file and move it to your path

tar -xvzf k3ai.tar.gz \
&& chmod +x ./k3ai \
&& sudo mv ./k3ai /usr/local/bin

Windows

Invoke-WebRequest -Uri "https://get-win.k3ai.in" -OutFile k3ai.zip

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)

 Expand-Archive -Path .\k3ai.zip

Mac

curl -sfL "https://get-mac.k3ai.in" -o k3ai.tar.gz

once downloaded untar the file and move it to your path

tar -xvzf k3ai.tar.gz \
&& chmod +x ./k3ai \
&& sudo mv ./k3ai /usr/local/bin

Arm64

curl -sfL "https://get-arm.k3ai.in" -o k3ai.tar.gz

once downloaded untar the file and move it to your path

tar -xvzf k3ai.tar.gz \
&& chmod +x ./k3ai \
&& sudo mv ./k3ai /usr/local/bin

Alternative method for Linux

If for any reason it fails just go straight away to https://github.com/kf5i/k3ai-core/releases and download the binary. Place it in your path and that's it.

or use the following

#Set a variable to grab latest version
Version=$(curl -s "https://api.github.com/repos/kf5i/k3ai-core/releases/latest" | awk -F '"' '/tag_name/{print $4}' | cut -c 2-6) 
# get the binaries
wget https://github.com/kf5i/k3ai-core/releases/download/v$Version/k3ai-core_${Version}_linux_amd64.tar.gz

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

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: https://k3s.io/

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

• Kubernetes KinD: https://kind.sigs.k8s.io/

• Kubeflow pipelines: https://github.com/kubeflow/pipelines

• Argo Workflows: https://github.com/argoproj/argo

• H2O Community: https://h20.ai

• Kubeflow: https://www.kubeflow.org/ - (coming soon)

• NVIDIA GPU support: https://docs.nvidia.com/datacenter/cloud-native/index.html

• NVIDIA Triton inference server: https://github.com/triton-inference-server/server/tree/master/deploy/single_server (coming soon)

• Tensorflow Serving: https://www.tensorflow.org/tfx/serving/serving_kubernetes:

  • ResNet

  • Mnist (coming soon)

• and many many others...

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

• clusterName: this is the name of the cluster (if applicable), it's useful to deploy multiple clusters of the same type

• clusterDeployment: local or cloud. For the cloud specs see below.

• spec: For each deployment, there are various options and binaries so dependening of where you're going to install we will take care of the right version+location. This is also useful if you want to test on a specific version.

• plugins: repo is the URL where the plugin are hosted. If you are using the public ones you may leave it empty, name is the name of the plugin as it appears from k3ai-cli list currently we do not support groups yet in the config file.

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.

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.

Apply a plugin or a plugin group

Usage:
  k3ai apply <plugin_name> [flags]

Flags:
  -g, --group   Apply a plugin group
  -h, --help    help for apply

Global Flags:
      --repo string   URI for the plugins repository.  
      (default "https://api.github.com/repos/kf5i/k3ai-plugins/contents/core/")

Init

With init you may deploy a cluster either local or remote

Initialize K3ai Client, allowing user to deploy a new K8's cluster, 
list plugins and groups

Usage:
  k3ai init [flags]

Examples:
k3ai init                                   #Will use config from $HOME/.k3ai/config.yaml and use interactive menus
k3ai init --config /myfolder/myconfig.yaml  #Use a custom config.yaml in another location(local or remote)
k3ai init --local k3s                       #Use config target marked local and of type k3s
k3ai init --cloud civo                      #Use config target marked as cloud and of type civo

Flags:
      --cloud string    Options availabe for cloud providers
      --config string   Custom config file [default is $HOME/.k3ai/config.yaml] (default "/.k3ai/config.yaml")
  -h, --help            help for init
      --local string    Options availabe k3s,k0s,kind

Global Flags:
      --repo string   URI for the plugins repository.  
      (default "https://api.github.com/repos/kf5i/k3ai-plugins/contents/core/")

List

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

List all plugins or plugin groups

Usage:
  k3ai list [flags]

Flags:
  -g, --group   List the plugin groups
  -h, --help    help for list

Global Flags:
      --repo string   URI for the plugins repository.  
      (default "https://api.github.com/repos/kf5i/k3ai-plugins/contents/core/")

Delete

Delete is used to remove a deployed plugin

Delete a plugin or a plugin group

Usage:
  k3ai delete <plugin_name> [flags]

Flags:
  -g, --group   Delete a plugin group
  -h, --help    help for delete

Global Flags:
      --repo string   URI for the plugins repository.  
      (default "https://api.github.com/repos/kf5i/k3ai-plugins/contents/core/")

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

The sample template install by defauly KinD

kind: cluster
targetCustomizations:
- name: localK3s #name of the cluster instance not the name of the cluster
  enabled: false
  type: k3s
  config: "/etc/rancher/k3s/k3s.yaml" #default location of config file or your existing config file to copy
  clusterName: demo-wsl-k3s #name of the cluster (this need to be the same as in a config file)
  clusterDeployment: local
  clusterStart: "sudo bash -ic 'k3s server --write-kubeconfig-mode 644 > /dev/null 2>&1 &'"
  spec:
  # If the OS is not needed may be removed so the three below are mutually exclusive, if not needed set them to null or remove it
    wsl: "https://github.com/rancher/k3s/releases/download/v1.19.4%2Bk3s1/k3s"
    mac: 
    linux: "https://get.k3s.io | K3S_KUBECONFIG_MODE=644 sh -s -"
    windows: 
    # Everything from this repo will be ran in this cluster. You trust me right?
  plugins: 
  - repo: 
    name: 
  - repo: 
    name: 

- name: localK0s #name of the cluster instance not the name of the cluster
  enabled: false
  type: k0s
  config: "${HOME}/.k3ai/kubeconfig" #default location of config file or your existing config file to copy
  clusterName: demo-wsl-k0s #name of the cluster (this need to be the same as in a config file)
  clusterDeployment: local
  clusterStart: "k0s default-config | tee ${HOME}/.k3ai/k0s.yaml && sudo bash -ic 'k0s server -c ${HOME}/.k3ai/k0s.yaml --enable-worker > /dev/null 2>&1 &' && sudo cat /var/lib/k0s/pki/admin.conf > $HOME/.k3ai/k0s-config"
  spec:
  # If the OS is not needed may be removed so the three below are mutually exclusive, if not needed set them to null or remove it
    wsl: "https://github.com/k0sproject/k0s/releases/download/v0.8.1/k0s-v0.8.1-amd64"
    mac: 
    linux: "https://github.com/k0sproject/k0s/releases/download/v0.8.1/k0s-v0.8.1-amd64"
    windows:
    # Everything from this repo will be ran in this cluster. You trust me right?
  plugins: 
  - repo: 
    name: 
  - repo: 
    name: 

- name: localKind #name of the cluster instance not the name of the cluster
  enabled: true
  type: kind
  config:  #default location of config file or your existing config file to copy
  clusterName: demo-win-kind #name of the cluster (this need to be the same as in a config file)
  clusterDeployment: local
  clusterStart: "kind create cluster"
  spec:
  # If the OS is not needed may be removed so the three below are mutually exclusive, if not needed set them to null or remove it
    wsl: "https://kind.sigs.k8s.io/dl/v0.9.0/kind-linux-amd64"
    mac: "https://kind.sigs.k8s.io/dl/v0.9.0/kind-darwin-amd64"
    linux: "https://kind.sigs.k8s.io/dl/v0.9.0/kind-linux-amd64"
    windows: "https://kind.sigs.k8s.io/dl/v0.9.0/kind-windows-amd64"
    # Everything from this repo will be ran in this cluster. You trust me right?
  plugins: 
  - repo: 
    name: jupyter-minimal
  - repo: 
    name: 

- name: localK3d #name of the cluster instance not the name of the cluster
  enabled: false
  type: k3d
  config:  #default location of config file or your existing config file to copy
  clusterName: demo-win-k3d #name of the cluster (this need to be the same as in a config file)
  clusterDeployment: local
  clusterStart: "k3d cluster create"
  spec:
  # If the OS is not needed may be removed so the three below are mutually exclusive, if not needed set them to null or remove it
    wsl: "https://kind.sigs.k8s.io/dl/v0.9.0/kind-linux-amd64"
    mac: "https://kind.sigs.k8s.io/dl/v0.9.0/kind-darwin-amd64"
    linux: "https://kind.sigs.k8s.io/dl/v0.9.0/kind-linux-amd64"
    windows: "https://github.com/rancher/k3d/releases/download/v3.4.0-test.0/k3d-windows-amd64.exe"
    # Everything from this repo will be ran in this cluster. You trust me right?
  plugins: 
  - repo: 
    name: jupyter-minimal
  - repo: 
    name: 

- name: remoteK3s #name of the cluster instance not the name of the cluster
  enabled: false
  type: k3s
  config: remote #default location of config file or your existing config file to copy  if Remote will be copy from remote location
  clusterName: demo-cluster-remote #name of the cluster (this need to be the same as in a config file)
  clusterDeployment: cloud
  clusterStart: 
  spec:
  # If the OS is not needed may be removed so the three below are mutually exclusive, if not needed set them to null or remove it
    wsl: 
    mac: 
    linux:
    windows:
    cloudType: civo
    cloudNodes: 1
    cloudSecretPath: $HOME/.k3ai/secret.txt
    # Everything from this repo will be ran in this cluster. You trust me right?
  plugins: 
  - repo: "https://github.com/alfsuse/demo-plugins"
    name: "demo"
  - repo: "https://github.com/alfsuse/demo-plugins-2"
    name: "demo2"

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 Code of Conduct and inclusivity document.‌

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 org.yaml​

• 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

  • If a week passes without receiving an invitation reach out on k3ai#community​

‌

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

  ​K3ai repositories.

‌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' contributors and owners 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:

  • no_parent_owners: defaults to false if not present; if true, exclude parent OWNERS files.

    Allows the use case where a/deep/nested/OWNERS file prevents a/OWNERS file from having any

    effect on a/deep/nested/bit/of/code

• reviewers: a list of GitHub usernames or aliases that are good candidates to /lgtm 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:

approvers:  
    - alice  
    - bob     
# this is a comment
reviewers:  
    - alice  
    - carol   
# this is another comment  
    - sig-foo # this is an alias

‌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:

aliases:  
    sig-foo:    
        - david    
        - erin  
    sig-bar:    
        - bob    
        - frank

‌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

    OWNERS file, and request their reviews on the PR

  • Choose suggested approvers, one from each OWNERS file, and list them in a comment on the PR

• Phase 1: Humans review the PR

  • Reviewers look for general code quality, correctness, sane software engineering, style, etc.

  • Anyone in the organization can act as a reviewer with the exception of the individual who

    opened the PR

  • If the code changes look good to them, a reviewer types /lgtm in a PR comment or review;

    if they change their mind, they /lgtm cancel

  • [FUTURE]Once a reviewer has /lgtm'ed, prow​

    (@k8s-ci-robot) applies an lgtm label to the PR

• Phase 2: Humans approve the PR

  • The PR author /assign's all suggested approvers to the PR, and optionally notifies

    them (eg: "pinging @foo for approval")

  • Only people listed in the relevant OWNERS files, either directly or through an alias, can act

    as approvers, including the individual who opened the PR

  • Approvers look for holistic acceptance criteria, including dependencies with other features,

    forwards/backwards compatibility, API and flag definitions, etc

  • If the code changes look good to them, an approver types /approve in a PR comment or

    review; if they change their mind, they /approve cancel

  • ​prow (@k8s-ci-robot) updates its

    comment in the PR to indicate which approvers still need to approve

  • Once all approvers (one from each of the previously identified OWNERS files) have approved,

    ​prow (@k8s-ci-robot) applies an

    approved label

• Phase 3: Automation merges the PR:

  • If all of the following are true:

    • All required labels are present (eg: lgtm, approved)

    • Any blocking labels are missing (eg: there is no do-not-merge/hold, needs-rebase)

  • And if any of the following are true:

    • there are no presubmit prow jobs configured for this repo

    • there are presubmit prow jobs configured for this repo, and they all pass after automatically

      being re-run one last time

  • Then the PR will automatically be merged

‌

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

  • Instead, explicitly write out /lgtm and /approve to help observers, or save the /lgtm for

    a reviewer

  • This goes against the idea of having at least two sets of eyes on a PR, and may be a sign that

    there are too few reviewers (who aren't also approver)

• Technically, anyone who is a member of the K3ai GitHub organization can drive-by /lgtm a

  PR

  • Drive-by reviews from non-members are encouraged as a way of demonstrating experience and

    intent to become a collaborator or reviewer

  • Drive-by /lgtm's from members may be a sign that our OWNERS files are too small, or that the

    existing reviewers are too unresponsive

  • This goes against the idea of specifying reviewers in the first place, to ensure that

    author is getting actionable feedback from people knowledgeable with the code

• Reviewers, and approvers are unresponsive

  • This causes a lot of frustration for authors who often have little visibility into why their

    PR is being ignored

  • Many reviewers and approvers are so overloaded by GitHub notifications that @mention'ing

    is unlikely to get a quick response

  • If an author /assign's a PR, reviewers and approvers will be made aware of it on

    their PR dashboard​

  • An author can work around this by manually reading the relevant OWNERS files,

    /unassign'ing unresponsive individuals, and /assign'ing others

  • This is a sign that our OWNERS files are stale; pruning the reviewers and approvers lists

    would help with this

  • It is the PR authors responsibility to drive a PR to resolution. This means if the PR reviewers are unresponsive they should escalate as noted below

    • e.g ping reviewers in a timely manner to get it reviewed

    • If the reviewers don't respond look at the OWNERs file in root and ping approvers listed there

• Authors are unresponsive

  • This costs a tremendous amount of attention as context for an individual PR is lost over time

  • This hurts the project in general as its general noise level increases over time

  • Instead, close PR's that are untouched after too long (we currently have a bot do this after 30

    days)

‌

Automation using OWNERS files‌

​prow​

‌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.‌

• ​cmd: tide​

  • per-repo configuration:

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

    • missingLabels: list of labels required to be missing for merge (eg: do-not-merge/hold)

    • reviewApprovedRequired: defaults to false; when true, require that there must be at least

      one approved pull request review​

      present for merge

    • merge_method: defaults to merge; when squash or rebase, use that merge method instead

      when clicking a PR's merge button

  • merges PR's once they meet the appropriate criteria as configured above

  • if there are any presubmit prow jobs for the repo the PR is against, they will be re-run one

    final time just prior to merge

• ​plugin: assign​

  • assigns GitHub users in response to /assign comments on a PR

  • unassigns GitHub users in response to /unassign comments on a PR

• ​plugin: approve​

  • per-repo configuration:

    • issue_required: defaults to false; when true, require that the PR description link to

      an issue, or that at least one approver issues a /approve no-issue

    • implicit_self_approve: defaults to false; when true, if the PR author is in relevant

      OWNERS files, act as if they have implicitly /approve'd

  • adds the approved label once an approver for each of the required

    OWNERS files has /approve'd

  • comments as required OWNERS files are satisfied

  • removes outdated approval status comments

• ​plugin: blunderbuss​

  • determines reviewers and requests their reviews on PR's

• ​plugin: lgtm​

  • adds the lgtm label when a reviewer comments /lgtm on a PR

  • the PR author may not /lgtm their own PR

• ​pkg: k8s.io/test-infra/prow/repoowners​

  • parses OWNERS and OWNERS_ALIAS files

  • if the no_parent_owners option is encountered, parent owners are excluded from having

    any influence over files adjacent to or underneath of the current OWNERS file

‌

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

• remove inactive people from OWNERS files

‌

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

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!

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 https://github.com/kf5i/k3ai-core/releases 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)

curl -fL "https://get.k3ai.in" -o k3ai.tar.gz

once downloaded untar the file and move it to your path

tar -xvzf k3ai.tar.gz \
&& chmod +x ./k3ai \
&& sudo mv ./k3ai /usr/local/bin

Windows

Invoke-WebRequest -Uri "https://get-win.k3ai.in" -OutFile k3ai.zip

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)

 Expand-Archive -Path .\k3ai.zip

Mac

curl -fL "https://get-mac.k3ai.in" -o k3ai.tar.gz

once downloaded untar the file and move it to your path

tar -xvzf k3ai.tar.gz \
&& chmod +x ./k3ai \
&& sudo mv ./k3ai /usr/local/bin

Arm64

curl -fL "https://get-arm.k3ai.in" -o k3ai.tar.gz

once downloaded untar the file and move it to your path

tar -xvzf k3ai.tar.gz \
&& chmod +x ./k3ai \
&& sudo mv ./k3ai /usr/local/bin

Alternative method for Linux

If for any reason it fails just go straight away to https://github.com/kf5i/k3ai-core/releases and download the binary. Place it in your path and that's it.

or use the following

#Set a variable to grab latest version
Version=$(curl -s "https://api.github.com/repos/kf5i/k3ai-core/releases/latest" | awk -F '"' '/tag_name/{print $4}' | cut -c 2-6) 
# get the binaries
wget https://github.com/kf5i/k3ai-core/releases/download/v$Version/k3ai-core_${Version}_linux_amd64.tar.gz

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

error: timed out waiting for the condition on xxxxxxx

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)

#Set a variable to grab latest version
Version=$(curl -s "https://api.github.com/repos/kf5i/k3ai-core/releases/latest" | awk -F '"' '/tag_name/{print $4}' | cut -c 2-6) 
# get the binaries
wget https://github.com/kf5i/k3ai-core/releases/download/v$Version/k3ai-core_${Version}_linux_amd64.tar.gz

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 issue)

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-cli delete

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 -

• Rancher K3d -

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.

   1. If you don't have docker installed at this point you have to follow this guide

   2. If you don't want to use Kind just go to step 3

3. If you got docker installed we will deploy Kind automatically and you're ready to move to Hello-Earth. If you don't want kind and/or don't want to install docker keep reading.

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

• k3ai init --local k3d

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

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 ****

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)

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

• We DO NOT install anything client-side (aka we don't want to be invasive) if not the minimal tools needed to run the solution (i.e.: k3s)

• We want to FAST

• We want to be LIGHTWEIGHT

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

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

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

    • plugins

      • demo-plugin

        • plugin.yaml

  • common

    • demo-plugin

      • deployment.yaml

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

plugin-name: demo-plugin
plugin-description: Demo of a custom local plugin
namespace: "default"
yaml:
  - url: "./commons/demo-plugin/deployment.yaml"
    type: "file"

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

apiVersion: v1
kind: Pod
metadata:
  name: shell-demo
spec:
  volumes:
  - name: shared-data
    emptyDir: {}
  containers:
  - name: nginx
    image: nginx
    volumeMounts:
    - name: shared-data
      mountPath: /usr/share/nginx/html
  hostNetwork: true
  dnsPolicy: Default

We are ready let's check the plugin list with

k3ai list --repo "<absolute path to root folder>/"

#k3ai list --repo "home/user/core/"

You should get something like this

k3ai list --repo "<absolute path to root folder>/"

Name                           Description
demo-plugin                    A simple demo of a local plugin

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

k3ai apply --repo "<absolute path to root folder>/"

Now let's check if the pod is running with

kubectl get pod shell-demo

#Output
NAME         READY   STATUS    RESTARTS   AGE
shell-demo   1/1     Running   0          3m9s

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

kubectl exec --stdin --tty shell-demo -- /bin/bash -c "apt-get update > /dev/null && apt-get -y install boxes > /dev/null &&  echo 'Hey, this is K3ai! Thanks for use this.' | boxes -d peek"

If everything goes right you should see something like this

/*       _\|/_
         (o o)
 +----oOO-{_}-OOo------------------------+
 |Hey, this is K3ai! Thanks for use this.|
 +--------------------------------------*/

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

k3ai delete --repo "<absolute path to root folder>/"

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

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

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

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 https://github.com/kubeflow/pytorch-operator **or the PyTorch site https://pytorch.org/

Quick Start

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

k3ai apply pytorch-op

Test You PyTorch-Job installation

We will use the MNISE example from the Kubeflow PyTorch-Job repo at https://github.com/kubeflow/pytorch-operator/tree/master/examples/mnist****

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

kubectl apply -f - << EOF
apiVersion: "kubeflow.org/v1"
kind: "PyTorchJob"
metadata:
  name: "pytorch-dist-mnist-gloo"
  namespace: kubeflow
spec:
  pytorchReplicaSpecs:
    Master:
      replicas: 1
      restartPolicy: OnFailure
      template:
        metadata:
          annotations:
            sidecar.istio.io/inject: "false"
        spec:
          containers:
            - name: pytorch
              image: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
              command: ['sh','-c','pip install tensorboardX==1.6.0 && mkdir -p /opt/mnist/src && cd /opt/mnist/src && curl -O https://raw.githubusercontent.com/kubeflow/pytorch-operator/master/examples/mnist/mnist.py && chgrp -R 0 /opt/mnist && chmod -R g+rwX /opt/mnist && python /opt/mnist/src/mnist.py']
              args: ["--backend", "gloo"]

    Worker:
      replicas: 1
      restartPolicy: OnFailure
      template:
        metadata:
          annotations:
            sidecar.istio.io/inject: "false"
        spec:
          containers:
            - name: pytorch
              image: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
              command: ['sh','-c','pip install tensorboardX==1.6.0 && mkdir -p /opt/mnist/src && cd /opt/mnist/src && curl -O https://raw.githubusercontent.com/kubeflow/pytorch-operator/master/examples/mnist/mnist.py && chgrp -R 0 /opt/mnist && chmod -R g+rwX /opt/mnist && python /opt/mnist/src/mnist.py']
              args: ["--backend", "gloo"]
EOF

If you have GPU enabled you may run it this way

kubectl apply -f - << EOF
apiVersion: "kubeflow.org/v1"
kind: "PyTorchJob"
metadata:
  name: "pytorch-dist-mnist-gloo"
  namespace: kubeflow
spec:
  pytorchReplicaSpecs:
    Master:
      replicas: 1
      restartPolicy: OnFailure
      template:
        metadata:
          annotations:
            sidecar.istio.io/inject: "false"
        spec:
          containers:
            - name: pytorch
              image: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
              command: ['sh','-c','pip install tensorboardX==1.6.0 && mkdir -p /opt/mnist/src && cd /opt/mnist/src && curl -O https://raw.githubusercontent.com/kubeflow/pytorch-operator/master/examples/mnist/mnist.py && chgrp -R 0 /opt/mnist && chmod -R g+rwX /opt/mnist && python /opt/mnist/src/mnist.py']
              args: ["--backend", "gloo"]
              # Change the value of nvidia.com/gpu based on your configuration
              resources:
                limits:
                  nvidia.com/gpu: 1 
    Worker:
      replicas: 1
      restartPolicy: OnFailure
      template:
        metadata:
          annotations:
            sidecar.istio.io/inject: "false"
        spec:
          containers:
            - name: pytorch
              image: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
              command: ['sh','-c','pip install tensorboardX==1.6.0 && mkdir -p /opt/mnist/src && cd /opt/mnist/src && curl -O https://raw.githubusercontent.com/kubeflow/pytorch-operator/master/examples/mnist/mnist.py && chgrp -R 0 /opt/mnist && chmod -R g+rwX /opt/mnist && python /opt/mnist/src/mnist.py']
              args: ["--backend", "gloo"]
              # Change the value of nvidia.com/gpu based on your configuration
              resources:
                limits:
                  nvidia.com/gpu: 1 
EOF

Step 2

Check if pod are deployed correctly with

kubectl get pod -l pytorch-job-name=pytorch-dist-mnist-gloo -n kubeflow

It should ouput something like this

NAME                               READY   STATUS    RESTARTS   AGE
pytorch-dist-mnist-gloo-master-0   1/1     Running   0          2m26s
pytorch-dist-mnist-gloo-worker-0   1/1     Running   0          2m26s

Step 3

Check logs result of your training job

 kubectl logs -l pytorch-job-name=pytorch-dist-mnist-gloo -n kubeflow

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

Train Epoch: 1 [55680/60000 (93%)]      loss=0.0341
Train Epoch: 1 [56320/60000 (94%)]      loss=0.0357
Train Epoch: 1 [56960/60000 (95%)]      loss=0.0774
Train Epoch: 1 [57600/60000 (96%)]      loss=0.1186
Train Epoch: 1 [58240/60000 (97%)]      loss=0.1927
Train Epoch: 1 [58880/60000 (98%)]      loss=0.2050
Train Epoch: 1 [59520/60000 (99%)]      loss=0.0642

accuracy=0.9660

Train Epoch: 1 [55680/60000 (93%)]      loss=0.0341
Train Epoch: 1 [56320/60000 (94%)]      loss=0.0357
Train Epoch: 1 [56960/60000 (95%)]      loss=0.0774
Train Epoch: 1 [57600/60000 (96%)]      loss=0.1186
Train Epoch: 1 [58240/60000 (97%)]      loss=0.1927
Train Epoch: 1 [58880/60000 (98%)]      loss=0.2050
Train Epoch: 1 [59520/60000 (99%)]      loss=0.0642

accuracy=0.9660

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

• Harsimran Singh Maan

  • Github: @harsimranmaan

  • Author of: Splunk ML Environment (SMLE) Labs Beta

  • Org: Splunk

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:

print ("Hello, Earth")

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 here

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 - https://www.kubeflow.org/docs/pipelines/overview/pipelines-overview/

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

k3ai apply kubeflow-pipelines

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

k3ai apply -f kubeflow-pipelines-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:

kubectl port-forward -n kubeflow svc/ml-pipeline-ui 8080:80

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

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

5. In another tab of your browser open the Kubeflow UI (http://<YOUR CLUSTER IP>:8888

6. Now let's move to the cell as the one below

# Specify pipeline argument values
arguments = {'a': '7', 'b': '8'}
# Launch a pipeline run given the pipeline function definition
kfp.Client().create_run_from_pipeline_func(calc_pipeline, arguments=arguments, 
                                           experiment_name=EXPERIMENT_NAME)
# The generated links below lead to the Experiment page and the pipeline run...

and change it to

# Specify pipeline argument values
arguments = {'a': '7', 'b': '8'}
# Launch a pipeline run given the pipeline function definition
kfp.Client("<YOUR CLUSTER IP>").create_run_from_pipeline_func(calc_pipeline, arguments=arguments, 
                                           experiment_name=EXPERIMENT_NAME)
# The generated links below lead to the Experiment page and the pipeline run...

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!

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. - **[

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

****

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 ****

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 ****

• --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 ****

Quick Start Guide

You only have to decide if you want CPU support:

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.

• Run CI/CD pipelines natively on Kubernetes without configuring complex software development products.

Learn more on the Kubeflow website: ****

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.

• 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

• 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: https://www.youtube.com/watch?v=K5jJRo1jKcY&feature=youtu.be

• Europe Cloud Conference 2020: https://youtu.be/NliBAWoe5ns?list=PLWWckTbWjUOEf-SqtQA_lc5fwC_67Scst

• All Things Open 2020: https://www.youtube.com/watch?v=1isURnUkYGY

• HubStation October Blog: https://hubstation.github.io/newsletter/2020/10/31/october.html

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

• Support for Rancher K3s clusters

• Kubectl is not the default command used

• Support for Civo automated cluster creation

• GPU plugin for V2

• H2O support for V2

October 2020

Released

• Kubeflow pipelines support

• Argo Workflows support

• GPU support

• Tensorflow Serving for ResNet models support

• Windows Subsystem for Linux support

• Civo Cloud support

Planned

• WSL improvements

• Cloud Deployment initial support

• Tensorflow Serving - MNIST support

• KFP SDK support

• Jupyter Notebooks support

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

• Showing empathy towards other community members

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

• Other conduct which could reasonably be considered inappropriate in a professional setting

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 https://www.contributor-covenant.org/version/1/4/code-of-conduct.html

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:

https://www.civo.com/signup****

Installing

All you have to do is simply type:

k3ai init --cloud civo

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:

kubectl config --kubeconfig="civo-k3ai-kubeconfig"

Step 3

One last thing and then we're done:

 k3ai apply <your favorite plugin>

enjoy your k3ai on https://civo.com****