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Information about ksonnet as used in Kubeflow

Kubeflow makes use of ksonnet to help manage deployments.

Installing ksonnet

Make sure you have the minimum required version of ksonnet: 0.13.1 or later.

Follow the steps below to install ksonnet:

  1. Follow the ksonnet installation guide, choosing the relevant options for your operating system. For example, if you’re on Linux:

    • Set some variables for the ksonnet version:

      export KS_VER=0.13.1
      export KS_PKG=ks_${KS_VER}_linux_amd64
    • Download the ksonnet package:

      wget -O /tmp/${KS_PKG}.tar.gz${KS_VER}/${KS_PKG}.tar.gz
    • Unpack the file:

      mkdir -p ${HOME}/bin
      tar -xvf /tmp/$KS_PKG.tar.gz -C ${HOME}/bin
  2. Add the ks command to your path:

      export PATH=$PATH:${HOME}/bin/$KS_PKG

Here are the ksonnet package download links for each operating system:

Deploying Kubeflow

After installing ksonnet, you can follow the Kubeflow getting-started guide to deploy Kubeflow.

Upgrading ksonnet

See the guide to upgrading Kubeflow.

Why Kubeflow uses ksonnet

ksonnet makes it easier to manage complex deployments consisting of multiple components. ksonnet is designed to work side by side with kubectl.

ksonnet allows you to generate Kubernetes manifests from parameterized templates. This makes it easy to customize Kubernetes manifests for your particular use case.

ksonnet treats environment as a first class concept. For example, you can define separate environments for your development, test, staging, and production deployments. For each environment, you can use ksonnet to deploy the same components with slightly different parameters to customize the deployment for a particular environment. For example, this feature makes it easy to run a job locally without GPUs for a small number of steps to make sure the code doesn’t crash, then move the deployment to the cloud to run at scale with GPUs.

More about ksonnet

ksonnet acts as a layer on top of kubectl. While Kubernetes is typically managed with static YAML files, ksonnet adds a further abstraction that is closer to the objects in object-oriented programming.

With ksonnet, you manage your resources as prototypes with empty parameters. Then you instantiate the prototypes into components by defining values for the parameters. This system makes it easier to deploy slightly different resources to different clusters at the same time. In this way you can maintain different environments for staging and production, for example. You can export your ksonnet components as standard Kubernetes YAML files with ks show, or you can deploy (apply) the components directly to the cluster with ks apply.

Some useful ksonnet concepts:

  • Environment: A unique location to deploy to. An environment includes:

    • A unique name.
    • The address of your Kubernetes cluster.
    • The cluster’s namespace.
    • The version of the Kubernetes API.

    Environments can support different settings, so you can deploy slightly different components to different clusters.

  • Prototype: An object that describes a set of Kubernetes resources and associated parameters in an abstract way. Kubeflow includes prototypes for tf-job (to run a TensorFlow training job), tf-serving (to serve a trained model), and a few others.

  • Component: A specific implementation of a prototype. You create a component supplying the empty parameters of a prototype. A component can directly generate standard Kubernetes YAML files, and can be deployed directly to a cluster. It can also hold different parameters for different environments.

Read more about the core ksonnet concepts in the ksonnet documentation.