ksonnet

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=<a href="https://ksonnet.io/#get-started">0.13.1</a>
     export KS_PKG=ks_${KS_VER}_linux_amd64
     

   • Download the ksonnet package:

     wget -O /tmp/${KS_PKG}.tar.gz https://github.com/ksonnet/ksonnet/releases/download/v${KS_VER}/${KS_PKG}.tar.gz \
       --no-check-certificate
     

   • 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
   

Creating a ksonnet application

This section shows you how to use ksonnet to deploy kubeflow into your existing cluster. The commands below find the cluster currently used by kubectl and create the namespace kubeflow.

export KUBEFLOW_VERSION=0.2.2
export KUBEFLOW_KS_DIR=</path/to/store/your/ksonnet/application>
export KUBEFLOW_DEPLOY=false
curl https://raw.githubusercontent.com/kubeflow/kubeflow/v${KUBEFLOW_VERSION}/scripts/deploy.sh | bash

This will create a ksonnet application in ${KUBEFLOW_KS_DIR}. Refer to deploy.sh to see the individual commands run.

Important: The commands above will enable collection of anonymous user data to help us improve Kubeflow. Do disable usage collection you can run the following commands

cd ${KUBEFLOW_KS_DIR}
ks param set spartakus reportUsage false

You can now deploy Kubeflow as follows

cd ${KUBEFLOW_KS_DIR}
ks apply default

Upgrading ksonnet

See the guide to upgrading Kubeflow.

Why Kubeflow uses ksonnet

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

ksonnet allows us to generate Kubernetes manifests from parameterized templates. This makes it easy to customize Kubernetes manifests for your particular use case. In the examples above we used this functionality to generate manifests for TfServing with a user supplied URI for the model.

One of the reasons we really like ksonnet is because it treats environment as in (dev, test, staging, prod) as a first class concept. For each environment we can easily deploy the same components but with slightly different parameters to customize it for a particular environments. We think this maps really well to common workflows. For example, this feature makes it really easy to run a job locally without GPUs for a small number of steps to make sure the code doesn’t crash, and then easily move that 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.