In this module we are going to get an overview of the different components that make up Kubeflow, and how to install them into our newly deployed Kubernetes cluster.
From Kubeflow's own documetation:
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow.
Kubeflow is composed of multiple components:
- JupyterHub, which allows user to request an instance of a Jupyter Notebook server dedicated to them.
- One or multiple training controllers. These are component that simplifies and manages the deployment of training jobs. For the purpose of this lab we are only going to deploy a training controller for TensorFlow jobs. However the Kubeflow community has started working on controllers for PyTorch and Caffe2 as well.
- A serving component that will help you serve predictions with your models.
For more general info on Kubeflow, head to the repo's README.
Kubeflow uses ksonnet templates as a way to package and deploy the different components.
ksonnet simplifies defining an application configuration, updating the configuration over time, and specializing it for different clusters and environments.
First, install ksonnet version 0.13.1, or you can download a prebuilt binary for your OS.
Then run the following commands to download Kubeflow:
KUBEFLOW_SRC=$(realpath kubeflow)
mkdir ${KUBEFLOW_SRC}
cd ${KUBEFLOW_SRC}
export KUBEFLOW_TAG=v0.4.1
curl https://raw.githubusercontent.com/kubeflow/kubeflow/${KUBEFLOW_TAG}/scripts/download.sh | bashKUBEFLOW_SRC a directory where you want to download the source to
KUBEFLOW_TAG a tag corresponding to the version to check out, such as master for the latest code.
# Initialize a kubeflow app
KFAPP=mykubeflowapp
${KUBEFLOW_SRC}/scripts/kfctl.sh init ${KFAPP} --platform none
# Generate kubeflow app
cd ${KFAPP}
${KUBEFLOW_SRC}/scripts/kfctl.sh generate k8s
# Deploy Kubeflow app
${KUBEFLOW_SRC}/scripts/kfctl.sh apply k8skubectl get pods -n kubeflow
should return something like this:
NAME READY STATUS RESTARTS AGE
kubeflow ambassador-b4d9cdb8-2qgww 1/1 Running 0 111m
kubeflow ambassador-b4d9cdb8-hpwdc 1/1 Running 0 111m
kubeflow ambassador-b4d9cdb8-khg8l 1/1 Running 0 111m
kubeflow argo-ui-6d6658d8f7-t6whw 1/1 Running 0 110m
kubeflow centraldashboard-6f686c5b7c-462cq 1/1 Running 0 111m
kubeflow jupyter-0 1/1 Running 0 111m
kubeflow katib-ui-6c59754c48-mgf62 1/1 Running 0 110m
kubeflow metacontroller-0 1/1 Running 0 111m
kubeflow minio-d79b65988-6qkxp 1/1 Running 0 110m
kubeflow ml-pipeline-66df9d86f6-rp245 1/1 Running 0 110m
kubeflow ml-pipeline-persistenceagent-7b86dbf4b5-rgndj 1/1 Running 0 110m
kubeflow ml-pipeline-scheduledworkflow-84f6477479-9tvhk 1/1 Running 0 110m
kubeflow ml-pipeline-ui-f76bb5f97-2s5qb 1/1 Running 0 110m
kubeflow mysql-ffc889689-xkpxb 1/1 Running 0 110m
kubeflow pytorch-operator-ff46f9b7d-qkbvh 1/1 Running 0 111m
kubeflow spartakus-volunteer-5b6c956c8f-2gnvb 1/1 Running 0 111m
kubeflow studyjob-controller-b7cdbd4cd-nf9z5 1/1 Running 0 110m
kubeflow tf-job-dashboard-7746db84cf-njdzk 1/1 Running 0 111m
kubeflow tf-job-operator-v1beta1-5949f668f7-j5zrn 1/1 Running 0 111m
kubeflow vizier-core-7c56465f6-t6d5p 1/1 Running 0 110m
kubeflow vizier-core-rest-67f588b4cb-lqvgr 1/1 Running 0 110m
kubeflow vizier-db-86dc7d89c5-8vtfs 1/1 Running 0 110m
kubeflow vizier-suggestion-bayesianoptimization-7cb546fb84-tsrn4 1/1 Running 0 110m
kubeflow vizier-suggestion-grid-6587f9d6b-92c9h 1/1 Running 0 110m
kubeflow vizier-suggestion-hyperband-8bb44f8c8-gs72m 1/1 Running 0 110m
kubeflow vizier-suggestion-random-7ff5db687b-bjdh5 1/1 Running 0 110m
kubeflow workflow-controller-cf79dfbff-lv7jk 1/1 Running 0 110m
The most important components for the purpose of this lab are jupyter-0 which is the JupyterHub spawner running on your cluster, and tf-job-operator-v1beta1-5949f668f7-j5zrn which is a controller that will monitor your cluster for new TensorFlow training jobs (called TfJobs) specifications and manages the training, we will look at this two components later.
If you want to remove the Kubeflow deployment, you can run the following to remove the namespace and installed components:
cd ${KUBEFLOW_SRC}/${KFAPP}
${KUBEFLOW_SRC}/scripts/kfctl.sh delete k8s