Deploy AIStore on Kubernetes

The repository includes supporting material for deploying AIStore on Kubernetes:

A Helm chart to install AIStore
Ansible playbooks to assist in preparing nodes to host AIStore
Documentation
A Helm chart for deploying aisloader, for sythetic GET loads
Terraform definitions for public cloud usage, such as GKE/GCP.

The repository is split from the main AIStore repo to facilitate GitOps-style deployments, free from the unrelated commit noise of the development repo.

Cloud Deployment

If you want to deploy a fresh Kubernetes cluster in the cloud with AIStore, please refer to the terraform directory of this repository.

Small Scale Experimental Deployments

It is assumed you want to deploy AIStore at reasonable scale on multiple nodes each with multiple drives. If you don't require such scale then consider deploying under Docker as illustrated in the main AIStore repo.

Deployment Documentation

You can deploy AIStore on Kubernetes in two ways. In both cases, some preparation and planning is needed; we suggest you read the deployment documentation first.

Deployment via Helm

The standard way to deploy AIStore at present is using Helm. You can find helm deployment steps in the standard deployment documentation.

Deployment via the AIStore Operator

AIStore can also be deployed using the AIStore operator, currently in beta.

With an operator based deployment, instead of deploying services directly, you define your AIStore cluster as a kubernetes custom resource.

The operator documentation can be found here, along with an accompanying walkthrough.

Using This Repository For GitOps-Style Deployment

We suggest cloning this repository and retaining the master branch as tracking this upstream master; create a new branch off of master and edit values.yaml etc., and point your CD tool at that branch. When you pull updates to the master you can pull and merge them into your private branch.