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Ray on GKE Templates

This repository contains a Terraform template for running Ray on Google Kubernetes Engine. See the Ray on GKE directory to see additional guides and references.

Prerequisites

  1. GCP Project with following APIs enabled

    • container.googleapis.com
    • iap.googleapis.com (required when using authentication with Identity Aware Proxy)

  2. A functional GKE cluster.

    • To create a new standard or autopilot cluster, follow the instructions in infrastructure/README.md
    • Alternatively, you can set the create_cluster variable to true in workloads.tfvars to provision a new GKE cluster. This will default to creating a GKE Autopilot cluster; if you want to provision a standard cluster you must also set autopilot_cluster to false.

  3. This module is configured to optionally use Identity Aware Proxy (IAP) to protect access to the Ray dashboard. It expects the brand & the OAuth consent configured in your org. You can check the details here: OAuth consent screen

  4. Preinstall the following on your computer:

    • Terraform
    • Gcloud CLI

Installation

Configure Inputs

  1. If needed, clone the repo
git clone https://github.com/GoogleCloudPlatform/ai-on-gke
cd ai-on-gke/applications/ray
  1. Edit workloads.tfvars with your GCP settings.

Important Note: If using this with the Jupyter module (applications/jupyter/), it is recommended to use the same k8s namespace for both i.e. set this to the same namespace as applications/jupyter/workloads.tfvars.

Variable Description Required
project_id GCP Project Id Yes
cluster_name GKE Cluster Name Yes
cluster_location GCP Region Yes
kubernetes_namespace The namespace that Ray and rest of the other resources will be installed in. Yes
gcs_bucket GCS bucket to be used for Ray storage Yes
create_service_account Create service accounts used for Workload Identity mapping Yes

Install

NOTE: Terraform keeps state metadata in a local file called terraform.tfstate. Deleting the file may cause some resources to not be cleaned up correctly even if you delete the cluster. We suggest using terraform destory before reapplying/reinstalling.

  1. Ensure your gcloud application default credentials are in place.
gcloud auth application-default login

  1. Run terraform init

  2. Run terraform apply --var-file=./workloads.tfvars.