Merge branch 'main' into website

.github/workflows/ci.yaml

@@ -0,0 +1,35 @@
+name: Terraform CI
+on: 
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+jobs:
+  Terraform-Lint-Check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: hashicorp/setup-terraform@v3
+        with:
+          terraform_version: "1.5.7"
+
+      - name: Terraform fmt
+        id: fmt
+        run: terraform fmt -check -recursive
+
+      - name: Terraform Init
+        id: init
+        run: |
+          terraform -chdir=applications/rag init 
+          terraform -chdir=applications/ray init 
+          terraform -chdir=applications/jupyter init 
+
+      - name: Terraform Validate
+        id: validate
+        run: |
+          terraform -chdir=applications/rag validate -no-color
+          terraform -chdir=applications/ray validate -no-color
+          terraform -chdir=applications/jupyter validate -no-color
+

.gitignore

@@ -12,11 +12,31 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-terraform.tfvars
-terraform.tfstate*
+## Archives
+**/*.tar
+**/*.tar.gz
+**/*.zip
+
+# Directories
+bin/
+deploy/
+
+# IDEs
+.idea/
+
+# Python
+__pycache__/
+
+# Terraform
+default.tfstate
+default.tfstate.backup
 .terraform*
 __pycache__/
 
 public/
 node_modules/
-resources/
+resources/
+terraform.tfstate*
+terraform.tfvars
+tfplan
+

CODEOWNERS

@@ -0,0 +1,4 @@
+# @name owns any files in the /benchmarks/
+# directory at the root of the repository and any of its
+# subdirectories.
+/benchmarks/  @achandrasekar @ahg-g @annapendleton

README.md

@@ -1,13 +1,172 @@
-# Kubernetes Engine Samples
-
-[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://ssh.cloud.google.com/cloudshell/editor?cloudshell_git_repo=https://github.com/GoogleCloudPlatform/ai-on-gke&cloudshell_tutorial=README.md&cloudshell_workspace=hello-app)
+# AI on GKE Assets
 
 This repository contains assets related to AI/ML workloads on
-[Google Kubernetes Engine (GKE)](https://cloud.google.com/kubernetes-engine/).
+[Google Kubernetes Engine (GKE)](https://cloud.google.com/kubernetes-engine/docs/integrations/ai-infra).
+
+## Overview
+
+Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers:
+
+- Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale
+- Flexible integration with distributed computing and data processing frameworks
+- Support for multiple teams on the same infrastructure to maximize utilization of resources
+
+## Infrastructure
+
+The AI-on-GKE application modules assumes you already have a functional GKE cluster. If not, follow the instructions under [infrastructure/README.md](./infrastructure/README.md) to install a Standard or Autopilot GKE cluster.
+
+```bash
+.
+├── LICENSE
+├── README.md
+├── infrastructure
+│   ├── README.md
+│   ├── backend.tf
+│   ├── main.tf
+│   ├── outputs.tf
+│   ├── platform.tfvars
+│   ├── variables.tf
+│   └── versions.tf
+├── modules
+│   ├── gke-autopilot-private-cluster
+│   ├── gke-autopilot-public-cluster
+│   ├── gke-standard-private-cluster
+│   ├── gke-standard-public-cluster
+│   ├── jupyter
+│   ├── jupyter_iap
+│   ├── jupyter_service_accounts
+│   ├── kuberay-cluster
+│   ├── kuberay-logging
+│   ├── kuberay-monitoring
+│   ├── kuberay-operator
+│   └── kuberay-serviceaccounts
+└── tutorial.md
+```
+
+To deploy new GKE cluster update the `platform.tfvars` file with the appropriate values and then execute below terraform commands:
+```
+terraform init
+terraform apply -var-file platform.tfvars
+```
+
+
+## Applications
+
+The repo structure looks like this:
+
+```bash
+.
+├── LICENSE
+├── Makefile
+├── README.md
+├── applications
+│   ├── jupyter
+│   └── ray
+├── contributing.md
+├── dcgm-on-gke
+│   ├── grafana
+│   └── quickstart
+├── gke-a100-jax
+│   ├── Dockerfile
+│   ├── README.md
+│   ├── build_push_container.sh
+│   ├── kubernetes
+│   └── train.py
+├── gke-batch-refarch
+│   ├── 01_gke
+│   ├── 02_platform
+│   ├── 03_low_priority
+│   ├── 04_high_priority
+│   ├── 05_compact_placement
+│   ├── 06_jobset
+│   ├── Dockerfile
+│   ├── README.md
+│   ├── cloudbuild-create.yaml
+│   ├── cloudbuild-destroy.yaml
+│   ├── create-platform.sh
+│   ├── destroy-platform.sh
+│   └── images
+├── gke-disk-image-builder
+│   ├── README.md
+│   ├── cli
+│   ├── go.mod
+│   ├── go.sum
+│   ├── imager.go
+│   └── script
+├── gke-dws-examples
+│   ├── README.md
+│   ├── dws-queues.yaml
+│   ├── job.yaml
+│   └── kueue-manifests.yaml
+├── gke-online-serving-single-gpu
+│   ├── README.md
+│   └── src
+├── gke-tpu-examples
+│   ├── single-host-inference
+│   └── training
+├── indexed-job
+│   ├── Dockerfile
+│   ├── README.md
+│   └── mnist.py
+├── jobset
+│   └── pytorch
+├── modules
+│   ├── gke-autopilot-private-cluster
+│   ├── gke-autopilot-public-cluster
+│   ├── gke-standard-private-cluster
+│   ├── gke-standard-public-cluster
+│   ├── jupyter
+│   ├── jupyter_iap
+│   ├── jupyter_service_accounts
+│   ├── kuberay-cluster
+│   ├── kuberay-logging
+│   ├── kuberay-monitoring
+│   ├── kuberay-operator
+│   └── kuberay-serviceaccounts
+├── saxml-on-gke
+│   ├── httpserver
+│   └── single-host-inference
+├── training-single-gpu
+│   ├── README.md
+│   ├── data
+│   └── src
+├── tutorial.md
+└── tutorials
+    ├── e2e-genai-langchain-app
+    ├── finetuning-llama-7b-on-l4
+    └── serving-llama2-70b-on-l4-gpus
+```
+
+
+### Jupyter Hub
+
+This repository contains a Terraform template for running JupyterHub on Google Kubernetes Engine. We've also included some example notebooks ( under `applications/ray/example_notebooks`), including one that serves a GPT-J-6B model with Ray AIR (see here for the original notebook). To run these, follow the instructions at [applications/ray/README.md](./applications/ray/README.md) to install a Ray cluster.
+
+This jupyter module deploys the following resources, once per user:
+- JupyterHub deployment
+- User namespace
+- Kubernetes service accounts
+
+Learn more [about JupyterHub on GKE here](./applications/jupyter/README.md)
+
+### Ray
+
+This repository contains a Terraform template for running Ray on Google Kubernetes Engine.
+
+This module deploys the following, once per user:
+- User namespace
+- Kubernetes service accounts
+- Kuberay cluster
+- Prometheus monitoring
+- Logging container
+
+Learn more [about Ray on GKE here](./applications/ray/README.md)
+
+## Important Considerations
+- Make sure to configure terraform backend to use GCS bucket, in order to persist terraform state across different environments.
 
-## Important Note
-The use of the assets contained in this repository is subject to compliance with [Google's AI Principles](https://ai.google/responsibility/principles/)
 
 ## Licensing
 
+* The use of the assets contained in this repository is subject to compliance with [Google's AI Principles](https://ai.google/responsibility/principles/)
 * See [LICENSE](/LICENSE)