Getting started

Deploying the Lemonade Stand AI Quickstart pattern

Prerequisites

An OpenShift cluster (version 4.18 or later). This pattern requires at least 1 NVIDIA GPU node for LLM inference.
- AWS: The pattern automatically provisions 1 g5.2xlarge GPU worker node (NVIDIA A10G) during installation. No GPU nodes need to be present before you deploy.
- Other providers and bare metal: A GPU node must already be part of the OpenShift cluster before you deploy this pattern. The pattern installs all required operators automatically.
- To create an OpenShift cluster, go to the Red Hat Hybrid Cloud console.
- Select OpenShift -> Red Hat OpenShift Container Platform -> Create cluster.
The Helm binary. For instructions, see Installing Helm.
The oc CLI tool. For instructions, see Getting started with the OpenShift CLI.
Additional installation tool dependencies. For details, see Patterns quick start.

Preparing for deployment

Procedure

Fork the ai-quickstart-lemonade-stand repository on GitHub. You must fork the repository to customize this pattern.

Clone the forked copy of this repository.

$ git clone git@github.com:your-username/ai-quickstart-lemonade-stand.git

Go to the root directory of your Git repository:
```
$ cd ai-quickstart-lemonade-stand
```

Run the following command to set the upstream repository:

$ git remote add -f upstream git@github.com:validatedpatterns-sandbox/ai-quickstart-lemonade-stand.git

Verify the setup of your remote repositories by running the following command:

$ git remote -v

Example output

origin	git@github.com:your-username/ai-quickstart-lemonade-stand.git (fetch)
origin	git@github.com:your-username/ai-quickstart-lemonade-stand.git (push)
upstream	git@github.com:validatedpatterns-sandbox/ai-quickstart-lemonade-stand.git (fetch)
upstream	git@github.com:validatedpatterns-sandbox/ai-quickstart-lemonade-stand.git (push)

Optional: To customize the deployment, create and switch to a new branch by running the following command:
```
$ git checkout -b my-branch
```
Make your changes, then stage and commit them:
```
$ git add <changed-files>
$ git commit -m "Customize deployment"
```
Push the changes to your forked repository:
```
$ git push origin my-branch
```

Deploying the pattern by using the pattern.sh file

To deploy the pattern by using the pattern.sh file, complete the following steps:

Log in to your cluster by following this procedure:
1. Obtain an API token by visiting the OAuth token request page.
2. Log in to the cluster by running the following command:
  $ oc login --token=<retrieved-token> --server=https://api.<your_cluster>.<domain>:6443
  Or log in by running the following command:
  $ export KUBECONFIG=~/<path_to_kubeconfig>
Deploy the pattern to your cluster. Run the following command:
```
$ ./pattern.sh make install
```

Verification

To verify a successful installation, check the health of the ArgoCD applications:

Run the following command:
```
$ ./pattern.sh make argo-healthcheck
```
It might take several minutes for all applications to synchronize and reach a healthy state. This includes downloading detector models, initializing the GPU operator, and starting the vLLM inference service.
Verify the Operator installation by navigating to Operators → Installed Operators in the OpenShift Container Platform web console. Confirm the following Operators are present:
- NVIDIA GPU Operator
- Red Hat OpenShift AI
- Node Feature Discovery Operator
- External Secrets Operator
After all applications are healthy, verify the inference service is serving by running:
```
$ oc get inferenceservice -A
```
Access the Lemonade Stand chatbot UI. Navigate to Networking → Routes in the lemonade-stand namespace and open the route URL for the lemonade-stand service.
Access the R Shiny monitoring dashboard. Navigate to Networking → Routes in the lemonade-stand namespace and open the route URL for the shiny-dashboard service.

Next steps

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