---
name: gke-inference-stack-deploy
description: Deploys the GKE base platform and inference-specific terra-services (GPU/TPU) for accelerated workloads.
version: 0.1.0
allowed-tools: kubectl gcloud terraform python3
mcp-servers:
  - kubernetes:
      reason: "Inspect cluster info, custom compute classes, pod statuses."
  - gcp:
      reason: "Verify GKE cluster status."
---

# Deploy GKE Inference Stack Skill

Follow these instructions to provision the GKE Base Platform and inference-specific terra-services.

## 1. Prerequisites & Platform Configuration

1.  **Ask the user** (only for information not provided in the user's prompt):

    - "What is the path to the repository?" -> Set ACP_REPO_DIR
    - "What is your Google Cloud project ID?" -> Set platform_default_project_id
    - "What GKE region would you like to use?" -> Set cluster_region
    - "What platform name would you like to set?" -> Set platform_name
    - "Would you like to deploy an Autopilot or Standard cluster?" -> Set cluster_type

2.  **Action**: Inject these values into the appropriate `tfvars` files (`${ACP_REPO_DIR}/platforms/gke/base/_shared_config/platform.auto.tfvars` and `cluster.auto.tfvars`) using `sed`.

    ```bash
    # Update platform variables
    sed -i 's/^platform_name.*/platform_name = "<platform_name>"/g' "${ACP_REPO_DIR}/platforms/gke/base/_shared_config/platform.auto.tfvars"
    grep -q "^platform_default_project_id" "${ACP_REPO_DIR}/platforms/gke/base/_shared_config/platform.auto.tfvars" || echo "platform_default_project_id = \"\"" >> "${ACP_REPO_DIR}/platforms/gke/base/_shared_config/platform.auto.tfvars"
    sed -i 's/^platform_default_project_id.*/platform_default_project_id = "<project_id>"/g' "${ACP_REPO_DIR}/platforms/gke/base/_shared_config/platform.auto.tfvars"

    # Update cluster variables
    sed -i 's/^cluster_region.*/cluster_region = "<cluster_region>"/g' "${ACP_REPO_DIR}/platforms/gke/base/_shared_config/cluster.auto.tfvars"
    ```

## 2. Accelerator & Hardware Configuration

1.  **Ask the user**: "Which accelerator(s) would you like to deploy for inference? You can select GPU, TPU, or both."

    - **Options**: GPU (`rtx-pro-6000`, `h100`, `h200`), TPU (`v6e`), or both.

2.  **Action**:
    - Determine if the target inference terra-services are `online_gpu`, `online_tpu`, or both based on the chosen accelerator(s).
    - Update accelerator configurations in the shared `tfvars` if necessary based on user selection.

## 3. Deploy Inference Platform & Prerequisite Terra-Services

Run the appropriate inference reference architecture deployment script based on the cluster type selected by the user. This provisions the core platform along with prerequisite inference services (such as Hugging Face and monitoring initialization). Always refer to existing scripts for execution rather than defining the `CORE_TERRASERVICES_APPLY` array explicitly.

- **For Standard Cluster**:
  ```bash
  "${ACP_REPO_DIR}/platforms/gke/base/use-cases/inference-ref-arch/terraform/deploy-standard.sh"
  ```
- **For Autopilot Cluster**:
  ```bash
  "${ACP_REPO_DIR}/platforms/gke/base/use-cases/inference-ref-arch/terraform/deploy-ap.sh"
  ```

## 4. Deploy Inference Terra-Services

Navigate to the specific inference terra-service directories (`online_gpu`, `online_tpu`, or both) and execute Terraform commands.

```bash
# Depending on what the user chose, add "online_gpu" and/or "online_tpu" to this array
declare -a selected_terraservices=( "online_gpu" "online_tpu" )

for inference_terraservice in "${selected_terraservices[@]}"; do
  cd "${ACP_REPO_DIR}/platforms/gke/base/use-cases/inference-ref-arch/terraform/${inference_terraservice}/"
  terraform init
  terraform plan -input=false -out=tfplan
  terraform apply -input=false tfplan
  rm tfplan
done
```

## 5. Verification

Retrieve GKE cluster credentials and verify the deployment. Note: if using NAP (Node Auto-Provisioning), nodes may not be visible until workloads are deployed.

```bash
gcloud container clusters get-credentials "<platform_name>" --region "<cluster_region>" --project "<project_id>" --dns-endpoint
kubectl get computeclasses
kubectl get namespaces # You should see the online_gpu and/or online_tpu namespace depending on what was deployed
```