# Install Users that do not have access to expensive GPUs or do not wish to spend money on GPUs for learning and testing can use the [GPU Operator Simulator](https://github.com/run-ai/fake-gpu-operator) software. This was originally created by the Run.AI team (acquired by Nvidia). This recipe describes how to configure, install and use the GPU Simulator Operator to reduce costs and simulate the presence of GPUs within a cluster. Note: In our testing, we have noticed a number of issues with the GPU Simulator Software. Please open a [GitHub issue](https://github.com/run-ai/fake-gpu-operator/issues) directly on the project's repo to report issues or request enhancements. --- ## Assumptions - You have provisioned or imported one or more Kubernetes clusters into a Project in your Rafay Org. - Ensure that you have not already deployed the Nvidia GPU Operator on the cluster. Note: In this recipe, you will notice that we are configuring and deploying the GPU Simulator software as a workload and not a Cluster Blueprint. Users can also deploy the simulator software to 100s of clusters in their Org by using a blueprint if required. --- ## Deploy GPU Simulator To deploy the GPU Simulator on the managed Kubernetes cluster, perform the following steps: --- ### Step 1: Create GPU Simulator Helm Repository Create a Helm repository that stores GPU Simulator Helm chart. - Log in to the controller web console and navigate to your Project as an _Org Admin_ or _Infrastructure Admin_. - Under **Integrations**, select **Repositories** and create a new Helm repository with the name `gpu-simulator`. - Add the GPU Simulator Helm Chart repository URL: _[https://fake-gpu-operator.storage.googleapis.com](https://fake-gpu-operator.storage.googleapis.com/)_ in the Endpoint and save it. --- ### Step 2: Create GPU Simulator Namespace Create namespace on the cluster for installing the GPU Simulator in. - Under **Infrastructure**, select **Namespaces** and create a new namespace with name `gpu-operator-resources`. - Click **Save and go to placement**. - Select the target cluster from the list of available clusters and click **Save and go to publish**. - Publish the namespace and make sure that it gets published successfully in the target cluster before moving to the next step. --- ### Step 3: Assign GPUs to Nodes You can assign GPUs to nodes by applying a label to specific nodes. Run the following command being sure to update the node name and the node pool name. ``` kubectl label node run.ai/simulated-gpu-node-pool= ``` Note: The node pool names are defined in the values.yaml file used when deploying the workload. The options in the provided values.yaml file are A100, H100 and T400. Additional node pool groups can be added to the values.yaml file as needed. --- ### Step 4: Deploy GPU Simulator Create a Workload to deploy the GPU Simulator Operator onto the cluster. - Under **Applications**, select **Workloads** and create a new workload with the name `gpu-simulator`. - Ensure that you select `Helm 3` for **Type**, `Pull files from repository` for **Artifact Sync**, `Helm` for **Repository Type** and select the namespace `gpu-operator-resources`. - Select the `gpu-simulator` **Repository** - Enter `fake-gpu-operator` for the **Chart Name** - Create a YAML file named `values.yaml` with the following YAML: ``` topology: nodePools: A100: gpuProduct: NVIDIA-A100 gpuCount: 4 H100: gpuProduct: NVIDIA-H100 gpuCount: 4 T400: gpuProduct: NVIDIA-T400 gpuCount: 4 ``` - Upload the `values.yaml` in the workload and **Save and Go To Placement**. - Select the target cluster from the list of available clusters and click **Save and go to publish**. - Publish the workload and make sure that it gets published successfully in the target cluster before moving to the next step. --- ### Step 5: Verify Deployment You can verify whether the resources related to GPU Simulator are properly deployed in the cluster. - Click on the Kubectl web shell. - Verify the GPU Simulator Operator deployment is up and running using the following command: ``` kubectl get pods -n gpu-operator-resources ``` --- ### Step 6: Deploy Test Application - Under **Applications**, select **Workloads**, then create a **New Workload** with the name `gpu-app`. - Set **Package Type** to `k8s YAML` - Select the **Namespace** as `gpu-operator-resources` and - Click **CONTINUE**. - Create a YAML file named `gpu-app.yaml` with the following YAML: ``` apiVersion: apps/v1 kind: Deployment metadata: name: sleepy-deployment labels: app: sleep spec: selector: matchLabels: app: sleep replicas: 2 strategy: rollingUpdate: maxSurge: 25% maxUnavailable: 25% type: RollingUpdate template: metadata: labels: app: sleep spec: containers: - name: sleep image: alpine command: ["sleep", "3600"] imagePullPolicy: IfNotPresent resources: limits: nvidia.com/gpu: 1 restartPolicy: Always ``` Take note that the application uses the GPU with the following lines: ``` resources: limits: nvidia.com/gpu: 1 ``` - Upload the `gpu-app.yaml` that was created before and then **go to the placement** of workload. - Select the target cluster from the list of available clusters and click **Save and go to publish**. - **Publish** the workload and make sure that it gets published successfully in the target cluster before moving to the next step. --- ### Step 7: Verify GPUs After deploying application in the cluster, let us verify that Kubernetes cluster is allocating the "simulated GPU" as requested by the workload. - Under **Infrastructure**, select **Clusters** - View the GPU information in the cluster card showing the number of GPUs in the cluster and the number of GPUs currently consumed by applications --- ## Recap Congratulations! Now, you have successfully deployed the GPU Simulator in a cluster to simulate GPUs running in the cluster.