> For the complete documentation index, see [llms.txt](https://docs.tensorwave.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tensorwave.com/welcome-to-tensorwave/kubernetes-quickstart.md). # Kubernetes Quickstart *** ### Installing Tooling We'll start by installing kubectl and k3d. kubectl is a command-line tool for managing kubernetes interfaces, and k3d is a lightweight wrapper to run k3s in Docker. Download and install the lateset release of kubectl using the folllowing commands: ```bash curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl" sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl ``` Next, install the latest release of k3d using this command: ```bash wget -q -O - https://raw.githubusercontent.com/k3d-io/k3d/main/install.sh | bash ``` {% hint style="info" %} Learn more about installing kubectl [here](https://kubernetes.io/docs/tasks/tools/install-kubectl-linux/), and installing k3d [here](https://k3d.io/v5.7.3/#installation). {% endhint %} *** ### Creating and Configuring a Cluster Now, you must create a cluster: ```bash k3d cluster create hello-world-cluster ``` Then go ahead and check your context: ```bash kubectl config current-context ``` This should list the cluster you just created, but if not, run the following command to switch to the needed context: ```bash kubectl config use-context k3d-hello-world-cluster ``` In order to operate with acceleration, Kubernetes must also be set up with the AMD GPU Operator and Labeler plugins. You can install these using the following commands: ```bash kubectl create -f https://raw.githubusercontent.com/ROCm/k8s-device-plugin/master/k8s-ds-amdgpu-dp.yaml kubectl create -f https://raw.githubusercontent.com/ROCm/k8s-device-plugin/master/k8s-ds-amdgpu-labeller.yaml ``` Then, go ahead and make your deployment manifest. Create a directory for your manifest and direct into it, then open up a `deployment.yaml` file. ```bash mkdir k8s-hello-world cd k8s-hello-world nano deployment.yaml ``` From there, paste in the following yaml: ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: hello-world spec: replicas: 1 selector: matchLabels: app: hello-world template: metadata: labels: app: hello-world spec: containers: - name: hello-world image: tensorwavehq/hello_world:latest resources: limits: amd.com/gpu: 1 volumeMounts: - name: dev-kfd mountPath: /dev/kfd - name: dev-dri mountPath: /dev/dri securityContext: runAsGroup: 110 volumes: - name: dev-kfd hostPath: path: /dev/kfd - name: dev-dri hostPath: path: /dev/dri ``` You'll notice there are a few extra configurations we added. These are necessary to running the pod with GPU acceleration. * ```yaml resources: limits: amd.com/gpu: 1 ``` * This specifies that the container requires 1 AMD GPU. You must explicitly request GPU resources so that Kubernetes can schedule the pod on a node with an available AMD GPU. * ```yaml volumes: - name: dev-kfd hostPath: path: /dev/kfd - name: dev-dri hostPath: path: /dev/dri ``` * These definitions allow the cluster to use the necessary volumes from the host for utilizing the AMD GPUs. * ```yaml volumeMounts: - name: dev-kfd mountPath: /dev/kfd - name: dev-dri mountPath: /dev/dri ``` * These mounts correspond to the above volumes, allowing the container to access the GPU hardware. * ```yaml securityContext: runAsGroup: 110 ``` * This security context runs the containers in the pod as the group ID 110, the render group, which is necessary for PyTorch to detect the devices properly (PyTorch is used in the hello world container). Continue by applying the manifest using the following: ```bash kubectl apply -f deployment.yaml ``` This should take a few minutes to create the container. You can monitor the status here: ```bash kubectl get pods -l app=hello-world ``` Once this output displays that `STATUS` is `Completed`, you're ready to check output. Running: ```bash kubectl logs -l app=hello-world ``` Should give an output of: ``` CUDA available: True Number of GPUs: 1 GPU 0: AMD Instinct MI300X ``` You'll notice that you only have one GPU. That's because, as covered earlier, we specified a resource limit of one. You may raise or lower this number as necessary. *** ### Teardown Navigate back to your base directory and remove your `k8s-hello-world` folder: ```bash cd ~ rm -rf k8s-hello-world/ ``` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. 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