1. GPU-Accelerated Machine Learning Pods on Kubernetes


    To deploy GPU-accelerated machine learning (ML) pods on a Kubernetes cluster, we will need to create the following:

    1. A Kubernetes cluster capable of provisioning nodes with GPU resources.
    2. A deployment or pod specification that requests GPU resources and runs the machine learning workload.

    We will use Pulumi and Python to describe and deploy the necessary infrastructure.

    Kubernetes Cluster with GPU Nodes

    First, we need to ensure our Kubernetes cluster has nodes with GPU capabilities. To facilitate this, we can use cloud provider-specific Kubernetes services like Google Kubernetes Engine (GKE) or Amazon Elastic Kubernetes Service (EKS) that support GPU-enabled nodes.

    For this demonstration, let's consider using Google Kubernetes Engine (GKE). You will need to create a node pool with the appropriate machine types and GPU accelerators. The google-native.container/v1.Cluster resource is used here.

    Pods with GPU Requests

    Once the cluster is ready, we define a pod or deployment with containers that request GPUs. This will indicate to Kubernetes that the container needs GPU resources. In the pod specification, we set resources.limits for the nvidia.com/gpu which indicates the number of GPUs the container is requesting.

    We will use the kubernetes.core/v1.Pod resource to define a Kubernetes Pod that requests GPU resources.

    Here's a full program that sets up the necessary infrastructure:

    import pulumi import pulumi_kubernetes as kubernetes import pulumi_google_native as google_native # Configurations for your Kubernetes cluster and GPU settings PROJECT_ID = 'your-gcp-project-id' CLUSTER_NAME = 'gpu-enabled-cluster' COMPUTE_ZONE = 'us-central1-a' MACHINE_TYPE = 'n1-standard-4' GPU_TYPE = 'nvidia-tesla-k80' GPU_COUNT = 1 NODE_COUNT = 2 # Create a GKE cluster with the necessary configurations to support GPUs cluster = google_native.container.v1.Cluster( "gpu-cluster", project=PROJECT_ID, name=CLUSTER_NAME, location=COMPUTE_ZONE, initial_node_count=NODE_COUNT, node_config=google_native.container.v1.NodeConfigArgs( machine_type=MACHINE_TYPE, guest_accelerators=[ google_native.container.v1.AcceleratorConfigArgs( accelerator_count=GPU_COUNT, accelerator_type=GPU_TYPE ), ], ), ) # Kubernetes provider to connect to the GKE cluster k8s_provider = kubernetes.Provider( "gke-k8s", kubeconfig=cluster.name.apply(lambda name: gke_cluster_kubeconfig(PROJECT_ID, name, COMPUTE_ZONE)), ) # Function to obtain the kubeconfig for the GKE cluster def gke_cluster_kubeconfig(project_id, cluster_name, compute_zone): # Here you would fetch the kubeconfig from GKE. This is a placeholder for the logic # needed to obtain the kubeconfig. return "your-gke-cluster-kubeconfig" # Define a pod that requests GPU resources gpu_pod = kubernetes.core.v1.Pod( "gpu-pod", metadata=kubernetes.meta.v1.ObjectMetaArgs( name="gpu-pod", labels={"app": "gpu-accelerated"}, ), spec=kubernetes.core.v1.PodSpecArgs( containers=[ kubernetes.core.v1.ContainerArgs( name="ml-container", image="your-ml-container-image", # Replace with your machine learning container image resources=kubernetes.core.v1.ResourceRequirementsArgs( limits={"nvidia.com/gpu": GPU_COUNT}, ), # Specify other container configurations (like env, ports, etc.) ), ], ), opts=pulumi.ResourceOptions(provider=k8s_provider), ) # Export the GKE cluster name pulumi.export('cluster_name', cluster.name) # Export the pod name pulumi.export('gpu_pod_name', gpu_pod.metadata.apply(lambda metadata: metadata.name))

    This program sets up a GKE cluster configured with GPU nodes and a pod that is capable of running GPU-accelerated ML workloads.

    Be sure to replace your-gcp-project-id, your-gke-cluster-kubeconfig, and your-ml-container-image with appropriate values for your project.

    To apply this Pulumi program, run the following commands after ensuring Pulumi CLI and GCP SDK are configured:

    pulumi up

    This will deploy the resources as specified in the program. After you've confirmed the preview looks correct, select 'yes' to continue with the deployment.