1. GPU-Accelerated Compute for LLMs on Kubernetes


    To set up GPU-accelerated compute resources for Language Model (LLM) processing on Kubernetes, we will use the following resources:

    1. Kubernetes Nodes with GPU support: We need nodes in our Kubernetes cluster that have GPUs attached to them. This typically involves using a specific machine type or instance type when creating the node pool that supports GPU workloads.

    2. ResourceQuota and LimitRange: To allocate GPU resources effectively among different namespaces or workloads, we can use Kubernetes' ResourceQuota and LimitRange objects to specify GPU resource constraints.

    3. NodeSelectors and Tolerations: We'll use nodeSelector and tolerations to ensure that our GPU-accelerated workloads are scheduled on the right nodes that have GPU support.

    4. Pod Specification with GPU requests: When defining our pod specifications within our deployments, we will include resource requests for GPUs so that Kubernetes can schedule these pods onto nodes with available GPU resources.

    Below is a program written in Python using Pulumi, that sets up a Kubernetes cluster (we'll use Google Kubernetes Engine for this example) with a node pool that has GPU support. It also includes an example of a Deployment with a pod that requests GPU resources.

    import pulumi from pulumi_gcp import container from pulumi_kubernetes import core_v1 as corev1 from pulumi_kubernetes import apps_v1 as apps # Create a GKE cluster with GPU-enabled nodes gke_cluster = container.Cluster("gpu-cluster", initial_node_count=1, node_config=container.ClusterNodeConfigArgs( machine_type="n1-standard-1", # Choosing a machine type that supports GPUs oauth_scopes=[ "https://www.googleapis.com/auth/compute", "https://www.googleapis.com/auth/devstorage.read_only", "https://www.googleapis.com/auth/logging.write", "https://www.googleapis.com/auth/monitoring" ], # Adding the necessary accelerators (GPUs) to the node configuration accelerators=[container.ClusterNodeConfigAcceleratorArgs( accelerator_count=1, accelerator_type="nvidia-tesla-k80" # NVIDIA Tesla K80 GPUs )] ), ) # Create a namespace for our LLM workloads llm_namespace = corev1.Namespace("llm-namespace") # Create a deployment that requests GPU resources gpu_deployment = apps.Deployment("gpu-deployment", metadata=apps.DeploymentMetadataArgs( namespace=llm_namespace.metadata["name"], # Deploying into the created namespace ), spec=apps.DeploymentSpecArgs( replicas=1, selector=apps.DeploymentSpecSelectorArgs( match_labels={ "app": "llm-gpu", }, ), template=corev1.PodTemplateSpecArgs( metadata=corev1.ObjectMetaArgs( labels={ "app": "llm-gpu", }, ), spec=corev1.PodSpecArgs( containers=[ corev1.ContainerArgs( name="llm-container", image="nvidia/cuda:10.0-base", # Using the CUDA image as an example resources=corev1.ResourceRequirementsArgs( requests={ "nvidia.com/gpu": 1, # Requesting one GPU }, ), ), ], node_selector={ "cloud.google.com/gke-accelerator": "nvidia-tesla-k80", # Ensuring the pod is scheduled on GPU-enabled nodes }, tolerations=[ # Toleartions ensure the pod can be scheduled on nodes with taints that match these. corev1.TolerationArgs( key="nvidia.com/gpu", operator="Exists", effect="NoSchedule", ), ], ), ), ), ) # Output the cluster name and the namespace pulumi.export("cluster_name", gke_cluster.name) pulumi.export("llm_namespace", llm_namespace.metadata["name"])

    This program creates a GKE cluster with nodes that are GPU-capable and a Kubernetes deployment that requests one GPU. The deployment is in the created namespace specifically set up for LLMs. We've used the NVIDIA CUDA image as an example container; in a real-world scenario, you would replace this with your LLM's container image.

    Additionally, the nodeSelector field ensures that the pods are placed onto nodes with the specified GPU (nvidia-tesla-k80). The tolerations are used in case the nodes with GPUs have taints applied to prevent other workloads from being scheduled on them.

    Please note that in real-world usage, you may need to customize the node machine types, the number of GPUs, and the image being used according to your specific requirements and the availability within your cloud provider. You may also require additional configuration for the NVIDIA drivers and CUDA libraries within the cluster.