1. GPU-enabled Kubernetes Pods for Deep Learning


    To set up GPU-enabled Kubernetes pods for deep learning purposes, you generally need to:

    1. Create a Kubernetes Cluster that has nodes with GPU capabilities.
    2. Configure a node with the appropriate GPU drivers and Kubernetes device plugin.
    3. Create a pod specification that requests GPU resources.

    In this program, we'll assume that you've got a Kubernetes cluster running with GPU-enabled nodes. The focus will be on crafting a pod specification that requests GPU resources for a deep learning task.

    Kubernetes manages GPUs through the device plugins framework. This allows Kubernetes to use GPUs as a schedulable resource similar to how it uses CPU and memory. Before we get started, ensure that your Kubernetes cluster has the Nvidia device plugin installed if you're using Nvidia GPUs. This is a critical component that makes GPUs available to your pods.

    Here's how the program will be structured:

    • Use the pulumi_kubernetes library to create Kubernetes resources.
    • Define a Pod resource with a container that requests GPU resources.
    • Use annotations or the resources configuration to specify the GPU request.

    When defining the Pod specification, you'll use the limits section under resources to specify the number of GPUs the pod requires. Different cloud providers might have different ways of specifying GPU resources, but for Nvidia GPUs, you would generally use nvidia.com/gpu: <number-of-gpus> to request GPU resources.

    Let's write the Pulumi program to create a GPU-enabled Kubernetes pod suitable for deep learning tasks.

    import pulumi import pulumi_kubernetes as k8s # Define the Pod that will run a container requesting GPU resources. gpu_pod = k8s.core.v1.Pod( "gpu-pod", metadata=k8s.meta.v1.ObjectMetaArgs( name="deep-learning-pod" ), spec=k8s.core.v1.PodSpecArgs( containers=[ k8s.core.v1.ContainerArgs( name="deep-learning-container", image="tensorflow/tensorflow:latest-gpu", # Docker image that supports GPU for deep learning. resources=k8s.core.v1.ResourceRequirementsArgs( # Define GPU resource limits here. # This specifies that the container requires 1 Nvidia GPU. limits={ "nvidia.com/gpu": 1 } ), # Other configurations for the container would go here, like command, args, volumeMounts, etc. ) ], # Node selector or other scheduling configurations would go here. ) ) # Export the name of the pod pulumi.export("pod_name", gpu_pod.metadata["name"])

    In the above program:

    • We defined a Kubernetes pod with the name deep-learning-pod.
    • It contains a single container named deep-learning-container, which uses a TensorFlow GPU-enabled Docker image. This image is set up to take advantage of GPU acceleration for deep learning tasks.
    • The resources section within the ContainerArgs is used to specify that the pod requires one Nvidia GPU.
    • The pulumi.export line is used to output the name of the pod that's been created.

    This program must be run in an environment where Pulumi is configured to communicate with your Kubernetes cluster. If the program executes successfully, the created pod will be scheduled to a node where a GPU is available, subject to Kubernetes' scheduling constraints and the availability of the required resources.