1. GPU-Accelerated Workloads on Kubernetes for Deep Learning


    Deploying GPU-accelerated workloads on Kubernetes for deep learning involves setting up a Kubernetes cluster, configuring nodes with GPU resources, and ensuring that your workloads can leverage the GPUs efficiently. This can be quite complex, but with Pulumi, we can automate the infrastructure setup.

    Here’s how you can go about it:

    1. Create a Kubernetes Cluster: We will start with provisioning a Kubernetes cluster, which will serve as the foundation for our workloads.

    2. Set up GPU Nodes: Once we have our Kubernetes cluster, we will add nodes that are equipped with GPUs. This is typically done using a specific node pool that requests instances with attached GPUs.

    3. Configure Workload to Use GPUs: After we have our GPU nodes ready, we’ll deploy a Kubernetes workload that is configured to use GPU resources. This generally involves setting proper resource limits and requests within the Pod specification to target GPU resources.

    For our program, we will use AWS as the cloud provider. This means we'll be creating an EKS (Amazon Elastic Kubernetes Service) cluster and provisioning GPU instances.

    Let's write the Pulumi program to set this up:

    import pulumi import pulumi_aws as aws import pulumi_eks as eks # Step 1: Create an EKS Cluster. # This will provision the control plane and configure networking. cluster = eks.Cluster("gpu-cluster") # Step 2: Create a GPU Node Group. # AWS's P2 or P3 instance types are GPU-equipped and suitable for deep learning tasks. # Make sure to replace `<YOUR_SUBNET_ID>` with your actual AWS subnet ID where you wish to provision these instances. gpu_node_group = eks.NodeGroup( "gpu-node-group", cluster=cluster.core, instance_type="p2.xlarge", desired_capacity=2, # Configure the desired number of nodes in the node group. subnet_ids=["<YOUR_SUBNET_ID>"] ) # Step 3: Deploy a Sample Deep Learning Workload. # This is a very basic example of a Kubernetes Pod that requests GPU resources. # The actual training/job script is not included in this example. # Make sure to include a proper image that can leverage GPU for deep learning tasks. gpu_pod = aws.core.v1.Pod( "gpu-pod", metadata={"name": "gpu-pod"}, spec={ "containers": [{ "name": "gpu-container", "image": "tensorflow/tensorflow:latest-gpu", "resources": { "limits": { "nvidia.com/gpu": "1" # Requesting 1 GPU for this workload. } } }] } ) # Exporting the cluster's kubeconfig. pulumi.export('kubeconfig', cluster.kubeconfig)

    In this program, we use the pulumi_eks module to create an AWS EKS cluster and then add a node group with GPU instances. We configure the node group to use p2.xlarge instance types, which are equipped with GPUs suitable for deep learning.

    We then set up a sample Pod spec to deploy a deep learning workload using TensorFlow with GPU support. The nvidia.com/gpu: 1 resource limit ensures that the pod will have access to the GPU resources.

    Remember to replace <YOUR_SUBNET_ID> with the ID of the AWS subnet where you want to provision the GPU nodes. You would also need to configure AWS access credentials and Pulumi to interact with your AWS account.

    After running this program with Pulumi, the kubeconfig is exported so you can interact with your cluster using kubectl and deploy your deep learning models.

    This is a foundational step in setting up your deep learning infrastructure, from here you can further refine your setups such as adding storage for datasets, setting up monitoring, and scaling the number of GPU instances as needed.